esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Impact of Network Latency on Teleoperation
### End-to-End Delay
Similarly, the green region is the downlink, the control data from the operator station to the vehicle actuators. * For digital visual interface (DVI) and liquid-crystal display (LCD), \(\Delta V_{d}\) involves a refresh time of 10 ms, a display time of 16.7 ms at 60 fps, and a frame buffer of 17 ms.
* For joystic control command transmission, \(\Delta T_{d,2}\) = 32 ms.
* For sensors and actuators, \(\Delta S_{d}+\Delta A_{d}\) is roughly 50 ms.
* Network jitter is approximately below 150 ms.
* For LTE, the average handover latency is 40 ms.  
Human operators have demonstrated the ability to perceive delays as low as 10 to 20 ms [28]. A latency of around 300 ms has a noticeable impact on the operator behaviour. In contrast, latencies of 1 s or more can drastically affect real-time operations. Such delays often cause operators to adjust strategies, transitioning from open-loop to closed-loop control methods, including start-and-stop or move-and-wait, sometimes even disregarding the visual feedback [18; 19]. The cognitive threshold for maintaining real-time performance is typically within 0.4 s. Upon receiving feedback from the vehicle, operators must respond within 0 to 3 s, with a minimum response time of 0.2 s [39; 40].
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Single-Layer Satellite Networks
#### 4.1.3 Virtual Topology
The algorithm can achieve more efficient and secure routing in highly dynamic environments than other routing algorithms. Future studies can replace two Q-tables with two neural networksdue to the increase in S-IoT nodes. However, double-Q learning has been rarely used in satellite routings. In order to improve the accuracy of routing optimization, more in-depth research on this type of learning algorithm is needed in academia. Comparisons of the key performance of partial dynamic routings in single-layer satellite networks are shown in Table 7.
## 2 Research Thesis and Structure  
In recent years, a considerable amount of research is centered on Fault-Tolerant Control (FTC) systems, which can be envisioned to maintain control objectives even if a fault occurs. The application of such system is frequently hindered by a lack of reliable sensors and the high cost of sensors [5; 6; 7]. Therefore, virtual sensors are applied, which make use of mathematical models of the process and other available measurements to estimate the unmeasured variables [8; 9]. Many approaches have been reported to design virtual diagnostic sensors: for instance, observer-based [10; 11], Kalman filter-based [12; 13] and parameter identification-based [14] approaches.  
At the center of the research is an innovative approach to virtual sensor design of longitudinal forces as well as torques acting onto an AGV using a Quadratic Boundedness (QB) approach [15]. This approach allows for including bounded disturbances acting on the AGV and avoids unnecessary state estimation because the state is fully available via measurements. One main advantage of the innovative approach is that the application of sophisticated tire models is not necessary. Frequently, the application of these kinds of models limits the performance of the approaches presented in the literature [16; 17]. Another main advantage of the proposed approach is that the design procedure is expressed in the form of Linear Matrix inequalities (LMIs), which can be solved using widely available computational packages.  
Consequently, the central research thesis can be stated as follows: _is it possible to design reliable virtual sensors for longitudinal forces and torques of automated guided vehicles based on a quadratic boundedness approach?_  
An innovative design of an AGV, which allows unlimited maneuvering capabilities but requires reliable sensor information is used both for the sake of explanation and validation of the approach.  
It should also be pointed out that such kind of problems can be potentially tackled with the so-called direct virtual sensors, which instead of exploiting the identified system dynamics are based strictly on the existing data [18; 19; 20; 21; 22]. This is, however, beyond the scope of the developments presented in this paper, which are strictly based on an analytical model of the AGV.  
The paper is organized as follows. Section 3 introduces essential preliminaries, which are necessary to undertake the problem being investigated. Section 4 describes the AGV, which will be used in this work for the sake of explanation and validation. Section 5 presents the mathematical foundation. Section 6 proposes a strategy for the design of the virtual diagnostic sensor. Section 7 presents the results of the application of the proposed strategy to the AGV. Section 8 shows experimental results, which clearly exhibit the performance of the proposed approach. Finally, the last section concludes the paper.
# Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF
## 2 Materials and Methods
### Feature Extraction Based on Sparse Envelope Spectrum
By properly increasing the value of \(r\), the similar frequency distribution in the decomposition results can be avoided;  
**Step 3:** Removing spectral basis with the low frequency region in \(\mathbf{W}\);  
**Step 4:** Computing the filtered spectrum according to Equation (9) by the remaining spectral basis;  
**Step 5:** Calculating the filtered signal and its envelope spectrum \(H_{\text{W}}(f)\);  
**Step 6:** Obtaining the envelop spectrum \(H_{H}(f)\) of each weight vector's corresponding frequency basis;  
**Step 7:** Getting the sparse envelope spectra with Equation (12);  
**Step 8:** Selecting the sparse spectrum with the maximum amplitude at the fault frequency in \(H(f)\) as the final result.
## 2 Methods
### Reverberation Time  
The measurements of reverberation time (RT) were done in a non-occupation mode in the early morning before the opening of the mall on Friday (the weekend), at the same locations used for the measurements of SPLs. No sound sources were present other than the watchers and the permanent sounds of some mechanical devices. The measurements were done while the microphone was held at a height of about 1.35 m from the ground and at least 1.5 m from the surrounding surfaces. The RT measurements are based on impulse excitation and measured in 1/1-octave frequency bands (500, 1000, and 2000 Hz). The decay is measured over the first 20 or 30 dB, after ignoring the first 5 dB of decay and avoiding measurements lower than 10 dB above the background noise, according to the ISO 3382-1 [16]. The measurements are extrapolated to the full 60 dB. The measurements of RT were repeated three times at each location, and the arithmetic mean was calculated.  
The recommended RT values differ in the literature from one source to another. For public areas in shopping centers and malls, the reverberation time is linked to the fire code. The NFPA 72 requires the Public Address and Voice Alarm (PA/VA) system to be intelligible and requests a speech intelligibility index (STI) of 0.5 or better for 90% of the space [17]. The STI is heavily influenced by the reverberation time. For very large spaces, such as shopping centers and malls, as well as airports, the aim is to achieve an RT of 1.1-1.32 s (averaged at frequencies of 500, 1000, and 2000 Hz). However, this can be very challenging due to the large volume of these spaces.
## 3 Results  
### Literature Search Results  
The literature search conducted across the PubMed, Embase, and Web of Science databases yielded a total of 4785 relevant articles. Specifically, 1864 were sourced from PubMed, 1209 were sourced from Embase, and 1730 were sourced from Web of Science. After the elimination of duplicate studies, the titles and abstracts were screened, resulting in 102 articles being preliminarily selected. A full-text screening further narrowed the field, excluding studies that did not meet the inclusion criteria. Six of these articles' entire texts could not be obtained, 12 lacked the requisite data for extraction, and 58 did not align with the research's subject. Ultimately, 26 articles were deemed suitable for inclusion in the meta-analysis (see Supplementary File S2 for details).
# Forensic Analysis of Blackhole Attack in Wireless Sensor Networks/Internet of Things
## 1 Introduction
AODV is one of the four prime routing protocols that are standardized by the Internet Engineering Task Force (IETF) MANET working group. AODV serves as the foundation for devising new routing protocols in the wireless networks domain [11; 12; 13]. We analyze the performance of AODV in mobile WSN and IoT under the BH attack. Further, a detailed forensic analysis of the basis of diverse parameters and aspects is performed. The main contributions of this research work are given below. * The BH attack in WSNs and IoT with the AODV routing protocol is investigated in a rigorous and generic manner. * A vulnerability analysis of the AODV protocol is performed, and a forensic analysis for static as well as mobile WSN and IoT for ad hoc and traditional modes under the BH attack is carried out. The traffic patterns in the network and on the nodes are examined with the assistance of various analytical tools to assess the attack damage. * An analogy between the DF investigation of a real-life WSN/IoT and a simulated environment is devised at the device, network, and cloud levels to correlate the procedures and tools on both sides. * The networks under mobile and static mode with variation in the number of total nodes and BH nodes are reconstructed to verify the observations of forensic analysis. Further, a damage analysis on the basis of strategical positioning and cooperation of the BH nodes is conducted. * Suggestions powered by this study are provided to design routing protocols that will be amenable to DF investigations. This paper is structured as follows: Section 2 describes the related work. The proposed methodology for forensic analysis, artifact collection framework, and an analogy between simulated and real-time DF investigations are presented in Section 3 followed by Section 4, which discusses the simulation environment and results. Finally, Section 5 concludes this work.
# Sample, Scalable Route to Produce Transparent Superhydrophobic/Hydrophilic Film Surfaces
## 3 Results and Discussion
### The Effects of the f-SiO\({}_{2}\) Concentration on WCA and Transmittance  
Samples of different concentrations of f-SiO\({}_{2}\) were prepared. Samples were designated according to their concentrations. The main properties, as well as the optical and surface properties, were analyzed using the transmittance and the water contact angle (WCA) of the samples. The WCA shown is the average angle for the three different spots on the sample surface. Figure 6 displays the average transmittance of the coating against the content of f-SiO\({}_{2}\). Figure 6 illustrates that as the content of f-SiO\({}_{2}\) in the coating increases from none to 100%, the corresponding WCA increases from hydrophilic of WCA = 30\({}^{\circ}\) to superhydrophobic of WCA = 158\({}^{\circ}\). Furthermore, the average transmittance decreases from 80.4% at 0% f-SiO\({}_{2}\) to 50% at 100% f-SiO\({}_{2}\) content. It is known that the surface wettability is influenced by various physical and chemical factors. The impact of two key variables--surface roughness and surface chemistry--on contact angle measurements has been widely reported [36]. Here, in the current study, the Si content is increasing, so the surface chemistry is changing. As previously reported in the XPS section, increasing the Si content will result in more hydrophobic behavior. However, this wettability change is accompanied by a change in the surface transmittance caused by the white color of f-SiO\({}_{2}\). Therefore, for self-cleaning the transparent panel, the optimal content of the nanoparticle would be 70%, because this content would result in the optimal balance between the optical properties and the surface properties, enabling acceptable transmittance and high WCA.
## 3 Results
### Prediction of SOC Using Different Predictive Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Normalized Coefficient**} & \multicolumn{1}{c}{**t**} & \multicolumn{1}{c}{**Significance**} \\ \cline{2-5}  & **Beta** & **Standard Deviation** & **Beta** & & \\ \hline (constant) & 7.42 & 1.44 & – & 5.15 & 0.00 \\ NDVIFeb. & \(-\)7.17 & 2.69 & \(-\)0.17 & \(-\)2.67 & 0.01 \\ NDVIJul. & 8.09 & 2.86 & 0.20 & 2.84 & 0.01 \\ NDVIJsept. & \(-\)11.22 & 1.85 & \(-\)0.33 & \(-\)6.07 & 0.00 \\ NDVIJan. & \(-\)5.12 & 2.50 & \(-\)0.14 & \(-\)2.05 & 0.04 \\ NDVIAug. & 5.20 & 1.88 & 0.19 & 2.77 & 0.01 \\ NDVIJun. & 5.30 & 1.81 & 0.15 & 2.93 & 0.00 \\ NDVIApr. & \(-\)4.78 & 2.28 & \(-\)0.12 & \(-\)2.10 & 0.04 \\ \hline \hline \end{tabular} Notes: NDVIFeb is the NDVI (normalized different vegetation index) values in February, and NDVIJul is the NDVI values in July, and so on.  
\end{table} 
Table 3: Stepwise multivariate regression model analysis. Figure 9: Experimental variogram and fitted models of SOC (soil organic carbon) through OK (ordinary kriging) interpolation. included 96.66% of the original NDVI information. Similarly, the cumulative LV of SOC was 67.87%, showing that this model explained 67.87% of the SOC information. This result indicated that the PLSR model can obtain substantial useful information for predicting SOC without redundant data. In the SVM model, \"epsilon (\(\varepsilon\))-SVR\" was used considering the runtime and efficiency in this study. In this model, the parameter cost was 3.16, and epsilon was 0.10, thereby presenting close fitting to the calibration data. Gamma was 0.032, which controlled the shape of the separating hyperplane. An increasing gamma usually increases the number of support vectors. The accuracy of the SVM model resulting from cross-validation was expressed by an RMSECV of 4.69 and a determination coefficient of cross-validation (R\({}^{2}\)cv) of 0.33. Thus, the SVM method for regression was used to simulate the SOC content and demonstrated a strong capacity compared with the abovementioned methods. In the ANN method, the optimum number of nodes (i.e., four) in the hidden layer should be determined after comprehensive consideration of RMSE acquired by calibration and cross-validation to create the SOC prediction model based on cross-validation (Table 5). Evidently, this ANN model for regression can be effectively applied to predict soil properties with the dimensionality reduction of data and remove the data redundancy.
## 2 Related Work  
### Visual Emotion Classification  
Facial expressions are one of the important bases for human emotional expression. The feature extraction methods for facial expressions mainly include traditional methods and deep learning methods.  
Traditional methods for facial expression recognition rely on low-level or artificially designed features, necessitating significant human involvement. These methods commonly include global feature extraction methods [18; 19], which obtain overall information regarding facial images; local feature extractions [20], which better capture local details; mixed feature extraction methods [21; 22], which combine the benefits of global and local features; and optical-flow-based methods [23], which capture the dynamics and changes in facial images. While these traditional, handcrafted methods for feature extraction have achieved noticeable success, they may not be applicable in complex scenes with large amounts of data.  
Deep learning networks, such as the convolutional neural network (CNN) [24; 25] and recurrent neural network (RNN) [26], have demonstrated strong performance in the recognition of facial expressions, leading researchers to gradually shift away from traditional methods and toward deep learning approaches [27; 28; 29].  
[PERSON] et al. [24] noted the effectiveness of CNNs for image recognition tasks as these networks are able to capture spatial features due to their large number of filters. They proposed a model comprising six convolutional layers, two max pooling layers, and two fully connected layers, which outperformed decision trees and feed-forward neural networks on the FER-2013 dataset. [PERSON] et al. [25] proposed a model comprising a face detection module based on the ensemble of three face detectors and a classification module that utilized an ensemble of multiple deep CNNs. To combine the multiple CNN models, they presented two schemes for learning the ensemble weights of the network responses: by minimizing the log likelihood loss and by minimizing the hinge loss. Their model achieved excellent results on both the FER and SFEW datasets.  
For modeling the spatiotemporal evolution of visual information, [PERSON] [26] presented the application of an RNN for modeling this spatiotemporal evolution via the aggregation of facial features to perform emotion recognition in video. [PERSON] [30] trained a CNN to extract both geometric and appearance features, and a long short-term memory (LSTM) to capture temporal and contextual information regarding facial expressions. The CNN-LSTM architecture enables a more comprehensive representation of facial expressions as it combines both spatial and temporal information.  
To more effectively utilize information regarding the key parts that convey emotion, [PERSON] et al. [31] proposed a facial expression recognition method incorporating an attention mechanism based on a CNN and LSTM. This model mined information regarding important regions more effectively than general CNN-LSTM-based models.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 2 Methodology
### Proportional-Integral-Differentiation (PID) Controller  
The PID controller is a widely used feedback mechanism in engineering fields such as industrial automation, robotics, and aerospace due to its rapid response and precise system control [49]. Current servo support systems for braced excavation mainly control deformation by setting and maintaining support axial forces within a predefined range. By introducing a PID controller, the system uses target deformation values as setpoints, continuously monitors deformation, and automatically adjusts the axial forces based on deviations to control the deformation closer to the target. This method controls deformation more directly, enhancing control precision and response speed. Therefore, the PID controller  
Figure 2: GRU unit.  
is considered as the control method for wall deflection in this study. Figure 4 shows the process to control wall deformations using a PID controller. First, the difference between the target deformation (setpoint, _SP_) and the actual monitored wall deformation (process variable, _PV(t)_) is defined as the error \(e(t)\):  
\[e(t)\ =\ SP\ -\ PV(t) \tag{10}\]  
where _SP_ is the target deformation, and _PV(t)_ is the actual deformation at time \(t\). In this system, the PID controller manages the bracing force (axial force) to control the wall deflections. Proportional (P) adjusts the axial force in proportion to the current error, which is the difference between the setpoint and the actual output. The larger the error, the greater the axial force. The integral component (I) accounts for the cumulative sum of errors over time, facilitating the elimination of long-term deformation and ensuring that the system output eventually reaches the target deformation. Meanwhile, the differentiation component (D) calculates the rate of error change by measuring the difference between the current error and the previous error and then dividing by the time interval. This rate indicates how quickly the system is deviating from its target. D can intervene before the error increases further. For instance, if a sudden wall movement causes a rapid increase in error, D predicts this trend and quickly adjusts the control signal to prevent further error increase. The \(P\), \(I\), and \(D\) are described in Equations (11)-(13).  
\[P(t)=K_{p}\cdot e(t) \tag{11}\]  
\[I(t)=K_{i}\!\int e(t)dt \tag{12}\]  
\[D(t)=K_{d}\frac{de(t)}{dt} \tag{13}\]  
By combining components _P(t)_, _I(t)_, and _D(t)_, the final output _u(t)_ can be written as Equation (14), which is used to control the axial force.  
\[u(t)=K_{p}\cdot e(t)+K_{i}\!\int e(t)dt+K_{d}\frac{d}{dt}e(t) \tag{14}\]  
Figure 3: Flowchart of the ST-GAT model.
## 7 Conclusions and Further Work  
This paper has considered an adaptive/predictive compensation scheme to help ameliorate the effects of packet losses in industrial DCSs employing wireless control loops. Experimental results have been reported that illustrate that for a small increase in packet processing and controller overheads, the method has the potential to improve upon contemporary schemes designed for use with industrial PID and PID-like controllers, even in cases when no specific knowledge of the process parameters are available. The proposed methods give robustness to packet losses around the control loop and exhibits bounded and relatively low computational complexity; as the majority of the overhead resides in the central controller node, they seem well suited for industrial DCS systems. Future work will concentrate upon further testing of the scheme using both HIL simulation and practical field testing.
## 3 Measurements
### Measurements with the Photonic/Wireless Compound System
This figure supports the system's capability to detect individualized gait signatures, offering potential for personalized gait profiling and anomaly detection in clinical and rehabilitation settings. Figure 9 presents the FBG wavelength shift signals recorded from seven different subjects during treadmill walking at 4 km/h. Each curve represents the strain profile over time for a full gait cycle, capturing the alternating phases of knee flexion and extension. The periodic nature of the waveforms reflects the cyclic biomechanics of walking, while inter-subject variations in amplitude and waveform shape highlight the influence of individual gait characteristics and anatomical differences. The figure demonstrates the system's sensitivity, repeatability, and ability to capture distinct gait signatures across multiple users, confirming its applicability for personalized gait monitoring in clinical and rehabilitation environments. Although amplitude differences are visible, the temporal alignment between FBG and IMU signals confirms their strong correspondence in gait cycle phase detection. The estimated angular deviation between the two remains within \(\pm 1^{\circ}\), supporting their complementary use in clinical applications.
## 5 Data Imbalance
### Limitations of Data Imbalance Handling Techniques  
#### 5.4.1 Overfitting and Noise Issues  
The oversampling method, similar to SMOTE, improves the dataset by increasing the number of minority class samples, but it often introduces noise, especially when the quality of the original dataset is poor [109]. These noisy samples can cause the model to capture these artificial features during training, leading to overfitting [110]. Overfitted models generally perform poorly when exposed to new data, making them less effective in real-world applications [111].