esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 \"Multistage Anchorage System + CFST\" Combined Support Technology
### Field Monitoring  
Except for numerical simulations, the surface convergence of the tentative roadway and the deformation of the CFST was monitored after the new support method had been applied. We chose five convergence monitoring sections (spacing = 10 m) in the tentative roadway and selected six monitoring points for each section. Only horizontal convergence was measured because the previous analysis showed that horizontal deformation was serious in the study area. Moreover, six CFSTs were selected, and four steel string strain gages were installed in each of these. The measurements lasted nearly 500 days and more than 20 groups of data were obtained.  
The average displacement curves of the surrounding rocks after maintenance are shown in Figure 22a, from which it is possible to see that the roadway shrank rapidly in the first 80 days and deformation then attenuated, after which it tended to become stable after about 320 days. At the last measurement, the two sides moved close to 75 to 90 mm, which is in agreement with the results of the numerical simulation. Moreover, Figure 22b reveals that the displacement of the CSFTs increased with time and the maximum deformation occurring on the left was only about 28 mm, which indicates that the tubes supply a support force and remain stable after more than 400 days.  
Figure 21: The surrounding rock’s plastic zone distribution under the new support scheme.  
Figure 22: Results of the site monitoring: (**a**) Curves of convergent deformation, (**b**) Displacement curves of CFST.  
The results of the numerical simulation and field monitoring and the photographs of the tentative roadway before and after the novel support scheme (depicted in Figure 23), show that the improved support \"multistage anchorage + CFST\" is able to control the surrounding rock's deformation in the deep roadway under high ground stress and broken rock mass and ensure that the roadway is stable.
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 3 Experiment and Result  
### Automated Welding Data Acquisition System  
Figure 2 shows the experimental setup of the welding experiment. The welding data acquisition system consists of a welding robot, a welding subsystem, and a signal acquisition subsystem. The welding subsystem consists of a Fronius TPS 600i welding power source, a wire feeder, a CMT welding torch, a welding table, and other accessories. In the welding process, the welding current and voltage are the main factors affecting the heat input, and their degree of adaptation to other parameters, such as welding speed, largely affects the quality of the weld. Due to assembly errors, the welding arc can become unstable, which in turn produces defects such as burn-through and a lack of fusion, which is reflected in the arc sound. Therefore, a multi-information acquisition subsystem was constructed for the current, voltage, and arc sound. The data acquisition subsystem consists of a signal acquisition card, sound sensor, and hall sensor. The frequency of the acquired signal is 32 kHz. The sound sensor converts the arc sound signal into an electric signal and  
Figure 1: Multi-channel time–frequency convolutional neural network.  
transmits it to the signal acquisition card. The hall sensor collects the welding current and voltage signal and transmits them to the signal acquisition card. The signal acquisition card converts the arc sound signal, welding current signal, and welding voltage signal collected by the sensor into a digital signal. As described in Section 2.1, the arc sound signal is transformed into a multi-channel time-frequency map by STFT, and convolutional neural networks are used for feature extraction and defect identification.
## 4 Node Insertion Algorithm for Determining Viewpoints
### Node Propagation Stoppage Criteria
#### 4.2.3 Checking Near Existing Saved Nodes  
The final check for node propagation stoppage is to see whether the current node being inserted is close to any saved nodes or not. This is necessary to ensure that the node propagation is stopped if it 'runs into' already existing nodes that were saved from a previous coverage planning process initiated from another start location.  
This criteria is detailed in Algorithm 6.  
```
Input: current point position \(\mathbf{p}\), coordinates of current position (i,j), current set of nodes S Output: Boolean, true if node propagation should be continued \(min_{S}\gets p_{V}\)/* Minimum Separation allowed between nodes */ for all nodes \(N\in S\)do \(dist\leftarrow Distance(N.\mathbf{p},\mathbf{p})\)/* Euclidean Distance between node N's position and current point position */ if\(dist<min_{S}\)then  return false  end if  end for return true
```  
**Algorithm 6**Checking near saved nodes - Stoppage criteria  
#### 4.2.3 Checking Near Existing Saved Nodes  
The final check for node propagation stoppage is to see whether the current node being inserted is close to any saved nodes or not. This is necessary to ensure that the node propagation is stopped if it 'runs into' already existing nodes that were saved from a previous coverage planning process initiated from another start location.  
This criteria is detailed in Algorithm 6.  
```
Input: current point position \(\mathbf{p}\), set of saved nodes S Output: Boolean, true if node propagation should be continued \(min_{S}\gets p_{V}\)/* Minimum Separation allowed between nodes */ for all nodes \(N\in S\)do \(dist\leftarrow Distance(N.\mathbf{p},\mathbf{p})\)/* Euclidean Distance between node N's position and current point position */ if\(dist<min_{S}\)then  return false  end if  end for return true
```  
**Algorithm 7**Checking near saved nodes - Stoppage criteria  
Figure 16: Node stoppage criteria (Direct).  
The value of \(p_{V}\) is chosen conservatively as the minimum separation allowed between nodes, as there will always be an overlap if the node separation is less than \(p_{V}\) (no matter which direction these viewpoints are angled, assuming \(p_{V}<p_{H}\)). Note that the Euclidean distance is used here with the assumption that the radius of curvature is large compared to the projection of viewing frustum on the surface.
# Novel Methodology for Condition Monitoring of
## 4 Experimental Setup
For this paper, a thermal image is captured every minute from the start to the end of the test, in order to monitor the thermal behavior of the gears of the gearbox transmission system. In this paper, an alternative study of four different wear conditions is proposed in the gears of the gearbox transmission system: healthy (HLT), and 25%, 50%, and 75% uniform wear, respectively. To produce the condition of wear failure in the gears, a gear factory was commissioned to manufacture it artificially. To produce the faults, the gears undergo a machining process where all their teeth are used by a tungsten cutter. Then, these gears are also subjected to a lapping process with the aim of making the wear induced on the gears as real as possible. Figure 4 shows the gears used for the tests carried out in this paper, with three different levels of uniform wear (25%, 50%, and 75%) and a healthy condition (HLT) being implemented to demonstrate the effectiveness of the proposed diagnostic methodology. Four experimental conditions are studied on the gearbox: HLT, and 25%, 50%, and 75% wear. Each test lasted 90 min, since in this time, the healthy motor reached thermal stability. Figure 3: Experimental test bench used to test and recognize gearbox wear.
## 1 Introduction
Additionally, most studies focus on either heating or cooling scenarios separately, rather than a comprehensive analysis encompassing multiple seasonal usage scenarios. Moreover, there has been a lack of detailed evaluations that utilize visual tools such as radar graphs to effectively convey the variation in solar radiation, insulation thicknesses, and associated energy demands and costs across different orientations. This study addresses these gaps by providing a comprehensive analysis that includes all cardinal and intermediate directions, offering a complete understanding of how solar irradiance influences optimal insulation thicknesses. Additionally, it introduces three alternative scenarios (cooling-only, heating-only, and both cooling and heating) to assess insulation performance under varying conditions. Correspondingly, the ideal thermal insulation thickness for each scenario is determined. Seasonal energy demands, payback periods, energy cost savings, savings rates, and optimum insulation thicknesses are also determined for various wall orientations. By developing new correlations for the total cost based on the insulation thickness for each direction and usage scenario, this work also provides a practical guidance, which has not been previously available in the literature.
## 2 Engineering Overview and Numerical Modeling
### Soil Constitutive Model and Support Structure Parameters  
(1) The built-in plastic hardening (PH) model in FLAC3D is selected for the soil constitutive models [22]. The model is similar to the hardening soil model in the Plaxis software(version: 3D\(-\)2013). It can consider the hyperbolic stress-strain relationship during the triaxial shear phase, plastic strain in mobilizing friction, stress-dependent elastic stiffness, plastic strain generated during the initial compression phase, elastic unloading/reloading behavior, the stress effect considering the preconsolidation strength, and the Mohr-Coulomb failure criterion. For a detailed description of the theoretical model, please see the literature [23]. In general, the model can capture the deformation behavior in foundation pit excavation well [24; 25].  
For any type of soil layer, the model contains 11 parameters: fundamental parameters, including the soil weight, Poisson's ratio un/reloading, and Earth pressure coefficient at rest; Mohr-Coulomb strength parameters, including the cohesion, friction angle, and dilatancy angle; stiffness parameters, including the primary loading stiffness (reference), geometric stiffness (reference), and un/reloading stiffness (reference); hardening parameters, including the stress dependency index; and the reference pressure (atmospheric pressure).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stage** & **Construction Sequences** \\ \hline
1 & Leveling the land, \\  & pile installation \\  & 1 st excavation to the depth of GL.\(-\)1.0 m, \\
2 & slope surface spray \\
3 & 2 nd excavation to the depth of GL.\(-\)5.0 m, the first layer of anchor \\  & installation at GL.\(-\)4.5 m \\
4 & 3 rd excavation to the depth of GL.\(-\)8.5 m, the second layer of anchor \\  & installation at GL.\(-\)8.0 m \\
5 & 4 th excavation to the depth of GL.\(-\)12.0 m, the third layer of anchor \\  & installation at GL.\(-\)11.5 m \\
6 & 5 th excavation to the depth of GL.\(-\)14.9 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Construction process information.  
Figure 2: The sectional view of the deep excavation.  
These parameters can be obtained from conventional geotechnical tests and the studies of [PERSON] et al. (2016) and [PERSON] et al. (2017). The abbreviated names and values of the variables are shown in Table 2.  
For the pit profile studied in this paper, the support structures include slope spray mixing (strength grade C20), a pile (strength grade C25), a crown beam (strength grade C25), a waist beam (strength grade C25), and an anchor (strength grade \(\Phi\)). Because these construction materials and support structures are produced according to normative standards, the relevant parameters can be obtained according to the standard. For example, C20 represents the standard value of concrete's compressive strength, which is 20 MPa, and the corresponding elasticity modulus is 25,500 Mpa. These parameters are given directly in Table 3.
## 2 Preliminaries and Models
### Communication Model  
As can be seen in the Figure 1, all SMs connect each other in a neighborhood by WiFi technique, which constructs public verifiable foundation. Each user would select randomly \(k\) pairwise nodes in one round and can ensure that if \(user_{i}\) chooses \(user_{j}\), then \(user_{j}\) chooses \(user_{i}\) and the keys between them are opposite mutually. The value \(k\) as a security parameter can take any value from 2 to \(n\), and depend on the specific application circumstance. The higher the value of \(k\) is, the higher the complexity is, and vice versa, and the scheme is more vulnerable to be attacked.
## 3 Thermal Control System Scheme and Unit Design
### Second-Stage Thermal Control System inside the Rack  
#### 3.3.1 Operating Principle  
As shown, the payload rack is connected to the TCB through the quick disconnector on the front panel. The inner part of the payload rack directly utilizes the liquid cooling resources provided by the TCB, and the resources are redistributed through the liquid cooling and thermal control terminal, which matches the flow rate of each channel according  
Figure 5: Working principle diagram of TCB and drive unit.  
Figure 6: Main structure of the fluid drive device.  
to the heat consumption of each load inside the module; according to the load quantity in the module, the thermal control terminal is matched with 2-3 channels of liquid supply branch, which is dynamically adjusted through the internal electric valve and transported to each load through flexible hose; the internal heating equipment of each experimental load is installed on the plate-fin-type cold plate to conduct heat to the fluid loop.  
As shown in Figure 7b, some racks adopt the method of gas-liquid mixed cooled; on the one hand, the medium of the TCB is provided to the cold plate for the equipment cooling. On the other hand, the heat source distribution of some loads inside the rack is relatively scattered, and the space size of the load is larger than others, which leads to low collection efficiency by using the liquid-cooled method, so an air loop bus is designed inside the rack; a set of parallel air ducts are arranged on the back of the rack to provide cold wind to scientific loads. The heat collected by the air loop is transferred to the liquid loop through the gas-liquid heat exchanger; the liquid loop eventually transfers the heat to the aircraft platform.  
For payloads with large flow rate demand inside the rack, as shown in Figure 7c, a secondary fluid loop is designed to collect the internal load's heat through an independent closed fluid loop; the flow rate can be adjusted by the rotation speed of the pump. After the heat of the secondary circuit is collected, the heat exchange with the TCB is realized through the liquid-liquid heat exchanger installed inside the rack.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology
### RL Controller Architecture
#### 3.3.1 Attention-Based Spatial Feature Encoder  
In the active tracking task, the quadrotor should first know the location of the dynamic target. To reduce the computational cost of the RL controller, a compact neural network architecture with powerful representational capacity is necessary. We leverage the convolutional block attention module (CBAM) [27], which consists of a 1D channel attention module and a 2D spatial attention module. The channel attention module focuses on 'what' is meaningful in the input image, while the spatial attention module tends to know 'where' informative parts are located. Given a raw image input, CBAM can help extract the spatial information of our dynamic target of interest.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Functional Characteristics of the Tumor Vascular System
#### 3.4.3 Irregular Blood Flow  
Blood flow within a tumor affects growth, metastasis, and treatment and is regulated by the difference between arterial and venous pressure, as well as by flow resistance, which depends on vessel architecture and viscosity [85; 102; 103].  
Within the tumor, microvascular pressures are increased due to venous compression, whereas drainage vein pressures are reduced [85]. Morphological abnormalities of blood vessels, such as weak connections between endothelial cells and pericytes, the absence of smooth muscle cells, basement membrane heterogeneity, and high blood viscosity due to vascular hyperpermeability, increase resistance to blood flow [85; 100; 102]. As a result, the total perfusion rate (blood flow per unit volume) decreases despite increased vascular density, leading to uneven blood flow or even reverse flow in some vessels [100; 102].  
Tumors are classified into regions according to perfusion rate: the vascular necrotic region (few or no blood vessels and dead tissue), the intermediate semi-necrotic region (some viable tissue), the region of stabilized microcirculation (stable blood flow), and the advanced front (slow growth) [104]. Perfusion heterogeneity exacerbates hypoxia, while reduced flow and increased permeability promote the accumulation of metabolic wastes, such as lactic and carbonic acid, lowering extracellular pH but maintaining intracellular pH at 7.4 [100; 102; 105].
## 6 Performance Evaluation
### Results by Epidemic, GSaR, TCCB, TC, and PRoPHETv2
#### 6.3.2 Case (b)  
To examine the performance against the node population, we vary the number of nodes from 100 to 150 and use \(L=20\) in the simulation. The rest of the settings are same as those used in Section 6.3.1.  
As shown in Figure 11, all policies can increase the successful delivery rate with a longer TTL, where the number of nodes is set to be N = 100 in Figure 11a,b, and N = 150 in Figure 11c,d. Epidemic and GSaR unbound replicate messages, thereby inducing more message overhead. Similar to Figure 9a, 10a and 11a,c depict the decrease in delivery rate when the TTL \(>8\) h in Epidemic and TCCB. Compared with Figures 9 and 11b,d, an increase in the overhead ratio is displayed. In the MPMF, the increase in overhead is lower with a larger TTL than the other policies. Although increasing the node population can increase the encounter rate, the delivery rate is not affected in the case of MPMF. In the MPMF, the number of message copies for each message is fixed; then, nodes with a copy of the message keep waiting for the destination without forwarding. This makes messages kept in the buffer longer. Because of the TTL timeout or buffer overflow, messages are unable to reach the destination node. Notice that in the case of NCCU, nodes are scattered in a large space, while nodes move in the same confined area in TVCM. Therefore, in the case of GSaR, messages have higher chances of reaching a destination. With the increase in contact frequency in the case of a larger node population, the numbers of forwarded messages in GSaR and PRoPHETv2 increase, thus resulting in a higher delivery rate and overhead ratio. In TC, with the increase in node population, the number of formed communities becomes larger. Therefore, a node can belong to several communities and help deliver messages to a target node, resulting in a higher delivery rate, as shown in Figure 11a,c.  
From the above results, we can explain that the MPMF policy is suitable for delivering messages with longer TTLs when the node population is small. However, given a larger node population, the policy can still result in lower overhead while maintaining a comparable delivery rate.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
###### Abstract  
After 30 years since the beginning of the Global Positioning System (GPS), or, more generally, Global Navigation Satellite System (GNSS) meteorology, this technique has proven to be a reliable method for retrieving atmospheric water vapor; it is low-cost, weather independent, with high temporal resolution and is highly accurate and precise. GNSS ground-based networks are becoming denser, and the first stations installed have now quite long time-series that allow the study of the temporal features of water vapor and its relevant role inside the climate system. In this review, the different GNSS methodologies to retrieve atmospheric water vapor content re-examined, such as tomography, conversion of GNSS tropospheric delay to water vapor estimates, analyses of errors, and combinations of GNSS with other sources to enhance water vapor information. Moreover, the use of these data in different kinds of studies is discussed. For instance, the GNSS technique is commonly used as a reference tool for validating other water vapor products (e.g., radiosounding, radiometers onboard satellite platforms or ground-based instruments). Additionally, GNSS retrievals are largely used in order to determine the high spatio-temporal variability and long-term trends of atmospheric water vapor or in models with the goal of determining its notable influence on the climate system (e.g., assimilation in numerical prediction, as input to radiative transfer models, study of circulation patterns, etc.).  
GNSS; GPS; water vapor; ground-based; methodology; validation; spatio-temporal; time-series; meteorology; climate
## 3 TVWS-DTV Coexistence: Field Measurements
### Field Tests Considering Short Distances
#### 3.1.2 Test Environment and Results
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline
**DTV** & \multicolumn{2}{c}{**Set-Top Box**} & **1** & **2** & **3** & **1** & **2** & **3** \\ \cline{2-7} \multicolumn{1}{c}{**Channel**} & \multicolumn{2}{c}{**Link Length [m]**} & \multicolumn{2}{c}{**15**} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{**50**} \\ \hline  & \(P_{\rm TX}\) TVWS (CH28) [Wp] & 0.030 & 0.030 & 0.045 & 0.030 & 0.030 & 0.040 \\ \cline{2-7}
27 & \(P_{\rm RX}\) DTV (CH27) [dBm] & \(-77.1\) & \(-77.0\) & \(-76.9\) & \(-77.0\) & \(-77.0\) & \(-77.0\) \\ \cline{2-7}  & \(P_{\rm RX}\) TVWS (CH28) [dBm] & \(-43.2\) & \(-43.1\) & \(-41.4\) & \(-44.3\) & \(-44.3\) & \(-43.3\) \\ \cline{2-7} Interf. & \(\mbox{PR}_{n+1}\) [dB] & \(-33.9\) & \(-33.9\) & \(-35.5\) & \(-32.7\) & \(-32.7\) & \(-33.7\) \\ \cline{2-7} \(n+1\) & \(\mbox{ACPR}_{\rm UPPER}\) [dB] & \(33.9\) & \(33.9\) & \(35.5\) & \(32.7\) & \(32.7\) & \(33.7\) \\ \cline{2-7}  & \(\mbox{ACPR}\)lower [dB] & \(-15.9\) & \(-16.1\) & \(-16.1\) & \(-16.1\) & \(-16.1\) & \(-16.1\) \\ \hline  & \(P_{\rm TX}\) TVWS (CH28) [Wp] & 0.030 & 0.030 & 0.040 & 0.030 & 0.045 & 0.054 \\ \cline{2-7}
29 & \(P_{\rm RX}\) DTV (CH29) [dBm] & \(-76.8\) & \(-76.8\) & \(-76.7\) & \(-76.8\) & \(-76.7\) & \(-76.7\) \\ \cline{2-7}  & \(P_{\rm RX}\) TVWS (CH28) [dBm] & \(-42.6\) & \(-42.6\) & \(-41.8\) & \(-43.7\) & \(-42.2\) & \(-41.7\) \\ \cline{2-7} Interf. & \(\mbox{PR}_{n-1}\) [dB] & \(-34.2\) & \(-34.2\) & \(-34.9\) & \(-33.1\) & \(-34.5\) & \(-35.0\) \\ \cline{2-7} \(n-1\) & \(\mbox{ACPR}_{\rm UPPER}\) [dB] & \(-16.2\) & \(-16.2\) & \(-16.3\) & \(-16.3\) & \(-16.3\) & \(-16.4\) \\ \cline{2-7}  & \(\mbox{ACPRlower}\) [dB] & 34.2 & 34.2 & 34.9 & 33.1 & 34.5 & 35.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test results with interference \(n-1\) and \(n+1\) at distances of 15 m and 50 m.  
The coexistence tests demonstrated that the recorded PR values were close to and consistent with those obtained in the laboratory tests. Taking as an example the PR measured on channel \(n-1\) and \(n+1\) using 5G-RANGE as the TVWS signal, the values of \(-31.38\) and \(-33.24\) were obtained in the laboratory, while in the field tests, the values obtained were \(-34.32\) and \(-33.73\), respectively. Thus, when the PR was below the identified limit, the reception of the DTV signal was noticeably affected through video observation. Among the main effects observed, the presence of visual artifacts, video freezing, and intermittent failures in signal demodulation stands out. These results indicate that the appropriate definition of the PR values is essential to ensure that TVWS can operate without compromising the DTV user experience.