esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 2 UAV Routing with an Emphasis on the Problem Type, Transportation Mode, and Degree of Automation
### Based on Modes of Transport
#### 2.2.2. Maritime-Based Transportation (Vessel Routing)  
The other relatively well-researched transportation mode is the vessel routing or maritime routing problems [50]. In maritime routing, in contrast to land-based modes of transportation, one generally must consider non-linear fuel-consumption models. The main concern with non-linear fuel-consumption models is that they make the solution of relevant models complicated [50; 51]. In this area, one also often encounters network design problems [50; 52] where the aim is to set up cyclical plans [53]. The same is often seen when designing, for example, airline flight schedules [54]. The focus of these is not dynamic routing such as covered by the traditional VRP and of particular relevance to the UAVRP.
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 4 EdgeX over Kubernetes
### Implementation of EdgeX over Kubernetes
The _kube-controller-manager_ ensures that the specified number of pod replicas operate in the cluster. Moreover, an EdgeX node can create, execute, and manage pods from the master node, and it can create a Kubernetes _Service_ through _kube-apiserver_. In this manner, Eok can dynamically distribute the necessary services to various EdgeX nodes through Kubernetes, and the distributed services can maintain high QoS by periodically managing resource status through _kublet_ and providing autoscaling through the HPA.
## 3 Results and Discussion  
### Absorption Spectroscopy  
Figure 1a,b shows the energy level diagram and the absorption spectra of two Tm\({}_{2}\)O\({}_{3}\):TeO\({}_{2}\)-Nb\({}_{2}\)O\({}_{5}\) glass samples with Tm\({}_{2}\)O\({}_{3}\) concentrations of 1.0 and 0.25 mol % in the range of 450-2050 nm. The other glass samples with different glass modifiers exhibit similar absorption characteristics but different absorption strengths, which will be discussed later. Table 1 summarizes the \(\Omega_{t}\)'s intensity parameters determined for three different glass hosts with 1 mol % ion concentration by employing J-O theory described above. In the radiative lifetime calculations for the decay from \({}^{3}\)F\({}_{4}\) and \({}^{3}\)H\({}_{4}\) levels to the ground state, we only used J-O intensity parameters derived from the absorption spectrum of the 1 mol % doped samples, where the signal to noise ratio is the highest. We also used the fact that the radiative lifetime is independent of active ion concentration. The average radiative lifetimes for the niobium containing glasses were determined as 2.76 ms and 0.37 ms for the sample TeNbTm, and 2.57 ms and 0.35 ms for the sample TeNbKTm. For the glasses containing zinc as a modifier (TeZnTm), we obtained average radiative lifetimes of 2.55 ms and 0.37 ms for the \({}^{3}\)H\({}_{4}\) and \({}^{3}\)F\({}_{4}\) levels, respectively.
## 2 Principle  
### The Basic Principle of Phase Measuring Deflectometry  
The PMD system is composed of an LCD screen, a camera, and the SUT. Structured light patterns displayed by the screen are reflected by the SUT and captured by the camera. Then, the slope data can be calculated by the system's geometrical model and calibration parameters. Finally, the slope data is integrated to obtain the SUT shape. The coordinates of the screen pixels and the coordinates of the SUT instead of the reference surface are obtained for the triangulation calculation. The slope data of the SUT is calculated as shown in Equation (1) [10; 22]:  
\[\begin{array}{c}\tan\theta_{x}(x_{m},y_{m})=\frac{\frac{x_{m}-x_{b}}{d_{m2 }x_{b}}+\frac{x_{m}-x_{c}}{d_{m2}x_{c}}}{\frac{y_{m2}}{d_{m2}x_{b}}+\frac{y_{m 2}}{d_{m2}x}}\\ \tan\theta_{y}(x_{m},y_{m})=\frac{\frac{y_{m}-x_{b}}{d_{m2}x_{b}}+\frac{y_{m 2}}{d_{m2}x_{b}}}{\frac{y_{m2}}{d_{m2}x_{b}}+\frac{y_{m2}}{d_{m2}x_{b}}}\end{array} \tag{1}\]  
where \(x_{m}\) and \(y_{m}\) are the coordinates of the reflection points; \(x_{s}\) and \(y_{s}\) are the coordinates of the screen pixels, which can be calculated by the phase-shifting algorithm [23; 24]; \(x_{c}\) and \(y_{c}\) are the coordinates of the camera; \(z_{m2s}\) and \(z_{m2c}\) are the distance between the SUT and the screen, respectively; and \(d_{m2s}=\sqrt{\left(x_{m}-x_{s}\right)^{2}+\left(y_{m}-y_{s}\right)^{2}+\left( z_{m2s}\right)^{2}}\) and \(d_{m2c}=\sqrt{\left(x_{m}-x_{c}\right)^{2}+\left(y_{m}-y_{c}\right)^{2}+\left( z_{m2c}\right)^{2}}\). Then, the SUT shape can be obtained by integrating the slope data using the Zernike modal method [25; 26; 27].
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Attitude Control
#### 4.3.2 Active Techniques
The comparison included the P + V controller, the P + V Double Integrator Controller, the Double Integrator Gain Tuning Controller, the 2 DOF Feed Forward Controller, the P + V Control Law Inversion Controller, the Open Loop Guidance Controller, and the Real-Time Optimal Controller. These are the standard controllers in the field. For the final control report, we tested and compared six other controllers based on controller quadratic cost, computation time, rise time, and stiffness. We measured these values based on how well the controller could move from rest to a position of one without any units. We then compared the results to those of a PD controller. Table 6 below shows the results. The different controllers have trade-offs, so to establish optimality the results depend on the specific system and its needs. For the CubeSat, low quadratic cost was the optimization criterion. Quadratic cost is a measure of acceleration over a time interval, which relates to power usage. Requirement 4.0 in Table 5 states that the power must be below 0.9 watts. This requirement was the most restrictive for the control system since the rise time requirement (requirement 3.0 was easily satisfied by all the controllers). As shown in Table 6, the PD controller has the lowest quadratic cost. To further verify the robustness of the PD controller, the rise time was measured as a function of the timestep. A low sensitivity would fulfill requirement 5.0. Since the Teensy microcontroller runs both the ACS and the flight code, the sensitivity of the rise time was tested to reduce the computational load for the ACS and integrate the controller with the rest of the flight software. It was found that the timestep had very little impact on the rise time. The flight computer could therefore run at a larger timestep if needed. See Table 7 below. The PD controller was confirmed as the best choice for our ACS system because of the minimal quadratic cost and the stability of the rise time for different timesteps.
# GPU Algorithms for Structured Sparse Matrix Multiplication with Diagonal Storage Schemes +
## 2 Related Work  
In the area of dense matrix multiplication, achieving high efficiency via GPU computing is well studied [9; 23; 24; 25]. Early work [26] on dense matrix multiplication using GPUs framed the computation as a multitexture multiplication and blending operation. The user-programmable shared memory provided by subsequent GPU architectures enabled higher-performing data-parallel schemes via shared memory and registers [27; 28]. The evolution of GPU math libraries employed more sophisticated code generation and kernel selection components to improve the performance of dense matrix multiplication. NVIDIA's cuBLAS library provides an extended _cublasGememEx_ interface where carefully trained heuristics allow the caller to select from among 24 different dense matrix multiplication algorithms. For the purpose of optimization, it is common for cuBLAS to consider a wide range of data-parallel and fixed-split [29] variants implemented via these algorithms and assemble each variant into its own architecture-specific kernel program.  
Unlike dense matrix multiplication, the implementation of sparse matrix multiplication in the current generic architectures is significantly more challenging. Among others, as identified by [PERSON] et al. [30], variations in the nature of the sparsity introduce (i) load balancing problems among threads, (ii) optimization problems associated with irregular memory access patterns, and (iii) memory management problems due to the difficulties associated with the prediction of the output size. The authors also pointed out that GPU implementation poses further challenges in sparse matrix multiplication. In addition to the serious workaround required for the limited amount of shared memory available in GPU architecture, any divergence in the execution paths of a warp of threads against the single-program, multiple-data (SPMD) nature of GPU thread execution contributes to performance hindrance.  
The GPU-based implementation of sparse matrix multiplication is available in CUSPARSE [31] and CUSP [32]. CUSP is termed an \"expansion, sorting, and compression (ESC) method\" due to the nature of its operation where it expands all values arising from scalar multiplication, sorts them, and finally compresses the entries with duplicate column indices. Consequently, the computational load is shifted to finding an efficient parallel sorting algorithm for the GPU (i.e., Radix-sort). This library routine stores the entire output on the GPU itself. Consequently, it only works on instances whose output size fits in the available global memory [33]). In addition, due to great variability in size, density, and structure, researchers [8] pointed out that it is reasonable to design special storage formats for matrices with certain characteristics to achieve better computational performance in the GPU.
## 3 Data and Statistical Analysis  
All outcome measures were collected at baseline (T0) and at the end of the training program (T1). Robotic outcome measures were computed from data of joint rotation recorded at a 100 Hz sampling rate and filtered with a sixth order Savitzky-Golay low-pass filter (8 Hz cut-off frequency). Data were analyzed off-line (MATLAB 2019a, Mathworks Inc., Natick, MS, USA). Wilcoxon Matched Pairs Tests were conducted to compare all outcome measures at T0 and T1 within each limb. Wilcoxon Matched Pairs Tests were conducted also to compare the differences in the outcome measures at T1 between the trained and control limb. Significance was set to \(p\) < 0.05. Correlations were analyzed using linear regression models and goodness-of-fit measures were computed to compare experimental and regression results (SPSS Statistics for Windows, Version 26.0. IBM Corp.: Armonk, NY, USA).
## 2 Simulation Design for Rock Movement and Surface Subsidence in Key Strata
### Similar Material Simulation Experiment  
Based on the theory of physical similarity simulation, the mechanical characteristics of coal and rock, and the geological conditions of the study area, a physical similarity simulation model with a geometric similarity constant of 1/100 was established [36]. The dimensions of the experimental model are 300 cm (length) \(\times\) 20 cm (width) \(\times\) 155.5 cm (height), with a total research height of 71.5 cm. The mechanical parameters of the coal and rock are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{**Thickness**} & **Model** & **Tensile** & **Compressive** & **Cohesion** & **Bulk** & **Volumetric** \\  & & & **(m)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline
14 & Loess & 18.1 & 18 & 0.089 & 0.42 & 0.44 & 1134 & 16.3 \\
13 & Silstone & 16.63 & 16.5 & 2.1 & 31.2 & 0.72 & 738 & 23.4 \\
12 & Medium-grained & 5.08 & 5 & 2.42 & 28.4 & 1.56 & 1487 & 23.8 \\
11 & Mudstone & 2.66 & 2.5 & 1.33 & 13.2 & 0.73 & 733 & 27.6 \\
10 & Fine-grained & 1.6 & 1.5 & 1.95 & 28.6 & 2.43 & 1536 & 23.2 \\
9 & Silstone & 4.93 & 5 & 2.3 & 32.1 & 0.56 & 756 & 24.1 \\
8 & Coarse-grained & 10.77 & 11 & 2.54 & 26.4 & 1.53 & 1433 & 22.8 \\
7 & Mudstone & 1.2 & 1.5 & 1.29 & 12.9 & 0.69 & 752 & 28.1 \\
6 & Fine-grained & 1.23 & 1.5 & 1.94 & 27.9 & 2.50 & 1531 & 23.0 \\  & sandstone & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main mechanical properties of model materials.  
Figure 1: Layout of the working face in the study area.  
In order to establish the relationship between the rock displacement of key strata-bearing structures and surface subsidence, five displacement monitoring lines were set up in the upper part of the 1-2 coal seam. These monitoring lines were positioned at distances of 12 cm, 23 cm, 37 cm, 53.5 cm, and 63.5 cm from the coal seam roof, and they were labeled as A to E accordingly. Lines A and B were located in the lower and upper sections of the sub-key stratum, while lines C and D were positioned in the lower and upper sections of the main key stratum. Line E was situated at the surface. The spacing between each monitoring point was 15 cm. Additionally, a leveling instrument was installed on the surface to monitor surface subsidence characteristics.
## 6 Applications of Mathematical Models
### SIAR Model  
SIAR (stable isotope analysis in R) mixing model, first established by [PERSON] et al. [50], is a logic prior distribution built based on Dirichlet distribution with Bayesian framework. It is used to estimate possible proportional source contribution, and then to determine the probability distribution for the proportional contribution of each source to the mixture. By defining the \(j\) isotopes of n compounds from k sources, SIAR model can be expressed as  
\[X_{ij}=\sum_{k=1}^{K}P_{k}(S_{jk}+c_{jk})+\varepsilon_{ij}, \tag{6}\]  
\[S_{jk}\sim N(\mu_{jk},\omega_{jk}^{2}), \tag{7}\]  
\[c_{jk}\sim N(\lambda_{jk},\tau_{jk}^{2}), \tag{8}\]  
\[\varepsilon_{ij}\sim N(0,\sigma_{j}^{2}), \tag{9}\]  
where \(X_{ij}\) is the isotope value of j of mixture \(i\), \(P_{k}\) is the proportion of source \(k\), \(S_{jk}\) is the source value \(k\) on isotope \(j\) from source \(i\) which obeys the normal distribution whose mean is \(\mu\) and variance is \(\omega\), and \(c_{jk}\) is the fractionation factor of isotope \(j\) from source \(i\) which obeys the normal distribution whose mean is \(\lambda\) and variance is \(\tau\). \(\varepsilon\) is the residual error which represents the variance of other compounds that cannot be quantified, and its mean and standard deviation are 0 under normal conditions.  
SIAR model has been successfully applied in the source analysis of nitrate pollution as it takes into the identification of over three potential pollution sources and reduces the uncertainties of mass balance mixing model. With SIAR model, [PERSON] et al. [66] successfully quantified the contribution ratios of five potential nitrate sources (precipitation, nitrate nitrogen fertilizer, ammonium nitrogen fertilizer, soil nitrogen, manure, and sewage). [PERSON] et al. [4], by using SIAR model, found that the nitrate of the irrigation water of Yellow River basin was mainly came from domestic sewage and manure (contribution ratio: 61-69%) and chemical fertilizer (contribution ratio: 12-16%). The nitrate pollution of rivers mostly caused by chemical fertilizer (contribution ratio: 21-58%), soil N (contribution ratio: 16-35%) and sewage and manure (contribution ratio: 4-49%). In comparison to other models, the SIAR model has many advantages. For instance, it considers isotopic fractionation and is able to evaluate the contribution ratios of multiple (more than three) pollution sources. Nevertheless, any slight change in the isotopic composition of pollution source will lead to a significant change in the results of SIAR model [83]. In addition, the contribution ratios of different pollution sources calculated by SIAR model are ranges instead of determined values.
# Fuzzy Neural Networks--A Review with Case Study
## 3 The Selected Fuzzy Networks
### Scaled Deep Fuzzy Network with the Application of Ordered Fuzzy Numbes  
To construct a deep fuzzy neural network, a modification of the McCulloch-Pitts neuron model has been proposed. However, instead of using traditional numerical values, the inputs, weights, and outputs are represented using Ordered Fuzzy Number (OFN) notation [75]. These neurons are then assembled into a network composed of deep, fully connected layers. The OFN notation allows for expressing uncertainty and imprecision in numerical data. Arithmetic operations with OFNs are carried out through two main steps:  
Step 1: The process begins by multiplying each input value X by its associated weight W, followed by summing all the resulting products to calculate a final value \(S\).  
\[S=W_{0}+\sum_{i=1}^{n}X_{i}W_{i} \tag{18}\]  
Step 2: Then the output Y using OFN arithmetic is calculated:  
\[Y=f(S). \tag{19}\]  
The elements within sets S, W, X, and Y are expressed as values using OFN notation. Equations (18) and (19) correspond to those in the McCulloch-Pitts neuron model, but here the variables are represented with OFN notation. Initially, the inputs are multiplied by their respective weights, followed by the calculation of an activation function.  
This approach requires implementing multiple layers:  
* The first layer performs fuzzification to convert the input data into OFN notation,
* The final layer carries out defuzzification to process the network's output data,
* Intermediate (deep) layers adapt the network's learning and training algorithms to effectively work with OFN arithmetic within the network.  
The operational structure of the proposed deep network is shown in Figure 4.  
Figure 4: Proposed scaled fuzzy neural network with Ordered Fuzzy Numbers.  
A network based on these principles, incorporating a fuzzy learning algorithm, was created and tested. The implementation was performed using the Python programming language.  
This approach enables flexible scaling of the network and does not limit the format of input or output data, allowing the network to be tested on the complete Iris dataset. The example code for defining the network does not differ from the definition of deep networks in frameworks familiar to researchers:  
networkCSVs = OFNNNeuralNetwork()  
networkCSVs.add_layer(Dense(4,2))  
networkCSVs.add_layer(Tanh(0))  
networkCSVs.add_layer(Dense(2,1))  
networkCSVs.add_layer(Tanh(0))  
The definition of the network requires specifying the layers along with the number of neurons, while the prepared network training function takes parameters in the form of the number of epochs and learning_rate.
## 3 Suboptimal Subcarrier and Power Allocation  
Since the optimization problem in Equation (4) is generally a NP-hard combinatorial problem. Furthermore, with non-linear constraints in C5, the feasible set becomes non-convex [21]. In an OFDMA system with \(K\) users and \(N\) subcarriers, there are \(K^{N}\) subcarrier allocation selections. Then an optimal power distribution can be utilized to obtain the optimal solution, while guaranteeing proportional fairness. Ideally, the subcarrier and power allocation should be performed jointly to obtain the maximum sum rate of the system. However, this incurs a computational burden. Hence, the approach of separating subcarrier and power allocation can be adopted to reduce the complexity, as the number of variables in the optimization problem is almost halved.
## 3 Comparative Analysis of Numerical Forward Simulation and Soil Trench Modeling Experiments
### Numerical forward Simulation of the Dual-Frequency Excitation Method
#### 3.2.2 Acquisition of Numerical Simulation Results  
In the simulation, an ultra-low frequency sinusoidal current was used as the excitation. The frequency ratio \(S=f_{\mathrm{H}}\mathrm{/}f_{\mathrm{L}}=13\) was selected for the dual frequency current, with a high frequency of 4 Hz and a low frequency of 0.308 Hz. The current amplitude is 100 mA.  
The measured potential will have real and imaginary parts since the Cole-Cole complex resistivity model is introduced in the numerical forward simulation. In this case, the imaginary part should be removed, and the real part of the potential signal should be utilized to calculate the apparent amplitude frequency. In Figure 7, there are 60 measurement points from left to right, which corresponds to 60 groups of high and low frequency  
Figure 6: Setting of the Cole-Cole model.  
Figure 7: Schematic of the simulation model.  
potential values. Taking measurement points 1-20 as an example, the apparent amplitude frequency \(F_{\text{s}}\) is calculated according to 20 groups of high and low potential differences, and Table 2 is obtained as follows. Where \(\Delta V_{H}\) (\(\Delta V_{L}\)) represents the potential difference between points M and N when high (low) frequency current is supplied. Significant digits of \(\Delta V_{H}\) and \(\Delta V_{L}\) were set to five in COMSOL and take the numerical precision to four decimal places.  
Similarly, when the device coefficient \(K\) value at 1-20 measurement points is calculated, the apparent resistivity can be calculated according to the \(\Delta V_{\text{MN}}\) obtained from numerical simulation, and the apparent resistivity under 1-20 measurement points is made as Table 3: Data of apparent resistivity.  
\begin{table}
\begin{tabular}{c c c} \hline \(\Delta V_{\text{MN}}\)/mV & **Device Coefficient** K & \(\rho_{\text{s}}\)/\(\Omega\)**-m** \\ \hline  
\hline \end{tabular}
\end{table}
Table 3: Data of apparent resistivity.  
\begin{table}
\begin{tabular}{c c c} \hline \(\Delta V_{\text{H}}\)/mV (Potential Difference in the Real Part at 4 Hz) & \(\Delta V_{\text{L}}\)/mV (Potential Difference in the Real Part at 0.308 Hz) & \(F_{\text{s}}\)/\(\%\) \\ \hline
238.00 & 240.70 & 1.122 \\
158.20 & 160.10 & 1.187 \\
113.94 & 115.34 & 1.213 \\
75.96 & 76.91 & 1.235 \\
50.64 & 51.28 & 1.248 \\
33.76 & 34.17 & 1
# Stripe-Assisted Global Transformer and Spatial-Temporal Enhancement for Vehicle Re-Identification
## 1 Introduction
Furthermore, we introduce spatial-temporal probability (STPro) based on kernel density estimation and fuse it with a visual feature distance as the synthesized similarity of vehicles. Comprehensive evaluations demonstrate that the global feature can learn more details when guided and that STPro can provide complements for vehicle re-identification, effectively. Our main contributions are summarized as follows:  
Figure 1: **Two significant challenges in vehicle re-identification. ** (**a**) Different vehicles share a similar appearance. (**b**) The same vehicle exhibits different visual patterns, and the regions of key details are non-corresponding. 1. We propose a novel SaGT method for vehicle re-identification that learns a discriminative global feature with the assistance of local feature learning, while also considering the redundancy, thereby only using the global feature for inference. 2. We design an SFM to construct stripe-based features that effectively capture details in stripe regions. Additionally, we introduce an SaGL to encourage the global feature to learn discriminative information from stripe-based features. 3. We introduce STPro to offer an additional metric to enhance vehicle re-identification relying on only visual features. We also explore the fusion of the visual feature metric and STPro to further improve vehicle re-identification. The rest of this paper is structured as follows. Section 2 provides an overview of related works. Our proposed approach is detailed in Section 3. In Section 4, the experimental results are presented and discussed. Finally, Section 5 concludes this paper.