562 research outputs found
Contribution of High-Yield Varieties Seeds to Major Food Crops Production, Yield and Area in Punjab - Pakistan
No full textThe present study is undertaken to measure the contribution of HYVs to major food crops (Wheat, rice, bajra, jowar and maize) production, yield and area in Pakistan Punjab. The study is based on secondary data for the last 44 years, i.e., from 1951-52 to 1994-95. For this purpose, a comparison of growth rates for production, yield and area is made (1) between period I (1951 to 1964) and period II 1965 to 1978). (2) between period II and period III (1979 to 1994) and (3) between period I and period IV (1951 to 1994). The study shows that the influence of HYV seed on production, yield and area for major food crops in Punjab is mixed. The contribution of HYVs to production, area and yield growth for wheat is remarkable. The adoption of HYVs has helped to accelerate the growth rate for production and yield for rice and maize in period II. In case of Jowar despite the fact that its area and production decreased its yield increased in period II, which may be due to the adoption of HYV seeds.Growth, Comparison, Periods, Time-series, Semi-log, HYV seed, Food crops, Area, Yield.
Inclusive Fitness Theory: is it still relevant in discussions of altruism?
No full textFor nearly 50 years, Inclusive Fitness Theory has provided researchers an avenue to understand altruistic interactions among individuals in a colony. It has recently come under fire by prominent academics suggesting it is unsatisfactory in describing altruism. This thesis aims to provide a history of inclusive fitness theory and dissect the arguments against the theory and in favor of it. Using scientific research articles from such publications as Nature and Science, I have collected information on the history of Inclusive Fitness Theory and the development of the theory over time. Furthermore, this thesis will also delve into the methods of testing Inclusive Fitness Theory as well as fields that have arisen due to the theory. Ultimately, using arguments made by opponents and proponents to the theory, conclusions will be drawn about the validity of the theory. While the arguments against the theory seem sound, they ultimately fail to provide alternative insights into the development of altruism in colonies, and moreover these arguments are successfully refuted by leaders in the field.M.S.Includes bibliographical referencesIncludes vitaby Daniel Wasim Awar
Refugee without refuge: Wasim, Phillip Adams, and a nation divided
No full textThis study follows on from previous work (Pedersen et al., 2008) that examined the situation of the stateless asylum seeker, Wasim. In the present study, a blog discussion stemming from an editorial about Wasim (Adams, 2008) was analysed. Participants were identified as 'Do-Gooders' and 'Do-Badders'; categories that indicate their orientation to asylum seeker debates (labels originated from the blog itself). We identified several features of the blog discussion. While similar themes and discursive devices were used by the two groups, they were used very differently. The Do-Gooders were more likely to offer accurate information about asylum seekers or Wasim and to show humani- tarian concerns for Wasim and others like him. The Do-Badders were more likely to display emotion, show the 'Phillip Adams Effect' (addressing the author of the editorial, Phillip Adams, in their sub- missions) and name-call. However, there was no significant difference with respect to whether participants addressed Wasim's situation specifically rather than focusing on the general issue of asylum seekers. The blog demonstrates, in microcosm, the divided orientation of Australians regard- ing asylum seekers
Online Balanced Allocation of Dynamic Components
No full textWe introduce Online Balanced Allocation of Dynamic Components (OBADC), a problem motivated by the practical challenge of dynamic resource allocation for large-scale distributed applications. In OBADC, we need to allocate a dynamic set of at most k vertices (representing processes) in > 0 clusters. We consider an over-provisioned setup in which each cluster can hold at most k(1+ε) vertices, for an arbitrary constant ε > 0. The communication requirements among the vertices are modeled by the notion of a dynamically changing component, which is a subset of vertices that need to be co-located in the same cluster. At each time t, a request r_t of one of the following types arrives:
1) insertion of a vertex v forming a singleton component v at unit cost.
2) merge of (u,v) requiring that the components containing u and v be merged and co-located thereafter.
3) deletion of an existing vertex v at zero cost. Before serving any request, an algorithm can migrate vertices from one cluster to another, at a unit migration cost per vertex. We seek an online algorithm to minimize the total migration cost incurred for an arbitrary request sequence σ = (r_t)_{t > 0}, while simultaneously minimizing the number of clusters utilized. We analyze competitiveness with respect to an optimal clairvoyant offline algorithm with identical (over-provisioned) capacity constraints.
We give an O(log k)-competitive algorithm for OBADC, and a matching lower-bound. The number of clusters utilized by our algorithm is always within a (2+ε) factor of the minimum. Furthermore, in a resource augmented setting where the optimal offline algorithm is constrained to capacity k per cluster, our algorithm obtains O(log k) competitiveness and utilizes a number of clusters within (1+ε) factor of the minimum.
We also consider OBADC in the context of machine-learned predictions, where for each newly inserted vertex v at time t: i) with probability η > 0, the set of vertices (that exist at time t) in the component of v is revealed and, ii) with probability 1-η, no information is revealed. For OBADC with predictions, we give a O(1)-consistent and O(min(log 1/(η), log k))-robust algorithm
Competitive Capacitated Online Recoloring
No full textIn this paper, we revisit the online recoloring problem introduced recently by Azar, Machluf, Patt-Shamir and Touitou [Azar et al., 2022] to investigate algorithmic challenges that arise while scheduling virtual machines or processes in distributed systems and cloud services. In online recoloring, there is a fixed set V of n vertices and an initial coloring c₀: V → [k] for some k ∈ ℤ^{> 0}. Under an online sequence σ of requests where each request is an edge (u_t,v_t), a proper vertex coloring c of the graph G_t induced by requests until time t needs to be maintained for all t; i.e., for any (u,v) ∈ G_t, c(u)≠ c(v). In the distributed systems application, a vertex corresponds to a VM, an edge corresponds to the requirement that the two endpoint VMs be on different clusters, and a coloring is an allocation of VMs to clusters. The objective is to minimize the total weight of vertices recolored for the sequence σ. In [Azar et al., 2022], the authors give competitive algorithms for two polynomially tractable cases - 2-coloring for bipartite G_t and (Δ+1)-coloring for Δ-degree G_t - and lower bounds for the fully dynamic case where G_t can be arbitrary.
We obtain the first competitive algorithms for capacitated online recoloring and fully dynamic recoloring, in which there is a bound on the number or weight of vertices in each color. Our first set of results is for 2-recoloring using algorithms that are (1+ε)-resource augmented where ε ∈ (0,1) is an arbitrarily small constant. Our main result is an O(log n)-competitive deterministic algorithm for weighted bipartite graphs, which is asymptotically optimal in light of an Ω(log n) lower bound that holds for an unbounded amount of augmentation. We also present an O(nlog n)-competitive deterministic algorithm for fully dynamic recoloring, which is optimal within an O(log n) factor in light of a Ω(n) lower bound that holds for an unbounded amount of augmentation.
Our second set of results is for Δ-recoloring in an (1+ε)-overprovisioned setting where the maximum degree of G_t is bounded by (1-ε)Δ for all t, and each color assigned to at most (1+ε)n/(Δ) vertices, for an arbitrary ε > 0. Our main result is an O(1)-competitive randomized algorithm for Δ = O(√{n/log n}). We also present an O(Δ)-competitive deterministic algorithm for Δ ≤ ε n/2. Both results are asymptotically optimal
Melipat Air: Jurus budaya pendekar Tionghoa: Lee Man Fong, Siaw Tik Kwie, Lim Wasim
No full textBuku ini semacam buku biografi yang disusun untuk mengingat keberadaan tiga tokoh budaya dan seni keturunan Tionghoa: Lee Man Fong, Siauw Tik Kwie, dan Lim Wasim. Ketiga tokoh tsb telah berjasa pada bangsa dan negara Indonesia. Meskipun mereka berkiprah dalam lingkungan seni rupa, jangkauan pikiran, kaki, dan tangannya melewati batas-batas profesinya. Mereka bertiga terkenal sebagai pelukis yang menjangkau wilayah keorganisasian, populer sebagai komikus, dan memiliki kontribusi tersembunyi bagi bangsa dan negara Indonesia
Airborne uygulamaları için bir kompakt yuvarlak polarize monopül slotted waveguide array
No full textIn modern radar and communication systems, high gain, high power capability and low-profile antenna systems are frequently utilized. In airborne and space borne applications, slotted waveguide antenna array is widely used due to its rigid structure, low-loss and high power handling capability. In this thesis, a compact, lightweight, high reection bandwidth, high gain and circularly polarzied slotted waveguide array antenna with monopulse capability operating in Ku-band is presented. Target application of this antenna is radio links on airborne platforms with built-in tracking capability . This thesis is divided into two parts: 1) Design of compact standing wave slotted array; 2) Design of a low-cost linear polarization (LP) to circular polarzition (CP) converter. In the first part of thesis, an antenna system is presented that has two key components: monopulse comparator/feed network and antenna section. The antenna is designed at 15 GHz and simulated using ANSYS HFSS. It is a planar array of 6x8 radiating slots with a gain greater than 22:5 dB at 15 GHz , side-lobe level (SLL) 13 dB , reection bandwidth of 1200 MHz. Monopulse comparator network is Magic Tee based waveguide structure. The antenna has maximum dimension of 10:8 cm * 10:7 cm 4 cm that makes it easy to house on an aircraft. In the second part of thesis, a LP to CP polarizer is presented. The proposed polarizer is a multi-layer metasurface based structure that converts incident linearly polarized wave into circularly polarized wave. The polarizer presented in this thesis is based on meander line and strip line hybrid concept. Each substrate layer is rotated 45 with respect to previous layer and is seperated by low permittivity material called spacer. The unit cell of the polarizer is square in shape with dimensions of 5:1mm 5:1 mm. Its axial ratio bandwidth is 2 GHz i.e: axial ratio is less than 3-dB from 14-16 GHz . It is also low-cost as compared to the available literature because thick substrates are used instead of high cost thin substrate and also one substrate layer is based on FR-4 laminate.Modern radar ve ileti sim sistemlerinde, yuksek kazan c, yuksek gu c kapasitesi ve du suk pro lli anten sistemleri s kl kla kullan lmaktad r. Havadan ve yere dayal uygulamalarda, rijit yap s , duuk kayb ve yuksek gu kullanma kabiliyeti nedeniyle oluklu dalga k lavuzu anten dizisi yayg n olarak kullan lmaktad r. Bu tez cal mas nda, Ku-band nda cal an monopuls yetene gine sahip kompakt, ha f, yuksek yans ma bant genili gi, yuksek kazan ve dairesel polarize oluklu dalgal dalga dizisi anten sunulmaktad r. Bu antenin hedef uygulamas , yerleik izleme ozelli gine sahip havadaki platformlardaki radyo ba glant lar d r. Bu tez iki bolume ayr lm st r: 1) Kompakt dura gan dalga oluklu dizisinin tasar m ; 2) Duuk maliyetli bir dourusal polarizasyon (LP) ile dairesel polarizasyon (CP) donuturucusunun ta sar m . Tezin ilk bolumunde, iki ana bile sene sahip bir anten sistemi sunulmu stur: monopuls kar s lat r c besleme a g ve anten bolumu. Anten 15 GHz'de tasarlanm s ve ANSYS HFSS kullan larak simule edilmi stir.15 GHz, yan lob seviyesi (SLL) 13 dB'de 22.5 dB' den daha fazla kazanc olan 6x8 yay lan oluklar n duzlemsel bir dizisidir.,1200 MHz yans tma bant geni sli gi. Monopuls kar s la st r c a g , Magic Tee tabanl dalga k lavuzu yap s d r. Anten, u cakta yerletirmeyi kolaylat ran maksimum 10.8 x 10.7 x 4 cm boyutuna sahiptir. Tezin ikinci bolumunde, bir LP-CP polarizoru sunulmu stur. Onerilen polarizer, olay do grusal polarize dalgay dairesel polarize dalgas na donu sturen cok katmanl bir metasurface bazl yap d r. Bu tezde sunulan polarizer, k vr ml ve duz cizgi hibrid konseptine dayanmaktad r. Her bir substrat katman onceki katmana gore 45 dondurulur ve aralay c ad verilen duuk geirgenlik materyali ile ayr l r. Polarizorun birim hucresi, 5.1 mm x 5.1 mm boyutlar nda kare eklindedir. Eksenel oran bant genili gi 2 GHz'dir, yani eksenel oran 14-16 GHz'den 3-dB'den azd r. Ayn zamanda, mevcut literature k yasla du suk maliyetlidir, cunku yuksek maliyetli ince substrat yerine kal n substratlar kullanlr ve ayrca bir substrat tabakas FR-4 laminat esasl d r
Design and analysis of a truncated elliptical-shaped chipless RFID tag
No full textThis article presents a novel polarization-insensitive chipless radio frequency identification tag having an encoding capacity of 11 bits. The proposed resonator design comprises discontinuous arc slots forming truncated elliptically shape offering 1:1 slot to bit correspondence with suppressed unwanted harmonic resonances. Electromagnetic performance analysis of the proposed tag design is done over an ungrounded Rogers RT duroid® 5880 laminate. The overall tag design covers a footprint of 15 × 15 × 0.508 mm 3 offering convincingly appreciable bit density of 4.88 bits/cm 2. The realized tags are analyzed for real-world electromagnetic performance resulting in an agreement between measured and computed results. The proposed work finds its applications in the food and beverage industry.Publisher versio
Public charging facility requirements for long-haul trucks in the EU: a trip chain approach
No full textAbstract of the research:
This research presents a trip chain-based model that evaluates the LBET's charging requirements in the year 2030 for the European continent. Following EU truck driver regulations, the research converts a four-step origin-destination (OD) matrix into LHT trip chains. We show that in our main case scenario of 15% LBET share, a minimum of 28800 slow (≤ 100 kW) and 9800 fast (≥ 1 mW) charging points are required to meet that energy demand, corresponding to daily energy requirements of 112 GWh. On average, the slow to fast charging points ratio is 3. Fast and slow charging points serve 12 and 2 LBETs daily, respectively. Our model suggests that it will be necessary to place charging stations every 25-35 km on highways where demand for charging is required.
The methodology:
We develop a method for placement of charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by LHT is identified using the ETISplus dataset. We develop a travel pattern for the long haul truck (LHT) to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving LHT. Break and rest locations for all moving LHTs are aggregated to suggest energy requirements if assuming these LHTs are BETs. The aggregated energy to charge stopped LBETs is used to identify the number and type of chargers within each suggested charging station.
Datasets details
The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers and Planned transport infrastructure.
The first dataset titled 'ChargerLocations' contains infromation about the locations of suggested charging stations, number and type of chargers, and number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:
Name
Description
Data Type
Unit
DTN30/MainDTN
number of electrified trucks in 2030
integer
number
ChE30
charged energy in Mega watt-hour from all charging (fast and slow)
float
Mega watt-hour
ChERM
charged energy in Megawatt hour with slow charging only (rest)
float
Mega watt-hour
MDTN_R
number of electrified trucks using slow chargers (rest)
integer
number
ChEBM
charged energy in Megawatt hour with fast charging only (break)
float
Mega watt-hour
MDTN_B
number of electrified trucks using fast chargers (break)
integer
number
NSCh2pD
number of slow chargers
integer
number
NFCh30m
number of fast chargers
integer
number
TotCha
Total number of chargers
integer
number
The second dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):
Name
Description
Data Type
Unit
ID_origin_region
Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3
Integer (9digits)
-
Name_origin_region
National name of NUTS-3 region of origin
String
-
ID_destination_region
Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3
Integer (9digits)
-
Name_destination_
region
National name of NUTS-3 destination region
String
-
Edge_path_E_road
List of the network edge IDs of the shortest path between the O-D pair, determined with Dijkstra's algorithm
String
-
Distance_from_origin_
region_to_E_road
Distance from the geometric centre of the origin region to the closest network node
Float
Kilometres [km]
Distance_within_E_
road
Distance of the shortest edge path between the O-D pair
Float
Kilometres [km]
Distance_from_E_
road_to_destination_
region
Distance from the geometric centre of the destination region to the closest network node
Float
Kilometres [km]
Total_distance
Sum of Distance_from_origin_region_to_E_road, Distance_within_E_road and Distance_from_E_road_to_destination_region
Float
Kilometres [km]
Traffic_flow_trucks_
2010
Number of trucks that drive between the O-D pair in 2010
Float
Number of trucks
Traffic_flow_trucks_
2019
Number of trucks that drive between the O-D pair after they had been scaled to 2019
Float
Number of trucks
Traffic_flow_trucks_
2030
Number of trucks that drive between the O-D pair according to the forecast for 2030
Float
Number of trucks
Traffic_flow_tons_
2010
Number of tons that are transported between the O-D pair in 2010 according to ETISplus
Integer
Tons [t]
Traffic_flow_tons_
2019
Number of tons that are transported between the O-D pair after they had been scaled to 2019
Integer
Tons [t]
Traffic_flow_tons_
2030
Number of tons that are transported between the O-D pair according to the forecast for 2030
Integer
Tons [t]
Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))
Name
Description
Data Type
Unit
Network_Node_ID
Unique network node ID
Integer (6 digits)
-
Network_Node_X
Longitude of the location of network node
Float
Degrees
Network_Node_Y
Latitude of the location of network node
Float
Degrees
ETISplus_Zone_ID
ID of the NUTS-3 region in which the network node is located
Integer
-
Country
Unique country code of the country in which the network node is located (country codes are defined by ETISplus)
String
-
Description of variables used in the network edges list (04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))
Name
Description
Data Type
Unit
Network_Edge_ID
Unique edge ID
Integer
(7 digits)
-
Manually_Added
Determines whether an edge had been manually added to the network (1) or not (0)
Binary-integer
-
Distance
Length of the network edge
Float
Kilometres [km]
Network_Node_A_ID
Unique ID of the network node that defines one end point of the network edge
Integer
-
Network_Node_B_ID
Unique ID of the network node that defines one end point of the network edge
Integer
-
Traffic_flow_trucks_2019
Number of trucks that drive on the edge in 2019 (both highway directions combined)
Float
Number of trucks
Traffic_flow_trucks_2030
Number of trucks that drive on the edge in 2030 (both highway directions combined)
Float
Number of truck
Public charging requirements for battery electric long-haul trucks in Europe: a trip chain approach
No full text<p>Contact details:</p>
<p>wasim.shoman at chalmers.se </p>
<p>waahh7 at gmail com</p>
<p><strong>Abstract of the research:</strong></p>
<p>Heavy-duty vehicles (HDV) account for less than 2-5% of the vehicles on the road in Europe but contribute to 15-22% of CO<sub>2</sub> emissions from road transport. Battery electric trucks (BETs) could be deployed on a large scale to reduce greenhouse gas emissions. However, they require sufficient charging infrastructure to support long-haul operations. Therefore, assessing the required charging locations, energy, and power requirements is critical. We use a trip-chain-based model to derive charging requirements for BETs in long-haul operation (travel times over 4.5 hours or over 360 km distance traveled) for Europe in 2030. We convert an origin-destination (OD) matrix into trip chains combined with European truck driving regulations to derive break and rest stops. We show that an average charging area (defined as a 25´25 km<sup>2</sup> square with each square that could include multiple charging stations and parking lots of multiple charging points) needs to have four to five times more overnight than megawatt charging points. We estimate that about 40,000 overnight charging points (50-100 kW, combined charging system, CCS) and about 9,000 megawatt charging system (MCS, 0.7 – 1.2 MW) points are required for 15% of trucks as BETs in long-haul operation. On average, 8 and 2 CCS and MCS chargers are required per charging area, and each MCS and CCS serve, on average, 11 and 2 BETs daily, respectively. Public charging entails about 110 GWh daily electricity demand in each charging area. The model can be applied to any region with similar data. Future work can consider improving the queuing model, assumptions regarding regional differences of BET penetration, and heterogeneity of truck sizes and utilization.</p>
<p><strong>The methodology:</strong></p>
<p>We develop a method to place charger locations in Europe that meets the demand of goods movements between regions while following EU driving regulations. The spatial resolution of regions is based on the Nomenclature of Territorial Units for Statistics (NUTS)-3 regions. The annual flow of goods transported by HDV is identified using the ETISplus dataset. We develop a travel pattern for the HDV to convert flows into trip chains with the traversed LHT number. The traveled routes between the regions are mapped. Locations of short period stops, i.e., breaks, and long period stops, i.e., rests, are allocated/assigned along traveled routes to construct a trip chain for each moving HDV. Break and rest locations for all moving HDVs are aggregated to suggest energy requirements if assuming these HDVs are BETs. The aggregated energy to charge stopped BETs is used to identify the number and type of chargers within each suggested charging station.</p>
<p><strong>Datasets details</strong></p>
<p>The presented datasets contain spatial information for generating charger stations with specifications according to charging needs. The datasets contain information about: Transport network model and edges, Transported flows, routes and flow center information data, region centers, and Planned transport infrastructure. </p>
<p>The first dataset titled 'ChargerLocations' contains information about the locations of suggested charging stations, the number and type of chargers, and the number of visited electrified trucks in 2030. It is a shapefile with the following details for its fields:</p>
<table>
<tbody>
<tr>
<td>Name</td>
<td>Description</td>
<td>Data Type</td>
<td>Unit</td>
</tr>
<tr>
<td>DTN30/MainDTN</td>
<td> number of electrified trucks in 2030</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>ChE30</td>
<td> charged energy in Mega watt-hour from all charging (fast and slow)</td>
<td>float</td>
<td> Mega watt-hour</td>
</tr>
<tr>
<td>ChERM</td>
<td> charged energy in Megawatt hour with slow charging only (rest)</td>
<td>float</td>
<td> Mega watt-hour</td>
</tr>
<tr>
<td>MDTN_R</td>
<td> number of electrified trucks using slow chargers (rest)</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>ChEBM</td>
<td> charged energy in Megawatt hour with fast charging only (break)</td>
<td>float</td>
<td> Mega watt-hour</td>
</tr>
<tr>
<td>MDTN_B</td>
<td> number of electrified trucks using fast chargers (break)</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>NSCh2pD</td>
<td> number of slow chargers</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>NFCh30m</td>
<td> number of fast chargers</td>
<td>integer </td>
<td>number</td>
</tr>
<tr>
<td>TotCha</td>
<td> Total number of chargers</td>
<td>integer </td>
<td>number</td>
</tr>
</tbody>
</table>
<p> </p>
<p>The second dataset titled (RestandBreaksPoints.shp) with information about the rest and break point locations. The dataset includes detailes about stop type, number of stopped trucks, and required charged energy. The dataset is a shapefile with "shp" format. </p>
<table>
<tbody>
<tr>
<td>
<p><strong>Name</strong></p>
</td>
<td>
<p><strong>Description</strong></p>
</td>
<td>
<p><strong>Data Type</strong></p>
</td>
<td>
<p><strong>Unit</strong></p>
</td>
</tr>
<tr>
<td>
<p>ID_origin_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_origin_region</p>
</td>
<td>
<p>National name of NUTS-3 region of origin</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>ID_destination_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_destination_<br>
region</p>
</td>
<td>
<p>National name of NUTS-3 destination region</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Rest</p>
</td>
<td>
<p>A value of ”1” indicates a rest stop</p>
</td>
<td>
<p>Boolean</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Break</p>
</td>
<td>
<p>A value of ”1” indicates a break stop</p>
</td>
<td>
<p>Boolean</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>ChaDisKM</p>
</td>
<td>
<p>Charged range within a trip for stopped the truck</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>km</p>
</td>
</tr>
<tr>
<td>
<p>ChaEnekWh</p>
</td>
<td>
<p>Charged energy within a trip for stopped the truck</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>KWh</p>
</td>
</tr>
<tr>
<td>
<p>MainDTN</p>
</td>
<td>
<p>Number of stopped trucks for the main electrification scenario (15%)</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>number</p>
</td>
</tr>
<tr>
<td>
<p>ChE30M</p>
</td>
<td>
<p>Charged energy for all stopped trucks</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>MWh</p>
</td>
</tr>
<tr>
<td>
<p>ChERM</p>
</td>
<td>
<p>Charged energy for the trucks stopping for rest</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>MWh</p>
</td>
</tr>
<tr>
<td>
<p>MDTN_R</p>
</td>
<td>
<p>Number of trucks stopping for rest</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>number</p>
</td>
</tr>
<tr>
<td>
<p>ChEBM</p>
</td>
<td>
<p>Charged energy for the trucks stopping for break</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>MWh</p>
</td>
</tr>
<tr>
<td>
<p>MDTN_B</p>
</td>
<td>
<p>Number of trucks stopping for break</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>number</p>
</td>
</tr>
<tr>
<td>
<p>geometry</p>
</td>
<td>
<p>X, Y coordinates</p>
</td>
<td>
<p>geometry</p>
</td>
<td>
<p>-</p>
</td>
</tr>
</tbody>
</table>
<p> </p>
<p> </p>
<p>The following dataset titled 'flowFile' with information about the transported flow between regions and the transported routes. The dataset is in "CSV" format. Details for its fields are explained as follows (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X):</p>
<table>
<tbody><tr>
<th>
<p><strong>Name</strong></p>
</th>
<th>
<p><strong>Description</strong></p>
</th>
<th>
<p><strong>Data Type</strong></p>
</th>
<th>
<p><strong>Unit</strong></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p>ID_origin_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 region of origin. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_origin_region</p>
</td>
<td>
<p>National name of NUTS-3 region of origin</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>ID_destination_region</p>
</td>
<td>
<p>Unique record ID with 9 digits decoding NUTS-3 code of destination region. First 3 digits decode NUTS-0, first 5 decode NUTS-1, first 7 decode NUTS-3</p>
</td>
<td>
<p>Integer (9digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Name_destination_<br>
region</p>
</td>
<td>
<p>National name of NUTS-3 destination region</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Edge_path_E_road</p>
</td>
<td>
<p>List of the <em>network edge IDs</em> of the shortest path between the O-D pair, determined with Dijkstra's algorithm</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Distance_from_origin_<br>
region_to_E_road</p>
</td>
<td>
<p>Distance from the geometric centre of the origin region to the closest network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Distance_within_E_<br>
road</p>
</td>
<td>
<p>Distance of the shortest edge path between the O-D pair</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Distance_from_E_<br>
road_to_destination_<br>
region</p>
</td>
<td>
<p>Distance from the geometric centre of the destination region to the closest network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Total_distance</p>
</td>
<td>
<p>Sum of <em>Distance_from_origin_region_to_E_road, Distance_within_E_road</em> and <em>Distance_from_E_road_to_destination_region</em></p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_trucks_<br>
2010</p>
</td>
<td>
<p>Number of trucks that drive between the O-D pair in 2010</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Number of trucks</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_trucks_<br>
2019</p>
</td>
<td>
<p>Number of trucks that drive between the O-D pair after they had been scaled to 2019</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Number of trucks</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_trucks_<br>
2030</p>
</td>
<td>
<p>Number of trucks that drive between the O-D pair according to the forecast for 2030</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Number of trucks</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_tons_<br>
2010</p>
</td>
<td>
<p>Number of tons that are transported between the O-D pair in 2010 according to ETISplus</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>Tons [t]</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_tons_<br>
2019</p>
</td>
<td>
<p>Number of tons that are transported between the O-D pair after they had been scaled to 2019</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>Tons [t]</p>
</td>
</tr>
<tr>
<td>
<p>Traffic_flow_tons_<br>
2030</p>
</td>
<td>
<p>Number of tons that are transported between the O-D pair according to the forecast for 2030</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>Tons [t]</p>
</td>
</tr>
</tbody>
</table>
<p>Description of variables used in the NUTS-3 regions dataset (02_NUTS-3-Regions). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p>
<table>
<tbody><tr>
<th>
<p><strong>Name</strong></p>
</th>
<th>
<p><strong>Description</strong></p>
</th>
<th>
<p><strong>Data Type</strong></p>
</th>
<th>
<p><strong>Unit</strong></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p>Network_Node_ID</p>
</td>
<td>
<p>Unique network node ID</p>
</td>
<td>
<p>Integer (6 digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Network_Node_X</p>
</td>
<td>
<p>Longitude of the location of network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Degrees</p>
</td>
</tr>
<tr>
<td>
<p>Network_Node_Y</p>
</td>
<td>
<p>Latitude of the location of network node</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Degrees</p>
</td>
</tr>
<tr>
<td>
<p>ETISplus_Zone_ID</p>
</td>
<td>
<p>ID of the NUTS-3 region in which the network node is located</p>
</td>
<td>
<p>Integer</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Country</p>
</td>
<td>
<p>Unique country code of the country in which the network node is located (country codes are defined by ETISplus)</p>
</td>
<td>
<p>String</p>
</td>
<td>
<p>-</p>
</td>
</tr>
</tbody>
</table>
<p>Description of variables used in the network edges list (Updated_04_network-edges). The dataset is in "CSV" format. (source: https://www.sciencedirect.com/science/article/pii/S235234092101060X))</p>
<table>
<tbody><tr>
<th>
<p><strong>Name</strong></p>
</th>
<th>
<p><strong>Description</strong></p>
</th>
<th>
<p><strong>Data Type</strong></p>
</th>
<th>
<p><strong>Unit</strong></p>
</th>
</tr>
</tbody><tbody>
<tr>
<td>
<p>Network_Edge_ID</p>
</td>
<td>
<p>Unique edge ID</p>
</td>
<td>
<p>Integer<br>
(7 digits)</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Manually_Added</p>
</td>
<td>
<p>Determines whether an edge had been manually added to the network (1) or not (0)</p>
</td>
<td>
<p>Binary-integer</p>
</td>
<td>
<p>-</p>
</td>
</tr>
<tr>
<td>
<p>Distance</p>
</td>
<td>
<p>Length of the network edge</p>
</td>
<td>
<p>Float</p>
</td>
<td>
<p>Kilometres [km]</p>
</td>
</tr>
<tr>
<td>
<p>Network_Node_A_ID</p>
</td>
<td>
<p>Unique ID of the network node that defin
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