esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Risk in Alborz: Insights from Geological Moment Rate Estimation and Fault Activity Analysis
## 4 Conclusions  
In conclusion, our study reveals that northern Iran, particularly the Central Alborz, is a highly active region in terms of seismicity. Our findings highlight the high risk of significant earthquakes in Tehran, Alborz, and Qazvin, given the long recurrence period of earthquakes with magnitudes greater than 6. Therefore, cities in high-risk areas, specifically the western side of the Central Alborz and the western side of the Eastern Alborz, are at the highest seismic risk due to their proximity to fault zones.  
The seismic hazard map generated in this study provides valuable information to mitigate the potential damage caused by future earthquakes. The estimated maximum horizontal acceleration for Tehran due to the Qasr-e Firuzeh and Sorhk-e Hesar fault systems is 0.5757, with a seismic potential of 6.3 Richter. These findings have significant implications for the region's urban planning, building design, and emergency response strategies.  
Furthermore, the geological moment rates, encompassing both seismic and aseismic deformations, provide insight into the potential of faults to release the stored elastic energy in the crustal rocks of the region. Our study identified the Eshqabad, Taleqan, and Mayamey faults as exhibiting the region's highest geological moment rates. These findings can aid in understanding the potential for future earthquakes and inform seismic hazard assessments in the area.  
In summary, our study highlights the need for continued monitoring and evaluation of seismic hazards in northern Iran to improve earthquake preparedness and mitigate potential damage to infrastructure and communities in the region.  
Conceptualization, A.M.N. and A.R; methodology, M.M.K. and S.M; software, A.M.N. and A.R; validation, M.N., S.S. and R.D.; formal analysis, S.S. and R.D; investigation, A.M.N. and A.R.; resources, R.D.; data curation, S.S.; writing--original draft preparation, A.M.N. and A.R; writing--review and editing, A.R. and R.D; visualization, M.N.; supervision, M.M.K. and A.R.; project administration, A.M.N. and S.M.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
This work is the result of a collaborative research study involving the Department of Geology at the University of Birjand, Iran; the Department of Earthquake Research at Shahid Bahonar University of Kerman, Iran; the International Institute of Earthquake Engineering and Seismology in Tehran, Iran; the Department of Geology at Shahid Bahonar University of Kerman, Iran; and the Department of Earth Sciences at Utrecht University, The Netherlands.  
The authors declare no conflict of interest.
# An Adaptive Moving Window Kriging Based on K-Means Clustering for Spatial Interpolation
## 2 Theoretical Background  
Before embarking on a systematic exploration of the proposed method, we provide some necessary theoretical backgrounds that can be attributed to two approaches: ordinary kriging and the K-means clustering algorithms.
###### Abstract
Using high-precision RT orbit and clock offset products from IGS-RTS, the accuracy of the RT POD of GRACE-C in the radial direction using spaceborne GPS observations reaches the centimeter level [18]. In summary, the accuracy of the RT POD of LEO satellites is improved by RT corrections. A highly accurate broadcast ephemeris is needed for the RT POD of an LEO satellite. The accuracy of a broadcast ephemeris can be improved using the PPP-B2b service. Although spaceborne GNSS data receivers do not receive PPP-B2b corrections currently, chips that can receive the broadcast ephemeris and PPP-B2b signal at the same time have been manufactured, e.g., the K803 board manufactured by SinoGNSS. Therefore, the RT POD of LEO satellites can be performed with the broadcast ephemeris and PPP-B2b services based on GPS and BDS observations. The present study takes the Tianjin University Satellite No. 1 (TJU-01) as an example to verify the feasibility of the RT POD of LEO satellites with the broadcast ephemeris and PPP-B2b service. It is the first to analyze the GPS and BDS LEO satellite orbit determination based on the PPP-B2b service. The TJU-01 satellite is an essential component of the Yunyao constellation, with more than 20 satellites to be networked. TJU-01 was launched in December 2021, has a mass of 20.53 kg, and operates in a 512 km Sun-synchronous orbit. It is equipped with an onboard GPS and BDS-2/3 multi-GNSS receiver for GNSS occultation and POD service. The RT POD performance of TJU-01 with a PPP-B2b service is demonstrated in detail. The rest of this article is arranged as follows. The mathematical principles and processing of the RT POD using PPP-B2b with its matching and correction algorithm are introduced and described. The variation characteristics, accuracy, and availability statistics of the PPP-B2b correction are then analyzed. The experimental results of the RT POD of the TJU-01 satellite using PPP-B2b and its improvement are next analyzed. Finally, conclusions are drawn from the results of the research.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 4 Proposed Framework: FL-LoRaMAC
### Optimizing Communication Bandwidth
#### 4.4.1 Model Pruning  
In a neural network, not all parameters are equally significant [27]. In other words, not all the connections between neurons of fully connected layers contribute equally to the model's prediction. The pruning technique can be used to reduce the complexity and time of execution by removing the insignificant connections between neurons. This is carried out by modifying the parameter values related to those connections to zero. The output of pruning is a sparsified weight matrix.  
Typically, pruning is accompanied by quantization or compression or both to obtain a smaller parameter matrix in terms of bytes, but this also increases the computation cost. In this particular scenario, training is performed on resource-constrained devices. Applying these methods to those devices will result in significant overhead. Instead, we can choose not to communicate these pruned parameters or send them with lower priority.
## 4 Remote Sensing Platforms Used in Peatland Research
### Satellite-Based Remote Sensing
#### 4.1.3. Carbon Stock Estimation  
Quantifying carbon stocks in peatlands is crucial for understanding their role in the global carbon cycle. Satellites provide a means for estimating carbon stocks over large areas. Several studies have utilised satellite data to map and estimate peatland carbon content [62; 63; 72; 73]. However, obtaining accurate carbon estimation through satellites requires ground validation and considering site-specific factors, such as vegetation type, peat depth, peat thickness, or direct traditional carbon content estimation in the laboratory. By combining spectral indices with field measurements, these studies have demonstrated the potential of remote sensing in capturing spatial variations in peatland carbon stocks.
## 3 Methodology
### Feature Extraction
#### 3.3.2 Rolling Mean  
The rolling mean is calculated over a given window, determining the number of data points to be involved in each average calculation. This rolling mean can be calculated by taking the average of consecutive data points in a rolling window. Hence, the rolling mean provides a smoothed representation of the underlying trend or pattern in the preprocessed inventory data.  
\[RM=\frac{1}{y}{\sum_{j=t-y+1}^{t}S_{n}^{*}} \tag{15}\]  
In this context, \(y\) represents the window size indicating the total number of data points used in each calculation. \(S_{n}^{*}\) represents the preprocessed data at a specific time \(t\).
## 6 Experiments
### Different Number of Cameras
These results show that applying a stereo matching algorithm to EBCA with five cameras can lead to satisfactory results even if results of this algorithm were poor when it was used with a stereo camera. Moreover, there are not significant differences between configuration 3L and 3P. Therefore, it is insignificant on which side of a central camera a third camera is placed when a configuration with three cameras is used. Nevertheless, it is possible that in some circumstances one of these configurations is better depending on the shape or illumination of viewed objects. The decrease in the value of BMP was associated with the increase in the value of the COV metric as shown in Figure 12b. This means that obtained disparity map contained overall less points for which disparities were not available. Charts obtained for GC and MC-CNN algorithms look peculiar because highest values were reported for two and five cameras. The reason is such that these algorithms provide data for almost all points of a disparity map when they are used with a stereo camera. Therefore, the COV metric for these algorithms drops when the EEMM is applied and the number of camera is increased  
Figure 12: Results obtained for EEMM used with different number of cameras. The figure presents values of the following quality metrics: (**a**) BMP, (**b**) COV and (**c**) BMB. to 3. Further increase in the number of cameras implies greater values of COV similarly as in case of SGBM and ELAS algorithms. Figure 12c presents error rate for points located in the background. The BMB metric becomes greater in case of all algorithms when the number of cameras increases from three to four and from four to five. It corresponds to the increase in the values of the COV metric showing that disparity maps contain values of disparities in almost all points. In case of GC and MC-CNN algorithms, the error rate in background is nearly equal to 100% when two and five cameras are used because these algorithms provide values of disparities in areas of images which should be marked with values indicating that the disparity is unknown. For this reason, results of MC-CNN need to be considered worse than results of ELAS in the configuration with five cameras, although values of BMP indicate that the quality of these algorithm is similar. ELAS in much larger extent correctly identifies points for which disparities need to be marked as unknown. MC-CNN generates incorrect data for these areas.
## 3 Blind Adaptive Beamforming Based on Direction Lock Loop
### Beamformer Construction  
The array vector \(\mathbf{a}(\hat{\theta}_{k})\) of the signal direction is constructed according to the DOA parameter estimated by DiLL in real time. The output power of the array is minimized, while the response of the signal direction is guaranteed with the unit array:  
\[\min_{\mathbf{w}}\left\{\mathbf{w}^{H}\mathbf{R}\mathbf{w}\right\}\,s.t.\, \mathbf{w}^{H}\mathbf{a}(\hat{\theta}_{k})=1 \tag{19}\]  
Using the linear constraint minimum variance (LCMV) method to solve the above equation, the optimal weight can be obtained as  
\[\mathbf{w}_{opt}=\frac{\mathbf{R}^{-1}\mathbf{a}(\hat{\theta}_{k})}{\mathbf{ a}^{H}(\hat{\theta}_{k})\mathbf{R}^{-1}\mathbf{a}(\hat{\theta}_{k})} \tag{20}\]  
The array-weighted output can be calculated as  
\[y_{out}(t)=\mathbf{w}_{opt}^{H}\mathbf{x} \tag{21}\]
## 3 Datasets and Methodology
### Datasets  
Our main goal was to gain new insight into the possibility of predicting earthquake occurrences using TEC value anomalies. To eliminate all non-related geodynamic effects (e.g., solar flares, coronal mass ejections, geomagnetic storms, and X-ray flux events), we excluded all earthquake events that took place during enhanced solar activity (X or M class solar flares and large scale coronal mass ejections).
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 6 Conclusions  
TSAER focuses on improving energy consumption and hot node issues in WBANs. TSAER divides the data types into emergency data and normal data and designs corresponding routing paths. On the one hand, in order to control the formation of hot nodes in the network, the protocol sets a heating threshold before the node reaches the high temperature threshold, divides the temperature of the node into different temperature intervals through these two thresholds, calculates the probability of data reception of the node in different temperature intervals, and delays the node to become a hot node by controlling the probability of data reception in the heating interval. On the other hand, in order to balance the network energy consumption, the benefit function of the node residual energy, the distance to sink, and the data reception probability is constructed. Through the selection of the largest neighbor node in the benefit function as the best forwarding node, the optimal transmission path is finally determined. Simulation results show that compared with the existing M-ATTEPMT and iM-SIMPLE, TSAER has significant advantages in meeting network QoS requirements, improving energy utilization, and controlling the formation of hot nodes. In future research, the influence of human movement diversity on data transmission in WBANs can be considered.  
All authors contributed to the study design. Y.M.: writing--original draft. G.Z.: formal analysis. X.W.: software. M.Z.: investigation. H.M.: data curation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Key project of Henan Province Science and Technology Research and Development Program Joint Fund (235200810040, 231111221500), Major Science and Technology Projects of Longmen Laboratory (231100220500), the National Natural Science Foundation of China (62072158).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 7: Analysis of average temperature rise.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 5 Discussion  
It might confound us, as to what would be the performance of DTCM without fuzzy inference system and why the fuzzy inference system plays such an important role in DTCM. From the perspective of theoretical analysis, since the complex relationship between influence factor and data transmission is not a type of mathematical mapping, and cannot be simply formalized as a deterministic or stochastic function. However, fuzzy inference can compute the membership degree of different influencing factors and determine their membership level, thereby judging whether the selection on data routing is reasonable or not. On the other hand, from the application level, without a fuzzy inference system, DTCM makes decisions on data routing through the amount of the remaining energy and cache space, but it is not clear how much impact they have on data transmission and which one presents a greater influence. Dynamic weight adjustment and the optimization of node classification are difficult to achieve. As the number of nodes increases, decisions on data routing from the DTCM will be more and more unreliable, so this type of algorithm lacks extensibility and self-adaptability.  
Figure 11: Mean and error from the experiments on network overhead comparison.  
Figure 10: Network overhead comparison from five algorithms with various cache spaces.  
Another issue that needs to be discussed is the energy level of mobile devices in the real world. DTCM is a type of linear optimization algorithm that can be processed in polynomial time, which indicates that it also can be implemented in mobile devices (mobile phone, laptop, etc.) with limited energy. Meanwhile, the mobility model, data storage, and energy consumption set in this experiment all mimic the real application scenario. To this end, DTCM shows a certain degree of extensibility and applicability in real application scenarios.
## 3 Constellation Set-Up (LEO)  
Based on the reasoning and dynamics mentioned in the chapters above, walker constellations with unique orientations are a way forward and respective simulations were planned and executed. It is not about the number of satellites that align through the constellation planes, but how optimally these satellites can perform with a minimum of resources utilized in orbit for a longer period of time. Hence, the utilization of Commercial-off-the-shelf (COTS) components becomes an essential vision of this proposed research. This needs to be supported with innovative trajectory optimization techniques for better coverage, understanding of on-orbit conditions and requirements for fulfilling them for maintaining cost-effective global constellations for their given lifecycles, respectively.  
The initial constellation set-up (pre-optimization) planned for this paper is given in Table 1 below:  
In a real-time scenario of the constellation meant for imagery and data transfer for disaster management applications, the number of satellites deployed would be less and are launched as per the needs of coverage and number of end-users and their locations. This set-up was made to analyze a bigger picture of a constellation performing a global operation  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of Satellites & 48 \\ Number of Planes & 08 \\ Mass (kg) & 85 \\ Inclination (Degrees) & 45 \\ Semi-major axis (Km) & 7000 \\ Eccentricity \(\mathbf{\xi}\) & 0.001 \\ Right Ascension of Ascending Node & \\ (RAAN-Degrees) & \\ Argument of Perigee & 270 \\ (Degrees) & 45 \\ Field of View (Degrees) & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the small satellite constellation.  
with multiple end-users worldwide. This will result in investigating the situations and conditions of operating larger constellations of small satellites over a significantly long-term period.  
The main reason for selecting the scenario given in Table 1 is to test the constellation operation at a Low-Earth Orbit (with a semi-major axis of 7000 km) where the influence of the atmospheric drag (specifically J2) is quite severe and the solar radiation pressure/winds are also heavily active in the region. The idea is to test the constellation under these severe conditions and analyze the coverage optimization for the best possible solution. If the constellation could survive this initial condition and operation to give solutions, then similar or higher altitudes could be highly productive for future missions.  
Adding to this, a walker constellation with 45-degree inclination is the first of a kind to be tested under these given situations and initial conditions, respectively. Additionally, with the ISS being at 408 km for conducting various Earth-benefitting experiments, the Low-Earth altitudes are of prime importance for disaster-management applications.  
The set-up is pictorially depicted in Figure 6 as below:
## 5 Restoration Plan Strategy after a Blackout in a Wide Area or a Country
### Case Studies  
#### 5.4.1 Case Study 1: The IEEE-39 Bus System  
The proposed algorithm was implemented by MATLAB and tested with the IEEE-39 bus system. There are three BS units and seven NBS units among the ten generators, the data of which are presented in Table 7. G8, G3, and G7 are BS units in islands 1, 2, and 3, respectively. All the other units are NBS units. After calculation by the proposed algorithm, the optimal network skeleton for each island is shown in Figure 9 with colored solid lines. Table 8 shows the restoration sequence and the corresponding restoration paths. For this scenario, we assume that all thermal units are in hot start time and the system load is 2150 MW. The total available production is 2740 MW, making the system generation rich. The total restoration time is 22.4 min. During that time, all the units and the loads were restored successfully. Comparing the restoration time of the A* Algorithm with other restoration mechanisms, it is proved that the proposed Algorithm is more efficient. For example, the restoration time in [88] is 36 min where the proposed genetic algorithm was validated also in the IEEE-39 bus system. In [84], where a Generic Restoration Milestone (GRM) is proposed and has also been validated in the IEEE-39 bus system, the restoration time is about 100 min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Node No.**} & \multirow{2}{*}{**Generator**} & **Generator** & **Output** & **Starting** & \multirow{2}{*}{**BS Unit**} \\  & & **Type** & **(MW)** & **Time (min)** & \\ \hline
30 & G1 & Gas & 430 & 3.5 & NO \\ \hline
31 & G2 & Gas & 520 & 4.8 & NO \\ \hline
32 & G3 & Hydro & 140 & 1.5 & YES \\ \hline
33 & G4 & Wind & 100 & 3.4 & NO \\ \hline
34 & G5 & Wind & 200 & 4.1 & NO \\ \hline
35 & G6 & Gas & 560 & 4.7 & NO \\ \hline
36 & G7 & Hydro & 130 & 1.6 & YES \\ \hline
37 & G8 & Hydro & 160 & 1.7 & YES \\ \hline
38 & G9 & Wind & 200 & 4.3 & NO \\ \hline
39 & G10 & Wind & 300 & 4.2 & NO \\ \hline \hline \end{tabular}
\end{table}
Table 7: Data generators for the IEEE-39 bus system and its classification.
[PERSON] [18] presented energy management concerning the minimization of energy cost with the utilization of RER, but the authors did not overview the load's categorization as it is required for optimal scheduling. [PERSON] et al. [9] discussed the internet of energy for DSM and smart grids, which lead to smart cities, but the authors did not emphasize end-user data privacy and security as well as the risk management for national security. [PERSON] [19] surveyed the literature on BEMS, considering building energy efficiency using control management strategies. However, the implementation of the energy policy for DSM was not covered in detail. The work in [20; 21] discussed the rage of BEMS with respect to the UN's sustainability goals. To address the gaps, this study presents a full investigation of the controllers and optimization for BEMS in terms of SDGs. The main contributions of this review are listed as follows:  
* This work summarizes optimizing algorithms and various control strategies in achieving energy reduction, together with their benefits and drawbacks. * This paper also presents the importance of commercial building load classification and categorization, energy policy, data privacy, and security to DSM. * The subject of passive and active design solutions for energy efficient retrofitting to ZEB is highlighted. * The study implies the development of an efficient BEMS that connects to the UN SDGs for achieving future sustainability through low carbon emissions, sustainable cities, green jobs, cost-effective energy supplies, and healthier living. The rest of the paper is organized as follows. In Section 2, a summary of load classification in commercial buildings is described. Conventional BEMS techniques are discussed in Section 3. A thorough discussion of current and advanced methods in BEMS is included in Section 4. Furthermore, optimization control strategies in BEMS are described in Section 5. A summary of future trends and issues is presented in Section 6. Finally, a discussion and conclusions are drawn in Section 7.
## 3 Proposed Method  
For frame-wise detection [33, 34] in low-bit-rate speech streams, the main challenge is how to extract steganalysis features from individual speech frames. As frame-wise detection cannot utilize long-term dependencies, a useful and promising approach is to construct complex features based on the codewords within each speech frame. Therefore, this paper proposes a frame-wise steganalysis method based on mask-gating attention and bilinear interaction mechanisms (MGA-BI), which can dynamically capture the importance of features and their interactions in a fine-grained way. The framework of our method is shown in Figure 1.