esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Trajectory Analysis Using Only Spatial-Temporal Information
#### 2.2.2 Trajectory Prediction and Recovery for Sparse Trajectory  
[PERSON] et al. proposed a model for predicting the future mobility of mobile users using such sparse trajectories based only on spatial-temporal dimensions [34]. Their model learns the trajectories of heterogeneous users accumulated in the past and predicts the future movements of users on the basis of their similarity with past users' data. However, that study focused only on predicting spatial-temporal information about how users move from one point to another; it did not aim to estimate a user group without spatial-temporal information.  
If it is possible to recover a complete trajectory for a sparse trajectory that lacks information, conventional methods could be applied to user-group classification. Various methods for such trajectory recovery have been proposed [35; 36; 37; 38]. [PERSON] et al. developed an attentional neural network-based model that recovers sparse trajectories by learning past accumulated historical trajectory data of users [35]. [PERSON] et al. also proposed a trajectory recovery model for similar problem settings using a graph neural network, and both models demonstrate high performance; however, these models do not assume a new user who does not store past historical data [36]. For this problem, [PERSON] et al. proposed a new method that mitigates the cold start problem of such new users by using a transformer [37]. However, these methods require large amounts of historical data linked to specific users. Conversely, since unique IDs of mobile users' location data covered by this study are reset on a daily basis, these methods cannot be applied to our problem settings.  
Feature Extraction Applicable to Sparse Trajectories Without Semantic Information: A Comparative Baseline  
[PERSON] et al. proposed a feature extraction method for classifying transportation modes of trajectories using a SDA [15; 16]. In this method, trajectories are converted into images and flattened into vectors, which are then used as inputs for a DNN constructed via SDA. Fine-tuning is performed using annotated labels, and the resulting improved DNN (IDNN) is employed to extract features from the trajectories. The semantic information associated with the trajectories is subsequently added, enabling high-accuracy transportation mode classification. In this study, we target trajectories that lack semantic information; however, the feature extraction method using the IDNN can still be applied. On the other hand, because IDNN-based feature extraction relies heavily on the shape of trajectories, it assumes dense trajectory data. Therefore, its performance on sparse trajectories remains uncertain. In our experiments, we compare the performance on sparse trajectories using features extracted by our proposed method and those extracted by the IDNN.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 7 Suggested Improvements
### Physical Security  
The physical security of components within the ACU is critical to prevent unauthorized tampering and maintain system integrity. Measures such as restricting access to the ACU and OBD connectors and using tamper-evident seals help mitigate physical attack risks. Additionally, a comprehensive physical security approach includes integrating a gateway to manage and secure communications within the AEB system. The gateway centralizes the management of CAN frames, enforces security policies, and monitors for communication anomalies, ensuring secure interactions between components.  
Furthermore, the incorporation of an HSM significantly enhances the security of AEB systems. HSMs provide a secure environment for storing and processing cryptographic keys, thereby protecting the communication between AEB components, as discussed by [PERSON] et al. [21]. They can be utilized for several critical security functions:  
* **Robust Authentication**: HSMs can manage and protect keys used to authenticate messages between AEB components, ensuring that only trusted sources can send commands. This authentication mechanism is crucial for maintaining the integrity and reliability of the AEB system.
* **Data Encryption**: HSMs can encrypt sensitive data transmitted between components, making them unreadable to potential attackers intercepting communications. This encryption ensures that even if data are intercepted, they cannot be used maliciously.  
By implementing these physical security measures, we can significantly enhance the resilience of the AEB system against physical attacks. Restricting access, using tamper-evident seals, integrating a secure gateway, and utilizing HSMs collectively form a robust physical security framework that safeguards critical components and ensures the overall integrity of the AEB system.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Attack** & **Component (s)** & **Attack Vector** & **Countermeasure** \\ \hline \multirow{2}{*}{DoS} & \multirow{2}{*}{ESC, AEB, SAS} & Injecting a higher volume of data to disrupt the service. & Establishing rate limits and validation inputs for the ports. \\ \hline \multirow{6}{*}{EoP} & \multirow{6}{*}{ESC} & The injection of malicious data through the serial communication port can unlock a deep level of control over the component. & Implementing the IDS/IPS of serial traffic and authentication mechanisms [20]. \\  & & & \\ \hline \end{tabular}
\end{table}
Table 4: _Cont._
###### Abstract  
Due to rapid urbanization, the development of megacities and metropolises worldwide is creating water scarcity, social-environmental risk, and challenges to the regions where water supply from rivers and alluvial aquifers is insufficient and unstable. Groundwater exploration in fractured bedrock of mountainous regions is thus a crucial issue in the search for substitute water resources. To achieve cost effectiveness on groundwater exploration, the use of comprehensive remote sensing (RS)- and geographic information system (GIS)-based models appears feasible. The required parameters selected and analyzed from the literature depend on the hydrogeological characteristics. This study intends to investigate and improve the proposed parameters and data sources upon those presented in the literature. A total of 17 hydrogeological units of concern was delineated from 105 complex geological formations of the geological sections and main rock types. The other parameters related to groundwater potential were derived from the digital elevation model and Landsat imagery. In addition, 118 drilling cores were inspected and in-situ well yield data from 72 wells were employed to assess the normalized groundwater potential index in the raster-based empirical GIS model with a higher spatial resolution. The results show that the accuracy of the interpretation of groundwater potential sites improved from 48.6% to 84.7%. The three-dimensional (3D) visualization of a thematic map integrated with satellite imagery is useful as a cost-effective approach for assessing groundwater potential.  
ground water exploration; hydrogeological units; groundwater potential; well yield +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
This research adopts the weighing method, the MIP test, SEM test and unconfined compression experiment to study the pore structure and mechanical properties of expansive soil  
Figure 10: The compression strength of expansive soil varies with the duration of dehydration under different conditions.  
under high temperature-drying and high temperature-humidity dehydration conditions, and then obtain the change rule of water content, pore volume distribution, micro-pore structure and mechanical properties. The conclusions of these studies are as follows:  
(1) The moisture contents of expansive soil samples after dehydration under two environmental conditions are different, and the time for dehydration to reach stability is different.  
(2) The environmental conditions have a significant effect on the volume distribution of large and medium pores, but little effect on small pores. Under high temperature and drying conditions, the volume of large pores decreases first and then increases, while the volume of medium pores decreases first and then stabilizes. Under high temperature and humidity conditions, the volume of large pores and medium pores decreases first and then keeps stable.  
(3) Under high temperature drying and high temperature humidity, the porosity of expansive soil samples decreases first and then fluctuates little.  
(4) The dehydration environment has a significant effect on the microscopic pore structure of expansive soil. After dehydration and stabilization in the high temperature drying environment, the microstructure of expansive soil presents a dense stack and a dense aggregate, and there are connected cracks. However, after dehydration and stabilization in the high temperature and humidity environment, the microstructure of expansive soil is dense and no obvious connected cracks are observed.  
(5) The environment and duration of dehydration have significant effects on the mechanical properties of expansive soil. With the process of dehydration, the mechanical failure characteristics of expansive soil samples change from creep deformation to brittle shear failure. The UCS of expansive soil under high temperature and drying environment increases first, then decreases and finally stabilizes, while the UCS of expansive soil under high temperature and humidity environment firstly increases and then stabilizes.  
[PERSON] performed the experiments and wrote the manuscript; [PERSON] and [PERSON] proposed the research method of analyzing the pore structure and mechanical properties of expansive soil. [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the financial support of the National Key R&D Program of China (Grant No.2019 YFC1509901) and the Natural Science Foundation of Hubei Province (No.2020 CFB373).  
Not applicable.  
The authors acknowledge the anonymous reviewers and Editor [PERSON] for their careful review and critique, which led to the significant improvement of the article.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 6 Results
### Throughput  
The network throughput is the result of aggregating all the correctly delivered traffic at the coordinator node. It is expected that situations in which the user nodes are capable of properly sensingthe channel, this metric is maximized. On the other hand, as the CSMA/CA performance gets impaired by an excessive link range between nodes (or even between coordinator and node), it is expected that the throughput decreases. Figure 11 depicts the obtained throughput for the two evaluated traffic conditions.  
It can be observed that Scenario 1 presents a performance close to the MAC-only simulation for a radius below 2.5 m. The reduced link range between coordinator and user nodes, and the advantageous location of the nodes in terms of CSMA/CA operation contribute to this result. Scenario 2 presents a similar behavior, but the distance at which throughput starts to diminish in respect to the MAC-only simulation is smaller. This occurs because the nodes placed on a regular mesh lose their mutual visibility before the coordinator-node link is affected by the geometry. In respect to Scenario 3, it is observed that it has the worst performance due to the presence of a hidden node. The commented trends apply to both traffic conditions, but the difference between Scenario 3 and Scenario 1 is sharper for saturated traffic than for reduced traffic necessities. This higher impact on performance is due to the effect of delivery probability, which is presented in the following subsection.
## Enhanced Single-Probe Method and Verification
### Calibration Procedure and Validation of the Proposed Method
The maximum values of magnitude error [\(\Omega\)], magnitude relative error, and phase error [\(\circ\)] are reported in Table 1, while average error and standard deviation are reported in Table 2 both for magnitude and phase. It is possible to appreciate that resistor measures are very accurate, with small errors at low frequency, where the probe coupling is weaker, and moderate errors in the tens of MHz range where inductively coupled methods are known to suffer from undesired parasitic effects. The capacitor measure shows moderate error around its self-resonance, where the impedance is minimum: this is not unexpected, since the sensitivity analysis presented \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & **Maximum** & **Maximum Percentage** & **Maximum Phase** \\  & **Magnitude Error [\(\Omega\)]** & **Magnitude Error** & **Error [\(\circ\)]** \\ \hline R 18 \(\Omega\) & \(-2.08\)\(\Omega\) @ 30 MHz & \(-10.3\)\% @ 30 MHz & \(-10.5\)\% @ 30 MHz \\ R 82 \(\Omega\) & \(-2.66\)\(\Omega\) @ 11.4 kHz & \(-3.25\)\% @ 11.4 kHz & \(1.87\)\% @ 10.5 kHz \\ R 250 \(\Omega\) & \(-24.6\)\(\Omega\) @ 11.8 kHz & \(-9.86\)\% @ 11.8 kHz & \(7.37\)\% @ 10.5 kHz \\ C 3.3 \(\mu\)F & \(-1.00\)\(\Omega\) @ 30.0 MHz & \(20.8\)\% @ 309 kHz & \(-16.4\)\% @ 359 kHz \\ L 100 \(\mu\)H & \(720\)\(\Omega\) @ 17.0 MHz & \(57.4\)\% @ 25.9 kHz & \(-28.1\)\% @ 43.8 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Maximum magnitude and phase errors from the measure of discrete RLC components. Figure 5: **(a)** Magnitude error (percentage) and **(b)** phase error from the measure of discrete RLC components. in Section 2.2 showed that the accuracy of the method is reduced when measuring small impedances, even in ideal conditions. An almost constant error is present at frequencies higher than the capacitor self-resonance frequency, which can be ascribed to the parasitic capacitance between the component under test and the impedance analyzer fixture ground. In fact, this additional capacitance is negligible at low frequency, where the capacitance of the capacitor under test is dominant, but its effect is evident at frequencies higher than the capacitor self-resonance frequency, where the capacitor under test behaves as a small inductor. Lastly, as far as the inductor measure is concerned, the same considerations drawn for resistors measures apply both in the tens of kHz and MHz ranges. Additionally, as mentioned, in the 10-100 kHz range a significant error due to capacitive effects is present. However, considering the percentage errors reported in Figure 5a, in the frequency range from 110 kHz up to 30 MHz, the inductor impedance measure is not less accurate than the other component ones.
## 2 Equipment Group Maintenance Planning Model Based on Service Age Regression Model
### Optimization Objective
#### 2.2.2 Maintenance Cost of Equipment Group  
The use of money represents the consumption of maintenance resources in maintenance tasks, including maintenance spare components, maintenance manpower, etc. In this section, the Kijima II model [26] is used to describe the impact of maintenance tasks on the mission capability of the equipment system. Maintenance activities will repair all the accumulated damage of the system, and the virtual age of the system will be reduced to a certain proportion.  
\[V_{ij}^{-}(k)=A(k)V_{ij}^{+}(k-1) \tag{9}\]  
where \(V_{ij}^{-}\) represents the virtual age of the system before the \(k\)-th maintenance task, and \(V_{ij}^{+}(k-1)\) represents the virtual age of the system after the \(k\)-th maintenance task.  
The maintenance work is divided into several levels, and then the maintenance cost required by the maintenance task of the equipment system at different levels is determined. In the series-parallel equipment system, the equipment is composed of several subsystems, and each subsystem is composed of several parallel components; then, the maintenance cost \(c\) associated with the maintenance of the \(f_{ij}\) level is  
\[c\big{(}f_{ij}\big{)}=c\Big{(}f_{ij}^{\max}\Big{)}\times f_{ij}/f_{ij}^{\max} \tag{10}\]
# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 2 Related Works
### Deep Matting Methods  
Due to deep learning networks' solid high-level semantic comprehension ability and excellent performance, deep learning methods have become increasingly popular in image matting tasks.  
In 2016, [PERSON] et al. [9] proposed DCNN, which utilizes a convolutional neural network to optimize the matting result of closed-form matting [7] and KNN matting [10] with the assistance of the original trimap. [PERSON] et al. [11] used a convolutional neural network to generate a trimap, then estimated alpha values by closed-form matting [7]. These works only used deep learning as optimization tools; no large-scale matting datasets were available for training. In 2017, [PERSON] et al. [12] proposed Deep Image Matting (DIM), an end-to-end convolutional neural network matting method. It consists of two stages: a matting encoder-decoder network and a small refinement network. Specifically, the image and trimap are fed into the matting encoder-decoder; the refinement network then uses the original image as auxiliary information to optimize the output from the previous stage, generating a refined alpha matte. They first released a large-scale matting dataset, which became essential to developing deep matting methods.  
Beginning with DIM, more and more deep learning methods were proposed and achieved good results. [PERSON] et al. [13] proposed AlphaGAN, which applies generative adversarial networks (GAN) to image matting, and it contains two key components: a generator and a discriminator. The generator generates alpha mattes of given images, and the discriminator evaluates the quality of the alpha mattes generated by the generator and helps the generator to produce better results. [PERSON] et al. [14] utilized channel attention and spatial attention mechanism to fuse low-level and high-level features and make networks focus on valuable channels and spatial regions. [PERSON] et al. [15] explored the semantic correlation between the trimap and image using a three-branch encoder. [PERSON] et al. [16] and [PERSON] et al. [17] combined the latest Transformer [18] with image matting and achieved good results. In recent years, there were also some works focused on trimap-free matting [14], portrait matting [19], and background-based matting [20]. There has been no semi-supervised deep matting method yet.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 3 Experiment Results and Analysis
### Data
#### 3.1.3 Input Matrix  
The electricity load data are not only influenced by the current state but also by the preceding moments. Consequently, this paper utilizes a sliding window to construct a data matrix as input for the forecasting model. The calculation process is outlined as follows:  
\[X=\begin{bmatrix}x_{1}^{1}&x_{2}^{2}&\cdots&x_{1}^{n}\\ x_{2}^{1}&x_{2}^{1}&\cdots&x_{2}^{n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m}^{1}&x_{m}^{2}&\cdots&x_{m}^{n}\end{bmatrix} \tag{15}\]  
\[Y=\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{m}\end{bmatrix} \tag{16}\]  
\[(y_{t+1},y_{t+2},\ldots,y_{2t})=f\!\left(\begin{bmatrix}x_{1}^{1}&x_{1}^{2}& \cdots&x_{1}^{n}\\ x_{2}^{1}&x_{2}^{2}&\cdots&x_{2}^{n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m}^{1}&x_{m}^{2}&\cdots&x_{m}^{n}\end{bmatrix}\right) \tag{17}\]  
\[(y_{t+2},y_{t+3},\ldots,y_{2t+1})=f\!\left(\!\left[\begin{array}{cccc}x_{1}^ {2}&x_{2}^{2}&\cdots&x_{2}^{n}\\ x_{3}^{1}&x_{3}^{2}&\cdots&x_{3}^{n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{t+1}^{1}&x_{t+1}^{2}&\cdots&x_{t+1}^{n}\end{array}\right]\!\right) \tag{18}\]  
where \(x_{t}^{n}\) denotes the feature values, which are derived from the decomposition of electricity load data. n denotes the number of features for each data point, and m denotes the total number of data points. \(y_{t}\) stands for the actual electricity load data, \(t\) represents the sliding window length, \(f\) denotes the forecasting method, and \((y_{t+1},y_{t+2},\ldots,y_{2t})\) represents the forecasting value. The forecasting process is illustrated in Figure 7.  
Figure 7: Sliding window to produce the input–output matrix.
## 3 Results
### Model Training Results  
After fine-tuning the model on the customer's specialized dataset, the key metrics have improved significantly, demonstrating its enhanced adaptation to the features of client images. Compared to the model trained during the Domain-Task Adaptation stage, the fine-tuned model shows substantial improvements in mAP performance across various intersection thresholds. The most notable increase was observed in the mAP@75 estimate, which rose by +127.6%. This sharp increase at a stricter IoU threshold indicates that fine-tuning enabled the model to achieve high accuracy even under rigorous evaluation criteria, which is crucial for reliable object detection in challenging conditions.  
In parallel with the improvement in average accuracy, there was a significant increase in recall (+54.0%), indicating the model's enhanced ability to identify true objects. At the same time, precision increased by 16.3%, suggesting that the model has become less prone to false positives, although the primary contribution to the improvement came from the enhanced recall. This balance between metrics demonstrates that the model has not only become more sensitive and capable of detecting objects but has also improved its reliability in practical applications.  
These results were obtained by testing the models on a test dataset that includes images from the deployment site of the model. To confirm the statistical validity of the fine-tuned model's accuracy, we conducted k-fold cross-validation (k = 5, 500 images) on a custom dataset. The average results from the cross-validation (mAP@50 = 0.8, Precision = 0.8, Recall = 0.71) closely matched the metrics obtained from the test dataset.  
Table 3 shows a comparative assessment of quality metrics on different models obtained when testing models on a test dataset.  
The following notation is used in Table 3: YOLOv11n.pt, a basic pre-trained model; Task-Adaptation.pt, a model that was trained on a specialized dataset; Client-Specific.pt, a model that was fine-tuned on a custom dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Specialized Dataset** & **Custom Training Dataset** & **Custom Testing Dataset** \\ \hline \multirow{3}{*}{Data source} & Kaggle, Internet, & \multirow{3}{*}{Greenhouse industry in Western Siberia} & Greenhouse industry in Western Siberia \\  & agro-biotechnical complex of Tyumen University & & \\  & 1300 & 500 & 50 \\ \multirow{2}{*}{Number of images} & Minimal occlusion, & \multirow{2}{*}{Occlusions, noise, dense foliage, a large number of small-scale objects} & Occlusions, noise, dense foliage, and a large number of small-scale objects \\  & uniform illumination & & \\ \multirow{2}{*}{Stage of use} & Domain-Task Adaptation & \multirow{2}{*}{Client-Specific Fine-Tuning} & \multirow{2}{*}{Evaluation} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of datasets.