esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Reliability Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**The Method Name**} & **Area1** & **Area2** & **Area3** & **Average** \\ \hline \multirow{3}{*}{Threshold Method} & Custom Threshold & \(-8.41\%\) & \(-12.27\%\) & \(-5.37\%\) & \(-8.68\%\) \\  & Otsu Threshold & \(-1.49\%\) & \(-33.10\%\) & \(-5.78\%\) & \(-13.46\%\) \\  & Adaptive Threshold & \(-0.92\%\) & \(-0.16\%\) & \(-0.33\%\) & \(-0.47\%\) \\  & Logistic Regression & \(-66.52\%\) & \(-47.37\%\) & \(-61.74\%\) & \(-58.54\%\) \\  & SVM & \(-66.25\%\) & \(-32.20\%\) & \(-60.49\%\) & \(-52.98\%\) \\  & Random Forest & \(-30.12\%\) & \(-49.73\%\) & \(-62.68\%\) & \(-47.51\%\) \\  & XGBoost & \(-27.16\%\) & \(-47.32\%\) & \(-29.35\%\) & \(-34.61\%\) \\  & Neural Network & \(-30.13\%\) & \(-42.96\%\) & \(-62.13\%\) & \(-45.07\%\) \\  & Decision Tree & \(-19.60\%\) & \(-46.28\%\) & \(-25.40\%\) & \(-30.43\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AUC index changes statistics of each machine learning algorithm. However, the water extraction effect of the threshold method is related to the remote sensing image data, and the water extraction effects of remote sensing images from different periods do not affect each other. The water extraction results of each algorithm were placed in the supplementary materials, as shown in Figure S4: Classification results of each algorithm in Area1 on October 4; Figure S5: Classification results of each algorithm in Area2 on October 4; Figure S6: Classification results of each algorithm in Area3 on October 4. It can be seen from the classification result diagrams that most of the machine learning pepper and salt phenomenon is very serious, and there is a large number of non-water \"noise\". The visual effects of various algorithms are also significantly different.
## 3 Results  
A total of 158 patients completed the first assessments of the study. However, 132 women completed the subsequent assessments after wearing the accelerometers for one week. Descriptive statistics of sociodemographic and clinical data are presented in Table 1.  
Aim 1: Associations between Subjective Measures of Physical Activity Intensity (IPAQ-s) and Objective Measure of Walking (Accelerometer)  
As shown in Table 2, walking behavior recorded by the accelerometer was positively associated with the perception of activity as vigorous (r = 0.765, \(p\) = 0.000). Self-efficacy was positively related to the perception of moderate- (r = 0.324, \(p\) = 0.001) and vigorous-intensity (r = 0.258, \(p\) = 0.007) IPAQ-s activity and to walking recorded by the accelerometer (r = 0.213, \(p\) = 0.026). In relation to symptoms, there was a negative significant correlation between the perception of moderate activity and depression (r = \(-\)0.217, \(p\) = 0.013). Similarly, there was a negative significant association between the accelerometer walking measure and both cognitive problems (r = \(-\)0.180, \(p\) = 0.040) and depression (r = \(-\)0.265, \(p\) = 0.002). Negative significant relationships were also shown between self-efficacy and pain (r = \(-\)0.272, \(p\) = 0.004), cognitive problems (r = \(-\)0.321, \(p\) = 0.001), and depression (r = \(-\)0.379, \(p\) = 0.000). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**FM Participants (\(n\) = 132)**} \\ \hline
**Sociodemographic Data** & \(n\) & **(\%)** & **M** & **(SD)** & **Max** & **Min** \\ \hline Age & & & 57.63 & 10.44 & 79 & 32 \\ Marital status & & & & & & \\ Married & 95 & (72.4) & & & & \\ Divorced or separated & 13 & (9.8) & & & & \\ Widow & 11 & (8) & & & & \\ Single & 13 & (9.8) & & & & \\ Educational level & & & & & & \\ Primary studies & 89 & (66) & & & & \\ Secondary studies & 31 & (24.6) & & & & \\ University studies & 12 & (9.4) & & & & \\ Employment status & & & & & & \\ Working & 30 & (22.7) & & & & \\ Housewife & 66 & (50) & & & & \\ Sick leave & 15 & (11.4) & & & & \\ Unemployed & 21 & (15.9) & & & & \\ \hline Clinical data & & & & * & & \\ \hline Medication & & & & & & \\ Analgesics & & & & & & \\ Yes & 104 & (86.7) & 4.37 & 2.54 & 5 & 0 \\ No & 28 & (13.3) & & & & \\ Sleeping pills & & & & & & \\ Yes & 77 & (65.9) & 3.20 & 3.30 & 3 & 0 \\ No & 55 & (34.1) & & & & \\ Antidepressants & & & & & & \\ Yes & 78 & (66.7) & 3.67 & 3.31 & 2 & 0 \\ No & 54 & (33.3) & & & & \\ Muscle relaxants & & & & & & \\ Yes & 49 & (31.9) & 1.27 & 0.55 & 4 & 0 \\ No & 83 & (68.1) & & & & \\ Others & & & & & & \\ Yes & 74 & (55.4) & 1.20 & 0.41 & 6 & 0 \\ No & 58 & (44.6) & & & & \\ \hline \hline \end{tabular} Note: M (Mean); SD (Standard Deviation);* data collected on the type of medications is number of pills per day.  
\end{table} 
Table 1: Sociodemographic and clinical data of participants.
# Dynamic Response Analysis of Airport Pavement under Impact Loading
## 2 Model Building
### Landing Gear Model and Parameters  
All loads acting on the pavement are transferred to the road surface through the wheels of the landing gear, and the configuration of the landing gear has a significant effect on the magnitude of the load on the pavement. Taking the Boeing 737-800 as an example, the main landing gear is in the form of a single-axis double-wheel, the maximum landing weight of the aircraft is 663.80 kN, and the conversion factor of the main landing gear is 95.0%. In order to simplify the calculation, this paper only constructs one of the main landing gear models and the dynamic load of a single tire is 4.83 t; the model schematic diagram of the main landing gear models is shown in Figure 1. The buffer system of the landing gear and the hub of the tire are made of structural steel, while the aircraft tires are generally made of multiple layers of rubber and carcass cord fabric combined with a simple tread pattern. In this paper, the radial aircraft tire is selected, including the tread, reinforcement layer, binding layer, tire body cord ply, sidewall, triangle rubber, wire loop, and heel. Figure 2 shows a diagram of the aircraft tire section. To ensure the quality of the grid, the tire is simplified into five parts: reinforcement layer, sidewall rubber, tire body, heel, and tread rubber. The tire thickness is 20 mm, and the Yeoh model is used to define the tire material. The model constants of each part are shown in Table 1[23] and a schematic diagram of the tire section is shown in Figure 3. As the overall structure of the tire has a high degree of symmetry, a three-dimensional tire geometry model can be obtained by rotating the tire section around the tire for one week based on the aero tire section.  
Figure 1: Schematic diagram of the main landing gear.
## 3 LR-Aided MIMO Detector Design  
The ML detector requires to search through all possible constellation points of the transmitted symbol vector \(\mathbf{x}\) in (3) within the lattice \(\mathbf{H}\mathbf{x}\), which can be expressed as \(\mathbf{\tilde{x}}=arg\min_{\mathbf{x}\in\chi^{2N}}\parallel\mathbf{y}- \mathbf{H}\mathbf{x}\parallel^{2}\). The ML detection requires high computational complexity. Hence, in order to reduce this complexity, LR-aided detectors were proposed in [9; 10], where the channel matrix \(\mathbf{H}\) in transformed into its equivalent channel matrix \(\tilde{\mathbf{H}}\), which is more orthogonal and better conditioned than \(\mathbf{H}\)[25; 26]. The LR-aided detector uses the new orthogonal channel matrix \(\tilde{\mathbf{H}}\), which gives more reliable estimation for the received signal than that of the detector that uses the original channel matrix \(\mathbf{H}\)[19].  
The new \(\tilde{\mathbf{H}}\) matrix can be obtained by transforming the MIMO equation as follows [19]:  
\[\mathbf{y} =\mathbf{H}\mathbf{x}+\mathbf{n}\] \[\Rightarrow\mathbf{y} =\mathbf{H}\mathbf{T}\mathbf{T}^{-1}\mathbf{x}+\mathbf{n}\] \[\Rightarrow\mathbf{y} =(\mathbf{H}\mathbf{T})(\mathbf{T}^{-1}\mathbf{x})+\mathbf{n}\] \[\Rightarrow\mathbf{y} =\tilde{\mathbf{H}}\mathbf{z}+\mathbf{n}. \tag{4}\]  
The new channel matrix is generated as \(\mathbf{\widetilde{H}}=\mathbf{H}\mathbf{T}\), where \(\mathbf{T}\) is a uni-modular matrix having a determinant of \(\pm 1\) and integer entries. Then, using the model in (4), the detector requires decoding \(\mathbf{z}=(\mathbf{T}^{-1}\mathbf{x})\) from the reduced-lattice constellation and then recovers the original constellation point by \(\mathbf{\hat{x}}=\mathbf{T}\mathbf{z}\) (Note that \(\mathbf{z}\in\mathcal{Z}\) integer set). Note that both \(\mathbf{H}\mathbf{x}\) and \(\mathbf{\widetilde{H}}\mathbf{z}\) produce the same point in the lattice but \(\mathbf{\widetilde{H}}\) is more orthogonal than \(\mathbf{H}\). Various LR algorithms have been proposed in literature in order to produce the \(\mathbf{T}\) matrix, where in this paper we focus on the LLL algorithm [11] and the ELR algorithm [13].
## 3 Analysis of the Scattering Phenomenon in Underground Mines and Distribution of Dust Particles
### Atmospheric and Dust Particles Scattering and Absorption Parameters in UM
In terms of \(k_{sca}^{Mie}\) and \(k_{abs}^{Mie}\), these coefficients can be modeled based on the Mie theory through a dust particle size distribution with arbitrary form \(n(r_{d})\), which is expressed as  
\[k_{sca}^{Mie}=\int_{0}^{\infty}\pi r_{d}^{2}Q_{sca}(r_{d},\lambda,m)n(r_{d})dr _{d}, \tag{8}\]\[k_{abs}^{Mie}=\int_{0}^{\infty}\pi r_{d}^{2}Q_{abs}(r_{d},\lambda,m)n(r_{d})dr_{d}, \tag{9}\]  
where \(r_{d}\) is the dust particle radius, \(m\) is the complex refractive index of dust particles, and \(Q_{sca}\) and \(Q_{abs}\) are the scattering and absorption coefficients respectively, which can be obtained as follows  
\[Q_{sca}(r_{d},\lambda,m)=\frac{2}{d_{s}^{2}}\sum_{n=1}^{\infty}(2n+1)\Big{(}|a _{n}|^{2}+|b_{n}|^{2}\Big{)}, \tag{10}\]  
\[Q_{abs}(r_{d},\lambda,m)=\frac{2}{d_{s}^{2}}\Bigg{[}\sum_{n=1}^{\infty}(2n+1)( a_{n}+b_{n})-\sum_{n=1}^{\infty}(2n+1)\Big{(}|a_{n}|^{2}+|b_{n}|^{2}\Big{)} \Bigg{]}, \tag{11}\]  
where \(d_{s}=2\pi r_{d}/\lambda\). Furthermore, \(a_{n}\) and \(b_{n}\) can be expressed as  
\[a_{n}=\frac{[\psi_{n}(md_{s})\psi_{n}(d_{s})](m-1)}{[\psi_{n}(md_{s})\xi_{n}( d_{s})](m-1)}, \tag{12}\]  
\[k_{sca}=k_{sca}^{Ray}+k_{sca}^{Mie}, \tag{4}\]0  
where \(\psi_{n}(d_{s})=d_{s}j_{n}(d_{s})\) and \(\xi_{n}(d_{s})=d_{s}h_{n}^{(1)}(d_{s})\) with \(j_{n}(d_{s})\) and \(h_{n}^{(1)}(d_{s})\) as the spherical Bessel function and spherical first kind Hankel function, respectively.
# Optical Cable Lifespan Prediction Method Based on Autoformer
## 5 Analysis of Cable Life Prediction Model Based on Autoformer
### The Impact of Time Window Length on the Performance of the Autoformer Model  
The time window length is a crucial parameter that can significantly affect the performance of time series forecasting models. To delve into the effect of the time window on model efficacy, this experiment employed models trained over various window lengths for comparative analysis.  
Figure 5 illustrates the impact of time window length on the Autoformer model's performance, as measured by RMSE, MAE, and MSE. Initially, increasing the time window length leads to improved performance, reaching an optimal point at a length of 60 days. However, further extending the time window results in performance degradation. This phenomenon can be attributed to two primary factors. Firstly, a wider time window may cause the model to overemphasize earlier data points, potentially neglecting the significance of more recent information. Secondly, larger time windows require processing a greater volume of data, which can increase the risk of overfitting. This is evidenced by the rising validation error observed with longer time windows, indicating a reduced ability to generalize to unseen data. Therefore, selecting an appropriate time window length is crucial for balancing model complexity and generalization capability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning Rate** & **RMSE** & **MAE** & **MSE** \\ \hline \(1\times 10^{-3}\) & 0.6091 & 0.3711 & 0.3535 \\ \(5\times 10^{-5}\) & 0.6088 & 0.3707 & 0.3496 \\ \(2.5\times 10^{-5}\) & 0.5893 & 0.3687 & 0.3473 \\ \(1.25\times 10^{-5}\) & 0.6141 & 0.3771 & 0.3609 \\ \(6.25\times 10^{-6}\) & 0.6186 & 0.3827 & 0.3591 \\ \(3.125\times 10^{-6}\) & 0.6121 & 0.3746 & 0.3601 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics for different learning rates.  
Figure 5: Impact of time window lengths on Autoformer model performance.  
Figure 6 presents the results of a similar experiment conducted with the Bi-LSTM + Attention model. As with the Autoformer model, when the input window is set to 60, the model achieves peak performance. However, deviating from this value leads to a decline in predictive accuracy, suggesting limitations in the model's ability to capture long-sequence temporal features.  
A comparison of the results depicted in Figures 5 and 6 reveals that the Autoformer model demonstrates greater robustness to variations in time window length compared to the Bi-LSTM + Attention model. Both models exhibit performance degradation with time windows exceeding 90 days, but the decline in RMSE is significantly more pronounced for the Bi-LSTM + Attention model. This suggests that the Autoformer model is more adept at capturing long-range dependencies within time series data, a capability not as pronounced in the Bi-LSTM + Attention model. These findings provide further evidence supporting the suitability of the Autoformer model for optical cable lifespan prediction tasks.
## 3 Results and Discussion
### Trace Elements in Sediments
#### 3.3.3 Arsenic  
The mean As content of sediment at site 1 was significantly higher than at all other sites. To elucidate the trend along the course of the MIW, sites 2 and 5 were again not included in Figure 5, where the As content at the different sampling sites is depicted. The As pollution at site 1 has been described in the past [54]. [PERSON] et al. [54] stated a content of  
Figure 4: Box plot including probable effect level (PEL) for Ni and Zn indicated with straight lines in sediments from sites along the course of the mining-influenced water.  
21 mg/kg As in sediment, which is lower than the mean content measured in this study (76.2 \(\pm\) 39.7 mg/kg). In general, arsenic pollution of sediments caused by AMD is a known issue. This is because As can replace S in most metal sulfide minerals (such as pyrite) due to their geochemical resemblance [55]. Similar to other trace elements and depending on factors such as dissolved oxygen and pH, As is easily soluble in MIW. Through treatment and neutralizing water parameters, the fate of dissolved As is determined by sorption onto, e.g., iron hydroxides and co-precipitation. This explains the significantly higher content on the site closest to the mine in our study [56; 57]. The PEL of 17 mg/kg was by far transcended in all samples at site 1. Additionally, the mean contents at sites 2, 3, and 7 were above the TEL. Especially sediment at site 1 was contaminated and problematic for benthic and thus also pelagic aquatic ecosystems. Even years after the implementation of the MIW treatment, the sediment at site 1 was still heavily polluted with As, showing an ongoing process of deposition of As compounds into the sediment. In the water phase, dissolved As was not detectable, but in the case of As, iron-rich sediment has proven to be the accumulating compartment for As [58] because prevalent As species display a high affinity toward iron oxides [55]. [PERSON] et al. [58] described that the transformation of the mainly occurring AMD iron precipitation (schwertmannite) to goethite over time potentially causes some of the previously retained As to be released. Accordingly, even though As precipitation occurred, a subsequent remobilization should be considered and potentially explains the content above the TEL at site 3.
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 5 Numerical Examples
### Multi-DOF Truss Calculation  
This section included two cases. The first case is a two-span truss structure, and the second is a four-span truss structure. The initial structures for both cases are shown in Figure 10, with a total length of 80 m and 160 m and a height of 4 m. The two-span structure contained 83 nodes with 154 DOFs, and the four-span structure contained 163 nodes with 306 DOFs. Both structures were subject to a 900 kN point load on the upper chord. The material information for both structures was the same as that of the example shown in Section 5.1.  
The two cases both employed the STSA algorithm for optimization calculations, with the only optimization objective being structural mass. Throughout the optimization process, the load application points were fixed and not subjected to movement. The optimization results information and figures are shown in Table 8 and Figure 11.  
It can be observed in Figure 11 that both results showed a reverse arch shape at the support and between the supports, which is consistent with mechanical requirements. The computational efficiency information of the structural optimization process is summarized in Table 9.  
Figure 11: Design results based on STSA: (**a**) case 1; (**b**) case 2.  
Figure 10: Initial layouts of the multi-span trusses: (**a**) case 1; (**b**) case 2.  
Based on the results in Table 9, it can be observed that the efficiency of using the selection criterion was improved by 13.7% in case 1 (with final DOFs of 143) compared to the complete analysis, and 30.45% in case 2 (with final DOFs of 272). In addition, as shown in the example in Section 5.1 (with final DOFs of 20), an improvement in computational efficiency could be achieved with increasing structural DOFs. On the other hand, the proportion of structural calculation to the structural optimization process also increased with the increase in structural DOFs.  
In summary, the selection criterion of reanalysis methods proposed in this paper was effective in improving computational efficiency, resulting in a significant reduction of computational time required for structural optimization, especially for structures with high DOFs, while ensuring calculation accuracy.
## 5 Conclusions  
This paper proposed a two-stream UDN for pedestrian detection, which includes two sub-networks trained jointly and used for judgement independently. The two sub-networks give prediction based on the overall or the upper 1/3 region of the bounding box, and they accept the V channel in HSV space, HOG feature map, Sobel feature map, and GrabCut feature map as input.  
In recent years, many deep learning methods [28; 32; 33] have obtained excellent results by extracting and combining complex deep features for pedestrian detection, but the models based on deep networks have critical requirements for the hardware shown in Table 4, and TOPS (Tera Operations Per Second) are computed according to the specifications [44; 45; 46; 47; 48; 49]. While the pedestrian detection is applied in an auto-driving platform, it cannot obtain huge computation resources. Table 5 lists the usual computation resources of popular auto-driving platforms. The proposed two-stream UDN can execute on a computer CPU with the speed of 0.076 s/img, which indicates that it can run on the hardware resources of the autonomous driving platform. Except for the reducing requirement for the hardware, improving true positive detections and reducing false positive detections are also important to promote the application of pedestrian detection, considering the environment context during the decision process is feasible, such as parameters whereby a pedestrian should not be located in the sky or on the surface of a car.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Type** & **Method** & **Hardware** & **TOPS** & **Time/img (s)** & **Parameters** \\ \hline \multirow{3}{*}{Shallow Nets} & SDN [22] & GTX 760 GPU & 76 & 0.1 & 1M+ \\  & UDN [21] & Intel Core i5 CPU & 0.46 & 0.051 & 53k \\  & Two-stream UDN (ours) & Intel Core i5 CPU & 0.46 & 0.076 & 89k \\ \hline \multirow{3}{*}{Deep Nets} & CCF [43] & Titan Z GPU & 968 & 13 & 30k \\  & CSP (MobileNetV1) [33] & GTX 1080 Ti GPU & 352 & 0.041 & 12.6M \\ \cline{1-1}  & CSP (ResNet-50) [33] & GTX 1080 Ti GPU & 352 & 0.061 & 40.6M \\ \cline{1-1}  & Feature Pyramid [32] & GTX 1070 GPU & 206 & 0.13 & 64M \\ \cline{1-1}  & RPN + BF [28] & Tesla K40 GPU & 243 & 0.5 & 230M \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of running time among the state-of-the-art methods.  
**Author Contributions:** Conceptualization, W.W. and L.W.; methodology, L.W.; software, W.W. and X.G.; validation, W.W., L.W. and X.G.; formal analysis, W.W.; investigation, X.G.; resources, B.Y.; data curation, X.G.; writing--original draft preparation, W.W.; writing--review and editing, W.W., L.W. and J.L.; supervision, B.Y.; project administration, B.Y.; funding acquisition, B.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded by National Natural Science Foundation of China (61876012, 61772049), and Beijing Natural Science Foundation (4202003).  
**Conflicts of Interest:** The authors declare there is no conflicts of interest regarding the publication of this paper.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 5 Future Perspectives  
This section outlines the prevailing trends in and perspectives on IoT satellite communications, underscoring significant advancements and research areas. The critical areas of focus include:* Global coverage: Satellite communications service providers have developed satellite constellations to offer global coverage, extending the reach of IoT to geographical regions where a constant coverage is not feasible due to technical or economic constraints [5].
* Cost reduction: Significant cost reductions are anticipated as satellite IoT communications technology and infrastructure continue to evolve. Like the impact of mass production and technological advancements in terrestrial LPWAN networks, these developments are expected to drive cost efficiencies in satellite IoT communications [33].
* Security and privacy: Cybersecurity in IoT satellite communications is increasingly relevant. Security measures should be considered from the initial stages of satellite design and production due to the higher adoption of satellite technology, which increases the risk of possible attacks [9].
* Energy efficiency: To promote the long-term sustainability of IoT projects, it is critical to develop technologies and protocols that reduce the energy consumption of IoT devices and extend battery life [72]. This aspect is particularly vital for low-cost, battery-powered devices in LEOs, a frequent component of IoT networks [44].
* Interoperability: While satellite communication is optimal in areas lacking terrestrial LPWAN networks, terrestrial communication remains preferable where such networks exist [73]. As a result, both terrestrial and satellite networks must coexist in IoT communications. This necessitates interoperability and the development of hybrid networks, which are expected to drive growth in the coming years [13].
* Integration with other technologies: Satellite IoT communications increasingly integrate with emerging technologies, like edge computing, artificial intelligence, and cloud processing, enabling advanced data analysis and real-time decision making [74].
* Specific applications: Various industries, including logistics, agriculture, energy, environmental monitoring, asset tracking, and disaster and emergency management, stand to benefit from this technology. It offers global connectivity and the capability to monitor and control devices in remote or cellular coverage-deficient locations.