esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Adverse weather conditions such as rain or fog pose various visual restrictions to drivers in maintaining a safe driving environment. In night situations, the risk is greater, the driver's workload increases, and the severity of traffic accidents is greater than on a clear day. In adverse weather at night, road lighting is almost the only means of providing direct visibility of the road surface to drivers, except for car headlights. Therefore, drivers rely on road lighting in adverse weather at night to determine the condition of the road surface and take driving measures according to the situation. However, all road lighting is developed and evaluated based on normal weather conditions. In fact, there is almost no development of road lighting to ensure visibility in adverse weather situations, and there is no related research. One of the reasons may be that there is no way to evaluate road lighting during adverse weather.  
The International Commission on Illumination (CIE) measures the road surface luminance, overall/longitudinal uniformity, threshold increment on dry roads, and overall uniformity of wet road surfaces [1] and classifies road lighting classes according to each value to judge the performance of road lighting [2]. However, there is no official method to evaluate road lighting performance in foggy and rainy situations. The reason is that, under normal clear weather conditions, the luminance of the road surface illuminated by road lighting is measured through a luminance meter. However, it is believed that during adverse weather such as fog or rain, atmospheric scattering occurs due to fog or rain particles in the atmosphere, making it impossible to measure the luminance value of the road surface [3]. In reality, the road environment that is fatal to drivers is adverse weather, so although road lighting performance evaluation measures should be actively carried out in adverse weather at night, little related research is being carried out due to the difficulty of implementing adverse weather environments. There are cases of experiments using obstacle recognition distance as a measure of effectiveness to evaluate road lighting during some adverse weather, and CIE 115:2010 also specifies that the purpose of road lighting is to make obstacles on the road visible [2]. Considering this, this study proposed obstacle recognition distance as a measure of effectiveness for evaluating road lighting during rain and fog. In order to verify whether obstacle recognition distance is appropriate as a measure of effectiveness for road lighting performance evaluation in adverse weather, an empirical experiment using subjects in various environments was conducted and comparative results were presented.
## 2 Materials and Methods
### Normalization and Weight Assignment  
All the data have been collected in an evaluation matrix, whose rows correspond to each alternative (i.e., each municipality), while each column corresponds to each selected criterion. In other words, the \(ij\)-th element of the evaluation matrix expresses the value of the \(i\)-th alternative relating to the attribute of the \(j\)-th criterion and describes the performance of each alternative regarding each criterion.  
It should be noted that criteria are represented through different scales and units. This precludes mutual comparisons. Thus, it is necessary to further homogenize the data contained in the evaluation matrix and proceed with comparisons through normalization. Through the preference function, the performance of the alternatives is transformed into a dimensionless value, ranging from o to 1. As a first attempt, we adopted the Type 1 preference function described in Table 1, which does not require the definition of any threshold. Subsequently, the linear preference function was also used.  
Finally, we attributed weights to each criterion. Through this step, decision makers can make their preferences explicit, since it is not ensured that all the criteria take on the same importance. We first decided to attribute the same weight to each criterion. Then, a sensitivity analysis was performed with varying weights.  
The risk maps shown in the following sections indicate three classes of risk levels, namely low, medium, high. It is emphasized that this classification must be intended as a pure ranking in terms of the relative urgence of investments. It does not at all intend to indicate the level of safety in absolute terms of the various municipalities. This classification answers the question as to whether the method can provide the priority level associated with a certain municipality and help to decide how to distribute investments over various municipalities.
# Circular Orbit Flip Trajectories Generated by E-Sail
## 3 Numerical Simulations and Discussion
### Case of a Transfer with a Single SWA
This specific mission concept, in the case of a single SWA maneuver, has been schematized in Figure 10, which represents the extended version of the scheme in Figure 2b. In particular, the magenta line in Figure 10 indicates the linear trajectory followed by the science probe after its release (with zero inertial velocity) at the COFT's aphelion point. Note that the same mission concept can also be applied to a DT case. Figure 8: Polar form of the COFT (black line) for a set of values of the propulsive acceleration \(\beta\) in a scenario with a single SWA. Black circle \(\rightarrow\) starting point; red star \(\rightarrow\) aphelion point; orange circle \(\rightarrow\) Sun; blue line \(\rightarrow\) parking orbit. The radial distance is normalized with the parking orbit radius \(r_{0}\). The mission analysis involving the insertion of a scientific probe into a linear trajectory is one of the possible extensions of the models discussed in this paper, and a couple of other potential extensions are briefly discussed in the conclusion section at the end of this paper. The next subsection, instead, describes the interesting characteristics of the optimal control law in two specific scenarios. Figure 10: Conceptual sketch of an extended mission scenario (in presence of a single SWA), in which a scientific probe is released at the aphelion point to cover a linear trajectory. See also Figure 2b. Figure 9: Case of a transfer with a single SWA: radial (\(r_{a}\)) and angular (\(\theta_{a}\)) position of the aphelion point as a function of \(\beta\).
###### Abstract  
The engineering technology of undercrossing an airport flight area is relatively mature; however, the use of shield tunnels crossing the operating airport flight area in high liquidity sandy cobble stratum has rarely been reported. To discuss the feasibility of a double-line shield tunnel undercrossing the airport flight area in a sandy cobble region. Based on the case of the Chengdu Metro Line 10 that undercrosses the Shuang-Liu Airport, which is located in sandy cobble region, the deformation and stress laws of airport runway pavement structures were investigated via a three-dimensional numerical model. Stratum displacement, ground settlement and pavement tensile stress under different tunnel depths were analysed. Then, the pavement tensile stress was taken as the safety evaluation index and the vulnerable area of the pavement structure was proposed. The results show that after excavation of the double-line tunnel, the maximum ground surface settlement occurred above the tunnel centreline and the ground settlement curves presented a V-type settlement trough instead of a W-type. The existence of a runway greatly limited the deformation of the surrounding soil, with increasing depth, the effect degree of the runway pavement on the soil settlement decreased. The most unfavourable region of the runway pavement structure under the influence of tunnel excavation was found. With increasing burial depth, the maximum settlement of the surface centre point decreased continuously. It is recommended that the tunnel burial depth not exceed 23.5 m in this project. According to the displacement and stress control limitation of the airport pavement, it can be judged that the shield construction method meets the structural stability requirements of the pavement. The calculated results provide a reference for the shield tunnel construction in the same geological condition areas.  
shield tunnel; undercrossing construction; settlement; stress; runway pavement Article
# _remote sensing_
## 3. HORUS Multiangle and Multispectral Observation Module
### Signal-to-Noise Ratio
Dark current noise is 125e\({}^{-}\)/s at 25 \({}^{\circ}\)C, so the square root of the dark current multiplied by the low exposure time is negligible. The readout noise is 5e\({}^{-}\) RMS, which is also negligible. Therefore, the SNR is approximately the square root of the detected signal. A summary of the parameters used in the SNR analysis is shown in Table 8. The HORUS predicted SNR values at different wavelengths and observation angles are summarized in Table 9, showing that the SNR performance is in line with the minimum values of other EO missions [51]. For what concerns the thermal control, the payload module shall be maintained in temperature as low and stable as possible during the images acquisition. The expected in orbit temperature range for an internal component of a CubeSat is between \(-\)5 and +40 \({}^{\circ}\)C.  
The non-operating temperature range for the sensor is \(-\)30/+80 \({}^{\circ}\)C, therefore, no active thermal control is needed while the sensor is switched off. The operating temperature of the sensor is 0/+50 \({}^{\circ}\)C, therefore, a well-defined thermal analysis and thermal testing shall be performed to evaluate the expected temperature during the operative phase and how the internal power dissipation of the sensor affects the temperature stability. However, as the sensor is located on a CubeSat face that shall not be facing the Sun, extremely high temperatures are not to be expected. The same can be applied for extremely low temperatures as the image acquisition is performed only while the CubeSat is illuminated by the Sun. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** \\ \hline Integration time & 17 ms (60 FPS) \\ Quantum efficiency & 50\% blue and NIR/60\% red and green \\ Dark current & 125e\({}^{-}\)/s \\ Read noise & 5e\({}^{-}\) RMS \\ Full well capacity & \(\sim\)10\({}^{6}\) e\({}^{-}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: SNR analysis parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **0\({}^{\circ}\) Nadir** & **26.1\({}^{\circ}\)** & **45.6\({}^{\circ}\)** & **60\({}^{\circ}\)** & **70.5\({}^{\circ}\)** \\ \hline
**Red** & 203 & 191 & 169 & 141 & 116 \\
**Green** & 177 & 167 & 148 & 123 & 101 \\
**Blue** & 150 & 142 & 125 & 105 & 86 \\
**NIR** & 305 & 288 & 254 & 213 & 174 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: SNR analysis results.
###### Abstract
The routing path defines the stretch encountered while completing the request from the publisher to the consumer as illustrated in Figure 1. The path with the lesser number of hops encounters lesser stretch [42]. Routing optimization leads to the efficient path for content retrieval. Hence, optimal routing path between consumer and publisher leads to a reduction in end-to-end latency [11]. Therefore, we have proposed a strategy to select the optimal routing path i.e., intelligent stretch optimization (ISO) with lesser stretch for haptic-driven inter-personal communication for AR/VR applications. The motivations of this paper are as follows:  
* First, an intelligent model should be designed to evaluate stretch in ICN to obtain an optimal stretch reduction scheme for improving ICN-based 5G network performance. * Second, reinforcement learning (RL) tends toward optimal stretch with an autonomous and intelligent decision based on previous experiences in the routing decision. This paper proposes a RL-based ISO strategy in ICN for haptic-driven AR/VR applications in TI based on the above considerations. The contributions of this paper are as follows:  
Figure 1: Overview of the basic ICN architecture for haptic-driven AR/VR applications. * The RL-based stretch reduction scheme has been proposed, promoting the optimal routing path selection with fewer hops. Integration of ICN, MEC, and 5G is employed to illustrate and evaluate the proposed routing scheme for stretch reduction for AR/VR users in TI. * We propose an iterative algorithm to optimize the content retrieval path between the AR/VR user and the content provider. Furthermore, through rigorous simulation variations, the convergence and optimality of the proposed strategy is proved. * We have pursued extensive simulation in this proposed strategy and proved that our RL-based stretch reduction strategy achieves significant routing path savings. Moreover, the proposed strategy opts for the path with the minimum stretch.
## 2 The Prototype and Modeling of the Proposed Isolation Platform
### Modeling
It can be seen that the stiffness coefficients in the stiffness matrix \(\mathbf{K}\) are dependent on the structural parameters. The parameters \(\theta\) and \(n\) are considered as adjustable parameters for the requirements of different applications. According to the stiffness matrix \(\mathbf{K}\), the linear stiffness equals zero in the \(\phi_{z}\) direction; thus, the minimum natural frequency of the system is zero. Therefore, it defined the value of the frequency where the first resonant peak occurs as the fundamental natural frequency of the proposed system. In this six-direction isolation system, the structural parameters' stiffness and damping coefficients \(k\) and \(c_{i}\) are difficult to be changed because the springs and dampers are fixed components, while the structural parameters of SLSs \(n\), \(\theta\), and \(l\) are easily adjusted. For \(M_{1}\) = 2.75 (kg), \(M_{2}\) = 0.34375 (kg), \(k\) = 10,000 (N\(\cdot\)m\({}^{-1}\)), \(L_{1}\) = 0.05 (m), \(L_{2}\) = 0.05 (m), \(L_{3}\) = 0.025 (m), \(J_{xc}\) = \(J_{yc}\) = 0.0573, \(J_{zc}\) = 0.11458, \(J_{xm}\) = \(J_{ym}\) = \(J_{zm}\) = 0.0001432, \(l_{1}\) = 0.2 (m), \(l_{2}\) = 0.5 (m), and \(c_{i}\) = 2 (N\(\cdot\)s\(\cdot\)m\({}^{-1}\)), the values of the natural frequencies of the system for different values of \(n\), \(\theta\), and \(l\) are shown in Figure 7.  
From Figure 7, the fundamental natural frequency of the isolation platform with SLSs can be adjusted by different structural parameters \(n\), \(\theta\), and \(l\). It can be seen that the fundamental natural frequency is reduced for increasing \(n\) or decreasing \(\theta\) and \(l\) of the SLSs in the isolation platform. As the layer of SLSs increases from one layer to five layers, the fundamental natural frequency reduces from about 70 rad/s to 20 rad/s, as raising the assembly angle of the connecting rods in SLSs from 0.4 to 1.2 rad, the natural frequency increases to about 110 rad/s, and for increasing the length of the rods to 0.4 m, the natural frequency increases to 50 rad/s. Therefore, adjusting the structural parameters of  
Figure 7: The fundamental natural frequency for different structural parameters: (**a**) different \(n\) when \(\theta\) = \(\pi/4\) rad and \(l\) = 0.2; (**b**) different \(\theta\) when \(n\) = 2 and \(l\) = 0.2 m; and (**c**) different \(l\) when \(n\) = 2 and \(\theta\) = \(\pi/4\) rad. the SLSs in the isolation platform system is effective in reducing the natural frequency, which could improve the isolation effectiveness of the system.
# A Review of Geometry, Construction and Modelling for Carbon Nanotori
## 5 Physical Roles, Properties and Applications  
Carbon nanotori possess similar physical properties to those of other carbon nanostructures, such as high thermal conductivity and high tensile strength [47; 48]. They are resistant to heat up to 4500 K before their structures start to deform [49], and their tensile strength ranges from 5.72-8.52 GPa [50]. However, these properties are dependent on their geometry and the method of their construction. The major and minor radii, representing the ring radius and the tube radius respectively, strongly influence the mechanical properties of the structures including deformation, vibration and buckling [51]. Nanotori with small radii are highly strained under bending loads [52]. the geometric parameters also play a critical role in the applications of carbon nanotori in electricity and magnetism as described in the following subsections.
## 3 Results and Discussion
In order to evaluate the degradation process of silicon segments of solar cells consisting of P-N junctions in the old battery and to compare it with the new ones, the volt-ampere characteristics of all three batteries are presented in Figure 2.4 As we can see, the volt-ampere curves of the bifacial-type battery, the silicon-based new battery and the battery used for 10 years basically match. Observing the trends of volt-ampere characteristics, it can be said that the active material of all three types of batteries--silicon--has a similar degree of alloying and the same internal resistance. Meanwhile, the matching of the volt-ampere characteristic curve of the old battery with the volt-ampere curves of new batteries (bifacial type and polycrystalline new) shows that the degradation of silicon segments over time is insignificant. Figure 2.5 Variation of solar battery power during the sunrise-sunset interval. Observations of the surface temperature of the solar cells over time are presented in Figure 2.6 Temperature measurements took place during daylight hours when the solar power plant actively produced electricity. For the measurements, 2 days with sufficiently different temperatures were chosen (the temperature difference sometimes reached up to 20 \({}^{\circ}\)C). Although the temperature differences were quite large, the amount of sunlight was similar, based on the meteorological statements. Observing the data of temperature measurements (2 days) on the surface of the solar modules, the temperature reached the maximum values around noon (about 25 and 45 \({}^{\circ}\)C, respectively) when the sun is at its highest point. At this moment, the most intensive production of electricity takes place in the solar power plant. It is known that temperature has a slight influence on the efficiency of a solar power plant, barely 0.5-1% (under ideal laboratory conditions). In real conditions, it is almost impossible to accurately determine the influence of temperature on the efficiency of a solar power plant, since the efficiency of a solar power plant depends not only on the  
Figure 2.7 Surface temperature fluctuations of solar battery between sunrise and sunset. Figure 2.8 Volt-ampere characteristics of different solar cells. temperature difference, but also on the angle of the sun between the surface of the battery and the radiation. In addition, the efficiency of a solar power plant strongly depends on the intensity of solar radiation. All the aforementioned factors (angle, radiation intensity, etc.) on which the efficiency of the solar power plant depends, have an incomparably greater influence than the ambient temperature, so that reliably assessing the influence of temperature on the efficiency of the solar power plant in real conditions is a difficult task.
## 3 Proposed Method
### Overlapping Group Sparsity with Lp-Pseudo-Norm (OSG-Lp) Model  
The L1-norm is commonly used as the fidelity term of impulse noise. However, the L1-norm is only the convex relaxation of the L0-norm. The \(p^{th}\) power of the Lp-norm (\(0\leq p\leq 1\), for simplicity, we name it Lp-pseudo-norm) is another relaxation of the L0-norm. In fact, the L1-norm constraint is a particular case of the Lp-pseudo-norm.  
Recently, the Lp-pseudo-norm [38; 39; 40; 41; 42] has attracted much attention in academia. [PERSON] and [PERSON] pointed out that the Lp-quasinorm is better for approximating the original L0-norm than the L1-norm and developed an iterative Lp-quasinorm shrinkage (LpS) solving the problem [43].  
The Lp-pseudo-norm makes an improvement on the sparsity-based shrinkage operator by introducing another degree of freedom, thus giving the model a better ability to depict the sparsity of an image in the gradient domain, as shown in Figure 1.  
Lp-pseudo-norm contour lines are given in Figure 1, where \(p=2\) and \(p=1\) represent L2- and L1-norms, respectively. Assuming that the image is contaminated by impulse noises with an absolute difference of \(\tau\), Figure 2 shows the schematic plots of anisotropic total variation contour lines \(\text{R}_{\text{APTV}}(\boldsymbol{F})=\|\boldsymbol{K}_{\boldsymbol{h}}* \boldsymbol{F}\|_{p}^{p}+\|\boldsymbol{K}_{\boldsymbol{v}}*\boldsymbol{F}\|_{p }^{p}(0<p\leq 1)\) intersecting with the fidelity term. As shown in Figure 2, the intersections of the contour lines with the fidelity term are more sparse for \(0<p<1\), as shown in Figure 2b, than for \(p=1\), as shown in Figure 2a, therefore the robustness of the model against noise is better.  
Based on the above analysis, the advantages of Lp-quasinorm regularization are listed as follows: (1) The LpS operator may converge to an accurate solution. (2) The Lp-quasinorm is more flexible than the L1-norm. This might be useful to adapt the degree of sparsity to the signal being processed. (3) The Lp-quasinorm feasible domain makes the solution robust to noise.  
Thus, the L1-norm-based OGS-TV could be extended to the Lp-pseudo-norm (abbreviated as OGS-Lp) [38, 39, 44] and is expressed as follows:  
\[\mathbf{F}=\underset{\mathbf{F}}{\text{argmin}}\|\mathbf{F}-\mathbf{G}\|_{p}^{p}+\mu[\varphi(\mathbf{ K}_{\mathbf{h}}*\mathbf{F})+\varphi(\mathbf{K}_{\mathbf{v}}*\mathbf{F})]. \tag{6}\]