esa-sceva/satcom-chunk-collection huggingface dataset:
# Fusion-Based Localization System Integrating UWB, IMU, and Vision
###### Abstract  
Accurate indoor positioning services have become increasingly important in modern applications. Various new indoor positioning methods have been developed. Among them, visual-inertial odometry (VIO)-based techniques are notably limited by lighting conditions, while ultrawideband (UWB)-based algorithms are highly susceptible to environmental interference. To address these limitations, this study proposes a hybrid indoor positioning algorithm that combines UWB and VIO. The method first utilizes a tightly coupled UWB/inertial measurement unit (IMU) fusion algorithm based on a sliding-window factor graph to obtain initial position estimates. These estimates are then combined with VIO outputs to formulate the system's motion and observation models. Finally, an extended Kalman filter (EKF) is applied for data fusion to achieve optimal state estimation. The proposed hybrid positioning algorithm is validated on a self-developed mobile platform in an indoor environment. Experimental results show that, in indoor environments, the proposed method reduces the root mean square error (RMSE) by 67.6% and the maximum error by approximately 67.9% compared with the standalone UWB method. Compared with the stereo VIO model, the RMSE and maximum error are reduced by 55.4% and 60.4%, respectively. Furthermore, compared with the UWB/IMU fusion model, the proposed method achieves a 50.0% reduction in RMSE and a 59.1% reduction in maximum error.  
indoor positioning; ultrawideband (UWB); visual-inertial SLAM; sensor fusion; Kalman filter +
Footnote †: journal: Applied sciences  
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Footnote 0: Accepted: 5 June 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] Fusion-Based Localization System Integrating UWB, IMU, and Vision. _Appl. Sci._**2025**, 15, 6501. [[https://doi.org/10.3390/app15126501](https://doi.org/10.3390/app15126501)]([https://doi.org/10.3390/app15126501](https://doi.org/10.3390/app15126501))  
Copyright: 6 2025 by the authors.  
License MDPI, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 3 Evaluation Methodology
NR metric models are trained on a database of images with known distortions, and they are limited to evaluating the quality of images with the same type of distortion. RR metrics involve a combination of FR and NR metrics. They are designed to predict the perceptual quality of distorted images with only partial information about the reference images. In this paper, we only use FR and NR metrics. We tried to use the RR VQM (Video Quality Metric) developed by NITA (National Telecommunications and Information Administration) [20], but we discovered that it is not compatible with 4K resolution videos; it is limited to assessing the quality of videos with up to 1260 image rows. There are several resources [21; 22; 23] claiming that the VQM score is very similar to VMAF, so we believe that not using VQM is not a critical issue in this research.
## 2 Rotating Bending Fatigue Test
### Fatigue Testing Conditions  
Rotating bending tests were performed using a dual-spindle rotating bending fatigue testing machine [24]. The fatigue testing machine consisted of an induction motor that transmitted power via a flat belt and rotated two spindles. Specimens were mounted on both ends of each spindle, and a dead-weight load was applied to them via a bearing situated at the tip. The maximum principal stress, \(\sigma\), was set by a dead weight, \(W\). The following equation was used to set a dead weight, \(W\).  
\[W=\frac{\sigma\pi d^{3}}{32 gK_{t}l} \tag{1}\]  
where \(d\) is the tip diameter of the specimen, \(g\) is the acceleration of gravity, \(K_{t}\) is the elastic stress concentration factor, and \(l\) is the distance from the center of the specimen to the weight-loaded area. A photo sensor continuously detected the spindle rotations while the repetitions were counted. Once the specimen fractured, the device triggered a microswitch that halted the timer and presented the specimen's fracture life.  
This fatigue testing machine can change the loading frequency by using an inverter in the induction motor driving unit. The minimum and maximum frequencies within the testable frequency range were tested. In this fatigue test, the loading frequency was set to 7 Hz and 70 Hz, and the fatigue tests were conducted as shown in Table 2. The fatigue tests were performed until failure or up to a limiting lifetime of \(2\times 10^{7}\) load cycles. The tests were performed in the air at room temperature.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **C** & **Si** & **Mn** & **P** & **S** & **Ni** & **Cu** & **Cr** \\ \hline JIS S10C & 0.10 & 0.23 & 0.40 & 0.018 & 0.008 & 0.02 & 0.01 & 0.02 \\ JIS S25C & 0.25 & 0.19 & 0.41 & 0.021 & 0.019 & 0.06 & 0.10 & 0.12 \\ JIS S35C & 0.35 & 0.26 & 0.67 & 0.016 & 0.007 & 0.04 & 0.05 & 0.10 \\ JIS S45C & 0.45 & 0.20 & 0.72 & 0.015 & 0.014 & 0.01 & 0.01 & 0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of each specimen (mass%).  
Figure 1: Configuration of the fatigue specimen.
# Utilizing Multi-Dimensional MmWave MIMO Channel Features for Location Verification
## 5 Numerical Results
### Impact of \(k_{a}\) on the Authentication Performance  
To investigate the impact of the distance between Eve-Bob and Alive-Bob on performance, we need to characterize \(\sigma_{a_{l,XB}}^{2}\) being the variance of the path gain of the \(l\)-th propagation path from the transmitter \(X\) to Bob. According to [31], baseband path gain \(\alpha_{l,XB}\) is written as \(\alpha_{l,XB}=\beta_{l,XB}\exp(-\frac{2\pi f_{c}d_{l,XB}}{c})\), where \(\beta_{l,XB}\) is the attenuation coefficient of \(l\)-th propagation path and \(f_{c}\) is carrier frequency. According to the Central Limit Theorem, the path gain can be modeled as a zero-mean complex circular symmetric Gaussian process and for large-scale fading channels, \(\sigma_{a_{l,XB}}^{2}\) can be modeled by applying ([32], Chapter 2), as  
\[\sigma_{a_{l,XB}}^{2}=K\bigg{(}\frac{d_{0}}{d_{l,XB}}\bigg{)}^{\varsigma}\Omega, \tag{67}\]  
where \(K\) is a reference path gain value; \(d_{0}\) is a reference distance for antenna far-field; \(\varsigma\) is a path loss exponent ranging between 2 and 5 in free space propagation wireless environments; and \(\Omega\) is a shadowing factor modeled as a log-normal random variable. Because the distance between the transmitter and receiver is much larger than antenna separation, we can use the approximation \(d_{l,XB}\approx\cdots\approx d_{L,XB}=d_{XB}\) for \(X=\{A,E\}\). From (67), we have \(\sigma_{a_{l,XB}}^{2}\approx\cdots\approx\sigma_{a_{l,XB}}^{2}=\sigma_{ax_{ BB}}^{2}\). Both [PERSON] and [PERSON] are assumed to be randomly deployed at arbitrary locations in a circular area centered on [PERSON], and there is no shadow fading in that area. By using (67), \(k_{a}\) by dB value without shadow fading is given by \(k_{a}=10\varsigma\log\Big{(}\frac{d_{AB}}{d_{XB}}\Big{)}\). We make an assumption (without loss of generality) that [PERSON] is in a fixed location, i.e., \(d_{AB}\) is a constant and we adjust the distance between [PERSON] and [PERSON] \(d_{EB}\) to obtain different \(k_{a}\).  
We show in Figure 5 how \(P_{d}\) varies with \(k_{a}\) under different \(P_{f}\)s and with the settings of (SNR = 10 dB, \(N_{r}\) = 128, \(L\) = 25, \(k_{\phi}\) = 2 dB, \(k_{\theta}\) = 2 dB). From Figure 5, \(P_{d}\) increases when \(k_{a}\) increases for a given \(P_{f}\). This clearly indicates that if [PERSON] is closer to [PERSON], she might be successfully detected by [PERSON], and [PERSON] might select an appropriate location in which she has a high probability of succesfully impersonating [PERSON]. In addition, the authentication performance under \(P_{f}\) = 0.08 outperforms that of the others, and the performance under \(P_{f}\) = 0.02 is poorest. This is because a lower \(P_{f}\) value means a stricter performance requirement and there would be a trade-off between \(P_{d}\) and \(P_{f}\). We can also see from Figure 5 that when \(k_{a}\) is fixed, \(P_{d}\) increases when \(P_{f}\) increases.
# Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
## 5 Segmented Global Path Planning
### Path Planning Algorithm  
A* algorithm is one of the most widely applied algorithms in global path planning in the indoor environment. As a heuristic algorithm, it uses both of shortest path and heuristic searching, so the cell in the grid map is computed by the value:  
\[\text{f(n)}=\text{g(n)}+\text{h(n)}, \tag{1}\]  
where g(n) is the length of the path from the start cell to the current cell through the selected sequence of cells, and h(n) is the heuristic distance from current cell to the goal. Heuristic provides the search direction pointing to the goal point. However, it does not take the room topological information into account, so there is a serious risk that A* provides the wrong search direction, which leads to large computational time and inefficient navigation, as shown in Figure 6a. Obviously, the map of the indoor environment (Map) is composed of multiple maps of different rooms (Map\({}_{i}\)):  
\[\text{Map}=\Sigma\text{Map}_{i}, \tag{2}\]  
and the robot must pass through the door when it moves from one room to another. Therefore, we propose to separate the global path into each topological area and use the APNs on both sides of the door as the endpoint of every path to run the path planning respectively, as described in Algorithm A3 in Appendix A. In this context, the heuristic can provide a more accurate search direction.  
To obtain the sequence of room passed when robot move from the start point to the goal point, we carry on topological path planning based on the topological map, as shown in Figure 6b. The red dotted line represents the topological path.  
Thus, the robot will pass through room B, room D, and room E, which are stored in the room sequence. We infer the corresponding ANPs sequence from the room sequence, and then the global path is segmented into five parts. In Figure 6c, blue dots indicate the ANPs sequence, and the red dotted line indicates the global path. Figure 6d-h shows the results of every path planning and their traversed cells. Through calculation, the number of traversed cells of A* is 30,571, while the number of  
Figure 5: Auxiliary navigation points. (**a**) Coordinates of the door endpoint, E\({}_{1}\) and E\({}_{2}\); dominant directions of the room, L; room semantic information, Room\({}_{1}\) and Room\({}_{2}\). (**b**) Perpendicular line A\({}_{1}\)A\({}_{2}\). (**c**) Point and upward orientation. (**d**) Point and downward orientation.  
our method is 1694. The decrease is 94.46%. It is found that our method provides a more accurate search direction for path planning in each topological area by using ANPs and greatly reduces the traversed cells and computational time.
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 2 The System Model
### The Uplink Communication Model
The channel matrix from the RIS to the BS, \(\mathbf{h}_{k}^{rb}\), and the channel vector from sensor \(n\) to the RIS, \(\mathbf{h}_{n,k}^{sr}\), are given by [16]  
\[\mathbf{h}_{k}^{rb} =\sqrt{d_{B,k}^{-\alpha_{B}}}\Bigg{(}\sqrt{\frac{\delta_{B}}{ \delta_{B}+1}}\tilde{\mathbf{h}}_{k}^{rb,l}+\sqrt{\frac{1}{\delta_{B}+1}} \tilde{\mathbf{h}}_{k}^{rb}\Bigg{)}, \tag{12}\] \[\mathbf{h}_{n,k}^{sr} =\sqrt{d_{R,n,k}^{-\alpha_{B}}}\Bigg{(}\sqrt{\frac{\delta_{R}}{ \delta_{R}+1}}\tilde{\mathbf{h}}_{n,k}^{sr,l}+\sqrt{\frac{1}{\delta_{R}+1}} \tilde{\mathbf{h}}_{n,k}^{sr}\Bigg{)}, \tag{13}\]  
where \(d_{B,k}\) and \(d_{R,n,k}\) represent the distances from the BS to the RIS and from the RIS to sensor \(n\), respectively. \(\alpha_{B}\) and \(\alpha_{R}\) are the path loss exponents, while \(\delta_{B}\) and \(\delta_{R}\) denote the Rician factors. The terms \(\tilde{\mathbf{h}}_{k}^{rb,l}\in\mathbb{C}^{G\times M}\) and \(\tilde{\mathbf{h}}_{n,k}^{sr,l}\in\mathbb{C}^{M\times 1}\) represent the LoS components from the RIS to the BS and from the sensor to the RIS, respectively. The small-scale fadingcomponents, \(\widetilde{\mathbf{h}}_{k}^{rb}\in\mathbb{C}^{G\times M}\) and \(\widetilde{\mathbf{h}}_{n,k}^{sr}\in\mathbb{C}^{M\times 1}\), follow the complex Gaussian distributions \(\mathcal{CN}(\mathbf{0}_{G\times M},\mathbf{I}_{G\times M})\) and \(\mathcal{CN}(\mathbf{0}_{M},\mathbf{I}_{M})\), respectively. Therefore, according to the uplink NOMA decoding protocol with successive interference cancellation (SIC), the signal from sensor \(n\) is decoded after those from the sensors with smaller indices (i.e., \(j<n\)), whose signals are assumed to have been successfully decoded and removed at the BS. Thus, the residual interference with sensor \(n\) originates only from the sensors with indices greater than \(n\). The signal-to-interference-plus-noise ratio (SINR) of sensor \(n\) is given by  
\[\gamma_{n}^{k}=\frac{p_{n}^{k}\left\|\mathbf{h}_{n}^{k}\right\|^{2}}{\sum_{j= n+1}^{N}b_{j,k}^{k}p_{j}^{k}\left\|\mathbf{h}_{j}^{k}\right\|^{2}+\sigma_{k}^{2}}, \tag{14}\]  
where \(\sigma_{k}^{2}\triangleq\sigma_{1,k}^{2}\left\|\mathbf{h}_{k}^{rb}\mathbf{ \Theta}_{k}\right\|^{2}+\sigma_{2,k}^{2}\) accounts for the effective noise at the BS, including thermal noise from both the RIS and the BS receiver. In digital communication theory, the SER for a single sensor is expressed in terms of the SINR as \(\text{SER}=Q\big{(}\sqrt{\gamma}\big{)}\), where \(Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}e^{-\frac{x^{2}}{2}}\ \mathrm{d}t\) denotes the Gaussian Q-function. By applying the above theory, the SER can be approximated as \(\text{SER}\approx e^{-\gamma}\)[3]. For sensor \(n\) at time \(k\), the SER is approximated as  
\[\text{SER}_{n}^{k}\approx e^{-\gamma_{n}^{k}}. \tag{15}\]
# Efficient Gamma-Based Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
## 3 Methodology  
In this section, the Gamma-nonlinear enhancement curve is formulated as a specialized mapping operator for low-light image restoration. Its theoretical foundation is derived from adaptive Gamma correction, ensuring pixel-wise adjustments and separate enhancement on the three RGB channels. To preserve critical feature discriminability during illumination transformation, a lightweight convolutional channel attention (GCA) module is strategically incorporated, achieving enhanced perceptual quality within a computationally efficient framework. Subsequent subsections are organized as follows: First, mathematical derivations of the GLE curve are formalized, followed by architectural specifications of the GCA mechanism. The hierarchical neural network topology is then delineated, concluding with an introduction of the hybrid loss function governing model training.
## 2 Experimental Set-Up and Test Methodology
### Experimental Plant  
The experimental system used to evaluate the configurations is schematized in Figure 1 for the direct expansion configuration and in Figure 2 for the indirect arrangement.  
The cycle, the same for both layouts, corresponds to a single-stage vapour compression system driven by a semi-hermetic compressor of 6.51 m\({}^{3}\cdot\)h\({}^{-1}\) displacement at 1450 rpm, a brazed plate condenser and a liquid receiver. The condenser, whose characteristics are detailed in Table 1, is cooled with an external loop working with water that allows for controlling the volumetric flow rate and inlet temperature as described in [23]. For the direct configuration (Figure 1), the refrigerant is sent through a 3/8\" 13.6 m liquid line to a commercial cabinet with doors (1875 mm long, 2071 mm height and 890 mm width). The cabinet, with a 1700 W rated cooling load at 25 \({}^{\circ}\)C and 50% HR, incorporates a controller that regulates the electronic expansion valve, whose superheating set point was set at 5 K.  
Figure 1: Schematic diagram of direct expansion refrigeration system.  
The evaporator of the cabinet for the direct expansion configuration is the commercial finned-tube evaporator used by the cabinet's manufacturer, whose characteristics are detailed in Table 1. Then the refrigerant is sectioned through a 1/2\" 13.4 m vapour line. In the indirect configuration (Figure 2), the refrigerant evaporates in a brazed plate heat exchanger (Table 1) controlled by another electronic expansion valve maintaining a degree of superheat of 5 K. This evaporator cools the secondary loop, working with Temper \(-20^{\oplus}\), which is then sent to the cabinet. The heat exchanger in the cabinet is another finned-tube heat exchanger (Table 1), again the commercial heat exchanger used by the cabinet's manufacturer. The SF is driven by a water pump (Wilo Yonos PICO-STG-15/1-13) with incorporated inverted with a nominal power of 74 W at 4800 rpm.  
For both configurations, the evaporator fans of the cabinet (three in total) were in continuous operation. The controller of the cabinet regulates the operation of the cooling devices through the air temperature measurement at the return to the heat exchanger of the cabinet according to the set point. For the direct configuration, this controller regulates the expansion valve of the cabinet and, for the indirect configuration, regulates the expansion valve of the brazed plate evaporator. The controller of the cabinet performs defrosting every eight hours through 2000 W electrical resistors. The defrosting period ends when the temperature at the evaporator fin surface reaches 5 \({}^{\circ}\)C. During defrosting, for the direct configuration the expansion valve of the evaporator is forced to close, and for the indirect, the expansion valve of the brazed plate heat exchanger and the secondary fluid pump are closed.
# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
### Objectives  
Based on the findings above, We developed a FL algorithm based on regularization and data sharing (FedRDS) to alleviate client drift issues caused by skewed label distribution. FedRDS addresses these issues by using a combination of regularization and data-sharing strategies. Specifically, FedRDS incorporates a regularization term into the FedAvg local update process, allowing the local optimal targets to closely approximate the global optimal targets and minimize the impact of client drift on the global target. Additionally, FedRDS uses data sharing to relieve the unfavorable factors of heterogeneous data distribution among clients. The proposed method provides improved the performance over existing solutions by combining regularization and data sharing. Moreover, FedRDS employs the widely used Stochastic Gradient Descent (SGD) optimization technique, which enhances the overall stability of the optimization process in heterogeneous network environments.  
Our work has made the following contributions:  
* We design an FL algorithm FedRDS for non-IID data, which restricts the update of local models to approach the global model via regularization and data sharing gradually, as well as narrows the gap between client data distribution to relieve the negative influence of client drift.
* We conduct extensive experiments on the MNIST, Fashion-MNIST, SVHN, and CIFAR-10 datasets. From the experimental results, it can be seen that the FedRDS outperforms other algorithms in terms of efficiency.  
The other parts of this paper are organized as follows: Section 2 elaborates on related work; Section 3 outlines the FedRDS algorithm and data-sharing principles; Section 4 consists of analyses based on experiments and concluding remarks; and Section 5 offers the conclusion.
# Eye State Identification Based on Discrete Wavelet Transforms
## 2 Theoretical Background
### Linear Discriminant Analysis  
The main goal of LDA is to project the original multidimensional data into a lower dimensional subspace with higher class separability [46; 47]. For this reason, it is also widely used as a dimensionality reduction algorithm as well as a classifier. LDA assumes that all the classes are separable and that they follow a Gaussian distribution. Let us consider a binary classification problem with training samples \(D=\{(\mathbf{x}(n),y(n)),(\mathbf{x}(n+1),y(n+1)),\ldots,(\mathbf{x}(n+N-1),y (n+N-1))\}\), where \(\mathbf{x}\in\mathbb{R}^{d}\) is the input feature vector and \(y\in\{-1,1\}\) is the class label. LDA seeks a hyperplane in the feature space that separates both classes. In the case of a multi-class problem with more than two classes, several hyperplanes are used [23]. The optimal separating hyperplane can be expressed as  
\[f(\mathbf{x})=\mathbf{w}\cdot\mathbf{x}+b, \tag{2}\]  
where \(\mathbf{w}\) is the projection vector and \(b\) is a bias term. The projection vector \(\mathbf{w}\) is defined as [48]  
\[\mathbf{w}=\mathbf{\Sigma}_{c}^{-1}(\boldsymbol{\mu}_{1}-\boldsymbol{\mu}_{2 }), \tag{3}\]  
Figure 1: Filter scheme for a multi-resolution DWT of an EEG signal sampled at \(128\,\mathrm{Hz}\), where \(g(n)\) and \(h(n)\) represent the impulse response of the high- and low-pass filters, respectively, and \(\downarrow 2\) represents downsampling by a factor of 2.  
where \(\mathbf{\mu}_{i}\) is the estimated mean of the \(i\)-th class and \(\mathbf{\Sigma}_{c}=\frac{1}{2}(\mathbf{\Sigma}_{1}+\mathbf{\Sigma}_{2})\) is the estimated common covariance matrix, i.e., the average of the class-wise empirical covariance matrices [48]. The corresponding estimators of the covariance matrix and the mean are calculated as follows  
\[\mathbf{\Sigma}=\frac{1}{N-1}\sum_{i=1}^{N}(\mathbf{x}(i)-\mathbf{\mu})(\mathbf{x}(i)-\mathbf{\mu} )^{T}, \tag{4}\]  
\[\mathbf{\mu}=\frac{1}{N}\sum_{i=1}^{N}\mathbf{x}(i). \tag{5}\]  
Once the projection vector has been calculated in the training phase, the predicted class for an unseen feature vector \(\mathbf{x}\) is determined by \(sign(f(\mathbf{x}))\). Thus, the assigned class to \(\mathbf{x}\) will be \(1\) if \(f(\mathbf{x})>0\) and \(-1\) otherwise.  
This classification algorithm will be applied over the features extracted with the DWT in order to estimate the ocular state of the user. Therefore, it will face a binary classification problem, i.e., open eye state (oE) or closed eye state (oE) state.  
LDA is probably the most used classifier for BCI design [32]. It has been successfully applied in different BCI systems, such as P300 spellers [49], MI-based applications for prostheses and orthosis control [50; 51], among others [23; 52]. LDA has a lower computational burden and faster rates than other popular classifiers such as SVM or ANN, which makes it suitable for the development of online BCI systems [23; 31].