esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Optimization Design for a Small Flat-Panel Synthetic Aperture Radar Satellite
## 3 Thermal Design Strategy
### Optimization of Thermal Design for the SAR Antenna
\(P\) stands for the heat consumption of a single T/RM, \(\tau\) represents the working duration of T/RMs within one orbital period, \(\tau_{0}\) signifies the duration of one orbital period, the subscript \(-z\) represents the antenna \(-\)Z side, the subscript \(h\) designates extreme high-temperature conditions, the subscript \(c\) indicates extreme low-temperature conditions, and the subscript \(MP\) refers to the antenna mounting plate. RU is composed of multiple thin plates, where heat is exchanged between the plates via radiation. A layer of polyimide germanium-coated film is situated on the outer surface of RU. The thermal equilibrium equation for RU is as follows. \[\frac{T_{RUlh}-T_{avg}}{\frac{1}{h_{T/RM-MP}}+\frac{\delta_{MP}}{\lambda_{MP}}+ \frac{1}{n_{RUl-MP}}}=\frac{Q_{+zh}-\sigma T_{RUlh}^{4}}{\frac{1}{\epsilon_{ RUl1}}+\frac{1}{\epsilon_{RUl}}+\cdots+\frac{1}{\epsilon_{RUlh}}+1} \tag{11}\]  
\[\frac{T_{RUlc}-T_{con}}{\frac{1}{h_{T/RM-MP}}+\frac{\delta_{MP}}{\lambda_{MP}}+ \frac{1}{n_{RUl-MP}}}=\frac{Q_{+zc}-\sigma T_{RUlc}^{4}}{\frac{1}{\epsilon_{ RUl}}+\frac{1}{\epsilon_{RUl}}+\cdots+\frac{1}{\epsilon_{RUlh}}+1} \tag{12}\]  
In the formula, the subscript \(+z\) represents the antenna's \(+\)Z side. By solving Equations (9)-(12) simultaneously, \(h_{T/RM-MP}\) and \(h_{RUl-MP}\) can be obtained. Subsequently, the adapted area of the TIM can be obtained. The derivation process of Formula (4) is as follows. Under extreme high-temperature conditions of the satellite, the operation of T/RMs is intermittent, and the temperature fluctuates. The temperature fluctuation amplitude (\(\Delta T_{h}\)) of T/RM can be estimated according to Equation (13). \[\Delta T_{h}=\frac{P\tau C_{MP}+0.5h_{1}P\tau^{2}}{C_{MP}C_{T/RM}+0.5h_{T/RM- MP}\tau(C_{MP}+C_{T/RM})} \tag{13}\]  
In the formula, \(C\) represents the heat capacity, and \(C_{MP}\) denotes the average heat capacity of the antenna mounting plate per individual T/RM. Following detailed design parameter iteration, the calculated value of the fluctuation amplitude \(\Delta T_{h}\) is 17 \({}^{\circ}\)C.  
After the aforementioned optimization analysis, it is evident that T/RMs can achieve zero-heating-power thermal control under reasonable TIM settings, while ensuring their temperature remains within the required range. Other components, such as PM and WPCU, do not necessitate special thermal design methods and can be installed using conventional thermal conductivity methods. The reason is that, compared with T/RM, PM and WPCU have high heat capacity, a larger installation area, and lower heat consumption.
# On-Orbit Calibration Method for Correction Microwave Radiometer of the HY-2 Satellite Constellation
## 5 Inversion and Verification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Latitude**} & \multirow{2}{*}{**Content**} & \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Compared**} & \multicolumn{2}{c}{**Mean Bias (mm)**} & \multicolumn{1}{c}{**RMS (mm)**} \\ \cline{4-5}  & & & & **Before/After** & **Before/After** & **Reduction** \\ \hline \multirow{2}{*}{\(\geq\)45\({}^{\circ}\)} & HY-2C & & AWV & \(-0.617/-0.454\) & 1.824/1.263 & 0.561 \\  & HY-2B & WPD & \(-3.512/-2.833\) & 10.631/7.287 & 3.344 \\ \multirow{2}{*}{HY-2D} & HY-2D & & AWV & \(-0.815/-0.512\) & 2.104/1.663 & 0.441 \\  & HY-2D & & WPD & \(-5.161/-3.414\) & 12.111/9.645 & 2.466 \\ \hline \multirow{2}{*}{\(<\)45\({}^{\circ}\)} & HY-2C & & AWV & \(0.471/0.341\) & 1.513/1.178 & 0.335 \\  & HY-2B & WPD & \(2.925/2.364\) & 9.142/7.011 & 2.131 \\ \multirow{2}{*}{HY-2D} & HY-2D & & AWV & \(-0.680/-0.453\) & 1.712/1.422 & 0.290 \\ \multirow{2}{*}{HY-2D} & & WPD & \(-4.182/-2.857\) & 10.138/8.155 & 1.983 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cross-checking before and after on-orbit calibration for products separated by the 45\({}^{\circ}\) north-south latitude. time. HY-2C satellite has more points than the other two satellites, to match the Jason-3 satellite at low latitude; meanwhile, HY-2B and HY-2D satellites have more crossovers with the Jason-3 satellite at high latitudes. Since observation errors of the space-borne radiometer are greater at high latitudes than at low latitudes, the increase in the proportion of high-latitude crossovers will lead to larger errors for cross-checking [36]. In Tables 4-6, it can also be found that the results of cross-checking between the HY-2C satellite and other satellites are better than those of the HY-2D satellite, which is affected by the uneven distribution of crossovers from the test dataset; the HY-2C satellite has more crossovers at low latitudes than other satellites. However, the above cross-checking shows that those differences between different satellite products after on-orbit calibration are all less than before calibration.
# A Review of Machine Learning Applications in Land Surface Modeling
## 2 LSMs: Importance, Then and Now
The need for LSMs to quantify such biogeochemical and biogeochemical feedbacks to the climate system has formed the basis of their recent development efforts. At present, LSMs have expanded from their initial simple biophysical configurations [20] to include representations of stomatal functioning [21], scaling information from leaf to canopy [22], soil moisture dynamic and surface hydrology [23; 24], crop processes [25; 26], land surface heterogeneity [27], dynamic vegetation [28; 29], urban environment [30], land cover management [31; 32], plant demographic processes and plant hydraulics [33], groundwater dynamics [34], soil microbial dynamics [35], leaf mesophyll process, nitrogen, phosphorus, carbon cycling and their mutual interactions [36]. The incorporation of processes in LSMs is driven by the need for extensive user communities,including ecologists, crop modelers, atmospheric scientists, biogeochemists, hydrologists, who explore interactions between different components of the system. Some widely used LSMs across the globe include Interaction Soil-Biosphere-Atmosphere (ISBA, [36]), The Community Land Model (CLM, [31]), JSBACH [37], Joint UK Land Environment Simulator (JULES, [38]), LPJ-GUESS [39], Noah-MP [40]. Along with the increasing capability of representing processes, LSMs are enhancing their spatial resolution as well, with the improvement in resolution of the atmospheric models. As the scope of LSMs broadens with the support of computational advancements, the questions of cognitive uncertainty and unresolved heterogeneity emerges as a challenge.
## 4 Design of Stand Fan and Simulation Results
### Parametric Design and Flow Analysis of Impeller  
Flow fields associated with impellers generated by different parameters are simulated and compared to obtain the best set of design parameters to construct the impeller shape. Firstly, various blade setting angles are selected and numerically evaluated to find the proper value for different performance requests. As illustrated in Figure 3, the setting angle is the blade angle on the hub formed by the horizontal line and the blade base line, which is the line connecting the leading edge and trailing edge of the airfoil. Furthermore, the twist angle is the difference between the blade angles at the inner end (hub) and the external end of the rotor blade.  
The simulation results show that the impeller with the same 55\({}^{\circ}\) setting angle and different twist angles (see Table 2) produce the flow rate at 2074 and 2174 CFM under the required torque of 0.238 and 0.267 N-m, respectively. Clearly, case B can deliver a higher flow rate while case A is the best design  
Figure 3: Definitions of the setting angle and twist angle for an axial flow fan. (**a**) Setting angle; (**b**) twist angle.  
in the viewpoint of energy efficiency. Note that, as illustrated in Figure 4, the associated flow field is appropriate for the human need and has a broad range of high-velocity distribution without severe flow disturbance near the fan outlet.  
Also, by comparing the calculated results for different twist angles, it is shown that the air flow rate decreases with an increasing twist angle. For example, when twist angles are of 25\({}^{\circ}\) and 30\({}^{\circ}\), their flow rates are reduced by 12% and 5%, respectively. As indicated in Table 2, the 55\({}^{\circ}\) setting angle incorporated with two arrangements of a 20\({}^{\circ}\) twist angle yield a proper flow rate. Hence, the set of 55\({}^{\circ}\) setting angle and 20\({}^{\circ}\) twist angle is adopted in the subsequent discussion on the incline angle of the rotor blade, which is defined as the circumferential angle difference between the blade hub and blade tip.  
The incline angle can be classified as backward and forward types with respect to the rotational direction. The simulation result reveals that the fan performance decreases by 6%-7% for a forward incline angle. Conversely, when the inclined angle is backward, the maximum flow rate increases while the torque needed also becomes larger. Among the cases studied here, the backward incline angle of 20\({}^{\circ}\) can yield a 2289 CFM flow rate under 0.267 N-m torque needed. Clearly, this flow rate presents a 38.8% enhancement over the original design (see Table 3). In addition, the outlet flow field is quite uniform and symmetrical with respect to the rotating axis. Thus, this optimized impeller is considered as the base impeller to improve the protective cover in the next stage.
# Finding Optimal Manipulator Arm Shapes to Avoid Collisions in a Static Environment
## 4 Optimization Algorithms
### Method 1--Iterative Algorithm with Collision Analysis
#### 4.1.1 Types of Collisions  
Collisions with an obstacle in the workplace can occur at any part of the robot. The significance of the collision and possible ways of dealing with it are different, so we need to classify the collisions into five categories: C1, C2,..., C5 (see Figure 6 and Table 2).  
Figure 5: Example of a simple workplace and a robot with the selected kinematic structure, where both the lower and upper arms bend according to the cubic Bézier curve. (a) Schematic model of the robot. (b) Visualization of collision bounding volumes.  
**C1:** Collisions of the base of the robot, the first link, and the first two joints (see an example in Figure 6b) are not related to the lengths and shapes of the arms higher on the kinematic structure, and can be eliminated only by changing the location of the robot, which is not a subject of this paper.  
**C2, C4:** Collisions within the lower or upper arm (Figure 6c,e) can be potentially fixed by changing the length or shape of the arm--making the arm elude the obstacle. This is the main focus of this paper.  
**C3:** Collisions that occur near the joint (elbow) between the lower and upper arm (see an example on Figure 6d) can potentially be fixed by changing the robot's location or by changing the lengths of the lower and upper arms. In this work, the robot's location is fixed; changing the arms' lengths is therefore the option to be considered.  
**C5:** Collisions at or near the end effector of the robot or the wrist (the highest three degrees of freedom; see Figure 6f) cannot be fixed in most cases because the robot must go to that place to fulfill the given task. An option is to change the required orientation of the TCP, unless it is forced by the technology or by other constraints. This is not a subject of this work.  
Figure 6: Simplified representation of the robot’s kinematic structure with zones corresponding to the collision categories, and examples of categories C1–C5.  
As already mentioned in Section 3.3, the lower and upper arms use collision spheres that cover the length of the arm. The type of a particular collision is determined as follows:  
* The first 10% of the spheres of the lower arm (counted from the bottom; rounded up) are considered too close to the shoulder joint and create C1 collisions.
* The last 10% of the spheres of the lower arm and the first 10% of the spheres of the upper arm (all counted from the bottom; rounded up) are considered too close to the elbow joint and create C3 collisions.
* The last 10% of the spheres of the upper arm (counted from the bottom; rounded up) are considered too close to the wrist joint and create C5 collisions.
* All other spheres create C2 or C4 collisions.
## 4 Results
### Morphotectonic Analysis
#### 4.2.2 Asymmetry Factor (Af)  
The results of the asymmetry factor parameter presented various degrees of basin asymmetry. Values of Vf indicated that the symmetrical sub-basin features were observed for six sub-basins (basins 1, 4, 8, 11, 13, and 15). Accordingly, the remaining sub-basins showed three asymmetrical divisions: class 1 asymmetry was recorded for sub-basins 3, 4, 5, and 9; class 2 asymmetry was observed for sub-basins 2, 7, 10, and 12; and class 3 asymmetry was recognized for just sub-basin 13 (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sub-Basins** & \begin{tabular}{c} **Area (A) in** \\ **km\({}^{2}\)** \\ \end{tabular} & \begin{tabular}{c} **Perimeter** \\ **(P) in km** \\ \end{tabular} & \begin{tabular}{c} **Length (Lu)** \\ **in km** \\ \end{tabular} & \
\begin{tabular}{c} **Elevations** \\ **in m** \\ \end{tabular} &
\begin{tabular}{c} **Nu (I, II, III, IV, and V)** \\ \end{tabular} & **Lu in km** \\ \hline
1 & 76.83 & 40.13 & 10.89 & 1:191 & 36 (18, 10, 6, 1, and 1) & 52.95 \\
2 & 223.28 & 92.61 & 27.18 & 41:618 & 73 (37, 15, 13, 8, and 0) & 137.40 \\
3 & 54.32 & 41.30 & 14.42 & 134:701 & 17 (9, 4, 4, 0, and 0) & 30.76 \\
4 & 188.40 & 76.12 & 21.22 & 188:1121 & 83 (42, 24, 11, 6, and 0) & 118.37 \\
5 & 458.20 & 118.5 & 39.31 & 181:1893 & 161 (81, 36, 21, 23, and 0) & 273.28 \\
6 & 190.92 & 70.32 & 21.22 & 336:1507 & 69 (35, 14, 17, 3, and 0) & 109.62 \\
7 & 39.30 & 27.68 & 8.27 & 608:1577 & 9 (5, 2, 2, 0, and 0) & 18.43 \\
8 & 21.75 & 25.59 & 6.21 & 844:2208 & 7 (4, 2, 1, 0, and 0) & 12.47 \\
9 & 146.08 & 77.53 & 18.59 & 1083:2142 & 53 (27, 14, 7, 5, and 0) & 91.35 \\
10 & 203.51 & 93.55 & 22.99 & 1082:2517 & 72 (36, 18, 7, 10, and 1) & 134.56 \\
11 & 35.57 & 25.96 & 8.61 & 1017:1901 & 15 (8, 4, 3, 0, and 0) & 21.03 \\
12 & 278.09 & 82.21 & 23.63 & 682:2585 & 103 (52, 26, 15, 10, and 0) & 158.92 \\
13 & 328.08 & 89.43 & 28.48 & 404:2657 & 123 (62, 29, 24, 8, and 0) & 197.31 \\
14 & 82.09 & 41.74 & 12.43 & 347:1978 & 31 (16, 8, 7, 0, and 0) & 44.86 \\
15 & 177.71 & 71.77 & 24.24 & 204:2099 & 65 (33, 12, 20, 0, and 0) & 99.41 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic geometric values of the studied sub-basins, stream number (Nu), and stream length (Lu).  
Figure 4: Map showing the sub-basin tilting directions and the different symmetry classes.  
Figure 3: (**a**) Locations of V\({}_{\rm f}\) calculated sections. (**b**) Values and classes of V\({}_{\rm f}\) parameter for the studied 15 sub-basins.
## 3 Results and Discussion
The IV curves of the corresponding sample are shown in Figure S6. IV curves for the five different Ge ranges introduced in the context of Figure 3 are compared in Figure S7. In addition to the IV and EQE measurements of freshly fabricated devices, a time-stability analysis was performed using an accelerated-aging procedure. A total of 12 CZTSe solar cells and 12 CZTGSe solar cells with Ge/(Ge + Sn) \(\approx\) 0.30 \(\pm\) 0.02 were kept in a controlled climate chamber at 85 \({}^{\circ}\)C for a total duration of 1000 h. Since the devices were not encapsulated, the relative humidity RH was maintained at 20% [31]. To rule out the mechanical stress by repeated sample measurements and to obtain more reliable data on the influence of handling, the best, the worst and one intermediate cell per sample was only measured initially and after the completion of the entire aging duration of 1000 h. These were found not to differ from the presented values and are also included in the corresponding boxplots at 0 h and 1000 h. The evolution of the sample efficiencies is summarized in Figure 6.  
In the accelerated-aging results, which compare the CZTSe and CZTGSe samples in terms of device efficiency, a distinct difference is visible. The CZTSe samples suffered from a quick drop in efficiency after the first 24 h of accelerated aging in the climate chamber (mean of 10.4% to approximately 7.0%). A slow but incomplete recovery of the efficiency up to 8.2% took place after the initial drop. The CZTGSe samples, on the other hand, did not feature the initial drop but also slowly improved their efficiency as the aging progressed from 6.5% to 8.4%. After 100 h of accelerated aging, the mean efficiency slightly overtook the reference. These results indicate that the long-term stability of the kesterite absorber can be improved by Ge incorporation, which needs more careful attention in future investigations.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 3 Problem Formulation  
An association and pairing approach is now addressed by jointly considering eMBB and uRLLC UEs' different demands. Since the uRLLC UEs should operate on a constrained received latency, DL latency should be considered during the connection process for uRLLC users. In contrast to uRLLC UEs' needs, eMBB communications typically demand a high DL data rate. Thus, the DL data rate is a crucial association measure for eMBB users. Therefore, the UE-slice association and the UE-slice pairing indicators can be designed to maximize the overall DL data rate for eMBB UEs and minimize the DL transmitted latency for uRLLC UEs. Thus, the user-slice association (U \(-\) S. A) and the UE-slice pairing (U \(-\) S. P) problem can be represented as,  
\[\text{O.P.T1}:\max\limits_{\{a,x\}}{[F_{1},-F_{2}]^{T}}, \tag{18}\]It can be observed that the O.P.T1 (18) is a nonconvex mixed-integer programming problem, where the ideal solution is challenging to obtain. The subsequent subsection will convert the problem into a pure combinatorial optimization issue. Therefore, we adopt a suboptimal method and decompose the O.P.T1 into two designed sub-problems, as described in the next sub-sections. The first sub-problem is to create the user-slice association decision matrix \(\mathcal{A}\). The second sub-problem is to create the user-slice pairing decision matrix \(\mathcal{X}\). Thus, we suggest solving this problem using two-sided matching, as seen in the next section.
## 3 Meteorological Data  
The meteorological data used herein was recorded on a prototype built in northern France (50\({}^{\circ}\)30\({}^{\prime}\)57.4\({}^{\prime\prime}\) N latitude, 2\({}^{\circ}\)39\({}^{\prime}\)24.2\({}^{\prime\prime}\) E longitude) from 19 th April to 26 th 2014. This period lies in the best season for solar home heating systems because solar gains are significant while outside temperatures remain quite low. The incident global solar radiation on the vertical facade of the building was measured by a pyranometer (CMP10-kipp and zonen) (Figure 3). The outside temperature was measured by a thermocouple inserted into a cylinder painted white and naturally ventilated, and wind speed was measured by an anemometer/wind vane installed near the facade containing the composite Trombe wall (Figure 4). All measurements were recorded with a time step of 5 min.  
Figure 3: Pyranometer used to measure the global vertical solar radiation.  
Figure 2: External view of the storage wall (**a**), and rear view of the experimental solar wall (**b**).  
The results of global solar flux measurements on the vertical facade, outdoor temperature and wind speed are shown in Figures 5 and 6. Please note that the solar flux values may appear to be high (1000 W/m\({}^{2}\)); this is due to the presence of a horizontal light-colored roof under the windows of the floor where the experiments were carried out (Figure 4).  
Figure 4: Sensors used to measure outside temperature (**a**) and anemometer/wind vane used to measure wind speed and direction (**b**).  
Figure 5: Meteorological data: \(\phi_{\text{glob\_vert}}\) denotes global solar radiation on the vertical facade of the wall, and T\({}_{\text{ext}}\) denotes outside air temperature.
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 3 Proposed Algorithms
### Setup for User-Specific Environment
#### 3.2.3 User-Drawn Group Path  
In a crowd simulation, the crowd does not stand still; it moves to the final destination. In this paper, we assume that the crowds are moving along a path that the user draws on the floor directly. The user-drawn path consists of a set of 2D points, and those points are connected while keeping the minimum distance between them. Note that we set a single path for each group instead of assigning a path to each individual agent. The 2D points of the path become the waypoints that the crowd follows. In Figure 6, a group is facing the waypoint \(W_{0}\) at a particular time, which is the closest point to the group on the path. If the group approaches \(W_{0}\) sufficiently closely, then \(W_{1}\) will be the current target instead. This process is iterated until the crowd reaches the final destination. To move from \(P\) to \(W\), which is the current target of the group, we have to calculate the group velocity \(V(t)\). Equation (3) is the formula that calculates the velocity and the update of the group positions:  
\[D(t)=\frac{W-P}{|W-P|} \tag{3}\] \[V(t)=D(t)\omega\Delta t\] \[P^{*}=P+V(t)\]  
where \(D(t)\in\mathbb{R}^{3}\) is the direction vector from the current position of the group \(P\) to the current target \(W\), \(V\in\mathbb{R}^{3}\) is the group velocity, and \(P^{*}\in\mathbb{R}^{3}\) is the new position of the group. \(\omega\in\mathbb{R}\) is the preferred group speed, which is initially set as a constant speed and uses a weight value to adjust the velocity of a group. The computed group velocity affects the short-range destinations of each agent in the group collectively, and Equation (4) is a representation of this:  
\[s_{i}^{*}=s_{i}+V(t) \tag{4}\]  
where \(s_{i}\) is an SRD of the \(i\)-th agent in the group and \(s_{i}^{*}\) is the new position of the SRD that moves from \(s_{i}\) by the amount of translation of the position of the group.  
Figure 5: A projection of the vertices of a 3D bird insignia model onto a 2D plane [19]. Red circles, yellow arrows, and yellow dots are represented to explain that the vertices of the model being projected onto a 2D plane. (**a**) A 3D model of bird insignia for crowd formation. (**b**) An example of a crowd formation formed by using the vertex positions of the model.
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology
### Fitness Function  
The fitness function is one of the crucial parts of an algorithm. It defines the accuracy of the algorithm. An accurate fitness function will not be biased by any parameter and provide more accurate results, which leads towards the selection of optimal CHs. As mentioned earlier, optimal CHs will enhance the cluster lifetime, which ultimately saves the network energy. After receiving the 3D position information and selecting the transmission range, each node calculates its fitness value and send it to neighbor nodes. EALC calculate fitness value using Equation (4):  
\[Fitness=\frac{w1\times Energy_{Res}}{(w2\times avg_{-dis})(w3\times delta _{-diff})} \tag{4}\]  
_Energy\({}_{ns}\)_ is node residual energy level, \"\(avg_{-dis}\)\" is the average distance to the neighbor nodes and _delta_diff_ is the delta difference. Delta difference is used for load balancing factor. \(w1\), \(w2\) and \(w3\) are weights for energy, average distance and delta difference respectively. Ideally, all CHs must have an equal number of nodes, but this is not possible in real world scenarios because nodes change their position very frequently which changes their degree of the neighborhood. Delta difference is the deviation of node's degree of the neighborhood from the ideal degree. It is calculated using Equation (5):  
\[D_{diff}=ABS\big{(}Ideal_{Degree}-Node_{Degree}\big{)} \tag{5}\]  
Figure 2: The relationship between SNR and PLR.