esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Carsharing Fleet Placement in Round-Trip Carsharing Service
However, car relocation is more difficult and more expensive, which is an even bigger burden to carsharing operators. In addition, with the trend of electronic cars, a charging station is easier to implement in the station-based services due to having fewer stations to implement than the free-float services. Therefore, a satisfactory solution associated with carsharing is multifarious. The work in this article is concerned with maximum user coverage and ease of access to the service (i.e., shortest distance to a station and flexibility in returning). It comprises the following:  
1. Developing a concept of station and their locations that maximize user coverage while giving a certain degree of flexibility when returning a car;
2. Maintaining the right balance between user coverage and ease of access to the service;
3. Considering or designing a suitable metric which can be used to determine the ease of access for users at a global scale. In this work, we propose a new method attempting to optimize the fleet placement in the station-based round-trip, will will be the first to tackle fleet allocation in round-trip carsharing. The model of this problem is called fleet placement problem (FPP). Fleet placement is really tedious and is usually performed manually by experts and hence is prone to errors due to lack of precision. The proposed methodology aims to maximize customer coverage, while minimizing the maximum walking distance between customers and the nearest vehicle. These two objectives are in conflict, thus, resulting in a bi-objective problem. Unlike previous solutions, the proposed model incorporates highly detailed street-level map data containing footprints of the buildings. The contributions proposed in this work are: mathematical formulation of the novel fleet placement problem (FPP) and its NP-hardness proof, correlation analysis of the two problem objectives, and the comparison of results between the manual placement and state-of-the-art heuristic and metaheuristic algorithms. The remainder of this paper is organized as follows. Related work on fleet location for carsharing services and similar location problems are presented in Section 2. The formulation of the FPP problem and the methodology to solve it are detailed in Section 3. In Sections 5 and 6 results from executions on real city instances are presented and discussed. Finally, conclusions and perspectives are provided in Section 7.
## 3 Results
### Physical-Mechanical Properties
#### 3.1.1 Physical Characterisation  
After sample elaboration, the main physical properties were collected and are shown in Table 4.  
Surface hardness was measured using a Baxlo Shore C durometer (Barcelona, Spain). As RGW was placed at the top of the 1 LU samples, surface hardness measurements are similar to those without the RGW layers. The main difference occurs when RGW is placed in the middle of the sample. E-0.7-P obtained the highest value (89 \(\pm\) 3 Shore C) and E-0.7 attained the lowest value (85 \(\pm\) 4 Shore C). This property remained almost the same for all the samples depending on perlite addition.  
Apparent density and thermal conductivity are the most important physical properties as they establish the suitability and viability of developing and commercialising this material. There exists a correlation between both values. The bigger the apparent density, the higher the thermal conductivity. In this study, the apparent density was reduced by  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sample** & **Surface Hardness (Shore C)** & \(\rho\) (kg/m\({}^{3}\)) & \(\Delta\)\(\rho\) & \(\lambda\) (mW/m\(\cdot\)K) & \(\Delta\)\(\lambda\) \\ \hline E-0.7 & 85.0 \(\pm\) 4 & 1100.0 & - & 300.00 & - \\ E-0.7-P & 89.0 \(\pm\) 3 & 1000.0 & \(-\)9\% & 225.00 & \(-\)25\% \\ E-0.7-1 LU & 85.0 \(\pm\) 3 & 798.6 & \(-\)27\% & 216.67 & \(-\)28\% \\ E-0.7-2L & 88.0 \(\pm\) 3 & 736.1 & \(-\)33\% & 203.85 & \(-\)32\% \\ E-0.7-P-1 LU & 89.0 \(\pm\) 4 & 580.6 & \(-\)47\% & 183.24 & \(-\)39\% \\ E-0.7-P-2L & 87.0 \(\pm\) 2 & 555.6 & \(-\)**49\%** & 146.83 & \(-\)**51\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical values obtained from physical characterisation tests.  
up to 49% in E-0.7-P-2L (555.6 kg/m\({}^{3}\)) compared to E-0.7. The addition of RGW led to a 27-33% reduction in apparent density. In terms of apparent density, there is no significant difference observed whether one or two RGW layers were added.  
Regarding thermal conductivity, there is a progressive decrease as the RGW was being added. This difference increased when both perlite and RGW were added. E-0.7's thermal conductivity value (300 mW/m\(\cdot\)K) was the highest, while E-0.7-P-2L (146.83 mW/m\(\cdot\)K) was the lowest. This leads to a potential thermal conductivity reduction of up to 51%. Perlite addition reduced this value by up to 25%, while RGW reached a 32% reduction. With this, all the samples assessed passed the EN 14246 standard [45] requirements related to thermal conductivity. These results will be discussed and compared with the literature results in the Discussion section.
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
## Materials and Methods
### Data
#### 2.2.3 MODIS GPP  
The GPP is the largest carbon flux into terrestrial ecosystems. Many peatlands include forests in the area. The PDF flux tower site also has a large forested area of \(\sim\)50 km\({}^{2}\). As usual for tropical forests, the photosynthesis in this area is high (more than 10 g C/m\({}^{2}\)/day). The 8-day MODIS GPP product (MOD17A2H) with 500 m resolution was used to estimate from the regional to national scales of GPPs for a long-term period (2002-2018). MOD17A2H (Collection 6) has improved its spatial resolution by using high-resolution reanalysis meteorological data (modern era-retrospective-analysis for research and applications (MERRA)-2 of Global Modeling and Assimilation Office of the National Aeronautics and Space Administration (NASA)) [39]. The MODIS GPP algorithm is a typical biochemical model that uses light use efficiency for each biome type based on the land cover map (MCD12Q1). Temperature and water stresses are the factors suppressing the potential GPP for the vegetation type. Absorbed photosynthetically active radiation (APAR) was calculated with solar radiations of reanalysis data, and the fraction of APAR (fAPAR) was multiplied with the incident PAR (IPAR) on the vegetative surface. Uncertainties in the data exist due to low PAR under the cloud-covered situations [40]. The MODIS sensor was installed on a Terra satellite, and its revisit time was every 10:30 in local time [34].
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 4 Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
### Efficiency Analysis  
To theoretically prove that the algorithm can satisfy the requirement of fault location speed, this section describes application of the time complexity of the aggregation analysis algorithm pseudocode to argue the computational efficiency of the algorithm.  
Lines 1-2 of DACFL set the initial pointer position and calculate the approximation gain of the starting section, which will be executed once in each recursion. Lines 3-6 traverse the sections of the distribution network, each of which takes constant time. The process will be interrupted by the appearance of T-type sections but can be continued in the subnetworks \(\boldsymbol{G}_{u}\) and \(\boldsymbol{G}_{v}\). According to aggregation analysis, lines 3-5 are executed once for each section of the distribution network. Line 7 determines the subnetwork type, executed once at each recursion. Line 8 and lines 10-32 correspond to the base and recursive cases, respectively, and are executed once per recursion at most. Each line takes constant time, except for lines 8 and 21. Lines 8 and 21 find the maximum value from the values of \(j-i+1\) and \(k-i+1\). Line 8 is executed for the base case. Line 21 is executed for the recursive case containing T-sections, but the same will be continued in the next recursion. Applying aggregation analysis, eventually, the approximation gain of each section will be taken out and compared once; thus, the overall number of operations in lines 8 and 21 is the number of sections in the distribution network.  
Let the distribution network contain \(n\) nodes, and the number of algorithm recursions is \(l\). \"Progressive notation\" is applied to represent the operation cost and total running time of each line [32], and the analysis results of this algorithm efficiency are summarized in Table 4.  
Figure 5: Flowchart of the algorithm.  
According to the \"Divide\" step of the pseudocode DACFL, the number of recursions depends on the number of downstream sections of the T-type sections in the distribution network; thus, \(l<n\). Combining the analysis results of Table 4, the time complexity of DACFL is  
\[\Theta(l)+\Theta(n)+\Theta(l)+\Theta(n)+\Theta(l)+O(l)+O(l)=\Theta(n) \tag{34}\]  
It can be seen that the pseudocode DACFL has a linear level of time complexity, thereby proving the efficient computational capability of the algorithm in this paper. It can be foreseen that the method proposed in this section has great potential for improving the computational efficiency of fault location in the context of large-scale distribution networks.
## 3 Results  
### Sediment Yield Volumes under Rainfall Events  
Figure 8a displays PWP evolution with time under different rainfall intensities. At the first minute, PWP increases rapidly in part because of surface water runoff concentration at the foot of the slope. High simulated rainfall intensities in our experiments result in the rapid appearance of surface water runoff. Figure 8b,c display moisture content at the top and middle of the slope, respectively.  
Figure 7: Lateral view of eroded soil sediment (debris).  
Apparently, the initial moisture content at the middle of the slope is higher than that at the top of the slope. Also, the moisture content at the middle of the slope increases faster compared with the moisture content at the top.  
Total soil sediment yield increases with rainfall duration for each intensity sequence. Similarly, at the same time step sediment, yield increases with rainfall intensity. Under 15 mm/h rainfall intensity, no soil loss occurred and thus there was no sediment data. Figure 9a shows a 3D curve of rainfall intensity, rainfall duration, and sediment. Figure 9b shows the 3D vertical view between rainfall intensity, duration, and sediment. Table 1 lists two potential fitted equations: (1) considering rainfall intensity and duration affecting the sediment separately, and (2) considering cumulative rainfall affecting the sediment. (An obvious linear relationship is observed.)  
Figure 8: Hydrological monitoring data: (**a**) p1, (**b**) m1 and (**c**) m2.  
Figure 9: 3D fitting curve (rainfall intensity (mm/h), rainfall duration (min), and sediment (e-3m\({}^{3}\)).  
(**a**) Optimum 3D fitting curve; (**b**) shadow map of optimum 3D fitting curve.  
Percentage of erosion can describe how much soil is eroded by the rainfall events in a certain area by percentage. Percentage of erosion (\(E\)) should follow the equations: \(E=0.013((ax+b\)y)+c) \(\times\) 100% or \(E=0.013\left(a(xy)+b\right)\times\) 100%.  
As shown in Table 1, the governing equation between cumulative rainfall and volume of eroded soil sediment is linear, as follows:  
\[(I-r)S_{l}\cdot t=a_{1}V \tag{10}\]  
\[R-R_{r}=a_{2}V \tag{11}\]  
where \(I\) is the rainfall intensity; \(r\) is the rainfall intensity threshold of sediment movement; \(S_{l}\) is the rainfall area; \(V\) is the volume of eroded sediment in time \(t\); \(t\) is the rainfall time; \(a_{1}\) and \(a_{2}\) are coefficients required by fitting the experimental data; and \(R\) and \(R_{r}\) are the cumulative rainfall and cumulative rainfall threshold of the initial sediment, respectively.  
Taking Equation (11) into Equation (9) in order to link the rainfall produces:  
\[L_{x\text{max}}=(2(R-R_{r})(\tan\alpha-\tan\beta)/a_{2}\tan\beta t\tan\alpha L_{ y})^{1/2} \tag{12}\]  
Considering the time domain, Equation (13) constructs the link between rainfall and maximum transport distance as follows:  
\[L_{x\text{max}}(t)=(2(R(t)-R_{r})(\tan\alpha-\tan\beta(t))/a_{2}\tan\beta(t) \tan\alpha L_{y}(t))^{1/2} \tag{13}\]
# Semantic Matching Based on Semantic Segmentation and Neighborhood Consensus
## 4 Experiments
### Application  
There are many applications of semantic matching. Here, we give an example of its application in label transfer. Manual labeling is very time-consuming work; however, if there are some known labels in the images, transferring them to other images through algorithms can greatly reduce labor costs. For example, we can transfer the foreground mask across images based on semantic consistency as shown in Figure 8. The first and third columns are semantically similar images. According to the estimated pixel-to-pixel semantic matches between them, the known foreground masks of one image are easily transferred to another one. The last column shows transfer results.  
Figure 8: Semantic label transfer. The second column shows the real foreground masks of \(A\). The last column is the estimated mask of \(B\), which is obtained by warping \(A\)’s mask according to the dense semantic matching between \(A\) and \(B\).  
Figure 7: Erroneous semantic correspondence due to occlusion. The orange solid line is the estimated match by our algorithm; the dashed line is the ground-truth match. The right image is a warping result according to dense semantic matching between source and target images, which should be semantically aligned with the target theoretically. The blue boxes mark the areas with mismatches and bad warping.
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 4 Hops-Based Back-Pressure Routing
### Queue Length-Based BP Routing  
BP routing originates from the wireless packet multi-hop network proposed by [PERSON] et al. in [37], which is an algorithm for dynamic traffic allocation based on the congestion gradient between adjacent nodes [22]. BP routing does not build a specific source-destination path, but only calculates the queue backlog for each slot. The data transmission path is determined according to the criterion of queue backlog maximization [12]. Given LSN \(G=(V,E)\), \((a,b)\) denotes the ISL between nodes \(a\), \(b\). At time slot \(t\), \(P_{a}^{c}(t)\) denotes the queue backlog at \(a\), while \(P_{b}^{c}(t)\) denotes the queue backlog at \(b\) at time slot \(t\). Thus, the queue backlog difference of packet flow \(c\) between \(a,b\) can be denoted by \(P_{a}^{c}(t)-P_{b}^{c}(t)\). Since multiple packet flows are usually converged on a single satellite, the backlog difference of the max queue on link \((a,b)\) can be defined as \(D_{ab}(t)\).  
\[D_{ab}(t)=\max_{c:(a,b)}[P_{a}^{c}(t)-P_{b}^{c}(t)]. \tag{7}\]  
On link \((a,b)\), the following strategy is adopted to assign the transmission rate to packet flow \(c\):  
\[\text{Maximize}:\sum_{a=1}^{N}\sum_{b=1}^{N}\mu_{ab}(t)D_{ab}(t) \tag{8}\]  
\[\text{s.t.}\mu_{ab}(t)\in\Gamma_{s(t)}, \tag{9}\]  
where \(\mu_{ab}(t)\) denotes the transmission rate of link \((a,b)\). \(\Gamma_{s(t)}\) denotes permitted transmission rate matrices in the network. Based on the above strategy, BP routing is suitable for all kinds of multi-hop networks to maximize throughput, which is satisfied under any flow arrival rate and channel state probability.
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 5 Experimental Analysis and Result
### Comparisons with Embedded Platforms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset and Type of Module** & & **The Best of FPGA** & **Jetson Nano** & **Raspberry** & **Nucleo** \\ \hline \multirow{8}{*}{Binary classification} & \multirow{4}{*}{\begin{tabular}{c} Cutter Oil \\ (k-NN) \\ \end{tabular} } & Accuracy & 99.36\% & 99.36\% & 99.36\% & 99.36\% \\  & & Latency & 7.81 us & 5.90 us & 10.51 us & 180.00 us \\  & & Power & 134 mW & 2120 mW & 1480 mW & 102 mW \\  & & PLP & 1046.54 & 12,508 & 15,554.8 & 18,360 \\  & & CPLP & 39,768.52 & 1,113,212 & 855,358.5 & 587,336.4 \\ \cline{2-7}  & & Accuracy & 98.73\% & 98.73\% & 98.73\% & 98.73\% \\  & & Latency & 11.12 us & 5.97 us & 18.77 us & 300.00 us \\  & & Power & 140 mW & 2110 mW & 1470 mW & 101 mW \\  & & PLP & 1556.8 & 12,596.7 & 27,591.9 & 30,300 \\  & & CPLP & 59,158.4 & 1,121,106 & 1,517,279 & 969,297 \\ \hline \multirow{8}{*}{Multiclass classification} & \multirow{4}{*}{\
\begin{tabular}{c} Cutter Oil \\ (k-NN) \\ \end{tabular} } & Accuracy & 99.36\% & 99.36\% & 99.36\% & 99.36\% \\  & & Latency & 7.81 us & 5.44 us & 10.25 us & 180.00 us \\  & & Power & 135 mW & 2120 mW & 1470 mW & 102 mW \\  & & PLP & 1054.35 & 11,532.8 & 15,067.5 & 18,360 \\  & & CPLP & 40,065.3 & 1,026,419 & 828,561.8 & 587,336.4 \\ \cline{2-7}  & & Accuracy & 83.69\% & 83.69\% & 83.69\% & 83.69\% \\  & & Latency & 5.60 us & 1.37 us & 5.82 us & 84.00 us \\  & & Power & 121 mW & 2060 mW & 1350 mW & 101 mW \\  & & PLP & 677.6 & 2822.2 & 7857 & 8484 \\  & & CPLP & 25,748.8 & 251,175.8 & 432,056.4 & 271,403.2 \\ \hline \multirow{8}{*}{Regression} & \multirow{4}{*}{
\begin{tabular}{c} Rain \\ (ANN) \\ \end{tabular} } & R2 & 0.8583 & 0.8583 & 0.8583 & 0.8583 \\  & & Latency & 8.73 us & 7.81 us & 35.77 us & 261.00 us \\  & & Power & 135 mW & 2140 mW & 1480 mW & 102 mW \\  & & PLP & 1178.55 & 16,713.4 & 52,939.6 & 26,622 \\  & & CPLP & 44,784.9 & 1,487,493 & 2,911,149 & 851,637.8 \\ \cline{2-7}  & & R2 & 0.9979 & 0.9979 & 0.9979 & 0.9979 \\  & & Latency & 13.47 us & 7.94 us & 43.73 us & 364.00 us \\  & & Power & 148 mW & 2610 mW & 1530 mW & 103 mW \\  & & PLP & 1993.56 & 20,723.4 & 66,906.9 & 37,492 \\  & & CPLP & 75,755.28 & 1,844,383 & 3,679,210 & 1,199,369 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Breakdown of platform implemented results.
# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 2 Algorithm Principle  
The basic principle of the proposed algorithm mainly includes five processes. Firstly, an improved voxel filter is used to down sample the source point clouds and the target point clouds, respectively. Secondly, the feature points are extracted by the ISS feature points detection algorithm to obtain a set of points with rich geometric feature information. Then the extracted feature points are described with the USC descriptor to form ISS-USC feature points descriptor. Next, the improved RANSAC algorithm is used for coarse registration to obtain the optimal initial transformation matrix. Finally, the ICP algorithm based on KD tree is used for fine registration. According to the final obtained transformation matrix, the source point clouds are transformed into the coordinate system under the target point clouds to complete the registration. The algorithm flow is shown in Figure 1.  
Figure 1: Algorithm flow chart.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 2 Background and Concept
### Service-Oriented Middleware (SOM) Architectures for WSNs  
The Service-Oriented Middleware (SOM) architectures are used to make service available and easily accessible by using standardized protocols without any concern about the details of implementation. SOM architecture helps WSN applications to develop over traditional development platforms which address these challenges. The WSNs connect to the SOA through different elements such as the middleware. In [17], SOM architecture considers the WSN as the service provider for user applications [17]. The middleware is implemented in or out of SOA, which is important for integrating/exchanging messages. Broker Registry is also responsible for allowing service discovery and making communication easier in SOA. Middleware has the reliability of messaging and guaranteeing that the messages reach the receivers. It has the ability to store messages for a long time and send multiple messages in parallel, resulting in increased speed in the execution of data messaging.  
The heterogeneous nodes in WSNs can impact the entire network's capability. In the case of a mismatch in data formats and structure exchange between nodes, the system should provide a mechanism for heterogeneous nodes to handle mismatch data, since all nodes communicate only with nodes of a similar data structure and exchange data formats model. The mismatching of communication types exists due to the implantation of different formats of data. There are some techniques that separate the service form application, i.e., dynamic allocation of resources and function level, which allow different applications access to similar nodes. This causes limitation, which can increase the complexity of the middleware's developed code. Most data aggregation techniques within SOA deal with simple data such as temperature, humidity and others. In this case, it is difficult to deal with complex data such as images and videos. The distributed middleware is used to combine services via networks. Logically, the network is located in the network layer but physically exists in the nodes [18]. The Service-Oriented Software Architecture is based on an adaptive middleware that is used for sensor networks. These nodes are connected only by the services of the middleware [19].  
Figure 3: Service-Oriented Architecture (SOA) [15].
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Solution of eMBB Allocation Problem
#### 4.1.1 Stage 1: RB Allocation  
For any given power allocation \(P\) and RIS phase shift matrix \(\Phi\), we propose a greedy algorithm-based RB allocation method to complete the optimal allocation of RBs. The algorithm solves the problem that the resource allocation cannot be completed properly in the case of a relatively low number of resources. Assume that all eMBB users have the same transmit power and satisfy the constraint (8). Each eMBB user selects and determines the RB allocation based on the channel condition and proportion. In each round of RB allocation, the eMBB user with the smallest proportional capacity has the priority. The detailed process is described in Algorithm 1.  
```
Input Number of RB \(R_{e}\) Proportionality constants \(\xi=[\varphi_{1}:\varphi_{2}:\cdots:\varphi_{E}]\) Output the allocation result Procedure /* Step 1: Determine the number of RBs initially assigned to the user */  initialize each user number of RB \(R_{e}\), make its meet: \(R_{1}:R_{2}:\cdots:R_{E}=\varphi_{1}:\varphi_{2}:\cdots:\varphi_{E}\) for\(e=1\):E \(R_{e}=\lfloor q_{e}R\rfloor\) \(R^{\prime}=\sum_{e=1}^{E}R_{e}\) /* Number of allocated RBs*/ \(R^{\prime}=R-R^{\prime}\) /* Number of unassigned RBs*/ endforfor\(e=1\):E if\(R_{e}=0\) \(R_{e}=1\) \(R^{\prime}=R^{*}-1\) endif endfor Allocate RBs to users based on proportions */ for\(e=1\):E  Allocate unallocated RBs with maximum gain to users \(R_{e}=R_{e}-1\) endforfor\(e=1\):E &\(R_{e}\
eq 0\)  Divide by \(\xi\) to calculate the capacity  According to the greedy strategy, get the minimum capacity in each iteration,  and choose the best RB for it endfor /* Step 3: Allocate the remaining RB according to the effective channel SNR of all users from best to worst, and each user can get at most one unallocated RB*/
```  
**Algorithm 1** RB allocation based on greedy algorithm