434 research outputs found
The Other Side of Moon: The Schengen Information System and Human Rights: A Task for National Courts. CEPS Working Document No. 288/April 2008
No full textThe recent proposals of the European Commission for a European Border Management Strategy are based on an almost blind faith in the use of large-scale databases, identification measures and biometrics for immigration and border control purposes. It is clear that these measures entail a risk to the protection of not only the right to privacy and the right to data protection, but also to the freedom of movement and the principle of non-discrimination. This paper by Evelien Brouwer, lecturer at the Law School of Utrecht University, considers the human rights implications of the Schengen Information System (SIS). Describing the case of Mr. and Mrs. Moon, who have been reported as “inadmissible” in the SIS for more than ten years, the difficulties for thirdcountry nationals trying to remedy a false or unlawful SIS report are highlighted. The Moon case illustrates that the outcome of national proceedings dealing with an SIS alert can be very different. The author concludes with recommendations to guarantee individuals’ rights to effective remedies and to improve the position and powers of national courts
Developing a data based method to quantify the effects of flight track, aircraft weight and engine setting on the received aircraft noise levels
No full textAirports in The Netherlands are subjected to tangent environmental laws to restrain pollution and noise nuisance. Amsterdam Airport Schiphol (AAS) is one airport dealing with this regulatory framework but nevertheless they are resolute to continue growth with respect to aircraft movements. To cope with the law related to aircraft noise, the department Stakeholder Strategy and Development (SSD) of AAS is responsible for the implementation of Noise Abatement Measures (NAMs). NAMs are used to minimize aircraft noise as to be able to maximize the number of aircraft movements within the environmental law as set by the Dutch government. SSD demands to be able to visualize the effect of a NAM by measuring aircraft noise with its Noise Monitoring System (NOMOS). However, in practice it appears that the effect of a NAM to the exposed noise level cannot easily be determined since the total set of measurements show a high degree of scattering. This is caused by the fact that many other parameters are contributing to the exposed noise level as, for example, engine setting and aircraft configuration. Therefore, AAS encounters difficulties evaluating the effectiveness of implemented noise reducing measures using the noise levels as measured by NOMOS. Hence, the research question becomes: How can the distinctiveness between noise measurements effectively be improved as to evaluate the direct effect of a Noise Abatement Measure to the measured noise level? As a first step towards answering this question, aircraft mass m and aircraft engine setting N1 were identified which were expected to mask the effect of a NAM to the measured noise level. Then, the Peak Find Method (PFM) is developed to determine N1 from the associated acoustic time series as retrieved from NOMOS. Thirdly, aircraft mass m was found to be very difficult to determine from aircraft performance theories. Therefore, the lift-off speed at take-off Vlof2 is taken as an aircraft mass representative. With the two predictors N1 and Vlof2 available and the measured maximum loudness levels Lmax retrieved from NOMOS, a Multivariate Linear Regression Analysis (MLRA) is carried out to assess the effect of the two predictors to variations in Lmax. Last, the identified MLRA model is used to subtract the contribution of N1 and Vlof2 from the received noise levels, hence leaving the direct effect of a NAM to the measured noise level. Initial correlation analysis showed no correlation between N1 and Lmax and neither between Vlof2 and Lmax. While the MLRA model is based upon the identified values of the predictors, it was therefore not expected that the high variations in Lmax would decrease when using these predictors, bearing in mind the results of both correlation analysis. And ultimately, by using the MLRA model only 7% of the total variation in Lmax could be explained, which turned out to be too less to evaluate the direct effect of a NAM to the measured noise level.Air Traffic Performance and the EnvironmentControl & OperationsAerospace Engineerin
Design and analysis of parachute triggering algorithms for re-entry vehicles
No full textMost re-entry vehicles utilize a Descent and Landing System (DLS) for a safe descent through the lowest part of the atmosphere. It usually requires deployment in a certain suitable range of flight conditions, which has to be estimated by limited means of navigation. The investigation performed is a comparison of currently used trigger methods and triggering algorithms which are based on correlation between in-flight measurements and the DLS triggering conditions for a ballistic re-entry vehicle, where the correlations have been extracted by multiple Monte Carlo campaigns. This approach gives an improvement of the estimation of the deployment conditions of a factor of two over direct measurements of the Mach number. The Mach number is determined to be the most critical parameter for the parachute deployment, because its opening range is the smallest compared to the dynamic pressure and the altitude. Furthermore a sensor sensitivity analysis is performed for a lifting entry trajectory in order to support an upcoming ESA re-entry mission. The velocity drift appears to be the dominant dispersion by a factor ten for the Mach estimation, if the Mach estimation is performed by estimating the drag using axial deceleration measurements. Such a method is the preferred method for the estimation of the deployment conditions, because it has an expected error of less than Mach 0.1. For the lifting-re-entry mission, a strategy was developed to have redundant parachute deployment triggering if a certain system on the vehicle fails. This strategy involves the use of an Inertial Measurement Unit, Global Positioning System measurements and as last resort, a static pressure probe. Furthermore it appeared that the vehicle can estimate its state using no inputs from the guidance navigation and control system for four minutes. Finally a case study has been performed to investigate the possibility to reduce the footprint by a dynamic parachute opening window. This has found to be ineffective on Earth, but could be effective for Mars re-entry using a parachute able to deploy beyond Mach 2.5, which would reduce the footprint 15 to 25 kilometers if the NASA MER missions are used as a reference; this would be a reduction of 30 to 50 % of the footprints of current missions. A higher opening velocity is already desired in order to be able to land on the Martian highlands. Modified parachute designs can be developed to incorporate both benefits.Space Systems EngineeringAerospace Engineerin
Waterverlies van de rivier de Hadejia
No full textDe rivier de Hadejia in Nigeria stroomt over de goed waterdoorlatende bodem van het Tsjaadbekken. De grondwaterstand in het Tsjaadbekken ligt op 5 meter of dieper onder de rivierbodem. Er stroomt dus water af vanuit de rivier door de bodem naar het grondwater. Het is belangrijk te weten wat de grootte van het waterverlies door de bodem is ten opzichte van het debiet in de rivier over een bepaald traject. Met behulp van een aantal theorieën om het waterverlies door de bodem te bepalen is een schatting gemaakt van de hoeveelheid water die door de bodem afstroomt. De belangrijkste factoren daarbij zijn de doorlatendheid van de bodem, de afmetingen van het doorstroomoppervlak en de diepte van de grondwaterstand. Ook is er een schatting gemaakt van hat waterverlies door verdamping. De afmetingen en het reliëf van het hoogwaterbed van de Hadejia bepalen, samen met het debiet, de oppervlakte van het overstroomde gebied. Met de afmetingen van het rivieroppervlak is daarmee de grote van het verdampingsoppervlak bepaald. Uit de berekeningen blijkt dat het waterverlies van de Hadejia voor ruim 90%, wordt veroorzaakt door afstroming door de bodem en slechts een klein gedeelte door verdamping.Hydraulic Engineering + WatermanagementCivil Engineering and Geoscience
Altimetric observation of wave attenuation through the Antarctic marginal ice zone using ICESat-2
No full textProgress Code: completedStatement: Track quality is annotated for each track in the "goodlog.txt" in each month's directory.Data from all ICESat-2 tracks underlying the publication Brouwer et al., 2022:<br/>Brouwer, J., Fraser, A. D., Murphy, D. J., Wongpan, P., Alberello, A., Kohout, A., Horvat, C., Wotherspoon, S., Massom, R. A., Cartwright, J., and Williams, G. D.: Altimetric observation of wave attenuation through the Antarctic marginal ice zone using ICESat-2, The Cryosphere, 16, 2325–2353, https://doi.org/10.5194/tc-16-2325-2022, 2022.<br/><br/>ICESat-2 is a high-precision laser altimeter in a polar orbit. Due to its narrow laser footprint (~17 m) and high measurement repetition rate (firing every 70 cm) it has the capability to resolve the passage of waves (of wavelength ~100 to 1000 m) through sea ice in the Antarctic marginal ice zone. This dataset provides the processed data underlying the ICESat-2 tracks published in Brouwer et al., 2022. The processing is described in that publication, but involves manual and automated filtering to remove inappropriate (cloud-affected) tracks, then a series of steps to convert segment heights into estimates of marginal ice zone width.<br/><br/>Rout<br/>A brief description of the dataset fields is provided below. For more information please see methods section of paper, and the following code: https://github.com/Jill-Brouwer/Brouwer-etal-2022-MIZ-code/blob/main/R-gam3batch.R<br/>• Rows describe FFT and FIRF techniques for calculating significant wave height<br/> o hm0bc: FFT with boxcar window<br/> o hm0hann: FFT with hanning window<br/> o std: 4 times the standard deviation of height<br/> o FXXXX refer to the FIRFs and the wavelengths ranging from 1500m to 38m<br/>• Columns describe the parameters from the segmented regression fits and resulting MIZ width estimate. <br/> o DC_slope_lin: Slope derived from linear regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment. <br/> o DC_slope_lin_error: standard error in DC_slope_lin generated by the regression function<br/> o DC_int_lin: Y intercept derived from segmented linear regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment. <br/> o DC_int_lin_err: standard error in DC_int_lin generated by the regression function<br/> o DC_bp_lin: break point estimated from R segmented function in corrected distance from ice edge (km units) and linear significant wave height estimates (m)<br/> o DC_miz_lin: Miz width estimated using the attenuation parameters DC_slope_lin and DC_int_lin<br/> o DC_RMSE_lin: RMSE of linear regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment.<br/> o DC_miz_lin_err: error in MIZ width estimate calculated by propagating errors in the DC_slope_lin_err and DC_int_lin_err<br/> o DC_slope_exp: Slope derived from exponential regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment. <br/> o DC_slope_exp_err: standard error in DC_slope_exp generated by the regression function<br/> o DC_exp_lin: Y intercept derived from exponential segmented regression fit to relevant significant wave height estimate (row value) and corrected distance of the section midpoint from ice edge (km units) for the outer/attenuation dominated segment.<br/> o DC_int_exp_err: standard error in DC_exp_lin generated by the regression function<br/> o DC_bp_exp: break point estimated from R segmented function in corrected distance from ice edge (km units) and log-transformed significant wave height estimates; exponential fit. <br/> o DC_miz_exp: Miz width estimated using the attenuation parameters DC_slope_exp and DC_exp_lin<br/> o DC_RMSE_exp: RMSE of exponential regression fit to relevant significant wave height estimate (row value) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment.<br/> o DC_miz_exp_err: error in MIZ width estimate calculated by propagating errors DC_slope_exp_err and DC_int_exp_err.<br/> o D_slope_lin: Slope derived from linear regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment. <br/> o D_slope_lin_err: standard error in D_slope_lin generated by the regression function<br/> o D_int_lin: Y intercept derived from segmented linear regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment. <br/> o D_int_lin_err: standard error in D_int_lin generated by the regression function<br/> o D_bp_lin: break point estimated from R segmented function in distance from ice edge (km units) and significant wave height estimates (m)<br/> o D_miz_lin: Miz width estimated using the attenuation parameters D_slope_lin and D_int_lin<br/> o D_RMSE_lin: RMSE of linear regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the outer/attenuation dominated segment.<br/> o D_miz_lin_err: error in MIZ width estimate calculated by propagating errors in the D_slope_lin_err and D_int_lin_err<br/> o D_slope_exp: Slope derived from exponential regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the outer/attenuation dominated segment.<br/> o D_slope_exp_err: standard error in D_slope_exp generated by the regression function<br/> o D_exp_lin: Y intercept derived from exponential segmented regression fit to relevant significant wave height estimate (row value) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment.<br/> o D_int_exp_err: standard error in DC_exp_lin generated by the regression function<br/> o D_bp_exp: break point estimated from R segmented function in corrected distance from ice edge (km units) and log-transformed significant wave height estimates; exponential fit.<br/> o D_miz_exp: Miz width estimated using the attenuation parameters D_slope_exp and D_exp_lin<br/> o D_RMSE_exp: RMSE of exponential regression fit to relevant significant wave height estimate (row value) and corrected distance of the section midpoint from ice edge (km units) for the outer (attenuation-dominated) segment.<br/> o D_miz_exp_err: error in MIZ width estimate calculated by propagating errors D_slope_exp_err and D_int_exp_err.<br/>• The final column parameters provide some extra values from the broken regression function that describe the inner ice-structure dominated fit:<br/> o DC_sgslop_lin: Slope derived from linear regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o DC_segslop_lin_err: standard error in DC_sgslope_lin generated by the regression function<br/> o DC_segint_lin: Y intercept derived from segmented linear regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o DC_bp_lin_err: standard error in DC_bp_lin generated by the regression function<br/> o DC_segslop_exp: Slope derived from exponential regression fit to relevant significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o DC_segslop_exp_err: standard error in DC_segslope_exp generated by the regression function<br/> o DC_segint_exp: Y intercept derived from segmented regression fit to log-transformed significant wave height estimate (row value, m units) and corrected distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o DC_bp_exp_err: standard error in DC_bp_exp generated by the regression function<br/> o D_segslop_lin: Slope derived from linear regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o D_segslop_lin_err: standard error in D_segslope_lin generated by the regression function<br/> o D_segint_lin: Y intercept derived from segmented linear regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o D_bp_lin_err: standard error in D_bp_lin generated by the regression function<br/> o D_segslop_exp: Slope derived from exponential regression fit to relevant significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o D_segslop_exp_err: standard error in D_segslope_exp generated by the regression function<br/> o D_segint_exp: Y intercept derived from segmented regression fit to log-transformed significant wave height estimate (row value, m units) and distance of the section midpoint from ice edge (km units) for the inner (ice-structure-dominated) segment.<br/> o D_bp_exp_err: standard error in D_bp_exp generated by the regression function<br/>• The final column, SIC_dist, lists the MIZ width derived from sea ice concentration data in km.<br/><br/>Rawheight <br/>Describes distance from ice edge in meters at 8m intervals and the matching height values interpolated from IS-2 altimeter heights with cubic spline. <br/><br/>Csv (mizfind)<br/>This is the data that was used in fitting the linear and exponential fits to significant wave height and distance from the ice edge for each 6.25km section midpoint, for final estimation of MIZ width. For more information please see methods section of paper and source code available here: https://github.com/Jill-Brouwer/Brouwer-etal-2022-MIZ-code/tree/main. <br/><br/>The following variables are for section midpoints with suitable significant wave height estimates:<br/>• Dist: midpoint distance from ice edge (in kilometers)<br/>• CDist: midpoint corrected distance from ice edge (in kilometers)<br/>• Hm0bc: significant wave height from spectral analysis of Icesat-2 interpolated height data using boxcar window<br/>• Hm0hann: significant wave height from spectral analysis of Icesat-2 interpolated height data using hann window<br/>• Std: significant wave height from the standard deviation (SD) of the Icesat-2 interpolated height data<br/>• FXXXX: significant wave height from spatial domain filters where XXXX corresponds to the wavelength used (in meters). <br/>• hm0bcerr: hm0bc error<br/>• hm0hannerr: hm0hann error<br/>• stderr: std error<br/>• FXXXXerr: error in FXXXX<br/>• Mplat, mplon: Latitude and longitude of section midpoint<br/>• Mpsic: sea ice concentration of section midpoint<br/>• Good_mpinds: lookup table of corresponding section midpoint indexes that had acceptable significant wave height estimates<br/><br/>The below variables are for all section midpoints from the ice edge - including those that do not have suitable significant wave height estimates. <br/>• all_sic_dist: Distance from ice edge in km for section midpoint<br/>• all_sic_corrdist: Corrected distance from ice edge in km for section midpoint<br/>• all_sic_vals: sea ice concentration value for section midpoint <br/>• all_lat, all_lon: latitude and longitude of all section midpoints<br/>• beam2mp_time: midpoint time extracted from IS-2 datase
The handover moment: Designing a framework that allows the aggregation of insights to allow a translation into an interaction that increases the likelihood of implementation
No full textThe Dutch Immigration and Naturalisation Service (IND) has been finding it increasingly difficult to carry out its task in recent years. Increasingly, they have been in the news negatively with reports such as poor conditions in Ter Apel, hopeless waiting times for applicants and having to pay penalty payments. Besides the media attention, it is also a point of discussion in politics at national and European level. The complex policy that will follow from this makes for a situation of tension for the IND. In order to respond appropriately to both politics and applicants, it is important for the IND to be agile and responsive as an organisation. To achieve this, the IND wants to become more innovative, which partly means retrieving ideas from the organisation and then experimenting with them with the aim of improving processes, also known as bottom-up innovation. Currently, one team within the IND, the Einsteinbrigade, is responsible for facilitating this bottom-up innovation. Although this team is very effective in identifying which innovations are of value to the organisation, they do not always manage to convey this value to those responsible for implementation.This thesis project explores what exactly underlies this phenomenon, and how design can address it. Through qualitative research methods, the following research question is dissected and explored:Why are some of the Einsteinbrigades completed experiments not followed up with an implementation project?The insights obtained revealed the root cause of this problem, but more importantly which bottlenecks lie below. These bottlenecks were translated into design goals from which a direction was chosen for the continuation of the project. It reads as follows:With my to be designed intervention, I want to achieve that Clients within the IND respond to the needs of the Business by making the Einsteinbrigade capable of effectively conveying the value of an experiment to the Client.Within this design goal, 6 principles were identified that have been proven to help implementation; (1)The expected benefit; (2)The compatibility; (3)Sensing surprise; (4)Perceiving multiples, (5)Embodying alternatives, and (6)Verbal Mastery. From testing these principles, strengths emerged that were incorporated into the final design.The design took the form of a framework in which the insights gained from the experiment can be compiled from which an interaction can be designed appropriate to the Client and the experiment. The interaction creates a unique experience for the Client, but above all makes it tangible and recognisable what impact the innovation is having.During the evaluation, it emerged that the interactions provide a unique moment within IND’s current meeting culture, and strongly contribute to conveying the value of the results. In addition, In addition, placing the insights in the framework allows new insights and connections to emerge, contributing to the narrative, and ultimately interaction. In addition, designing the interaction stimulates creativity, which was recognised as a necessary replacement instead of the current way of presenting results.In conclusion, it must be acknowledged that this design is not conclusive for the Einsteinbrigade problem. It is a first step in the right direction of making the IND a mature organisation where bottom-up innovation is central to improving processes. Ultimately, it is recommended that to execute successful bottom-up innovation, there must be an organisation-wide leadership with corresponding clear agreements.Strategic Product Desig
Dynamic Adaptive Epidemic Control: A case study of anticipatory action to cholera outbreaks in Cameroon
No full textResponding rapidly to epidemic outbreaks presents significant challenges, due to resource, capacity and time limitations. Anticipatory Action (AA) is a newly emerging strategy in the field of humanitarian aid, designed to preemptively address potential crises. By taking impact-reducing actions before a disaster strikes, AA seeks to minimize human loss. However, AA frameworks currently use static prepared-in-advance plans. As a result, AA is not sufficiently able to deal with the uncertainty levels in the onset and spread of epidemics. Effective epidemic control requires plans that can adapt to a constantly changing environment and incoming information, such as the number and location of suspected cases, weather forecasts and population movement, while balancing flexibility with an effective management approach. We show how the (DAPP) framework for decisionmaking under deep uncertainty can be adapted to enhance the common anticipatory action approach with flexibility and effective management for epidemic control. More specifically, we show how DAPP allows to take into account newly available information and change the strategy to minimize human loss. We illustrate it with a case study of cholera in Cameroon, for which the French, Netherlands, and Cameroon Red Cross, supported by EHESP, are developing an early action protocol and a model that assesses the cost-effectiveness of actions for different risk levels and external shocks. Our results suggest that DAPP increase flexibility and coordination in anticipatory action for epidemics and helps optimizing early action strategies, which could have larger implications for global disease control.Engineering and Policy Analysi
Testing for value stability with a meta-analysis of choice experiments: River health in Australia
No full textWhile meta-analysis is typically used to identify value estimates for benefit transfer, applications also provide insights into the potential influence of design, study and methodological factors on results of non-market valuation experiments. In this paper, a metaanalysis of sixteen separate choice modelling studies in Australia with 130 individual value estimates relating to river health are reported. The studies involved different measures and scales of river health, so consistency was generated by transforming implicit prices from each study into a common standard of WTP per kilometer of river in good health. Tobit models have been used to identify the relationships between the dependent variable (WTP/km) and a number of variables. The results demonstrate that values are sensitive to marginal effects, with lower WTP/km for larger catchments, and higher WTP/km when river health is in decline. Values are also lower when river health has been defined by a subset of benefit types, such as recreation uses, vegetation health, fish health or bird populations. While there is evidence that the framing of the choice sets and descriptions of attributes have systematic impacts on values, there is very little evidence that choice dimensions, collection methods, sample sizes, response rates, statistical methods or publication status have influenced value estimates. Tests of apparent author effects show that these become insignificant when other explanatory variables are included in the models.non-market valuation, choice modelling, meta analysis, river health, Environmental Economics and Policy,
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