esa-sceva/satcom-chunk-collection huggingface dataset:
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 4 Results and Discussion
### Partial Guidance  
In the partial guidance model, pedestrians near an exit move immediately to the exit door, while other pedestrians follow the nearest UAV, which guides them to the nearest exit. The results for AET and mortality for partial guidance models are presented below.  
(a) Average Evacuation Time  
In Figure 3, the average evacuation time is plotted against different UAV locations as the number of pedestrians increases from 450 to 7200 for opposite and adjacent exit doors. AET is calculated as the average time taken by each evacuee. The results show that AET was highest when the evacuation process ran without UAVs (None); it was around 9 min when CD = 0.5; and it increased slightly, reaching 10 min, when CD = 2 for both placements of exit doors.  
On the other hand, using UAVs in the evacuation process led to a decrease in AET. For instance, when CD \(\leq\) 0.5, AET was less than 8 min; it increased slightly to just above 8 min when CD = 1 for all three UAV positions and both placements of exit doors. When the hall was crowded, that is, where two people were located within one meter of each other, AET increased to around 9 min. In all tested situations involving the partial guidance model, the two placements of the four exit doors (opposite or adjacent)had no significant effect on the AET.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & & **Assigned Value** \\ \hline Environment & & Indoor square hall of 3600 m\({}^{2}\) \\ \hline \multirow{4}{*}{UAV} & No. & 0, 4 \\ \cline{2-3}  & Position & Middle, random, near exit \\ \cline{2-3}  & Speed & 3.5 m/s \\ \cline{2-3}  & Guidance Model & Partial, Full \\ \hline \multirow{4}{*}{Pedestrians} & No. & 450, 900, 1800, 3600, 7200 \\ \cline{2-3}  & Position & Random \\ \cline{2-3}  & Speed & Varies based on Table 2 \\ \hline \multirow{2}{*}{Exit} & No. & 4 \\ \cline{2-3}  & Position & Opposite, adjacent \\ \hline \multirow{2}{*}{Obstacles} & No. & 6 \\ \cline{2-3}  & Size & Long pillar of 1 m radius \\ \hline \multicolumn{3}{l}{Evacuation time} & 15 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters and assigned values.
# TrackInk: An IoT-Enabled Real-Time Object Tracking System in Space
## 6 Conclusions and Future Work  
In this paper, we proposed a low-cost, portable, and IoT-enabled real-time satellite tracking system called TrackInk. The name TrackInk, derives from the observation that the system can efficiently track (Track) and then point out the exact location of the object in space (Ink). Our proposed approach takes advantage of the GPS. The employed sensors contain GPS chips capable of working by sending coordinates accurately. We designed and developed this system to utilise hobby-grade materials with full implementation. That said, we provide a complete set of hardware-based performances for the system. We also provide a detailed discussion on the achieved results by a real-time object tracking example. In future, we intend to implement a wireless system control feature from ThingSpeeak, such that the user is able to change the functionality of the system without interacting with the physical device. The current implementation requires the user to press a physical button to change the automation status, and also requires changing code to modify the priority of tracked objects.  
Additionally, future work could be done by creating a network of devices in multiple locations which could view a satellite from multiple angles. This would allow for increased viewing times of a satellite, along with the option to view a satellite from multiple angles at the same time. Higher quality cameras could also be implemented in order to give sufficient system feedback for precise movement. The issue of security in such portable devices also needs to be considered, along with data privacy concerns. Nevertheless, this is a separate direction of research that we leave for future work.  
[PERSON], [PERSON], [PERSON] and [PERSON] planned the paper, structured the article, and contributed to the core research problem formulation. [PERSON] and [PERSON] helped in the project design. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# RBF Neural Network Based Backstepping Control for an Electrohydraulic Elastic Manipulator
## 2 Electrohydraulic Elastic Manipulator Dynamics
### Mechanical Dynamics
The dynamics of the EEM can be represented as follows:  
\[\ddot{\theta}_{2}+f_{2}(\theta_{2},\dot{\theta}_{2})+g_{2}(\theta_{2},\dot{ \theta}_{2})(\tau_{ext_{2}}+\tau_{12})=0 \tag{14}\]  
\[\ddot{\theta}_{1}+f_{1}(\theta_{1},\dot{\theta}_{1})+g_{1}(\theta_{1},\dot{ \theta}_{1})\Big{(}\tau_{21}+\tau_{ext_{1}}+\tau_{fc}-\tau_{obs}\Big{)}=0 \tag{15}\]  
where (14) is the outer link equation and \(f_{2}(\theta_{2},\dot{\theta}_{2})\), and \(g_{2}(\theta_{2},\dot{\theta}_{2})\) are known functions in the outer link dynamics; \(\tau_{ext_{2}}\) is the external torques and the unknown function; and \(\tau_{12}\) is coupling torque generated  
Figure 3: The forces acting on the two-link manipulator. by the variable stiffness. The EEM dynamic equation is represented by (15). \(f_{1}(\theta_{1},\dot{\theta}_{1})\) and \(g_{1}(\theta_{1},\dot{\theta}_{1})\) are the known functions of the manipulator dynamics; \(\tau_{ext1}\) is the external torques and modeling error. When the system stiffness is adjusted by ASBM online, the coupling torque impacting the EHSs will alter with respect to the system stiffness. This issue and other uncertainties in the system are challenges for the position control of the EHSs. This paper proposes an indirect adaptive controller to guarantee the stability and robustness of the EHSs in the existence of uncertainties. From (1), (4)-(6), and (15), the system dynamics are represented as follows:  
\[ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=g_{1}x_{3}+f_{1}+\delta_{1}(t)\\ \dot{x}_{3}=g_{0}u+f_{0}+\delta_{2}(t)\end{array}  \tag{16}\]  
where \(x=\left[ \begin{array}{cc}x_{1}&x_{2}&x_{3}\end{array} \right]^{T}=\left[ \begin{array}{cc}\theta_{1}&\dot{\theta}_{1}&F_{els}\end{array} \right]^{T}\); \(\delta_{1}(t)\) is the uncertainties, such as fiction, external torques, and modeling error, in the mechanical dynamics of the EEM; \(\delta_{2}(t)\) is the uncertainties, such as leakages and modeling error, in the hydraulic dynamics; and \(f_{i}(i=0,1)\) and \(g_{i}(i=0,1)\) are defined in Appendix B. **Assumption 1**. : _The perturbation, \(\delta_{i}(t)\), (\(i=1,2\)) changes with respect to time and is bounded by \(\|\delta_{i}(t)\|\leq\eta_{i}\)._
# Studying the Cable Loss Effect on the Seismic Behavior of Cable-Stayed Bridge
## 2 Three-Dimensional FE Model of the Cable-Stayed Bridge
### Verification of Dynamic Characteristics of Numerical Model  
In order to evaluate the dynamic characteristics of the FE model developed, an eigenvalue analysis was conducted, and the results were compared with the measured natural frequencies of the same bridge obtained from previous research [17]. The results of the eigenvalue analysis for the major modes are shown in Figure 5, and the comparison results are listed in Table 3. The differences in the vertical modes are within 2%, while those in the horizontal modes are 13.67
# EconLedger: A Proof-of-ENF Consensus Based Lightweight Distributed Ledger for IoVT Networks
## 5 Experiment and Evaluation
### Performance Evaluation
#### 5.2.1 Network Latency  
Figure 4 presents the network latency for EconLedger with respect to completing an entire epoch round of PoENF consensus protocol given the number of validators varying from 5 to 20. For each test point, we let all validators perform tasks simultaneously and waited until the bundle of tasks is finished. The latency includes the round trip time (RTT) and service processing time on the remote host. Broadcasting an ENF \(tx\) needs \(\mathcal{O}(K)\) communication complexity and \(K\times\mathcal{O}(1)\) computation complexity for verification. Thus, the total complexity is \(\mathcal{O}(K)\) such that latency of ENF collection \(\mathcal{T}_{ec}\) is linearly scale to committee size \(K\). Chain finality requires all validators to broadcast their vote among committee members so that it has the same complexity as ENF collection. Thus, the delay of chain finality \(\mathcal{T}_{cf}\) is almost linear scale to \(K\).  
The green line in Figure 4 shows the latency of block proposal \(\mathcal{T}_{bp}\), which indicates how long a proposed block could be accepted by all validators in the PoENF committee. The communication complexity of block proposal is \(\mathcal{O}(K)\), which is similar to ENF collection and chain finality. However, during block generation and verification processes, PoENF algorithm requires a validator using Equation (1) to compute ENF scores based on collected proofs from others, and it has computation complexity of \(\mathcal{O}(K^{2}d)\). As a result, the total complexity of block proposal is \(\mathcal{O}(K^{2}d+K)\). In general, ENF samples size \(d\) is a small value such as 60, and it has less effect on computation cost than \(K\) does. Thus, \(\mathcal{T}_{bp}\) is almost linearly scaled relative to \(\mathcal{O}(K^{2})\). The total latency shows that EconLedger takes about 2 s to finish an epoch cycle of PoENF committee consensus (\(\mathcal{T}_{ec}+\mathcal{T}_{bp}+\mathcal{T}_{cf}\)) given the committee size \(K=20\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **Dell Optiplex-7010** & **Dell Optiplex 760** & **Raspberry Pi 4 Model B** \\ \hline \multirow{2}{*}{CPU} & Intel Core TM i5-3470 & Intel Core TM E8400 & Broadcom ARM Cortex \\  & (4 cores), 3.2 GHz & (2 cores), 3 GHz & A72 (ARMv8), 1.5 GHz \\ \hline
**Memory** & 8 GB DDR3 & 4 GB DDR3 & 4 GB SDRAM \\ \hline
**Storage** & 350 G HHD & 250 G HHD & 64 GB (microSD) \\ \hline
**OS** & Ubuntu 16.04 & Ubuntu 16.04 & Raspbian (Jessie) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of experimental nodes.
## 3 Particle Swarm Optimization
### Resemblance with Least Mean Square and Recursive Least Squares  
The PSO, LMS, and RLS algorithms share certain resemblances in terms of their learning mechanisms and optimization objectives. PSO is an algorithm where particles within a swarm collectively explore and exploit the search space to find optimal solutions. Similarly, both the LMS and RLS algorithms are adaptive filtering techniques used in signal processing and parameter estimation [68]. They aim to adjust the internal parameters iteratively to minimize the error between the predicted and actual outputs.  
One resemblance between PSO, LMS, and RLS is their learning mechanism. In PSO, particles adjust their positions and velocities based on their individual experiences and the collective knowledge of the swarm [69]. This learning process allows particles to explore the search space and exploit promising regions. Similarly, in LMS and RLS, the algorithms update their weight vectors or coefficients based on the input data and the discrepancy between the predicted and actual outputs. This iterative learning mechanism in all three algorithms enables them to converge toward optimal solutions or parameter estimates.
## 4 Experiments
### Comparison with the SOTA Methods  
#### 4.2.1 Quantitative Comparisons  
To verify the effectiveness of our proposed model, we compared it with six state-of-the-art UAV object detection approaches on the VisDrone dataset. The methods we compared with are RRNet [39], DPNet-ensemble [36], SMPNet [40], YOLOv8 [25], DPNetV3 [40], YOLOv9 [26], PP-YOLOE [41], YOLOv10 [28], RT-DETR [27], TPH-YOLOv5 [34], and CZ Det [42]. The scores of these methods are presented in Table 1.  
Table 1 presents the test results of the current mainstream detection model on the VisDrone2019 dataset and compares the detection results of our AFE-YOLOv8 model with those of other models.
## 2 Related Works
### STFT-Based Approaches  
Short-time Fourier transform (STFT) analyzes the frequency characteristics of signals over time using a fixed-size time window and is currently one of the most actively researched methods in CNN-based fault diagnosis.  
[PERSON] et al. converted three-phase current time-series signals into STFT-based time-frequency images and input them into a CNN model to evaluate its diagnostic performance. Their approach demonstrated superior accuracy compared to LightGBM models [22].  
[PERSON] et al. applied STFT-based CNN methods to effectively detect faults in the multifunction vehicle bus (MVB) systems of railway vehicles [23].  
[PERSON] and [PERSON] performed a fault diagnosis for three-phase induction motors by combining STFT-based time-frequency images with a weighted probability ensemble deep learning (WFDL) framework. By integrating predictions from different CNN architectures based on probability weighting, they achieved a higher diagnostic accuracy and stability compared to individual models [24].  
[PERSON] et al. introduced Differential-STFT into the STFT-based diagnostic process to emphasize changes in time-frequency characteristics, thereby effectively extracting robust fault features in noisy environments [5].  
STFT-based methods excel in capturing and visualizing variations in frequency characteristics over time. When combined with CNNs, they provide an excellent diagnostic performance. However, due to the inherent trade-off between time and frequency resolution, the optimal selection of window size and step size is required [25].  
The RGB mapping, wavelet transform (DWT/CWT), and STFT-based image transformation techniques reviewed above are all representative approaches that construct CNN input data using three-phase signals for fault diagnosis. In this study, we experimentally apply these established image transformation methods not to the full set of three-phase current signals but instead to the single current component (Id) obtained via the D-Q synchronous reference frame. Our goal is to verify whether CNN-based fault diagnosis can achieve a sufficient performance and utility using only the Id component. Through a quantitative comparison of performance and practicality between conventional three-phase-based methods and the proposed Id-based approach, this study aims to offer a concrete guideline for constructing input data in CNN-based motor fault diagnosis systems.
# Hybrid CNN-NLP Model for Detecting LSB Steganography in Digital Images
## 6 Conclusions  
This work presented a hybrid approach based on convolutional neural networks and natural language processing to detect LSB steganography in digital images. The combination of visual and linguistic analyses allowed the development of a system capable of identifying semantic patterns in image content and hidden data, overcoming the limitations of traditional unimodal approaches. The results obtained in experimental settings indicate that integrating both branches can significantly improve detection capacity, especially when the embedded messages have textual coherence.  
The CNN-NLP model developed in this work achieves efficient metrics and redefines the traditional approach to digital steganalysis by integrating a semantic validation layer for the hidden message. Unlike the architectures evaluated, which focus solely on binary detection, the CNN-NLP enables the verification of whether the extracted content is linguistically coherent, thus elevating the analysis from a technical to an interpretive perspective.  
Likewise, the inclusion of the NLP module allows the filtering of statistical false positives, providing a second line of verification based on natural language processing. The progressive analysis with different dataset sizes showed that the proposed architecture maintains stability, generalization capacity, and a low error rate, even in scenarios with visual noise or highly complex messages, making it a robust, replicable, and scalable alternative for detecting steganography in digital images.  
Significant challenges were identified, such as the need for more realistic data, the computational complexity of the model, and limitations in accurately extracting hidden text. These aspects unlock new opportunities for future research aimed at improving the system's robustness, efficiency, and applicability.  
Conceptualization, K.A., D.G., A.Y., and H.E.; methodology, K.A., D.G., A.Y., and H.E.; project administration, A.Y.; supervision, A.Y.; validation, K.A. and D.G.; writing--original draft, K.A., D.G., A.Y., and H.E.; writing--review and editing, K.A., D.G., A.Y., and H.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
Creating noninvasive deep learning tools to help clinicians monitor SMS voice symptoms could greatly improve patient outcomes. The objective of this study is to develop a convolutional neural network (CNN)-based classification model capable of accurately identifying individuals with SMS from voice samples. By applying data augmentation techniques, the authors aim to enhance the performance of these models and contribute to the early diagnosis of this disease. Consequently, it is hypothesized that the extraction of discriminative acoustic features such as MFCC and CPP, combined with data augmentation techniques, will enable CNNs to identify underlying patterns in voice signals that differentiate individuals with SMS from healthy controls. In addition, existing voice databases identified in the literature lack data from Spanish-speaking SMS patients. This study aims to fill this gap by using a database that encompasses 20% of SMS patients in Spain. Furthermore, to address the challenge of imbalanced datasets, this research work incorporates sixteen distinct variations of the SMOTE. Each SMOTE variation is employed to generate synthetic data points for the minority class, effectively balancing the class distribution within the dataset. The paper is organized as follows: the state of the art is presented in the following section. Section 3 then describes the corpus and the synthetic data augmentation techniques, the convolutional neural network architecture and a subsection is devoted to present the methodology. Subsequently, Section 4 points out the results and the Section 5 provides the discussion. The conclusions are presented in Section 6.
## 2 Materials and Methods
### Method of Collecting Real-World Data to Determine Model Parameters  
Operational data were collected from a transportation company that owns 34 trucks adapted for the transportation of oversized cargo. The selected vehicles were a homogeneous group moving in a closed area (trucks without trailers or semi-trailers). The gross vehicle weight (GVW) of the trucks was 50,000 kg, 10 \(\times\) 6 (11 units) and 10 \(\times\) 4 (24 units) configuration. All trucks had 4 torsion axles and disc brake systems on non-driving axles. A drum brake system was installed on the drive axles. All trucks had the same tire size installed.  
The tests were conducted from February 2020 to November 2021 and began after the brake pads were replaced on a particular truck. The brake pads used were from the same manufacturer. Completion of the study was defined as the thickness of the brake pads outside the serviceable range designated by the manufacturer (new 30 mm thick, worn < 8 mm thick). During the study, 5 trucks were eliminated from the analysis (3 trucks had mechanical or thermal damage to the brake pads, and 2 had driver changes).  
The source data were read via diagnostic equipment from Knorr-Bremse (Munchen, Germany), Wabco (Friedrichshafen, Germany) and specialized CL2000 CAN 2.0A software from CSS Electronics (Aabyhoej, Denmark). Air brake system pressure signals and vehicle  
Figure 1: Procedure for determining the statistical model.  
load were recorded via the CAN-BUS (J1939 protocol, OBD2). The recording of variable data was grouped in the recorder at 1000 km intervals. During brake pad replacement, the number of kilometers was read and rounded up to 100 km. The collected source data were contained in two matrices A = [129 \(\times\) 88] and B = [145 \(\times\) 88], where A--brake system pressure data matrix and B--vehicle load range data matrix. The collected source data were clustered according to the established algorithm (Table 1).  
Due to the large number of data, the source material was converted to statistically significant information [29; 30]. In this study, the value of the coefficient of variation related to a range of 1000 km was used for each explanatory variable. The critical value of the coefficient of variation (Var-co.) was set at \(<\)10% [31]. The explanatory variable Y determines the brake pad wear period in kilometers for individual trucks T\({}_{i}\) = {T\({}_{1}\), T\({}_{2}\),..., T\({}_{29}\)}. The explanatory variables X\({}_{i}\) = {X\({}_{1}\),..., X\({}_{4}\)} represent the number of brake pedals used within a certain range of brake system operating pressures per 1000 km. The explanatory variables X\({}_{i}\) = {X\({}_{5}\),..., X\({}_{9}\)} represent the number of individual truck load ranges per 1000 km with respect to GVW.
# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 4 Methodology
### Model Training
#### 4.2.4 Extreme Gradient Boosting  
Extreme Gradient Boosting (XGBoost) is a classification and regression algorithm based on the Gradient Boosting Decision Tree (GBDT), a supervised learning method based on function approximation by optimizing specific loss functions and applying several regularization techniques. [34] Figure 17 is the flowchart outlining the use of the XGBoost algorithm in this paper.  
Figure 17: Flowchart of XGBoost algorithm.  
Suppose the training dataset D and the sample and target label in it are expressed as  
\[D=\{(x_{1},y_{1}),(x_{2},y_{2}),(x_{3},y_{3}),(x_{4},y_{4}),\ldots,(x_{n},y_{n})\} \tag{11}\]  
The prediction function of the \(i^{th}\) sample is stated as [34]  
\[\hat{y}_{i}=\sum_{n=1}^{N}f_{n}(x_{n}),f_{n}\in F \tag{12}\]  
where \(f_{n}(x_{n})\) is the discriminant function of the \(n^{th}\) tree to the \(i^{th}\) data and \(F\) is the vigorous model of integration of the \(n\) Decision Tree model [34].  
As shown in Figure 17, the initial tree predicts a value then takes the deviation of the value from the true value and adds a tree to learn the deviation.  
\[\hat{y}_{i}^{0}=0,\ y_{i}^{\uparrow}=f_{1}(x_{i})+\hat{y}_{i}^{0},\ y_{i}^{ \uparrow}=f_{2}(x_{i})+\hat{y}_{i}^{0}+\hat{y}_{i}^{\uparrow} \tag{13}\]  
When the approach applies to all trees,  
\[\hat{y}_{i}^{\uparrow}=\sum_{n=1}^{N}f_{n}(x_{n})=\hat{y}_{i}^{(t-1)}+\alpha f _{t}(x_{i}),0<\alpha<1 \tag{14}\]  
where \(\alpha\) is the learning rate [34].  
We minimize the following regular objective to find out the set of functions used in the model [35].  
\[L^{(t)}=\sum_{i=1}^{N}l\Big{(}\hat{y}_{i}\hat{y}_{i}^{(t-1)}+f_{t}(x_{i})+ \alpha f_{t}\Big{)} \tag{15}\]  
The 9 th equation is the optimal loss function for a fixed tree structure. There will be several hundreds of possible trees. Instead of exploring all possible tree structures, XGBoost greedily creates a tree. The division that results in the maximum loss reduction is chosen.