esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 GNC System with LOS-Based Model
### AUV Control System  
The motion control system of the AUV performs maneuvers in all translational motions (surge, sway, and heave) and in the yaw rotational motion with an individual PID controller for the north, east, and down positions and yaw orientation of the NED coordinate system. The separate PID controller approach is selected after comparing the velocity and position PID controllers in the previous study [28]. Furthermore, the motion control system does not involve compensation for the environmental forces due to waves and ocean currents that can affect the AUV's ability to simplify the vehicle implementation. Table 3 shows the PID parameters used in this study. These parameters were estimated using the MATLAB-Simulink Control toolbox [38] for a certain time response (\(T_{\text{PID}}=0.5\) s) and transient behavior (\(K_{\text{tb}}=0.9\)) parameter definition. Then, the AUV performed the necessary field tests in the sea environment to verify the correct PID parameters estimation in all motion controllers.
# Analysis of Optimal Loading Angle in Dynamic Flattened Brazilian Disc Splitting Test for Concrete
## 4 Experimental Procedures  
### Specim Preparation  
To investigate the optimal loading angle in the flattened BD test for SCC, this paper manufactured the molds with the loading angles [40,41]\(\theta\) of 20\({}^{\circ}\), 22\({}^{\circ}\), 24\({}^{\circ}\), 26\({}^{\circ}\), 28\({}^{\circ}\), and 30\({}^{\circ}\) by a 3D printer with the material of high-strength resin. The height of the model is 25 mm, the inner diameter is 50 mm, and the outer diameter is 60 mm, as shown in Figure 10. The printing accuracy is 0.01 mm, which could meet the requirements of flatness and parallelism of the loading surfaces.  
According to the ratio in Table 3, the concrete specimen was mixed into a uniform slurry state and poured into the molds. The specimens were demolded one day after pouring and solidified for 28 days in a stable environment with a temperature of (20 \(\pm\) 2) \({}^{\circ}\)C and humidity of 95%.  
A layer of white paint was sprayed on the front face of the specimen with black speckles spotted for DIC algorithm. Five strain gauges, numbered 1#, 2#, 3#, 4#, and 5#, were equally attached to the opposite face of the specimen along the loading diameter direction, which assisted to monitor the cracking development (Figure 11). The basic physical and mechanical properties of SCC are list in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Density/(g.cm\({}^{-3}\))** & **Peak Stress/MPa** & **Peak Strain** & **Elastic Modulus/MPa** & **Deformation Modulus/GPa** & **Poisson’s Ratio** \\ \hline SCC & 2.425 & 52 & 0.0213 & 4.981 & 1.732 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic physical and mechanical properties of concrete.  
Figure 10: Mold sketch and 3D-printed molds.
# Temporal Changes in Land Use, Vegetation, and Productivity in Southwest China
## 2 Data and Methods
### Methods  
The normalized difference vegetation index (NDVI) can reflect the growth and coverage of surface vegetation [50; 51]. Therefore, the NDVI values of different LULC types were extracted to represent the growth state of this vegetation type. Vegetation has obvious inter-annual and seasonal variation characteristics, and its NDVI value in the season of the most active growth stage can more accurately represent the vegetation growth state in this region [52]. In order to reduce the NDVI error caused by the seasonal changes in vegetation, this paper used NDVI and GPP values in the growing season (May-October) to analyze vegetation activities, which accurately reflected the status of vegetation cover and GPP in southwest China. To minimize the effect of cloud contamination and atmospheric variability, we calculated the annual growing season NDVI by using the maximum value composites (MVC) method [53]. The MODIS NDVI and GPP data in the study area were resampled to the monthly scale using ArcGIS10.4, with a spatial resolution of 1 km. Additionally, by averaging each pixel, the average values from May to October were obtained as the growing season NDVI and GPP for each year from 2000 to 2019, which meant in pixels of 500 \(\times\) 500, there was, on average, one gC unit of GPP per 1 m\({}^{2}\).  
Simple linear regression was used to analyze the annual trends of average NDVI and GPP values in the growing season of different LULC in southwest China from 2000-2019. All data were analyzed using Python software. Additionally, referring to related research [54], the vegetation coverage was divided into lowest coverage (0 \(<\) NDVI \(\leq\) 0.35), low coverage (0.35 \(<\) NDVI \(\leq\) 0.55), medium coverage (0.55 \(<\) NDVI \(\leq\) 0.75), high coverage (0.75 \(<\) NDVI \(\leq\) 0.85), and highest coverage (0.85 \(<\) NDVI \(\leq\) 1), five grades. The gravity center migration can show the change directions in the region's center of gravity. Therefore, the change directions in the region's NDVI and GPP gravity centers were calculated by gravity center migration. This, in turn, can reflect the import of human activities or climate change on the migration of vegetation zone. The main LULC types in southwest China were cropland, forest, and grassland, and other land types (shrub, water, snow/ice, barren, impervious, and wetland) accounted for only 2% of the total area (Figure 1). Therefore, this paper mainly analyzed the change characteristics of three LULC types of cropland, forest, and grassland, the temporal and spatial distribution of NDVI and GPP in the growing season of different LULC types, as well as the regular change in the center of gravity.
## Appendix A

Table 10: Notation of parameters used for traffic model, network design (MILP), network operation (EWA), and evaluation. The objective (A1) aims at minimizing the installation cost of the network. The constraints (A2) model the IP routing as a flow along a single path for each source-destination pair. The constraints (A3) allow IP flow only in one direction on installed logical links. The constraints (A4) ensure that enough capacity is installed along lightpaths through the physical topology to route the demands of all time periods while respecting a maximum utilization \(\delta\) of the lightpaths. With the constraints (A5) and (A6) a router module with sufficient capacity to accommodate both backbone and aggregation traffic is chosen, and with the constraints (A7) a sufficient number of fibers are installed. To abbreviate notation we define the maximum demands emanating and ending at node \(a\) as:  
\[d_{a}^{+}=\sum_{b\in V}d_{a,b}\quad\text{and}\quad d_{a}^{-}=\sum_{b\in V}d_{b,a}.\] (A11)  
Then, via (A4) and (A5) we employ an undirected demand routing scheme, i.e., demand from a source to a target is routed along the same path as demand in the opposite direction, and the necessary capacity along the path has to suffice for the maximum of the demand in each direction. We require that IP traffic is routed along a shortest path through the network of installed logical links. To this end, node potentials \(\hat{v}_{b}^{a}\) are introduced (cf., for instance, [47] (Section 7.2)). In general, the offset \(\hat{v}_{a}^{a}\) for \(a\in V\) could be any number, while only the difference \(\hat{v}_{b}^{a}-\hat{v}_{a}^{a}\) matters; due to the constraints (A10), \(\hat{v}_{b}^{a}\) can be directly interpreted as the length of the shortest path between \(a\) and \(b\) (number of logical links). The shortest path routing is then ensured by the constraints (A8), which restrict routing path lengths to \(\hat{v}_{b}^{a}\), and the constraints (A9), which force the values of \(\hat{v}_{i}^{a}\) and \(\hat{v}_{j}^{a}\) to differ by at most 1, if nodes \(i\) and \(j\) are connected by a link in the logical topology (indicated by variable \(\hat{\varphi}_{i,j}\) and by the constraints (A3)).
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 3 Methodology
### Noise Transfer Model in Forward Path  
#### 3.1.1 Derivation of Noise Transfer Functions  
The transfer function \(H_{n_{m}}^{d_{i}}(s)\), which describes the conversion of Brownian noise into EIN in acceleration, is expressed as follows:  
\[H_{n_{m}}^{d_{i}}(s)=\frac{1}{K_{m}}, \tag{23}\]  
The derivation process of the noise transfer function can be found in Appendix A.  
To enhance readability and streamline the analysis, we combine the noise contributions from the pre-amplifier \(n_{c}(t)\) and the filter \(n_{v}(t)\) into a single term representing the total electronic noise of the forward path, \(n_{cv}(t)\). This combined noise term is defined as follows:  
\[n_{cv}(t)=n_{c}(t)\times K_{c}^{v}+n_{v}(t). \tag{24}\]  
The combined noise model follows the previously established model in Equation (22). The transfer function for the total electronic noise of the forward path, \(H_{n_{cv}}^{d_{i}}(s)\), is expressed as follows:  
\[H_{n_{cv}}^{d_{i}}(s)=\frac{1}{K_{m}H_{d}^{d}(s)K_{d}^{c}K_{c}^{v}}. \tag{25}\]
## 4 Results  
### Spatial Distribution of Vacant Housing  
The latest four scene images of the Luojia-1, mainly from June 2018 to June 2019, were selected to calculate the HVR of Xi'an city. We selected all images of the Luojia-1 covering the study area during this time period, including 19 August 2018, 23 August 2018, 31 October 2018, and 11 March 2019. The results of the four grid-based HVRs were obtained based on the remote sensing data and methods of spatial analysis (Figure 6).  
The HVR results were divided into seven levels (i.e., extremely low: 0-0.05; lower: 0.05-0.1; low: 0.1-0.2; medium: 0.2-0.4; high: 0.4-0.5; higher: 0.5-0.6; extremely high: >0.6) to more intuitively observe the spatial distribution patterns of housing vacancies in the main urban area of Xi'an city, The result indicates that the spatial distribution of the HVRs for the main urban area of Xi'an city was highly consistent with the observation of the nighttime lights, and the results of this paper (Figure 6) show that the grid was used as the basic unit, and the spatial resolution was consistent with the Luojia-1 data (130 m). The results present that, the areas with a low HVR were mainly distributed in Beilin District, Yanta District, and Lianhu District, and most of the HVRs in these three districts were less than 0.1, and the full areas were concentrated. The areas with a high HVR were mainly distributed in Weiyang District, Baqiao District, and Xincheng District and mainly distributed in the marginal areas of these three districts, of which the new urban areas were mostly commercial areas and office areas. On the whole, the distribution of the HVRs was scattered, unlike other areas.
# Double Unloading Gas Control Technology for Fracturing Soft Coal Seams in Overlying Key Strata
## 3 Analysis of Simulation Results
### Drilling and Extraction Rules before and after Stress Relief of Key Strata
#### 3.3.2 Effective Extraction Radius  
According to the findings in Figure 13, it is evident that the effective extraction radius increases as the stress load of the overlying rock formation decreases and as the borehole diameter increases. For stress loads of 13 MPa, 10 MPa, 8 MPa, and 5 MPa in the overlying rock layer, the effective extraction radius of the hole drilling was measured to be 3 m, 3.2 m, 3.4 m, and 3.6 m, respectively. On the other hand, the extraction radius of the ordinary borehole was found to be 1.15 m, 1.23 m, 1.3 m, and 1.4 m. These results can be attributed to the expansion and deformation of the coal matrix and the opening up of pore fissures in the coal layer. This suggests that an increase in overburden stress within the coal seam significantly hinders gas extraction, while increasing the diameter of boreholes can help expand the effective extraction radius. Consequently, unloading coal seam stress through hydraulic fracturing and large-diameter borehole technology in overlying strata can effectively improve the extraction effect.
## 4 Results  
### Landslide Susceptibility Assessment Results  
After optimizing the parameters of each model, landslide susceptibility zoning in the study area was conducted using four distinct models. The susceptibility indices produced by these models were imported into ArcGIS, where the natural break method was applied to divide the area into five zones: very low susceptibility (VL), low susceptibility (L), moderate susceptibility (M), high susceptibility (H), and very high susceptibility (VH) (Figure 6). In the figure, Buffer-LR/-SVM/-GBDT/-RF represent the susceptibility maps generated from the \"Buffer\" dataset, while Slope-LR/-SVM/-GBDT/-RF correspond to the maps derived from the \"Slope\" dataset. Although the susceptibility zones varied slightly among models, the distributions of the VL and VH zones remained consistent. The VH zones were primarily concentrated in the northern part of Badong County, particularly along rivers, whereas the VL zones were predominantly located in the southern region.  
The ROC curves for the eight scenarios are shown in Figure 7. The AUC values for the Buffer-LR/-SVM/-GBDT/-RF models were 0.721, 0.844, 0.829, and 0.824, respectively, whereas the AUC values for the Slope-LR/-SVM/-GBDT/-RF models were 0.763, 0.855, 0.880, and 0.889, respectively. These findings indicated that, irrespective of the machine learning model employed, landslide susceptibility predictions based on non-landslide sample datasets from slope units achieved higher prediction accuracy than those derived from buffer zone datasets.  
To evaluate the performance of the four models on unknown data and examine the impact of different sampling strategies on susceptibility results, the accuracy, precision, recall, and F1 score were calculated for each model using the test set. Table 4 presents a detailed comparison of these metrics for the four models under two distinct non-landslide sample selection strategies.  
The table revealed that the slope dataset outperformed the buffer dataset across all metrics. Among the models, the RF model demonstrated superior accuracy, precision, and F1 score on both datasets compared with the others.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1 Score**} \\ \cline{2-9}  & **Buffer** & **Slope** & **Buffer** & **Slope** & **Buffer** & **Slope** \\ \hline LR & 0.6054 & 0.7187 & 0.6037 & 0.7138 & 0.6034 & 0.7282 & 0.6036 & 0.7209 \\ SVM & 0.7139 & 0.8040 & 0.7227 & 0.7939 & 0.6967 & 0.8227 & 0.7095 & 0.8080 \\ GBDT & 0.7222 & 0.8312 & 0.7241 & 0.8285 & 0.7202 & 0.8382 & 0.7221 & 0.8321 \\ RF & 0.7299 & 0.8385 & 0.7381 & 0.8406 & 0.7098 & 0.8341 & 0.7237 & 0.8373 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance metrics for four models with two sampling strategies.
## 5 Case Study
### Comparative Control Strategies  
To compare the effectiveness of the joint control strategy for the wind storage system based on TPA-BiGRU proposed in this paper, five control strategies are used for simulation and analysis. The experimental data are derived from 31 days of data for the first half of 2019 for the wind farms mentioned above.  
1. TPA-BiGRU: Temporal pattern attention mechanism combined with the bidirectional gated recurrent unit. The new decision model proposed in this paper.
2. TPA-BiLSTM: The GRU in TPA-BiGRU is replaced with LSTM, and the bidirectional structure and attention mechanism are retained to verify the computational efficiency advantage of GRU in the joint control of the wind storage system by comparison.
3. BiGRU: The temporal pattern attention (TPA) module in TPA-BiGRU is removed, and only the bidirectional GRU is retained for quantifying the contribution of the attention mechanism to multi-timescale feature extraction.
4. AROCS: Advanced Rolling Optimal Control Strategy with Model Predictive Control (MPC) framework, the core of which is to dynamically adjust the power allocation of the wind storage system through Rolling Horizon Optimization. AROCS stands for comparative experiments on optimization models.
5. PI control: Traditional proportional-integral feedback control. The real-time deviation of the wind storage system is used as an input, and through PI control, the regulation command of the energy storage is output to regulate the battery storage output, thus reducing the deviation of the output of the wind storage system. PI control stands for classical feedback control.
## 3 Results
### Experimental Data Analysis
#### 3.4.4 Comparative Experiment  
Comparative experiments are a research methodology used to evaluate the performance variations between different models by applying them to the same task under controlled, consistent conditions. This helps to determine the most cost-effective detection model based on a comprehensive evaluation of factors such as processing cost and benefits.  
To evaluate the usefulness of the model under consideration, this study conducts a comparison with prominent object detection models, including those from the YOLOv series, using the same dataset. The conclusions drawn from the comparative investigations are shown in Table 2.  
Table 2 presents a comprehensive comparison of deepfake face detection models across both performance and efficiency metrics. YOLOv9-ARC achieves the best overall performance, attaining the highest mAP (0.751), recall (0.925), and F1-score (0.849), while maintaining a high precision (0.784), second only to YOLOv3. These results suggest that YOLOv9-ARC is highly effective at detecting positive samples (i.e., forged images) with minimal compromise in misclassification control.  
From an efficiency perspective, YOLOv9-ARC records an inference time of just 32 ms and 128 GFLOPs--substantially faster and lighter than many competing models, including YOLOv5x and EfficientNet-B0. This indicates that the proposed architectural enhancements, such as AKConv and CBAM, improve feature extraction and attention without imposing additional computational burden.  
Furthermore, compared to lightweight models like MobileNetV3-Large and YOLOv8n, YOLOv9-ARC delivers significantly better detection accuracy while maintaining comparable computational cost. This trade-off demonstrates that YOLOv9-ARC is not only suitable for high-performance systems but also adaptable to real-time or resource-constrained environments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **mAP** & **P** & **R** & **F1** & **Inference Time (ms)** & **FLOPs (G)** \\ \hline YOLOv3 & 0.677 & 0.789 & 0.919 & 0.848 & 52 & 139 \\ \hline YOLOv5 & 0.686 & 0.750 & 0.802 & 0.775 & 47 & 135 \\ \hline YOLOv5x & 0.674 & 0.738 & 0.808 & 0.771 & 56 & 141 \\ \hline YOLOv8 & 0.729 & 0.710 & 0.843 & 0.770 & 44 & 133 \\ \hline YOLOv8n & 0.707 & 0.772 & 0.756 & 0.764 & 36 & 126 \\ \hline YOLOv9 & 0.716 & 0.766 & 0.771 & 0.768 & 45 & 130 \\ \hline EfficientNet-B0 & 0.693 & 0.742 & 0.799 & 0.769 & 68 & 390 \\ \hline MobileNetV3-Large & 0.681 & 0.730 & 0.784 & 0.756 & 29 & 60 \\ \hline Ours (YOLOv9-ARC) & 0.751 & 0.784 & 0.925 & 0.849 & 32 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiment results table.  
Overall, these results underscore the effectiveness of the proposed modules in enhancing robustness to low-quality deepfake images and validate the model's suitability for practical deployment scenarios where both speed and accuracy are critical.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 4 Correctness and Security Analysis
### Security Analysis
#### 4.2.6 Resistance to Replay Attacks  
This attack involves retransmitting previously eavesdropped data reports to the receiver without modifying the content. The CP/FN adds timestamps before sending messages to indicate their timeliness. By checking the timestamps, the receiver FN/CSO can resist replay attacks.
# L-Band Erbium Fiber Laser with Tunable and Narrow Sub-kHz Linewidth Output
## 3 Results and Discussion
As displayed in Figure 7, the output power and center wavelength fluctuations of the proposed EDF laser were also measured through a 40 min observation time. In the available tuning bandwidth, the largest and smallest variations in each output power and wavelength were between 0.47 (at 1558.0 nm) and 0.08 dB (at 1618.0 nm), and between 0.032 (at 1608.0 nm) and 0.008 nm (at 1573.0 nm), respectively, as seen in Figure 7. Therefore, this demonstrated that the L-band EDF laser structure can achieve high stability in output power and center wavelength over the whole wavelength-adjusting bandwidth. Figure 6: Attained 3 dB Lorentzian linewidth by self-heterodyne method in the entire wavelength span from 1558.0 to 1618.0 nm, as shown by the blue square symbols. The top diagram is the electrical spectrum measured and fitted at 1618.0 nm wavelength.
# Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials
## 3 Methodology
### Algorithms
#### 3.3.4 Feedback for Received Vaccine  
The recipients who have received their vaccination will fill out the feedback regarding the process starting from the registration till successfully being vaccinated, also about any post-vaccination symptoms or any side-effect encountered as given in Algorithm 4. The recipient will get the access only if the recipient identification in valid. After the validation process the recipient will submit the feedback and save the data on blockchain. In case of any post vaccine effect the peers can check the fault with the help of vaccination lot identification. The data is immutable and this feature disables any kind of tampering by third parties.  
```
RID: Recipient's ID V_ID: Vaccine Lot ID
1. Procedure Feedback(RID)
2. If RID is valid then
3. Grant access
4. mapping(bytes V_ID to String feedback) public feedback
5. mapping(bytes V_ID to bool side_effect) public side_effect
6. Save feedback to blockchain
7. End if
8. Else
9. RID is not valid
10. End else
11. End procedure
```  
**Algorithm 4** Feedback for Received Vaccine  
Before starting the distribution process the vaccine producer has to provide the set of rules such as freezer temperature limits in order to initiate the process. The freezer time will automatically get initiated as and when the blockchain network transacts the first block that is called as genesis block of the blockchain network. As freezer time equals to the current time of the block.  
The feedback page about the vaccination process. The first consists of feedback that is of string type the user can directly type the feedback. Secondly, side effect is of Boolean type it will only take either true or false values as shown in Figure 5. With the help of feedback feature the conflict in vaccination process are easy to identify and check. Also, it will be recorded in blockchain network for the future use to analyze the vaccination process.