esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis on the Isostatic Bipod Mounts for the HERA Mission LIDAR
## 2 Previous Research  
The Planetary ALTimeter (PALT) mechanical design has been optimized on different aspects to meet the requirements established for the HERA mission. In [6; 7; 8], the HELENA LIDAR's performance was evaluated regarding weaker requirement of shock, static simulation of 5G in two different directions, and thermal conductive simulations between \(-40\)and +60 \({}^{\circ}\)C. The main objective was to study the secondary mirror deformation towards the base of the telescope, and maximum stresses. Two different materials (aluminum and titanium) were assessed for the upper part of the LIDAR telescope. The simulations revealed a higher factor of safety, and a minor displacement of the secondary mirror towards the base of the telescope for the titanium material. This modification was implemented on PALT.  
In order to guarantee that the best optical performance is maintained, the primary mirror shall be a-thermalized, which is possible by the optimizing the isostatic bipod mounts (see Figure 1). Furthermore, the assembly and alignment procedures of the primary mirror should be as simple as possible to avoid damages on the mirror. This paper presents an optimization of the isostatic bipod mounts design, and details on the assembly procedure.  
Figure 1: (**a**) HELENA design; (**b**) HELENA engineering model. (**c**) PALT design. Several transformations were made since HELENA design, with the isostatic bipod mounts being a critical design improvement.
## 4 Results and Analyses
### Settlement of Ground Surface
When \(t=0.30\)\(D\), the \(S_{\text{max}}\) is reduced by about 18.2% compared with the case of \(t=0.0\)\(D\). Similarly, it shows that the CR has no significant effect in reducing the settlement of the ground surface. Figure 6 shows the variation of the maximum surface settlement with \(t\) under different reinforcement strategies. In general, both the RR and CR limit the transmission of the stress release from the following tunnel excavation to the preceding tunnel, but do not directly limit its transmission to the surface, so that the surface settlement has not been significantly reduced by the RR and CR to the allowable value (\(S_{\text{max}}<20\) mm). For the LR and TR, the soil layer around the following tunnel is reinforced, which can effectively decrease the disturbance to the ground surface due to excavation of the following tunnel.
# The Accuracy Assessment of Lithospheric Density Models
## 3 Model Uncertainties
### Errors Due to Lithospheric Thickness Uncertainties  
In sub-lithospheric mantle geoid modeling, the lithospheric thickness information is used to compute the lithospheric-mantle gravitational potential \(V^{LM}\) (in Equation (39)) and the LAB potential \(V^{LAB}\) (in Equation (41)). We then established a relation between the error of sub-lithospheric mantle geoid \(\varepsilon_{N^{\text{SM}}}\) and the error of lithospheric thickness \(\varepsilon_{L}\) in the following form:  
\[\varepsilon_{N^{\text{SM}}}=-\frac{1}{\gamma_{0}}\bigg{(}\frac{\partial V^{LM} }{\partial L}+\frac{\partial V^{LAB}}{\partial L}\bigg{)}\varepsilon_{L}\]  
\[=-\frac{3}{\gamma_{0}}\frac{\text{GM}}{\text{R}}\frac{\text{GM}}{\overline{ \mathfrak{p}}^{\text{Earth}}}\sum_{n=0}^{\pi}\frac{1}{2n+1}\sum_{k=0}^{n+2} \binom{n+2}{k}\frac{\left(-1\right)^{k}}{\text{R}^{k+1}(k+1)}\sum_{m=-n}^{n} \left(L_{\text{n},\text{m}}^{(k+1)}+\hat{L}_{\text{n},\text{m}}^{(k+1)} \right)\text{Y}_{\text{n},\text{m}}(\Omega)\]  
\[=-\frac{3}{\gamma_{0}4\pi\text{r}}\frac{\text{GM}}{\text{R}}\sum_{n=0}^{\pi} \sum_{k=0}^{\pi}\binom{n+2}{k}\frac{(-1)^{k}}{\text{R}^{k+1}(k+1)}\]  
\[\times\iint\limits_{\Phi}\ \left[\delta\rho^{LM}\big{(}\Omega^{\prime}\big{)}+ \Delta\rho^{\text{L}/\Lambda}\right](k+1)L^{k}\big{(}\Omega^{\prime}\big{)} \varepsilon_{L}(\Omega^{\prime})\text{ P}_{\text{n}}(t)\ d\Omega^{\prime}. \tag{43}\]  
After disregarding terms \(k\geq 1\), the expression in Equation (43) simplifies to the following:  
\[\varepsilon_{N^{\text{SM}}}\cong-\frac{3}{\gamma_{0}4\pi}\frac{\text{GM}}{ \text{R}^{2}\overline{\mathfrak{p}}^{\text{Earth}}}\iint\limits_{\Phi}\ \left[\delta\rho^{LM}\big{(}\Omega^{\prime}\big{)}+\Delta\rho^{\text{L}/ \Lambda}\right]\varepsilon_{L}\big{(}\Omega^{\prime}\big{)}\ \sum_{n=0}^{\pi}\text{P}_{\text{n}}(t)\ d\Omega^{\prime}. \tag{44}\]  
For \(n=0\), we have the following:  
\[\varepsilon_{N^{\text{SM}}}\cong-\frac{3}{\gamma_{0}4\pi}\frac{\text{GM}}{ \text{R}^{2}\overline{\mathfrak{p}}^{\text{Earth}}}\iint\limits_{\Phi}\ \left[\delta\rho^{LM}\big{(}\Omega^{\prime}\big{)}+\Delta\rho^{\text{L}/ \Lambda}\right]\varepsilon_{L}\big{(}\Omega^{\prime}\big{)}\ d\Omega^{\prime}. \tag{45}\]  
When assuming that the largest contribution to the error \(\varepsilon_{N^{\text{SM}}}\) comes from lithospheric thickness uncertainties in the vicinity of the computation point (for which the error is estimated), the global integration domain in Equation (45) could be disregarded. Instead, we reduce a functional relation only for a position of computation point. We then write the following:  
\[\varepsilon_{N^{\text{SM}}}\approx-\frac{3}{\gamma_{0}4\pi}\frac{\text{GM}}{ \text{R}^{2}\overline{\mathfrak{p}}^{\text{Earth}}}\Big{(}\delta\rho^{LM}+ \Delta\rho^{\text{L}/\Lambda}\Big{)}\ \varepsilon_{L}. \tag{46}\]  
Inserting for \(\gamma_{0}\approx\text{GM}/\text{R}^{2}\) in Equation (46), we arrive at the following:  
\[\varepsilon_{N^{\text{SM}}}\approx-\frac{3}{4\pi}\bigg{(}\frac{\delta\rho^{LM }+\Delta\rho^{\text{L}/\Lambda}}{\overline{\mathfrak{p}}^{\text{Earth}}} \bigg{)}\ \varepsilon_{L}. \tag{47}\]
## 6 Simulation Results and Analysis  
This section uses the STK simulation software version 6.0 to build a system scenario in order to obtain real satellite and inter-satellite physical link information, apply Pytorch 2.2.2 and MATLAB 2024a to simulate the algorithms proposed in this paper and the algorithm in the literature [40], and analyze the performance differences between the two. This paper presents a novel cost model for an LEO satellite edge computing system, addressing the challenges of computation offloading. It introduces a joint computation offloading and resource allocation strategy, which decomposes the problem into two sub-problems: optimizing computation offloading using game theory and allocating communication resources via the Lagrange multiplier method. Ref. [40] is selected as a baseline due to its relevance to IoT applications and its use of energy consumption as a cost metric, which aligns with our study's objectives. However, our approach differs significantly in methodology, as we employ a DQN-based optimization rather than the game-theoretic and Lagrange multiplier methods used in [40]. To enable a fair comparison, we replace our proposed DQN-based optimization algorithm with the JCORA method from [40] while keeping all other simulation parameters unchanged. This allows us to directly compare the performance impact of different optimization strategies under the same system conditions.
# Analysis and Design of Infrared Search and Track System with Afocal Zoom Telescope
## 3 Image Wandering Analysis of an IRST
### Specifications  
The design requirements are summarized in Table 1. In this, a cooled focal plane array (FPA) with an effective area of 9.6 mm \(\times\) 7.68 mm and pitch size of 15 \(\upmu\)m \(\times\) 15 \(\upmu\)m is picked, and the total diagonal image size of 12.3 mm is used as the real image height FOV. The focal length range of 60 mm-360 mm is specified to fulfill infrared searching and tracking. The relative distortion, modulation transfer function (MTF), dimensions, and back focal length are restricted accordingly to achieve good imaging quality and compact size. The working frame frequency of IRST is 50 fps, and the maximum integration time is about 16 ms. The image wandering was determined to be about 0.6 pitch size.
# A State-of-Art-Review on Machine-Learning Based Methods for PV
## 2 Machine Learning, Deep Learning and Related Methods
Typically, the two terms are well synthesized with the image shown in Figure 1:  
The model's error is the difference between predicted and observed/actual values. Suppose one has a very accurate model: this means that the error is very low, indicating a low bias and low variance (as seen on the top-left circle in Figure 1). If the variance increases, the data are spread out more which results in lower accuracy (as seen on the top-right circle in Figure 1). In this case, the average model's error could be the same as in the first case but sometimes the error is greater and more spread out around the same mean value. If the bias increases, the error calculated increases (as seen on the  
Figure 1: Bias vs. variance trade-off. bottom-left circle in Figure 1). High variance and high bias indicate that data are spread out with a high error (as seen on the bottom-right circle in Figure 1). This is a bias-variance tradeoff. In essence, bias is a measure of error between what the model captures and what the available data is showing, while variance is the error from sensitivity to small changes in the available data. A model having high variance captures random noise in the data. For the field of interest of this paper, the most used ensemble methods are:  
* Random Forest (RF) (bagging ensemble method);
* XGBoost or LightGBM (boosting ensemble method). Very few papers have tested stacking solutions. **3. Literature Review of Review Paper for Each of the Fields of Interest in PV**  
In Table 1 are listed review papers concerning ML-based methods to forecast power production from PV; note that only recent papers, i.e., from 2018 till 2021, have been taken into consideration. Some notes for every paper listed in Table 1 summarize what the reader can expect from reading it. **4. Latest Research in PV Power Forecasting**  
This section describes the latest ML-based methods that have been employed in literature to forecast power production from PV, published from the year 2018 till the year 2021.
## 4 Case Study for Signal Processing of Pulsed Thermography
### Advances of DWT  
[PERSON] et al. [75] proposed the detection of damage to the cylindrical shell using DWT analysis in a post-processing technique based on active IRT as one of the NDT. ABAQUS simulation was used to analyze the transient heat transfer problem of the damaged double-sided shell. Afterward, 1D- and 2D-DWT were applied to emphasize the location of damage to the thermal response of the structure, and a deep neural network was used to reduce image noise. [PERSON] et al. [76] proposed a method for extracting features from the histogram of oriented gradients (HOG) coefficient vectors using IRT and reducing HOG vectors by combining continuous coefficients. Therefore, the sub-band HH was obtained using 2D-DWT, and the image's desired feature extraction was performed. In addition, a technique for classifying normal and abnormal using the Support Vector Machine (SVM) binarization classification method is presented. [PERSON] et al. [77] proposed an automated defect detection technique for bearings of rotating machines using IRT. The thermal image was filtered with 2D-DWT, and detectability was improved using PCA. Through this, images were acquired for multi-resolution analysis in the time and frequency domains. [PERSON] et al. [78] presented experimental results for shape and size analysis at various depth stages of GFRP composites using LIT. To improve the SNR of inclusions, PCA and DWT techniques based on pixel-level data fusion were applied. As a result, it was demonstrated that the higher the frequency range in the region where inclusions are detected, the more significant the improvement in the SNR value.
## 3 General Opportunities, Challenges, and Applications
### Main Challenges
#### 3.3.3 Infrastructure and Bandwidth Issues  
A solid infrastructure is required for FL to work properly. However, difficulties such as unreliable networks and restricted bandwidths, particularly in edge devices, might pose substantial impediments. These limits might delay model updates and training, thereby impacting real-time decision-making and overall system performance [24].
## 2 System Model and Identification  
### PMLSM Model  
The PMLSMs are used as the actuators in the gantry system and the mathematical model is derived below. The dynamic model [2] in the \(d-\)\(q\) reference frame is represented by  
\[v_{d}=R\dot{i}_{d}+L_{q}\frac{\dot{a}_{d}}{dt}-\frac{\pi}{\tau}v_{P}L_{d}\dot{ i}_{q} \tag{1}\] \[v_{q}=R\dot{i}_{q}+L_{q}\frac{\dot{a}_{q}}{dt}+\frac{\pi}{\tau}v_{ P}L_{q}\dot{i}_{d}+\sqrt{\frac{2}{3}}\frac{\pi}{\tau}\lambda_{\text{max}}v_{P}\]  
where \(v_{d}\) and \(v_{q}\) denote the d-axis and q-axis voltages, \(L_{d}\) and \(L_{q}\) are the d-axis and q-axis inductances, \(\dot{i}_{q}\) and \(\dot{i}_{d}\) are the q-axis and d-axis currents, respectively, \(\tau\) is the pole pitch, \(v_{P}\) and \(R\) are the velocity and resistance of the mover, and \(\lambda_{\text{max}}\) denotes the maximum of sinusoidal flux linkage. By (1), the derivative of these currents can be obtained as  
\[\begin{bmatrix}\frac{\dot{a}_{d}}{dt}\\ \frac{\dot{a}_{d}}{dt}\end{bmatrix}=\begin{bmatrix}-\frac{R}{L_{d}}&-\frac{\pi }{\tau}v_{P}\frac{L_{d}}{L_{d}}\\ -\frac{\pi}{\tau}v_{P}\frac{L_{d}}{L_{q}}&-\frac{R}{L_{q}}\end{bmatrix}\begin{bmatrix} \dot{i}_{d}\\ \dot{i}_{q}\end{bmatrix}+\begin{bmatrix}\frac{v_{d}}{L_{d}}\\ \frac{1}{L_{q}}\begin{pmatrix}v_{q}-\sqrt{\frac{2}{3}}\frac{\pi}{\tau}\lambda_ {\text{max}}v_{P}\end{pmatrix}\end{bmatrix} \tag{2}\]  
The dynamic equation of the mechanical part is expressed as  
\[F_{e}=M\dot{v}_{P}+Bv_{P}+F_{L}=M\ddot{x}+B\dot{x}+F_{L} \tag{3}\]  
where \(M\), \(B\), and \(x\) are the mass, viscous friction coefficient, and displacement of the stage, respectively, \(F_{L}\) denotes the external loading, and \(F_{e}\) represents the electromagnetic force, which is given by  
\[F_{e}=\frac{3\pi}{2\tau}\big{[}\big{(}L_{d}-L_{q}\big{)}\dot{i}_{d}\dot{i}_{q} +\lambda_{\text{max}}\dot{i}_{q}\big{]} \tag{4}\]  
Since the PMLSM exhibits a uniform air gap on the stator surface, the condition \(L_{d}=L_{q}=L\) can be assumed and reduces (4) to  
\[F_{e}=\frac{3\pi}{2\tau}\lambda_{\text{max}}\dot{i}_{q}=k_{t}\dot{i}_{q} \tag{5}\]  
where \(k_{t}=\frac{3\pi}{2\tau}\lambda_{\text{max}}\) is the force constant. Let \(v_{dr}\) and \(v_{qr}\) be the \(d\)- and \(q\)-axis voltage commands and remove the coupled terms, then the derivative of the currents shown in (2) can be rewritten:  
\[\begin{bmatrix}\frac{\dot{a}_{d}}{dt}\\ \frac{\dot{a}_{d}}{dt}\end{bmatrix}=-\frac{R}{L}\begin{bmatrix}\dot{i}_{d}\\ \dot{i}_{q}\end{bmatrix}+\frac{1}{L}\begin{bmatrix}v_{dr}\\ v_{qr}\end{bmatrix} \tag{6}\]  
From (5), the q-axis current \(i_{q}\) produces the motor driving force \(F_{e}\). Integrating (3), (5), and (6), and then applying a Laplace transform, provides the transfer function from the voltage command \(V(s)\) to the displacement \(X(s)\):  
\[\frac{X(s)}{V(s)}=\frac{K_{t}}{MLs^{3}+(MR+BL)s^{2}+BRs} \tag{7}\]  
Figure 1 shows the model block diagram of the PMLSM system.
## 2 Design of the Energy Harvester
### Power-Generation Bicycle Lane Using Piezolectric Material Conceptual
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Value** & **Unit** \\ \hline Overall dimensions (length \(\times\) width \(\times\) thickness) of piezoelectric modules & 71.0 \(\times\) 25.4 \(\times\) 0.76 & mm \\ \hline Density & 7.8 & g/cm\({}^{3}\) \\ \hline Dielectric constant (1 KHz) & 3800 & - \\ \hline Piezolectric voltage coefficient g\({}_{33}\) & 19.0 & V\(\cdot\)m/N \(\times\) 10\({}^{-3}\) \\ \hline Piezolectric voltage coefficient g\({}_{31}\) & \(-\)9.5 & V\(\cdot\)m/N \(\times\) 10\({}^{-3}\) \\ \hline Piezolectric charge(displacement) coefficient d\({}_{33}\) & 650 & m/V \(\times\) 10\({}^{-12}\) \\ \hline Piezolectric charge(displacement) coefficient d\({}_{31}\) & \(-\)320 & m/V \(\times\) 10\({}^{-12}\) \\ \hline Capacitance & 190 & nF \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of piezoelectric material properties. The voltages were measured across ten trials, each conducted with and without the spring installed on the power-generating floor; the average of the values thus obtained was calculated, as shown in Table 4. The experimental results indicated that, on installing the spring, the average voltage was higher than that without the spring. Due to the mounting of the spring, the piezoelectric material oscillated with higher amplitudes and frequencies. A rectifier and a 1 uF capacitor were connected to the piezoelectric modules to convert the AC to DC at night. Moreover, the ripple voltage at the output was reduced, and the current was increased by transmitting it through the Darlington circuit for supplying the illuminating system. For the daytime battery charging circuit, a 12 V nickel-metal hydride (NiMH) battery was used to connect the boost converter and the Darlington circuit in order to maintain the expected voltage level. Furthermore, to confirm that the battery was chargeable, the 3-pin IC model MC7805 was used to provide a constant output voltage to the load. The combination of these circuits from both designs utilized electronic switches to separate day and night periods. During actual use, the Darlington circuit was powered using solar energy. The energy obtained from the piezoelectric device through the energy harvesting circuit increased to 72 mA and 3.1 V. Therefore, when a bicycle passed over the power generator, it excited four piezoelectric devices, increasing the  
Figure 10: Voltage signal under excitation by mechanical force, as captured from an oscilloscope: (**a**) rectified voltage signal; (**b**) voltage signal boosted by boost converter. Figure 9: Piezoelectric energy harvesting circuit. power (3.1 \(\times\) 0.072 = 0.8928 W) by a factor of 4. When passing through the step-up circuit, which was used to increase the voltage to 12 V, a current of 13 mA was obtained. Over a distance of 1 m, a total of 32 piezoelectric plates were installed. Thus, the electric power (12 \(\times\) 0.0186 = 7.1424 W) increased by a factor of 32.
## 2 Methods
### Improved Grey Wolf Optimizer (IGWO)  
The Grey Wolf Optimizer (GWO) algorithm, notable for its simplicity, minimalistic parameter requirements, and robust convergence capabilities, has found initial applications across diverse fields including constraint optimization, drone route optimization, scheduling of module assembly, optimization of PI controllers, and workshop scheduling. However, the GWO algorithm faces challenges in maintaining a balance between its global and local search abilities. Furthermore, the optimal positioning of the leading wolf, a crucial element in directing the group, is not always guaranteed. These limitations often result in the algorithm prematurely converging to local optima and exhibiting slow convergence rates.  
To overcome these challenges, the algorithm has undergone significant improvements:  
1. Population Initialization Stage  
The integration of cubic chaotic mapping with a refraction-based reverse learning strategy guarantees the diversity and uniqueness of the initial population. This approach effectively disperses initial solutions throughout the search space, mitigating the risk of early convergence on suboptimal solutions.  
2. Position Vector Update Stage  
Adopting a nonlinear convergence factor instead of a linear one significantly improves the algorithm's global and local search efficiency. This change enhances the wolves' movement dynamics, enabling more effective exploration of the search space and the ability to escape local optima.  
3. Enhancements to Step Size and Leadership Influence  
Adjusting the step size proportional weight and amplifying the role of the leading wolf allow the algorithm to circumvent local optima, thereby enhancing search efficacy and solution precision. Emphasizing the leading wolf's position ensures the search is strategically oriented towards the most promising regions of the search space.  
These strategic enhancements address the GWO algorithm's inherent limitations, thereby bolstering its exploration and exploitation capabilities. This results in improved convergence speeds and higher accuracy in identifying optimal solutions, making the improved GWO algorithm a more effective tool for tackling complex optimization challenges.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 3 Visibility Calculation
Therefore, the final requirement for ensuring visibility is that the link length \(d_{i,j}\) in Figure 4 should be smaller than the length threshold \(d_{i,j}^{max}\), as shown in (17):  
\[d=\sqrt{\left(\left|\mathbf{R}_{j}\right|\right)^{2}-\left(\frac{\left|\mathbf{ R}_{j}\times\left(\mathbf{R}_{j}-\mathbf{R}_{i}\right)\right|}{\left|\mathbf{R}_{j}- \mathbf{R}_{i}\right|}\right)^{2}}>r_{E}, \tag{2}\]0  
Based on the same distance, optical systems guarantee a greater Shannon capacity than RF (Radio Frequency) systems [2]. Furthermore, FSA with equal-length time segments is used to describe and evaluate the visibility calculation. Through experiments, we chose the most appropriate time segment length that allows the visibility among the inter-plane links to be utilized to the greatest extent. The constellation network period is divided into fixed equal-length time segments and transform the dynamic state into fixed states. In time segment \(k\), in order to consider that two antennas are visible, the requirement that the two antennas are visible at any time within time segment \(k\) must be met. According to the above analysis, the visibility between any two antennas \(\forall i\in A\) and \(\forall j\in A\) in the constellation in time segment \(k\) is determined as (18):  
\[v_{i,j,k}= \begin{cases}1,&if\ (\ref{eq:1})\ and\ (\ref{eq:2})\ and\ (\ref{eq:2})\ and\ (\ref{eq:2})\ and\ (\ref{eq:2})\ at\ all\ the\ time\ points\ in\ k,\\ 0,&otherwise.\end{cases}  \tag{18}\]
## 4 Experiments and Results
### Results
#### 4.4.1 Metrics  
Commonly used metrics include precision, recall, and F-score, which are essential in quantifying the accuracy and effectiveness of proposed method and also bench-marking it against other proposed models. This section explain briefly the metrics we used.  
The precision metric is determined by dividing the number of correctly classified positive samples by the total number of samples classified as positive, including those that were classified incorrectly. This metric serves as an indicator of the model's ability to accurately classify samples as positive. The formula is shown in Equation (7), where TP refers to the true positive and FP represents the false positive.  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
Recall is derived by dividing the number of positive samples that were correctly classified as positive by the total number of positive samples in the dataset, as illustrated in Equation (8). This metric is used to evaluate the model's capacity to identify positive samples accurately. Higher values of recall indicate that the model is better at detecting positive samples.  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]The F1-score is the harmonic mean of precision and recall values for a classification problem. The F1-score symbolizes high precision as well as high recall. It presents a good balance between precision and recall, and gives good results on imbalanced classification problems.  
\[F1\ Score=\frac{2*Precision*Recall}{Precision+Recall}=\frac{2*TP}{2*TP+FP+FN} \tag{9}\]  
In multi-class classification with imbalanced data, the main consideration will be the Macro F1-Score. The formula is illustrated with Equation (10), where n represents the number of class involved. In the DFU classification, n is equal to 4.  
\[\mathit{Macro}\ F1\ Score=\frac{\sum_{i=1}^{n}F1\ score}{n} \tag{10}\]