esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Orthogonal Experimental Design Scheme  
The surface texture characteristics of EAC are intrinsically linked to coarse aggregate content and gradation. Therefore, two mix design parameters--coarse aggregate volume ratio and proportion of aggregates \(>\) 9.5 mm--were selected to reflect coarse aggregate dosage and gradation, alongside two conventional parameters (water-binder ratio and sand ratio), forming four factors in the orthogonal experimental design. Based on existing research on the mix proportion design of EAC [24; 25; 26; 27; 28] and technical requirements specified in the Specification for Mix Proportion Design of Ordinary Concrete (JGJ 55-2011) [29], the appropriate ranges for each parameter were determined as follows: water-binder ratio 0.40-0.46, sand ratio 28-34%, coarse aggregate volume ratio 40-44%, and proportion of aggregates \(>\) 9.5 mm 30-70%. After comprehensive consideration of factors including specimen fabrication costs, number of experiments, parameter range settings, and the three-level experimental design which facilitates inflection point analysis, each factor was assigned three levels based on the aforementioned ranges, and an L\({}_{9}\)(3\({}^{4}\)) orthogonal array was adopted to organize laboratory tests for physical and mechanical properties. The factor-level assignments are detailed in Table 1, with the Orthogonal test scheme provided in Table 2. Here, Factors A, B, C, and D correspond to water-binder ratio, sand ratio, coarse aggregate volume ratio, and proportion of aggregates \(>\) 9.5 mm.  
To rationally analyze the surface texture characteristics, SRNR performance, and mechanical properties of EAC, two indicators--exposed aggregate depth and exposed aggregate area ratio--were used to characterize the surface texture. The pendulum value and noise value were employed to evaluate the SRNR performance, respectively. The 28-day compressive and flexural strengths were used to assess the mechanical properties of EAC. Therefore, a total of six performance indicators were tested to optimize the EAC mix proportion. To minimize potential errors during EAC preparation and testing, three parallel specimens were fabricated for each mix proportion in the orthogonal experiment, resulting in 27 specimens in total. The corresponding experimental results for each group were determined as the average value of the three specimens. The experimental error was controlled using the absolute error percentage of three parallel data relative to the median value. For exposed aggregate depth and area ratio, an absolute error threshold of 5% was established: when the error exceeded 5%, the median value was retained as the valid measurement if only one datum exceeded the threshold, whereas the entire specimen group was invalidated if both data exceeded the threshold. Similarly, for the pendulum value and noise value, the absolute error tolerance was adjusted to 3%, while the tolerance for 28-day compressive and flexural strength tests was set at 15%.
## 2 Materials and Deposition Method
### Sample Characterization  
The XRD analysis of the C\({}_{60}\) thin film was conducted using a Rigaku miniflex 600 diffractometer (Rigaku, Tokyo, Japan). The X-ray diffraction technique was employed using CuK\(\alpha\) (40 kV of current) radiation within a 2-theta angle range from 5 to 40\({}^{\circ}\) with a 0.02 step. Infrared spectroscopy (FTIR) was performed to identify the chemical bonds in Carbon C\({}_{60}\) thin film by using an Agilent Cary 600 instrument (SpectraLab Scientific Inc., Markham, ON, USA), equipped with an ATR diamond, and specifically within the wave number range of 4000-500 cm\({}^{-1}\). The high-resolution images of the microstructures of the films were acquired utilizing an environmental scanning electron microscope (E-SEM), type Prisma ThermoFisher Scientific (Waltham, MA, USA), with a tungsten filament. Finally, the basic elements of the films were analyzed by energy dispersive spectrometry (EDS). The error of the EDS detector for Carbon (C) and Oxygen (O) varies from \(\pm\)0.5 to \(\pm\)2%.
## 1 Introduction
The goal is to promote the integration of XAI in digital forensic procedures by improving model transparency, trust, and evidentiary reliability in AI-assisted IDSs. The proposed method is innovative in combining SHAP and LIME within a forensic framework, evaluated across XGBoost and TabNet models. It introduces forensic-specific metrics (fidelity, stability, Jaccard) and aligns with legal standards, offering a novel and practical contribution to explainable AI in cybersecurity. The main contributions of this work are summarized as follows:  
* Comparative Application of XAI Techniques in Cybersecurity: A rigorous comparison of SHAP and LIME is conducted in the context of intrusion detection, focusing on their interpretability contributions in forensic scenarios. * Dual-Model Evaluation Framework (XGBoost and TabNet): The study contrasts two machine learning paradigms--a tree-based ensemble model (XGBoost) and a deep learning architecture for tabular data (TabNet)--to assess how the explanation methods perform across different model types. * Multi-Criteria Explainability Assessment: The evaluation includes quantitative metrics such as the Jaccard similarity index, fidelity score, and explanation stability, providing a multidimensional perspective on the quality of explanations. Use of a Realistic and Public Benchmark Dataset: The UNSW-NB15 dataset is employed to ensure realism, replicability, and alignment with network intrusion detection use cases. * Forensic-Centric and Trust-Oriented Interpretability: The study shows how SHAP and LIME can support evidence validation, regulatory compliance, and transparent decision-making in cybersecurity contexts. The remainder of this paper is structured as follows. Section 2 provides a comprehensive review of related work, emphasizing the integration of explainable artificial intelligence (XAI) techniques in intrusion detection systems. Section 3 outlines the materials and methods, detailing the dataset characteristics, model architectures (XGBoost and TabNet), preprocessing pipeline, and implementation of SHAP and LIME for forensic interpretability. Section 4 presents the experimental results, including classification performance, global and local explainability analyses, comparative evaluations, and metric-based assessments of fidelity, stability, and feature consistency. Section 5 discusses the implications of the findings, highlights limitations, and offers strategic recommendations for the application of XAI in forensic intrusion detection. Finally, Section 6 concludes the paper, summarizing the main contributions and identifying directions for future research.
## 4 Dataset Description
### File Structure  
Multiple types of files are present inside the dataset. For the reference data, we provide the observation data in Novatel proprietary log file format, and the RINEX files extracted using Novatel Application Suite software. The observations from the base station are provided in RINEX files.  
For Android devices, the raw logs are produced in a simple CSV format. This format was chosen to allow compatibility with the GNSSLogger app (presented in Section 3.3). Each line of the CSV file represents the data from a sensor at a specific timestamp. While multiple clocks might be defined inside the device (e.g., GNSS clock, system clock), all entries provide two timestamps: a UTC time in Unix format based on the global system clock, and a time counter since the last time the smart device was booted. The former is used for temporal indication but it might not be very accurate, as explained in Android documentation [43], and it is recommended to use the latter for more accurate time intervals.  
We note that, while it is possible to request a specific sampling frequency through the Android API, our empirical experiments have shown that the accuracy of the sampling clock can be poor. Requesting for a too high or too slow sampling interval might lead to truncated sampling, based on the physical limitation of the sensor. It is recommended to interpolate the values of a log to the wanted sampling frequency before any processing is performed. The time information discussed above should be sufficient for accurate clock alignment between the sensors and with the GNSS clock.
## 4 Results and Discussion
### Consensus on Benefits
#### 4.2.3 Training Method  
Fourteen papers agreed that AR application helped in training personnel and referred to AR as a learning and educational tool. [PERSON] et al. (2013) mentioned that using AR for communication helped to improve the learning curve of trainees significantly, and fewer errors were made on site [42]. In addition, AR eased the transferability of knowledge between the participants in the learning activities [43]. Regardless of the type of device in use (i.e., head-mounted displays, cellphones, or tablets), the type of environment, or the type of project an application is being implemented in, this benefit does not depend on other contextual factors, based on the literature. This highlights the importance of AR in sharing information and expanding knowledge through communication in the industry, especially, since all the identified papers agreed on this topic.
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Training Performance  
The training loss intuitively reflects the proposed models' learning ability and convergence degree throughout each epoch. The training losses of models with different LSTM layers trained on four different datasets are shown in Figure 3.  
To prevent overfitting during the training of the models, the early stopping regularisation method was used, with the patience value set to five. Two more experiments were conducted to validate the model's generalisation capability:  
1. Each trained model was subjected to a series of speech inpainting tests on ten speech signals that were entirely independent of its own dataset. The restored speech signals consistently exhibited good MOSs and listening quality across various gap lengths.
2. In order to further validate the generalisation capability, we conducted additional tests to examine the models by training them on a specific dataset and applying them to completely different datasets. In this experiment, a model was first trained on a dataset consisting of only female/male voices, and the model was then applied to restore male/female speech signals from completely different datasets. The inpainting results also demonstrated high MOSs and listening quality across various gap lengths.  
As a result, we can reasonably state that no overfitting occurred during the models' training process and the models generalised well on all the different datasets used in the experiment.
## 4 Results and Discussion
### Performance Comparison of Different Feature Extractors  
The performance of six kinds of features was compared, and the experimental results are shown in Table 2. Model parameters were set as D = 2, C = 8. Experimental results were cross-validated by five folds and five runs. Average recall, precision, _F1_score_ and their standard deviations were given as assessment indices. TX_fea was obtained by concatenating three typical traditional features: SIFT, LBP and the statistics of GLCM. VGG-16_fea [22], ResNet18_fea [23], DenseNet-121_fea [24] and EffentNet-B0_fea [25] were the activation vectors of four different ImageNet-trained CNNs. It can be observed that the features extracted by ImageNet-trained CNNs showed greater advantages than the traditional feature descriptors. This indicates that the features extracted by ImageNet-trained CNNs have better discrimination and integrity, and can be transferred to the pathological image analysis tasks.  
Figure 5: Performance with different model parameters.
# Improvement of Hexacopter UAVs Attitude Parameters Employing Control and Decision Support Systems
## 5 FEM Decision Support  
The hexacopter was conceived to provide a robust structure for transporting large payloads. The aim of this approach is to ensure the stability of the hexacopter during stationary flight manoeuvres, to set out a composite simulation model for different analysis types and to validate the computational model in order to synchronize analytical, experimental and numerical results. FEM model reduction, efficiently tuning the discretization parameters with the available computational resources, was also a computational goal.  
The problem in the case of six propellers is that the rotating domains are close to each other, and the narrow space causes modelling and computational convergence issues. The results obtained from the FEM study were employed to optimize flight parameters, such as the rotor speed. Another important target of CFD simulations is the power requirements and the evaluation of the lift and drag forces.  
Hover flight is one of the most important flight regimes of the hexacopter, when the UAV requires maximum stability. In package delivery tasks, the fixed-point turbulences are essential, as they may act in the close proximity to buildings or even to the ground, especially in urban areas. This was considered when creating the flow domain around the hexacopter. The air pressure under the hexacopter is higher the closer the hexacopter is to the ground. It is therefore important to know the air pressure values so that the hexacopter remains stable. On the hexacopter frame, the pressure increases correspondingly as it approaches the ground or a target. Some of the turbulences are redirected on the drone components and on the rotors. The hexacopter can work in areas with dust, sand and even snow, which can then interact with the vehicle. This is another reason why the CFD study is essential to ensure the stability and safe operation of the hexacopter.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 3 Background Concepts
### Summary Notes  
In summary, de-identification is a broad term that includes both anonymization and pseudonymization. Figure 1 represents the spectrum of data privacy.  
The \"pure\" de-identification only removes the identifiers without any additional analysis of the re-identification risk based on the remaining data--that can include quasi-identifiers--and therefore is a poor technique that must be used carefully for simple and restricted contexts. Since anonymization completely removes any direct and indirect identifiers, it is the most suitable method for the data to be fully open. It is worth noting that anonymized data is complex or, in many cases, impossible to link across multiple sources. Furthermore, the guarantee of non-re-identification is timely as future datasets released might be linked to the anonymized data, allowing re-identification. Due to the poor quality of de-identification methods achieved by the pseudonymization technique compared to anonymization, until now, most of the known re-identification attacks have been successful on pseudonymized data [39, 41]. Another drawback of pseudonymization is the difficulty in obtaining metrics that quantify the practical risk of re-identification or even the success rate of the process. Of the various scientific sources consulted, only applications of similarity on numerical data (e.g., [42]) or limited to categories (e.g., [43]) were identified, with an apparent void regarding the application, in this context, of similarity algorithms of texts (e.g., Levenshtein distance, Jaro-Winkler or Jaccard index [44, 45, 46]). As such, the reliability of the process will ultimately depend on the responsible analyst and it is impossible to guarantee that all the necessary transformations were carried out correctly to remove the individual character of each record.  
As pointed out by [PERSON] and [PERSON] [47], the growing lack of trust in de-identification, due to a number of noticed cases, and focus on re-identification risks may result in data custodians believing they should not waste their time even attempting to de-identify personal information prior to making it available for secondary purposes.
## 8 Conclusions  
The geophysical study carried out by means of TDEM sounding in Liceras (Soria, Spain) demonstrates the value of this geophysical research system as a support for hydrogeological studies in Mesozoic formations. The difference in resistivity of the Cretaceous, Jurassic and Triassic formations allows for obtaining geoelectric profiles that can be correlated with the local and regional geological and hydrogeological structure, which allows for the definition of the structure and position in the subsurface of the Mesozoic sequence outcropping in the study area. In addition, the low resistivities could indicate a low hydrochemical quality of the groundwater, usually associated with the gypsum of the Triassic formation.  
This spatial information obtained by Time-Domain Electromagnetic is essential when considering the design of works to capture hydrogeological Mesozoic resources in order to solve the quality and supply problems existing in the area, as it defines the ideal location and depth for the execution of potential works.  
Therefore, combining the pre-existing geological data with TDEM geophysical data has shown to be a methodology that can solve similar problems in Mesozoic formations and can produce savings for both public and private institutions in their investment in hydrogeological resources.  
Figure 12: Hydrogeological Interpretation based on the 1:50,000 geological cartography of Spain, including position of the proposed catchment borehole.  
**Author Contributions:** Conceptualisation, J.C.-G., D.P.-S. and J.M.B.-H.; methodology, J.C.-G., D.P.-S. and J.L.H.-P.; software, J.C.-G. and P.C.-G.; validation, J.C.-G.; formal analysis, J.C.-[PERSON] and [PERSON]; investigation, [PERSON]-[PERSON], [PERSON]-[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; resources, [PERSON]-[PERSON], [PERSON] and [PERSON]-G.; writing--original draft preparation, [PERSON]-G., [PERSON], [PERSON] and [PERSON]-G.; writing--review and editing, [PERSON]-G. and [PERSON]-N.; visualisation, [PERSON]-G.; supervision, [PERSON]-G.; project administration, [PERSON]-G. and [PERSON]-G.; funding acquisition, [PERSON] and [PERSON]-H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to thank the Water Supply Service of the Regional Government of Castile and Leon (Servicio de Abastecimiento de Aguas de la Junta de Castilla y Leon) for supporting the above research project and for their consent to show the results obtained in a research article. This paper is part of the Research Project I+D+i/PGC2018-094566-B-C21, supported by the MCIN/AEI/10.13039/501100011033/ and FEDER \"Una manera de hacer Europa\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Controller Switching Mechanism for Resilient Wireless Sensor-Actuator Networks
## 3 System Overview
### System Model  
Figure 1 shows the system model of CPSs: a physical system, a computing system, and networks. The physical system includes a sensor and an actuator. First, a clock-driven sensor periodically samples the plant output every \(T_{s}\). Thereafter, an event-driven controller calculates the actuation commands as soon as the sensor data arrive. The computing system monitors and controls the state of the plant. The observer estimates the state of the physical system and a feedback controller. Generally, event-driven actuators can immediately respond to updated actuation commands.  
The physical system is modeled as a linear time-invariant (LTI) single-input single-output system. The computing system consists of an observer, a feedback controller, and an anomaly detector, where each component estimates the state of the physical system, calculates the control input signal, and determines the system anomalies [37]. The physical and computing systems were designed in the discrete-time domain. We consider the dynamics of the physical system as follows:  
\[\begin{split}& x(k+1)=Ax(k)+Bu(k),\\ & y(k)=Cx(k).\end{split} \tag{1}\]  
where \(A\in\mathbb{R}^{n\times n}\) is the system matrix containing one or more eigenvalues with magnitudes greater than \(1\), \(B\in\mathbb{R}^{n}\) is the input matrix, \(C\in\mathbb{R}^{1\times n}\) is the output matrix, \(x(k)\in\mathbb{R}^{n}\) is the state of the physical system, \(u(k)\in\mathbb{R}\) is the control input, and \(y(k)\in\mathbb{R}\) is the sensor output. For the LTI system of (1), we assumed that the \((A,B)\) pair was controllable, and the \((A,C)\) pair was observable. The eigenvalues of \(A\), whose magnitude is greater than \(1\), become the unstable poles of the transfer function form of (1) from \(u\) to \(y\).  
We consider the observer-based feedback controller on the computing system as follows:  
\[\begin{split}&\hat{x}(k+1)=A\hat{x}(k)+Bu(k)+L(y(k)-C\hat{x}(k)), \\ & u(k)=Py_{r}(k)-K\hat{x}(k),\end{split} \tag{2}\]  
where \(\hat{x}(k)\in\mathbb{R}^{n}\) is the state estimate, \(y_{r}(k)\in\mathbb{R}\) is the reference signal, \(P\) is a scalar gain, \(K\in\mathbb{R}^{1\times n}\) is the state-feedback controller gain, and \(L\in\mathbb{R}^{n}\) is the observer gain. We assume that \(K\) and \(L\) in (2) are appropriately selected to stabilize the system of (1). In other words, they were selected such that both \(A-BK\) and \(A-LC\) were Schur matrices. Gain \(P\) was selected to satisfy \(C(I_{n}-A+BK)^{-1}BP=1\) to achieve asymptotic tracking for a constant \(y_{r}\), where \(I_{n}\) is the n-dimensional unity matrix.
## 5 Belief Model
#### NUTS (No-U-Turn Sampler) Belief Model Update
A sample is taken from the set of points generated during the trajectory, usually by uniform sampling from the accepted positions in the tree. This process is repeated for \(N\) iterations to form the resultant Markov chain. The step size \(\epsilon\) and the maximum tree depth \(D\) may be adapted during a warm-up phase to improve the performance of the sampler. ```
Input: Initial position \(\theta^{(0)}\), step size \(\epsilon\), max tree depth \(D\), number of iterations \(N\) Output: Sequence of samples \(\{\theta^{(n)}\}_{n=1}^{N}\) for\(i\gets 1\)to\(N\)do  Sample momentum \(p^{(0)}\sim\mathcal{N}(0,I)\);  Set \(j\gets 0\), \(\theta^{\prime}\leftarrow\theta^{(i-1)}\), \(p^{\prime}\gets p^{(0)}\);  Set \((\theta^{+},p^{+},\theta^{-},p^{-})\leftarrow(\theta^{\prime},p^{\prime}, \theta^{\prime},p^{\prime})\); whilestopping criterion not metdo  Choose a direction \(v_{j}\sim\{+1,-1\}\) uniformly; if\(v_{j}=+1\)then  Expand the tree in the positive direction; else  Expand the tree in the negative direction;  end if ifU-Turn detectedthen break;  end if \(j\gets j+1\); if\(j=D\)then break; // Limit the maximum tree depth  end if  end if \(\theta^{(i)}\leftarrow\) A new sample from the trajectory ;  end while
```  
**Algorithm 1**Simplified No-U-Turn Sampler (NUTS)  
Our research employs the NUTS algorithm to effectively generate samples within each query iteration, thereby enhancing the alignment between belief and user models. This update improves the cosine similarity measure in the context of active preference learning.