esa-sceva/satcom-chunk-collection huggingface dataset:
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 5 Conclusions  
This paper presents unique challenges, solutions and recommendations for ICT expansion for developing country like Nepal. The unique geographical division of Nepal, ranging from high mountainous regions to flat Terai regions, enables as well as demands suitable localized research and innovation required to properly deploy appropriate broadband networks to make it affordable and sustainable, particularly to rural communities. We presented a study on different factors to realize affordable broadband services for rural communities and recommend solutions to make sustainable and affordable broadband services for rural communities. Our study recommends developing vision for suitable subsidization, provision for dedicated rural broadband policy, cost effective deployment of a wired rural network with newer technologies including SDN and IPv6, free content delivery to rural citizens, and many other solutions. Energy optimization in ICT networks has a large contribution to organizational OpEX savings, with a significant reduction in CO\({}_{2}\) emissions. Our experimental results show that the proposed SoDIP6 network helps ISPs save a huge amount on annual OpEX compared to that of legacy IPv4 based networks, while providing sustainable and affordable services to underserved rural communities.  
Conceptualization, B.R.D.; Methodology, B.R.D., D.S.B. and D.B.R.; Software, B.R.D.; Validation, B.R.D., D.B.R. and S.R.J.; Formal Analysis, B.R.D.; Investigation, B.R.D., D.B.R.; Resources, S.R.J.; Data Curation, B.R.D.; Writing--Original Draft Preparation, B.R.D.; Writing--Review & Editing, D.B.R. and S.R.J.; Visualization, B.R.D.; Supervision, D.B.R. and S.R.J.; Project Administration, B.R.D., D.S.B. and S.R.J.; Funding Acquisition, D.S.B. and S.R.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by University Grant Commission-Nepal (FRG/74_75/Engg-1), Bhaktapur, Nepal.  
We are thankful to following organizations for their supports in this research. (1) Center for Applied Research and Development (CARD), Institute of Engineering, Tribhivan University, Kathmandu, Nepal, (2) Norwegian University of Science and Technology (NTNU), Trondheim, Norway, and (3) Nepal Academy of Science and Technology (NAST), Lalipur, Nepal.  
The authors declare no conflict of interest.
## 3 Thermal Analysis of the WCLL COB Segment
### WCLL COB Thermal Optimization and Determination of Its Thermal Field
#### 3.2.2 Determination of a Thermal Field for the Whole WCLL COB Segment  
The outcomes of the previous optimization study were used in order to represent in the most realistic way the thermal field arising in the whole WCLL COB segment. Indeed, starting from the optimized thermal field calculated in each of the seven equatorial cells, an interpolation procedure [5] was performed to obtain seven different sets of functions of the radial and the toroidal variable. Then, a proper Fortran subroutine (named \"UDISP\") was set up to provide the poloidal dependence so to obtain a different 3D thermal field for each of the seven WCLL COB poloidal regions. Lastly, the seven thermal fields were stitched together to give the 3D spatial temperature distribution for the whole WCLL COB segment.  
To perform the interpolation, each equatorial cell was divided into 13 different regions: one for the SW-FW-SW and one for the manifolds region; one for each vertical SP, for a total of five regions; and six for each horizontal SP, identified by the five poloidal-radial SPs. Successively, a polynomial function of the radial and toroidal variables was drawn for each region to represent its temperature distributions. A set of 13 polynomial functions was then built up for each COB poloidal region to reproduce the thermal field in the most accurate way. In particular, a 14 th degree polynomial function of two variables (r and t directions) was selected for the SW-FW-SW regions, up to 9 th degree polynomial function of one variable (r direction) for the manifolds, up to 10 th degree polynomial functions of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **O1** & **O2** & **O3** & **O4** & **O5** & **O6** & **O7** \\ \hline n. channels & 4 & 4 & 4 & 4 & 4 & 6 & 6 \\ DWTs layout & v06b ref. & alt. 22 & alt. 24 & alt. 24 & alt. 24 & alt.22 & v06b ref. \\ Tmax on SPs [\({}^{\circ}\)C] & 546.7 & 534.0 & 531.4 & 538.9 & 538.2 & 539.2 & 501.0 \\ Tmax of FW [\({}^{\circ}\)C] & 478.2 & 484.6 & 497.8 & 504.6 & 504.0 & 474.6 & 479.2 \\ \hline \end{tabular}
\end{table}
Table 4: BZ and FW-SW cooling layout per poloidal region.  
Figure 8: Alternative DWTs layout equipped with 26, 24, and 22 tubes.  
two variables (r and t directions) for each SPh, and up to 14 th degree polynomial functions of one variable (r direction) for each SPv. Concerning the Caps, since their cooling system has not yet been studied in detail, a polynomial function of one variable (r direction) was obtained from the SWs region and applied to the whole plate, while, on the corresponding FW region, the same polynomial function of the nearest poloidal region (namely O1 and O7) was used.  
The obtained thermal field is depicted in Figure 9 with some details at different poloidal heights of the segment. Moreover, a uniform temperature value of 300 \({}^{\circ}\)C was considered for the supports (namely the attachment system) in charge of connecting the segment to the VV.
# Study on Frost Resistance of the Carbon-Fiber-Reinforced Concrete
## 2 Experimental Program of CFRC
### Test Procedure  
The test process for the frost resistance of each concrete specimen strictly complied with the Standard for Test Methods of Long-Term Performance and Durability of Ordinary Concrete [14]. To saturate the specimens, the 24-day concrete specimens were placed into 20 \({}^{\circ}\)C water for four days. The 28-day specimens were cleaned, and the water on surface was wiped off. After weighing, three specimens from each type of concrete were selected for compression tests. Simultaneously, the remaining specimens were frozen and thawed by a JCD-400-type slow freeze-thaw machine (Figure 2). In the freeze-thaw process, the temperature of specimens was reduced to \(-\)18 \({}^{\circ}\)C within 2 h and remained between \(-\)20 \({}^{\circ}\)C and \(-\)18 \({}^{\circ}\)C for 4 h. Then, warm water was injected into the box to thaw the specimens, and the temperature was kept between 18 \({}^{\circ}\)C and 20 \({}^{\circ}\)C for 4 h. After 50 freeze-thaw cycles,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mix No.**} & \multicolumn{8}{c}{**Material Dosage (kg/m\({}^{3}\))**} \\ \cline{2-10}  & **Cement** & **FA** & **CA** & **Water** & **Carbon Fiber** & **D** & **HRWR** & **DA** \\ \hline PC & 360 & 530 & 1366 & 144 & 0 & 2.88 & 1.44 & 0.72 \\ CF0.25 & 360 & 530 & 1366 & 144 & 0.6 & 2.88 & 1.44 & 0.72 \\ CF0.50 & 360 & 530 & 1366 & 144 & 1.2 & 2.88 & 1.44 & 0.72 \\ CF1.00 & 360 & 530 & 1366 & 144 & 2.4 & 2.88 & 1.44 & 0.72 \\ CF1.50 & 360 & 530 & 1366 & 144 & 3.6 & 2.88 & 1.44 & 0.72 \\ CF2.00 & 360 & 530 & 1366 & 144 & 4.8 & 2.88 & 1.44 & 0.72 \\ \hline \hline \end{tabular}  
* CA, carbon aggregate; CF, carbon fiber; D, dispersant; DA, defoaming agent; FA, fine aggregate; HRWR, high-efficiency water-reducing agent; PC, plain concrete.  
\end{table}
Table 2: Variation of mix design.  
the specimens were taken out to be weighed and tested. The compression strength test was conducted using an electro-hydraulic servo universal testing machine with an ultimate bearing capacity of 5000 kN in the Key Laboratory of the School of Transportation Science and Engineering, Harbin Institute of Technology. The test methods were conducted strictly in accordance with the Standard for Test Methods of Concrete Physical and Mechanical Properties [15]. Tests of the specimens after 50, 100, 150, 200, 250, and 300 freeze-thaw cycles were conducted. Each kind of specimen was tested three times, and the average values are reported in this study. When the average mass loss of the specimen exceeded 5% or the strength loss exceeded 25%, the next compressive test for this type specimen was terminated.
# AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval
## 2 Related Work
Such aggregations are done in numerous concepts in which different pooling mechanisms are introduced. Feature pooling [39] can extract specific features from a given patch from an input image. The architecture uses CNN as local patch descriptors, and the CNN is combined with a feature pooling strategy. Further, features of each patch are again placed in a grid, and CNN is used to aggregatethe local descriptors. The problem with the architecture is that the local descriptors are not resolution-independent. Local-descriptor based architectures mostly introduce new aggregation and pooling techniques than investigating patch-based feature extraction techniques. Selective convolutional descriptor aggregation [40], sum-pooled convolutional [41], part-based weighting aggregation [42], NetVLAD [43] are some of the examples of local-descriptor feature aggregation techniques. Such techniques are either feature-centric or aggregation-centric. Therefore, in most cases, either the rich features are poorly aggregated, or the shallow features are strongly aggregated. In contrast to the other local descriptors, the proposed AutoRet focuses on both feature extraction and feature aggregation. AutoRet extracts local features from the \(3\times 3\) patches from the input image. Moreover, as the other local descriptors miss the resolution-independent features, AutoRet focuses on such concern by using Spatial Pyramid Convolution (SPP) [44]. Moreover, to firmly increase the range of feature extraction for a given patch, Atrous Spatial Pyramid Convolution (ASPP) [45] is used. Both SPP and ASPP are used for object detection and segmentation purposes, designed for pinpointing object features from a given input. Consequently, AutoRet is trained based on deep metric learning. Deep metric learning solves the problem of complex data connectivity issues for self-supervised training [14]. Self-supervised learning retrieval systems often apply clustering to generate pseudo labels [20; 46]. Therefore, some self- supervised retrieval systems require a pre-defined number of classes [46; 47]. In contrast, AutoRet does not require any pre-defined number of classes. AutoRet specifically implements the AutoEmbedder [48] strategy for training the embedding model. AutoEmbedder approach can work in a self-supervision [49], which can be also mixed with labeled data. Therefore, the proposed algorithm can be applied to partially labeled data.
## 3 Results and Discussion
### Results Analysis
The integrated model has the capability to accurately replicate the abrupt expansion of the breach breadth resulting from the instability of the wedge, as depicted in Figure 11b, and the variation in the breach bottom elevation fits well with the actual data, as shown in Figure 11c. As shown in Figure 12: the water depth map of the upstream and downstream areas at various time points during the dam failure. This map clearly illustrates the progression of the flood in the downstream area, the decrease in water level in the upstream reservoir, and the steady reduction in the flooded area. Figure 11: The Comparison between the measured and the simulated of the Tangjiashan landslide dam failure: (**a**) outflow discharge; (**b**) breach width; and (**c**) breach bottom elevation. The discharge process measured at the downstream Beichuan and Tongkou locations was chosen for comparison with the simulation findings of the coupling model. Among these, the distance between the Beichuan and the barrier dam is 7 km, while the Tongkou is located 33.5 km away from the barrier dam. Figure 13 illustrates that the flood arrival time at Beichuan is 1 h ahead of the actual time, and the peak discharge of the breach is 315 m\({}^{3}\)/s (4.78%) higher than the measured value. The flood discharge process at the Tongkou dam is accurately represented during the rising stage. However, the peak discharge is lower than the reported value of 594 m\({}^{3}\)/s (10.61%). Additionally, the flood flow is generally lower during the falling stage. The primary cause of the inaccuracy is that during the initial phase of the dam break, the simulated discharge process exceeds the measured discharge process. This results in an accelerated rise in discharge and an increase in the  
Figure 12: Water depth maps of the upstream and downstream at different times of Tangjiashan dam break flood. peak discharge at Beichuan. The discrepancy between the discharge attenuation rate observed in the simulation results and the measured discharge attenuation rate is primarily due to the precision of the topography data. The simulation of the Tangjiashar Barrier Lake break event demonstrates that the integrated model accurately replicates the peaking discharge, breach width, bottom elevation, flood propagation, and typical section discharge process. This indicates that the model is effective in simulating the dam break event of the Tangjiashan Barrier Lake.
## 3 System Model
### Energy Model  
The energy of a node is directly proportional to propagation distance '\(n\)' when the threshold distance (\(n_{0}\)) exceeds the value of '\(n\)'. The following expression represents the total energy used by each node to transmit the '\(M\)'-bit data packet.  
\[E_{tx}(M,n)=E_{elec}\times M+\varepsilon_{am}\times M \tag{1}\]  
\[\varepsilon_{am}=\left\{\begin{array}{ll}\varepsilon_{f}\times n^{2},&\mbox{ when }n\leq n_{0}\\ \varepsilon_{p}\times n,&\mbox{when }n>n_{0}\end{array}\right. \tag{2}\]  
where \(E_{tx}\) is the total energy required for data transfer, \(E_{elec}\) is the dissipation of energy per bit, \(\varepsilon_{f}\) is the energy used for amplification in the free space model, \(M\) is the number of bits, and \(n_{0}\) is the threshold transmission distance.  
The receiver's energy consumption is given by  
\[E_{rx}(M)=E_{elec}\times M \tag{3}\]  
\[E_{sum}=E_{rx}+E_{tx} \tag{4}\]  
where \(E_{rx}\) is the total energy required for received data and \(E_{sum}\) represents the total energy loss for a WSN.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 3 Constellation Optimization Design
### Optimization of Satellite and Payload Deployment
The function of lines 13 in Algorithm 2 is mainly to calculate the three domains' indicators. The complexity of such calculation is \(O(3 LN^{2})\). Therefore, the complexity of the whole algorithm is \(O(LN^{2})\), which is an acceptable amount of calculation.
# Eight Element Wideband Antenna with Improved Isolation for 5G Mid Band Applications
## 5 Results and Discussion  
Figure 19 illustrates the fabrication of the proposed eight-element antenna on a flexible PET substrate. Detailed analysis of the proposed antenna in terms of the S-parameters, radiation patterns, MIMO performances, and comparative analysis with existing works in the literature are discussed in the further section.
## 5 Conclusions  
The databases of tide gauge records include many segmented records, where data originate from tide gauges having different sea and land contributions to the relative sea level signal, and the segments are misaligned, or suffer from other quality issues, such as earthquakes. These segmented records should not be considered as a single tide gauge record for assessing sea level rate of rise and acceleration. Many records in the PSMSL and NOAA database are segmented, and they should be quality flagged.  
We proposed a simple technique to find alignment inaccuracies, by placing breakpoints in the gaps between every two segments and computing the relative offset between every two segments needing the same values at the breakpoint from left and right extrapolations, to test suspicious alignments. Applied to the records of Aden, Mumbai, and Karachi, the technique showed inaccuracies in the alignments of the different segments. Extremely worrying is the misalignment of the reading from the novel tide gauges, contemporarily set up in the three locations (as well as many other locations worldwide) about the year 2007, versus the reading of the historical tide gauges for the same locations.  
While breakpoint techniques may be used to discover misalignments, their efficacy strongly varies from case to case, depending on the length of the segments and the phasing of the periodic oscillations. Segmented records should be quality flagged. No use should be made of records resulting from the composition of segments to infer sea level rates of rise or accelerations. If a tide gauge record has many gaps, there are doubtful alignments, it originates from different tide gauges, the record is affected by crustal movements such as earthquakes, the instrument has been damaged, the land below the tide gauge is subsiding, or it suffers from other quality issues, it should not be considered a single quality record, but quality flagged and disregarded for the purpose of computing rates of rise and accelerations over the full time of the record. There is a clear need for quality assurances for all the data sets that serve the purpose of supplying vital information to policymakers. The more reliable the data sets, the better the policy that can be based on these data.  
**Author Contributions:** This is a single author contribution. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The author declares no conflict of interest.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## Appendix A Channel Models
### Air-to-Ground (A2G) Communication Channel Model  
The utilized air-to-ground propagation model is based on the probability of line of sight and is used for UAV-to-terrestrial-vehicle communication.  
In time slot \(t\), the path losses for LoS and NLoS from \(i^{th}\) UAV \(U_{i}\) to \(j^{th}\) vehicle \(V_{j}\) are given by:  
\[PL_{LoS,U_{i},V_{j}}(t)=PL_{FS,U_{i},V_{j}}(t)+\eta_{LoS}\] (A1) \[PL_{NLoS,U_{i},V_{j}}(t)=PL_{FS,U_{i},V_{j}}(t)+\eta_{NLoS}\] (A2)  
where \(PL_{FS,U_{i},V_{j}}(t)\) is free space path loss, which is calculated as:  
\[PL_{FS,U_{i},V_{j}}(t)=20 log(4\pi d_{U_{i},V_{j}}(t)f/c)\] (A3)  
In time slot \(t\), a connection between \(i^{th}\) UAV \(U_{i}\) and \(j^{th}\) vehicle is LoS with a probability, \(p_{LoS,U_{i},V_{j}}(t)\) or NLoS with a probability, \(p_{NLoS,U_{i},V_{j}}(t)\). They are calculated as:  
\[p_{LoS,U_{i},V_{j}}(t)=\frac{1}{1+m\times exp(-n(\theta_{i}(t)-m ))}\] (A4) \[p_{NLoS,U_{i},V_{j}}(t)=1-p_{LoS,U_{i},V_{j}}(t)\] (A5)  
Then, the average received power of \(U_{i}\) from \(V_{j}\) is  
\[P_{U_{i},V_{j}}(t)=P_{tx,U_{i}}+G_{tx,U_{i}}+G_{rx,V_{j}}-\bar{P}L_{U_{i},V_{ j}}(t)\] (A6)where the average path loss is  
\[\bar{P}L_{U_{L},V_{j}}(t)=p_{LoS,U_{L},V_{j}}(t)PL_{LoS,U_{L},V_{j}}(t)+p_{NLoS,U_{ L},V_{j}}(t)PL_{NLoS,U_{L},V_{j}}(t) \tag{10}\]
## 3 Analysis of Strain Transfer Influential Factors
### The Influence of Stacking Numbers  
The average strain transfer ratios are compared by changing the stacking numbers of CFRP laminates. Stacking numbers are set to be 4, 8, 12, 16, and 20 which are commonly used for the fabrication of CFRP laminates. To improve the universality of the comparison, the configuration of CFRP laminates is designed by repeating three kinds of stacking units \([0/0/0/0]\), \([45/-45/45/-45]\), and \([90/90/90/90]\) separately. The average strain transfer ratios are calculated with different stacking numbers as shown in Figure 5. It can be observed that the ratios are significantly affected by the stacking numbers. All of the three curves show downward trends with the stacking numbers increasing, but the trends weaken gradually. The ratio of \([0/0/0/0]\) stacking unit is the highest, while the ratio of \([90/90/90/90]\) stacking unit is the lowest for the same stacking number. When stacking numbers increase from 4 to 20, the ratios fall by 7.2%, 11.9%, and 15.4% for stacking units \([0/0/0/0]\), \([45/-45/45/-45]\), and \([90/90/90/90]\) respectively. Therefore, it can be concluded that the average strain transfer ratios decrease with the increase of stacking numbers, and the decreasing trend is sharper for CFRP laminates with bigger direction angles.  
Figure 4: Strain transfer distribution ratio curves with stacking directions \([0/0/0/0]_{48}\), \([45/-45/45/-45]_{48}\), and \([90/90/90/90]_{48}\).
## 8 Results and Discussion
### Selection of APs with K-Means  
Three kinds of approaches to the selection of APs were conducted on each cluster at the same time: random, strongest, and maximum spatial variance. Figure 12 shows the CDF of error for the selected approaches with q = 20, and AP reduction is 50% (number of APs = 17). The maximum spatial variance exhibits superior performance as compared with the other selected APs approaches.  
Furthermore, the performance of the maximum spatial variance with 17 APs (50% reduction) is similar to the performance of all APs (34 APs). This confirms previous work [11]. We found the redundancy of MSSID occurred when SSIDs were exploited in the same fingerprinting vectors.  
Table 5 summarizes the accuracy of the proposed system with various kinds of selection APs. The selection of informative APs leads to a reduction in computational cost of 50%, as well as the reduction that comes from the clustering technique. The performance of the K-means-based proposed system with various values of q (number of clusters) is illustrated in Figure 13. As we can see in the figure, the average error is decreased when the number of informative APs is increased. The maximum spatial  
Figure 11: K-means clustering. (**a**) Cumulative Distribution Function (CDF) of Error; (**b**) mean error versus number of clusters.  
Figure 10: K-means clustering. (**a**) With a single SSID; (**b**) with multiple SSIDs.
## 2 Materials and Methods
### Climate Data and Land Cover Map  
To assess the interaction of environmental drivers and vegetation, monthly air temperature (at a 2 m height) and soil moisture content (surface 0-7 cm depth, root zone 28-100 cm depth) based on ERA-5 reanalysis data were downloaded from Copernicus Climate Change Service ([[https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)]([https://cds.climate.copernicus.eu/](https://cds.climate.copernicus.eu/)), accessed on 1 July 2020).  
Global monthly precipitation based on Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG, Version 6, Final run, 2007-2019) at a monthly scale and a 0.1\({}^{\circ}\) spatial resolution was collected from the NASA Precipitation Processing System ([[https://pps.gsfc.nasa.gov/](https://pps.gsfc.nasa.gov/)]([https://pps.gsfc.nasa.gov/](https://pps.gsfc.nasa.gov/)), accessed on 1 August 2020).  
The National Dynamic Land Cover Dataset (DLCD) was used in this research, obtained from Geoscience Australia and the Bureau of Agricultural and Resource Economics and Sciences ([[http://www.ga.gov.au/scientific-topics/earth-obs/landcover](http://www.ga.gov.au/scientific-topics/earth-obs/landcover)]([http://www.ga.gov.au/scientific-topics/earth-obs/landcover](http://www.ga.gov.au/scientific-topics/earth-obs/landcover)), accessed on 1 July 2020). Given that some biome types were only covered by a few pixels over the study region, closed Tussock grassland, dense shrubland, and closed forest were, respectively, re-grouped into open/closed Tussock grassland, dense/open shrubland, and closed/open forest (Figure 1). This dataset, validated with abundant field sites, was aggregated to a 0.5\({}^{\circ}\) spatial resolution by the most frequent values.