esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Consumption Analysis
The parameters are for the Semtech XE1205 transceiver used in our experiments and are listed in Table 1. Points \(A\), \(B\),..., \(G\) are the network energy consumption equilibrium points for two neighboring data rates. They can be used to select an optimal data rate, to minimize network energy consumption, for a given RSSI value. For example, when \(A<RSSI<B\), data transmission rate \(R=2.4\) kbps will achieve minimum network energy consumption. To verify the analysis of Equation (11) and (12), we performed an experiment with off-the-shelf TinyNodes over an indoor link. In the experiment, the transmitter sent packets to the receiver at \(5\) Hz with \(4\) different data rates (\(9.6\), \(20\), \(76\), and \(152\) kbps). Each experiment lasted for \(15\) minutes, and we repeated the same experiment \(5\) times. Then, we computed the energy consumption for the experiments of using the parameters listed in Table 1. The results shown in Figure 1 shows a similar trend to Figure1. The _Semtech XE1205 transceiver provides only 7 RSSI levels (0-6)_ so the number of decision points in Figure 1 is smaller than for Figure1. For example, if \(RSSI=5\) (Point C in the Figure), \(R=76\) kbps consumes the minimum network energy according to Figure 1.  
Although we can in theory choose the optimal data rate to minimize network energy consumption according to Equation (11) and (12), in practice they are difficult to obtain because of their dependence on channel condition which is unknown in advance and dynamic over time. Therefore, in the next section, we will introduce an adaptive MAC protocol, which is tailored to handle channel dynamics for the purpose of selecting an optimal data rate.
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 2 Related Works
A directed acyclic graph (DAG) was used to characterize the task model, and the nodes and edges of the task model were mapped to the steady-state graph to find the minimum task completion delay. The authors employed a binary particle swarm algorithm to optimally solve the optimal mapping problem and verify the algorithm's feasibility in comparison with ground cloud processing and other basic scheduling algorithms. [PERSON] et al. [4] constructed a satellite edge cluster computing architecture using LEO and geostationary earth orbit (GEO) satellites as edge nodes for collaborative task computing and characterized the logical relationships and constraints among subtasks using the DAG model. Furthermore, the author designed a scheduling algorithm that considered the dynamic changes in the priority and link bandwidth of subtasks in different time slots. At each scheduling moment, the unresolved subtasks were assigned to the appropriate satellite nodes for processing to ensure that the corresponding metrics of interest were optimized. Most authors included all edge computing satellites in the spectrum of task scheduling assignable nodes in the work mentioned above. In fact, due to factors such as unequal population distribution and varying business demands, the load of each satellite node varies significantly. Enough computing power for new task processing is difficult for high-load satellites. However, this problem has not been considered in any the above research. At the same time, the satellite was powered by solar energy, and the energy consumption was an optimization target of great interest in satellite terrestrial networks [22; 23]. This was basically not mentioned by the authors in the above work. As a result, the remainder of this paper will focus on investigating a strategy in which satellite nodes with high loads are excluded from task scheduling in the dynamic LEO satellite network task scheduler. Task latency and energy consumption will be considered together. In addition, the research concentrates on the dependent tasks. The difference between our work and the existing literature is summarized in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Independent** & **Dependent** & **Dynamic** & **High-Load** & **Task** & **Energy** \\  & **Task** & **Task** & **Network Topologies** & **Satellites** & **Delay** & **Consumption** \\ \hline [11; 19] & \(\sur\) & & & & \(\sur\) & \\ \hline [20] & \(\sur\) & & & & \(\sur\) & \\ \hline [4; 21] & \(\sur\) & \(\sur\) & & & \(\sur\) & \\ \hline Our paper & \(\sur\) & \(\sur\) & \(\sur\) & \(\sur\) & \(\sur\) & \(\sur\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the literature.
## Methodology
### Mathematic Relationship of Residuals on Different Frequencies
Applying the Gauss elimination method [35], the inverse matrix of \(\textbf{A}^{\textbf{T}}\textbf{A}\) can be derived as follows:  
\[\left(\textbf{A}^{\textbf{T}}\textbf{A}\right)^{-1}=\left[ \begin{array}{cc} \frac{1+\gamma_{2}^{2}}{2n\left(1+\gamma_{2}^{2}\right)-n\left(1+\gamma_{2} \right)^{2}}&\frac{1+\gamma_{2}}{2n\left(1+\gamma_{2}^{2}\right)-n\left(1+ \gamma_{2}\right)^{2}}\textbf{I}\\ \frac{1+\gamma_{2}}{2n\left(1+\gamma_{2}^{2}\right)-n\left(1+\gamma_{2}\right) ^{2}}\textbf{E}&\textbf{B}\end{array} \right], \tag{8}\]  
where  
\[\textbf{B}=\frac{\left(1+\gamma_{2}\right)^{2}}{\left(2n\left(1+\gamma_{2}^{2 }\right)-n\left(1+\gamma_{2}\right)^{2}\right)\left(1+\gamma_{2}^{2}\right)} \textbf{E}+\frac{2n\left(1+\gamma_{2}^{2}\right)-n\left(1+\gamma_{2}\right)^{ 2}}{\left(2n\left(1+\gamma_{2}^{2}\right)-n\left(1+\gamma_{2}\right)^{2} \right)\left(1+\gamma_{2}^{2}\right)}\textbf{I}. \tag{9}\]  
Substituting Equation (8) into (6), the residual vector can be explicitly expressed as follows:  
\[\textbf{V}=\left[\begin{array}{cc}\frac{\gamma_{2}^{2}}{n\left(1+\gamma_{2 }^{2}\right)}\textbf{E}-\frac{\gamma_{2}^{2}}{1+\gamma_{2}^{2}}\textbf{I}&- \frac{\gamma_{2}}{n\left(1+\gamma_{2}^{2}\right)}\textbf{E}+\frac{\gamma_{2} }{1+\gamma_{2}^{2}}\textbf{I}\\ -\frac{\gamma_{2}}{n\left(1+\gamma_{2}^{2}\right)}\textbf{E}+\frac{\gamma_{2} }{1+\gamma_{2}^{2}}\textbf{I}&\frac{1}{n\left(1+\gamma_{2}^{2}\right)}  
GPS, GLONASS, and BDS signals with 1 Hz sampling rate. The data spans a period of time from 10-20 to 10-200. Generally, the orbital repeat times are 86,155 s for GPS, seven days and 84,442 s for GLONASS, 86,165 s for BDS GEO/IGSO, and six days and 84,697 s for BDS MEO, respectively. Taking the orbital repeat time and data length into consideration, the compromised number of days for residual stacking is seven for GPS and BDS IGSO/GEO, and one for GLONASS and BDS MEO.
## 3 Experimental Results
### Coal Sample M3  
Coal sample M3 was under the experimental condition in which the gas pressure was 0.7 MPa and the confining pressure 4 MPa. The axial stress was loaded until the coal sample M3 was damaged.  
Figure 5: Experimental results of coal sample M2. (**a**) Unloading axial stress; (**b**) unloading confining pressure; (**c**) loading confining pressure; (**d**) loading to secondary damage.  
Then, the permeability evolution in the process of unloading the axial stress and confining pressure and loading the axial stress to the secondary damage of the coal sample M3 was measured. The following curves were obtained, as shown in Figure 6.  
It can be seen from Figure 6a that the axial stress of the coal sample M3 was loaded to 20 MPa under the condition of 4 MPa confining pressure, and then, the initial damage occurred. In the process of unloading axial stress from 20 MPa to 1 MPa, the permeability of coal sample M3 increased slowly. When the axial stress was unloaded to 1 MPa, the permeability increased by about 1.3 times that before unloading the axial stress.  
According to Figure 6b, when the confining pressure was unloaded from 4 MPa to 1 MPa, the fractures binding force of coal sample M3 decreased, and the permeability increased gradually. When the confining pressure was unloaded to 1 MPa, the permeability was about 1.25 times that before unloading the confining pressure.  
It can be seen from Figure 6c that when the confining pressure of the coal sample M3 was loaded from 1 MPa to 3 MPa, the fractures of the coal sample M3 were re-closed, and the gas seepage channel became smaller. The permeability range decreased approximately linearly, and the permeability decreased about 1.22 times when the confining pressure was increased by 2 MPa.  
It can be seen from Figure 6d that when the axial stress was loaded from 1 MPa to 13 MPa, the coal sample M3 underwent secondary damage. The peak intensity of the initial damage of the coal sample M3 was 1.76 times that of the secondary damage. During the whole process, the permeability of the coal sample M3 first decreased and then increased. Compared with Figure 6a, it can be seen that the permeability of the coal sample M2 after initial damage was about 0.64 mD, and the permeability minimum value in the secondary damage process was about 0.62 mD. However, in general, the permeability of coal sample M3 in the secondary damage process was higher than that during the  
Figure 6: Experimental results of coal sample M3. (**a**) Unloading axial pressure; (**b**) unloading confining pressure; (**c**) loading confining pressure; (**d**) loading to secondary damage.  
initial damage process, which was consistent with the permeability evolution of coal samples M1 and M2 in the same processes.
# Structure Approximation-Based Preconditioning for Solving Tempered Fractional Diffusion Equations
## 5 Numerical Experiments  
This section evaluates the computational efficiency of the proposed preconditioning strategy through numerical experiments. All computations were executed in MATLAB (R2022b) on a workstation equipped with a 4.00 GHz AMD Ryzen 9 7940H processor, 16.00 GB RAM, and the Windows 11 OS. We solve the variable-coefficient tempered FDEs:  
\[\begin{cases}\frac{\partial u(x,t)}{\partial t}=d(x)\Big{(}_{0}\mathbf{D}_{x}^ {\beta,\lambda}+_{x}\mathbf{D}_{1}^{\beta,\lambda}\Big{)}u(x,t)+f(x,t),\\ u(0,t)=0,u(1,t)=0,t\in[0,1],\\ u(x,0)=0,x\in[0,1].\end{cases} \tag{15}\]  
The source term is given as follows:  
\[f(x,t)=e^{-\lambda x}x^{3}(1-x)^{3}-td(x)\Bigg{[}e^{-\lambda x} \sum_{m=0}^{3}\left((-1)^{m}\left(\begin{array}{c}3\\ m\end{array}\right)\frac{\Gamma(4+m)}{\Gamma(4+m-\beta)}x^{3+m-\beta}\right)\] \[+e^{\lambda(x-2)}\sum_{j=0}^{30}\frac{3j}{7!}\left(\sum_{m=0}^{3} \left((-1)^{m}\left(\begin{array}{c}3\\ m\end{array}\right)\frac{\Gamma(4+m+j)}{\Gamma(4+m+j-\beta)}(1-x)^{j+3+m-\beta} \right)\right)\Bigg{]}\] \[+2 td(x)\big{(}\lambda^{6}\big{)}e^{-\lambda x}x^{3}(1-x)^{3}\]  
and the exact solution is \(u(x,t)\simeq te^{-\lambda x}x^{3}(1-x)^{3}\).  
The resulting linear system of the discretization is solved by the GMRES method with preconditioners. We test \(P_{3}^{-1}\) and denote it as \(P_{TAI}^{-1}(l)\), where \(1\) represents the number of interpolation points. For comparison, we also test the diagonal and circulant splitting preconditioner [17], the skew-diagonal Toeplitz splitting preconditioner based on \(\tau\) ma trix [22], and the Strang circulant approximate inverse preconditioner [25]. These three preconditioner are denoted as \(P_{DCS}^{-1}\), \(P_{SDTAS_{\tau}}^{-1}\) and \(P_{CAI}^{-1}(l)\), respectively.  
In all experiments, \(\lambda=1.5\), \(\beta=1.2\). We searched for the optimal number of interpolation points l in the proposed preconditioning matrices \(P_{TAI}^{-1}(l)\). For the two experiments, we choose the optimal number of interpolation points l to be 8 and 12, respectively. We specify the same grid density in both space and time \(\Delta t=h\), and for a certain initial point \(u^{0}\). The iterative procedure is initialized with the zero vector and halts once the relative residual error satisfies \(\|r_{k}\|/\|r_{0}\|<10^{-7}\), with a cap of 1000 iterations imposed to prevent divergence.
## 1 Introduction
Despite the potential advantages of OBIA combined with Sentinel-2 imagery and Random Forest classification, relatively few studies have applied this combination for monitoring deforestation and forest degradation in the Amazon. This approach offers the potential for more accurate detection of changes in forest cover, which is particularly important for monitoring forest degradation. For instance, [PERSON] et al. used OBIA to investigate the influence of mining projects on LULC changes in the southeastern area of the Brazilian Amazon, resulting in overall accuracies of all the classified maps higher than 94% [19]. [PERSON] et al. demonstrated the effectiveness of Random Forest with OBIA for land cover change detection in the Brazilian savanna (Cerrado) biome, achieving overall accuracies of around 88% [20]. Regarding the forest areas, conservation units play a vital role in protecting the Amazon's biodiversity and ecosystem services. Established according to the Brazilian National System of Nature Conservation Units (SNUCs), these areas are crucial for preserving the forest's integrity and helping mitigate climate change [21]. However, their effectiveness can vary depending on management strategies and local contexts. Recent studies have shown that while protected areas generally reduce deforestation rates, their impact can differ significantly across regions and management types [22; 23]. Understanding these variations is essential for optimizing conservation strategies. The heterogeneity of the Amazon presents both challenges and opportunities for researchers and policymakers seeking to understand and mitigate the impacts of land use changes [24]. This study focuses on two distinct areas within the Brazilian Amazon: Manaus and Porto Velho. These areas represent different stages of development and deforestation pressures, offering valuable insights into the diverse challenges faced across the Amazon. The objectives of this study are as follows: (1) to produce accurate LULC maps for Manaus and Porto Velho from 2018 to 2023 using Sentinel-2 imagery and OBIA with Random Forest classification; (2) to analyze LULC distribution in the study areas; (3) to compare LULC changes and conservation effectiveness between Manaus and Porto Velho, considering their different deforestation and degradation yearly rates; and (4) to quantify the total area of deforestation and forest degradation of conservation units with different management strategies and their surrounding areas to evaluate their effectiveness. By addressing these objectives, this study aims to contribute valuable insights to the ongoing discourse on Amazon conservation and inform evidence-based policymaking for sustainable forest management in the region. The use of advanced remote sensing techniques and machine learning algorithms provides a detailed and accurate assessment of LULC changes, while the comparison between different types of conservation units offers crucial information for improving protected area management strategies.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Decoder  
Symmetric with the encoder, the decoder is developed based on regular Swin Transformer blocks and patch expanding layers [42] to up-sample the feature map to the original resolution, as shown on the right part of Figure 2. Contrary to the patch merging layer in the encoder, the patch expanding layer up-samples the extracted feature by \(2\times\). Specifically, given the dimension of the input is \(2C_{1}\), the patch expanding layer uses a linear layer to double the feature dimension to \(4C_{1}\). Then, a rearrange operation is conducted to expand the resolution of the input features by \(2\times\), and the dimension of the feature is reduced to one-quarter of the ascended dimension to be \(C_{1}\).  
With skip connections, the up-sampled features in the decoder are fused with the features from the encoder. Shallow features are concatenated with deep features to reduce the loss of the spatial information during down-sampling, and a linear layer keeps the dimension unchanged. The process of skip connection can be formulated as:  
\[y^{l+1}=F(y^{l}\copy x^{l}) \tag{11}\]  
where \(x^{l}\) and \(y^{l}\) refer to the feature from the encoder and the decoder, respectively, while \(y^{l+1}\) refers to the input of rearrange operation. \(\copy\) represents the channel-level concatenation. \(F(\cdot)\) represents the linear layer.
# Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
## 4 Methodology
### Application of Dynamic Thermal Rating to Sicilian Transmission Grid  
The aim of the study was to evaluate the potential of DTR in the Sicilian territory. Both current and future scenarios are characterized by high electric power flows produced by renewable sources. The network congestion caused by these flows represents one of the main challenges for the Sicilian electricity system, and the Dynamic Thermal Rating technology can contribute to facing this challenge [1]. A key aspect in the correct application of Dynamic Thermal Rating is the positioning of the devices, and the important innovation of this study was choosing the location of DTR devices based on the specific characteristics of each REZ. Specifically, the work carried out to assess the potential of Dynamic Thermal Rating in Sicily was divided into several phases as follows:  
* Climatic study of Sicilian municipalities for Dynamic Thermal Rating. The location of these devices on the territory depends on the identification of hot spots. A study of the climatic conditions typical of the Sicilian territory was carried out. This made it possible to identify the differences in terms of thermal exchange propensity in the macro-areas of the region;
* Implementation of the Dynamic Thermal Rating calculation algorithm proposed by IEEE [14] on Python software (version 3.13.1). Based on the previous climatic study, DTR simulations were carried out for the Sicilian territory using this tool;
* Identification of critical lines based on the simulations carried out. Considering both the current configuration and the 2027 forecasts for Renewable Energy Zones in the area, several hot spots were identified on the Sicilian transmission lines. The installation of DTR devices in these areas would significantly improve the management of the transmission system;
* Brief economic considerations demonstrating the economic sustainability of DTR devices;
* Comparison with current applications of Dynamic Thermal Rating in Sicily.
## 4 Results and Discussion  
### Experimental Datasets  
For the purpose of fair comparison with other KTs, experiments were conducted on the same public datasets to obtain the models' performance. These datasets were the classical datasets that are commonly exploited in KT research. Descriptions of the datasets are shown in Table 2.  
The ASSISTment2009 dataset comes from the log data of students performing math exercises in 2009, which were collected by the ASSISTments platform [32]. The original version of the dataset contained 123 skills. Skills corresponded to exercise tags, such as prime numbers or linear equations in math problems. For the convenience of representation, the exercise tags were mapped to some numbers in the dataset. In other words, a skill ID represented an exercise tag in these KT datasets. Later, it was found that there were duplicate records of the data, which affected the reliability of the prediction results of the DKT model [33]. Therefore, experiments in this paper adopted the version after removing duplicate records. The total number of skills involved in this version of the dataset has been reduced to 110.  
The ASSISTment2015 dataset comes from the data collected by the ASSISTments platform in 2015.  
The Synthetic-5 dataset is from the paper which proposed DKT. The dataset is a set of unreal datasets constructed for experiments and does not correspond to the actual skills [12]. However, the dataset is of good quality and is still suitable for many experimental studies in KT founded on DL.  
The ASSISTchall dataset was gathered when learners utilized the ASSISTments platform and was used for an educational data mining competition in 2017.  
The Statics2011 dataset originates from online college-level statistics lessons [34].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Students** & **Skills** & **Records** \\ \hline ASSISTment2009 & 4151 & 110 & 325,637 \\ ASSISTment2015 & 19,840 & 100 & 683,801 \\ Synthetic-5 & 4000 & 50 & 200,000 \\ ASSISTchall & 1709 & 102 & 942,816 \\ Statics2011 & 333 & 1223 & 189,297 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Introduction of the datasets.  
When using the datasets mentioned above, the student whose records are less than or equal to two were regarded as invalid records and were removed from the sample sets, as the student records with too few practice records could not reflect the real learning situation of the student and were not applicable to the time-series model [21]. There were various situations leading to a student only practicing twice. The student may have already mastered a particular skill, and thus did not need practice. The student may also have given up the exercise because it was too difficult. So, the student records with two practice records did not seem reliable enough to reflect students' real learning situation. The training of neural networks relies on reliable samples on a large scale to obtain accurate results. So, we removed the students with less than two practice records, which could affect the whole model.  
A portion of the dataset is shown in Figure 2.
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 3 Ground Target 2D Localization Scheme
### Second Stage Spatial Filtering  
At this stage, the azimuth of the detected targets is estimated using a high-accuracy DoA estimation technique. The accuracy of azimuth estimation was increased by generating a new set of steering angles \(\Phi_{DoA}=\{\phi_{DoA,1},\phi_{DoA,2},\ldots,\phi_{DoA,N_{\phi_{DoA}}}\}\) composed of a high number of steering directions with low azimuth increments between them. The new beamforming weights vector is calculated according to a maximum array directivity criteria, \(\mathbf{w}(p,\phi_{DoA})\). To produce the DoA estimation the Minimum Variance Distortionless Response (MVDR) algorithm is considered [20]. The estimated DoA is the angle \(\phi\) where a maximum of the beamformer output spectrum is obtained. The beamformer spectrum is calculated with Expression (8).  
\[\begin{split} S_{m,p}(\phi)=\mathbf{w}(p,\phi_{DoA})^{T}\cdot \widehat{\mathbf{R}}_{\mathbf{s}_{s},\mathbf{s}_{s}}(m,p)\cdot\mathbf{w}(p, \phi_{DoA})\\ \widehat{\mathbf{R}}_{\mathbf{s}_{s},\mathbf{s}_{s}}(m,p)=\mathbf{ s}_{s}\cdot\mathbf{s}_{s}^{H}\end{split} \tag{8}\]  
where \(\widehat{\mathbf{R}}_{\mathbf{s}_{s},\mathbf{s}_{s}}(m,p)\) is the estimation of the instantaneous spatial covariance matrix for the snapshot \(\mathbf{s}_{s}(m,p)\) at target location \((m,p)\).  
Figure 5: Target’s range estimation by means of 5 points around the maximum. The real target range is 125 m, while the maximum of the CAF is obtained at 132 m. The estimated range with this approach is 124.1 m.