esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Indoor Localization Architecture and Methodology
### The Extended Kalman Filtering Algorithm Model  
This paper uses the EKF filtering [29; 30] function to filter the obtained RSSI average value and effective CSI value, and then combines the distance-related mathematical ranging model to reduce the impact of noise on the system and improve the ranging accuracy. The observation equation comes from the RSSI-based ranging model and effective CSI-based ranging model, which is shown as follows.  
\[h[X(k)]=\left[\begin{array}{c}RSSI_{antenna_{1}}-10\sigma_{1}log_{10}(dist(X_{k},X_{ ref_{1}})/d_{0})\\ \vdots\\ RSSI_{antenna_{L}}-10\sigma_{L}log_{10}(dist(X_{k},X_{ref_{1}})/d_{0})\\ CSI_{effantenna_{1}}-\frac{c}{f_{c}}\sqrt{\frac{\sigma}{(4\pi dist(X_{(k)},X_{ (ref_{1})}))^{n}}}\\ \vdots\\ CSI_{effantenna_{L}}-\frac{c}{f_{c}}\sqrt{\frac{\sigma}{(4\pi dist(X_{(k)},X_ {(ref_{1})}))^{n}}}\end{array}\right] \tag{18}\]  
where \(d\) is the distance between the transmitter and receiver in indoor environments. \(RSSI_{antenna_{L}}\) and \(CSI_{effantenna_{L}}\) represent the RSSI value and the effective CSI at a distance \(d_{0}=1\) m from the receiver reference point, respectively. \(L=p*q\), where \(p\) and \(q\) represent the number of antennas at the transmitting node and the receiving node respectively. \(c\) is the radio velocity, \(f_{c}\) is the central frequency of CSI. \(n\) is the path loss attenuation factor, and \(\sigma\) is the environmental factor, and \  
\[R_{k}=\begin{bmatrix}R_{11}&0&\cdots&0\\ 0&R_{22}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&0&R_{2L2L}\end{bmatrix} \tag{20}\]  
The \(R_{k}\) value is affected by the indoor environment. When the indoor environment is more complicated, the value is larger. Since the system model is linear, \(A(k)=I\) and \(G=I\).
# Estimation of SAR Average in Rats during 5G NR Chronic Exposure
## 2 Materials and Methods
### Dosimetry Study  
The whole-body average SAR values (exposure volume) are 0.08 W/kg for the general public and 0.4 W/kg for RF EMF workers, regardless of the frequency of radiation [2; 25]. The SAR value is determined as follows:  
\[SAR=\frac{d}{dt}\left(\frac{dW}{dm}\right)=\frac{\sigma|E|^{2}}{\rho}\left[ \frac{W}{kg}\right], \tag{1}\]  
where \(m\), \(t\), and \(W\) are mass [kg], time [s], and energy [J] of the EMF, respectively. \(\sigma\) is the specific electrical conductivity of a biological tissue in [S/m], \(E\) is the E-field strength in a matter [V/m], and \(\rho\) is the density of a matter [kg/m\({}^{3}\)] [3; 25].  
There are several experimental methods for determining SAR, including waveguide methods, calorimetric and thermometric methods, as well as methods based on measuring the EMF components in biomaterials [3; 26]. The methods are not always applicable, as they require special instruments and have certain restrictions when dosimetry is applied to measure radiation absorbed by living things. Therefore, in a real-life setting, it is not always feasible to accurately determine SARs.  
There are many publications [20; 21; 24; 27; 28] on the qualitative assessment of SAR for biological subjects. The studies investigated the frequency dependence of SAR with geometric dimensions of objects, as well as when exposed to different polarizations of radiation.  
In [24], the expressions are given for assessing the whole-body average SAR at the EMF free-space power density \(S_{inc}\), for different ratios \(L/\lambda\), where L, \(\lambda\) are the lengths of the body and the wave, respectively. In particular, for \(L/\lambda>0.4\):  
\[SAR_{avg}=\frac{5.954}{f_{GHz}}\frac{S_{inc}L}{m}\left[\frac{W}{kg}\right], \tag{2}\]  
where \(m\) is the mass of the animal in kilograms, \(L\) is in meters, and \(S_{inc}\) is in W/m\({}^{2}\)[24]. It has been shown that expression (2) describes well the distribution of SAR from the frequency when the object is oriented parallel to the E-polarization. In the limit, expression (2) tends to the \"optical\" description of absorption (\(1-Refl\)).  
In vivo determination of SARs for a group of rats kept in a normal cage was rendered complex due to many impacts. During the prolonged and one-sided EMF exposure in the cage, the rats were located in different proximities from the antenna and were free to move randomly. In addition, the weight of the rats changed during the experiment. All of the above factors contributed to an accurate SAR estimate.  
Given the above, a rough estimate of the SAR can be expressed as:  
\[SAR_{avg}=\frac{5.954}{f_{GHz}}\frac{\langle S_{inc}\rangle\langle L\rangle}{ \langle m\rangle}, \tag{3}\]  
where \(S_{inc}(r)=P_{A}G/4\pi r^{2}\), \(P_{A}\) is the power supplied to the antenna [W], \(G\) is the antenna gain [dB], \(r\) is the distance from the antenna [m], and \(<>\) is the averaging.
## 4 Example Verification and Engineering Application
### Engineering Application
The calculation method for cables B and C mirrors that of cable A and will not be reiterated here. The results for all three cables are summarized in Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Anchor Cable A**} & \multicolumn{2}{c}{**Anchor Cable B**} & \multicolumn{2}{c}{**Anchor Cable C**} \\ \(P\) **(kN)** & \(S\) **(mm)** & \(P\) **(kN)** & \(S\) **(mm)** & \(P\) **(kN)** & \(S\) **(mm)** \\ \hline
234 & 6.79 & 198 & 5.78 & 216 & 6.52 \\
390 & 14.22 & 330 & 12.58 & 360 & 14.65 \\
546 & 20.07 & 462 & 19.03 & 504 & 22.74 \\
624 & 27.83 & 528 & 27.12 & 576 & 30.55 \\
702 & 36.08 & 594 & 34.90 & 648 & 37.44 \\
780 & 42.71 & 660 & 40.25 & 720 & 44.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pull-out test data. Figure 10: Load–axial displacement curves of the anchor cable.
# Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization
###### Abstract  
Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.  
underwater visual localization method; line segment features; PL-SLAM +
Footnote †: journal: Applied  
Article
## 4 Results
### Electron Loss Data Analysis  
Since geomagnetic storms and substorms are known to induce electron precipitation, the first part of the analysis was centred on the geomagnetic indices (Figure 3).  
Our analysis was extended from 25 January to 8 February 2023 and there are perturbed magnetic conditions on the mainshock day (6 February). In the whole time interval considered, we found several electron bursts (\"events\") and some extensive perturbations that could not be correlated to the seismic event. They were detected mainly at low latitudes and around the SAA. Therefore, these loss phenomena were hardly linkable to the Turkey seismic event occurring at medium latitudes. On 28 January 2023 (i.e., 9 days after EQ), we identified three different electron bursts of low duration, visualised from MetOp-01, and NOAA-18 and 19, at latitudes and longitudes close to the EQ epicentre. Detection times occurred at around 12:30, 13:15, and 20:30 UT. The three detections are shown in Figure 22, where the electron flux level of these loss events is notable, reaching 1500-2000 counts\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\cdot\)str\({}^{-1}\), as much as the flux levels detected for the extended phenomena.  
Figure 21: Ionosonde anomaly on 20 November 2022 likely associated to the 6 February 2023 Mw7.8 Turkey EQ (indicated by the black arrow), compared to the relationship between \(\Delta T\cdot R\) and \(M\) previously found by the analysis of 33 M6+ EQs in the vicinity of Japan during the years 1985–2000 [78] (solid line).  
Moreover, a significant and extensive electron loss phenomenon was detected from MetOp-01 on 7 February 2023, at unusual geographical positions around, and always eastwards of, the epicentral area. The record is shown in Figure 23, which describes an electron flux of up to 5500 counts\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\cdot\)str\({}^{-1}\), from 0\({}^{\circ}\) to 30\({}^{\circ}\) in latitude and from 80\({}^{\circ}\) to 90\({}^{\circ}\) in longitude. The loss phenomenon lasted more than 8 min, around 04:15 UT. Contrary to other cases of extended electron precipitation, this phenomenon was not detected by other satellites, therefore limiting its possible time extension. Bouncing altitudes corresponding to these longitudes are much higher than the satellite altitude, so electron detections should have been a very strong perturbation from the trapped conditions.  
Figure 22: Electron bursts were detected by (**a**) NOAA-18, (**b**) NOAA-19, and (**c**) MetOp-01 on 28 January 2023.
## 2 Feature Extraction in Multiple Domains  
### Feature Extraction in the Time Domain  
The statistical methods are widely used to capture key process characteristics that reveal the hidden information of the signal. In traditional ways, the statistics are computed upon the whole sample, losing the time-varied features of non-stationary signals. To overcome this problem, a window-basedcontinuous statistical analysis (CSA) method is proposed to obtain the statistic patterns of a sample in a time scale where the output is time series as shown in Figure 1.  
When a signal with a time length of \(\Delta t\) passes through the continuous statistical analysis, it is converted into several time sequences where each sequence corresponds to a statistic index, e.g., skewness. In this process, a point of the output time sequence is obtained by statistics over a certain width of data region with this point as the center. Instead of calculating over the whole sample, the results by this method are time-dependent, given as  
\[sta(t)_{i,j}=f[s_{i}(t{-}1/2\delta t,\ t{+}1/2\delta t)], \tag{1}\]  
where \(sta(t)_{i,j}\) donates the time series corresponding to the \(j^{\text{th}}\) statistic index of the \(i^{\text{th}}\) sample \(s_{i},f\) is the statistic function, \(t\) is time, and \(\delta t\) is the region width that keeps constant in the calculation. Symmetric extensions of \(\delta t\)/2 are made on the two ends of the signal, separately, to obtain the results in the borders.
## 4 Conclusions  
A Cu/SiO\({}_{2}\)/Pt structure showed the resistive switching property in the atmosphere. Oxygen and H\({}_{2}\)O molecules reacted with the Cu metal to form a Cu\({}_{\mathrm{x}}\)O layer during the forming process, and then the Cu\({}_{\mathrm{x}}\)O layer assisted the Cu ionization process. Thus, Cu ions migrated through the SiO\({}_{2}\) layers under an electric field. Hence, the formation and rupture of Cu conducting filaments are responsible for the RS. However, the Cu/SiO\({}_{2}\)/Pt structure cannot form a Cu\({}_{\mathrm{x}}\)O layer in a vaporless environment and thus did not show the RS property. A GO layer was inserted into the Cu/SiO\({}_{2}\)/Pt structure to fabricate the Cu/GO/SiO\({}_{2}\)/Pt structure. The GO layer was full of oxygen-related groups and hence reacted with the Cu electrode to form the Cu\({}_{\mathrm{x}}\)O layer. The Cu\({}_{\mathrm{x}}\)O layer assisted the Cu ionization process, and Cu conducting filaments could thus be formed within the SiO\({}_{2}\) layer, reversibly enabling RS in a vaporless environment. The RS of the GO sample in the atmosphere was also characterized. Although the GO sample had slightly poorer RS in the N\({}_{2}\) ambient than in the atmosphere, it had high endurance (\(>3000\) cycles) and high retention (\(>10^{4}\) s). Therefore, the Cu/GO/SiO\({}_{2}\)/Pt structure is suitable for next-generation NVM applications.  
Investigation, C.-Y.L.; methodology, C.-P.Y.; funding acquisition, C.-Y.L.; writing--original draft, C.-Y.L.; formal analysis, C.-H.L.; data curation, C.-C.L.  
This research was funded by the Ministry of Science and Technology of the Republic of China, grant number MOST 107-2221-E-992-081.  
The process support from the Taiwan Semiconductor Research Institute is gratefully acknowledged. The authors declare no conflict of interest.
# Low-Speed Bearing Fault Diagnosis Based on Permutation and Spectral Entropy Measures
## 3 Experimental Set-Up  
In this study, several thrust ball bearings in a test-rig oriented to reproduce rolling contact fatigue were used for experimental testing (Figure 1a). As this test-rig can operate at very low rotational speed condition (from 3 rpm) and high loads can be applied to the bearing [62], the configuration is in concordance to the conditions used in several and prominent publications found in the literature [63; 64; 33; 65], and thus the configuration was appropriate for this study.  
The installed bearings were FAG 51226 thrust ball bearings. For the experimental set-up, one bearing was used for each scenario--a healthy scenario (HS), damage 1 scenario (D1), and damage 2 scenario (D2). The faults from the damaged bearings D1 and D2 were artificially seeded to the shaft washers by electro-discharge machining before the tests, to reproduce one of the main faults in low-speed bearings [66]. The type and size of the damage seeded in the bearings were chosen after reviewing the several types of damage reported in the literature--natural damage [28], accelerated damage [67], and artificial damage [68]. Each damaged bearing had three circular damage of the same size that were equally spaced by 1 mm from each other (Figure 1c). D1 has circular damage with diameters of 1.5 mm and for D2, 3 mm. All circular damage are 0.3 mm deep.  
The vibration signal data set used in this study was obtained by operating the bearings continuously at 5 rpm and 10 rpm over time, which were acquired with a 356A16 ICP acceleration sensor (PCB Piezoelectronics). The sensors were attached to the surface of the upper support disc (Figure 1b) by a wax adhesive. The raw data were acquired at 10.24 kHz from the sensor using a data acquisition system from National Instruments (cDAQ 9174), and later, the raw data were processed as described in the following section.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 5 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Series** & **MODEL** & **RMSE** & **MAPE** & **MAE** & **ARV** & **IA** \\ \hline \multirow{8}{*}{**Fortaleza**} & ARIMA [32; 33] & 0.2138 & 61.59 & 0.1456 & 1.2968 & 0.7355 \\  & RF [69; 70] & 0.1746 & 55.63 & 0.1173 & 0.9224 & 0.8215 \\  & GB [37; 38] & 0.1772 & 53.20 & 0.1161 & 0.8342 & 0.8260 \\  & SVR [34; 71] & 0.1942 & 55.88 & 0.1211 & 0.8148 & 0.8083 \\  & MLP [39; 72] & 0.1849 & 58.32 & 0.1257 & 0.8780 & 0.8115 \\  & ELM [40; 41] & 0.1927 & 59.09 & 0.1304 & 0.9796 & 0.7919 \\  & DBN [42; 43] & 0.1892 & 57.10 & 0.1263 & 1.0258 & 0.7917 \\  & HetDS(1,5) & 0.1864 & 54.57 & 0.1194 & 0.8251 & 0.8163 \\  & HetDS(1,10) & 0.1808 & 52.70 & 0.1156 & **0.7878** & 0.8267 \\  & HetDS(1,20) & 0.1797 & 52.28 & 0.1154 & 0.8031 & 0.8262 \\  & HetDS(3,5) & 0.1767 & 52.40 & 0.1155 & 0.8335 & 0.8261 \\  & HetDS(3,10) & 0.1726 & 51.90 & 0.1137 & 0.8118 & 0.8327 \\  & HetDS(3,20) & **0.1720** & **51.51** & **0.1132** & 0.8144 & **0.8332** \\  & HetDS(5,5) & 0.1777 & 53.80 & 0.1176 & 0.8814 & 0.8201 \\  & HetDS(5,10) & 0.1764 & 53.84 & 0.1171 & 0.8748 & 0.8224 \\  & HetDS(5,20) & 0.1762 & 53.85 & 0.1169 & 0.8730 & 0.8230 \\  & Hetmean & 0.1802 & 55.30 & 0.1205 & 0.9473 & 0.8113 \\  & Hetmedian & 0.1832 & 55.11 & 0.1208 & 0.9539 & 0.8070 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results  
This section defines the compared routing protocols' performance evaluation and simulation results. We have recently analyzed and developed seven routing protocols: RACE-SM, EH-UWSN, MuLSi-Co, EERBCR, MuLSi, GCORP, and EEDG. For the simulation, we used MATLAB. We have randomly deployed the 200 sensor nodes in a \(1000\times 1000\) simulation area. The simulation parameters are listed in Table 1. Initially, we have considered three main performance metrics: energy consumption, PDR, and end-to-end delay.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion  
### Systematic Approach  
The simulation systematically explored network performance by analyzing parameter variations. A 10 dB RSSI margin was set for the Wi-Fi network, following industry best practices for ensuring stable wireless communication. For Trusted Wireless, a 15 dB margin was chosen to account for multi-hop signal degradation, enhancing network reliability in industrial settings. An 80% Fresnel Zone clearance was maintained, aligning with standard recommendations to minimize diffraction losses and maintain strong signal integrity over long distances. These thresholds ensure resilience against environmental factors such as interference and extreme weather [32; 33; 34].  
This method enabled the implementation of proposed designs using the minimal transmission power and antenna gain required to minimize unwanted noise. However, in certain cases, such as sectors 2 and 3 of the Wi-Fi network and cluster head T/1/235/0/96 in the Trusted Wireless network, the use of taller structures was unavoidable and necessary due to challenges like long distances, elevation differences, and terrain that obstructed the Fresnel zone.
## 3 Conformity Review According to Fairway Design Standard
### Analysis of Marine Traffic Congestion for Fairways
Based on the results of the maritime traffic survey conducted in Section 3, the congestion level according to the traffic \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fairway**} & **Speed** & **Basic Traffic Volume** & **Practical Traffic Volume** \\  & **(Knots)** & **(Number of Vessel/h)** & **(Number of Vessel/h)** \\ \hline \multirow{3}{*}{Wando} & 10 & 245.1 & 61.3 \\  & 12 & 294.1 & 73.5 \\  & 15 & 367.6 & 91.9 \\ \hline \multirow{3}{*}{Yeosu} & 10 & 295.3 & 73.8 \\  & 12 & 354.3 & 88.6 \\  & 15 & 442.9 & 110.7 \\ \hline \multirow{3}{*}{Masan} & 10 & 245.1 & 61.3 \\  & 12 & 294.1 & 73.5 \\  & 15 & 367.6 & 91.9 \\ \hline \multirow{3}{*}{Gadeok} & 10 & 248.0 & 62.0 \\  & 12 & 297.6 & 74.4 \\ \cline{1-1}  & 15 & 372.1 & 93.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Basic traffic capacity and practical traffic capacity by destination fairway. capacity for each fairway in Table 5 was analyzed, and the congestion level per hour for 7 days is shown in Figure 6.  
As the degree of congestion is a comparison of the actual traffic capacity with the actual maritime traffic volume, 100% means that ships are passing at the practical traffic capacity, and because the fairway is saturated, the traffic of ships is concentrated at a specific time. Thus, it is necessary to disperse the constant mass. According to Figure 3b,d, we found that congestion on the Yeosu fairway and Gadeok fairway was over 100% at a specific time; thus, it was analyzed that it is necessary to solve the congestion of the fairway. Table 6 shows the average hourly congestion and peak-time congestion evaluation results for each fairway. On the Wando fairway, the average hourly congestion level was up to 1.4%, and the congestion level during the hour with the most traffic was 9.1%, indicating considerable leeway in the fairway capacity. In the Yeosu fairway, the average hourly congestion level was up to 25.5%, and the congestion level during the busiest time period was 101.9%. The average congestion of the Masan fairway was up to 6.5% per hour, and the congestion level during the busiest hour was 50.0%, indicating a certain amount of leeway. In the Gadeok fairway, the average hourly congestion level was the highest, at 36.9%, the highest among the target fairways, and the congestion level during the busiest time period was  
Figure 6: (**a**) Hourly congestion evaluation result of the Wando fairway, (**b**) the Yeosu fairway, (**c**) the Masan fairway, and (**d**) the Gadeok fairway. 205.2%, which was analyzed to significantly exceed the practical traffic capacity. However, because there is still plenty of traffic in other time zones, it may be a short-term measure to reduce congestion by distributing traffic to other time zones. However, this cannot be regarded as a fundamental solution; furthermore, if the traffic volume increases and ships become larger due to port development in the future, the probability of marine accidents may increase considerably, depending on the congestion level of the fairway.
# Blast Loading Prediction of Complex Structures Based on Bayesian Deep Active Learning
## 4 Experimental Results and Analysis
### Comparison with Other Methods  
To validate the superiority of the BDAL model in predicting blast loading, FCNN [8], decision tree [16], random forest [16], XGB [16], and DENN [13] models were constructed to replicate the original study's structure. These models were trained on a dataset containing 324 \(\times\) 200 samples and evaluated on a test set of 36 \(\times\) 200 samples. The performance evaluation results for the different models are presented in Table 5.  
Figure 7: Change in model prediction accuracy with the amount of training data. (**a**) MAPE was used as the model accuracy evaluation index; (**b**) R\({}^{2}\) was used as the model accuracy evaluation index.  
As shown in Table 5, for blast loading predictions in complex structures, the BDAL model exhibits the smallest prediction error and the highest accuracy, indicating superior predictive performance. Furthermore, unlike other models that can only provide predictions of blast loading, the BDAL model is capable of quantifying the uncertainty of its results, making it more reliable for practical applications. Additionally, the BDAL model achieves results comparable to those obtained with the full training dataset while using only 60% of the training data, whereas the accuracies of other models decline as the training data volume decreases. This leads to the lowest model construction cost for BDAL.  
In summary, the BDAL model enables rapid and reliable predictions of blast loading with a reduced amount of training data, making it a valuable approach for deep-learning-based blast loading prediction.