esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Theory of Regions  
The theory of regions (TR) for control synthesis was developed by [PERSON] et al. [9; 10; 11; 12] using PN tools for adding a control place \(P_{c}\) to the initial PN model with initial marking \(M_{0}(P_{c})\) and its incidence vectors \(C(P_{c},\.)\). This theory is reformulated by a linear system composed of three kinds of conditions; the reachability conditions, the marking/transition separation instance (MTSI) equations, and the basic cycle conditions. By resolving this linear system, one can compute a PN supervisor composed of a set of control places. Each control place \(P_{c}\) must fulfill the reachability conditions, i.e.:  
\[M_{0}(P_{c})+C(P_{c},.)\overset{\rightarrow}{\Gamma}_{M}\geq 0. \tag{1}\]  
\(\overset{\rightarrow}{\Gamma}_{M}\): is the path between \(M_{0}\) and \(M\).  
In addition, each PN monitor to add should solve at least one prohibited event \((M,\ t)\). The MTSI condition relative to the couple \((M,\ t)\) is:  
\[M_{0}(P_{c})+C(P_{c},.)\overset{\rightarrow}{\Gamma}_{M}+C(P_{c},t)<0. \tag{2}\]  
Finally, \(P_{c}\) must satisfy cycle equations:  
\[\sum\
olimits_{t\in T}C(P_{c},t).\overset{\rightarrow}{\sigma}[t]\ =\ 0,\ \forall \sigma\in\Delta. \tag{3}\]  
\(\overset{\rightarrow}{\sigma}[t]\) is the algebraic sum of occurrences of \(t\) in \(\sigma\). \(\Delta\) is the set of existing cycles in RG.  
Notably, many MTSI conditions may obtain the same solutions by resolving the linear system composed of Equations (1)-(3). As a result, the number of PN controllers will be much smaller than the set of MTSI conditions. The following example of Figure 1 is an application of the TR for control synthesis.  
For this prohibited state problem, one can forbid the following event \((M_{5}\xrightarrow{T_{1}}M_{7})\) (see Figure 1b) since \(M_{7}\) is a blocking state. Based on the TR, there are:  
Seven reachability conditions:  
\[M_{0}(P_{c})\geq 0,\]  
\[M_{1}(P_{c})\ =\ M_{0}(P_{c})+C(P_{c},T_{1})\geq 0,\]  
\[M_{2}(P_{c})\ =\ M_{0}(P_{c})+C(P_{c},T_{1})+C(P_{c},T_{2})\geq 0,\]  
\[M_{3}(P_{c})\ =\ M_{0}(P_{c})+C(P_{c},T_{1})+C(P_{c},T_{2})+C(P_{c},T_{3})\geq 0,\]  
\[M_{4}(P_{c})\ =\ M_{0}(P_{c})+2C(P_{c},T_{1})+2C(P_{c},T_{2})+C(P_{c},T_{3}) \geq 0,\]  
\[M_{5}(P_{c})\ =\ M_{0}(P_{c})+2C(P_{c},T_{1})+2C(P_{c},T_{2})\geq 0,\]  
\[M_{6}(P_{c})\ =\ M_{0}(P_{c})+2C(P_{c},T_{1})+C(P_{c},T_{2})\geq 0.\]  
One cycle equation:  
\[C(P_{c},T_{1})+C(P_{c},T_{2})+C(P_{c},T_{3})+C(P_{c},T_{4})\ =\ 0.\]  
One MTSI condition:  
\[M_{7}(P_{c})\ =\ M_{0}(P_{c})+2C(P_{c},T_{1})+2C(P_{c},T_{2})+C(P_{c},T_{1})<0.\]  
Therefore, the linear system contains nine equations. Its resolution leads to the controlled PN (see Figure 1c). The controller \(P_{c}\) is characterized as follows:  
\[M_{0}(P_{c})\ =\ 2;C(P_{c},.)\ =\ (-1,\ 0,\ 0,\ 1).\]  
Figure 1: Application of the theory of regions in the (**a**) Petri net (PN) model, (**b**) reachability graph, and (**c**) controlled PN model.
# Water Use Efficiency Spatiotemporal Change and Its Driving Analysis on the Mongolian Plateau
## 2 Materials and Research Methods  
### Study Area  
The Mongolian Plateau (87\({}^{\circ}\)43\({}^{\prime}\)-126\({}^{\circ}\)04\({}^{\prime}\) E, 37\({}^{\circ}\)22\({}^{\prime}\)-53\({}^{\circ}\)23\({}^{\prime}\) N) is part of the Central Asian Plateau. It has both arid and semiarid areas, experiences a typical continental climate, and includes the entire territory of Mongolia and the Inner Mongolia Autonomous Region of China [45]. The Mongolian Plateau is bordered by high-altitude mountains to the north and is adjacent to the Gobi Desert to the south [46; 47]. Affected by the climate, the annual precipitation level in the Mongolian Plateau is low, and droughts are frequent. The average annual precipitation in most areas is 200 mm, which occurs mainly from June to August. It is windy in winter and spring, while the average temperature in January is \(-\)26 \({}^{\circ}\)C and that in July is 17 \({}^{\circ}\)C. The plateau also has complex ecological zones, including meadow steppes, typical steppes, alpine steppes, desert steppes, coniferous forests, broadleaf forests, and the Gobi Desert; see Figure 1 for details. Vegetation data were obtained from the National Atlas of Mongolia, and an IMG vegetation map with a scale of 1:1,000,000 was rasterized to a resolution of 0.083\({}^{\circ}\)[48; 49].
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 6 Obtained Results
### Sumny Day  
A sunny day is characterized by high irradiance levels at midday and typically only requires two switching actions when considering a reconfigurable system. The initial switch action involves the PVG division when the irradiance reaches the \(P_{\mathit{UID\,light}}\) threshold, and the second switch action involves the PVG grouping when the power diminishes and falls below the \(P_{\mathit{UID\,low}}\) threshold.  
Figure 13 shows the power supply to the grid when a reconfigurable team system and a non-reconfigurable system are tested. Figure 13a shows the output power of the two inverters used in a reconfigurable team system. Figure 13b shows the output power of the non-reconfigurable system (red trace) and the output power of the reconfigurable team system (blue trace), the latter obtained as the sum of the output powers of the two inverters used.  
Figure 13: Performance of non-reconfigurable (fixed) and reconfigurable (team) systems on a sunny day. (**a**) AC power delivered by two inverters in the reconfigurable system; (**b**) AC power delivered by fixed and team system; and (**c**,**d**) details of the performance difference between the two compared systems in low-load conditions.  
As depicted in Figure 13a, initially, one of the team system inverters is utilized. However, once the threshold is reached, the second inverter is added (PVG division). At the end of the period, when the power is lower than the threshold, the reverse action of grouping takes place. As is expected, a small drop at the first switching point can be observed in the total AC power. This power loss can be seen in Figure 13b as a small glitch and in more detail in Figure 13c.  
As seen in Figure 13c,d, the non-reconfigurable and reconfigurable systems demonstrate distinct performances in the two regions where the power remains below the threshold. Throughout this period, the reconfigurable system operates with a single inverter, while the non-reconfigurable system operates with both inverters. It can also be observed that the reconfigurable system delivers more power to the grid, showing greater efficiency in low-load conditions.  
The energy supplied to the grid by the two tested systems is calculated from the instantaneous power data recorded by the PC used in the system. The power curves are numerically integrated using MATLAB and the results obtained are presented in Table 3. As discussed in the previous section, the last column represents the energy lost from generation during switching, as can be seen, the reconfigurable (team) system generates slightly more power than the fixed system.
## 4 Results and Analysis
### Long-Term Deformation Velocity and Maximum Seasonal Deformation in the Northern QTP  
After determining the time series deformation derived from the raw phase, the long-term deformation trend and maximum seasonal deformation magnitude can be calculated. The spatial distributions of the maximum seasonal deformation magnitude are shown in Figure 7.  
Due to the limitation of the revisit period of ALOS data, we calculated the long-term deformation velocity (Figure 7a,b) and the maximum seasonal deformation (Figure 7e,f) of ALOS-2 and Sentinel-1 data. The experimental results show that the average maximum seasonal deformation of the two sensors is 66 mm. As shown in Figure 7a,b,e,f, the area with large maximum seasonal deformation may tend to undergo strong subsidence and uplift. From the ALOS-2 result, the average maximum seasonal deformation during 2015-2020 is 68.74 mm. According to the Sentinel-1 result, the average MSD during 2017-2021 is 64.85 mm. The areas with high MSD are mainly concentrated in glacier outwash plains and braided stream plains. We selected two typical areas, A and B. The upper left of study area A is located in Duoergai Co Lake, surrounded by glaciers to the north. The MSD is large, which may be due to glacial erosion. A large amount of ice deposits is brought to low-lying areas, affecting the water storage capacity and ice melting capacity of permafrost. In addition, a large MSD occurred in thermal karst landforms, such as area B, which is characterized by the formation of hot melt lakes and proglacial lakes. Due to the thermal influence of underground ice in the permafrost, the land shrinks and settles, and finally melted water flows along the groove of the slope. In addition, we reveal the relationship between the maximum seasonal deformation and the long-term deformation velocity. From Figure 7g,h, there is a weak correlation between MSD and LTD, with coefficients of  
Figure 7: The long-term deformation velocity and maximum seasonal deformation. (**a**) Long-term deformation velocity during 2015–2020 from ALOS-2. (**b**) Long-term deformation velocity during 2017–2021 from Sentinel-1. (**c**) ALOS-2 maximum seasonal deformation. (**d**) Sentinel-1 maximum seasonal deformation. (**e**) Maximum seasonal deformation during 2015–2020 from ALOS-2. (**f**) Maximum seasonal deformation during 2017–2021 from Sentinel-1. (**g**) ALOS-2 LTD and MSD. (**h**) Sentinel-1 LTD and MSD.  
determination (\(R^{2}\)) [65] of 0.208 for ALOS-2 and 0.301 for Sentinel-1. This shows that the relation between MSD and LTD is not simply linear and is related to geomorphic types, moisture, ice and other factors.
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 2 Related Work
Adopting the study in [19] could be the promising solution for the environments with high-rise buildings, however, the priori knowledge containing the shape of the building could require the large amount of data, time, and expenditure. Our algorithm implicitly considers the variable path loss, by sharing the current number of connected mobile devices. References [20; 21] designed an UAV deployment problem where the UAV act as a flying base station while connecting to the mobile cellular users or the device-to-device (D2D) communication nodes. There is a difference between our work and this paper since the proposed system in [20] uses a single UAV. However, the approach to the solution which formulates to the disk covering problem [22] is similar to our research, since our algorithm adopts the sphere-shaped network range model. In [23], the authors proposed the multi-UAV deployment algorithm optimizing the downlink coverage probability and the coverage lifetime. Unlike [20], the solution adopts disk packing problem [24] and derive the optimal position of each UAV. However, disk packing problem results in the blind spot of the network coverage region, which could drop the network performance. Also, optimizing the coverage lifetime might not be notable since the large amount of the power is consumed at the motors in UAV. [PERSON] et al. [25] designed a multi-UAV network formation game to optimize and stabilize the formation. Also, each UAV's movements are determined by the virtual force, which let the UAVsmaintain the multi-hop connection and avoid the physical collision. Even though the overall concept and approach are similar to our paper, there is a notable difference in terms of the amount of the formation research process. The main contribution of [25] is the expansion of the wireless backhauling area by modifying the node link status, which is addressed in our work called diffusion (See Section 4.4). In addition to the multi-hop network deployment, we applied an additional force to let the formation lean toward to the traffic concentrated area of the ground. In Section 5, we investigated the performance in the cases with and without the additional traffic-aware force.
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 4 Experimental Results
### The Performance of the Proposed Method  
Because the number of cluster centers \(k\) and threshold greatly affect the coding efficiency in the application of the quantized value adjustment (QA) technique [18], we evalu  
Figure 4: Eight test images.  
ate how the QA technique and these parameters influence the bitrate and image quality in this section.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 7 Completing the Algorithm and Flowchart of the Process  
The ultimate aim underlying this study, as indicated in the abstract, was to use a drone for small package delivery to locations not easily accessible (e.g., mountain rescues) and to disabled people living in cities. To achieve this goal, a number of initial conditions must be met.  
For the sake of this project, a 10 \(\times\) 10 km\({}^{2}\) territory will be assumed somewhere in a mountainous area, with an altitude of 250 m from the highest terrain point. This gives the WS origin, which has to be modelled. There are excellent SW tools capable of creating a 3D model of this environment based on the \"Google map\" (where the territory ought to be selected). The wind direction and wind strength sensors should be placed somewhere near (or directly within) this territory. These sensors provide data via the IoT to the \"MeteoCloud\" (a proxy server developed for Data Collection with Database and Data Management functions) which serves to create a so-called \"Wind Map\" of the environment.  
The first step is to perform the configuration of the WS, which means that the obstacles need to be increased by the radius of the sphere around the drone (modelling the environment). The next step is WS partitioning, where the environment is divided into cubes; then the occupied spaces (where the obstacles are) and free spaces are designated. In conjunction with this process, the center points of the free-spaces (cubes) must be designated, and based on these, the distance calculations between the center points (edges of the graph-\(e_{(j)}\)-) can begin. By designating the _START-GOAL_ pair, the graph orientation can be specified. The  
Figure 14: The calculated optimal trajectory from the START (left bottom corner) to the GOAL positions in a headwind situation. The start and goal coordinates: S3 = [8, -2, 2]; G3 = [15, 15, 15].  
Figure 13: The calculated optimal trajectory from the START (left bottom corner) to the GOAL positions in a crosswind situation. The start and goal coordinates: S2 = [0, 6, 0]; G2 = [13, 13, 13].  
resulting velocity of the drone can be calculated by the drone represented as a point (where the orientation and the average speed of the drone are given) and the wind conditions (the resulting velocity can speed up, or slow down the average velocity of the drone, based on the wind direction and speed). The power can be calculated (Equation (16)) from the resulting velocity, which is directly related to the battery energy (Equation (20)), through the drone's energy consumption (Equation (9)).  
As a result, the flight time over the segments (edges) of the graph is acquired, which is directly connected to the resulting velocity of the drone. If the wind increases its average speed, the flight time decreases and the energy consumption is less. Eventually, the final execution path can be selected based on cost functions (Equation (15)) of the calculated paths. The process described above is repeated following each update of data from IoT \"MetecCloud\".
## 5 Simulation and Results  
In this section, we present the performances of the clustered distributed system with the proposed SH-DFS using Monte Carlo simulation results. We assumed that local sensors with random initial energy from 0.1 to 0.5 /battery were randomly deployed in an area with dimensions 100 m \(\times\) 100 m. The FC was at (50 m, 50 m). In addition, we made the \(k\)th local sensor's probability of false alarm \(P_{jk}\) - U(0.0, 0.2). The probability of error bit \(P_{dk}\) in the non-ideal channel was assumed to be 0.05, and it was the same for each local sensor for convenience.  
For the \(k\)th local sensor, its observation \(y_{k}\) followed a Gaussian distribution:  
\[\left\{\begin{array}{c}f(y_{k}|H_{1})=\frac{1}{\sqrt{2\pi}\sigma}\exp\left\{ -\frac{(y_{k}-E(S+N))^{2}}{2\sigma^{2}}\right\}\\ f(y_{k}|H_{0})=\frac{1}{\sqrt{2\pi}\sigma}\exp\left\{-\frac{(y_{k}-E(N))^{2}}{ 2\sigma^{2}}\right\}\end{array}\right. \tag{23}\]  
where \(E(N)\) is the mean value of noise, \(E(S+N)\) is the mean value of signal and noise, and \(\sigma^{2}\) is the variance of the Gaussian noise.
# An Efficient Blockchain Transaction Retrieval System
## 3 System Implementation
### Blockchain Transaction Retrieval System Architecture  
In this section, we introduce the blockchain retrieval system architecture. As shown in Figure 3, it mainly includes four modules: collection module, retrieval module, blockchain storage module and retrieval structure maintenance module.  
**Collection Module** : The primary responsibility is block collection/parsing and blocks retrieval analysis. The collection module obtains the block after the upper consensus module of the blockchain node, verifying that the block and sends the complete block data to the storage module for storage. At the same time, the block content is analyzed to extract the characteristic information needed for retrieval. The collection module sends it to the maintenance module of the retrieval structure to establish or update the retrieval structure. Block retrieval analysis extracts the retrieval targets and sends the retrieval features, including the account address and time stamp, to the retrieval module.  
Figure 3: Blockchain retrieval system architecture.  
Figure 2: The I2B+ tree.  
**Storage Module**: The primary responsibility is to store/obtain the block, and store/update the retrieval structure. The block data received from the collection module are stored in a file, which contains a mapping table between block hash and block offset. Block files can be stored locally or on IPFS. After the retrieval module retrieves the block's hash, which contains the transaction record, the system can obtain the block through the storage module. At the same time, the time retrieval structure and account address retrieval structure are stored for retrieval by the retrieval module.  
**Retrieval Structure Maintenance Module**: The primary responsibility is to update and maintain various retrieval structures: update and keep the acquired retrieval feature information, including account address and timestamp, for the corresponding retrieval structure.  
**Retrieval Module**: The primary responsibility is to perform single-feature retrieval or multi-feature hybrid retrieval based on the retrieval feature information to determine the block that contains the transaction record.
## 4 Results
### Arrhythmia Classification Using DA-BSMs for Coarse-Grained Databases
#### 4.2.1 Evaluation with SPH Database
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **Pretrained** & **WSL** & **SB** & **GSVT** & **AFIB** & **SR** \\ \hline Coarse-only & & & 0.9521 & 0.8353 & 0.7538 & 0.8441 \\ \hline Pretraining-only & & & 0.9822 & 0.8874 & 0.8530 & 0.9333 \\ \hline DA-BSM & & & 0.9810 & **0.9115** & **0.8872** & **0.9409** \\ \hline \end{tabular}
\end{table} 
Table 5: F1-scores for the classification of specific arrhythmia rhythm types in the SPH dataset (4-class classification: SR, AFIB, GSVT, SB; number of samples: SR = 2225, AFIB = 2225, GSVT = 2307, SB = 3889). To visually demonstrate the advantages of the DA-BSM framework, Figure 6 presents the t-distributed stochastic neighbor embedding (t-SNE) [41] visualizations of the ECG data distributions at different stages of the proposed method. In this figure, panel (A) depicts the raw data distribution without any preprocessing or adaptation, where significant overlap among rhythm classes (AFIB, SB, SR, GSVT) in the ECG signals is evident. Panel (B) illustrates the limited class separation achieved by training exclusively with weakly supervised learning (WSL) on coarse-grained annotations, without pretraining. Panel (C) highlights the improved separability obtained from pretraining alone on the fine-grained MITDB dataset. Panel (D) shows significantly enhanced and clearly distinguishable rhythm clusters by the combination of pretraining and weakly supervised adaptation. These results visually underscore the effectiveness of our proposed approach in enhancing class distinction, robustness, and adaptability across heterogeneous ECG datasets.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 3 Results
### Ablation Experiments
#### 3.3.2 Effectiveness of the Dual-ASPP Structure  
To evaluate the impact of DANet adding ASPP structures twice in the image encoding phase, we conducted ablation experiments. Two control groups were set up: DANet as the control group, Experimental Group I with one set of ASPP structures added, and Experimental Group II without ASPP structures added. We conducted semantic segmentation experiments for each group and obtained relevant evaluation index data. The experimental data are shown in Table 2.  
The DANet network structure was found to enhance _Re_ by 1.16%, _road IoU_ by 1.49%, and _F1-score_ by 1.23% compared to the network structure lacking the ASPP structure. In  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental Group**} & \multicolumn{3}{c}{**Evaluation Indicators (\%)**} \\ \cline{2-4}  & _Re_ & _Road IoU_ & _F1-Score_ \\ \hline Xception & 69.91 & 64.88 & 70.36 \\ MobileNetV2 & 69.72 & 65.17 & 70.51 \\ DarkNet & 70.87 & 65.31 & 71.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Verification of the effectiveness of DarkNet network in extracting feature information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experimental Group**} & \multicolumn{3}{c}{**Evaluation Indicators (\%)**} \\ \cline{2-4}  & _Re_ & _Road IoU_ & _F1-Score_ \\ \hline I & 70.32 & 64.56 & 70.64 \\ II & 69.71 & 63.82 & 69.85 \\ DANet & 70.87 & 65.31 & 71.08 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation index of segmentation results of ablation test.  
Figure 9: Segmentation results: (**a**) image, (**b**) label, (**c**) segmentation result.  
comparison to the network with only one set of ASPP structures, _Re_ improved by 0.55%, and _road lou_ improved by 0.75%, _F1-score_ improved by 0.44%; Figure 10 presents the experimental results for Experimental Group I, Experimental Group II, and DANet.  
The ablation test results graphs in Figure 11 demonstrate that DANet's prediction results had fewer false positives and misses, as well as smoother predicted road edges compared to the two experimental groups. These results indicate that the ASPP structure of the two groups can enhance the performance of semantic segmentation.  
Figure 11: Comparison of prediction results with other network structures. (**a**) Image; (**b**) label; (**c**) U-Net; (**d**) DeepLab V3+; (**e**) HRNet-V2; (**f**) PSPNet; (**g**) SegNet; (**h**) SENet; (**i**) D-LinkNet; (**j**) DANet.  
Figure 10: (**a**) Image; (**b**) label; (**c**) Experimental Group I; (**d**) Experimental Group II; (**e**) DANet.