esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work
In all the above mentioned work, the improvement in the computation rate is mainly focused. However, the battery life of UE is ignored and the number of sub-tasks in which an application should be divided for partial offloading is assumed to be a fixed value. The authors in [30] investigate the computing problem in ultra-dense cellular networks with a multi-user, multi-server scenario. The problem is divided into two phases, i.e., selection of MES and offloading decision. In the first phase, the UEs are grouped with one MES on the basis of physical distance and workload. While in the second phase, a distributed offloading strategy based on the genetic algorithm is proposed to get an adaptive offloading decision. The authors in [31] investigate the computational offloading of DNN-driven AI applications in 5G-enabled MEC networks. The authors consider dynamic task offloading and propose an online algorithm to reduce the energy consumption of UEs and MESs. The authors in [32; 33] present a theoretical model for partial offloading in MEC, only for divisible application. They assume that the components of the task can be executed in parallel. To minimize UE energy consumption, a game-theoretic approach is presented in [34]. A deep learning-based MDP technique is presented in [35]. The authors propose a gradient-based deterministic policy for computational offloading in MEC networks to solve the optimization problem. This work considers a dense distributed cellular network with multi-user, multi-server, and multi-tasks scenario. The authors consider the mobility of MESs and UEs to propose an area-based parallel task offloading model to achieve low latency for delay sensitive applications. Similarly, the authors in [36] also propose a deep reinforcement learning approach in a dynamic MEC networks. The paper optimizes the MES selection for offloading and computing power allocation jointly. Deep learning approaches are considered in [37; 38; 39; 40] to minimize the service delay of the UE. Furthermore, the authors in [41] propose a deep imitation learning technique to minimize the service delay of MDs. The main focus is on the service delay, therefore, their cost function only depends on execution and transmission delays. However, for a better quality of service, it is necessary to take an accurate decision about the offloading policy and component size. Most of the related work considered either service delay or energy consumption or both as a cost function. To the best of our knowledge, in the cost function, there has been no consideration of task-division-process of getting different components of a task.
## 1 Introduction
In this study, we analyze the characteristics of ionospheric plasma perturbations before four strong EQs with magnitudes equal to or larger than Ms 6.9 occurred during this period by using the observations from the Plasma Analyzer Package (PAP) and Langmuir Probe (LAP) onboard CSES-01. Our findings support the possibility of using in-situ plasma observation results from the LEO satellite to examine EQ precursor signatures in the ionosphere.
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.6 Average Traffic Time for Different Nodes  
The purpose of this simulation is to observe how sensor nodes impact connection times for the four protocols. Figure 11 compares the average traffic times for the protocols for various node counts. The chart clearly shows that when more nodes are deployed, network traffic times for both the CSMA and SFAMA protocols grow, especially for the CSMA protocol. Our protocol has a network traffic time that is 92.7 percent and 89.5 percent lower than those of the CSMA and SFAMA protocols, respectively, when 140 nodes are used. Additionally, it is just 2.3% slower than the T-LOHI protocol's traffic time. The simultaneous occurrence of RTS operations in the time domain brought on by a significant submerged delay in propagation results in a variety of collision situations for the CSMA protocol. However, the other three MAC protocols can effectively prevent the collision of the same nodes, and then, reduce network traffic time, while taking into account the uncertainty in underwater time and space. By efficiently using longer data lines as the traffic load rises, the CAPC and SFAMA protocols outperform the CSMA protocol in terms of throughput. High latency caused by submerged space and time uncertainty is a difficulty that the T-LOHI protocol attempts to solve. The sender is given time slots, each of which has a duration that is equal to the time it takes to transmit a packet plus its maximum propagation delay. Comparing this method to the CSMA and SFAMA protocols, it decreases collisions and retransmissions. Last but not least, the chosen protocol makes use of a clustering hierarchy structure to avoid transmission collisions by synchronizing time and managing energy. It thus cuts down on traffic on the network significantly.  
Figure 10: Different traffic loads vs. average delay.
###### Abstract  
The progress in telemedicine can be observed globally and locally. Technological changes in telecommunications systems are intertwined with developments in telemedicine. The recent COVID-19 pandemic has expanded the potential of teleconsultations and telediagnosis solutions in all areas of medicine. This article presents: (1) an overview of milestones in the development of telecommunications systems that allow progress in telemedicine and (2) an analysis of the experiences of the last seven conferences of telemedicine and eHealth in Poland. The telemedicine and eHealth conferences have grown steadily in Poland since their inception in the late 1990s. An exemplary conference program content was used to assess the scientific maturity of the conference, measured by the indices of research dissemination and the impact of publications. The overview presents progress in selected areas of telemedicine, looking at local developments and broader changes. The growing interest in telemedicine in the world's medical sciences is demonstrated by visibility metrics in Google Scholar, Pubmed, Scopus and Web of Science. National scientific events are assumed to raise interest in the population and influence the creation of general policies. As seen in the example of Poland, the activity of the scientific community gathered around the Polish Telemedicine Society led to novel legal acts that allowed the general practice of telemedicine during the SARS-CoV-2 pandemic. Local scientific conferences focusing on telemedicine research can be a catalyst for changes in attitudes and regulations and the preparation of recommendations for the practice of telemedicine and electronic health. On the basis of the results of this study, it can be concluded that the progress in telemedicine cannot be analyzed in isolation from the ubiquitous developments in technology and telecommunications. More research is needed to assess the cumulative impact of long-standing scientific conferences in telemedicine, as exemplified by the telemedicine and eHealth conferences in Poland.  
e-health; telemedicine; information systems; publication impact; COVID-19 pandemic, telehealth 2022
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of the Bottom Non-Singular Sliding Mode Guidance Law
#### 5.2.2 Performance of the NSMG Law with Different Impact Time  
To evaluate the performance of the NSMG law under different impact time, the designated impact time is set to 45 s, 65 s, 85 s and 105 s, respectively. Other parameters are the same as the parameters used in Section 5.2.1. Simulation results are shown in Figure 5.  
The legends represent different simulation situations. For example, \"t\({}_{\text{d}}\) = 45 s\" represents the simulation results obtained by using the NSMG law when the designated impact time is 45 s.  
As can be seen from Figure 4(a), the UAV can attack the target at a designated time. The longer the designated impact time is, the more obvious the lateral maneuverability of the UAV will be. As can be seen from Figure 4(b),c, in the initial stage, since the estimated value of the UAV's time-to-go is less than the desired time-to-go, the leading angle increases and then gradually converges to zero. Therefore, the corresponding acceleration command increases in the initial stage, and then converges to zero with the decrease of the leading angle, as shown in Figure 4(d). At the same time, when the designated time is small, that is, the error of the initial impact time is small, although the acceleration command of the UAV increases in the initial stage, it does not exceed the boundary of the acceleration command. However, when the designated time is large, that is, the error of initial impact time is large, the acceleration command of the UAV will reach the specified boundary in the initial stage, and the larger the error of initial impact time is, the longer the duration will be.  
Figure 4: Simulation results of the non-singular sliding mode guidance (NSMG) law and the sliding mode control (SMC) law.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning
#### 4.6.1 Smog Profiling over Zywie
Start and goal locations are marked with red and green tetrahedrons, respectively. The area of highest predicted pollutant concentration is highlighted by a blue sphere, while a red prism represents the obstacle volume above Zywiec. The experiment was conducted 30 times, each with a unique seed for the random number generator. The outcomes are presented in Table 8. Metrics such as FT, LEN, SMOO, EEE, and NCOL were derived from the simulation data, while CT, COST, and CFEs were obtained from the GPP output. Given the low simulation time step and high waypoint density, SMOO was computed by sampling every 500 th waypoint. The final row (Ref) provides the metrics for the reference path. Out of the 30 paths generated, 25 meet the success criteria, achieving a success rate of 83.33%. Every path had NCOL \(>\) 0. Paths 4, 5, 10, 22, and 24 breached the FT limit, with the longest recorded FT being 3236.482 s for Path 24. This extended duration was due to an unnecessary control waypoint, which led to a helical ascent (as illustrated in Figure 6). The CT limit was not assessed as the LPP was not utilized in this scenario. Path 24 is both the longest (LEN = 71,025 m) and the most energy-demanding (EEE = 93.491 Wh). In contrast, Path 15 stands out as the shortest and most efficient, with an FT of 2135,013 s, a length of 46,868 m, and an energy expenditure of just \(\text{EEE}=61.633\) Wh. Among all paths, Path 25 is the smoothest, boasting an SMOO value of 0.887, while Path 4 is the roughest, with the lowest smoothness score of 0.801. Compared to the human expert's reference path, 19 out of 30 paths (63.33%) show improvements in FT, EEE, and LEN. Furthermore, based on simulation data, 28 paths are smoother than the reference path. The median values for the GPP-generated paths (LEN = 49,005 m, FT = 2233.620 s, \(\text{EEE}=64.467\) Wh, and \(\text{SMOO}=0.872\)) also outperform the respective metrics of the reference path. Manually designed paths with a low number of waypoints present significant challenges for the simulated controller to follow accurately. This limitation results in oscillations, especially along the measurement segment of the path, as highlighted in the top right section of Figure 6.  
In the subsequent experiment, stages 5 to 7 from Table 7 were included, with 30 independent runs conducted as before. Table 9 presents the results. The CT column uses a plus notation, where each term represents a different segment of the path. For example, the entry \(32.446+0.171\) in the first row indicates that CT for the first segment is \(32.446\) s, while for the second segment it is \(0.171\) s. This notation can be applied to any number of segments, with the first term here indicating the GPP-generated path and the second term referring to the path generated by LPP. Paths 14 and 21 fail to meet the FT success criterion, with durations of \(3789.433\) s and \(2995.963\) s, respectively, making them the longest and least energy-efficient paths.
# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 7 Results and Analysis
### Wind Power Analysis  
Wind power is a widely available RES, therefore it is one the most popular and emerging power generation sources. The predictive analytics were performed on wind power data of Maine wind farms, ISO New England. According to the annual report, Maine wind farms annually produce approximately 900 MW energy, which contributes almost 14% of the total electricity in Maine. The wind power is directly proportional to the wind speed. In Maine, USA, the wind speed is affected by seasonality. The wind power in autumn is higher compared to the other seasons. The reason behind this is the fastest winds in coastal area of Maine, where the wind turbines are installed.
## 3 Implementation and Evaluation of the Method
### Sensitivity Evaluation  
To determine how the proposed hash function behaves against small modifications made to the input signal, six types of modifications were made in selected recordings. After this, the codes of the original signal were compared with those of the modified signals by using \(HD\) and \(PCC\). The modifications made to the test signals are listed below:  
1. Modifying the amplitude of a sample
2. Reversing the signal in time
3. Muting a word
4. Cutting out an audio fragment
5. Changing from 16 to 8 quantization bits
6. Changing from 16 to 24 quantization bits  
For the first modification, it was decided to change only the tenth sample of the signal to 0; if the amplitude of this sample is already 0, the next sample is changed. For the second modification, the signal reversion was performed without modifying any of the samples. In the third modification, a word lasting less than 1 s was silenced. In the fourth modification (trimming), a fragment of the signal was eliminated (lasting less than 1 s). Finally, in the fifth and sixth modifications, the 16-bit signal was changed to 8 and 24 bits, respectively.  
When calculating the \(PCC\) and making the histogram of the data, it is possible to observe that most of the data are concentrated in values around 0, which implies that there is no linear relationship between the compared codes (Figure 4).  
According to the above, it was found that for the tests performed no collisions were detected.
# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 3 Problem Description
### Optimization Goals  
In this paper, two metrics of task processing delay and energy consumption are considered, and the problem of resource allocation and task offloading is combined to maximize system utility as the optimization objective. Comparing task latency with the maximum latency \(T_{n}\), more remaining time means higher utility. Comparing the offloading energy consumption with the energy consumption of local processing, higher relative improvements in energy consumption mean higher utility. The utility function of user \(n\) for local processing versus offloading to the MEC server for processing is expressed as  
\[u_{n}^{l}=\beta_{n}^{t}.\frac{T_{n}-t_{n}^{m}}{T_{n}} \tag{8}\]  
\[u_{n}^{m}=\beta_{n}^{t}.\frac{T_{n}-t_{n}^{m}}{T_{n}}+\beta_{n}^{e}\frac{e_{n} ^{l}-e_{n}^{m}}{e_{n}^{l}} \tag{9}\]  
The utility function of the whole system is defined as the cumulative sum of all user utility values, denoted as  
\[U(\mathcal{F},S)=\sum_{n\in N}u_{n} \tag{10}\]  
where the computational resource allocation \(\mathcal{F}=\Big{\{}0<f_{n}^{m}\leq F,n\in N_{off}\Big{\}}\), and the offloading policy \(S=\Big{\{}s_{n}^{l},s_{n}^{m}\Big{\}}\).  
The joint resource allocation and task offloading problem is described as a system utility maximization problem, denoted as  
\[\frac{max}{\mathcal{F},S}U(\mathcal{F},S) \tag{11}\]  
\[s.t.\] \[\text{C1}:\ 0<f_{n}^{m}\leq F,\forall n\in N_{off}\] \[\text{C2}:\ \sum_{n\in N_{off}}f_{n}^{m}\leq F^{total},\forall n \in N_{off}\] \[\text{C3}:\ s_{n}^{l}+s_{n}^{m}\leq 1,\ \forall n\in N\] \[\text{C4}:\ s_{n}\in\Big{\{}s_{n}^{l},s_{n}^{m}\Big{\}},n\in N\]  
where C1 is a constraint on the allocation of computing resources available to user \(n\) in the MEC server, and C2 is a constraint on the overall computing resources in the edge layer. C3 and C4 are offload policy constraints, and for each computing task, only one offload policy can be selected at a time.  
Problem (11) is a mixed nonlinear programming (MINLP) problem, and it is nonconvex and NP-Hard. Using the Tammer decomposition method, the problem can be decomposed into multiple subproblems with separated objectives and constraints. The constraints C1 and C2 for resource allocation and constraints C3 and C4 for offloading decision \(S\) are decoupled from each other, so Equation (11) can be transformed into  
\[\underset{S}{\text{max}}(U^{*}(S)) \tag{12}\]  
\[s.t.\text{ C3,C4}\]  
where \(U^{*}(S)\) is the optimizing function for the resource allocation problem  
\[U^{*}(S)=\underset{\mathcal{F}}{\text{max}}(U(\mathcal{F},S)) \tag{13}\]  
\[s.t.\text{ C1,C2}\]  
Therefore, Problem (11) can be decomposed into a computational resource allocation problem and a task unloading strategy problem, and the optimal unloading decision and resource allocation results can be obtained by iterations.
# Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP
## 3 System Analysis
### Bundle Forwarding  
To model the current routing approach to a space DTN, where routing decisions are performed for each bundle separately, consider that each end-to-end flow \(i\) is transmitted over multiple paths, as given by its forwarding matrix \(R^{(i)}\). Each element \(R^{(i)}_{a,b}\) of \(R^{(i)}\), with \(0\leq R^{(i)}_{a,b}\leq 1\), denotes the fraction of the flow traffic \(\lambda_{i}\) to be handled by the edge \((a,b)\in\mathcal{E}\), and \(R^{(i)}_{x,y}=0\) if \((x,y)\
otin\mathcal{E}\). Let \(\mathcal{R}\) denote the set of forwarding matrices for all the flows, where \(\mathcal{R}=\{R^{(1)},R^{(2)},\ldots,R^{(w)}\}\). Note that each bundle of flows \(i\) is forwarded probabilistically according to the assigned matrix \(R^{(i)}\) (i.e., from node \(a\in\mathcal{V}\), each bundle of flows \(i\) is sent to node \(b\) with a probability \(R^{(i)}_{(a,b)}/\sum_{(a,c)\in\mathcal{E}}R^{(i)}_{(a,c)}\)).  
A few constraints are needed. For a given flow, the source emits but does not receive traffic, and thus  
\[\sum_{b}R^{(i)}_{s_{i},b}=1,\;\;\;(s_{i},b)\in\mathcal{E}. \tag{5}\]  
While bit errors may occur during each transmission, it can be assumed that the DTN can recover from such errors either through the automatic repeat request (ARQ) of the LTP or custody transfer of the bundle protocol. In such a case, the sink receives exactly the same amount of traffic sent by the source and does not emit any traffic:  
\[\sum_{a}R^{(i)}_{a,t_{i}}=1,\;\;\;(a,t_{i})\in\mathcal{E}. \tag{6}\]  
Additionally, any intermediate node \(a\in\mathcal{V}-\{s,t\}\) forwards all its incoming flow traffic:  
\[\sum_{(c,a)\in\mathcal{E}}R^{(i)}_{(c,a)}=\sum_{(a,d)\in\mathcal{E}}R^{(i)}_{ (a,d)}. \tag{7}\]
## 2 Theoretical Background
### Model Order Reduction (MOR)  
Model Order Reduction (MOR) techniques are mathematical tools designed to reduce the dimensionality of complex systems while preserving their essential dynamic characteristics. Several approaches exist, including Center Manifold, Lyapunov-Schmidt (L-S), Galerkin projection, and Proper Orthogonal Decomposition (POD). Among these, POD is widely adopted for constructing ROMs in FEA due to its computational efficiency and its ability to capture dominant spatial patterns.  
POD-based ROM development follows a well-defined sequence of steps. Full-order simulations are first performed at selected design points, generating a set of system state snapshots (e.g., displacement, stress, or temperature fields). These snapshots are assembled into a snapshot matrix \(\mathbf{A}\), where each column corresponds to a single system state. The matrix is then subjected to Singular Value Decomposition (SVD):  
\[A=U\Sigma V^{T} \tag{2}\]  
where:  
* \(\mathbf{A}\in\mathbb{R}^{m\times n}\) is the snapshot matrix, with \(m\) representing the number of degrees of freedom (DOFs) and \(n\) the number of collected snapshots;
* \(\mathbf{U}\in\mathbb{R}^{m\times m}\) is a matrix of orthonormal left singular vectors, representing the dominant spatial modes;
* \(\mathbf{\Sigma}\in\mathbb{R}^{m\times n}\) is a diagonal matrix containing singular values in descending order, indicating the relative importance of each mode;
* \(\mathbf{V}^{T}\in\mathbb{R}^{n\times n}\) contains the right singular vectors.  
By truncating the decomposition and retaining only the most relevant modes (those associated with larger singular values), a reduced-order approximation is obtained. This reduced basis preserves the key physical features of the original system while dramatically lowering computational cost.
## 4 Case Study
### Analysis of Results  
The on-site monitoring points of the dry beach length of the tailings pond are displayed in Figure 13. The actual length of the dry beach measured on site was used to verify the calculation results, as shown in Figures 17-19. In this case, the minimum calculation error is 2.10%, the maximum calculation error is 3.46%, and the average error is 2.70%. This proves that the method used in this study is feasible and reliable. The dry beach length of the tailings pond represented obvious changes with different seasons. From monitoring point B, it can be concluded that from January 2022 to February 2023, the length of dry beach was shortened from 411.46 m to 343.18 m, and then to 390.44 m. The variation characteristics of dry beach length in 2023 were shorter in summer and longer in winter. This was because rain fell in summer, and the water level rose, submerged the dry beach, and shortened its length. In winter, there was less rainfall, the water level dropped, and the dry beach was exposed more. However, monitoring points A and C were located on the most two sides of the reservoir area. Due to the influence of topography, the dry beach length was relatively less affected by the season.  
Figure 16: (**a**) Line fitting; (**b**) length calculation of monitoring points A and B; (**c**) length calculation of monitoring point C.  
Figure 15: (**a**) Detection results; (**b**) edge detection results.  
Figure 19: Measurement and calculation results of monitoring point C.  
Figure 17: Measurement and calculation results of monitoring point A.  
Figure 18: Measurement and calculation results of monitoring point B.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 3 The Proposed Scheme
### Region Head Joining  
In the proposed GB-FERMA, we use Fermat points to find the shortest path to construct an energy efficient path for grid-based geocasting. If there are more than two target regions needed to collect data, the GB-FERMA constructs a tree structure by selecting the two region heads farthest from the sink. As shown in Figure 7, the gateway \(G_{1}\) could be found by using the gateway selection process. A new region III could then be joined into the tree-based transmission path. Gateway \(G_{1}\) and the region head \(H_{3}\) conduct the gateway selection process, and then an energy efficient tree-based shortest path can be constructed.  
Figure 6: Energy efficient grid-based geocasting path construction.  
Figure 7: Region head joining.