esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Mobile edge computing (MEC)-enabled satellite-terrestrial networks (STNs) can provide task computing services for Internet of Things (IoT) devices. However, since some applications' tasks require huge amounts of computing resources, sometimes the computing resources of a local satellite's MEC server are insufficient, but the computing resources of neighboring satellites' MEC servers are redundant. Therefore, we investigated inter-satellite cooperation in MEC-enabled STNs. First, we designed a system model of the MEC-enabled STN architecture, where the local satellite and the neighboring satellites assist IoT devices in computing tasks through inter-satellite cooperation. The local satellite migrates some tasks to the neighboring satellites to utilize their idle resources. Next, the task completion delay minimization problem for all IoT devices is formulated and decomposed. Then, we propose an inter-satellite cooperative joint offloading decision and resource allocation optimization scheme, which consists of a task offloading decision algorithm based on the Grey Wolf Optimizer (GWO) algorithm and a computing resource allocation algorithm based on the Lagrange multiplier method. The optimal solution is obtained by continuous iterations. Finally, simulation results demonstrate that the proposed scheme achieves relatively better performance than other baseline schemes.  
mobile edge computing; satellite-terrestrial networks; inter-satellite cooperation; offloading decision; resource allocation +
Footnote †: journal:  
00 000-0001-6600-0000][PERSON]; [PERSON]; [PERSON]; [PERSON] Inter-Satellite Cooperative Offloading Decision and Resource Allocation in Mobile Edge Computing-Enabled Satellite-Terrestrial Networks [2023]2366830, doi:10.3390/s2302068
# Evaluation of Tacotron Based Synthesizers for Spanish and Basque
## 3 Methodology
### Robustness Improvement  
The attention instability during inference makes the models unreliable when synthesizing a large amount of utterances without any supervision. This robustness issue of Tacotron 2 has already been observed and investigated. Some works propose modifications over the current attention mechanism with the intention of reducing the alignment errors, as related in [28; 29]. Other techniques propose the injection of prior knowledge into the system to improve the training process of the current attention mechanism [18; 30]. In this work, we applied the latter, specifically we implemented a pre-alignment guided attention following the work described in [18]. This method improves the model stability while also enhances the training efficiency.  
The principle of pre-alignment guided attention is introducing an explicit target in order to compute an additional loss term described by Equation (1).  
\[Loss_{align}(A,\alpha)=\frac{1}{T}\sum_{i=1}^{T}\sum_{j=1}^{T^{\prime}}(A_{ij }-\alpha_{ij})^{2} \tag{1}\]  
where \(A_{ij}\) denotes the ground truth time aligned phoneme sequence and \(\alpha_{ij}\) is the predicted alignment by the attention module.  
This new loss eases the learning of the attention during the training process. The explicit target provided to the model is the ground truth time-aligned phoneme sequence. To create it, we used the Montreal Forced Aligner [31] and the phoneme sequence obtained with the linguistic front-end for Spanish and Basque described in Section 2.1.  
Figure 3 shows the final architecture of the system. The input of the baseline system is now the phoneme sequence instead of the original character sequence. This architecture includes the additional loss term computed using the ground truth alignment of the phoneme sequence and the predicted alignment obtained in the attention model.  
Figure 4: Alignment of two different sentences and inspection area of the error detection algorithm: (**a**) correctly aligned sentence; (**b**) incorrectly aligned sentence.  
The training of the system was performed according to the same hyper-parameters used in the training of the baseline. In this case, no pre-trained models were used and the five voices were trained from scratch. These models are hereinafter referred to as Taco-PAG.
## 5 Understanding Decentralised Identity Systems
### Blocksstack
#### 5.6.1 User Registration, Authentication, and Key Revocation Process  
The user registration process in Blockstack involves a two-phase procedure:  
The first process begins with the user preordering a name, which generates a hash that is kept on the blockchain. After a mandatory waiting period, the name is linked to the blockchain, completing the registration process. The name is cryptographically connected to the blockchain's public key. This ensures that every name is unique and secure. Names are registered in certain namespaces, which determine the cost and renewal procedures for the names. The blockchain stores these namespaces as part of the identity management procedure [67].  
Then, during the authentication process, Blockstack uses an SSO system in which each user enters their Blockstack ID, which is uniquely linked to a public key via blockchain. When a user submits the appropriate private key as proof of ownership, they are authenticated. This method protects and authenticates a user's identity without relying on centralised systems [68].  
Blockstack's key revocation approach is beneficial when keys are lost. The name transfer process allows users to transfer ownership of their Blockstack ID to another address. When the key is revoked, all subsequent operations on the connected name are disabled, protecting the user's identity from unauthorised access [67].
## 5 Bayesian Prediction
### One-Sample Prediction  
To obtain a one-sample prediction, we begin by assuming that the observed sample, under a progressive Type-II censoring scheme, is represented as \(X=(X_{1},\ldots,X_{r+1},\ldots,X_{m})\), derived from a generalized Pareto distribution. The censoring scheme is denoted as \((R_{1},\ldots,R_{m})\). We denote the failure times for a future sample of size \(K\) as \(W_{1}\leq W_{2}\leq\cdots\leq W_{K}\). Additionally, let \(W_{r}\) (for \(1\leq r\leq K\)) represent the \(r\)-th failure of the future sample, which we aim to predict based on the observed samples. We will denote the recorded values of \(W_{r}\) as \((w_{r_{j}})\), arranged in order.  
Based on the function presented in Equation (2), we can derive the corresponding cumulative distribution function (CDF) as follows:  
\[F(W|X,\alpha,\lambda) =\int_{X_{r}}^{W}f(u|x,\alpha,\lambda)\mathrm{d}u \tag{48}\] \[=j\frac{R_{r}!}{(R_{r}-j)!j!}\sum_{n=0}^{j-1}(-1)^{j-1-n}\frac{(j- 1)!}{(j-1-n)!n!(R_{r}-n)}\] \[\times[1-(1-F(x_{r}))^{n-R_{r}}(1-F(w))^{R_{r}-n}]. \tag{49}\]  
We will obtain the predictive survival function.  
\[H(h|\hat{x},\alpha,\lambda)=\frac{P(w>h|x,\alpha,\lambda)}{P(w>x_{r}|x,\alpha, \lambda)}=\frac{\int_{h}^{\infty}f(u|x,\alpha,\lambda)\mathrm{d}u}{\int_{x_{ r}}^{\infty}f(u|x,\alpha,\lambda)\mathrm{d}u}. \tag{50}\]  
Utilizing the posterior distribution of \(\alpha\) and \(\lambda\), we derive the posterior predictive cumulative distribution function (\(G(w|\hat{x})\)), along with the associated survival function (\(S^{*}(w|\hat{x})\)):  
\[G(w|\hat{x})=\int_{\alpha,\lambda}F(w|\hat{x},\alpha,\lambda)\pi(\alpha, \lambda|\hat{x})\mathrm{d}\mathrm{d}\lambda, \tag{51}\]  
\[S^{*}(w|\hat{x})=\int_{\alpha,\lambda}S(w|\hat{x},\alpha,\lambda)\pi(\alpha, \lambda|\hat{x})\mathrm{d}\mathrm{d}\lambda. \tag{52}\]  
Next, we will derive the Bayesian prediction interval \((L_{1},U_{1})\), which corresponds to a confidence level of \(100(1-\tau)\%\):  
\[S^{*}(L_{1}|\hat{x})=1-\frac{\tau}{2},S^{*}(U_{1}|\hat{x})=\frac{\tau}{2}. \tag{53}\]  
Following this, we can generate the predictive estimation for the future \(r\)-th ordered lifetime:  
\[\widehat{w_{r}}=\int_{0}^{\infty}\int_{0}^{\infty}H(x_{r}|\alpha,\lambda)\pi (\alpha,\lambda|x)\mathrm{d}\lambda\mathrm{d}\alpha, \tag{54}\]  
where  
\[H(x_{r}|\alpha,\lambda)=\int_{x_{r}}^{\infty}uf(u)\mathrm{d}u. \tag{55}\]  
We have observed that the integrals mentioned above cannot be solved analytically. Utilizing the MH algorithm, we derive the predictive estimation of \(W_{r}\) as follows:  
\[\widehat{w_{r}}=\frac{1}{M-M1}\sum_{i=M+1}^{M}H(x_{r}|\alpha_{i},\lambda_{i}). \tag{56}\]
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
###### Abstract  
Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from \(O(K^{3})\) to \(O(K^{2})\) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate.  
massive MIMO; B5G networks; wireless communication; optimized AOR; meta-heuristic algorithms; particle swarm optimization; signal detection +
Footnote †: journal: _arxiv.org/abs/1208.00022_
# A Study on Analyzing Travel Characteristics of Micro Electric Vehicles by Using GPS Data
## 3 Methodology
### Characteristics of Roads Traversed by Micro-EVs  
By analyzing the characteristics of the roads traversed by the micro-EVs, the conditions under which the micro-EV can be most effectively utilized were identified. For this purpose, we utilized road network data provided by the Korea Transport Database (KTDB). The road network data include information about the width and length of road segments.  
Figure 3: Experimental sites for driving performance comparison of micro-EVs and conventional vehicles.  
Using the road network data, roads located near the micro electric vehicle's position were extracted. Since GPS devices sometimes exhibit errors, it is difficult to assume that GPS data accurately represent the location of a micro-EV at every moment. Therefore, potential errors of the GPS device must be taken into account. According to the GPS performance standards provided by the U.S. Department of Defense, the error of GPS devices ranges from 15 to 33 m at most [32]. Thus, in this study, roads within 30 m of the micro electric vehicle were considered as the roads traveled by the micro-EV. Subsequently, the road widths and lengths were categorized based on different types of micro-EV services, which helped to analyze the characteristics of the roads primarily used by micro-EVs.
## 5 Results
### Continuous Security Monitoring Strategy and Integration of Advanced Technologies  
Internal audits can assess how cybersecurity and IT areas implement continuous real-time monitoring tools that detect abnormal or suspicious activities. These tools include intrusion detection and prevention systems (IDS/IPS), network traffic monitoring, user and entity behavior analysis (UEBA), and SIEM (Security Information and Event Management), which aggregates and analyzes security data in real-time to identify potential threats. If the institution's strategy fails to integrate these technologies or present consistent results effectively, the audit may recommend a feasibility study and create an action plan to adjust the processes.  
Interviewee INT14 made the following comment in his context unit regarding the topic: _\"Investing in cutting-edge technology for threat monitoring, detection, and response can mean the difference between a minor breach and a catastrophe. Advanced tools such as AI for anomaly detection and automation for incident response enable a more dynamic and adaptive approach to addressing cyber risks and significantly increase an organization's ability to respond to threats\"_.  
Modern tools for continuous monitoring and automated anomaly detection enable real-time oversight of financial operations and rapid identification of suspicious activities [42]. This shifts auditing toward proactive risk management, keeping pace with evolving cyber threats. Ref. [43] presents an analytics-driven tool that detects, predicts, and mitigates threats in real time, enhancing the integration between technology and risk governance.  
The evolution of continuous security monitoring is closely linked to advances in AI, which enhances financial organizations' ability to anticipate and address cyber threats [44]. AI automates incident detection and response, improving cybersecurity risk management [42]. AI and machine learning also streamline the analysis of large data volumes, boosting efficiency and accuracy [45], though challenges like model bias remain [41].
# A Method of Discriminating Between Power Swings and Faults Based on Principal Component Analysis
## 4 Simulation Tests
### Test Results Based on Two-End System
Figure 8: The waveform of the three-phase voltage under a power swing of 2 Hz. Figure 7: Trajectory of measured impedance during power swing. Table 2 shows the performance of the proposed method under different power swing situations, where \"\(f_{\text{ps}}\)\" represents the oscillation frequency, \"\(t_{0}\)\" represents the system of the power swing, and \"PS\" indicates the test result of the power swing. It is observed in Table 2 that the proposed method can identify power swings reliably, and it is not affected by the starting moment. Table 3 shows the performance of the proposed method under different three-phase shorting fault situations, where \"Distance\" represents the distance from the fault point to the measuring point, \"\(t_{0}\)\" represents the occurring moment of the fault, and \"F\" indicates the test result of the fault. It is observed in Table 3 that the proposed method can also identify faults reliably, and is not affected by the starting moment of the fault. For testing the effectiveness of the PSD function of the proposed method, several scenarios were simulated. During the simulation, a power swing with an oscillation frequency of 5 Hz occurred at 1.2 s. Different fault distances and power angles between the two ends during the occurrence of the fault were also considered in the simulation. The test results are presented in Table 4, where \(\delta\) represents the power angle between the two ends. According to the results of No. 1-5 shown in the table, the proposed method can reliably detect faults during power swings. Moreover, since \(Z_{\text{m}}\) is equal to \(Z_{\text{n}}\) in the simulation \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \(f_{\text{ps}}\) **(Hz)** & \(t_{0}\) **(s)** & \(C_{\text{avr}}\) & **Result** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 2: Test results under different power swing situations. Figure 9: The value of \(C\) under a power swing of 2 Hz. system shown in Figure 6, the fault point 150 km away from the measuring point is actually the oscillation center. From the results of No. 6-12, it is observed that the proposed method can also reliably detect a fault occurring at the oscillation center during a power swing.
## 2 Radio Propagation Measurement Method
### Radio Propagation Measurement Environment
#### 2.2.1 Simple Measurement Environment  
Figure 2(a) shows a photograph of the selected S environment, where there is only one building. The building has different heights on the left (16 m) and right (11 m) sides. As shown in Figure 2(a), the wall of Building C is made of brick, and there is one door and two windows in the center of Building C, but the overall structure of the walls is flat with few bumps and hollows. The location where the receiving antenna was placed is the rooftop of another building which is concrete.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Simple Propagation Environment** & **Complex Propagation Environment** \\ \hline Measurement time & 5 s & 10 s \\ Migration length \(L\) & 1.5 m & 3 m \\ Movement speed of UAV \(v\) & & 0.3 m/s \\ UAV altitude \(H\) & & 10–30 m \\ \hline Distance between & & \\ the nearest building and & 5 m & 10 m & 5 m \\ transmitting antenna (UAV) \(Dt\) & & \\ Distance between & & \\ the nearest building and & 5 m & 10 m & 15 m \\ receiving antenna \(Dr\) & & \\ \hline Frequency \(f\) & & 2.5 GHz \\ Transmitting antenna & & Half-wavelength dipole antenna \\ Receiving antenna & & Half-wavelength dipole antenna \\ Antenna arrangement & Horizontal installation & Horizontal installation & Vertical installation \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radio propagation measurement parameters.  
Figure 3: Simple measurement environment: (**a**) photograph of measurement environment; (**b**) top view of the analytical model and (**c**) flight path of the UAV.  
Figure 3b depicts the top view of the measurement environment with a detailed diagram of each parameter. The distance \(D_{t}\) between the transmitting antenna and Building C and the distance \(D_{r}\) between the receiving antenna and Building C were set to the same distance, \(D\). The transmitting and receiving points were located 5 m and 10 m from the center of the right-hand building as shown by the X marks in the figure. The receiving antenna was placed 1 m above the rooftop of another building. Half-wavelength dipole antennas were used for the transmitting and receiving antennas.  
Figure 3c illustrates the flight path of the UAV which was moved along an axis parallel to the ground and Building C, i.e., the \(x\)-axis. The UAV flew at 5 m intervals from 10 m to 30 m above the ground. The migration length of the UAV was set to 1.5 m to reduce the variation in the spatial propagation loss component of the received signal during movement, and the movement speed of the UAV \(v\) was set to 0.3 m/s to eliminate as much as possible the effect of Doppler frequency.
## 3 Data Fusion Method Based on Self-Adaptive
### Determination of Training Target Values Based on Neighborhood Search
The neighborhood search method can effectively improve the performance of the model when the true value cannot be determined. By analyzing the results shown in Figure 10, it can be observed that as the rotation angle increases, the angular error tends to increase when the training output value is set to the IMU solution value. In contrast, when the training output value is set to the target value obtained by the neighborhood search method, the rotation error is reduced compared to the IMU solution value and is closer to the situation where the true rotation angle value is used as the training output value. This finding indicates that using the target values obtained by the neighborhood search method as the output target values in backpropagation algorithm model training can more effectively determine the target values during training, thereby optimizing the performance of the model. Therefore, the results of this simulation experiment support the use of the target values obtained by the neighborhood search method as the output target values in the training process of the backpropagation algorithm to improve the model's accuracy and robustness.
# Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation
## 4 Results and Discussion  
Execution logs gave the total execution time per number of surrogates as shown in Figure 4 by the point marks, and the presented dashed lines are fitted by the linear least square method. Line slopes for each parallelism strategy are 280.385 min/surrogate (Data Parallelism) and 65.257 min/surrogate (Task Parallelism). Therefore, the quickening can be determined as  
\[Quickening=\frac{t_{Data\ Parallelism}}{t_{Task\ Parallelism}}=\frac{280.385}{65.257}=4.297. \tag{4}\]  
Figure 3: Beowulf cluster used in the experiments. Lines between devices may represent multiple Ethernet cables for diagram cleanliness purposes. Cluster nodes are prefixed with “lps”. Detailed cluster configuration is informed in the Appendix A.  
The achieved \(\sim\)4.3 quickening shows that Task Parallelism is significantly faster than Data Parallelism. After analyzing logs, positive quickening can be explained by three main factors, and it is negatively impacted by another.  
First, quickening explanation is data locality since data are stored on a local disk in Task Parallelism versus being transferred by the network in Data Parallelism. In addition, the former has to transfer channel data for every surrogate, while, the latter, locally read signal data only once for each two channel permutation surrogates.  
The second factor is sub-optimum node utilization caused by cluster heterogeneity illustrated in Figure 5. This happens in Data Parallelism because data are equally divided across computing nodes with different performance, causing faster nodes, which finished data processing, to wait for slower nodes.  
The third factor is caused by the fact of Data Parallelism surrogate datasets are generated only by host node, forcing all computing nodes to wait for surrogate dataset generation. Task Parallelism does not suffer from the same problem, as while one surrogate dataset is generated by one computing nodes, it does not block another computing node.  
The asynchronous nature of tasks are negatively affecting Task Parallelism quickening, when task pool is exhausted, some computing nodes are left without any task, reducing the quickening enhancement.  
About node performance difference, Figure 5 suggests that it may be caused by different random-access memory (RAM) sizes across cluster nodes, since slowest nodes (_lps02_, _lps04_, _lps05_ and _lps08_) have the smallest RAM amount (8 GiB, 8 GiB, 8 GiB and 12 GiB, respectively) as listed in Table 1 in Appendix A.  
An analogy can be made between MapReduce and presented parallelism strategies. In Figure 2a,b, dashed and doted arrows would correspond to respectively map and reduce operations. Keeping the same analogy, this study would be about the balance between communication and computation to optimize MapReduce DTE runtime performance.  
Figure 4: Data and Task Parallelism total execution time per number of surrogate signals. Point marks show numerical results in minutes. Lines show data fitted by the linear square method.