esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress in Pressure Sensors for Wearable Electronics: From Design to Applications
## 2 Pressure Transduction Mechanism and Design Principles
### Other Types  
Other transduction mechanisms have also been utilized to sense external pressure. The transistor-type pressure sensor is a good candidate for a highly-sensitive pressure sensor. Transistors are mainly studied for organic field-effect transistors, and their sensitivity is defined by the deformability of the gate dielectric layer under applied pressure, similar to capacitive-type pressure sensors [95; 96; 97]. Similarly, various methods have been introduced to improve the deformability of the dielectric layer. [PERSON] et al. fabricated a pyramidal PDMS dielectric layer with a photolithography-based silicon mold, and the sensor showed an extremely high sensitivity of 514 kPa\({}^{-1}\)[98]. In addition, materials with excellent deformability, such as PDMS and Ecoflex, have been widely used [99; 100].  
The triboelectric sensor is another type of promising self-powered pressure sensor. Triboelectric pressure sensors are based on triboelectric nanogenators (TENGs), whose mechanism is based on the coupling effect of triboelectrification and electrostatic induction. For TENGs, the contact/separation mode is mainly used, with two dielectric layers. To optimize the triboelectric effect, dielectric materials located at the opposite ends of the triboelectric series are demanded. In addition, a larger surface area of the dielectric layer is preferred to induce more charges on their surface. Silicon holds with pyramidal structures are widely used to increase the surface area of the dielectric layers [101; 102; 103; 104]. However, a silicon mold is usually produced with a complex and time-consuming process. As a method of using a low-cost mold rather than a silicon mold, [PERSON] et al. introduced a sandpaper mold to modify the surface of the layer, resulting in a sensitivity of 0.51 V/kPa [105]. [PERSON] et al. used a C. _zephrine_ leaf as a mold, and the sensitivity of the pressure sensor was boosted with a 14-fold increase compared with a flat surface [106]. Electrospun NFs are also frequently employed due to their fast and simple fabrication process, as well as the high surface area of the NFs [107; 108; 109; 110].
## 4 Development of Machine Learning (ML) Models
### ML Models Description  
#### 4.2.1 Random Forest (RF)  
According to [PERSON] and [PERSON] (2018), random forest (RF) has been applied successfully in a wide variety of projects and has seen tremendous acceptance in many disciplines, thus, its inclusion in this study. RF also has the capability of ranking the importance of all the input variables contributing to the prediction of the target variable.  
The predictive abilities of multiple decision trees are harnessed by Random Forest, an ensemble method. To practice each decision tree, bootstrapped samples are used and the predictive capabilities of all the trained trees are aggregated to form the final model. A number of predictors, _mtry_, was randomly chosen in constructing the trees to be considered at each node during the recursive binary splitting instead of using all the predictors ([PERSON] and [PERSON], 1993). This gives the technique its name, random forest. At each node, a different set of predictors are used for node splitting; therefore, the variance of the resulting model is significantly reduced compared to the individual regression tree. In training the decision trees, each split is done to obtain two regions \(R_{1}\) and \(R_{2}\) as in Equation (2).  
\[R_{1}(j,\ s)\ =\ \big{\{}X\big{|}X_{f}\ <\ s\big{\}}\ \textit{and}\ R_{2}(j,\ s)\ =\ \big{\{}X\big{|}X_{f}\ \geq\ s\big{\}} \tag{2}\]  
where \(j\) is the index in the predictor space with an upper limit of _mtry_ and \(s\) is the cut point for the split.  
The objective is to obtain \(j\) and \(s\) values that minimize the function (Equation (3)).  
\[\sum_{i:x_{i}\in R_{1}(j,s)}\left(y_{i}-\hat{y}_{R_{1}}\right)^{2}+\sum_{i:x_{i} \in R_{2}(j,s)}\left(y_{i}-\hat{y}_{R_{2}}\right)^{2} \tag{3}\]  
with \(\hat{y}_{R_{1}}\) is the mean response for the training observations in \(R_{1}(j,s)\); and \(\hat{y}_{R_{2}}\) is the mean response for the training observations in \(R_{2}(j,s)\).  
This process is repeated until there is no decrease in residual sum of squares by further splitting, at which point the terminal node is reached. The number of predictors to be considered in the splitting at the nodes, \(mtry\), is a hyperparameter that has been calibrated using 5-fold cross-validation (CV) to achieve an optimum value for the best prediction output in training the random forest model [27]. The optimal \(mtry\) was then used to fit the final model.
## 1 Introduction
These groups are further subdivided, as indicated in Figure 2. Under the evolutionary techniques, Genetic Algorithm (GA) and Differential Evolution (DE) are placed. A GA is an evolutionary algorithm that generates solutions to optimization and search problems. To achieve the best results, it employs techniques that are influenced by natural selection. Selection, cross-over, and mutation are examples of such techniques. Researchers have also utilized hybridized GA with different bio-inspired algorithms. Differential Evolution-Genetic Algorithm (DE-GA) is more accurate and requires less time to complete. The technique works well in terms of accuracy and time complexity due to the rise in the population vector size. By carefully selecting the design parameters and employing superior hybrid methods, efficiency and forecast accuracy might be improved. Under the Swarm-based techniques, four different and unique techniques are listed. In terms of network lifespan and packet delivery ratio, the Genetic Algorithm-Particle Swarm Optimization (GA-PSO) method is found superior. When compared to the shortest path, PSO, and GA approaches, hybrid GA-PSO increased the lifetime from 12 percent to 23 percent, from 8 percent to 15 percent, from 5 percent to 13 percent, and packet delivery ratio from 9 percent to 16 percent, from 6 percent to 11 percent, and from 5 percent to 9 percent for large scale networks. Particle Swarm Optimization-Ant Colony Optimization (PSO-ACO) hybrid optimization technique evaluates the shortest path for data transfer from the cluster head to the base station. This proposed technique also evaluates more average remaining energy, a greater number of alive nodes, and better throughput when simulated by taking 100 sensor nodes. The particle Swarm Optimization-Grey Wolf Optimization (PSO-GWO) approach has enhanced the exploration ability by preventing PSO from falling into local minima. This hybrid technique also improved the performance of the network as compared to ABC, PSO, and GWO meta-heuristic methods [1; 2; 3; 4; 5; 6; 7]. Similarly, different bio-inspired algorithms as shown in Figure 2, are hybridized with each other based on their contribution and limitations and have been discussed in the subsequent sections.
## 3 Materials and Methods  
### Research Object  
The experimental site was situated on the Jiefangbei Underground Ring Road in Yuzhong District, Chongqing, China. The large number of curves on the ring road provided a rich research object for this study. Therefore, fourteen curves with small radii and poor alignment on the underground ring road were studied (Figure 1). The technical indicators of the 14 curves are listed in Table 1. Meanwhile, the Jiefangbei Underground Ring Road has a design speed of 30 km/h, with a lane width of 3.5 m and a shoulder width of 0.5 m. The loop is a two-lane road, and the tunnel end walls feature a vertical design. The specific real structural characteristics are shown in Figure 2. The entire underground road is not equipped with any guardrail facilities. Additionally, the road and curve in the study exhibit directional characteristics, with both sharing the same operational direction. The specific direction is indicated by the arrow in Figure 1.  
According to the Urban Road Engineering Design Specification CJJ37-2012 [9], the minimum radius for setting superelevation at a design speed of 30 km/h is generally 85 m, and the minimum radius without setting superelevation is 150 m. Therefore, for urban underground roads, we consider curves with R \(\leq\) 85 m as extreme small radius curves,  
Figure 1: Curve schematic diagram.  
Figure 2: Realistic view of tunnel structural features.  
curves with 85 m \(<\) R \(\leq\) 150 m as general small radius curves, and curves with R \(>\) 150 m as ordinary radius curves.
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 2 Materials and Methods
### Localization of 3D Objects
Calculating each transformation yields  
\[P^{\prime}_{e}=R\cdot P+R\cdot T_{e} \tag{14}\]  
\[P^{\prime}=R\cdot P \tag{15}\]  
Then, we can calculate \(T\) using the difference between \(P^{\prime}_{e}\) and \(P^{\prime}\) as follows:  
\[P^{\prime}_{e}-P^{\prime}=R\cdot T_{e} \tag{16}\]  
Assuming that \(R\) is invertible (i.e., an inverse exists), to directly calculate \(T\), we use \[R^{-1}\cdot(P^{\prime}_{e}-P^{\prime})=T_{e} \tag{17}\]  
This calculation is valid under the assumption that the inverse of \(R\) exists and can be computed. To perform the actual calculation, specific values for \(R\), \(P^{\prime}_{he}\), and \(P^{\prime}_{h}\) are needed. Therefore, \(T\) can be calculated as follows:  
\[T_{e}=R^{-1}\cdot(P^{\prime}_{e}-P^{\prime}) \tag{18}\]  
This depends on the actual values of \(R\), \(P^{\prime}_{e}\), and \(P^{\prime}\), Thus, we address the challenge of accurately determining the camera positioning in 3D space by considering potential GPS errors and their impact on 3D point projections in 2D imaging. In our computational analysis, we sought to refine the estimation of the translation vector \(T\), which represents the displacement from erroneous measurements to accurate world coordinates. Given a series of individual translation vectors \(T_{i}\), derived from multiple observations or point pairs, the most prevalent method for determining an optimal translation vector is to compute the mean of these vectors. Each translation vector \(T_{i}\) is computed as the difference between the corresponding erroneous camera point \(P^{\prime}_{e}\) and the reference world point \(P^{\prime}\). Then, the aggregate translation vector \(T_{\text{mean}}\) is calculated as the average of all individual \(T_{i}\) vectors as follows:  
\[T_{\text{mean}}=\frac{1}{n}\sum_{i=1}^{n}T_{i} \tag{19}\]  
where \(n\) is the total number of observations. This averaging process effectively reduces the random errors present in individual observations under the assumption that these errors are unbiased and normally distributed. The calculation is performed by summing all individual translation vectors and then dividing by the number of vectors to yield the mean translation vector \(T_{\text{mean}}\). From a least-squares error perspective, this approach is equivalent to minimizing the sum of the squared differences between \(T_{\text{mean}}\) and each \(T_{i}\), thereby providing a robust estimate of the actual translation required to correct the measurement discrepancies.
## 5 Experimental Results
### Discussion  
To properly interpret the results, it is crucial to understand the implications of false positives and false negatives within the context of crime detection. A false positive occurs when the model incorrectly predicts the presence of a criminal incident in the video when, in fact, no crime is occurring. This type of error results in false alarms, which can lead to the unnecessary mobilization of resources and distract authorities, thereby negatively affecting operational efficiency. Conversely, a false negative occurs when the model fails to detect a crime that is taking place. This type of error is particularly critical as it represents a failure to identify real crimes, thereby jeopardizing public safety. The inability to detect a crime in a timely manner may prevent authorities from intervening promptly, potentially leading to serious consequences, including harm to individuals. Therefore, minimizing false negatives is essential for ensuring the overall effectiveness of a surveillance system.  
Table 7 demonstrates that the CrimeDetection dataset yields the most favorable results in terms of both false positive and false negative rates, making it the most efficient and promising dataset for effective crime detection in this context. In comparison, the RWF2000 dataset exhibits a higher false negative rate (21.0%), indicating that the model is more likely to miss actual criminal incidents. Similarly, the RLVS dataset shows a higher false positive rate (22.5%), resulting in an increased occurrence of false alarms compared to the CrimeDetection dataset.
# Research on Lifespan Prediction of Cross-Linked Polyethylene Material for XLPE Cables
## 4 Conclusions  
In this paper, the changing law of lifespan prediction parameters, corresponding with the operating time of XLPE cables, was investigated by widely accepted methods. On this basis, some parameters that significantly correlated with the operating time were obtained. Finally, a method based on a BP neural network for determining the operating time of XLPE cables was proposed. The main conclusions are as follows:  
1. With the increase in operating time, some macro and micro changes in the cross-linked polyethylene material are noticed. Under a high temperature and strong electrical fields, chemical bonds break gradually, which leads to an increase in the content of micromolecules along with polar impurities and defects. In this case, the gel content and crystallinity of samples decrease with the increase in operating time, which, in turn, leads to a decrease in the lifespan prediction parameters, including the tensile strength, the breaking elongation, the dielectric permittivity, the dielectric loss and the breakdown strength, along with the hardness of samples.
2. Through the correlation calculation between lifespan parameters and operating time, it turns out that the parameters, including the gel content \(C_{gg}\), the crystallinity \(X_{C}\), tensile strength \(\sigma\), ultimate elongation \(\delta\), the dielectric permittivity \(\varepsilon\) and the dielectric loss \(J_{tan}\), are significantly correlated with the operating time, which can be used to evaluate the aging degree of XLPE cables and propose a lifespan prediction method to determine the operating time of XLPE cables. Moreover, the lifespan prediction parameters, including the hardness \(A\) and the average breakdown strength \(E_{b}\), are correlated with the operating time. On this basis, these parameters can be used as auxiliary indices in evaluating the aging effects of XLPE cables.
3. Due to the high accuracy of the experimental verification, it turns out that the lifespan prediction method proposed in this paper can be used to determine the operating time of XLPE cables from Factory A in future research.  
The authors gratefully acknowledge the contributions as following: [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 51907029). The authors thank all members of the high voltage research team in Southeast University for their hard work in helping us to obtain the experimental data in this paper.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Samples** & \(a_{1}\)**/Year** & \(a\)**/Year** & \(\sigma_{1}\) **(\%)** \\ \hline A\({}_{6}\) & 8.081 & 8 & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Predicted and actual aging time of composite insulators.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 7 Conclusions  
Vehicular Ad hoc Networks are subjected to many vulnerabilities and security attacks because of their open and decentralized features. Black hole attack is one of the most serious attacks that affect the data availability in VANETs. In this paper, we proposed a new approach capable of detecting black hole attack in real time and without performing any updates in the routing algorithm. The findings reveal that the present study may provide an unprecedented approach that uses the multivariate normality tests to detect routing security attacks in VANETs. We tested our detection scheme in a more realistic environment using a realistic traffic model. The real time monitoring of the network traffic is a big advantage of our approach since time is a critical factor in detecting incidents in the VANETs.  
Further, our approach relies on the multivariate concept, which is very useful in the context of our study. VANETs are highly dynamic, and their behavior must be characterized by more than one variable rather than an individual one. In that regard, our approach has the ability to distinguish legitimate behavior from malicious behavior based on multiple network characteristics simultaneously. We involved three main important network metrics that had not been previously combined for black hole detection, namely the throughput, the dropped packets ratio, and the overhead traffic ratio. It is also worth noting that our research demonstrated the relevance of employing data rescaling techniques (ZSN, MMN, and NDS) in improving the data sensitivity to the normality conformity analysis. Further in-depth details regarding these techniques can be tried in the future to get more insights into the differences between these methods and others from the literature.  
To the best of our knowledge, this is the first study that combines rescaling techniques with multivariate data traffic for routing security attack detection. As a next step, additional experiments should be undertaken to provide further evaluation of the proposed scheme, including testing other routing security attacks. The proposed detection scheme can be enhanced by integrating other performance metrics and implementing a reaction scheme to countermeasure the routing attacks.  
Conceptualization, S.A. and S.E.H.; methodology, S.A. and M.-A.E.H.; validation, M.H.; formal analysis, S.A., M.H. and S.E.H.; investigation, S.A. and S.E.H.; resources, S.A. and S.E.H.; data curation, S.A. and S.E.H.; writing--original draft preparation, S.A., M.H. and S.E.H.; writing--review and editing, S.A., M.H. and S.E.H.; visualization, S.A., M.-A.E.H. and S.E.H.; supervision, M.H.; project administration, M.H. and S.E.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Ultra-wideband (UWB) localization is one of the most promising approaches for indoor localization due to its accurate positioning capabilities, immunity against multipath fading, and excellent resilience against narrowband interference. However, UWB researchers are currently limited by the small amount of feasible open source hardware that is publicly available. We developed a new open source hardware platform, Wi-PoS, for precise UWB localization based on Decawave's DW1000 UWB transceiver with several unique features: support of both long-range sub-GHz and 2.4 GHz back-end communication between nodes, flexible interfacing with external UWB antennas, and an easy implementation of the MAC layer with the Time-Annotated Instruction Set Computer (TAISC) framework. Both hardware and software are open source and all parameters of the UWB ranging can be adjusted, calibrated, and analyzed. This paper explains the main specifications of the hardware platform, illustrates design decisions, and evaluates the performance of the board in terms of range, accuracy, and energy consumption. The accuracy of the ranging system was below 10 cm in an indoor lab environment at distances up to 5 m, and accuracy smaller than 5 cm was obtained at 50 and 75 m in an outdoor environment. A theoretical model was derived for predicting the path loss and the influence of the most important ground reflection. At the same time, the average energy consumption of the hardware was very low with only 81 mA for a tag node and 63 mA for the active anchor nodes, permitting the system to run for several days on a mobile battery pack and allowing easy and fast deployment on sites without an accessible power supply or backbone network. The UWB hardware platform demonstrated flexibility, easy installation, and low power consumption.  
UWB; indoor localization; open source; hardware; shield; DW1000; Zolertia RE-Mote; ranging; external antenna; sub-GHz 1
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Active BCI  
This category centers on the active BCI systems and consists of four studies presented in Table 6. One of the studies [51] features a hybrid BCI system that integrates active and reactive BCI technologies.  
[PERSON] and [PERSON] [51] designed a hybrid BCI-AR system that combines MI and SSVEP to navigate a quadcopter. EEG data were collected using actiCHamp with six electrodes, and a lowpass filter at 40 Hz was applied in collection. CCA was used for the SSVEP classification, while FBCSP was used for the MI classification. To test their system, two subjects took part in the experiment, and both of them managed to successfully navigate the quadcopter.  
[PERSON] et al. [52] developed two games utilizing the beta/alpha ratios of EEG signals as a degree of concentration. A single EEG channel in combination with HoloLens was employed for the two experiments. In the first game, users attempted to hit targets positioned in the mixed reality space using bullets controlled by hand gestures and concentration. Meanwhile, in the second game, the user had to focus on outperforming their opponent. One-way ANOVA was performed to evaluate the experiments.  
[PERSON] et al. [53] designed an active AR-BCI system that utilized voluntary eye blinks to control a robot. They developed an eye blink detection algorithm in order to identify the long blink and the double blink from the EEG data while effectively filtering out noise and the normal blink. The results indicated that the blink-based input method of the eye, as opposed to the gesture-based input, resulted in a reduced user input time.  
[PERSON] et al. [54] developed a brain-controlled robotic arm system based on MI using MR visual guidance to enhance training efficiency and EEG signal classification accuracy. The system integrated EEG signals for task switching and motor imagery to control the robotic arm, utilizing a combination of CSP and SVM for signal classification. Eight subjects participated in the experiment, using a 64-channel EEG cap with data processed through the actiCHamp amplifier. The results showed a significant improvement in classification accuracy after visually guided training, with an average increase of about 10% in accuracy and a notable enhancement in kappa values.