esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
### System and Technical Challenges
#### 7.1.5 Data Privacy and Security  
DT systems rely on vast amounts of data, ranging from real-time sensor data to GPS and camera feeds, throughout the lifecycle of transportation infrastructure. While this data is critical for the functionality of DTs, it also raises significant concerns about privacy and security. The potential misuse or breach of sensitive data poses a serious risk, making data privacy and security essential for gaining public trust and support for DT technologies [61].  
Future policies must establish clear guidelines on data privacy, usage, and security for DT applications across all lifecycle stages. For instance, during the planning and design phase, regulations could define the types of data that can be collected, their intended uses, and the duration for which they can be stored [112]. In the construction phase, robust measures such as encryption, access controls, and regular audits should be implemented to prevent unauthorized access and data breaches [126]. During the operation and maintenance phase, continuous monitoring of data security is essential to ensure the integrity of real-time data streams.  
Beyond technical safeguards, ethical considerations must also be addressed. Policymakers and researchers must ensure that DT systems are designed and deployed in ways that promote equitable access to transportation services and avoid exacerbating existing inequalities in urban mobility. By embedding ethical guidelines into the development and use of DTs, stakeholders can create a framework that balances innovation with social responsibility [127].  
The effective application of DT concepts to transportation infrastructure faces several technical and systemic challenges, including data quality and integration, real-time processing, model accuracy, system interoperability, and data privacy. Addressing these issues is critical to unlocking the full potential of DTs in creating smarter, more efficient, and sustainable transportation systems throughout their entire lifecycle.  
Future efforts must prioritize collaboration among researchers, industry experts, and policymakers to develop solutions that overcome these challenges. By fostering innovation while ensuring ethical and secure practices, the transportation sector can harness the transformative power of DTs to improve urban mobility and infrastructure management, from planning and construction to operation, maintenance, and decommissioning [46]. This holistic approach ensures that transportation infrastructure remains adaptive, resilient, and responsive to evolving demands, ultimately contributing to long-term sustainability and societal well-being.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 2 Related Work
### SSVEP-Based BCI Enhancements  
As a rule, scalp electroencephalography (EEG) is used as a tool to provide continuous registration of a user's neural activity for further transmission to BCIs. The EEG signals have high temporal resolution [9; 39], and the approach involves a non-invasive process of measuring the electrical activity of the cerebral cortex and does not require surgical intervention. This provides a high safety level for users of such BCIs, compared to BCIs based on electrocorticography (ECoG) [40]. The latter requires mandatory surgical intervention and, thus, poses a high level of risk, making EEG-based BCIs a safer and more promising technology for universal application.  
It is recognized that BCI performance, which has been foremost associated with information transfer rates, is considerably better with calibration than in calibration-free schemes [41]. With respect to SSVEP-based BCIs, it is believed that a subject-specific type of calibration is capable of yielding the best performance. However, the time and effort spent on such individual training sessions are considered to be the most serious disadvantages of this approach [42]. Correspondingly, up-to-date research in the field focuses on (a) reducing the data amounts that need to be gathered from a particular subject by reusing some existing data [41], (b) collecting the data more intensively, e.g., through several channels [43], and (c) making more intensive use of available data via smarter calibration algorithms [33].
## 4 Overview of GNSSs
### Galileo
#### 4.2.3 GALILEO Ground Segment  
Both the ground control segment and the ground mission segment, along with the following infrastructure, are included in Galileo's ground segment:  
* Galileo Control Centers (GCCs), implementing ground control functions and ground missions at each location;
* Galileo's worldwide network of sensor stations (GSS), which collects and transmits Galileo measurements and data in real time;
* A worldwide network of Galileo Uplink Stations (ULS) that distributes and transmits mission data to the Galileo constellation;
* A worldwide network of telemetry, tracking, and control stations (TTC stations) that collect and transmit telemetry data generated by Galileo satellites and distribute and transmit control commands required to maintain the Galileo satellites and constellation under nominal operating conditions.
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 5 Experiments
### Parameter Sensitivity Studies  
To investigate the impact of certain parameter settings in the model on traffic flow prediction results, parameter sensitivity tests were conducted on the data embedding dimension \(d\) and the number of layers for spatiotemporal self-attention \(l\) on the PEMS04 dataset. For the data embedding dimension \(d\), this research tested values of \(\{4,8,16,32,64\}\), and the results are shown in Figure 8a in terms of MAE and MAPE. For the number of layers for spatiotemporal self-attention \(l\), this method tested values of \(\{1,3,5,7,9\}\), and the results are shown in Figure 8b in terms of MAE and MAPE.  
Figure 7: Ablation studies. (a) Ablation study on PEMS04. (b) Ablation study on PEMS07.  
Figure 8a demonstrates that the predictive performance of the model follows a trend of initially increasing and then decreasing as the embedding dimension \(d\) increases, reaching its optimum at \(d=8\). This is because as the embedding dimension increases from small to large, the embedded information increases, enhancing the expressive power of the model and enabling it to capture more crucial information. However, as the embedding dimension continues to increase, the number of parameters also increases, which may lead to model overfitting and consequently result in a decline in performance. This indicates that in selecting the embedding dimension, it is necessary to balance the model's expressive power and the risk of overfitting, thereby choosing an appropriate embedding dimension to achieve optimal predictive performance.  
From Figure 8b, it can be observed that the predictive performance of the model exhibits an increasing and then decreasing trend as the number of self-attention layers increases. The model achieves its optimal predictive performance when the number of layers is \(l=7\), and subsequent increases in the number of layers lead to a decline in the model's predictive capability. This is because in the initial stage, as the number of layers \(l\) increases, the model captures an increasing amount of key information. However, when the number of layers exceeds a certain threshold, the model starts to overfit, leading to a decrease in the accuracy of the predictions.  
Figure 8: The effects of the parameters. (**a**) The effects of the embedding dimension \(d\). (**b**) The effects of spatial–temporal self-attention layer \(l\).  
In summary, the model DSTSAN is sensitive to the data embedding dimension \(d\) and the number of self-attention layers \(l\).
# UAV Charging Station Placement in Opportunistic Networks
## 3 System Model  
We consider a system consisting of a finite set of both mobile nodes and UAVs. All nodes and UAVs can wirelessly communicate and exchange messages when they come within a certain proximity of each other or when they are within the radio range of each other without relying on any fixed infrastructure.  
Due to the constantly moving nature of the nodes, the connection among them changes over a time span \(T\). The model can be represented by the graph \(G=(N,U,E)\), at a certain time in \(T\), where \(N\) indicates the set of nodes, \(U\) the set of UAVs, and \(E\) the set of edges. An edge \(e_{ij}\) exists between \(i\) and \(j\), \(\forall i\in N|U\) and \(\forall j\in N|U\) lies within the radio range for each other, and, hence, can share and exchange data over the provided wireless medium. The system model can, thus, be represented by a seven-tuple graph as follows  
\[G=(N,U,E,T,\alpha,\beta,\gamma) \tag{1}\]  
where,  
\[\alpha:N\times T\rightarrow\{0,1\} \tag{2}\] \[\beta:U\times T\rightarrow\{0,1\}\] (3) \[\gamma:E\times T\rightarrow\{0,1\} \tag{4}\]  
\(\alpha\) in Equation (2) is the node presence function that indicates whether a given node exists at a given time, \(\beta\) in (3) is the function that indicates whether a given UAV exists at a given time, and similarly, \(\gamma\) in (5) is the edge presence function that indicates whether a given edge exists at a given time.  
It should be noted that the graph G is not necessarily be connected at every \(t_{i}\), where \(0<t_{i}<T\). Nodes could be disabled over the time span \(T\) either due to being switched off by the user exiting radio range.  
At any given time \(t\), where \(0>t>T\), when a message is created at a source node, an end-to-end path between the source and the destination nodes may not exist in the underlying graph \(G\) since nodes and/or drones may be on or off due to their proximity to each other at the current moment.  
In our system model, a message sent from a source node \(u\) travels in a journey through a sequence of sub-graphs \((g_{1},g_{2},...,g_{n})\), where \(g_{i}\subset G\) until it reaches a destination node \(v\). The message is sent, carried, and forwarded in a manner determined by different routing schemes (see net section). When two nodes meet, they seize this opportunity to distribute the messages they are holding in order to increase the number of nodes carrying these messages depending on the routing scheme that was selected. Each node determines the messages it is interested in collecting and is willing to serve as their carrier from the current underlying subgraph \(g_{i}\subset G\) to another subgraph \(g_{j}\subset G\).  
Given the previous assumptions, the representation of the graph G described in (1), and a set of charging stations \(C=(C_{1},C_{2},...,C_{n})\), find the locations \(L=(L_{1},L_{2},...,L_{n})\) of C that gives the best values of the network metrics being used.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 6 Non-Thermal Preservation Techniques
### Cold Plasma
Cold plasma technology also has substantial potential against common food-borne pathogens such as _Salmonella_, _Listeria_ and the Tulane virus. For _Salmonella_ inactivation, about 39 kV energy for 3.5 min was found to be significant in the reduction in load to about 3.7 CFU/cube [54]. Cold plasma treatment at 70 kV was applied for various periods on packaged meat at 4 \({}^{\circ}\)C and showed effective inactivation against _Salmonella_ and _campylobacter_ spp. and improved the meat's sensory characteristics [55].
## 5 Simulations Setup and Results
### Simulation Results
#### 5.2.2 Accuracy versus the Number of Malicious Attempts  
In this simulation, we measured the accuracy in percentage versus the number of malicious attempts, by increasing the number of malicious attempts by three for each measurement (please refer to Figure 3). Our results show that LAPP has an accuracy of 99.98% at 27 malicious attempts, while the other methods are between 99.46% and 99.58%.  
Figure 2: Computation time of auditing (number of challenged data blocks).
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 1 Introduction
NotationA bold-face upper case letter denotes a matrix while a bold-face lower case letter denotes a vector; \((\cdot)^{*}\),\((\cdot)^{T}\),\((\cdot)^{H}\) denote conjugate, transpose, and conjugate-transpose, respectively; \(A\bigodot B\) denotes the Hadamard product or entry-wise product of the matrices \(A\) and \(B\); \(A\bigotimes B\) denotes the Kronecker product of the matrices \(A\) and \(B\); \(tr(\cdot)\) is a trace function; \(E(\cdot)\) and \(var(\cdot)\) represent expectation and variance of a random variable, respectively; \(\|\mathbf{X}\|_{F}\) denotes the Frobenius norm of the matrix \(\mathbf{X}\); \(|x|\) denotes the absolute value of \(x\); \(det(\mathbf{X})\) stands for the determinant of \(\mathbf{X}\); \(diag([x_{0},x_{1},\ldots,x_{N-1}])\) denotes an \(N\times N\) diagonal matrix with diagonal entries \(x_{0},x_{1},\ldots,x_{N-1}\); \(\lfloor x\rfloor\) denotes the largest integer smaller than \(x\); \(\mathbf{I}_{N}\) is an \(N\times N\) identity matrix; and finally, superscript \(\mathbb{C}^{T\times N}\) gives the dimension of a complex matrix.
###### Abstract  
Oil pollution in soil-groundwater systems is difficult to remove, and a large amount of residual oil is trapped in the low permeable layer of the heterogeneous aquifer. Aromatic hydrocarbons in oil have high toxicity and low solubility in water, which are harmful to the ecological environment. Chemotactic degrading bacteria can perceive the concentration gradient of non-aqueous phase liquid (NAPL) pollutants in the groundwater environment, and enrich and proliferate around the pollutants, thus achieving a more efficient and thorough remediation effect. However, the existing theoretical models are relatively simple. The physical fields of oil-water two-phase flow and oil-phase solute convection and diffusion in water are not coupled, which further restricts the accuracy of studies on bacterial chemotaxis to NAPL. In this study, geometric models based on the actual microfluidic experimental study were constructed. Based on the phase field model, diffusion convection equation and chemotaxis velocity equation, the effects of heterogeneity of porous media, wall wettability and groundwater flow rate on the residual oil and the concentration distribution of chemotaxis bacteria were studied. Under all of the simulation conditions, the residual oil in the high permeable area was significantly lower than that in the low permeable area, and the wall hydrophilicity enhanced the water flooding effect. Chemotactic bacteria could react to the concentration gradient of pollutants dissolved into water in the oil phase, and enrich near the oil-water interface with high concentration of NAPL, and the density of chemotactic bacteria at the oil-water interface can be up to 1.8-2 times higher than that in the water phase at flow rates from 1.13 to 6.78 m/d.  
NAPL; heterogeneous porous media; chemotactic bacteria; residual oil; convection; diffusion +
Footnote †: journal: Applied Sciences
# A Study on Design and Control of the Multi-Station Multi-Container Transportation System
## 3 Results
### Overview of the Controlled Module Characteristics
#### 3.1.2 The Characteristics of the Lifting Module and the Line Switching Module  
The two modules (the lifting module--Figure 12 and the line switching module--Figure 13) are controlled in a sequential manner. The transportation sequence will be considered as follows:  
1. Sub-conveyor of the module has reached the right position.
2. Let the goods move into the lifting module or switch-line module.
3. Initiate the lifting or line switching operation.
4. Transport the goods into the next forward motion conveyor module.  
The module can merely handle one container at a time. Therefore, if the module is handling one container, the previous modules must wait until the module completes the transport sequence. Conversely, if there is no container left to be transported (being lifted or switched), the module will stop. Two RFID readers are mounted on both two ends of the module to identify if a container is entering or leaving the module. In addition, the proximity sensors help to limit the movement of the conveyor module.  
Figure 11: Sequential activation of the forward motion conveyor in the forward motion module.  
Figure 12: The structure of the lifting module with sensors.  
Figure 10: The structure of a forward motion module with sensors.
###### Abstract  
In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play a crucial role thanks to its simple structure and performance-oriented tuning process. To improve the control performance, advanced gain-scheduling methods are used to schedule the PID control gains based on the operating conditions and/or tracking error. However, determining the scheduled gain is a major challenge, as PID control gains need to be determined at each operating condition. In this paper, a model-assisted online optimization method is proposed based on the modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) to obtain the optimal gain-scheduled PID controller. Model-assisted offline optimization through computer-in-the-loop simulation provides the initial scheduled gains for an online algorithm, which then uses the iterative NSGA-II algorithm to automatically schedule and tune PID gains by online searching of the parameter space. As a summary, the proposed approach presents a PID controller optimized through both model-assisted learning based on prior model knowledge and model-free online learning. The proposed approach is demonstrated in the case of a nonlinear valve system able to obtain optimal PID control gains with a given scheduled gain structure. The performance improvement of the optimized gain-scheduled PID control is demonstrated by comparing it with fixed-gain controllers under multiple operating conditions.  
PID control; genetic algorithm; nonlinear system; gain scheduling; EGR valve
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 2 Related Work
### Semi-Supervised Semantic Segmentation  
Different from supervised learning which needs plenty of labels and unsupervised learning which does not require labels, semi-supervised learning uses a mixture of a few labeled data and a large amount of unlabeled data, and it performs training to make the prediction effect as close as possible to that of fully supervised learning. [PERSON] et al. [19] proposed to generate false labels by mixing two images to supervise the network. [PERSON] et al. [20] presented a data enhancement method for semantic segmentation. These methods all perform data augmentation of images, but for assembly images lacking color and texture, they are not useful for assembly image segmentation.  
Generative Adversarial Network (GAN) [21] is a commonly used approach to perform semi-supervised semantic segmentation. [PERSON] et al. [22] used adversarial learning to generate pseudo-labels from unlabeled datasets and conducted network training to improve network performance. [PERSON] et al. employed a discriminator of the fully convolutional network to discriminate the input image at the pixel level to generate a confidence image to supervise the training process and realize semi-supervised learning. [PERSON] et al. [23] adopted a double-branch structure, where the upper branch uses the method of [PERSON] et al. and the lower branch uses the Mean Teacher classifier, which can effectively reduce the false detection rate and enhance network performance. The main ideology of semi-supervised learning is to train the network through pseudo-labels generated by many unlabeled datasets to improve the segmentation capability of the network further.  
Although fully supervised semantic segmentation has high accuracy, it will consume a lot of human and financial resources for data labeling. Unsupervised semantic segmentation does not require data labeling, but the segmentation accuracy is not high, so it cannot meetthe high-precision requirements of assembly monitoring. Inspired by the literature [22], this paper proposes an adversarial learning AdvSemiSeg-MA network, which reduces the number of data annotations while ensuring high accuracy.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 5 Analyzing the Selected Features
### Feature-Selection Overview  
The feature selection provides insights into which features the model relies on, but it does not explain why a feature is important. Domain knowledge is crucial for understanding the context and significance of certain features. Through Table 10, we can analyze the results obtained from the feature-selection techniques simultaneously.  
\begin{table}
\begin{tabular}{l l} \hline \hline \(n\) & **Selected Features** \\ \hline
2 & TYP/DHW \\
3 & ARE/TYP/DHW \\
4 & ARE/TYP/BED/DHW \\
5 & ARE/TYP/BED/HAC/DHW \\
6 & ARE/TYP/BED/HAC/DHW/COE \\
7 & ARE/TYP/BED/HAC/DHW/COE/WPU \\
8 & ARE/TYP/BED/HAC/DHW/COE/WPU/NPE \\
9 & ARE/TYP/BED/HAC/DHW/COE/WPU/NPE/U18 \\
10 & ARE/TYP/BED/HAC/DHW/COE/WPU/NPE/U18/PET \\ \hline \hline \end{tabular}
\end{table}
Table 8: Features selected with maximum \(POS_{F}(Q)\).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \(n\) & **K-Means Accuracy** & **AHC Accuracy** & **SOM Accuracy** \\ \hline
2 & 0.6489 & 0.5957 & 0.6596 \\
3 & 0.6489 & 0.5957 & 0.6596 \\
4 & 0.6277 & 0.5957 & 0.6595 \\
5 & 0.6809 & 0.7021 & 0.7128 \\
6 & 0.6596 & 0.6383 & 0.6596 \\
7 & 0.6596 & 0.6277 & 0.6809 \\
8 & 0.6809 & 0.6383 & 0.6383 \\
9 & 0.6596 & 0.6277 & 0.6914 \\
10 & 0.6489 & 0.6064 & 0.6489 \\ \hline \hline \end{tabular}
\end{table}
Table 9: FRNN model accuracy for \(n\) features selected.  
While feature-selection techniques shed light on which features are influential in the model, they do not inherently provide the rationale behind them. Thus, a deep understanding is essential to comprehend the context and significance of these features. For that analysis, we focus on features common to at least three selection methods.  
Thus, we will analyze the number of people living in a dwelling (NPE), the number of heating and air conditioning units (HAC), the number of hot water units (DHW), the number of bedrooms (BED), and the value of family income (INC).