esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Accelerated Over-Relaxation Method for Robust Signal Detection: A Metaheuristic Approach
## Appendix B Numerical Example of Proposed OAOR Method
### Step 3: Optimize Relaxation Parameters Using Novel PSO  
Use the novel PSO algorithm to find the optimal relaxation parameters \(\omega^{*}\) and \(\gamma^{*}\). Set the PSO parameters as follows:  
* Population size: \(N_{p}\).
* Maximum iterations: \(t_{\max}\).
* Inertia weight: \(w_{\max},w_{\min}\).
* Acceleration coefficients: \(c_{1},c_{2}\).
* Search space: \(\omega\in(\omega_{\min},\omega_{\max})\), \(\gamma\in(\gamma_{\min},\gamma_{\max})\).  
Initialize the particles' positions and velocities randomly:  
\[\mathbf{x}_{1} =(\omega_{1},\gamma_{1}), \mathbf{v}_{1} =(v_{\omega,1},v_{\gamma,1})\] \[\mathbf{x}_{2} =(\omega_{2},\gamma_{2}), \mathbf{v}_{2} =(v_{\omega,2},v_{\gamma,2})\] \[\vdots \vdots\] \[\mathbf{x}_{N_{p}} =(\omega_{N_{p}},\gamma_{N_{p}}), \mathbf{v}_{N_{p}} =(v_{\omega,N_{p}},v_{\gamma,N_{p}})\]  
Evaluate the fitness function for each particle:  
\[f(\mathbf{x}_{1}) =f_{1}, f(\mathbf{x}_{2}) =f_{2}, \cdots,f(\mathbf{x}_{N_{p}}) =f_{N_{p}}\]  
Set the personal best (\(\mathbf{p}_{\text{best}}\)) for each particle and the global best (\(\mathbf{g}_{\text{best}}\)):  
\[\mathbf{p}_{\text{best,1}} =\mathbf{x}_{1}, \mathbf{p}_{\text{best,2}} =\mathbf{x}_{2}, \cdots,\mathbf{p}_{\text{best,}N_{p}} =\mathbf{x}_{N_{p}}\] \[\mathbf{g}_{\text{best}} =\mathbf{x}_{\min}\]  
where \(\mathbf{x}_{\min}\) is the particle with the minimum fitness value.  
Iteration 1: Update the inertia weight and acceleration coefficients:  
\[w(1) =w_{\max}\cdot e^{-\frac{1^{2}}{2(0.5\max)^{2}}}\] \[c_{1}(1) =2+0.5\cos\left(\frac{\pi\cdot 1}{2}\right)\] \[c_{2}(1) =2-0.5\cos\left(\frac{\pi\cdot 1}{2}\right)\]  
Update the velocities and positions of the particles:  
\[\mathbf{v}_{1}(2) =w(1)\cdot(v_{\omega,1},v_{\gamma,1})+c_{1}(1)\cdot(\mathbf{p}_{ \text{best,1}}-\mathbf{x}_{1})+c_{2}(1)\cdot(\mathbf{g}_{\text{best}}- \mathbf{x}_{1})\] \[\mathbf{x}_{1}(2) =\mathbf{x}_{1}+\mathbf{v}_{1}(2)\] \[\vdots\] \[\mathbf{v}_{N_{p}}(2) =w(1)\cdot(v_{\omega,N_{p}},v_{\gamma,N_{p}})+c_{1}(1)\cdot( \mathbf{p}_{\text{best,}N_{p}}-\mathbf{x}_{N_{p}})+c_{2}(1)\cdot(\mathbf{g}_{ \text{best}}-\mathbf{x}_{N_{p}})\] \[\mathbf{x}_{N_{p}}(2) =\mathbf{x}_{N_{p}}+\mathbf{v}_{N_{p}}(2)\]  
Evaluate the fitness function for the updated particles:  
\[f(\mathbf{x}_{1}(2)) =f_{1}(2), f(\mathbf{x}_{2}(2)) =f_{2}(2), \cdots,f(\mathbf{x}_{N_{p}}(2)) =f_{N_{p}}(2)\]  
Update the personal best and global best:  
\[\mathbf{p}_{\text{best,1}} =\mathbf{x}_{1}(2), \mathbf{p}_{\text{best,2}} =\mathbf{x}_{2}(2), \cdots,\mathbf{p}_{\text{best,}N_{p}} =\mathbf{x}_{N_{p}}(2)\] \[\mathbf{g}_{\text{best}} =\mathbf{x}_{\min}(2)\]  
where \(\mathbf{x}_{\min}(2)\) is the particle with the minimum fitness value at iteration 2.  
Iteration 2 Iteration \(t_{\text{max}}\): After \(t_{\text{max}}\) iterations, the novel PSO algorithm converges to the optimal relaxation parameters: \[\omega^{*} =\omega_{\text{best}}\] \[\gamma^{*} =\gamma_{\text{best}}\]
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 5 Results
### South America  
In South America, Brazil (Figure 21) and Peru (Figure 22) are the two countries with the largest number of actual COVID-19 deaths. In Brazil, the number of daily deaths has undergone a sort of plateau from June to August 2020, followed by a descending branch (Figure 21a). In Peru, the official data on daily deaths show two isolated peaks, which the model fits smoothly with a single maximum (Figure 22a). For both countries, the model predicts increasing trends of the total number of deaths (Figures 21b and 22b).  
The third and fourth South American countries for the total number of COVID-19 deaths are Colombia (Figure 23) and Argentina (Figure 24). In Colombia, the number of daily deaths experienced a peak in August 2020, followed by a descending trend (Figure 23a). In Argentina, the epidemic seems to be still at an early stage and the first peak of daily deaths has not been reached yet (Figure 24a). The model predicts a moderately increasing trend of the total number of deaths for Colombia (Figure 23b) and a strongly increasing trend for Argentina (Figure 24b).  
Figure 19: Actual and predicted trends in Canada: (**a**) daily deaths, (**b**) total deaths.  
Figure 20: Actual and predicted trends in North America: (**a**) total deaths; (**b**) daily deaths.  
Figure 21: Actual and predicted trends in Brazil: (**a**) daily deaths, (**b**) total deaths.  
Figure 22: Actual and predicted trends in Peru: (**a**) daily deaths, (**b**) total deaths.  
Figure 25 shows the time trends obtained for the whole South America by summing the available data and model predictions for each country. According to the model, the daily number of deaths is expected to undergo a descending trend in the next months (Figure 25a). Figure 25b shows the actual and forecast number of total deaths in the continent. The predicted future trend is moderately increasing. Error bars correspond to the error rate (\(-\)2.5%, +5%)/15 days estimated through the validation study. The forecast total number of COVID-19 deaths on 31 December 2020 in South America is 330,821 + (\(-\)50,726, +101,452).
# COSMIC-2 Mission Summary at Three Years in Orbit
## 4 Conclusions  
COSMIC-2's first three years in orbit have been very successful. Following payload activation and checkout, neutral atmosphere and ionosphere calibration/validation activities began and led to incremental product releases as validation/verification milestones completed. At this time all but one of the baseline products are released and made available to operational centers and the research community. The mission is making clear positive impacts to operational weather and space weather specification and prediction and is contributing high quality data to science applications in the areas of weather, space weather, and climate. In the future, new TGRS ionosphere products specifying the presence or absence of scintillation events will provide additional important information to space weather centers. We also expect that incremental flight software and ground processing updates will further enhance the quantity and quality of both neutral atmosphere and ionosphere data products.  
Writing--original draft preparation, J.-P.W.; writing--review and editing, J.-P.W., W.S.S., J.J.B., W.X.-S. and C.-Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Oceanic and Atmospheric Administration under contract DOCEA133W16 CQ0051. FORMOSAT-7/COSMIC-2 is a partnership sponsored by the National Oceanic and Atmospheric Administration, United States Space Force, and Taiwan's National Space Organization.  
Not applicable.  
All data used herein are available via CDAAC and TACC [29; 30].  
We would like to acknowledge the support and contributions of program partners from The Aerospace Corporation, Central Weather Bureau, Jet Propulsion Laboratory/CaliforniaInstitute of Technology, Joint Center for Satellite Data Assimilation, National Central University, National Cheng Kung University, National Oceanic and Atmospheric Administration, National Space Organization, SRI International, University Corporation for Atmospheric Research, United States Space Force, and University of Texas at Dallas. The authors declare no conflict of interest.
## 4 Results
### Experiment 3 Results: Four Devices, Distance 550 m  
The results obtained in Experiment 3, featuring a distance of 550 m between the gateway and four LoRa devices, showed that the only setting where 100% of the packets were received was BW = 125 kHz, CR = 4/6 and SF = 7. Analyzing this setting in the previous experiments, Experiment 1 featured some damaged packets and a high RSSI average (-58 dBm), compared to the best setting in Experiment 2, with an average RSSI value of -50 dBm. In Experiment 2, this setting had an acceptable behavior, meaning that all the transmitted packets were received correctly, but it did not stand out as a setting with the best average RSSI values (the value for this setting was -73 dBm, when the highest average value of Experiment 2 was -66 dBm approximately).  
It is important to note that this experiment considered only 44 settings, by discarding 28 settings that did not yield good results in Experiments 1 and 2. The discarded settings include those with SF equal to 11 and 12 (24 settings) and the settings with an SF of 10 and a BW of 125 kHz (4 settings).  
There is a considerable decrease in the percentage of received packets in most of the settings. This is explained because more than one LoRa device is being used at the same time (most of the time, four in each setting) and the four devices have the same parameter values (mainly SF), which can generate packet collisions, according to [19]. Only in 25% of the settings 100% of the packets were received; while 47% of the settings featured a reception rate between 99% and 75%, and 23% of the settings featured a reception rate below 75%.  
The behavior of the best RSSI level when increasing the BW is stable.  
The baseline setting obtained an average RSSI value of -105.75 dBm and 91.67% of received packets, which was close to the best setting (SF = 7, BW = 125 kHz, CR = 4/6), with an average RSSI value of -103.27 dBm and 100% packets received.  
The settings with the better RSSI levels and the better percentages of packets received in Experiment 3 are presented in Table 2.  
Figure 10 shows the behavior of the best setting of this experiment, which shows some variability in the RSSI levels obtained for each of the four devices (approximately 8 dBm).  
\begin{table}
\begin{tabular}{c c c} \hline
**Bandwidth (kHz)** & **Coding Rate** & **Spreading Factor** \\ \hline
125 & 4/6 & 7 \\
125 & 4/8 & 7 \\
500 & 4/5 & 10 \\
250 & 4/6 & 9 \\ \hline \end{tabular}
\end{table}
Table 2: Better settings in Experiment 3.  
Figure 11 presents the contour graphs for Experiment 3. As in Figure 8, only the denominator values of the CR are presented.  
The first graph (relationship between CR and BW) shows that the most desirable values of RSSI are obtained with CR settings close to 4/8, and a BW close to at 500 kHz. The second plot (relationship between SF and BW) shows that a low SF (7) and a BW close to 500 yields the most desirable RSSI. Finally, the third graph (relationship between SF and CR) shows that there is no specific area for ideal RSSI values, when compared to the other graphs. However, CR = 4/8 and SF = 10 yields the best RSSI levels.
## 2 Literature Review
### Applications of Machine Learning in Facilities Management
#### 2.3.4 Applications in Sanitary Facility Management  
The success of LSTM networks in these domains suggests promising applications for sanitary facility optimization. Several key parallels exist between these applications and sanitary facility management. The temporal pattern recognition capabilities proven in energy management translate directly to restroom usage, which exhibits similar short-term and long-term patterns. The multi-variable dependencies handled in photovoltaic production forecasting mirror the complex interrelationships between occupancy, time of day, and gender distribution in sanitary facility usage. Furthermore, the real-time adaptation capabilities demonstrated in seismic response prediction prove valuable for adjusting to varying occupancy patterns and special events in facility management.  
These successful applications provide a strong foundation for extending LSTM technology to sanitary facility planning. The demonstrated capabilities in handling complex temporal patterns, multiple variables, and real-time adaptation align perfectly with the challenges of optimizing restroom facilities in modern office buildings. Such methods excel at handling diverse inputs, scaling well, and uncovering usage patterns that simpler models miss. Nonetheless, broader data gathering and proper validation remain crucial to confirm their reliability in restroom planning.
## 3 Results
### Comparison to Mechanical Extensometer  
In this section, the application of the developed measurement system is shown. For this purpose, tests were done with different materials under force control and closed-loop condition using a servo-hydraulic testing machine Instron 8502. Figure 5 shows the optically measured strain signal in comparison with the strain measured by a tactile extensometer (Maytec PMA-12/V7/1 [31]) for a force-controlled triangular cycle with a frequency of 10 Hz. The critical points for the closed-loop control in strain-controlled experiments are the turning points where the strain rate \(\dot{c}\) changes from positive to negative and vice versa. The optical sensor shows less noise than the mechanical sensor and pretty smooth transitions. Apparently, the bandwidth of the system is limited by the mechanics, not by the strain sensor measuring only 120 strain values per cycle-less than 400 as recommended by ASTM E606.  
As the system works marker-free, a calibrated digital dial gauge (Sylvac S_Dial Work Nano 2.5 mm [27]) was used to measure absolute elongation values. They were found to be in the range of \(\pm 0.5\) micrometers, like the mechanical extensometer (Maytec PMA-12/V7/1 [31]). However, the  
Figure 4: Performance test results processing two subsets per image. The height of the bars gives the total processing time for two subsets per image, the numbers within each field give the processing time for each step in microseconds.  
calibration accuracy of the gauge was 0.38 micrometer (1\(\sigma\)). Therefore, the reproducibility of the elongation was measured in a 'zero-strain-test' where the specimen is moved without load. In this case, the reproducibility of the strain measurement was found to be in the range of \(10^{-5}\) (1\(\sigma\)) with maximum and minimum in a range of \(\pm 3\times 10^{-5}\)[32]. So the measurement accuracy can be considered similar to the mechanical extensometer or optical ones based on IC-GN [5].
## 3 Methodology
### Verification of Accuracy  
Seven methods were used to evaluate the accuracy of the remote sensing image data of Poyang Lake, including the OA, Kappa coefficients, f1, and miou, as shown in Table 3.  
According to the above table, the OA and Kappa values of the SMDPSO+LFE classification results are the highest at 97.56% and 0.95, respectively, while the OA and Kappa values of the water index method are the lowest at 92.32% and 0.8390, respectively.  
Comparing with the results of SMDPSO, the OA of SMDPSO+LFE increased to 97.56%, the Kappa coefficient increased to 0.95. This proves that linear enhancement is effective for improving the accuracy of water extraction. Therefore, SMDPSO+LFE is considered to be the most suitable method for the extraction of surface water in the Poyang Lake area.
## 4 Applications of Intelligent Acoustic Monitoring
### Public Safety and Security  
This subsection presents the use of acoustic monitoring to detect threats such as shots, explosions, the presence of drones or firearms in public spaces, in the context of shot detection and weapon classification [96; 97; 98; 99], explosion detection using smartphones [100], weapon detection in images [101], and drone detection [102].
## 3 Strain Gauges and Distributed Optic Fiber Strain Measurements
### Berea Sandstone--Increasing Confining Pressure
#### 3.1.2 Optic Fiber Measurement--Rayleigh Frequency Shift  
Figure 5a presents the Rayleigh frequency shift measurements encompassing the three sensor regions with increasing confining pressure. The horizontal axis represents the distance along the optical fiber from the end of the pump light. A close-up over the sensor region A (rock sample) is presented in Figure 5b. The Rayleigh frequency shift is seen to increase (indicating compressive strain) with increasing confining pressure.  
It is noted that the measured increase in the frequency shift is not uniform over the length interval attached to the core sample. Instead, from the monitoring points 1 to 11 within the length interval 18.89-19.30 m, a down-then-up trend may be identified as the optic fiber goes around the core sample peripheral in spiral; and this spatial trend becomes increasingly pronounced at higher confining pressure. The difference between the highest and lowest measured frequency shift between points 1 and 11 when the confining pressure is increased to 12 MPa is approximately 15 GHz, which is about 15% of the mean value (\(\sim\)100 GHz).  
Figure 6 presents the mean value of frequency shifts measured at the 11 monitoring points (which are plotted separately in Figure S2) as the confining pressure is increased from 0.5 MPa to 12 MPa, and their mean value at each pressure step compared with the baseline value (sensor region B). It is seen that the frequency shift for the free-standing optic fiber in the pressure vessel is only a small fraction (<10%) of that measured from the rock, while there is practically no response from the free-standing optic fiber outside the pressure vessel (sensor region C in Figure 5a).  
Figure 4: Berea core strain gauge measurements showing considerable differences between the vertical and hoop strains and a bi-linear increase in the strains with increasing confining pressure.  
Figure 5: (**a**) Rayleigh frequency shift measurements at different confining pressures: across the all three sensor regions; (**b**) close-up in the sensor region A (Berea core). The locations of a few selected monitoring points are marked.
## 3 Integrity Monitoring Method of Space-Ground Integration
### Architecture of Integrity Monitoring Method
Both methods can achieve integrity enhancement for medium- and high-orbit GNSSs, but they have different reliability requirements and capabilities for the LEO satellites, so the planning of the solution should be based on specific needs. The following sections will provide a detailed introduction to these two methods and conduct simulation analyses. Figure 3: The architecture of the integrity monitoring method of space–ground integration.
## 1 Introduction
### Literature Review
For OD matrix reconstruction, traditional methods mainly rely on traffic survey data, which is costly with long update cycles. [PERSON] and [PERSON] introduced dynamic origin-destination demand estimation using automatic vehicle identification data without requiring market-penetration rates [13]. [PERSON] et al. developed origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data, achieving MAPEs lower than 19% [14]. OD matrix estimation methods based on trajectory data can provide dynamic, real-time OD information. [PERSON] et al. enhanced travel time prediction through high-resolution origin-destination approaches with multi-dimensional features, achieving an RMSE of 202.89 s [15]. Through continuously optimized data processing algorithms and statistical modeling techniques, significant progress has been made in addressing data sparsity and bias issues, providing a more reliable data foundation for traffic planning and management. In traffic congestion detection and prediction, researchers have utilized the spatiotemporal characteristics of trajectory data to develop various congestion identification algorithms. [PERSON] et al. demonstrated that deep learning models significantly outperformed conventional machine learning in traffic congestion prediction based on GPS trajectory data [16]. [PERSON] et al. proposed urban traffic congestion estimation and prediction methods using floating car trajectory data with fuzzy comprehensive evaluation [17]. [PERSON] et al. studied catastrophe boundary extraction and the evolution of expressway traffic flow states [18]. [PERSON] et al. developed a large-scale road network traffic congestion prediction based on recurrent high-resolution networks [19]. [PERSON] and [PERSON] provided a comprehensive survey of deep learning applications in congestion detection, prediction and alleviation [20]. These methods can monitor network congestion status in real time and, combined with advanced data fusion techniques and intelligent decision-making algorithms, are evolving toward complete congestion warning and control strategy generation systems, providing strong technical support for implementing proactive traffic management. However, existing research still has deficiencies in the following aspects. First, there is a lack of systematic data preprocessing methods, resulting in inconsistent data quality. Second, the accuracy of trajectory point-to-road network matching needs improvement, affecting the accuracy of subsequent parameter extraction. Third, the consistency and accuracy issues in multi-parameter extraction have not been well resolved. Fourth, the reusability and practicality of methods need improvement, as many research outcomes are difficult to apply in practical engineering.
## 3 Results and Analysis of the Shaking-Table Test
### Earthquake Response of the Test Model Analysis
#### 3.5.2 Floor-Displacement Response  
Figure 22 shows the four types of one-way and two-way input ground motions under the action of rigid isolation and soft soil isolation in the X-direction (maximum displacement response) with respect to the average floor displacement response.  
As shown in Figure 22, for a PGA of 0.1 g, four bidirectional types of ground motions are observed under the action of earthquake for the rigid and soft soil foundations; the floor-displacement response on the change rule and the one-way input fields are similar. The common ground motion under the action of two-way input, each layer displacement response peak and one-way approximates to the final input. A two-way input is applied to the near-field ground motion under the action of pulse, and the two peaks of the foundation displacement of the isolation layer were obtained as slightly larger than those obtained using the one-way input conditions; therefore, the displacement of the top floor is large. The two-way input of far-field harmonic ground motion shows two peaks in the foundation displacement of the isolation layer, which are slightly smaller than those obtained under the one-way input conditions; therefore, the top-floor displacement is lesser than that in other cases. Thus, in a practical test between rigid or soft soil layer spacing on the displacement response of the structure, a one-way input situation showed no obvious difference. In comparison, the two-way input ground motion over a long period may increase and/or  
Figure 22: One- and two-way ground motions under the action of floor-displacement response (PGA = 0.1 g). (**a**) Near-field ordinary ground motion; (**b**) Far-field ordinary ground motion; (**c**) Near-field pulse ground motions; (**d**) Far-field harmonic ground motions.  
decrease the displacement response of the isolation layer; thus, the structure of the top floor displacement could be increased or decreased.