esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Force Model  
For orbital motions around the asteroids, the two strongest perturbations are the non-spherical gravity and the SRP. For large-size asteroids such as Eros, the non-spherical gravity is much stronger than the SRP. For Eros, non-spherical gravity is several orders of magnitude larger than the SRP for low-altitude orbits within about ten kilometers above the asteroid's surface. Therefore, it is appropriate for us to use the simplified force model.  
The gravity field in the asteroid's body-fixed frame can be described as  
\[V=V_{0}+\Delta V=\frac{GM}{r}+\frac{GM}{r}\sum_{l=2}^{\infty}\sum_{m=0}^{l}( \frac{a_{e}}{r})^{l}P_{lm}(\cos\psi)(C_{lm}\cos m\lambda+S_{lm}\sin m\lambda) \tag{1}\]  
where \(a_{e}\) is the reference radius, \(P_{lm}\) is the associated Legendre polynomials, \(\psi\) is latitude, and \(\lambda\) is longitude. \(C_{lm}\) and \(S_{lm}\) are spherical harmonic coefficients. For the example case used in our study, the coefficients are adapted from Ref. [1] and displayed in Table 1.  
Physical parameters of Eros are shown in Table 2[1]. In our study, normalized units are used. The mass unit and the length unit take values in Table 2. The time unit (T) is defined as \(\sqrt{[L]^{3}/G[M]}\), and its value is also displayed in Table 2. In this paper, all the units are normalized unless specified.  
\begin{table}
\begin{tabular}{|c c|c c|c c|} \hline \(C_{10}\) & \(0.000000\) & \(C_{22}\) & \(0.082538\) & \(C_{41}\) & \(-0.000106\) \\ \(C_{20}\) & \(-0.052478\) & \(S_{22}\) & \(-0.027745\) & \(S_{41}\) & \(0.000136\) \\ \(C_{30}\) & \(-0.001400\) & \(C_{31}\) & \(0.004055\) & \(C_{42}\) & \(-0.017495\) \\ \(C_{40}\) & \(0.012900\) & \(S_{31}\) & \(0.003379\) & \(S_{42}\) & \(0.004542\) \\ \(C_{11}\) & \(0.000000\) & \(C_{32}\) & \(0.001792\) & \(C_{43}\) & \(-0.000319\) \\ \(S_{11}\) & \(0.000000\) & \(S_{32}\) & \(-0.000686\) & \(S_{43}\) & \(-0.000141\) \\ \(C_{21}\) & \(0.000000\) & \(C_{33}\) & \(-0.010337\) & \(C_{44}\) & \(0.017587\) \\ \(S_{21}\) & \(0.000000\) & \(S_{33}\) & \(-0.012134\) & \(S_{44}\) & \(-0.008939\) \\ \hline \end{tabular}
\end{table}
Table 1: Gravity harmonic coefficients of Eros.
## 7 Simulation Parameters
### Analysis of DPD-TANVEER and PA-ROBINA with Number of Dead Nodes and Packet Transmitted  
It is evident from Figure 13 that the comparison between DPD-TANVEER (which is a sub-variant of TANVEER for delivery of data Packet, see [22]) and the sub-variant of our proposed approach PA-ROBINA (which is used for enhancing the node length to cover the maximum of relay nodes and I\({}_{\text{AFN}}\)) is represented. With time intervals, the performance of the network including the number of dead nodes was plotting. Initially, the benchmark approach of DPD-TANVEER had a smaller number of dead nodes until the first 50 s to  
Figure 11: Analysis of Number of dead nodes with I\({}_{\text{AFN}}\) and its effects.  
100 s, and the total number of dead nodes found less than one were ignorable. Suddenly, the next interval of 50 s, like in 150 s until it reaches 250 s, the number of nodes increased, which was eight nodes for PA-ROBINA and around ten nodes for DPD-TANVEER. During path adjustment, most of the nodes are easily covered under the radius of the destination.  
Furthermore, the PA-ROBINA also considered forwarder nodes distances; therefore, the closer nodes were easily covered. Meanwhile, in DPD-TANVEER, the speed purpose nodes are called watchman nodes, responsible for providing the immediate alternate rotation for dead nodes. As watchman nodes use some amount of energy, the time to recover and rescue the dead nodes is longer than ROBINA. As the number of dead nodes is decreased, the packed transmission is increased. It is earlier mentioned that the procedure of both TANVEER and ROBINA techniques, therefore, consider and analyze the effect  
Figure 12: Analysis of path loss with baseline schemes.  
Figure 13: Analysis of the number of dead nodes compared with DPD-TANVEER.  
of Figure 13 on Figure 14. Although, the packet transmission of PA-ROBINA is majorly increased and dominant over DPD-TANVEER.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 3 Experiment Analysis  
The proposed method is first evaluated using normal data without maneuvers, since the precise orbit products for satellites during maneuvering are not available. Afterwards, the method is further tested on seven actual maneuver cases, including two GPS maneuver events and five BDS maneuver events.  
Figure 2: Processing scheme of the clock-constrained reverse precise point positioning (RPPP) precise orbit determination (POD) strategy for maneuvering satellites. GPS—global positioning system; BDS—Bedou navigation satellite system; ZTD—zenith tropospheric delays; ISB—inter-system bias.
## 3 RAD System
### Main Modules of RAD System
#### 3.2.1 Traffic Analyzer
If, while in intrusion detection mode, Snort detects attacks, it notifies the fault/anomaly detection submodule in the traffic analyzer to properly defend against it (e.g., by dropping the attack traffic). The following are examples of _Snort rules_ used to detect certain attacks (i.e., ICMP flooding and TCP SYN flooding attacks), _Snort input_, and _Snort output_ in the log file. **Snort Rule**:  
_If ICMP flooding attack with any IP/port to any IP/port exceeding 10 ICMP messages in 1 s_  
alert icmp any any -> any any (msg:\"ICMP FLOODING ATTACK DETECTED\"; GID:1; sid:10000011; rev:001; itype:8; threshold: type threshold, track by dst, count 10, seconds 1; classtype:icmp-event;)  
**Snort Input**:  
_// Mounting ICMP flooding attack by hping3 attack tool (SrcIP 10.0.0.1, DstIP 10.0.0.22)_  
hping3 -1 - flood -a 10.0.0.1 10.0.0.22  
**Snort Output**:  
10/07-13:54:37.429751 **[**]** [1:10000013:1] ICMP FLOODING Attack DETECTED **[**]** [Classification]: Generic ICMP event] [Priority: 3]  
{ICMP} 10.0.0.1 -> 10.0.0.22:66530
2. **Snort Rule**:  
_// TCP SYN flooding attack with any IP/port to any IP/port exceeding 100 SYN messages in 1 s_  
alert tcp any any -> any any (msg:\"DDOS ATTACK Detected (TCP SYN Flooding)\"; flags:S; threshold: type threshold, track by_dst, count 100, seconds 1; sid: 10000003; rev:1;)  
**Snort Input**:  
_// Mounting TCP SYN flooding attack by hping3 attack tool (SrcIP 10.0.0.1, DstIP 10.0.0.22, interval 100 \(\mu\)s)_  
Source 10.0.0.1 >> hping3 -i u100 -S 10.0.0.22  
**Snort Output**:  
10/07-14:15:37.839869 **[**]** [1:5000008:0] DDOS ATTACK Detected (TCP SYN Flooding) **[**]** [Priority: 0] [TCP] 10.0.0.1 -> 10.0.0.22:3334  
To defect a fault in switches (e.g., link or switch down), the fault/anomaly detection submodule monitors the status of switches and links between them. This submodule is also supported by a module called \"link discovery\" in SDN, which monitors all link information, whether it is in regard to the addition or deletion of a link.
###### Abstract  
Understanding spatial and temporal variability in soil organic carbon (SOC) content helps simultaneously assess soil fertility and several parameters that are strongly associated with it, such as structural stability, nutrient cycling, biological activity, and soil aeration. Therefore, it appears necessary to monitor SOC regularly and investigate rapid, non-destructive, and cost-effective approaches for doing so, such as proximal and remote sensing. To increase the accuracy of predictions of SOC content, this study evaluated combining remote sensing time series with laboratory spectral measurements using machine and deep-learning algorithms. Partial least squares (PLS) regression, random forest (RF), and deep neural network (DNN) models were developed using Sentinel-2 (S2) time series of 58 sampling points of bare soil and according to three approaches. In the first approach, only S2 bands were used to calibrate and compare the performance of the models. In the second, S2 indices, Sentinel-1 (S1) indices, and S1 soil moisture were added separately during model calibration to evaluate their effects individually and then together. In the third, we added the laboratory indices incrementally and tested their influence on model accuracy. Using only S2 bands, the DNN model outperformed the PLS and RF models (ratio of performance to the interquartile distance RPIQ = 0.79, 1.36 and 1.67, respectively). Additional information improved performances only for model calibration, with S1 soil moisture yielding the most stable improvement among three iterations. Including equivalent indices of the S2 indices calculated using soil spectra obtained under laboratory conditions improved prediction of SOC, and the use of only two indices achieved good validation performances for the RF and DNN models (mean RPIQ = 2.01 and 1.77, respectively).  
soil organic carbon; deep learning; Sentinel-2; spectral indices; Sentinel-1; soil moisture; radar indices; visible-near-infrared spectra 2023
# A Two-Stage Optimization Model for Airport Stand Allocation and Ground Support Vehicle Scheduling
## 6 Numerical Study
### Results and Discussion  
#### 6.2.1 Stand Allocation Problem  
Figure 6 shows the Pareto front of the stand allocation problem solved by the LS-NSGA-II model. Considering three objectives: taxi fuel consumption, ferry vehicle driving time, and the sum of the squares of the idle times, the model provides the optimal solutions for different objective functions after iterations. In Figure 6, \(Z_{1}\) represents the taxi fuel consumption, \(Z_{2}\) represents the ferry vehicle driving time, and \(Z_{3}\) represents the sum of the squares of the idle times.  
Table 7 provides the results of some solutions from the Pareto front. Solution 1 will serve as the input for ground support vehicle scheduling. Figure 7 shows the convergence of fitness values under NSGA-II and LS-NSGA-II. We can find that in the early stages of algorithm iteration, the LS-NSGA-II algorithm is able to find the optimal solution more quickly; in the later stages, it escapes local optima and converges earlier.  
The Gantt chart in Figure 8 illustrates the final stand allocation scheme. The taxi fuel consumption is 17,969.15 kg, the ferry vehicle driving time is 760, and the squared stand idle time is 452.77. Each segment in the chart represents the time a stand is occupied by a flight. It can be seen from the chart that flights between 50 and 220 min are dense, and the allocation of the contact stands is relatively balanced in this scheme.
# Predictable Trajectory Planning of Industrial Robots with Constraints
## 3 Predictable Linear and Circular Trajectory Planning  
### Predictable Linear Trajectory Planning  
If the constraint defined in Equation (1) is zero, the method illustrated in Section 2 degenerates into linear trajectory planning. Because the shortest path in the Cartesian space is a linear path, the Riemannian metrics \({}^{d}g\) and \({}^{o}g\) become [9],  
\[\left\{\begin{array}{c}{}^{d}g=(dp)^{2}\\ {}^{o}g=(dn)^{2}+(do)^{2}+(da)^{2}\end{array}\right. \tag{20}\]  
The geodesic equations are  
\[\left\{\begin{array}{c}\frac{d^{2}q_{i}}{dt^{2}}+\sum\limits_{k,j}^{d}\Gamma ^{i}_{kj}\frac{dq_{k}}{dt}\frac{dq_{j}}{dt}=0\\ \frac{d^{2}q_{i}}{dt^{2}}+\sum\limits_{m,b}^{o}\Gamma^{l}_{bm}\frac{dq_{b}}{ dt}\frac{dq_{n}}{dt}=0\end{array}\right. \tag{21}\]  
We predict that the end-effector performs linear paths with the joint trajectories derived by Equation (19). The velocity and acceleration are constant and zero respectively. The Cartesian trajectories are smooth. These predictions are validated as follows.  
The joint variables \(\theta_{1},\cdots,\theta_{k_{0}}\) are regarded as local coordinates of the position space. The components of the position vector \(p_{x}\), \(p_{y}\) and \(p_{z}\) can also be chosen as coordinates. The Riemannian metric defined in Equation (19) can be rewritten as  
\[{}^{d}g=(dp)^{2}=(\ dp_{x}\ dp_{y}\ dp_{z}\ )I(\ dp_{x}\ dp_{y}\ dp_{z}\ )^{T}, \tag{22}\]where \(I\) is a 3 \(\times\) 3 identity matrix. The geodesic equations become  
\[\left\{\begin{array}{l}\frac{d^{2}p_{x}}{dt^{2}}=0\\ \frac{d^{2}p_{y}}{dt^{2}}=0\\ \frac{d^{2}p_{z}}{dt^{2}}=0\end{array}\right. \tag{23}\]  
which are equations of the linear path. Since geodesic has no relation to the coordinates, Equations (19) and (21) represent the same geodesic. The robot end-effector will move linearly under the joint trajectories determined by Equation (19). The velocity and acceleration are constant and zero respectively from Equation (21). The end-effector motion properties can be made before the calculation of the geodesic method. The Cartesian trajectories are smooth by the theory of the ordinary differential equation.
# An Improved Lightweight Model for Defect Detection on Paths in Images
## 2 Materials and Methods
### Attention Mechanism Module  
Incorporating context from various scales is crucial for detecting targets that vary in size. By integrating context data across scales, the detection performance can be significantly enhanced [22]. [PERSON] et al. [23] proposed a module for multi-scale feature acquisition, designated as LM-fem. Additionally, they developed the S-ECA module, which leverages multi-scale contextual data. To further improve the model's ability to detect small-scale features and multi-scale defects [24; 25], and to improve the expressive power of the detection head, the detection head of YOLOv8 was replaced with DyHead [26], in the optimization of the aforementioned model. DyHead utilizes self-attention mechanisms to operate on the feature hierarchy (L), spatial (S), and channel (C) dimensions. By employing multi-attention to combine three dimensions, with each attention mechanism focusing on a single dimension, the principal computational equations are as follows:  
\[\mathrm{W(F)}=\pi_{\mathrm{C}}\left(\pi_{\mathrm{S}}\left(\pi_{\mathrm{L}} \left(\mathrm{F}\right)\cdot\mathrm{F}\right)\cdot\mathrm{F}\right)\cdot \mathrm{F} \tag{1}\]  
Here, \(\pi\mathrm{L}(\cdot)\), \(\pi\mathrm{S}(\cdot)\), and \(\pi\mathrm{C}(\cdot)\) represent three distinct attention functions applied to dimensions L, S, and C, respectively, as follows:  
Figure 4: Structure of ADown module.  
Scale-aware Attention \(\pi_{\text{L}}\):  
\[\pi_{\text{L}}(\text{F})\cdot\text{F}=\sigma(\text{f}(\frac{1}{\text{SC}}\sum_{ \text{S},\text{C}}\text{F}))\cdot\text{F} \tag{2}\]  
Spatial-aware Attention \(\pi_{\text{S}}\):  
\[\pi_{\text{S}}(\text{F})\cdot\text{F}=\frac{1}{\text{L}}\sum_{\text{d=1}}^{ \text{L}}\sum_{\text{k=1}}^{\text{K}}w_{\text{U}\text{k}}\cdot\text{F}(\text{l}; \text{p}_{\text{k}}+\Delta\text{p}_{\text{k}};\text{c})\cdot\Delta\text{m}_{ \text{k}} \tag{3}\]  
Task-aware Attention \(\pi_{\text{C}}\):  
\[\pi_{\text{C}}(\text{F})\cdot\text{F}=\text{max}\Big{(}\alpha^{1}(\text{F}) \cdot\text{F}_{\text{c}}+\beta^{1}(\text{F}),\alpha^{2}(\text{F})\cdot\text{F} _{\text{c}}+\beta^{2}(\text{F})\Big{)} \tag{4}\]  
By performing self-attention operations between feature hierarchies, DyHead can capture features of different scales and effectively fuse them.  
The spatial awareness attention module includes offset learning and a 3 \(\times\) 3 convolution, focusing on every spatial position on the feature map. By learning the offsets and sparse sampling, the model can prioritize discriminative regions within the image, enhancing its spatial location awareness. Task-aware attention adjusts to various tasks, including classification and bounding box regression, by dynamically enabling or disabling feature channels, improving the model's adaptability to various tasks. DyHead serializes and stacks the three attention modules mentioned above, allowing each module to independently operate on a dimension of the feature tensor, enhancing the expressive power of the features.
## Appendix A Calculation of Daily Returns and Semivariance
### Daily Returns Calculation  
Daily returns for each asset were calculated using log-returns (logarithmic returns). Log-returns are preferred over simple returns in financial time series analysis, particularly when aggregating returns over multiple periods, as they are additive across time and approximate continuous compounding. The daily log-return for an asset \(i\) on day \(t\), denoted as \(\Re_{it}\), is computed as  
\[\Re_{it}=\ln\frac{P_{i,t}}{P_{i,t-1}},\]  
where  
* \(P_{i,t}\) is the closing price of asset \(i\) on day \(t\);
* \(P_{i,t-1}\) is the closing price of asset \(i\) on day \(t-1\);
* In denotes the natural logarithm.
## 1 Introduction
Various artificial intelligence studies have been published to quantitatively detect spinal deformities or scoliosis using only the radiographic images, but this study is characterized as an artificial intelligence study using feature importance analysis based on parameters extracted from a computer vision-based posture analysis system (Table 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & \begin{tabular}{c} **Feature** \\ **Importance** \\ \end{tabular} & **Dataset** & **Modeling** & **Outcome** \\ \hline [PERSON] et al. & None & \begin{tabular}{c} Radiographic \\ images \\ \end{tabular} & Deep learning & \begin{tabular}{c} Scoliosis angle \\ measurement \\ \end{tabular} \\ \hline [PERSON] et al. [12] & None & \begin{tabular}{c} Radiographic \\ images \\ \end{tabular} & \begin{tabular}{c} Machine \\ learning \\ \end{tabular} & \begin{tabular}{c} Surgical \\ outcome \\ \end{tabular} \\ \hline [PERSON] et al. [13] & None & \begin{tabular}{c} Radiographic \\ images \\ \end{tabular} & \begin{tabular}{c} Machine \\ learning \\ \end{tabular} & \begin{tabular}{c} Biomechanic \\ prediction \\ \end{tabular} \\ \hline [PERSON] et al. [14] & None & \begin{tabular}{c} Radiographic \\ images \\ \end{tabular} & Deep learning & \begin{tabular}{c} Scoliotic area \\ prediction \\ \end{tabular} \\ \hline This study & \begin{tabular}{c} Shoulder height \\ difference, wrist \\ height difference, pelvic height \\ difference, etc. \\ \end{tabular} & Tabular data & \
\begin{tabular}{c} Machine \\ learning \\ \end{tabular} &
\begin{tabular}{c} Feature \\ importance \\ analysis and \\ postural \\ deformity \\ prediction \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Artificial intelligence research using feature importance analysis to detect spinal deformity. In this study, postural deformity as a result of prediction probability was derived for the early detection of suspected scoliosis through artificial intelligence models based on skeleton point information extracted from the computer vision-based posture analysis system (CVPAS) using non-ionizing radiation. Moreover, contributions of prognostic factors to the results were analyzed by the inference process for the prediction outcomes.
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
###### Abstract  
The driving tasks of a maize harvester are exhausting because of the varying soil, maize conditions, and the long working time. Operators must adjust and optimize the internal settings of the harvester to modify the working parameters and reduce the harvest loss. In this paper, an intelligent control system for maize picking harvest was investigated for automatic adjustment and minimization of maize picking losses. A prediction model based on experimental data was used to predict the maize picking loss rate, and the rotational speed of the pulling rollers, operating speed, and header height were optimized to minimize the maize picking loss. The intelligent control system allows use of manual or automatic controls; the controller adjusts the rotational speed of pulling rollers, the operating speed, and header height based on the measured picking losses in automatic mode. The designed automatic control system comprises faster and slower loops. The fuzzy proportional-integral-derivative (PID) method is used to optimize the rotational speed of pulling rollers and the operating speed in the faster loop, and the PID method is used to regulate the header height in the slower loop. Field experiments were conducted to evaluate the effectiveness and stability of the system. The system test results showed that all working parts respond quickly, and the overshoot and steady-state errors of each working part were relatively small. Regardless of the load condition, the established control strategy could optimize each working parameter of the maize harvester. In experiments, maize picking loss rates of 1.676% and 1.386% were obtained, which meet the requirements of maize harvesting.  
maize picking loss rate; intelligent control system; prediction model; automatic control
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 1 Introduction
However, most of the tailing ponds and dumps were not properly rehabilitated and showed a quasi-continuous release of radioactive contamination into the local environment [50; 54]. In case of the sudden destruction of the tailing ponds due to earthquake-induced landslides in the Mailuu-Suu catchment, both clean and radioactive polluted soils may be substantially expanded through the river networks and transported across the Kyrgyz-Uzbek border, adding a potential transboundary conflict to the existing political and environmental problems [54; 56]. To date, most studies related to landslide focused on the assessment of landscape stability and the development of LSI, but the evaluation of VOL has been largely ignored [15; 24]. However, proper risk management plans and engineering practices can only be achieved by a simultaneous assessment of LSI and VOL. Central Asian countries, due to limited research budgets, despite being frequently struck by earthquake and burdened by radioactive legacy sites, are in particular need of research in this field. Therefore, this study aims to (1) identify the landslide prone areas in the Mailuu-Suu catchment, utilising maps of LSI and VOL under a series of earthquake scenarios from moderate to extreme and (2) examine the relationships between nine environmental variables and the spatial distributions of LSI and VOL, in order to determine the most important variables which should be considered as priorities in future risk management plans.
# Optical Lensless-Camera Communications Aided by Neural Network
## 3 Lensless Camera Communication System Experimental Platform
### Hardware Design of Sender Module  
The sender consists of a microcontroller, an amplifier circuit, and a switch circuit. In the experimental process, we selected the STM32F429 as microcontroller. The MAX4427 chip was used in the amplification circuit. The microcontroller is used to generate modulated signals that are then amplified by the amplification chip to form 2A sink current. We also selected an IRF54 ON FET with a short response time (about 20 ns) as the switch to drive the LED to transmit modulated visible light signals. Figure 9 shows the circuit diagram of the sender.