esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Scene Construction for Seismic Damage of Building Ceilings and Furniture
## 4 Case Study
### Parametric Study  
As an only parameter for a given ground motion, PGA has significant influence on the earthquake indoor scene. To analyze such influence, the horizontal displacements of a ceiling tile and a chair in the constructed scene with different PGAs are studied, respectively. As aforementioned, PGA for the maximum considered earthquake is 0.4 g for the building in case study [40], hence the range of PGA is from 0 to 0.4 g. The THAs of the building are performed using the El Centro ground motion within the PGA of 0-0.4 g, and thus the movements of a ceiling tile and a chair are simulated based on the results of the THAs and the proposed physical models, respectively. Consequently, the curves between the simulated horizontal displacements and PGAs are illustrated in Figure 14.  
It can be observed that the relationship between the horizontal displacements of a ceiling tile and PGAs exists two stages. In the first stage, the ceiling tile does not separate from the entire ceiling according to the corresponding fragility curve in FEMA P-58, thus the horizontal displacement is zero. The threshold of PGA when the ceiling tile starts to fall is 0.3 g, as shown in Figure 14. In the second stage, the horizontal displacements have a linear relationship with PGAs. Because the building is strictly designed following the seismic design code of China [40], its structure almost keeps elastic within the PGA of 0-0.4 g, leading to the linear seismic response (e.g., floor velocities and accelerations) with PGAs. In addition, the horizontal displacements of a falling tile also have a linear relationship  
Figure 13: Constructed indoor scenes for different earthquake levels, (**a**) original scene; (**b**) minor earthquake; (**c**) moderate earthquake; (**d**) severe earthquake.  
with the velocities according to Equation (1). Consequently, the relationship between the horizontal displacements of the tile and PGAs is linear. Contrastingly, the horizontal displacements of the chair has a square relationship with the floor accelerations according to Equation (10) and the rigid body dynamics [26]. Therefore, as shown in Figure 14, the horizontal displacements of the chair have a nonlinear relationship with PGAs.
# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 2 Materials and Methods
### Objectives  
The main goal of the EEO software is to answer all rapid-mapping challenges to the best of its ability. First of all, the chains have to support all the data provided during RM activations, which means handling a wide variety of sensor types, resolutions and data, which are disseminated differently from one satellite product to another. Regarding Sentinel-1/2 data, the download is included in the pipeline, regardless of the size of the area or the time series.  
There is a special focus on managing thematic products that can be automatized. This can differ from one sensor to another. For example, it is easy to delineate fire areas when SWIR bands are available (Sentinel-2), but becomes trickier when only a classic RGBNIR stack is provided (SPOT6/7). Some themes are also harder to map than others. For example, landslide mapping is very contextual, while flood mapping is more easily handled.  
But the most important constraint of these chains is to deliver reliable insights to the experts in a short time. Indeed, the goal is that the operator spends as little time as possible in thematic extraction, saving time for the analysis phase. To achieve this, the chains have to be fully automated, from downloading data, to the thematic extraction into vector packages, in addition to dedicated, rapid-mapping delivery. They need to be as fast as possible, without losing their thematic reliability. Last but not least, they should be as user-friendly as possible, to ensure that the operators will use them without losing time.  
In addition to answering the rapid-mapping challenges, having fully automatic end-to-end chains ensures a normalized and reproducible work environment shared by operators, and reduces the subjectivity of the thematic mapping.
## 4 Case Study
### Simulation Results
#### 4.2.1 Case 1 Consensus Controller without Leader Regardless of Geographic Factors  
This case is used to test the consensus algorithm without considering geographic factors proposed in this paper. The communication graph of BESSs is shown in Figure 3a, and the whole system is working in islanded mode. The consensus controller begins to work at 10 s. All BESSs will reach consensus at 12 s. In addition, there is a \(1.26+j1.26\) per unit (\(p.u\)) load decrease after 20 s and a \(2.52+j2.52p.u\) load increase after 30 s. It is noteworthy that load changes are fully distributed among loads. As can be seen from Figure 5, whatever the load decreases or increases, active and reactive power sharing, frequency, voltage and energy level of all BESSs also can be synchronized and their final state can reach the average of their initial value. In addition, it can be observed that the consensus controller can act as the primary frequency/voltage controller.
## Materials and Methods
### Data Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Shoe Collar Condition**} & \multicolumn{3}{c}{**Pairwise Post Hoc**} \\ \cline{2-7}  & **High** & **Elastic** & **Low** & **H vs. E** & **H vs. L** & **E vs. L** \\ \hline \multicolumn{7}{c}{**Anterior single-leg jump landing**} \\ \hline Dorsiflexion ROM (\({}^{*}\)) * & 10.40 (5.19) & 12.63 (4.28) & 12.50 (4.16) & 0.031 (0.511) & 0.043 (0.446) & 0.718 (0.078) \\ Eversion ROM (\({}^{*}\)) & 10.47 (3.48) & 11.72 (3.43) & 10.09 (2.76) & 0.286 (0.362) & 0.691 (0.121) & 0.030 (0.524) \\ Peak plantar flexion moment (Nm/kg) & 2.38 (0.38) & 2.21 (0.36) & 2.24 (0.35) & 0.095 (0.459) & 0.789 (0.383) & 1.000 (0.084) \\ Peak inversion moment (Nm/kg) & 0.48 (0.24) & 0.58 (0.43) & 0.51 (0.31) & 0.442 (0.287) & 0.696 (0.108) & 0.437 (0.187) \\ \hline \multicolumn{7}{c}{**Lateral single-leg jump landing**} \\ \hline Dorsiflexion ROM (\({}^{*}\)) & 18.11 (5.13) & 20.50 (3.50) & 20.62 (2.39) & 0.058 (0.544) & 0.059 (0.627) & 0.907 (0.040) \\ Eversion ROM (\({}^{*}\)) & 8.85 (3.13) & 11.04 (4.29) & 9.20 (4.74) & 0.005 (0.583) & 0.752 (0.087) & 0.116 (0.407) \\ Inversion ROM (\({}^{*}\)) * & 12.10 (0.315) & 15.00 (5.88) & 12.97 (4.25) & 0.028 (0.615) & 0.323 (0.233) & 0.054 (0.396) \\ Peak plantar flexion moment (Nm/kg) & 2.42 (0.36) & 2.39 (0.49) & 2.51 (0.48) & 1.000 (0.070) & 1.000 (0.212) & 0.785 (0.247) \\ Peak inversion moment (Nm/kg) & 0.29 (0.21) & 0.37 (0.32) & 0.31 (0.21) & 0.402 (0.296) & 0.704 (0.095) & 0.496 (0.222) \\ Peak inversion moment (Nm/kg) & 0.35 (0.30) & 0.41 (0.26) & 0.38 (0.26) & 0.471 (0.214) & 0.689 (0.107) & 0.743 (0.115) \\ \hline \hline \end{tabular} Note. * represents a significant difference within a subject factor. High (H), Elastic (E), and Low (I) represent three football shoe conditions: high collar, elastic collar, and low collar, respectively.  
\end{table} 
Table 1: Mean (standard deviation) of biomechanical variables and pairwise post hoc \(p\)-value (Cohen’s dz) in ankle joint during tasks in the high-, elastic-, and low collar shoe conditions.
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 2 Methodology
### Simulation  
Simulations were performed to see the effect of the wind farm controller. For simulations, three wind turbines in series and aligned to the wind were used. The reason why only three wind turbines were chosen is because, in the previous study, the power regulation of the most upstream wind turbine was found to be mostly effective on the two downstream wind turbines [16].  
As a wind turbine, the 5-MW NREL research wind turbine was used. The specification of the wind turbine is shown in Table 1.  
The three wind turbines were assumed to be in line with the wind direction and the wind turbine spacing was varied from 4 RD to 10 RD with an interval of 3 RD. In addition, to find the effect of the proposed wind farm control algorithm, only the most upstream wind turbine was used for power regulation to reduce calculation time and avoid convergence in local minimum. The simulation results  
Figure 4: Structure of wind turbine controller [21] Conventional wind turbine controller with torque and pitch control has been modified to have a DPPT control algorithm and to receive power demand from a higher level wind farm controller.  
were compared with the results with a baseline wind farm controller available in the literature [33; 34]. The baseline wind farm control satisfies the command from the transmission system operator when the command is lower than the available power from the wind farm. Each wind turbine receives its share in power generation as a power command, which is proportional to its available power normalized by the total available power in the wind farm. If the command from the TSO is higher than the available power such as the total rated power of the wind farm which is the case considered in this study, the most upstream wind turbine will receive command which is greater than its rated power. As the result, the most upstream wind turbine will extract as much energy from wind as possible, but limited to its rated power by the pitch controller of the wind turbine. The algorithm dispatches power commands to individual wind turbines based on their own available powers, \(P_{av,i}\), as shown in Equation (7) [33; 34],  
\[\text{WFC}_{\text{CMD},i}=\frac{\text{P}_{av,i}}{\sum\text{P}_{av,i}}\times \text{TSO}_{\text{CMD}} \tag{7}\]  
Because the wind turbines with more available power (upstream wind turbines) get higher power commands from the wind farm controller, the baseline control is also known as \"greedy control\". Therefore, it does not consider either the power increase or maximum load reduction in the whole wind farm.
###### Abstract  
In this study, an experimental analysis of the thermal behavior and wear of polymer and hybrid polymer gears produced with the Fused Deposition Modeling (FDM) method was performed. Compared to conventional polymer gear manufacturing methods, the FDM process represents an energy-efficient material forming method. The low thermal conductivity of polymer gears has an impact on heating, which limits their application. The novelty of this research is an experimental analysis on hybrid polymer gears, and, for this purpose, a new hybrid polymer gear design with aluminum and steel inserts has been proposed. An in-house-developed non-mechanically closed-loop test rig was used to investigate Polyamide (PA) gears under different loads. An accelerated step load test procedure was employed, while the gears' bulk temperature was recorded with a thermal imaging camera. The print quality affected the tooth flank surface roughness, so polymer gears with two different print qualities were initially produced. Hybrid polymer gears were produced with a higher print quality, since the print quality had an influence on the heating and wear. The correlation between the bulk temperature and wear was observed for all of the tested gears. A novel design of hybrid polymer gears with aluminum inserts achieved up to a 9 \({}^{\circ}\)C (17%) lower bulk temperature and a higher wear resistance.  
additive manufacturing; bulk temperature; FDM; gear design; PA; wear resistance +
Footnote †: journal: Applied sciences
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 5 Test Results and Analysis  
### Reliability Testing  
The overall physical system is shown in Figure 12.  
To test the reliability and anti-interference ability of the FEC algorithm in the wireless communication of this system [30; 31], a series of system performance verification experiments were conducted in a site simulating real test conditions. The experiments simulated wireless communication environments under different interference intensities and recorded and analyzed the changes in the bit-error rate (BER) and packet loss rate, respectively [32]. The specific test method is as follows: First, a series of interference intensity levels were set, ranging from \(-95\) dBm to \(-75\) dBm. These values reflect the interference intensities from low to high. Under each interference intensity level, the BER and packet loss rate were measured both without FEC and with FEC. Subsequently, the same test method was  
Figure 11: UDP top-level logic diagram.  
Figure 12: Physical diagram of the overall system.
# Spatiotemporal Analysis of Evapotranspiration and Effects of Water and Heat on Water Use Efficiency
## 2 Materials and Methods
### Gross Primary Production (GPP) Data  
The MOD17 product is the first continuous satellite-driven dataset that monitors global surface vegetation productivity and provides 8-day composite GPP products, net photosynthesis production products, and annual total GPP and NPP numerical products [34]. In MODIS satellite products, the standard product mod17a2h GPP of version 6 is a cumulative 8-day composite product with a spatial resolution of 1 km. The GPP of this product was estimated according to the BIOME-BGC model of radiation utilization efficiency [35]. In addition, the algorithm of this product was based on the relationship between the total photosynthesis and the amount of photosynthetically active radiation absorbed by the plant canopy. For detailed calculation, please refer to [PERSON]'s literature [36]. In this study, GPP data from 2000 to 2014 were selected, and the spatial resolution was resampled to 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). The GPP product was applied to calculate WUE.
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 6 Experimental Results
### Forgery Detection and Localization Results on GRIP Dataset  
Table 3 summarizes the performance on GRIP dataset where the proposed method exceeds other methods in both classification and localization indexes. The evaluations in different types of copy-move situations are also considered. Specifically, 4 attacks includes Gaussian noise addition and JPEG compression to the copy-move images, rotation and scaling to the copied regions. Figure 7a indicates that our method is better than other methods in term of localization \(F\)-measure with different levels of Gaussian noise added to the copy-move images. Figure 7b shows that different CMFL methods handling JPEG compression situation with a slight difference. In case the copied regions are rotated or scaled, [PERSON] et al. [20], [PERSON] et al. [32], and our method sequentially perform better than the rest (Figure 7c,d). The proposed method performs better than other methods when the changes of the copied regions are small. On the contrary, its performance declines for larger rotation angle and scaling factor. Figure 8 illustrates the CMFL examples of the proposed method on GRIP dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Case \(A\)** & **Case \(B\)** & **Case \(C\)** \\ \hline MICC-600 & 87.6 & 98.3 & 98.3 \\ GRIP & 100 & 100 & 100 \\ CASIA 2 & 97.5 & 99.6 & 99.4 \\ \hline \end{tabular}
\end{table}
Table 1: Average ratios when the host image of the query image is retrieved (%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\mathbf{C_{P}}\) & \(\mathbf{C_{R}}\) & \(\mathbf{C_{F}}\) & \(\mathbf{L_{P}}\) & \(\mathbf{L_{R}}\) & \(\mathbf{L_{F}}\) \\ \hline [PERSON] et al. [29] & 69.8 & 88.1 & 77.9 & 86 & 88 & 87 \\ [PERSON] et al. [26] & - & - & - & 90.2 & 93.7 & 91.9 \\ [PERSON] et al. [27] & 97.5 & 86.2 & **91.5** & - & - & 91.8 \\ Proposed method & 88.6 & 92.5 & 90.5 & 90.8 & 95.5 & **93.1** \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison on MICC-600 dataset (%).  
Figure 6: The mean average precision obtained by 3 scenarios of image retrieval performed by the proposed clustering algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{C_{P}}\) & \(\mathbf{C_{R}}\) & \(\mathbf{C_{F}}\) & \(\mathbf{L_{P}}\) & \(\mathbf{L_{R}}\) & \(\mathbf{L_{F}}\) \\ \hline [PERSON] et al. [20] & - & - & - & - & - & 95.33 \\ [PERSON] et al. [24] & - & - & 94.61 & - & - & 94.06 \\ [PERSON] et al. [27] & 100 & 100 & **100** & - & - & 94.66 \\ [PERSON] et al. [28] & - & - & 96.63 & - & - & 92.98 \\ [PERSON] et al. [32] & - & - & - & - & - & 95.77 \\ Proposed method & 96.3 & 98.8 & 97.5 & 96.2 & 97.4 & **96.8** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison on GRIP dataset (%).  
Figure 7: Pixel level \(F\)-measure comparison in the GRIP dataset with different types of copy-move attacks.
## 2 Smartphone Technology and Built-in Sensors
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Functionalities** & **Units of Measure** & **Possible Role in Solar PV Apps** \\ \hline GPS (global positioning system) & Measures x, y, z-coordinate (position) & Degree, minute, second & Location awareness (to obtain information of solar PV sites) \\ \hline Optical image sensor & Acquires optical images or videos & - & Photo viewer, video playing, and augmented reality (AR) visualization \\ \hline Accelerometer & Measures acceleration force & m/s\({}^{2}\) & Estimate tilt of PV module or roof \\ \hline Magnetometer & Measures the ambient magnetic field & \(\mu\)T & Estimate orientation (aspect) of PV module or roof \\ \hline Proximity sensor & Measures the distance of any object & cm or m & Estimate distance between PV module or other components \\ \hline Gyroscope & Measures a device’s rate of rotation & rad/s & Rotates map-screen orientation (PV design on roof or rooftop *) \\ \hline Barometer & Measures the ambient air pressure & hPa or mbar & Estimate weather conditions (in situ ambient air pressure) \\ \hline Ambient temperature sensor & Measures the ambient temperature & \({}^{\circ}\)C & Estimate weather conditions (in situ ambient temperature) \\ \hline Humidity sensor & Measures the ambient relative humidity & \% & Estimate weather conditions (in situ ambient relative humidity) \\ \hline Ambient light sensor & Measures the ambient light level & Lx & Estimates solar radiation \\ \hline Microphone & Converts sound wave into electric signal & - & Records status of the site by voice \\ \hline \hline \end{tabular}  
* Roof PV refers to a PV system mounted on the (mostly inclined) building roof while rooftop PV indicates a PV system installed (or mounted) on the upper flat place of a building that a person can stand up.  
\end{table} 
Table 1: Typical sensors embedded in the smartphones and their roles in solar photovoltaic (PV) apps (added from [18]). for a specific purpose in each field, rather than a smartphone manufacturer. To secure commercialization as an external sensor for smartphones, problems in terms of sensing accuracy and price must be overcome.
# Blockchain Applications in the Military Domain:
## 3 Materials and Methods  
This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure transparency and rigor. The process consisted of four stages: identification, screening, eligibility, and inclusion, as detailed below [50]. A comprehensive search was conducted across multiple academic databases, including Scopus, IEEE Xplore, and Web of Science. This search yielded 120 records, complemented by 10 additional records identified through other sources such as the gray literature, reference lists, and conference proceedings. After removing 15 duplicates, a total of 115 records remained for screening. The titles and abstracts of the 115 records were screened to assess their relevance to blockchain applications in the military domain. Articles were excluded if they did not focus on military-specific applications, lacked discussions of blockchain technology, or were non-peer-reviewed. This step excluded 60 records, leaving 55 articles for full-text review. The remaining 55 full-text articles were assessed against the inclusion and exclusion criteria. Articles were excluded for the following reasons:  
* Five articles were unrelated to blockchain applications in the military domain;
* Four articles had methodological limitations, such as insufficient data or lack of rigor;
* Three articles presented redundant or overlapping content.  
A total of 43 articles met the inclusion criteria and were included in the final qualitative synthesis. These articles comprehensively address blockchain applications in the military, focusing on security, logistics, supply chain management, and privacy. This systematic process is summarized in the PRISMA flowchart (Figure 2), which illustrates the progression from the initial identification of records to the final inclusion of 43 studies in the review.  
Figure 2: Flowchart of PRISMA methodology.
## 6 Simulation Results
### Network Performance Parameters: Definitions
#### 6.2.5 Transmission Loss  
The transmission loss of the compared schemes, which is computed by using the Thorp model, is shown in Figure 15a-c. Transmission loss depends on the transmission distance, bandwidth efficiency and the attenuation loss of the transmitted signal. A larger distance between nodes causes a high transmission loss, which further increases, due to the death of intermediate nodes. In the case of AURP and AEERP, the intermediate nodes quickly die due to the excessive burden, which leads to relatively high transmission loss in these schemes. The distant nodes transmit data at a longer distance, which increases the transmission loss at the end of the network lifetime. The transmission loss of AEDG is less due to balanced energy consumption and the longer stability period. Figure 15a,b shows the impact of node density on the transmission loss; high node density means a relatively greater number of communication links, which leads to high transmission loss. On the other hand, Figure 15c shows the direct relation between network area and transmission loss. To sum up, these figures show that the relative impact of node density on transmission loss is far greater than the relative impact of the network area on transmission loss.  
Figure 14: Packet transmission rate. (**a**) 10 nodes deployed in 100 m \(\times\) 100 m; (**b**) 100 nodes deployed in 100 m \(\times\) 100 m; (**c**) 100 nodes deployed in 10 km \(\times\) 10 km.  
Figure 15: Transmission loss. (**a**) 10 nodes deployed in 100 m \(\times\) 100 m; (**b**) 100 nodes deployed in 100 m \(\times\) 100 m; (**c**) 100 nodes deployed in 10 km \(\times\) 10 km.