esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Information
### Augmented Reality Environment Setup with Kinect V2  
In this paper, we use a video see-through method to construct an augmented reality environment. We used Microsoft Kinect V2 camera for this purpose. Kinect V2 has the advantage of being able to simultaneously capture a color image, depth image, and IR image through multiple cameras, but calibration is required separately for analysis, using sensors of different resolutions [15]. Figure 5 shows the hardware configuration and information for Microsoft Kinect V2.  
The image input through the color camera has a resolution of (1920 \(\times\) 1080), but the image input through the depth sensor has a resolution of (512 \(\times\) 424). We performed image calibration through the following algorithm for the analysis of two different images. The following Table 1 and Figure 6 show the image calibration algorithm and the result of our method:  
\begin{table}
\begin{tabular}{c} \hline
**Image Calibration Algorithm.** \\ \hline
**Input: Color Image Matrix, Depth Image Matrix** \\
**Output: Calibrated Image Matrix** \\ \hline /Preprocessing for Calibration \\ undistort color, depth image \\ create Calibrated Image Matrix for store calibration image [Depth.row * Depth.col] \\ \hline /Calibration Method (Loop Equation to Mapping Color \& Depth Image) \\ for y = 0, y \textless{} Depth.row, y++ \\ for x = 0, x \textless{} Depth.col, x++ \\ float DepthValue = Depth(y,x) //get row, col depth value \\ vec3f DepthCoord = DepthToW(y, x, DepthValue) //create depth coordinate \\ vec2i ColorCoord = wToColor(DepthCoord) //depth coordinate to color coordinate \\ //Store color coordinate data to Calibrated Image Matrix (y,x) \\ Calibrated Image Matrix (y,x) = Color Image Matrix (ColorCoord[0], ColorCoord[1]) \\ \hline \end{tabular}
\end{table}
Table 1: Color and Depth Image Matrix Calibration Algorithm for Image Analysis [16].  
Figure 5: Microsoft Kinect V2’s Hardware Structure Information (Resolution, Frame per Second (FPS)).  
We derived the Calibrated Image Matrix from the matching algorithm, and analyzed the image of the matrix to extract meaningful information. Significant information, such as a desk or wall that can interact with the enhanced object, was detected in this study. The contours detection method provided by the image processing library OpenCV was used to detect (x, y) coordinates and (z) coordinates in the color image matrix, and to group them into one bounding box form. The information was then stored in the GPU buffer of the physics simulator to interact with the augmented object. Figure 7 shows the results of detecting squares using image processing from a video frame.
# Blockchain-Based Decentralized Federated Learning Method in Edge Computing Environment
## 4 Experiments and Results  
### Experiment Setting  
We built a simulation experiment environment on an Intel(R) Xeon(R) server with two Gold 6248 processors @ 2.50 GHz (Intel Corporation, Santa Clara, CA, USA). We used Java version 1.8 to implement edge servers and the blockchain system, and Python version 3.7 to implement the local device environment. The local device communicates with the edge server through Socket. In the simulation environment, local devices are distributed within the coverage range of a 250 m radius of each edge server. The channel gain between them is modeled as \(30.6+36.7 log_{10}(distNode_{ks})\) dB using the block fading model. Table 1 shows the simulation parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Network Bandwidth & 20 MHz \\ Shooting Power \(p_{k}\) & 200 mW \\ Power Spectral Density & \(-95\) dBm/Hz \\ Uplink Data Size \(D_{k}\) & [3000, 4000] kb \\ \(\mu,\eta\) & 0.5,0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter settings.  
We use the ResNet18 implemented by PyTorch as the federated learning network model, and the dataset is CIFAR-10. In experiments, the CIFAR-10 dataset will be randomly divided into multiple copies with different sizes. The local device will randomly obtain one copy as the local data sample.
## 5 Analysis and Discussion
### Ablation Study  
To gain a deeper understanding of the individual contributions of each component in the proposed ensemble model, we conducted an ablation study. The purpose of this study was to assess the individual impacts of the transformer, CNN, and LSTM architectures on the overall performance of the SER system. By systematically removing each component and evaluating the model's performance, we aimed to identify the relative importance of these architectures in capturing emotional information from speech data.  
For the ablation study, we created four variant models by removing each of the key components:  
Figure 18: Receiver operating characteristic curves for the datasets: (**a**) the EMO-DB, (**b**) the RAVDESS, (**c**) SAVEE, (**d**) IEMOCAP, and (**e**) SHEIE.  
Ensemble model without transformer (EM-T): This variant excluded the transformer block, which is responsible for capturing the contextual information and long-range dependencies in the speech data.  
Ensemble model without CNN (EM-C): In this variant, we removed the CNN layers that extract local temporal features from the input sequences.  
Ensemble model without LSTM (EM-L): This variant eliminated the LSTM layers, which are designed to capture and learn the long-term dependencies and temporal relationships within the audio sequences.  
Ensemble model with four common emotion classes (EM-4E): In this variant, we focused on the four common emotion classes (happy, angry, sad, and neutral) across the RAVDESS, EMO-DB, SAVEE, and IEMOCAP datasets. These four datasets were used for training, while the SHEIE dataset was used solely for testing.  
All other components and hyperparameters of the ensemble model remained unchanged during the ablation study. We evaluated each variant model on the five datasets (RAVDESS, EMO-DB, SAVEE, IEMOCAP, and SHEIE) using the same evaluation metrics as the complete ensemble model. Table 10 presents the performance comparison of the complete ensemble model and its variants on the five datasets. The results demonstrate the individual contributions of the transformer, CNN, and LSTM architectures to the overall performance of the SER system.
## 3 Development of a Laboratory Setup  
The challenges of integrating a measurement system into an existing beam path are complex. Different wavelengths must be used so that a separation can be made between the measuring system and the process laser. At the same time, the optics used are designed only for the process laser, so that strong chromatic aberrations must be expected here [28]. Thus, a suitable choice of the measuring wavelength is no longer trivial. An ultra-short pulse laser (USP) with a central wavelength of 1030 nm is used for the machining process. The laser has a pulse width of 500 fs and a pulse frequency of 10 kHz.  
Based on the parameters of the laser, the scanner and the optics used, the central wavelength of the LCI measurement system can now be determined. For this purpose, different potential wavelengths were first tested by means of an optical simulation with the replica of the machine's beam path. At the same time, one of the most important components is the beam splitter, which is intended to separate the two beams. Since the beam path is optimized for a wavelength of 1030 nm, the wavelength of the measuring system should be as close to this as possible. As shown in Equation (2), a high spectral width of the light source is advantageous. To take these boundary conditions into account, a beam splitter with a steep slope is necessary.  
The combination of the NFD01-1040 beam splitter from Semrock and the Superlum SLD-471 UBB was identified as the optimum combination, see Figure 1. In the use case considered here, the smallest structure is at a depth of 1 \(\upmu\)m, so that the resolution of the measuring system must cover this. Equation (1) shows a theoretical axial resolution of 3.632 \(\upmu\)m for the light source used with a central wavelength of 930 nm and a FWHM of 115 nm. It indicates the ability of the system to differentiate between different layers tomographically, but here a topographic measurement is made. For topographic evaluations, the repeatability must be taken into account, which is 0.36 \(\upmu\)m, as will be shown later. Therefore, all structures of the use case can be resolved. To ensure optimal utilization of the broadband light, a suitable spectrometer is required that can capture the entire spectral width.
## 3 Results and Discussion of the Physical Experiment
### Wave Overtopping
For the non-standing barrier, overtopping discharges are less than the results of the EurOtop, except for the minimum relative free board (which is the case for the 50-year return period and the sea level during the storm surge with the SLR in Table 4) because the wave energy is reduced by armor units, and the vertical wall has a short overhang that minimally blocks wave overtopping. However, the overtopping discharge is about 1.2 times greater than the predicted results \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **State of** \\ **Movable Barrier** \\ \end{tabular} & \begin{tabular}{c} **Designed Sea Level** \\ **(year)** \\ \end{tabular} & \begin{tabular}{c} **Return Period** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **\(H_{m0}\)** \\ **(sec)** \\ \end{tabular} & \begin{tabular}{c} **\(T_{m-1,0}\)** \\ **(sec)** \\ \end{tabular} & \begin{tabular}{c} **\(d\) (m)** \\ \end{tabular} & \begin{tabular}{c} **h (m)** \\ \end{tabular} & \begin{tabular}{c} **\(R_{c}/H_{m0}\)** \\ \end{tabular} & \begin{tabular}{c} **\(L_{m-1,0}\)** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **S\({}_{m-1,0}\)** \\ \end{tabular} & \
\begin{tabular}{c} **\(H_{c}^{\prime}\)** \\ \end{tabular} &
\begin{tabular}{c} **\(L_{m-1,0}^{\prime}\)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Non-standing} & Approx. H.H.W. & 10 & 0.120 & 2.17 & 0.0388 & 0.254 & 1.127 & 7.352 & 0.0163 & 0.0112 \\ \cline{2-10}  & Approx. H.H.W. & 50 & 0.128 & 3.00 & 0.0388 & 0.254 & 1.106 & 14.052 & 0.0091 & 0.0055 \\ \cline{2-10}  & Sea level during storm & 10 & 0.121 & 2.23 & 0.0746 & 0.290 & 0.822 & 7.764 & 0.0155 & 0.0230 \\ \cline{2-10}  & surge + SLR & 50 & 0.128 & 3.00 & 0.0746 & 0.290 & 0.777 & 14.052 & 0.0091 & 0.0120 \\ \hline \multirow{4}{*}{Standing} & Approx. H.H.W. & 50 & 0.128 & 3.00 & 0.0888 & 0.254 & 2.272 & 14.052 & 0.0091 & 0.0055 \\ \cline{2-10}  & sea level during storm & 10 & 0.121 & 2.23 & 0.0746 & 0.290 & 2.107 & 7.764 & 0.0155 & 0.0230 \\ \cline{2-10}  & surge + SLR & 50 & 0.128 & 3.00 & 0.0746 & 0.290 & 1.992 & 14.052 & 0.0091 & 0.0120 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters to compare overtopping discharge at the model scale. at the minimum relative free board, presumably because the wave energy is not reduced by the armor units because of high incident wave conditions. For the standing barrier, the overtopping discharges are less than those of EurOtop, since incident waves are returned to the seaside by two bullnoses. To secure the right view, the height of the standing movable barrier should be decreased. Thus, the height of the bullnose should be increased and the number of the bullnose should be increased to reduce wave overtopping. Figure 8: Definition of the related parameters; (**a**) composite vertical wall in the EurOtop manual; (**b**) non-standing barrier; (**c**) standing barrier.
# Autonomous Vehicles in Rural Areas: A Review of Challenges, Opportunities, and Solutions
###### Abstract  
The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policy-makers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts.  
autonomous vehicles; rural areas; mobility; intelligent transportation systems; infrastructure modifications; traffic simulation 2025
# A Photo Identification Framework to Prevent Copyright Infringement with Manipulations
## 3 Copyright Photo Identification Framework
### Image Verification  
#### 3.4.1 Image Alignment  
In the image verification step, a matched image pair from the image hashing step in Section 3.3 is verified based on the measured similarity between them. Because the query image possibly has geometric manipulation such as cropping or resizing, it is necessary to geometrically align the query and its matched image. In our framework, since the photos taken in different views should be predicted as different, the transform between the query and matched image is assumed to be in the affine space rather than projective space. By using matched image features, the alignment process computes an transform matrix and then extract an overlapping region based on it. Consequently, the image structure including edge contrast between two images is deemed to be similar which is necessary for image similarity measurement in the next subsection.  
As shown in Figure 8, we use image features by using Accelerated KAZE (AKAKZE) [46] for image feature matching. The AKAZE is a more computationally efficient method than KAZE. It can computes robust features within milliseconds while having comparable feature matching accuracy with well-known feature extractors such as SIFT [47] and SURF [48]. From the feature descriptors computed on each image, the affine transform matrix **M** is estimated by using RANSAC (RANdom SAmple Consensus) [49] that computes the robust solution to the outlier matches. The detailed procedure of estimating the affine transform matrix by using AKAZE feature matches is described in the Algorithm 2.
## 3 Results and Discussion
### GPX and TYROX Activities  
In control tissues, GPX only increased in the seed tissue as germination progressed, until stage S3 (2-3 leaves) (Figure 3). This in agreement with [PERSON] et al. [7,27]. In roots and sprouts, a decreasing GPX activity was observed (Figure 3).  
Figure 3: Effect of tyrosine treatment on guaiacol peroxidase (GPX) activity in _V. fuba_ seeds, roots, and sprouts at the early stages of development: S1: 3–4 days, S2: 8–9 days, and S3: 2–4 leaves. The bars indicate \(\pm\) standard error (\(n=3\)). _”*”_ indicates significant differences between controls and tyrosine treatments according to [PERSON] (\(P<0.05\)).  
GPX elicitation has been described as an early indicator of environmental changes in plants [15]. Indeed, the elevation of GPX activity has been reported as the only more general response to stimuli, such as metal exposure in _V. faba_[40]. Interestingly, [PERSON] [41] found that different cultivars of fava bean had different GPX responses to water deficit, and only the Reina Mora cultivar exhibited an increase in GPX activity.  
Our results do not reflect any increased GPX activity with tyrosine treatment. On the contrary, GPX activity decreased or did not vary, depending on the organ (Figure 3). GPX has been proposed as a phenolic marker metabolism; thus, precursors derived from shikitimate route could be used by GPX for phenolic polymerization in structural development processes (germination) [29]. In our experiment, tyrosine treatment implies abundant phenolics, suggesting no need of increment in GPX activity. TYROX activity was not detected in all our experiments. The positive control for tyrosine oxidase (Beta vulgaris roots) was run in parallel in all samples and yielded proper activities 0.6 mmol min\({}^{-1}\) g\({}^{-1}\) DW.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 2 Materials and Methods
### Composite Fibers Preparation  
During the research, three types of composites were fabricated, SBS/graphite, SBS/CNT, and SBS/graphite/CNT, with different amounts of functional phases. However, all the materials were prepared using similar procedures. The first stage of preparing the composite was dissolving an SBS polymer in chloroform using a heated magnetic stirrer to achieve a low-viscosity solution. In the meantime, ultrasound mixing of CNT and graphite was performed in chloroform with a small addition of dissolved polymer to break up the agglomerates using a Vibra-Cell VCX130 sonicator with 80% maximum power for 30 min. The maximum power of the used sonicator was equal to 130 W and the repetition rate was 20 kHz. The sonication process was carried out in a cylindrical 45 mm diameter container and using a 6 mm diameter sonication tip placed approximately 10 mm from the bottom of the container. After breaking up the carbon particle agglomerates, the carbon/solvent suspension was added to the remaining part of the dissolved polymer. Our previous studies showed that the breaking of the agglomerates of carbon structures using ultrasound in a polymer solution, allows for the fabrication of a composite with greater homogeneity than the breaking of carbon structures in the solvent alone [10]. Carbon particles were mixed with polymer using an MS7-H550-Pro magnetic stirrer for 2 h in an opened container to slowly evaporate chloroform. When the mixture reached a high viscosity that prevented further mixing with a magnetic stirrer, it was additionally stirred by hand and then poured into a large container to completely evaporate the solvent. Evaporation of the solvent was performed in a dryer at 50 \({}^{\circ}\)C for 24 h. After the evaporation of the solvent, the composite was palletized and formed into fiber using a hot mixing extrusion process.  
To prepare the composite fibers, a single screw extruder was used. The extruder machine was equipped with two independent heating zones and replaceable nozzles with different diameters. This allowed for the control of the extruded fibers' diameters.  
To fabricate highly homogenous and continuous fibers, the extrusion temperatures were set to 150 \({}^{\circ}\)C in both heating zones. Lowering the temperature caused insufficient plasticization of the matrix material, resulting in inhomogeneous fibers and the difficult extrusion of long fibers. On the other hand, a higher extrusion temperature caused the material to flow too quickly, making it impossible to achieve a consistent fiber diameter and also caused the thermal degradation of the matrix.
## 6 Featured Application  
The main application of the proposed solution in this paper is in the 5G field. The transport network has a critical role to play in 5G by providing the backhaul and new fronthaul infrastructure that will enable all the interesting high bandwidth and ultra-low latency applications for millions of end devices. The separation of the remote radio unit (RRH) from the baseband unit (BBU) pools, dictated by 5G for supporting the high densification of the cells and the adoption of interference management procedures, is opening an unchartered territory in the 5G fronthaul network. The industry consensus is  
Figure 8: The number of wavelengths per optical ring is reported as a function of the probability \(p_{r}\) of residential sub-area. The peak offered traffic \(A_{0,r}\) for a residential sub-area is fixed equal to 16 Gbps while the ratio \(\omega=\frac{A_{0,r}}{A_{0,r}}\) is varied from 0.5 to 2. The percentage \(\gamma\) of used RRUs equals 0.6. \(R=2\) optical rings are considered. The values of the other parameters are chosen as the ones in Figure 5.  
Figure 7: The number of wavelengths per optical ring is reported as a function of the probability \(p_{r}\) of residential sub-area. The percentage \(\gamma\) of used RRUs is varied from 0 to 1. The peak offered traffic is \(A_{0,b}=26\) Gbps and \(A_{0,r}=16\) Gbps for business and residential sub-area, respectively. \(R=2\) optical rings are considered. The values of the other parameters are chosen as the ones in Figure 5.  
that the physical medium for the 5G fronthaul network will be fiber. However, beyond this consensus point, there are many options being put forward and much debate including choice of transport protocols and even basic physical layer decisions for fiber. While promising solutions based on eCPRI and Ethernet technology are under investigation, we propose a feasible solution able to provide high bandwidth and dynamic resource allocation to lead high saving in realizing the transport network.
## 1 Introduction
Simulation results show that the performance of the proposed method achieves the ML method as well as the CRB under most circumstances, with the benefit of much less computational demand. The rest of the paper is arranged as follows. Section 2 gives the model of the array output signals of UCA and known waveforms, along with some necessary assumptions. Section 3 introduces the ML and interferometer-based DOA estimator with known waveforms, respectively. Section 4 recalls CRB of 2D DOA without known waveforms and derives CRB with known waveforms. Section 5 carries out several simulations to verify the proposed method. Conclusions are summarized in Section 6. The following notations are used in the paper: matrices and vectors are denoted by bold upper-case and lower-case letters, respectively. \((\cdot)^{T}\), \((\cdot)^{*}\), \((\cdot)^{H}\) and \((\cdot)^{-1}\) denote transpose, conjugate, conjugate transpose and inverse, respectively. \(\mathds{E}\{\cdot\}\), \(\text{diag}\{\cdot\}\), \(||\cdot||\) and \(\odot\) denote expectation, diagonalization, Euclidean norm and Hadamard product. \(|\cdot|\), \(\text{Re}\{\cdot\}\) (or \(\widetilde{(\cdot)}\)), \(\text{Im}\{\cdot\}\) (or \(\widetilde{(\cdot)}\)) and \(\arg\{\cdot\}\) denote modulus, real part, imaginary part and argument of a complex number, respectively. \(\mathbf{I}\) and \(\mathbf{0}\) denote identity and zero matrix of proper size.
## 3 Indoor Positioning Systems
### Systems Based on Sound  
Sound signals, consisting of pressure waves that propagate in the air, benefit from the fact that sound travels at a much slower speed than electromagnetic signals, thus allowing us to measure the time between the emission and arrival of a signal with much less effort [25; 97].  
The emission time is usually measured by the simultaneous transmission of a radio signal and an audible signal because the radio signal reaches the sensor almost instantly, and the sound signal reaches the sensor later. Therefore, the difference between these two times can be used to calculate the distance between two points. Sound-based systems can be classified into audible and inaudible sound systems.  
Although audible and inaudible acoustic applications have shown excellent levels of accuracy, strong variations are also reported in certain scenarios, as the speed of sound is affected by temperature and humidity [50].