esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
###### Abstract
[25] used Wi-Fi and visible light RSS as fingerprints to achieve positioning, which was then applied to a simultaneous localization and mapping (SLAM) system. However, those existing positioning methods still require the positioning target within multiple anchors' coverage area, which not only limits the size of the localization area but also necessitates that the receiver must have a wider field of view (FOV) to ensure signal reception. To this end, we propose an advanced hybrid RF-VLP indoor positioning system that synergistically integrates the strengths of radio frequency and visible light technologies to overcome the inherent limitations of conventional methods. The proposed approach enables precise localization by utilizing a single photodetector (e.g., a photodiode or solar cell) to acquire signals from a single LED anchor, eliminating the need for multiple LEDs or fixed RF anchors. To enhance system efficiency and scalability, we incorporate a BLE mesh network to facilitate inter-receiver communication and provide auxiliary spatial information for positioning. This system combines VLP with BLE-based RF localization to address challenges faced by traditional positioning systems, such as low accuracy in RF localization and the dependence on multiple anchors in VLP systems. By reducing infrastructure requirements and enhancing system adaptability, this approach is well suited for deployment in complex indoor environments. Experiments show that this system achieves an overall root mean square error (RMSE) of 26.1 cm within an area of 1.93 m \(\times\) 1.93 m, corresponding to an accuracy enhancement of approximately 28.5% over the traditional BLE-only localization approach. Furthermore, when compared to single LED-only positioning, an improvement of 74% is achieved due to the single LED anchor yielding localization error over 1 m.  
This paper is organized as follows: Section 2 presents the system overview along with the proposed localization algorithm. Section 3 details the prototype design and experimental setup, while Section 4 offers an in-depth analysis of the experimental results. Finally, Section 5 concludes the paper and discusses potential future research directions.
# Efficient Next-Hop Selection in Multi-Hop Routing for IoT Enabled Wireless Sensor Networks
###### Abstract
Moreover, cyberspace should also be managed accordingly to efficiently handle sensing bottlenecks and delays in data exchange. This paper proposes a scheme called EMRP that predicts routing paths by considering and analyzing the residual battery of sensors. The major contributions of the paper are as follows. 1. We reviewed the relevant literature for adaptive next-hop selection mechanisms and identify the research problems. 2. We devised a time slot selection mechanism based on the connection degree and maximum rank to collect data from all the member nodes. 3. Using the time slot selection mechanism, we proposed and developed the EMRP, a next-hop selection algorithm for multi-hop routing based on the residual node energy. The remaining part of the paper is arranged as follows. The literature review is discussed in Section 2. Section 3 formulates the problem and the system model. Section 4 presents the proposed EMRP scheme. Section 5 presents the results and analysis. Section 6 concludes the proposed research work and considers future work.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 3 Dynamic Characteristics of Positioning Stage
### Variation of Modal Frequency with Preload  
Figure 8 shows the frequency responses of the stage assessed at different positions, which are associated with the vibration motion of pitching, rolling, and yawing modes, respectively. It was found from the frequency responses that the stage with different preloads exhibited different vibration frequencies and amplitudes. For stages with low, medium, and high rigidity, the vibration frequencies of the yawing mode were measured as 246, 295, and 323 Hz, respectively. A similar phenomenon was also observed in rolling and pitching motions.  
Table 3 compares the vibration frequencies of the positioning stage obtained by the vibration test and finite element modeling. This shows that the positioning stage with  
Figure 8: Frequency responses of the stage with different preloads: (**a**) yawing mode; (**b**) rolling mode; and (**c**) pitching mode.  
Figure 7: Fundamental vibration modal shapes of stage: (**a**) yawing mode; (**b**) rolling mode; and (**c**) pitching mode.  
high preload linear guides had a higher modal frequency. The frequencies of the three vibration modes increased by approximately 20% and 31%, respectively, when the linear guide preload was adjusted from lower to medium and higher amounts, respectively, equivalent to an increase in the stage rigidity by 44% and 72%. In addition, according to the measured frequency responses, the stage with lower preload exhibited higher modal amplitude compared with the higher preloaded stage. Moreover, the modal amplitude could be affected to different extents according to the linear guide preload.  
The finite element modeling predictions agreed well with the measured values, with a maximum difference of 6.5%. The influences of the linear guide preload on the vibration characteristics of the stage were also observed from the finite element prediction. The variations in the predicted frequencies ranged from 16% to 35% when the preload of the stage was changed from low preload to medium and high preloads. This indicates that the modeling of the linear guide plays an important role in describing the mechanical characteristics of a physical feeding stage, particularly the contact stiffness of the rolling elements, which is a vital factor in determining the rigidity of the linear guide and the stage.  
As demonstrated in the radial compressive tests, the linear guide preload is caused by the interference between the rolling elements and raceways. A high preload is induced in the sliding blocks with a larger interference at the rolling interface. The interference decreases once the rolling elements or the raceways wear out during long-term service, which will decrease the preload of the sliding block, and hence reduce the rigidity of the stage as well as its natural vibration frequency. Observing the frequency response function of the stage clearly demonstrates the tendency that the preload not only affects the vibration frequency but also the modal amplitude.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Results
#### 6.3.3 Detection Capacity  
In this section, performance of GAFOR in terms of detection capacity (also referred as filtering capacity or detection power) is compared to existing schemes as shown in Figure 13. The network size is represented with the \(x\)-coordinate while detection capacity by the \(y\)-coordinate. The robustness of the our scheme is evaluated using different network sizes and \(FTRs\). The detection capacity is defined in Equation (12) as  
\[DetectionCapacity{=}\frac{NumberofFilteredAttacks}{NumberofAttacks}. \tag{12}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **FND** & & & **PND** & \\ \hline
**FTR** & **CCEF-RC** & **DEF** & **GAFOR** & **CCEF-RC** & **DEF** & **GAFOR** \\ \hline
50\% & 1.02 & 2.92 & 3.64 & 1.8 & 2.18 & 2.29 \\
70\% & 0.98 & 2.81 & 3.38 & 1.88 & 2.18 & 2.31 \\
90\% & 1.00 & 2.71 & 3.10 & 1.79 & 2.21 & 2.34 \\ Avg. & 1.00 & 2.81 & 3.37 & 1.82 & 2.19 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network Lifetime—FND and PND.  
Figure 12: Energy-efficiency.  
It is observed that, on average, the detection capacity of compared schemes is similar with trivial differences, while significantly extending network lifetime.
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 5 Experiment  
### Experiment Setup  
To validate the collision-free path planning using the proposed MPAPF, experiments were conducted in a complex situation only for collision-free path planning. The manipulator was fixed at an arbitrary position without a mobile base due to the limitations of the experimental environment. As shown in Figure 19, the experiment environment consisted of a shelf with four targets, one of which was placed behind a partition to make it difficult to approach.  
The experiments were conducted under the assumption that the positions of obstacles and targets were known without sensors for detecting the environment. Both the shelf and the partition were represented in the form of point clouds. As mentioned in Section 3, in order to reduce computation time, all shelf parts and partition were represented as clusters. The distance threshold between obstacle clusters and joints was set to 100 mm and the safety distance for the repulsive potential was set to 50 mm. All point clouds were structured at intervals of 50 mm and 110 points for the shelf and 44 points for the partitions, respectively. The position selection algorithm was not applied and only the experiment verification of collision-free path planning was conducted. The target object arrival orientations were arbitrarily set. The paths from the initial posture to each target were generated using MPAPF and were organized to return to the initial position in reverse. The same manipulator and system were used in the experiment as in the simulator. The experiment was conducted by directly inputting the joint trajectory calculated on the PC into the manipulator through U2D2 communication converter by Robotis. The manipulator was equipped with an integrated device--DYNAMIXEL by Robotis--which allows the user to input an angle value, enabling the robot to follow it without requiring a controller or driver. The overall environment system diagram is shown in Figure 20.  
Figure 19: Experiment environment. Four targets are shown inside the red boxes and the circled numbers indicate their picking sequence.
## 2 Methodology
### Numerical Setup
For the initial position in \(y\)-direction, the mean values of all experiments for each orientation were used. Since the mass distribution was not symmetrical in the experiments, the container was 3D modelled as shown in Figure 2. The container trajectories and velocities shown in the paper are referred to the centroid of the container. The inertia of the container was first calculated in DualSPHysics by modelling the container as in Figure 2 with a \(dp\) = 0.0005 m. This had the only purpose of letting DualSPHysics compute very accurately the inertia matrix (i.e., the \(3\times 3\) matrix of the moments of inertia). Since it was known that the mass distribution in the container was not symmetrical and the position of the centre of mass was not known from the experiments, this position was prescribed and calibrated with the experimental data. An eccentricity between 0.0025 m and 0.004 m in the longer dimension was considered in a series of calibration tests, with the latter value giving the best results and therefore used in the present study. The addition of the eccentricity resulted in a larger effect at the onset of the container motion with differences noticeable mainly in the \(y\)-direction; the differences in terms of centroid velocity along the \(y\)-direction (which have maxima of order of \(10^{-1}\) m/s) are up to 0.134 m/s at the motion onset and they become negligible after the container stabilised in the dam break flow. The centre of mass and inertia, taken from the high-resolution inertia matrix computation were subsequently prescribed at the start of each full-size simulation where a solid container was used due to particle size restrictions. Figure 2: 3D modelled container for high resolution “container only” simulation. Finally, every simulation was performed on a Windows server equipped with a Nvidia Tesla P40 24 GB GPU, Intel Xeon Silver 4108 and 64 GB of Random Access Memory (RAM). With this configuration, 5 s of simulated time resulted in approximately 13 h of computing time applying the coupled DualSPHysics-CHRONO model.
# Defense and Security Mechanisms in the Internet of Things: A Review
## 6 Discussion
IDSs offer a more reactive approach, enabling analysis and incident response after detection. IPSs, on the other hand, introduce an element of proactive protection, but their operation may introduce an additional risk of blocking legitimate traffic (so-called false positives). \begin{table}
\begin{tabular}{l l} \hline \hline
**Reference** & **Method, AI Techniques, Environment with Datasets and Key Results or Findings** \\ \hline
[141] & • & IDS, anomaly detection \\  & • & GB, DT \\  & • & IoT; NSL-KDD, BoT-IoT, IoT-23 \\  & • & Model performance measures more than 99.9\% \\ \hline
[142] & • & DDoS attack detection \\  & • & LSTM, CNN \\  & • & Agriculture 4.0, CIC-DDoS2019 \\  & • & Precision 100\% \\ \hline
[143] & • & DoS attack detection \\  & • & DT, RF, KNN, SVM \\  & • & IoT, IoTID20 \\  & • & Best results for DT and RF \\ \hline
[144] & • & anomaly detection \\  & • & Multi-scale discrete wavelet decomposition and a dual graph attention network \\  & • & IoT \\  & • & The multi-head self-attention mechanism enhances the accuracy of anomaly detection by outputting prediction and reconstruction findings, while simultaneously decreasing the model parameters \\ \hline
[145] & • & Anomaly detection and prediction to mitigate \\  & • & Multiple filter ensemble with a swarm-based wrapper, bagging classifier \\  & • & IoT, NSL-KDD IDS \\  & • & Accuracy 99.8\% \\ \hline
[146] & • & blackhole attacks detection, attack and anomaly detection \\  & • & K-RF means algorithm with Flawless Trust formulation \\  & • & MANET-IoT \\  & • & Accuracy 99.8\% \\ \hline
[147] & • & anomaly detection \\  & • & Wavelet transform and K-means clustering \\  & • & Intel Berkeley Research Lab dataset \\  & • & Accuracy 97\%, fast anomaly detection \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of anomaly and cyber-attack detection methods. Also, in Figure 3, we summarise the accuracy and precision of the proposed defense methods confirmed by the authors. Here, we assigned only solutions where such values are indicated. The authors of other papers did not highlight received accuracy. It is worth mentioning that the proposed methods achieve high accuracy in anomaly and cyber-attack detection. Due to the limited amount of information available regarding the effectiveness (accuracy) of the methods considered for detecting attacks and anomalies in IoT environments, we decided to conduct experimental studies. These studies aimed to verify the effectiveness of selected defense mechanisms based on various datasets commonly used in the literature, such as CIC-DDoS2019 or IoTID20. The experiments focused on methods based on algorithms widely used in attack detection in IoT environments. The choice of algorithms such as Random Forest (RF), Long Short-Term Memory (LSTM), Support Vector Machines (SVMs), or Graph Neural Networks (GNNs) was derived from how frequently they appear in the scientific literature, the effectiveness reported in existing studies, and the potential for their application in various IoT contexts.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### Fingerprint Image Preprocessing
#### 3.2.1 Histogram Equalization  
Histogram equalization is a process that aims to distribute the gray levels in the image so that they are equally distributed across their range. This process efficiently resets the brightness value of every pixel based on the image histogram and intends to extend the pixel value distribution to increase the perceptional information. In this work, contrast limited adaptive histogram equalization (CLAHE) is employed. While traditional histogram equalization works on the entire image, the CLAHE operates locally on the image in small regions called tiles. CLAHE increases the contrast of small tiles and incorporates the neighboring tiles in the image using bilinear interpolation which removes the boundaries that are artificially caused. Additionally, the 'clip limit' factor is applied to avoid excessively saturating the image specifically the inhomogeneous areas that have high peaks in the histogram of certain image tiles due to many pixels that fall within the same range of gray level [16]. Figure 4 shows the histogram-equalized results of a fingerprint image where improvements in average intensity and contrast are apparent. Furthermore, the histogram distributed over the entire intensity increases the contrast and the average intensity level in the equalized image histogram is higher (lighter) than the original intensity level [17].
# Carbon Nanocomposites in Aerospace Technology: A Way to Protect Low-Orbit Satellites
## 2 Corrosion Factors and Mechanisms in Low Orbit Environment
### LEO Environmental Challenges and Material Requirements
At the altitude of 300 km, 80% of gaseous species are _AO_ and 20% is composed of nitrogen molecules, with the _AO_ density varying from about 10\({}^{8}\) to (2-4) \(\times\) 10\({}^{9}\) atoms \(\times\) cm\({}^{-3}\), depending on the level of solar activity [34]. The exact flux value is determined by various factors such as altitude, orbital inclination, solar activity and time of day. Despite the low number of species present, high velocities of spacecraft provide a translational energy of around 5 eV per _AO_ atom, which is sufficient to cause oxidative decomposition and bond breaking in polymers [35; 36]. Interactions with _AO_ cause surface erosion, chemical changes, morphological changes, the formation of particulates, material contamination and space vehicle glow which interfere with the function of optical sensors [31]. Charge prevention or minimization is another aspect which needs to be considered when undertaking the design of spacecraft materials. Since polar regions are characterized by low-density high energy plasmas and other regions can contain low energy plasmas, spacecraft can accumulate a static electric charge that could interfere with the function of spacecraft electronic systems. To prevent this, surface properties of the material such as  
Figure 4: The impact of the collision with 1.59 mm aluminium spheres on the integrity of solar arrays. The experiment used sphere velocities typically detected in the collisions between space debris and satellites. Reprinted with permission from [PERSON] et al., 2017 [32]. © 2017 Elsevier. electrical resistivity, secondary electron emissivity and photoelectron emissivity should be properly selected [33]. Thus, superior tensile strength, higher thermal stability and outstanding chemical resistance are among the key factors that need to be considered during the selection of materials for low orbit satellites. Some other properties, e.g., electron emissivity, could also be important.
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results
### Results on ZH300
#### 5.1.2 PRD Types  
We used the SP-CRN (LSTM) as the model with periodicity component to perform the PRD proposals. Note that SP-CRN refers to SP-CRN (LSTM) from now on. An input sequence length of five months was selected as the baseline setting because it was the best for the SP-CRN model in the previous experiment (Figure 5). Table 1 shows the comparison of SP-CRN for the three proposals in different settings. The SD shows the dispersion to the average RMSE value. The SP-CRN-1 (1,1,0) with an RMSE of 74.22 m is slightly better than the SP-CRN-1 (0,1,0) baseline result (RMSE of 76.89 m). The result of SP-CRN-1 (1,1,0) indicated that only the nearby-time representation of plus and minus one month in one prior periodic cycle was more important than the more than two months (\(n_{pre}>1\)) for ZH300 prediction. All settings for the SP-CRN-2 proposal results showed a worse prediction accuracy compared to the baseline except SP-CRN-2 (0,4,0) with an RMSE of 70.58 m, indicating the importance of the information of the previous four periodic cycles for ZH300 prediction and possibly for the four-year oscillation of ENSO [53]. All settings of SP-CRN-3 outperformed the baseline result, of which SP-CRN-3 (0,0,2) had the best with an RMSE of 67.97 m. The results clearly showed that the change over cycles significantly improved ZH300 prediction accuracy. In summary, using SP-CRN for periodicity in ZH300 prediction was most effective for periodic-depth differencing representation (PRD-3) followed by periodic-depth representation (PRD-2) and nearby-time representation (PRD-1).  
Figure 5: The average RMSE of the comparison methods in different length of sequential input for ZH300 prediction. The error bar indicates the SD of the RMSE.
## Materials and Methods  
### Sliding Window for Real-Time Processing  
Figure 3 shows a connection example of R peaks detected per successive sliding window for real-time processing. In the figure, \(R_{t}(n)\) denotes the \(n\)-th detected R-peak in the \(t\)-th sliding window. In the proposed method, the R peak is detected in the sliding window unit for real-time processing. Thereafter, overlapping R peaks (\(R_{t}(n-1)~{}=~{}R_{t+1}(1)\) and \(R_{t}(n)~{}=~{}R_{t+1}(2)\) in the figure) are detected between the sliding windows to merge the detected R peaks for each sliding window, and the detected R peaks in the front (\(t\)-th) and rear (\(t\)+1-th) sliding windows are connected using the detected overlapping R peaks. The setting for the sliding window is set considering bradycardia. To get a successive R peak sequence even for bradycardia (less than 60 heartbeats per minute), the length of the sliding window and overlapped interval between sliding windows should be at least 3 s and 1 s, respectively. In the proposed method, the size of sliding window is set to 2000 samples (about 5.6 s) based on MIT-BIH DB (360 samples/s), and the step size of sliding window is set to 1600 samples. This means the overlapped interval between sliding windows is 400 samples (about 1.1 s). Thus, the length of the sliding window and overlapped interval can be adjusted according to the sampling rate of the measured ECG signal.