esa-sceva/satcom-chunk-collection huggingface dataset:
# Correlation Study of Auroral Currents with External Parameters During 10-12 October 2024 Superstorm
## 4 Discussion
### Mean Current Density and Latitude
This was likely due to the fact that the diffuse precipitation becomes more important for conductance enhancement in the evening and early morning sectors [28]. Concerning latitudinal differences, FACs and PEJs shift more equatorward at dusk (night) compared to dawn (day). These local time differences in MLat of the ionospheric current system are also consistent with the May storm results. As both hemispheres exhibit a similar duskward equatorward displacement compared to dawn, and due to the poor correlation with IMF By, IMF By may not significantly influence the duskside latitudinal displacement. The duskside partial ring current might be a plausible reason, which could extend the ionospheric currents to lower latitudes at dusk [11]. FACs and PEJs in the summer hemisphere tend to be positioned more poleward than in the winter hemisphere in most cases, which is generally consistent with the May storm, due to the summer-winter difference in ionospheric conductivity. However, there were differences between these two geomagnetic storm events: during the May storm, the upward FACs and PEJs on the nightside were located at a higher MLat in the winter hemisphere when compared to the summer hemisphere, while in the October storm, in addition to the upward FACs at night, both the daytime FACs and PEJs were also located more poleward in the winter hemisphere when compared to the summer hemisphere. The May and October 2024 storms provide a unique opportunity to distinguish hemispheric and seasonal differences, as they occurred at opposite seasons in two hemispheres. Generally, the hemispheric asymmetries in FACs, PEJ density, and latitude can be attributed to seasonal variations in ionospheric conductivity. However, subtle hemispheric differences persist, which cannot be explained by the seasonal differences but may be due to hemispheric asymmetries in the geomagnetic field geometry. Last but not least, our analysis confirmed that the auroral oval represented by FACs and PEJs might extend to approximately 51\({}^{\circ}\) MLat around midnight, making mid-latitude aurora sightings possible. Thus, it provides further evidence that the aurora observed at mid-latitudes should be an electromagnetic phenomenon associated with geomagnetic storms or solar wind activity rather than a phenomenon that occurs due to chemical reactions in the upper atmosphere, for example, sky airglow.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 2 Space Debris Detection
### Temporal Feature Extraction
The affinity function \(f\) between feature elements \(q\) and \(S_{q}\) can be computed by  
\[w_{q}=f(q,S_{q})=\sum_{n^{\prime}=1,h^{\prime}=h-dt,w^{\prime}=w-dr}^{N,h+dr,w +dr}qK(n^{\prime},h^{\prime},w^{\prime})^{\mathrm{T}} \tag{3}\]  
where \(w_{q}\in\mathbb{R}^{N(2r+1)^{2}\times 1\times 1\times 1}\). Then the augmented feature \(q^{\prime}\) in \(C_{T^{\prime}}\) can be calculated as a weighted sum of the embedding feature \(V(n^{\prime\prime},h^{\prime\prime},w^{\prime\prime})\in\mathbb{R}^{N(2r+1)^ {2}\times 1\times 1\times 1}\) with weight \(w_{q}\):  
\[q^{\prime}=w_{q}V(n^{\prime\prime},h^{\prime\prime},w^{\prime\prime}) \tag{4}\]  
where \(q^{\prime}\in\mathbb{R}^{\widehat{\mathbb{C}}\times 1\times 1\times 1}\). \(1\leq n^{\prime\prime}\leq N,1\leq h^{\prime\prime}\leq H,1\leq w^{\prime \prime}\leq W\). The parallel CSA structure with different window sizes and dilations can capture the multiple scales and various speed cues simultaneously. Obviously, the sliding window size should be larger than the space debris size (but not too large to reduce computational costs). With regard to the speed, a higher value means the object will cross a wider space in the same interval as shown in Figure 6. Based on the space debris size analyzed in Section 4.1, both the window size \(r\) and dilation are set to \(\{1,2\}\).
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 6 Discussion
### Threats-to-Validity  
We can see from the experimental results, that the probability of the scheme disclosing the user's location is low, as the dummy location we generated is as close as possible to the user's location, which makes it hard for the attacker to distinguish which one is the user's real location. Suppose \(u_{1},u_{2},u_{3},...,u_{n}\) are locations and \(u_{i}\) is the user's location. In the first case, the number of locations with the same query probability as the user's location is greater than k. Based on such conditions,the enhanced-DLP scheme obtains the historical query probability of the dummy locations in the anonymous set as the same, and we get an anonymous set \((u_{1},u_{i},u_{3},u_{4},u_{5})\), therefore, the attacker can not get the user's location information. In the second case, the number of the historical query probability of the user location is the same as the historical query probabilities of other locations (not less than 1) and an anonymous set \((u_{4},u_{6},u_{9},u_{i},u_{10})\) is obtained by the enhanced-DLP scheme. This anonymous set guarantees a larger entropy approximate to the optimal value, thus the attacker cannot recognize the user's location. In the third case, if there is no location similar to the user's real location. The enhanced-DLP scheme randomly chooses one location from the candidate set and adds to the anonymous set, and then uses the greedy scheme to select the remaining k-1 dummy locations, which changes the regular pattern of the location in the anonymous set. It is guaranteed that the anonymous set contains the maximum entropy, and thus the attacker cannot easily recognize the user's location.
# Capacity-Operation Collaborative Optimization for Wind-Solar-Hydrogen Multi-Energy Supply System
## 4 Case Study
### Basic Parameters  
The WSH-MES system, located in Zhangbei (41.2\({}^{\circ}\) N, 114.7\({}^{\circ}\) E), was subject to an exhaustive analysis to optimize its key equipment capacity and its hourly operation across the year. The optimization targeted a range of capacity parameters, encompassing the size of the SF, and the capacities of CSP, TES, PV, WP, PEME, HST, and PEMFC.  
The LSTM model adopts a single hidden layer structure, and the node number of the hidden layer is 50. The Adam method is applied in training of the LSTM model, and the batch size is set as 64. The dropout is set as 0.1 to avoid overfitting. The last year data were used as the test set and the previous data were divided into training and validation sets. The validation set was used to evaluate the generalization performance of the model. The training set was used to train the LSTM model. The training curve of DNI is shown in Figure 5. The trained LSTM model is predicted on the training set and validation set, respectively. The training set and validation set errors are shown in Table 5. From the results in the table, it can be seen that the validation set error of each parameter is slightly lower than the training set error except for DHI, and the validation set error of DHI is also close to the training set error. It indicates that the prediction model constructed by using the LSTM algorithm has no overfitting phenomenon.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Unit** & **Range** \\ \hline \(x_{1}\) & - & [0–776] \\ \(x_{2}\) & MW & [0–150] \\ \(x_{3}\) & h & [0–20] \\ \(x_{4}\) & MW & [0–150] \\ \(x_{5}\) & - & [0–66] \\ \(x_{6}\) & MW & [0–50] \\ \(x_{7}\) & m\({}^{3}\) & [0–10] \\ \(x_{8}\) & MW & [0–50] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization interval of capacity parameters.  
Figure 6: The original and predicted values of hourly weather date [33,43].  
To reflect the prediction performance of the LSTM model, a Persistence model is used for a comparative analysis. The results are shown in Table 6. The results show that the error of the LSTM model is significantly lower than the Persistence model, indicating that the prediction performance of the LSTM model for each parameter is significantly better than the Persistence model.
## 4 Monitoring System Framework  
### Components of the Multi-Source Monitoring System  
#### 4.1.1 Force Monitoring on Constant-Resistance Large-Deformation Anchor Cables  
In order to ensure that the supporting structure has sufficient supportive strength and adapts to dynamic pressure in the rock surrounding the roadway, the roof formed under the pillar-free self-formed roadway method is supported by a cable that utilizes the effect of a negative Poisson's ratio (an NPR cable, Figure 4). The NPR type is shown in Table 1 below. When the forces on the anchor cable are less than the constant resistance value, the anchor cable is in an elastic working state. When the anchor cable experiences instantaneous dynamic pressure, it will be stressed. When the pressure is greater than the constant resistance value, the constant resistance device will absorb energy by generating slip deformation, ensuring that the anchor cable does not break and continues to play its bearing role.  
During support by the NPR cable, the stress state is sensed by an FBG anchor cable stress sensor as shown in Figure 5. And its specific parameter models are shown in the Table 2. The basic principle of this sensor is that a high-stability and high-sensitivity strain fiber grating is placed within a steel pressure cylinder. When the load causes axial deformation of the steel cylinder, the fiber grating deforms along with it and the stress change causes a change in the reflection wavelength of the fiber grating. The system is set to measure the data of the fiber Bragg grating anchor cable force sensor every 1 min. The measured data can be read by the fiber Bragg grating demodulator to obtain the wavelength reading that causes the deformation of the steel cylinder and then the load value of FBG anchor cable force sensor can be calculated by substituting calibration coefficient.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NPR Type** & **Specification Type** & \
\begin{tabular}{c} **Constant Resistance Value** \\ **(kN)/Elongation (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Constant Resistance** \\ **Device (mm)** \\ \end{tabular} \\ \hline
20t Constant & HZS20-300-0.5 & 200/300 & \(\varphi\):65/L:500 \\ Resistance Anchor & HZS35-300-0.5 & 350/300 & \(\varphi\):73/L:500 \\
50t Constant & HZS50-300-0.5 & 500/300 & \(\varphi\):98/L:500 \\ Resistance Anchor & HZS85-1000-1.0 & 850/1000 & \(\varphi\):133/L:1000 \\
85t Constant & HZS85-2000-2.0 & 850/2000 & \(\varphi\):133/L:2000 \\ Resistance Anchor & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Engineering scale types of negative Poisson’s ratio (NPR).  
Figure 4: Structural diagram of a constant-resistance large-deformation anchor cable.
## 3 System Model and Methodology
### Analysis of Proposed Technique  
In this section, the BER analysis of the proposed technique is discussed. The performance is analyzed for the MIMO channel with a ULA having half-length spacing.  
The received signal is expressed mathematically as  
\[\mathrm{Y(t)=s(t)+n(t)} \tag{7}\]  
If the r(t) is the impulse response of the ULA channel is given by  
\[\mathrm{r(t)=e^{-i2\pi\frac{\mathrm{s}}{\lambda}}} \tag{8}\]  
Therefore, the equation can be written as  
\[\mathrm{y(t)=r(t)\times s(t)+n(t)} \tag{9}\]where n(t) is the noise and s(t) represents the data transmitted. The error \(\rm{p_{e}}\) for DWT-OFDM is given by [28]  
\[\rm{P_{e}}=\frac{\sqrt{M}-1}{\sqrt{M}\log_{2}\sqrt{M}}\,erfc\Bigg{[}\sqrt{\frac{ 3\log_{2}M^{E_{b}}_{N}}{2(M-1)}}\Bigg{]} \tag{10}\]  
where M is the M array QAM modulation and \(\rm{\frac{E_{b}}{N}}\) is SNR.  
The BER for the millimeter wave MIMO LOS is given by [29]  
\[\rm{BER}=\,P_{e}\bigg{(}\frac{E_{b}}{N}\frac{m}{n}\bigg{)} \tag{11}\]  
Then BER for the MIMO DWT can be expressed as  
\[\rm{BER}=\frac{1}{2}\,P_{e}\sqrt{\bigg{(}\frac{E_{b}}{N}\frac{m}{n}\bigg{)}} \tag{12}\]  
where m is the number of transmitter antennas and n is number of receiver antennas.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 3 Convolutional Neural Network
### Pooling Layer  
The function of the pooling layer is to reduce the input picture size to reduce the dimensionality of each feature map and maintain important features. This has many advantages, such as reducing the parameters for subsequent layers, accelerating system running, and reducing over-fitting.  
Similar to the convolutional layer, the pooling layer uses a filter to extract the values of various regions for operation; however, the final output is free from the activate function (the function of the convolutional layer is ReLU).  
The pooling average value after each convolutional layer is calculated by Equation (11) [14]:  
\[X_{ij}^{[l]}=\frac{1}{MN}\sum_{m}^{M}\sum_{n}^{N}X_{iM+m,jN+n}^{[l-1]} \tag{11}\]  
where \(i\) and \(j\) are the positions of the output map, and \(M\) and \(N\) are the pooling sample sizes in two orthogonal dimensions.  
If the input image size has an odd number of pixels or cannot be divided exactly (the filter is not 2 \(\times\) 2), it can be handled using peripheral zero fill or by abandoning edges.
# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 4 Methodology Proposal
### Stage 4: Graph Transformation  
The aim of this stage is to incorporate new attributes, nodes, or edges into the original graph, selecting subgraphs or executing queries to address the questions formulated in the initial phase.  
This stage consists of four tasks guided by the questions defined in stage 1: (4.1) Modify Topology, which can improve the graph's representation, enhancing the support for its analytical process. This task becomes necessary when the nodes or relationships initially present in the graph are insufficient to address the analysis questions. One example is to add new relationships for all product nodes purchased together. The reader can refer to [56] for guidance in this task. (4.2) Include Structural Attributes, which provides additional information in nodes and relationship attributes for a more comprehensive understanding of the graph's structure to enhance the analysis. Examples encompass the integration of attributes such as degree centrality, PageRank centrality, Louvain community, and Jaccard similarity into node attributes, as well as including the Dijkstra path as relationship attributes. The reader can refer to [52] for guidance in this task. (4.3) Apply Summarization, which is useful to reduce the graph's complexity by creating higher-level abstractions, allowing users to gain insights and identify patterns more quickly by grouping nodes, relationships, or both. For example, the graph can be summarized using attributes such as degree centrality, Louvain community, or other relevant measures. The reader can refer to [57] for guidance in this task. (4.4) Generate Subgraphs, which contains filtered and modified elements from the original graph, structural attributes, and summarization results. Each subgraph serves as a focused representation of the original graph, tailored to meet the specific analytical requirements of the users. Examples include the overall graph structure, a homogeneous graph, and specific nodes of interest. The reader can refer to [58] for guidance in this task.  
The result of this stage consists of one or more subgraphs or graphs with nodes or edges with new attributes.
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 4 Experiment and Result Analysis
### Process of Experiment  
#### 4.4.1 Ablation Experiment  
To assess and analyze the effectiveness of the proposed enhancement strategies, ablation studies were performed using the VisDrone dataset, with the corresponding results detailed in Table 4.  
As evidenced by the data presented in Table 4, the following observations can be made.  
Experiment v8s-0 represents the original YOLOv8s algorithm, achieving a baseline mAP of 36.3%. This serves as the reference point for all subsequent improvements.  
Experiment v8s-1 replaces the original C2f module with the proposed C2f-DWR module. Without increasing computational cost, the mAP improves by 0.9% (from 36.3% to 37.2%), demonstrating the effectiveness of the C2f-DWR module in enhancing feature representation.  
Experiment v8s-2 replaces the neck part of the traditional YOLOv8 structure with the proposed GLSA-BiFPN module. Compared to v8s-0, the mAP improves by 1.7% (from 36.3% to 38.0%), while reducing the number of parameters by 28.8% and the model size by 27.9%. This indicates that the GLSA-BiFPN module not only enhances feature extraction, but also significantly reduces model complexity.  
Experiment v8s-3 introduces the lightweight shared convolutional detection head (LSCDH) on the basis of v8s-0. Compared to v8s-0, the mAP improves by 6.3% (from 36.3% to 42.6%), proving that the LSCDH can better capture diverse semantic information in the features, leading to a substantial performance boost.  
Experiment v8s-4 adopts the GLSA-BiFPN structure on the basis of v8s-1. Compared to v8s-0, the mAP improves by 1.6% (from 36.3% to 37.9%), while reducing the model size by 30.2%. This further validates the efficiency of the GLSA-BiFPN module in balancing performance and model complexity.  
Experiment v8s-5 adds the lightweight shared convolutional detection head (LSCDH) on the basis of v8s-1. Compared to v8s-0, the mAP improves by 6.5% (from 36.3% to 42.8%), highlighting the significant contribution of the LSCDH to detection accuracy.  
Experiment v8s-6 integrates both the GLSA-BiFPN module and the lightweight shared convolutional detection head (LSCDH) on the basis of v8s-2. Compared to v8s-0, the mAP improves by 8.4% (from 36.3% to 44.7%), while reducing the model size by 31.6%. This demonstrates the synergistic effect of combining the GLSA-BiFPN module and the LSCDH, achieving the best performance among all experiments.  
The data recorded in the ablation experiments above indicate that each improvement proposed in this study enhances the model's detection performance to a certain extent.
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Dataset
#### 4.1.1 Extraction of Geological Parameters
This approach also made it possible to create synthetic images with high spectral and spatial resolution, which is ideal for analyzing textures and geological structures. Spectral ratios constitute a powerful tool for improving the discrimination of geological formations from satellite images. This study used several combinations of bands to accentuate spectral contrasts and minimize topographical and shading effects specific to the mountainous region of the Western Highlands of Cameroon. Among the ratios applied, the ratios OR7/OR4, OR5/OR4, and OR5/OR7 made it possible to reduce interference linked to incident light while enhancing the spectral differences between geological materials, such as basalts, enginess, and granites. These ratios were chosen because of their sensitivity to the mineralogical characteristics of lithological formations, particularly in the visible and near-infrared. Additional ratios, such as OLI5/OL7 (highlighting clay and iron variations), OLI/OLI2 (used for differentiating bare soils and vegetation), and gloss index (BI), have been calculated to analyze the physical properties of exposed surfaces. In parallel, standardized indices were used to link the vegetation cover to the underlying mineral alteration, thus revealing zones of mineralization and fractures associated with underground flows. These ratios and indices have been integrated into a robust analytical framework, providing crucial variables for training classification models and lithological mapping.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 2 Research on the Strain Transfer Model  
### Surface-Bonding Stain Transfer Model  
Polyimide FBGs are always bonded to the surface of a substrate with adhesive. The layout of a strain transfer system through electronic microscope is shown in Figure 4.  
Figure 3: The strain transfer experiment of the soft layer encapsulated fiber with the distributed sensing interrogator ODISI OBR 4600. (**a**) The LUNA OBR 4600 distributed sensing system; (**b**) Fiber bonded to the cantilever beam; (**c**) Strain measured by OBR 4600 along the fiber; and (**d**) Strain transfer measured in contrast to strain transfer ratio calculated based on the theoretical strain transfer model. According to the surface-bonding layout of the fiber, a core-bonding-substrate three-layer strain transfer model was established. The 2-D layout of the model is shown in Figures 5 and 6. This model was based on the following three assumptions:  
1. Core, bonding, and substrate are all linear elastic materials. 2. Because there is no coating, the various interfaces of core-bonding and bonding substrates can be assumed to be perfect contacts, and accordingly, the subjected axial stresses do not exceed the maximum stress, which produces a relative slip between the various interfaces. 3. Polymide coating of the fiber can actually be neglected, as to say the polyimide FBG can be considered as uncoated FBG. The representation of the adhesive bonding structure can be considered as a triangle. Based on the above assumptions, \(r_{g}\) represents the radius of the FBG; \(h_{m}\) represents the thickness of the upper bonding layer; \(h_{c}\) represents the thickness of the lower bonding layer; and \(H\) represents the total thickness of the bonding layer and fiber grating. The micro-stresses of bonding layer and fiber  
Figure 4: The cross section of the surface bonded bare Polyimide FBG. Figure 5: The model of the surface-bonded fiber. Figure 6: The structure of surface-bonded fiber. grating elements are \(d\sigma_{c}\) and \(d\sigma_{g}\), respectively. \(\tau_{gc}\) is the shear stress between the fiber grating and bonding layer. \(\tau_{c}\) represents the shear stress between bonding layer and substrate layer. Assuming the bonding length \(2L\) and sensor bonding width \(D\), since the model is symmetrical along axis h, the right part of the model was selected for analysis. Applying the principle of static equilibrium to the first layer of fiber grating can produce an equilibrium equation in the \(x\) direction:  
\[\pi r_{g}\tau_{gc}\big{(}x,r_{g}\big{)}dx+\frac{1}{2}\pi r_{g}{}^{2}\sigma_{g} =\frac{1}{2}\pi r_{g}{}^{2}\big{(}\sigma_{g}+d\sigma_{g}\big{)} \tag{1}\]  
This was simplified to produce the following:  
\[\pi r_{g}\tau_{gc}\big{(}x,r_{g}\big{)}=\frac{1}{2}\pi r_{g}{}^{2}d\sigma_{g} \tag{2}\]  
Similarly, for the infinitesimal segment of the lower bonding layer and selection of the segment \(0\leq h\leq h_{c}\), according to actual experiment and the properties of the adhesive, the bonding layer had a triangle shape after the adhesive coagulated.