esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods  
### Study Area  
The Qinghai-Tibet Plateau is located at 26\({}^{\circ}\)00\({}^{\prime}\)12\({}^{\prime\prime}\) N\(-\)39\({}^{\circ}\)46\({}^{\prime}\)50\({}^{\prime\prime}\) N, 73\({}^{\circ}\)18\({}^{\prime}\)52\({}^{\prime\prime}\) E\(-\)104\({}^{\circ}\)46\({}^{\prime}\)59\({}^{\prime\prime}\) E [30], which is located from the Pamirs Plateau in the west to the Hengduan Mountains in the east, the southern margin of the Himalayas in the south, and the Kunlun-Qilian Mountains in the north. The Qinghai-Tibet Plateau is approximately 2945 km long from east to west and 1532 km wide from south to north, with an area of approximately 2.57 \(\times\) 106 km\({}^{2}\), accounting for 26.8% of China's total land area. The Qinghai-Tibet Plateau includes the Tibet Autonomous Region and parts of the Qinghai Province, Xinjiang Uygur Autonomous Region, Gansu Province, Sichuan Province and Yunnan Province. The Qinghai-Tibet Plateau has the highest altitude and largest area, and it is the most-recently formed plateau in the world, which is a large tectonic geomorphic unit. The terrain is inclined from northwest to southeast. In the Qinghai-Tibet Plateau, it is cold and dry in the northwest and warm and humid in the southeast. The dry and wet seasons are distinct, and the annual and daily temperature ranges are large. The vegetation is characterized by alpine desert, alpine meadow and grassland landscapes, and the soil is mainly composed of coarse textures, thin soil layers and poorly developed alpine meadow soil and alpine cold desert soil in the Qinghai-Tibet Plateau.
# Registration of Dental Tomographic Volume Data and Scan Surface Data Using Dynamic Segmentation
## 3 Results & Discussion
### Result
#### 3.1.4 MICP Registration Results: Different Length Values  
To identify the optimal length for generating the intensity profiles of the four data sets, MICP is performed as the length values are varied from 1 to 10 (Table 3). The length values 1 and 2 are too short to find edge points on the intensity profiles. Also, the D value increases with the length value. The registration result changes depending on the length used, and 3-6 seem to be good lengths for generating the intensity profile due to the lower D values obtained. Note that, based on this length test, all MICP registration results in this paper use 4 as the length when generating the intensity profile.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Data** & **Normal Correction** & \(\mathbf{N_{MICP}}\) & \(\mathbf{E_{MICP}}\) (mm) & \(\mathbf{SD_{MICP}}\) (mm) & \(\mathbf{D_{MICP}}\) (mm) \\ \hline \multirow{4}{*}{Set no.1} & \multirow{4}{*}{Maxillary} & No & 212,125 & 0.3145 & 0.7173 & 0.6178 \\  & & Normal sampling & 0.3146 & 0.7198 & 0.6172 \\  & & Remeshing & 180,186 & 0.2882 & 0.6703 & 0.6219 \\ \cline{2-7}  & \multirow{4}{*}{Mandible} & No & 160,112 & 0.3953 & 1.0815 & 1.1811 \\  & & Normal sampling & 0.3723 & 1.0094 & 1.1774 \\  & & Remeshing & 131,545 & 0.3357 & 0.9217 & 1.1679 \\ \hline \multirow{4}{*}{Set no.2} & \multirow{4}{*}{Maxillary} & No & 0.8431 & 1.8762 & 1.2145 \\  & & Normal sampling & 0.8374 & 1.8500 & 1.2461 \\  & & Remeshing & 185,546 & 0.7412 & 1.6113 & 1.2730 \\ \cline{2-7}  & \multirow{4}{*}{Mandible} & No & 0.4203 & 1.2217 & 1.1116 \\  & & Normal sampling & 0.4071 & 1.1760 & 1.1070 \\  & & Remeshing & 95,438 & 0.2830 & 0.8040 & 1.0955 \\ \hline \multirow{4}{*}{Set no.3} & \multirow{4}{*}{Maxillary} & No & 0.3828 & 1.4925 & 0.4458 \\  & & Normal sampling & 0.3339 & 1.2376 & 0.4350 \\  & & Remeshing & 51,398 & 0.3337 & 1.2289 & 0.4466 \\ \cline{2-7}  & \multirow{4}{*}{Mandible} & No & 0.5730 & 1.9817 & 0.5957 \\  & & Normal sampling & 0.5000 & 1.6143 & 0.5366 \\  & & Remeshing & 78,657 & 0.4998 & 1.6196 & 0.5387 \\ \hline \multirow{4}{*}{Set no.4} & \multirow{4}{*}{Maxillary} & No & 0.2920 & 1.1811 & 0.6204 \\  & & Normal sampling & 0.2602 & 1.0053 & 0.5959 \\ \cline{1-1}  & & Remeshing & 56,335 & 0.2515 & 0.9704 & 0.5891 \\ \cline{1-1} \cline{2-7}  & \multirow{4}{*}{Mandible} & No & 0.1457 & 0.5928 & 0.9164 \\ \cline{1-1}  & & Normal sampling & 0.1084 & 0.3837 & 0.9146 \\ \cline{1-1}  & & Remeshing & 44,472 & 0.1059 & 0.3801 & 0.9298 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Registration result of modified iterative closest point (MICP) after normal correction.  
Figure 7: Registration result of modified iterative closest point (MICP) after normal correction.
## 3 Experimental Results and Discussion
### Dynamic Stress of Soil  
The dynamic stress distribution of soil is shown in Figure 8. When the vibration reaches 10,000 times, the pile-soil stress ratios of S1/S2 and S1/S8 are about 2 and 1.8 under dynamic loading. When the static load is 41.0 kPa, the pile-soil stress ratios of S1/S2 and S1/S8 are about 2.5 and 5.8 under static loading. The pile-soil stress ratio under dynamic loading has some attenuation compared to static loading. Compared to the pile-soil stress ratio under static loading, the amplitude of the pile-soil stress ratio under dynamic loading does not change much. Where the pile soil stresses in S1/S8 are substantially reduced, i.e., the stronger the soil arch effect under static loading, the greater the weakening under dynamic loading.  
The dynamic stress of soil decreases gradually from the center to both sides, and the attenuation trend of dynamic stress is smaller than that of static stress. In the longitudinal direction of the embankment, the dynamic stress amplitudes of S1 and S7 are similar, and the changing trend of stress with the increase in vibrations is also synchronized. At the initial stage of the dynamic load, the dynamic stress amplitude is about 5 kPa. When the vibration frequency increases to 7000 times, the dynamic stress reaches its peak value and stabilizes at about 20 kPa. The dynamic change trend of soil between piles with vibration times is similar to the \"N\" type, and the change trend at the top of piles is the opposite. The dynamic of pile-top soil increases slowly at the beginning and fluctuates steadily when it reaches a certain value. It can be seen that the development of the arch effect mainly occurs before the vibration reaches 7000 times. With the increase in vibration times, the soil at the top of the pile is gradually wedged to form the arch foot, and the dynamic stress gradually rises. When the number of vibrations is 3500-7000 times, the soil arch is formed slowly, the load on the top of the pile becomes bigger, and the soil arch effect starts to exert influence, so the dynamic stress of the soil between the piles starts to decrease. When the soil arch reaches a stable state after the vibration times reach 7000, the dynamic stress also fluctuates around a stable value. As shown in the dynamic stress trends of S6 and S8 in Figure 8d,f, the stronger the soil arching effect, the faster the soil dynamic stress decay. The soil arch reaches a stable state, and then as the vibration increases the overall sinking of the upper soil, the dynamic soil stress increases slowly because the soil between the piles is compressed again.  
Figure 7: Tensioned membrane effect.
# Sphero-Conical Modeling for the Estimation of Very Long Baseline Interferometry Invariant Point
## 4 Estimation of IVP Using TLS  
In general, only the dependent variables were considered to have random measurement errors in the conventional least-squares adjustment, whereas the independent variables were treated as error-free. By TLS, we refer to a least-squares estimator that minimizes the random errors in all data variables [16; 17; 29], sometimes called \"general least squares\" [17; 30].  
The coordinates of the targets mounted on the VLBI antenna can be estimated from surveying data such as slope distances, and horizontal/vertical angles; thus, all three components have errors, together with the covariance between parameters. Therefore, we must consider applying the TLS method to estimate the IVP coordinates based on the coordinates of the targets.  
If the target is related to one rotation only, either horizontal or vertical rotation, the corresponding row (upper or lower) in \(w_{i}\), \(A_{i}\), and \(B_{i}\) should be considered accordingly. (See the detailed derivation of the IVP estimation within GHM in Appendix B). Collecting all the related equations leads to the following GHM [16; 29; 31; 32]:  
\[\mathbf{w}=A\mathbf{\xi}+B\mathbf{e},\quad\mathbf{e}\sim(\mathbf{0},\sigma_{0}^{2}P^{-1}), \tag{3}\]  
where \(\mathbf{w}\) is the observation vector, \(A\) is the design matrix, \(\mathbf{\xi}\) is the parameter vector, \(B\) is the transformation matrix, \(\mathbf{e}\) is the observation error vector, \(\sigma_{0}^{2}\) is the variance component, and \(P\) is the weight matrix for the observation. The combined unknown parameters can be explicitly represented by  
Figure 5: The rotation of targets in elevation represented by a cone with the apex at the IVP. The slant height \(c_{l}\) is the individual unknown for each vertical target, while the coordinates of the IVP are common for all targets.  
\[\begin{split}\mathbf{\xi}&=\left[\mathbf{\xi}_{1}^{T}\quad\mathbf{\xi}_{2 }^{T}\quad\mathbf{\xi}_{3}^{T}\right]^{T}\\ \mathbf{\xi}_{1}&=\left[\alpha\quad\beta\quad\gamma \right]^{T}\\ \mathbf{\xi}_{2}&=\left[r_{1}\quad\cdots\quad r_{n_{k}} \right]^{T}\\ \mathbf{\xi}_{3}&=\left[c_{1}\quad\cdots\quad c_{n_{l}} \right]^{T}.\end{split} \tag{4}\]  
As can be seen in Equation (4), the unknowns are grouped by their types \((\mathbf{\xi}_{1},\mathbf{\xi}_{2},\mathbf{\xi}_{3})\); the first group \((\alpha,\beta,\gamma)\) represents the coordinates of the IVP, as explained above, and the second and third groups are related to the horizontal and vertical motions, respectively, while \(n_{k}\) and \(n_{l}\) are the corresponding numbers of targets. \(\mathbf{\xi}_{2}\) and \(\mathbf{\xi}_{3}\) are the nuisance parameters, which are the radii of the horizontal circles and the slant heights of the cones, respectively, and are of no primary concern. However, these parameters must be estimated simultaneously to complete the model with solid and robust solutions.
## 4 WSN Deployment Approaches Based on the SA Algorithm
\(\text{f}_{\text{min}}(\text{X})\): Measures the minimum separation between a sensor whose position is shifted d units in \(\text{R}^{2}\) and all the others: \(\text{x}_{\text{i}},\text{x}_{\text{j}}\in\text{X},\forall\,\text{i}\
eq\text {j}\text{f}_{\text{min}}(\ \left(\text{X}\backslash\{\text{x}_{\text{i}}\}\right)\cup\{\text{x}_{\text{i} }+\text{d}\})=\min(\delta(\text{x}_{\text{i}}+\text{d},\text{x}_{\text{j}}))\)  
X is K-survivable if for any simultaneous failure of K nodes in X, every surviving node \(\text{x}_{\text{i}}\) lies at least within the coverage of another surviving node \(\text{x}_{\text{j}}\), and each one must be able to communicate with the sink either directly or indirectly. Maximizing \(\text{f}^{\text{K}}(\text{X})\) implies the maximization of \(\text{f}_{\text{min}}(\text{X})\), and ensures that the set of nodes of the solution will be separated by the maximum distance allowed. The sub-function \(\text{f}_{\text{s}}(\text{X})\) computes the interest collected and penalizes the overlapping which occurs by the intersections of the sensing regions of the nodes. In [14] another approach was developed to provide maximum coverage with improved connectivity properties, allowing a minimum number of hops between all nodes and the sink. Based on the objective function in [8], and as shown in Equation (3), a new sub-function was added to compute the average of the number of hops between all the nodes and the sink. In this case, maximizing the objective function will minimize the function of average hops. \[\hat{F}^{K}(X)=\ \left\{ \begin{array}{c}\gamma.f_{min}(X)-\sigma.\ f_{\alpha}(X)+\ f_{s} \ (X)\,\ \text{if}\ X\ \text{is}\ K-\text{survivable}\\ \\ C,\ \text{otherwise}\end{array} \right. \tag{3}\]  
\(f_{\alpha}(X)\) is the function of the average number of hops defined by Equation (4) as follows:  
\[f_{\alpha}\big{(}\ X-\{x_{i},s\}\cup\{x_{j}+d\ \}\ \big{)}=\ \frac{1}{N-1}\times\sum_{j=1}^{N-1}\frac{\sum_{j=1}^{P}nbr\_hops \big{(}s,x_{j}\big{)}}{P_{j}} \tag{4}\]  
where, \(s\) is the sink node, \(p_{j}\) represents the total number of paths between \(s\) and \(x_{j}\), \(d\) is an arbitrary distance oriented in a random direction, and \(nbr\_hops\) (\(s\), \(x_{j}\)) computes the number of hops between the sink and the node \(x_{j}\). While the first algorithm based on SA in [13] tries to optimize deployment only by maximizing coverage, the second approach in [14], searches for the optimal placement of nodes by maximizing coverage while ensuring a \(k\)-connected graph. Moreover, both approaches consider a fixed number of sensor nodes.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 3 In-Orbit Payload Performance
### Top-of-Atmosphere Radiometric Validation
This can be explained by the 760 nm oxygen absorption line being better resolved by HYPSO-1 than RadCalNet. In [22], the DLR Earth Sensing Imaging Spectrometer (DESIS), which is a hyperspectral imaging spectrometer, Level 1 product (ToA reflectance) are compared with RadCalNet ToA reflectance. They state that the DESIS spectra are inconsistent at wavelengths lower than 430 nm due to atmospheric scattering. An inconsistency is also observed when comparing the HYPSO-1 ToA reflectance with RadCalNet ToA reflectance at these wavelengths. Figure 11 provides a comparison of the ToA reflectance at the RadCalNet test site in Gobabeb with HYPSO-1 ToA reflectance and Sentinel-2 ToA reflectance. The analysis given here used images that were captured one day apart from the RadCalNet data since there were no captures from the same day during the campaign period. The ToA reflectance changes every day and throughout the day. The RadCalNet sites are located to be pseudo-invariant, meaning that their reflectance properties are stable relative to other natural targets [42]. It is an important factor when analyzing the ToA reflectance and the relative error for the Gobabeb captures in Figure 11. \begin{table}
\begin{tabular}{l l l} \hline
**Capture Time, UTC+0** & **Solar Zenith Range** & **Elevation Angle** \\ \hline
2022-07-24T18:03Z & 29.26–29.65 & 24.8\({}^{\circ}\) \\
2022-09-03T08:39Z & 45.07–47.06 & 77.3\({}^{\circ}\) \\
2022-10-05T17:49Z & 48.65–50.69 & 59.4\({}^{\circ}\) \\
2022-10-10T18:23Z & 46.70–48.92 & 52.9\({}^{\circ}\) \\
2022-10-11T18:11Z & 47.74–49.97 & 74.0\({}^{\circ}\) \\ \hline \end{tabular}
\end{table} 
Table 7: Shows the HYPSO-1 metadata for captures in Figures 11 and 12. Figure 11: ToA reflectance spectral comparison of HYPSO-1, Sentinel-2, and RadCalNet calibration sites Railroad Valley Playa (**left**) and Gobabeb (**center**). Relative error, which is calculated by dividing HYPSO-1 ToA reflectance with RadCalNet ToA reflectance and subtracting 1 (**right**).
# Application of Fiber Bragg Grating Sensing Technology for Bol Force Status Monitoring in Roadways
## 4 Application of FBG Sensors for Bolts Force Monitoring in Coal Mine Roadway  
### Engineering Overview and Monitoring Scheme  
The engineering application place is the No. 14301 longwall working face of the Shaqu coal mine, Lvliang City, Shanxi Province, China, as shown in Figure 7a. This working face has an average inclination of 4\({}^{\circ}\), a burial depth of 400 m, a vertical stress of about 10 MPa, a minable length of 1145 m, and a width of 220 m. It is identified as a highly gassy mine by the Chongqing Branch of the China Coal Research Institute, with a gas content of 81.84 m\({}^{3}\)/t.  
When the monitoring system was installed, the working face had been mined to 750 m from the roadway adit. A monitoring observation station was arranged 150 m in front of the working face.  
Figure 6: Pressure values of the pressure gauge and the FBG sensor under different forces.  
Figure 7: Location of the No. 14301 working face of the Shaqu coal mine and the layout of the bolt force status monitoring (BFSM): (**a**) location of the Shaqu coal mine; (**b**) plan view of the No. 14301 working face; (**c**) arrangement locations of two sensors; (**d**) picture of sensor installation in the roadway.  
An FBG interrogator and an upper computer were provided in the anti-explosion cabinet, which was placed in the substation in the mining area. The plan view of the No. 14301 working face and the layout of the observation station are shown in Figure 7b. The bolt force status test place was the track roadway of the No. 14301 working face. The roadway had a rectangular section with a width of 4.2 m and a height of 2.8 m. Two FBG bolt force sensors (Sensor-1 and Sensor-2) were installed on the roadway roof, 1.4 m from both sides of the roadway. The roadway section support and sensor installation positions are shown in Figure 7c. A picture of the installation of the FBG bolt force sensor in the roadway is shown in Figure 7d.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 2 Methods and Principles
### RES Module  
Convolutional module attention module (CBAM) [27] can solve the problem of what and where the image features are utilizing a channel attention module and a spatial attention module. Therefore, we form an improved RES module by combining residual connectivity between three layers of convolution and CBAM. The RES module structure is shown in Figure 1.  
The attention module, a pivotal data processing technique in machine learning, empowers neural networks to concentrate on specific local features within the input data for effective information extraction. CBAM contains two sequential sub-modules, namely the channel attention module (CAM) and the spatial attention module (SAM), as shown in Figure 2.  
\[\begin{split}\text{M}_{\text{c}}(\text{F})& =\sigma(MLP(AvgPool(\text{F}))+MLP(MaxPool(\text{F})))\\ &=\sigma\Big{(}\textbf{W}_{\textbf{1}}\Big{(}\textbf{W}_{ \textbf{0}}\Big{(}\textbf{F}_{\textbf{avg}}^{\textbf{c}}\Big{)}+\textbf{W}_ {\textbf{1}}(\textbf{W}_{\textbf{0}}(\textbf{F}_{\textbf{max}}))\Big{)}\end{split} \tag{1}\]  
CAM (Figure 3a) is used to learn by integrating the spatial information of the input feature maps after integrating them into a multilayer perceptron (MLP) through average pooling and maximum pooling operations, and the activation function is ReLU. The output features of the two MLPs are summed element-by-element to generate the channel attention value M\({}_{\text{C}}\) through the Sigmoid activation function.  
\[\begin{split}\text{M}_{\text{s}}(\text{F})&=\sigma \big{(}f^{7\times 7}\big{(}[AvgPool(\text{F});MaxPool(\text{F})]\big{)}\big{)}\\ &=\sigma\big{(}f^{7\times 7}\Big{(}[\textbf{F}_{\textbf{avg}}^{ \textbf{c}};\textbf{F}_{\textbf{max}}^{\textbf{c}}]\Big{)}\Big{)}\end{split} \tag{2}\]  
Figure 1: The architecture of the proposed RES module.  
Figure 2: The architecture of the CBAM attention module.  
SAM (Figure 3b) takes the feature maps output from the CAM as input and performs channel-based global maximum pooling and global tie pooling operations on the feature maps to obtain two H \(\times\) W \(\times\) 1 feature maps, where H and W are the height and width of the feature map. The Concat operation splices the two feature maps, performs a convolution operation with a convolution kernel of \(7\times 7\), and reduces the number of channels to 1, i.e., we obtain a feature map of H \(\times\) W \(\times\) 1. Finally, a Sigmoid activation function generates the spatial attention value M\({}_{S}\). This is then multiplied with the input feature map to obtain the final result.
## 3 Empirical Study
### Empirical Study on the Reliability of RPEM Economic Evaluation
#### 3.2.2 Determine the Range of Ore Extraction Grade Values for the Mine  
The average geological grade of the mine is 26.3% (as shown in Table 1). When using the non-pillar sublevel caving method for mining, if the ore dilution rate fluctuates between 15% and 20%, the change in the average ore grade g of the mine can be calculated by Equation (14):  
\[\begin{array}{ll}g&=g_{1}\cdot(1-\rho)\\ &=26.3\%\times[1-(15\%\sim 20\%)]\\ &=21.04\%\sim 22.36\%\end{array} \tag{14}\]  
In the formula, \(g_{1}\) is the average geological grade of the deposit, %; \(\rho\) is the ore dilution rate, %.  
Therefore, when the mine adopts the non-pillar sublevel caving method for mining, the variation range of ore grade in the mine is 21.04-22.36%.
# 5G Beyond 3 GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts
## 4 Cross-Border Use Case Implementations
### Advanced Driving  
The advanced driving use case involves the participation of different vehicles and infrastructures in order to cooperate and provide information for planning and operating a complex manoeuvre, as depicted in Figure 4. Here, each participant may use a different communication technology. Thus, the interoperability provided by multi-modal/multi-radio technologies of 5G [14] is critical to allow the information exchange from all the surrounding actors. Moreover, sidelink communications such as PC5 C-V2X and 5G NR sidelink [22] add flexibility and versatility enabling data flows between peers once the parties are connected and roles are accepted. As the manoeuvre requires a synchronised response and coordination from vehicles, the network needs to deal with the timely delivery of messages to foster a solid and consistent knowledge at once. Here, the generalised Precision Time Protocol (gPTP) provides reliable time synchronisation as declared in 3 GPP TS 23.501 [28]. Furthermore, as the scenario timeframe for such advanced driving use cases is usually small, it is important to minimise communications interruptions and outages while some participants are migrating towards other cells (handover) or other networks (roaming) with low-latency migrations between the network infrastructures.  
Figure 3: General scenario of the proposed solution.  
From the latency perspective, advanced roaming techniques [11] boost the migration of sessions along vehicles, and sidelink communications [22] simplify the protocol to exchange coordination messages to create more direct and quick communication among the surrounding participants. Specifically, roaming techniques get more complex in cross-borders to speed up data sessions across different systems and domains.  
Accurate positioning from 5G NR [19] with meter accuracy further enhanced with the next 3 GPP releases will bring sub-meter accuracy, and will complement onboard sensor-based measures; multicast networking bridged by FeMBMS (further evolved multimedia broadcast multicast service) technology [24] will ease synchronous data sharing across the manoeuvring participants; and edge computing will provide higher-level vision of the driving situation by gathering data from all the actors in place. Overall, such additional features, could enforce the use case results, adding data accuracy, boosting communications protocols and reducing exchanged messages, thereby gaining efficiency from reduced overheads.  
This use case also includes RSUs, V2X communications and multi-PLMN aspects as declared in 3 GPP TR 22.886 [40], but it can go beyond by creating of a multi-tier architecture for the server adding edge computing services [11] to provide the advanced driving service with close-to-zero latency and generating summaries and reports for a central server monitoring.