esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Radiative Transfer
### Radiance Transmission  
The transmittance \(T_{I}^{g}\) along a slant light path through a plane parallel atmospheric layer with gaseous absorption can be computed with [PERSON]'s law  
\[T_{I}^{g}(\tau_{g},\zeta) =e^{-\zeta\ \int K(z)\ dz} \tag{2}\] \[=e^{-\zeta\ \tau_{g}}\]  
Figure 1: Basic radiative transfer setup with an absorbing atmosphere, a surface with Lambertian reflectance, and an optically thin semi-transparent layer which can partly transmit, absorb, or scatter light in an isotropic way. \(F_{0}\) is the solar incoming flux, \(\theta_{0}\) and \(\theta\) are the solar and satellite zenith angles, and \(I\) is the radiance reaching the satellite instrument split into components as discussed in the main text. Red represents radiation originating from direct illumination of the surface. Green represents radiation originating from direct illumination of the scattering layer. Arrows represent radiance components reaching the satellite instrument originating from the surface (solid) or from the scattering layer (dashed). Wavel lines represent diffuse radiant fluxes.  
with \(K\) being the absorption coefficient, \(z\) the height above the surface, \(\tau_{g}\) the total vertical optical thickness, and \(\zeta=1/\cos\ \theta\) the light path extension for the zenith angle \(\theta\).  
Considering light scattering and absorption within the scattering layer, the fraction of light transmitted through the scattering layer becomes  
\[T_{I}^{g}(\tau_{e},\zeta)=e^{-\tau_{e}\ \zeta}=1-S_{I}(\tau_{s},\tau_{e},\zeta)-A_ {I}(\tau_{s},\tau_{e},\zeta); \tag{3}\]  
with \(\tau_{e}=\tau_{a}+\tau_{s}\) being the extinction optical thickness, i.e., the sum of absorption (not to be confused with gaseous absorption) and scattering optical thickness. \(S_{I}\) and \(A_{I}\) are the fraction of scattered and absorbed radiance within the scattering layer:  
\[S_{I}(\tau_{s},\tau_{e},\zeta) = \frac{\tau_{s}}{\tau_{e}}\ [1-T_{I}^{s}(\tau_{e},\zeta)] \tag{4}\] \[A_{I}(\tau_{s},\tau_{e},\zeta) = \frac{\tau_{a}}{\tau_{e}}\ [1-T_{I}^{s}(\tau_{e},\zeta)] \tag{5}\]
## 3 Analysis of Influence Factors on Deformation Allowance of Loess Tunnel
### Analysis of Influence of Moisture Content on Deformation Allowance  
Affected by the moisture content in the loess area, the mechanical properties of the loess body can rapidly deteriorate, potentially leading to instability and posing construction safety risks. In soil tunnel construction, uncontrolled initial support clearance displacement and unstable surrounding rock complicate the determination of the tunnel's deformation  
Figure 8: Analysis of deformation allowance caused by surrounding convergence of grade V surrounding rock.  
Figure 7: Analysis of deformation allowance caused by crown settlement of grade V surrounding rock.  
allowance. [PERSON] et al. [69] studied the settlement and deformation mechanism of shallow buried loess tunnel surrounding rock after the increase of surrounding rock moisture content caused by rainfall. Through a large amount of on-site monitoring data, it was shown that the stress on tunnel support increased after the increase of moisture content, and the settlement of surrounding rock continued to increase. They simultaneously provided corresponding rectification measures. [PERSON] et al. [70] classified the geotechnical environment level and immersion level of loess tunnels, and provided a calculation and analysis method for the settlement deformation of loess foundation in tunnels. Considering the structural condition of loess, the Taisha formula was used to calculate the surrounding rock pressure of the tunnel, and the relationship between the surrounding rock pressure and the structural degree of loess was obtained. However, few scholars have provided corresponding reserved deformation values for the moisture content of tunnel surrounding rock under different loess soil types. Through an analysis of 57 monitoring sections in three soil types--clayey loess, silty loess, and sandy loess--the relationship between tunnel arch settlement and water content was examined. Deformation allowance recommendations are provided. In Figure 9, moisture content primarily falls within the range of 6% to 26%, with a notably high proportion concentrated between 11% and 23%. The influence of moisture content on loess tunnels in sandy loess sections is more significant than that in clayey loess and silty loess sections. With a 10 cm deformation allowance, the guarantee rate is 64.29%, while increasing it to 20 cm raises the guarantee rate to 94.64%. Taking soil type into account, crown settlement in clayey loess and silty loess sections typically remains below 100 mm due to moisture content influence, allowing a 10 cm deformation allowance. However, in sandy loess sections, extensive cumulative vault settlement occurs due to significant moisture content effects. Therefore, tunnels in sandy loess sections should have a deformation allowance of 20 cm.
# How Does C-V2X Help Autonomous Driving to Avoid Accidents?
## 2 Solution for C-V2X VRUCW
### Autonomous Driving System Integration with C-V2X  
As shown in Figure 1, we designed the system integration solution between C-V2X OBU and autonomous driving control unit in real life [19]. Because the specification of OBU is a standard as a vehicle equipment including operating system, power supply, operational temperature and radio frequency band, this integration can adapt to all kinds of vehicles as long as they are in accordance with the international standards.  
Firstly, RSU as a roadside equipment receives information from outside sources, such as AI servers, 4G/5G telecommunication network. Then, it broadcasts this information by PC5 interface using a unified message format in its coverage field, the maximum reachable radius can be one kilometer if there are not obstacles such as buildings or trees. Secondly, OBU is connected with control unit Industrial PC (IPC) of the autonomous driving system via Transmission Control Protocol (TCP)/Internet Protocol (IP) protocol. The OBU with 12V power is provided by vehicle, and antennas of OBU should be installed outside of vehicle for better messages receiving. After a successful 3-way TCP/IP handshake connection between OBU and control unit of autonomous vehicle, OBU can receive Global Navigation System Satellite (GNSS) and Controller Area Network (CAN) messages from the vehicle's control unit. Thirdly, combining with vulnerable road user information from RSU, OBU can instantly judge the status of road safety and notify autonomous driving control unit.
## 3 Results and Discussion  
### Evaluating the Cause of Cracks on the MSE Wall Facing at the Case Site  
The results of the electrical resistivity survey were analyzed to evaluate the cause of the cracks in the wall facing of the MSE wall at the case site. Although electrical resistivity surveys are not very quantitative in terms of ground properties, they are useful in determining the overall subsurface conditions (soil type, presence of voids, groundwater conditions, etc.).  
Figure 8 shows the results of the electrical resistivity survey of the MSE wall.  
Figure 8a to Figure 8c show the results of the original ground exploration focused on the front of the MSE wall. There were no abnormal areas in ER-1-1 and ER-1-2 (Figure 8a). However, four abnormal areas were identified as a result of the ER-1-3 exploration. Based on the past construction history of the MSE wall, areas A and D were among the abnormal areas evaluated due to the drainage pipes in the original ground in front of the MSE wall. Moreover, area C was identified as being affected by a collector well, which is a reinforced concrete structure. Area B, which is the exploration result for the ground where the corner of the MSE wall is located, was analyzed to be an abnormal area caused by low ground stiffness since there was no history of underground construction structures. This means that it had a great influence on the occurrence of longitudinal cracks in the facing of the MSE wall. Area E, which can be seen in the exploration results of ER-1-4, was also identified as being affected by underground structures. Therefore, it was analyzed that the low ground stiffness of the original ground in the structurally vulnerable corner induced the displacement of the MSE wall due to subsidence, based on the exploration results of the original ground in the front of the MSE wall.  
Figure 8d to Figure 8e show the exploration results of the reinforced earth mass in the MSE wall. It was found that there was no abnormal area in ER-2-1, but two abnormal areas were confirmed in ER-2-2 and ER-2-3. Areas F and G correspond to the corners of the MSE wall, and the abnormal areas are connected. In addition, these areas have similar longitudinal cross-section locations to area B. In other words, it was found that the problems (such as groundwater infiltration) in areas F and G, which extended to area B, caused the continuous deformation of the corner on the MSE wall.  
Therefore, the risk area for the MSE wall of the case site was determined to be the corner based on the results of the electrical resistivity survey. In other words, this area was evaluated as requiring continuous monitoring. The longitudinal crack status of the corner is shown in Figure 9.  
Figure 7: Field experiments with the traditional monitoring method (total station) and digital image photogrammetry (commercial imaging devices, digital camera, and cellphone camera).  
Figure 8: _Cont._
## 3 The Proposed Protocol
### Selection of the Key Node
CI is the Consistency Index and is calculated by : \(CI=\dfrac{\lambda_{\text{max}}-n}{n-1}\)  
CR is the Consistency Ratio and is calculated by : \(CR=\dfrac{CI}{RI}\)  
where RI is the Random Index, whose values are cited in Table 5.  
When selecting the key node, it can be any of the UAVs, which reduces the possibility of a specific UAV being subject to the targeted DoS attack. Once the key node in the cluster is identified, GCS forms a list of session keys for each node in the cluster, and sends it to that key node, and encrypts it using its session key. This is shown in Figure 4.  
Because the key node changes periodically, or when the key node is compromised or attacked, the key node needs to be reelected to ensure the security of the UAV cluster communication network, as shown in Figure 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number Rating** & **Verbal Judgment of Preferences** \\ \hline
1 & Equally preferred \\
3 & Moderately preferred \\
5 & Strongly preferred \\
7 & Very strongly preferred \\
9 & Extremely preferred \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A fundamental 1 to 9 scale. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Criteria** & \(A\) & \(B\) & \(C\) & **Priority Vector** \\ \hline \(A\) & 1 & 3 & 7 & 64.34\% \\ \(B\) & 1/3 & 1 & 5 & 28.28\% \\ \(C\) & 1/7 & 1/5 & 1 & 7.38\% \\ \hline \(\lambda_{\text{max}}=3.0967\), \(CI=0.0484\), \(CR=8.34\%<10\%\) (acceptable).  
\end{table} 
Table 3: Paired comparison matrix. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dimension** & **RI** \\ \hline
1 & 0 \\
2 & 0 \\
3 & 0.58 \\
4 & 0.9 \\
5 & 1.12 \\
6 & 1.24 \\
7 & 1.32 \\
8 & 1.41 \\
9 & 1.45 \\
10 & 1.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Random Consistency Index (RI).
## 3 FGRL for Mapless Navigation
### RL-Based Mapless Navigation  
In discrete RL tasks, the learning of appropriate actions can be achieved by Deep Q-Network (DQN) [38], and in order to resolve the overoptimistic value estimates of the max operator, the Double Deep Q-Network (DDQN) algorithm [39] is introduced. In continuous RL tasks, the policy gradient method Deep Deterministic Policy Gradient (DDPG) [40] is adapted from DQN, and it updates the Q-values in the same way as DQN. Thus, the overestimation bias still needs to be taken into consideration. In this work, we adopt the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm [41] to construct the mapless navigation framework for UGVs, as it can further improve DDPG by addressing variance reduction.
## 2 Materials and Methods
### Analysis of Volatiles  
Five grams of a sample and 20 mL of deionized water were placed into a 50 mL centrifuge tube and, after sufficient shaking, the sample was ultrasonicated for 30 min, and then centrifuged at 4 \({}^{\circ}\)C for 10 min at 8000\(\times\)\(g\). Then, 8 mL supernatant and 20 \(\upmu\)L internal standard (2-Methyl-2-butanol, 100 \(\upmu\)g/mL) was placed in a 20 mL headspace vial containing 3.0 g NaCl, tightly capped with a silicon septum, extracted with PDMS/CWR/DVB, equilibrated at 65 \({}^{\circ}\)C in a thermostatic bath for 15 min, and extracted for 20 min at the same temperature under stirring with a 400 r/min shaking speed. After extraction, the fiber was immediately introduced into the injection port of gas chromatography-mass spectrometry to thermally desorb the analyte at 250 \({}^{\circ}\)C for 180 s.  
The volatiles were analyzed and identified on a Trace GC Ultra gas chromatograph-DSQ II mass spectrometer (Thermo Electron Corp., Waltham, MA, USA) equipped with an HP-INNOWAX capillary column (60.0 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technology, Santa Clara, CA, USA). Mass spectrum was generated in the electron impact (EI) mode at 70 eV ionization energy using the full scan mode (35 to 400 amu).  
The following is the GC operation condition: the carrier gas was helium (purity > 99.999%) at a flow rate of 0.8 mL/min and the inlet temperature was 280 \({}^{\circ}\)C with splitless injection. The oven temperature was maintained at 35 \({}^{\circ}\)C for 1 min, followed by an increase of 3 \({}^{\circ}\)C/min to 280 \({}^{\circ}\)C, and then programmed to 230 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min, and held for 5 min. The flavor compounds were tentatively identified by matching mass spectrum with NIST05 spectrum database and verified by comparison of their Kovats retention indices (RI) with the RIs reported in other studies, which was calculated by using C8-C20 n-alkanes [23].  
Quantitative analysis of flavor compounds was carried out using the internal standard method. In this process, the area of each chromatographic peak was automatically integrated, and the ratio of the peak area of the internal standard to the peak area of each component in the sample was calculated. As a result, the content of each flavor component was quantified.
## 3 Results and Discussion
### Analysis of Subjective Questionnaires Survyes in the Cooling Condition  
Figure 8 shows a comparison of the TSV, CSV, and CLV after rest using the basic, ventilation, and cold water-cooling seats at a temperature of 27.5 \({}^{\circ}\)C as the indoor temperature condition during summer. As a result of the TSV for the basic seat, 50% voted the neutral areas which are \"warm\" or \"not cool,\" and the remaining 50% voted for \"warm,\" \"hot,\" and \"feel hot.\" However, when using the ventilation seat, 50% of the subjects chose neutral,  
Figure 6: Topographical distribution of average power density in each frequency band on the head for various seat types.  
Figure 7: Improvement of biosignals according to seat type, compared to the basic seat.  
as in the case of the basic seat, and 33.3% and 16.7% voted \"cool\" and \"warm,\" respectively. When using the cold water-cooling seat, 71% of the subjects answered \"cool,\" and the other 29% answered in a neutral area. According to the analysis of the CSV, when the basic, ventilation, and cold water-cooling seats were used, the response rate for the comfort index 2 indicating satisfaction was the same for all three conditions. However, when ventilation and cold water-cooling seats were used, the comfort index was higher than 0 (neutral), and the average comfort level increased by 5.8% and 13.9%, respectively, compared to the basic seat. Among the three cases, when the subject was resting using the cold water-cooling seat, all subjects responded with a comfort index of 0 or a little higher, thus it was the most satisfactory condition regarding thermal comfort. This means that the use of the ventilation and cold water-cooling seats provided a comfortable environment to the subject for rest in the indoor space during summer.  
As a result of the analysis of CLV, for the basic seat, the subjects responded with a minimum attention index of \(-3\) to a maximum of 2; however, the percentage that responded with 0 or less was approximately 67.1%, indicating that the concentration of the subject decreased. However, compared to the basic seat, when the ventilated and cold water-cooling seats were used, the attention concentration index improved by 4.6% and 32.3%, respectively, providing favorable conditions for improving the subject's concentration. As a result of a comprehensive analysis of the results obtained in this study, it is considered that the use of the ventilation and cold water-cooling seats provided a pleasant environment to the subject and increased concentration, compared to the basic seat at an indoor temperature of 27.5 \({}^{\circ}\)C. Comparing the three options of this study, the cold water-cooling seat provided the highest concentration and the most comfortable condition for rest, according to the results of the subjective questionnaire.
## 4 Topology and Control Strategy for Proposed Power Converter
### DC/AC Inverter and Control  
Figure 8 shows a typical topology of a two-stage grid-connected PV system, which includes a DC/DC boost converter and a DC/AC converter. The first-stage DC/DC boost converter connected to the PV panel steps up the output DC voltage of the DC bus to a level appropriate for the DC/AC inverter, based on the MPPT algorithm.  
Figure 8: Control strategy of DC/AC inverter.  
Figure 7: Augmented state-space feedback linearization control strategy.  
The DC/AC inverter control strategy is based on the instantaneous powers of the \(d\)-\(q\) frame references applied for pulse-width modulation (PWM) control. According to the instantaneous power theory, the active power \(p\) and reactive power \(q\) can be written as in Equation (10):  
\[\left\{\begin{array}{l}p=\frac{3}{2}(e_{d}i_{d}+e_{q}i_{q})\\ q=\frac{3}{2}(e_{d}i_{q}-e_{q}i_{d})\end{array}\right., \tag{12}\]  
where \(e_{d}\), \(e_{q}\), \(i_{d}\), and \(i_{q}\) are \(d\)-\(q\) components of the grid voltage and \(d\)-\(q\) components of the output current, respectively. \(|E|\) is the amplitude of the grid voltage. On the \(d\)-\(q\) frame references under the voltage-oriented control (\(V_{OC}\)) method, \(e_{d}=|E|\), \(e_{q}=0\). Thus,  
\[\left\{\begin{array}{l}p=\frac{3}{2}e_{d}i_{d}\\ q=\frac{3}{2}e_{d}i_{q}\end{array}\right.. \tag{13}\]  
Ignoring the inverter attenuation losses, the instantaneous value of the DC-link power \(p_{dc}\) can be described as shown in Equation (14):  
\[p_{dc}=i_{dc}u_{dc}. \tag{14}\]  
The active power \(p\) and \(p_{dc}\) could be represented as  
\[i_{dc}u_{dc}=p=\frac{3}{2}e_{d}i_{d}. \tag{15}\]  
The control scheme of the grid-side inverter is shown in Figure 8. The internal \(d\)-\(q\) current loop references can be calculated from the external loop, and the PI controller is used to regulate the \(d\)-\(q\) grid currents [45; 46; 47]. The outputs of the \(d\)-\(q\) current controllers, \(\Delta V_{gd}\) and \(\Delta V_{gq}\), which denote a variation of the grid voltage, are added to the d-q grid voltages to obtain the dynamic response with the feedforward of the grid voltage [48]. In order to operate in terms of unity power factor with grid, \(I_{qref}\) is set to 0.
## 4 Results and Discussion
Figure 10 shows the RMSE according to the thermal conductivity substituted into theoretical analysis for various measurement directions (\(\theta\): \(0^{\circ},30^{\circ},45^{\circ},60^{\circ}\), and \(90^{\circ}\)). \[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{N}\left(\chi_{cal}-\chi_{\rm exp}\right)^{ 2}}{N}}\ (N=\text{ Relative positions}) \tag{14}\]  
Figure 10: _Cont._  
Based on these results, Figure 11 shows the thermal conductivity according to the measurement direction derived using the transparent anisotropic collinear method, the average of literature values in the principal axis directions (\(k_{11}\), \(k_{22}\)), and the calculated values that can be derived through the vector operation of Equation (10). It was found that the measurement values were accurate because absolute errors from the thermal conductivity of A-plane sapphire glass in the principal axis direction from various literature were less than about 4 W/(mK). It was also found that the thermal conductivity according to the measurement direction was accurate because errors from the values calculated based on the average of literature values were less than 4 W/(mK). Table 8 summarizes the measured values and errors. Figure 11: Experimental results for various measurement directions.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Scattering Mechanisms and Radar Observatory Techniques  
The polarimetric technique deals with the electromagnetic waves (polarization of the full-wave vector), throughout the frequency spectrum, by controlling the coherent polarization properties of the optical and radio waves: this allows detecting where there are either abrupt or gradual changes in the refraction index (or permittivity, magnetic permeability, and conductivity). When an electromagnetic wave passes from one medium to another, changing, therefore, refraction index, or when it strikes an object such as a radar target and it is reflected, it allows a polarization control to acquire knowledge of the characteristic information about the reflectivity, e.g., the orientation and shape of the reflecting object. In classical radars, i.e., amplitude-only radars, the information about a target is mainly obtained from the energy of the returned pulse; interferometric SAR exploits fully the phase and Doppler information, but not the polarization information of the electromagnetic vector wave-scatterer interrogation process [116; 117; 118; 119].  
A polarimetric, coherent electromagnetic scattering model has been developed for short-branch vegetation analysis. The polarimetric radar back-scatter analysis for soybean plants with truck-mounted scatterometer using L and C-band radar for different values of soil-humidity concentration was, in particular, performed by [PERSON] et al. [120]. This model was used to investigate the soybean field's physical parameters, including soil moisture from a polarimetric set of AIRSAR images [121]. Using polarimetric SAR (PolSAR) data and electromagnetic scattering models depending on plant morphology, one can approximate the backscattering behavior of rice crops. The accuracy of such an approach has been tested in two different bands (X and C) with soil measured biophysical parameters in rice fields. Moreover, the proposed metamodel-based biophysical probabilistic parameter-retrieval algorithm has allowed rice crop height estimation using PolSAR data with both high accuracy and low computation cost [120]. [PERSON] et al. reported that the comparison between predicted performances of monostatic and bistatic radars in vegetation monitoring [122] shows the basic scattering mechanisms [123; 124]. [PERSON] et al. studied radar backscatter measurements of sea ice made in May 1977 with a surface-based FM-CW scatterometer from 1-2 to 8.5-17.5 GHz [125].