esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation  
### Introduction of the Experiment  
To validate the applicability and feasibility of the length-angle mixed intersection adjustment method presented in this article, an experimental test is conducted on a section of track at a rocket sled test site. The experimental flow is shown in Figure 13, and the specific experimental steps are as follows:  
1. Verify the repeatability of the distance measurement of the tracking instrument;
2. Place the total station in the global position and the laser tracker at L1;
3. Measure the reference points with total station under current station, and the target points with the laser tracker. If the measurement task is not finished, move the laser tracker to the next station and repeat the step 3; otherwise, proceed with step 4;
4. Solve the measurement data using the length-angle mixed intersection adjustment model;
5. Evaluate the accuracy of the distance between target points after adjustment.  
Figure 12: Standard deviation of target points after adjustment compared to networking with laser trackers.  
Figure 13: Flow chart of the experimental process.  
The equipment used in the experiment included a Leica MS60 total station and a Leica AT402 laser tracker. The laser tracker is moved along a track approximately 669 m in length, adopting a station-transfer method. As shown in Figure 14, the laser tracker measured both target points and reference points, undergoing 10 station transfers, with 11 laser tracker station positions labeled L1-L11 spaced approximately 60 m apart. The total station, T1, is positioned in the middle of this track segment to measure reference points. The distance between adjacent target points is about 3 m, and reference points are placed around the track to ensure that each laser tracker station can observe a sufficient number of reference points.  
The observations of all target points and reference points along the specified track segment are listed in Table 1, which shows the number of measurement points observed by each station.  
Constructing the error equation according to Formula (13), the number of measurement points and stations is, respectively, as follows:  
* \(n_{1}\) is the number of target points: 224;
* \(n_{2}\) is the number of reference points: 38;
* \(m_{1}\) is the number of laser trackers: 11;
* \(m_{2}\) is the number of total stations: 1;
* The number of unknown parameters is 819;
* The number of equations is 972;
* The number of redundant observation equations is 153.  
Substituting these parameters into Formula (16), we can obtain a solution of equations.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Point Type/Station** & **T1** & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7–L10** & **L11** \\ \hline Target points & - & 25 & 25 & 25 & 25 & 25 & 27 & 25 & 25 \\ Reference points & 38 & 7 & 6 & 5 & 6 & 6 & 6 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of different types of measurement points observed by each station.  
Figure 14: Distribution of measuring stations and points at the experimental site.
## 8 Conclusions
The honeycomb hexagonal transmitter design provided a 19% improvement in system performance with a spacing of 1 cm, which slightly decreased to 16% when the spacing was reduced to 1 mm, a change that also made the array size 100 times smaller. Despite this reduction, its performance remains commendable, especially since, at \(s=1\) mm, our largest array is smaller than one of the smallest industrial headlight modules, Samsung's PixCell LED. This result emphasizes the versatility of our design, which not only adapts well to compact dimensions but also allows automotive manufacturers the flexibility to tailor lighting solutions to meet their specific size and aesthetic requirements while ensuring effective communication ranges. These findings highlight the effectiveness of optimizing the half FOV \(\phi_{\frac{1}{2}}^{\prime}\) and the orientation vectors, controlled by \(d_{0}\), of each LED in the array transmitters. Our method allows for achieving the desired irradiance patterns and optimizing communication across different transmitter configurations. This study shows the critical role of adjusting the geometrical configuration of the transmitter LED array in optimizing VLC performance. Future research should explore further optimizations of LED arrays and assess the impact of dynamic environmental conditions on system performance. Such work may include validating simulation results with experimental data and conducting more comprehensive analyses related to real-life conditions. The performances of different LED arrays in outdoor conditions, such as different weather conditions like smoky and foggy atmospheres, can be examined to provide data for real-world applications. In this regard, future experimental studies can be compared with our simulation-based analysis, with external weather factors included, to make important contributions to the development of LED-based communication systems. Conceptualization, A.D., M.O. and H.Y. ; methodology, A.D., M.O. and H.Y. ; software, A.D. and M.O. ; validation, A.D., M.O. and H.Y. ; formal analysis, A.D., M.O. and H.Y. ; investigation, A.D., M.O. and H.Y. ; resources, A.D., M.O. and H.Y. ; data curation, A.D. and M.O. ; writing--original draft preparation, A.D. and M.O. ; writing--review and editing, A.D., M.O. and H.Y. ; visualization, A.D. and M.O. ; supervision, H.Y. ; project administration, H.Y. ; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the Bogazici University Scientific Research Fund (BAP), Istanbul, Turkey, under contract number 19061, and by the BAP Research Universities Support Program (ADP), which is supported by the Turkish Council of Higher Education (YOK) under contract number 50005 (23 AYOKADP1). Not applicable. The data are contained within the article. The authors wish to thank in advance the anonymous reviewers for their valuable time in reviewing this paper. The authors declare no conflicts of interest.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain  
[PERSON]  
The advancement of laser ultrasonics has increased rapidly in recent years, providing applications for materials characterization as well as for industrial utilization, as a quality control device. The wide-ranging capabilities for high-temperature in-situ analysis of a variety of microstructural characteristics offers a multitude of possibilities for usage in R&D. To date, this is the only known method that has been successfully deployed for in-situ materials characterization, as well as in the harsh environment of the metalworking industry. Combined with the enablers, introduced by the fourth industrial revolution, and the conjunction of a laser ultrasonic system with a Smart Production Lab, it has great potential to contribute to lower rejection rates, better recyclability, and consequently to a more sustainable production. In this review, the potential for systemic sustainability is explained throughout a part of the value chain, in the context of Industry 4.0. In addition, the integration of the methodology into a miniaturized Smart Production Lab is demonstrated, with the intention of incorporating it as a substantial part of the creation of a digital twin. Such a lab is designed to serve as an interface between laboratory and industry, in order to reveal the possibilities of digital transformation, Industry 4.0, and the application of highly flexible systems such as the laser-ultrasonic system for companies.  
laser ultrasonics; in-situ material testing; quality control; I 4.0; real-time monitoring; Smart Production Lab
# ViTFER: Facial Emotion Recognition with Vision Transformers
## 2 Related Work
### Emotion Recognition Methodologies Based on Machine Learning  
Various classic machine learning approaches such as K-nearest neighbor and support vector machines are used to address the facial emotion recognition challenge. [PERSON] et al. [13] generated local binary pattern (LBP) histograms from multiple small segments of the image, integrated them into a single feature histogram, and then classified emotion using a linear programming (LP) technique. [PERSON] and [PERSON] [14] first added a wavelet energy feature (WEF) to a face picture, extracted features using [PERSON]'s linear discriminant (FLD), and then classified emotion using the K-nearest neighbor (KNN) method. [PERSON] et al. [15] implemented a boosting technique for classification and an enhanced wavelet transform for 2D feature extraction, termed contourlet transform (CT). [PERSON] and [PERSON] [16] integrated face recognition into FER to improve individual expression recognition, and they employed a radial basis function (RBF) neural network for classification.  
[PERSON] et al. [17] used SVM to analyze two feature descriptors, facial landmarks descriptor and center of gravity descriptor, and applied them for facial feature extraction. In their thorough study, [PERSON] et al. [18] assessed several facial renderings depending on local statistical features and LBPs using different forms of SVM. The primary downside of the observed conventional approaches is that they only evaluated frontal images for facial emotion recognition since features from front-face views differ in typical feature extraction methods.
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 2 System Model and Problem Formulation  
This study investigates a secure UAV communication system within an Internet of Things environment, consisting of a communication destination and \(K\) potential eavesdroppers (Eves). The UAV initiates its mission at a designated starting point, transmits sensitive information to the communication destination during its flight, and ultimately reaches a specified endpoint. We assume that any of the \(K\) potential passive eavesdroppers may intercept the information intended for the communication destination. Therefore, UAV flight trajectories must be optimized to guarantee the security of UAV communication downlinks. Specifically, in this UAV-aided IoT communication system, the objective is to maximize the system's achievable secrecy rate and ensure the security of the information received by the legitimate ground node.
## 5 Simulation Results and Discussion
### Simulation Results
#### 5.3.2 Case II: Complete Link Failure with a Partially Failed Backup Path  
In this case, we configure the SCN \(\mathcal{N}\) in such a way that a backup path exists. The backup path is considered through the Internet, which is modelled using network \(\mathcal{N}_{2}\). Node \(v_{1}\) is connected to the Internet through network \(\mathcal{N}_{3}\). Node \(v_{6}\) is connected to the Internet through network \(\mathcal{N}_{4}\). Thus, the backup path comprises networks \(\mathcal{N}_{2}\), \(\mathcal{N}_{3}\), and \(\mathcal{N}_{4}\). To configure the backup path in the partially failed state, we configure the Internet with 75% background traffic. Furthermore, network \(\mathcal{N}_{1}\) is disabled for 150 s to 210 s. The SCN is simulated from 110 \(s\) to 210 \(s\).
## 3 Numerical Methodology and Procedure
### Governing Equation for Piston Movement  
The primary forces acting on the piston are gravity and pressure-induced forces. Secondary forces, including body forces, flow-induced forces, and frictional forces, are negligible in this calculation. The force balance for the piston is described by [PERSON]'s second law as follows:  
\[m\frac{d^{2}X}{dt^{2}}=P_{rs}A_{rs}-P_{ms}A_{ms}+P_{fs}A_{fs}+P_{fe}A_{fe}-P_{ re}A_{re}-mg \tag{6}\]  
where \(A_{rs}\) is the displacement area of the rear step surface of the piston, \(A_{ms}\) is the displacement area of the mid step surface of piston, \(A_{fs}\) is the displacement area of the front step surface of the piston, \(A_{fe}\) is the displacement area of the front end face of the piston, and \(P_{re}\) is the displacement area of the rear end face of the piston. \(P_{rs}\), \(P_{ms}\), \(P_{fs}\), \(P_{fe}\), and \(P_{re}\) are the corresponding pressure differences acting on the areas. Additionally, \(m\) is the piston mass, and \(g\) is the acceleration of gravity, i.e., \(g\) = 9.80 m/s\({}^{2}\).  
When the piston reaches the end of its stroke and impact occurs, the rebound velocity \(v\) of the piston is calculated as follows:  
\[v=-\mathrm{k}\overrightarrow{v}_{\rm i} \tag{7}\]  
where k is the rebound coefficient; \(\overrightarrow{v}_{\rm i}\) is the piston impact velocity.  
When the time step is sufficiently small, the piston acceleration is assumed to be constant within each time step. Consequently, the piston velocity \(v^{n+1}\) for the \((n+1)\)th time step can be determined as follows:  
\[v^{n+1}=v^{n}+a^{n}\Delta t \tag{8}\]  
where \(a^{n}\) is the \((n+1)\)th time step acceleration of the piston.  
Furthermore, the piston displacement \(X^{n+1}\) for the \((n+1)\)th time step can be obtained from the following:  
\[X^{n+1}=X^{n}+\left(\frac{v^{n}+v^{n+1}}{2}\right)\Delta t= X^{n}+v^{n}\Delta t+\frac{\mathrm{a}^{n}\Delta t^{2}}{2} \tag{9}\]
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 2 Related Work  
### GPS Spoofing in UAVs  
To ensure navigation performance, many UAVs are equipped with GPS modules. The convenience of GPS also creates many vulnerabilities for UAVs. Attackers can interfere with GPS spoofing to mislead the localization of GPS modules [12]. GPS spoofing can cause the remote controller to lose control of the UAV, and then the attackers can control the UAV to conduct crimes and threaten public safety and private security.  
The goal of GPS spoofing of UAVs is to leverage fake GPS signals to interfere with UAVs' navigation [13]. The navigation systems of the UAVs are said to be captured once control is secured over their position and velocity estimates [14]. To counteract GPS spoofing, the UAVs need to recognize the attacks.  
UAVs can leverage hardware information derived from the autopilot to detect attacks. Ref. [15] analyzes the camera feed on UAVs, inertial measurement units (IMUs), and the velocity generated based on GPS signals. The difference in velocity derived from the GPS and IMUs can be used to detect GPS spoofing [16]. When GPS spoofing is detected, the UAV under attack can leverage IMUs to maintain a steady position [12]. IMUs are typically immune to interference from SDRs due to their use of physical sensors that operate at low frequencies, shielded enclosures, and a lack of dependence on radio frequency signals.  
IMUs are designed to measure motion and rotation and do not rely on external signals susceptible to jamming.  
Machine learning algorithms can also detect GPS spoofing of UAVs. In [17], the ensemble models of machine learning were used to detect GPS spoofing signals. A support vector machine (SVM) outperforms conventional detection methods [18]. Some research also proves that deep learning is effective at detecting GPS spoofing attacks [19; 20].  
The current methods can detect GPS spoofing of UAVs remotely and can recognize attacks effectively [21] and efficiently [22]. However, these methods need ground assistance to detect attacks, which cannot extend the range of UAV missions. The range is limited to the coverage of the ground assistance, which is not flexible, efficient, or compact. A compact method of GPS spoofing can reduce the cost of ground assistance and extend the range of UAV missions.
## 2 Background
### Structure of the Internet  
We can broadly identify three constituents in a network: the core, edge and access networks [9], presented as concentric circles in Figure 1. Let us note that we do not consider here the size of ISPs and how they would be interconnected. The core network acts as a backbone and its routers support multiple telecommunication interfaces to switch and forward packets at the highest speed. An edge router, sitting in close proximity to the end users, is connected to one or multiple core routers. The outermost circle is composed of the access networks, connected to the edge routers. These networks vary widely depending on the underlying access technologies; they might be either wired (e.g., DSL, Cable) or wireless (e.g., LTE, WiFi) and will support any network-capable device, from PCs to \"smart\" phones. Understandably, the access networks offer a wide range of data transfer speed depending on the technology used, the available bandwidth, the customer's subscription plan, _etc_.
## 3 Results and Discussion
### Copper  
The mean Cu concentration in the _A. tuberculosis_ samples was 6.86 \(\pm\) 0.51 mg/kg with a range of 4.10-18.20 mg/kg (Table 1). According to FAO [36] a daily intake of 2-3 mg of copper through the diet is sufficient to meet the nutritional needs of adults, establishing a maximum tolerable intake of 0.5 mg Cu/kg body weight per day. Other sources suggest an upper daily intake limit of 10 mg [32]. The copper levels reported in the three sectors can be considered acceptable for the consumption habits of the region. Furthermore, these results are much lower than those reported in Estero Salado, Guayas province, which had copper concentrations of up to 204.1 mg/kg.  
However, in Figure 5, it is observed that the letters of the Tukey statistical method indicate significant differences between the means of copper concentrations and the other metals. This means that it is likely that these differences are not due to chance but are due to real factors, such as environmental pollution, industrial activity, or soil geology.  
In the same figure, it is observed that copper concentrations are higher in La Libertad (8.5636 \(\pm\) 4.27 mg/kg; range 4.20-18.20 mg/kg) than in Puerto El Morro and General Villamil Playas (5.30 \(\pm\) 1.02 mg/kg; range: 4.10-7.50 mg/kg). This suggests that La Libertad may be more contaminated with copper than the other two locations.  
To determine the cause of the differences in copper concentrations, a more detailed analysis is required. This may include studying the sources of contamination in the area, soil and water conditions, and industrial activity.
## 2 LEO Satellite Networks Model
### Satellite Networks Topology  
The LEO system is modeled as a direct graph \(G=(V,E)\), where \(V\) represents the set of satellites and \(E\) represents the set of inter-satellite links (ISLs). Each satellite has four ISLs, including two intra-plane ISLs and two inter-plane ISLs [19]. Due to the extreme variation of the angular velocity of inter-plane ISLs--the ISLs within cross-seam--the north and south pole area cannot be built. As to simplify the change of network topology, we adopt the Virtual Node (VN) strategy to set up a satellite networks topology. In VN-based topology, a virtual node is supposed to be the current physical satellite, which is above the specific surface of the earth [20]. A virtual node and a physical satellite correspond one to one at any time, and the correspondence will change if a physical satellite moves out of the coverage of current VN or into the coverage of another VN [21]. The process of correspondence changing is called handoff. When a handoff happens, the state information will be transferred from the former physical satellite to the latter. In this way, rotating physical satellites can be converted into fixed virtual nodes, and the dynamic topology is also transformed into an accordingly static one. As it is shown in Figure 1, we construct the LEO satellite networks based on STK.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### Deep-Learning-Based IDS
#### 2.4.3 Gated Recurrent Unit  
A gated recurrent unit (GRU) was proposed by [PERSON] et al. [24] in 2014 and it synthesizes a single update gate by combining the forgetting gate and the input gate. It also changes the cell state and the hidden state, among other things. The final model is more straightforward than the standard LSTM model. Figure 2 depicts a cell of a GRU.  
Figure 1: A cell of LSTM.  
GRU gates are calculated using the following equations:  
\[z_{t}=\sigma\ (W_{z}\ [\ h_{t-1},x_{t}]) \tag{7}\]  
\[r_{t}=\sigma\ (W_{r}\ [\ h_{t-1},x_{t}]) \tag{8}\]  
\[\tilde{h}_{t}=\ tanh(\ w[r_{t}\ o\ h_{t-1},x_{t}) \tag{9}\]  
\[h_{t}=(1-z_{t})\ o\ h_{t-1}\ +\ z_{t}\ o\ \tilde{h}_{t} \tag{10}\]  
In principle, GRU is similar to LSTM, with an update gate calculated using Equation (7), a reset gate calculated using Equation (8), a memory calculated using Equation (9), and a candidate hidden layer calculated using Equation (10). The sigmoid function \(\sigma\) limits the output between 0 to 1, and the \(tanh\) function limits the output between \(-1\) to 1. \(Wz\), \(Wr\), and \(W\) are the parameter matrix. Both the update and reset gates calculate the memory of the current input and the previous moment.
## 4 Experimental Results and Discussion
### Total Power Consumption and Efficiency  
The PMS' power efficiency (\(\eta\)), as defined in (4), is presented in Figure 11. The peak end-to-end efficiency measured was \(\sim\)50.7% for a 3.3 output voltage. Table 4 summarizes and compares the presented work to previously reported systems.  
\[\eta=\frac{P_{out}}{P_{in}}\times 100=\frac{P_{MFC}-\sum P_{losses}}{P_{MFC}} \times 100\approx\frac{P_{MFC}-[I_{out}(R_{MFC}+R_{switch})+P_{Loss_{ Counter}}]}{P_{MFC}}\times 100. \tag{4}\]  
Figure 11: Measured Efficiency versus Output Voltage.  
Figure 10: Internet of things sensor node. (**a**) Hardware setup; (**b**) supercapacitor voltage waveform and (**c**) temperature measurements.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 2 Model Construction Principle  
### Variational Mode Decomposition  
As a signal decomposition method, VMD can decompose the signal into multiple local band components and extract the local features of the signal [18]. The theoretical basis of VMD is to express the signal as the superposition of a series of modulated signals, in which each modulated signal has a central frequency and bandwidth range. VMD can decompose the original signal by iterating it to obtain the optimal modulation signal. The constrained variational model expression is as follows:  
\[\left\{\begin{matrix}\underset{\{u_{k}\},\{\omega_{k}\}}{\min}&\left\{\sum \limits_{k}\left\|\partial_{t}\left[\left(\delta(t)+\frac{j}{\pi t}\right)*u_ {k}(t)\right]e^{-j\omega_{k}t}\right\|^{2}\right\}\\ &s.t.\underset{k}{\sum}u_{k}(t)=f\end{matrix}\right. \tag{1}\]  
where \(u_{k}\) represents K IMF components obtained by decomposition; \(\omega_{k}\) is the center frequency of each mode; \(f\) is the input signal.  
For constrained variational problems, they are usually transformed into unconstrained variational problems and then solved. Here, the quadratic penalty term and Lagrange multiplier method are introduced to achieve this goal, and the alternating direction multiplier method is employed to find the global optimal point:  
\[\begin{split} L(\{u_{k}\},\{\omega_{k}\},\lambda)& =\alpha\left\{\sum\limits_{k}\left\|\partial_{t}\left[\left(\delta(t)+ \frac{j}{\pi t}\right)*u_{k}(t)\right]e^{-j\omega_{k}t}\right\|^{2}_{2}\right\} +\\ &\left\|f(t)-\sum\limits_{k}u_{k}(t)\right\|^{2}_{2}+\left\langle \lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\right\rangle\end{split} \tag{2}\]  
where \(\alpha\) is a penalty factor and \(\lambda\) is Lagrange multiplication operator.