esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Hazard Evaluation and Strain Dynamics in the
## 2 Tectonic Settings
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Historical Period (AC)**} & \multicolumn{6}{c}{**Instrumental Period (1900–)**} & **Focal** \\ \hline
**Date** & **Lat.** & **Lon.** & **Mag.** & **Date** & **Lat.** & **Lon.** & **Mag.** & **Mech.** \\ \hline
94 & 38.75 & 30.5 & VIII & 1905 & 38.81 & 28.52 & 6.1 & - \\
1595 & 38.99 & 29.39 & VII & 1924 & 39.51 & 28.4 & 5.5 & - \\
1611 & 38.74 & 28.68 & V & 1942 & 39.27 & 28.19 & 5.5 & - \\
1728 & 39.09 & 28.97 & V & 1942 & 39.1 & 27.8 & 6.1 & - \\
1860 & 39.3 & 29.05 & VIII & 1944 & 38.79 & 29.31 & 5.9 & - \\
1862 & 38.8 & 30.5 & VIII & 1969 & 39.14 & 28.48 & 7 & \\
1873 & 38.76 & 30.55 & VI & 1969 & 39.25 & 28.44 & 6 & \\
1887 & 38.73 & 29.75 & VIII & 1970 & 39.21 & 29.51 & 7.2 & \\
1896 & 39.3 & 29.2 & VII & 1970 & 39.03 & 29.76 & 5.8 & \\
1897 & 38.93 & 28.2 & VI & 1971 & 39.05 & 29.71 & 6 & \\
1899 & 39.4 & 28.13 & VII & 2011 & 39.13 & 29.08 & 5.9 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Historical and instrumental period earthquakes that occurred on and around the SFZ [34; 77; 78; 79]. Figure 2: Epicenter distribution of M \(\geq\) 3 earthquakes that occurred in the SFZ and its surroundings during the historical and instrumental periods (circles represent instrumental-period earthquakes, with their colors indicating event depths and their sizes representing event magnitudes; yellow stars denote historical earthquakes, red lines represent active faults [34]). (Mw: 7.2) on the SFZ was not fully released by the 2011 Simav earthquake (Mw: 5.8). The study further highlighted that a significant portion of the SFZ, particularly the Saphane Segment (>1 bar) located in the eastern part of the fault zone, is still subjected to significant stress accumulation. Additionally, in the models proposed by [PERSON] et al. [39] and [PERSON] et al. [76], the eastern part of the SFZ, which represents a block boundary and thus experiences significant deformation, has also experienced destructive earthquakes. Historical and instrumental period earthquake records indicate the occurrence of numerous earthquakes within the ASFS that have generated surface ruptures. The 1921 Doganhisar-Ilgun (M: 5.4), 1946 Ilgun-Argthanh (M: 5.5), 2000 Sultandag1 (M: 6), and 2002 Cay earthquakes (M: 6.5 and 6.2) that occurred within the ASFS are considered part of a northwestward earthquake migration along the fault system [50]. The possibility of this earthquake migration continuing westward is of significant relevance to the SFZ.
# A Point Cloud Registration Algorithm Based on Weighting Strategy for 3D Indoor Spaces
## 2 Weighting Strategy of Targets in Semantic Segmentation  
### The Objective Weighting Method  
The objective weighting method utilizes objective and measurable metrics, rather than subjective judgments, for weight assignment to ensure fairness and rationality. In our task of point cloud registration, the coordinates, color information, and category labels of each point cloud data point are totally quantifiable. Therefore, using the objective weighting method for weight assignment aligns well with the requirements of the application background. Common objective weighting methods include the standard deviation method, the entropy weight method, and the criteria importance through intercriteria correlation (CRITIC) method [26; 27; 28]. The standard deviation method's basic idea is to calculate the variance Si of the data under each indicator. We obtain the weight value for the ith indicator by dividing Si by the sum of all Si values. Larger variances result in higher weights. This method is relatively simple and easy to implement, effectively distinguishing between different indicators. However, it assumes that all indicators are equally important, and a larger variance only indicates higher discrimination ability for different scenarios, not necessarily the importance of the indicator [29]. The entropy weight method, on the other hand, calculates entropy weights based on the dispersion of data for each indicator using information entropy. Afterward, we apply certain corrections to these entropy weights, leading to more objective indicator weights. This method allows for weight adjustments and exhibits adaptability, but it cannot handle strong correlations between indicators; thus, there is a risk of unreasonable weight assignments [30]. The CRITIC method comprehensively assesses the objective weights of indicators based on the comparative intensity of evaluation indicators and the conflict between indicators. It takes into account both the variability in indicator values and the correlation between indicators. Larger numerical values do not necessarily indicate greater importance; instead, the method utilizes the inherent objectivity of the data for scientific evaluation. Comparative intensity is defined as the magnitude of the differences in values between various evaluation schemes for the same indicator, expressed in the form of standard deviation. A larger standard deviation indicates greater fluctuation, implying larger differences in values between schemes, leading to higher weights. Correlation coefficients represent the conflict between indicators. If two indicators exhibit a strong positive correlation, indicating lower conflict, then the weights will be lower. The CRITIC method is used in this paper to give semantic segmentation targets weights. It takes into account the strong correlation between point cloud sets of the same target as well as positional relationships like occlusion and overlap between different targets that affect the registration process.
## 1 Introduction
### The Design of Switches  
The three-electrode planar spark gap high voltage switch was composed of two main electrodes and trigger. The main electrode includes the cathode and anode, as shown in Figure 1. The main electrode is semicircular in shape and placed in the opposite position. The trigger electrode is located in the middle of the two main electrodes. Its principle is to load high DC voltage between the cathode and anode of the switch. When a specific pulse voltage signal is applied to the trigger electrode, a high voltage gap is formed between the cathode and the trigger electrode. A certain number of ions or electrons are produced by the breakdown field strength, and the particles and gas undergo the collision multiplication process, resulting in the instantaneous conduction of the anode and cathode of the switch. In Figure 3, \(a\) represents the width of the trigger electrode, \(b\) represents the gap width of the main electrode, and \(R\) represents the radius of the main electrode.  
As can be seen from Figure 3, the key structures of the planar three-electrode switch include the main electrode radius (_R_), the main gap (_b_), the trigger gap ((_b_ - a_)/2) and the trigger electrode width (_a_). The diameter R of the two main electrodes is a semi-circular structure with a diameter of 4.0 mm \(\pm\) 0.5 mm. The purpose of this design is to ensure that a uniform electric field exists between the electrode gaps as much as possible, so as to improve the service life of the switch. With the input of the trigger signal, the switch conduction first occurs in the trigger gap. Therefore, reducing the trigger gap is beneficial to improve the working reliability of the switch, but the gap is too small, which affects the working voltage range and safety of the switch. Combined with the machining accuracy, the structural design parameters for the plane three-electrode spark gap high-voltage switch are shown in Table 1.
## 3 Attitude Determination
### Attitude Filtering  
The epoch-by-epoch MC-LAMBDA attitude solution is further processed using an unscented Kalman filter (UKF) [26]. For a leveled platform (_i.e._, for small \(\theta\) and \(\psi\)), the kinematic equations of the attitude angles are given as [27]:  
\[\alpha_{i}=F\alpha_{i-1}+v_{i-1}^{\alpha} \tag{20}\]  
where the state vector \(\alpha_{i}=\left[\phi_{i}\,\dot{\phi}_{i}\,\theta_{i}\,\dot{\theta}_{i}\,\psi _{i}\,\dot{\psi}_{i}\right]^{\tau}\) consists of attitude angles and angular rates, and the state transition matrix \(F\) is given as:  
\[F=I_{3}\otimes\left[\begin{array}{cc}1&T\\ 0&1\end{array}\right] \tag{21}\]  
where \(T\) is the sampling interval. The process noise \(v_{i-1}^{\alpha}\) has a zero mean normal distribution with variance-covariance matrix \(Q_{v^{\alpha}v^{\alpha},i-1}\), which is given as:  
\[Q_{v^{\alpha}v^{\alpha},i-1}=\mathrm{diag}\left([\sigma_{\phi}^{2},\;\sigma_{ \theta}^{2},\;\sigma_{\psi}^{2}]\right)\otimes\left[\begin{array}{cc}T^{3}/ 3&T^{2}/2\\ T^{2}/2&T\end{array}\right] \tag{22}\]with \(\sigma_{\phi}\), \(\sigma_{\theta}\) and \(\sigma_{\psi}\) the process noise standard deviations. The observation model reads:  
\[\zeta_{i}=h(\alpha_{i})+w_{i}^{\alpha} \tag{23}\]  
with \(\zeta_{i}\) given by \(\left(\hat{R}^{k}(\hat{Z}^{k})\right)\) at epoch \(i\). The nonlinear observational function \(h(\alpha_{i})\) is defined by Equation (8), and the observation noise \(w_{i}^{\alpha}\) is assumed to have a zero mean normal distribution with covariance matrix \(Q_{w^{\alpha}w^{\alpha},i}\), which is given by \(Q_{\hat{R}^{k}(\hat{Z}^{k})\hat{R}^{k}(\hat{Z}^{k})}\) at epoch \(i\).  
The use of the above constant-velocity model [28] reflects the fact that the frame is rotating at a constant rate. For the two-antenna scenario considered in real-data analyses (Figure 2), only heading and elevation angles are estimable. Hence, a reduced state space model consisting of only heading, elevation and their rates is used in Section 5. The recursive filter is initialized with two-point initialization [28] and propagated with process noise standard deviations of \(\sigma_{\phi}=0.01^{\circ}\mathrm{s}^{-\frac{3}{2}}\) and \(\sigma_{\theta}=0^{\circ}\mathrm{s}^{-\frac{3}{2}}\) (_i.e._, dead reckoning filtering for elevation constraining to horizontal 1D rotation).
###### Abstract  
Current water color remote sensing algorithms typically do not consider the vertical variations of phytoplankton. Ecolight with a radiative transfer program was used to simulate the underwater light field of vertical inhomogeneous waters based on the optical properties of a eutrophic lake (i.e., Lake Chaohu, China). Results showed that the vertical distribution of chlorophyll-a (Chla(z)) can considerably affect spectrum shape and magnitude of apparent optical properties (AOPs), including subsurface remote sensing reflectance in water (\(r_{rs}(\lambda,z)\)) and the diffuse attenuation coefficient (\(K_{x}(\lambda,z)\)). The vertical variations of Chla(z) changed the spectrum shapes of \(r_{rs}(\lambda,z)\) at the green and red wavelengths with a maximum value at approximately 590 nm, and changed the \(K_{x}(\lambda,z)\) from blue to red wavelength range with no obvious spectral variation. The difference between \(r_{rs}(\lambda,z)\) at depth z m and its asymptotic value (\(\Delta r_{rs}(\lambda,z)\)) could reach to \(\sim\)78% in highly stratified waters. Diffuse attenuation coefficient of downwelling plane irradiance (\(K_{d}(\lambda,z)\)) had larger vertical variations, especially near water surface, in highly stratified waters. Three weighting average functions performed well in less stratified waters, and the weighting average function proposed by [PERSON] et al., (2005) performed best in highly stratified waters. The total contribution of the first three layers to \(r_{rs}(\lambda,0^{-})\) was approximately 90%, but the contribution of each layer in the water column to \(r_{rs}(\lambda,0^{-})\) varied with wavelength, vertical distribution of Chla(z) profiles, concentration of suspended particulate inorganic matter (SPIM), and colored dissolved organic matter (CDOM). A simple stratified remote sensing reflectance model considering the vertical distribution of phytoplankton was built based on the contribution of each layer to \(r_{rs}(\lambda,0^{-})\).  
vertical distribution of phytoplankton; underwater light field; subsurface remote sensing reflectance; weighting average function +
Footnote †: journal: Applied Soft. 2019, 9, 1635; doi:10.3390/app9081635
## 4 Curved Radome Physical Optic Analysis
Apart from the incident angle variation, the discontinuity of the FSS could lead to the phase difference in the radome as well. With the radome covered, the difference of the antenna radiation field results from having different incident angles at the radome inner surface. Thus, the influence of the incident angles must be taken into consideration when calculating the total radiation diagram. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(A_{0}\) & \(A_{1}\) & \(A_{2}\) & \(A_{3}\) & \(A_{4}\) & \(A_{5}\) \\ \hline
0.2387 & \(-\)1.203 & 34.63 & \(-\)902.7 & 8912 & \(-\)32,160 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The polynomial coefficients of Equation (21). Figure 5: Radome structure: (**a**) Geometric median cross section of the radome; (**b**) internal photo of the manufactured radome. The physical optic method is adopted in order to consider the influence of different incident angles. Combining this method--the radome structure and horn antenna position--the incident angle at every point of the radome can be ascertained. Thus, the incident angle can be utilized to compute the small scale curved radome transmission coefficients, while considering the radome as a local plane. For each radiation direction, the intersection position of rays and the radome in the transmitting direction can be determined. At the intersection positions, the incident wave polarization is decomposed into a TM mode, which is parallel to the incident plane, and a TE mode, which is perpendicular to it. Multiplying these by the radome transmission coefficients and then synthesizing all the data, the radiation fields in the radiation direction are eventually acquired. Figure 7 shows the 10 GHz radiation patterns with and without the radome, the experimental results are also shown. The radome is experimented in the microwave anechoic chamber and a wideband highly directional horn antenna is fixed in the radome as shown in Figure 5a. The radome and the antenna are fastened to the rotation axis of the antenna automated test system in the chamber. Far field of the radiated electric field is received by another horn antenna. All the results are eventually compared in Figure 7 after data processing. Normalization of the data are not shown for the reason that the normalized curves overlap each other at both ends and cannot indicate the details. It can be seen in Figure 7 that the experimental and simulated results agree well with each other. When the observing angle is within the range of \(-30^{\circ}\) to \(30^{\circ}\), the radiation patterns are in accord with each other, which reveals a high transmissivity in the vertical incident angle and nearby. Along with the observing angle increase, the transmitted field intensity decreases, which might be attributed to the physical optic method limitations. Figure 6: Radome 10 GHz transmission coefficients of TE and TM modes with different incident angles: (**a**) Normalized transmission coefficients; (**b**) transmission coefficients phase. Figure 7: The 10 GHz calculation and experiment radiation patterns with and without the radome.
## 3 Fault Location Method
### Topology Search Algorithm  
When a fault occurs in the power distribution line, there will be a fault current flowing from the substation to the fault point, then the FIES between the substation and the fault point will be triggered. Generally speaking, the faulted section is on the nearest downstream of the last triggered FIE. So, if the last triggered fault-indicating equipment is identified, the faulted section can be located. Based on the line list proposed in Section 2.2, a topology search algorithm to find out the last triggered fault-indicating equipment is proposed. This algorithm is not only applicable to single fault, but also to multiple faults on different branches.  
In this method, there are five variables: \(i,j,k,m,n\). Variable \(k\) represents the equipment number of fault-indicating equipment in the iteration process. Variables \(j\), \(k\), \(m\) and \(n\) are iteration parameters to record the times of iterations and judgment conditions. Array \(R[]\) is used to record the equipment number of the last triggered fault-indicating equipment. Array \(A[]\) is used to store the triggered equipment number of a branch during the iteration process.  
The procedure for the topology search algorithm is explained in detail in the following steps, and the flow chart of this method is shown in Figure 3.  
1. Set the initial values of \(k,j\) and \(m\) as 1; set the initial values of \(i\) and \(n\) as 0;
2. Check the status of the \(k\)th FIE. If the status is \"1\", then continue to the next step; if the status is not \"1\", go to step 7;
3. Check the quantity of the FIES that are on the nearest downstream of the \(k\)th FIE. If there is only one equipment, then continue to the next step; if there are more than one piece of equipment, go to step 5; if there is no equipment, assign \(k\) to \(R[m]\) and set \(m=m+1\), then go to step 6;
4. Check the status of the nearest downstream equipment. If the status is not \"1\", assign \(k\) to \(R[m]\) and set \(m=m+1\), then go to step 6; if the status is \"1\", then assign the equipment number of the downstream FIE to \(k\) and go back to step 3;
5. Check the status of all the nearest downstream FIES. If the status of all FIES is not \"1\", assign \(k\) to \(R[m]\) and set \(m=m+1\), then go to step 6; if the quantity of FIES in status \"1\" is greater than 1, assign the quantity of FIES in status \"1\" to \(n\), and assign the numbers of them to \(A[i+1]\),..., \(A[i+n]\) separately, then set \(i=i+n\), go to step 6; if there is only one FIE in status \"1\", assign its number to \(k\) and go back to step 3;
6. Determine the magnitude of \(i\) and \(j\). If \(j>i\), go to step 7; otherwise, assign \(A[j]\) to \(k\) and set \(j=j\) + 1, then go back to step 3;  
Figure 2: Flow chart of fault location method.  
Step 7 Stop the iteration and output \(R[]\). Each number in \(R[]\) means that there is a fault on the nearest downstream section of the fault-indicating equipment.
# Wearables for Industrial Work Safety: A Survey
## 3 State-of-the-Art Techniques in the Field of Industrial Wearables
### Localization Techniques
These systems require an initial complex ground deployment and set-up by the manufacturer on-site. The pusetololites need access to a power source and a clear line of sight for best precision. This solution was still not applied in the IIoT area to the best of the authors' knowledge but should be considered a promising one. Other positioning methods proposed for underground workspaces are: ZigBee, Visible Light Communication [169], WiFi, BLE, Inertial Measurement Units [170], image-assisted person localization [171]. Most of them require a large density of beacons and stations, offering meter accuracy. For example, in the USA, since 2006, all mine operators must adopt electronic tracking systems, RFID being the most popular solution [172]. To conclude, choosing the appropriate localization techniques in each case compromises accuracy, coverage, power consumption, scalability, and price. When discussing localization techniques for industrial wearables, we need to consider that we usually deal \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Localization** & **Typical env-t.** & **Accuracy** & **Additional Details** \\ \hline LoRa & Time-based, & & & The key advantages of this technology are coverage \\ [157; 158] & Power-based & Outdoor & 100 m-level & range (up to 20 km), low consumption, bigger stability \\ \cline{3-5}  & & & & than in case of WiFi and BLE [159]. However, low accuracy prevents the spread of the technology as a localization solution. \\ \hline Sigfox [160; 161] & Power-based & Outdoor & 100 m-level & Advantages and disadvantages are similar to LoRaWAN technology. \\ \hline RFID [132; 162; 163] & Power-based & Indoor & cm-level & This technology ensures high precision but in a low range (approx. 15 m). \\ \cline{2-5}  & Angle-based & Indoor & m-level & Less accurate than passive RFID but has bigger range (approx. 150 m). \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._with low throughput, low power, small size, and specific locations (underground, underwater). The position is an essential parameter for work safety providing and remains one of the main accuracy-related issues (especially in indoor and underground conditions) and smoothness of tracking.
## 3 Results and Discussion  
### Proximate Composition  
The chemical compositions of the raw beef burgers and artichoke extracts used during reformulation are presented in Table 2. The beef burger samples reported 62.46-66.43% moisture, 2.46-2.90% ash, 17.54-19.14% protein, 7.99-12.16% fat, 1.32-4.52% carbohydrates, and 0.81-1.93% dietary fiber. Proximal composition results showed a significant decrease (\(p\) < 0.05) in the fat and protein content in fat-replaced burgers compared to CB. Other authors have observed a similar trend in beef burgers reformulated with different emulsions of tiger nut [28], walnut [12], avocado [45], or chia and linseed oils [13]. These differences in proximate composition found in the present investigation can be explained by the different compositions between beef fat and the hydrogel emulsion. As indicated in Section 2.3, the EVOO emulsion was composed of 56% water and 37.3% oil. Therefore, the replacement was expected to result in a decrease in the fat content and an increase in moisture. In addition, animal fat contains approximately 10% protein [13], resulting in a small decrease in the protein content of fat-substituted burgers. Ash content was also significantly affected after fat replacement. Barros et al. [8] observed a similar trend in the increase of ash in burgers reformulated with algal and wheat germ oil emulsions. This variation may be due to the Prosella content present in the EVOO emulsion, which was used as a jellifying agent.