esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Generation Equations  
Equation (A1) illustrates the generation process of wind technologies, highlighting its dependency on wind speed forecasts (\(W_{t}\)), the minimum input (\(W^{in}\)), and maximum output (\(W^{out}\)) speeds required for generator operation. Values below the minimum do not generate sufficient energy, while exceeding the maximum speed risks compromising the generator's proper functioning. The equation takes into account the nominal power (\(P_{\gamma}\)) and the nominal speed (\(W^{rate}\)). It also introduces an exponent (\(m\)) that shapes the generation curve, typically assuming values of 1, 2, or 3, as per [46].  
It is important to note that wind speed varies with height, meaning the speed at ground-level measurement points (such as meteorological stations) differs from the speed at the height of a wind turbine's axis. To accommodate these variations in speed, the wind speed correction is applied using [PERSON]'s exponential law, as detailed in [47].  
\[f(W)\to w_{k}*gen_{wt}=\begin{cases}0,&\text{if }W_{t}<W^{in}\\ P_{y}*\frac{(W_{t})^{m}-(W^{in})^{m}}{(W^{rate})^{m}-(W^{in})^{m}}&\text{if }W^{in} \leq W_{t}\leq W^{rate}\\ P_{y},&\text{if }W^{rate}<W_{t}\leq W^{out}\\ 0,&\text{if }W_{t}>W_{out}\end{cases}\] (A1)  
Furthermore, constraint (A2) determines the minimum and maximum power generation capacities for diesel generators, as explored in [48]. This calculation is crucial for ensuring energy production aligns with operational constraints and environmental standards.  
\[f(D)\to gen_{dt}=P\_DG_{k}^{min}*v_{H}\leq p_{H}\leq P\_DG_{k}^{max}*v_{H}\] (A2)  
Solar technology generation (A3) depends on the number of panels (\(N_{p}v\)), the nominal capacity under standard test conditions (\(P_{p}vstc\)), the irradiation on the module (\(IrrPv\)), and the solar radiation under standard test conditions (\(G_{stc}\)), typically 1000 W/m\({}^{2}\). The temperature coefficient \(kt\) (usually assumed to be between 0.003 and 0.006/\({}^{\circ}\)C), the cell temperature (\(tc_{t}\)), the reference temperature \(t_{ref}\) (commonly assumed to be 25 \({}^{\circ}\)C), and the derating factor (\(fpv\)) are also significant factors in this calculation, as detailed in [35].  
\[f(S)\to w_{k}*gen_{st}=\begin{cases}0,&\text{if }IrrPv_{t}\leq 0\\ P\_{p\_{p\_{p\_{p\_{p\_{p\_{p\_{p\_p\_p\_p\}}}}}}}}*\frac{IrrPv_{t}}{G_{stc}}*( 1+kt*(tc_{t}-t_{ref}))*fpv,&\text{if }IrrPv_{t}>0\end{cases}\] (A3)  
Note that to guarantee linearity, a set of panels with a determined \(N_{p}v\) is introduced into the data; thus, it is not a decision but a parameter.
# Exploiting Frequency Characteristics for Boosting the Invisibility of Adversarial Attacks
## 2 Related Works
### Frequency Principle of Adversarial Examples  
Currently, research on the principles of adversarial examples suggests that shallow feature maps of neural networks typically extract edge and texture features, which highlights the importance of high-frequency information for final classification. In [18], [PERSON] et al. theoretically prove the frequency principle (FP) for DNNs through analyzing the spectral response and spectral deviation of DNNs. They reveal the causes and effects of the FP, demonstrating that DNNs exhibit a significant bias towards the information of different frequencies during decision making. The dependence of the model on high-frequency signals is also directly related to the phenomenon of adversarial examples. [PERSON] et al. [12] propose a practical attack method without a box that introduces small but effective perturbations through a hybrid image transformation (HIT) without changing the semantic information of the image. The authors demonstrate that the HIT can effectively deceive multiple target models and detectors with low computational costs and high success rates.  
However, recent research indicates that the conclusion that adversarial examples are high-frequency perturbations is incorrect. In detail, [PERSON] et al. [19] propose a frequency-analysis-based method for quantifying the adversarial robustness of DNNs. The authors first define a novel frequency sensitivity index (FSI) to measure the model's sensitivity to perturbations in different frequency ranges. The analysis shows that adversarial examples do not rely only on high or low frequencies, but the impact of the used dataset should be considered.
## 2 Materials and Methods
### Machine Learning Algorithms Used in the Study
#### 2.4.5 Support Vector Regression  
Support vector machine (SVM) finds extensive application across diverse prediction scenarios, encompassing tasks related to classification and regression. In the domain of regression, it is commonly referred to as support vector regression (SVR) [90]. SVR operates by transforming the model's inputs into a higher-dimensional space, a fundamental step in its training process based on the principles of structural risk minimization (SRM) [91]. Facilitating this transformation is the kernel function, tasked with mapping inputs into this expanded space to enable complex processing.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 5 Numerical Simulation
### Verification of the Bottom Non-Singular Sliding Mode Guidance Law  
In order to fully verify the non-singular sliding mode guidance law with impact time control designed in this paper, the following simulation examples are designed for simulation verification.  
2.1 Comparison of Non-Singular Sliding Mode Guidance Law (NSMG) Law and Sliding Mode Control (SMC) Law  
In this case, the performance of the NSMG law and the SMC law are compared. The velocity of the UAV is 330 m/s, the initial position is \((0,0)\) m, the initial flight path angle is 0 deg. The navigation gain is set to \(N=3\). The maximum acceleration of the UAV is 5 g and \(g=9.8\) m/s\({}^{2}\). The target position is \((10,0)\) km and the designated impact time is set to 45 s.  
\[a_{M} =a_{M}^{eq}+a_{M}^{dis}\] \[=\left[\left\{1+\frac{\dot{r}}{V_{M}}\left[1+\frac{\sigma_{M}^{2 }}{2(2N-1)}\right]+\frac{-r\dot{\lambda}\sigma_{M}}{(2N-1)V_{M}}\right\} \times\text{Csign}\left(\dot{\lambda}\right)-\frac{2\dot{r}\dot{\lambda}}{r} \right]/\left[\frac{\cos\sigma_{M}}{r}-\frac{Cr\sigma_{M}\text{sign}\left( \dot{\lambda}\right)}{(2N-1)V_{M}^{2}}\right]+K_{M}^{dis}\text{sign}(S) \tag{49}\]  
where  
\[K_{M}^{dis}=M/\text{sign}\left[\frac{\cos\sigma_{M}}{r}-\frac{Cr\sigma_{M} \text{sign}\left(\dot{\lambda}\right)}{(2N-1)V_{M}^{2}}\right] \tag{50}\]  
\(C\) and \(M\) are positive constants, and the form of the guidance law is denoted as SMC. The non-singular sliding mode guidance law denoted NSMG proposed in this paper is compared with the guidance law denoted SMC proposed in [33] for simulation, and the variation curves of the UAV's flight trajectory, leading angle, impact time error and acceleration command over time are obtained, as shown in Figure 4.  
It can be seen from Figure 4c that the SMC law cannot make the UAV attack the target at the designated time. At this time, the acceleration of the UAV is 0, and the UAV directly flies to the target at a constant speed with a flight time of 30.3 s, which shows that when the leading angle is 0, the SMC law cannot be started, while the NSMG law can attack the target at the designated time. Therefore, when the initial leading angle is 0, the performance of the non-singular sliding mode guidance law with impact time control proposed in this paper is better than the SMC law.
## 5 Open Challenges and Future Directions  
### Data scarcity and Standardization  
A fundamental challenge in applying AI to GPR interpretation is the lack of large, annotated, and diverse datasets. Unlike fields such as computer vision or natural language processing, GPR datasets are often proprietary, application-specific, and expensive to acquire. This scarcity hampers the training of high-performing deep learning models and makes fair benchmarking across studies difficult. Moving forward, the GPR community would benefit from collaborative initiatives to develop open-access benchmark datasets, incorporating a range of soil conditions, sensor types, and target materials. Synthetic data generation tools such as gprMax can help supplement real-world data, but their limitations must be carefully acknowledged [76; 77; 78].
## 2 Adaptive Spatial Feature Fusion Depth Completion
### Loss Function  
The loss function in this paper consists of two components: Sparse Loss and Smoothness Loss.  
**Sparse Loss:** It aims to ensure that the predicted dense depth map has the same depth value on pixels containing depth information as in the sparse depth map, as shown in the following equation:  
\[L_{dense}=\left\|D_{gt}-D_{pred>0}\right\|^{2} \tag{6}\]  
where \(D_{gt}\) is the ground truth of the depth map and \(D_{pred}\) is the dense depth map output using the DepthNet network. In the depth map, a pixel value greater than 0 indicates a valid value.  
**Smoothness Loss:** Considering that the depth information predicted by the network is independent of each other, this may lead to large abrupt changes in depth information between consecutive pixels, resulting in discontinuities between image pixel values. To solve this problem, Smoothness Loss [18] is introduced in this paper to increase the overall smoothness of the predicted image. The calculation formula is:  
\[L_{smooth}=\left\|D_{pred}\right\|^{2} \tag{7}\]  
In summary, the overall loss function in this paper can be expressed as:  
\[L=\alpha L_{dense}+\beta L_{smooth} \tag{8}\]  
where \(\alpha\) and \(\beta\) are the weight parameters of the loss function to balance the contributions of Sparse Loss and Smoothness Loss.
## 4 Conclusions  
WPU was prepared by covalent grafting of BTA on the polymer backbone as a pendant group (WPU-g-BTA). Using this WPU-g-BTA, we developed a series of WPU-g-BTAs containing N-alkylated amine cross-links (WPU-g-BTA-A, WPU-g-BTA-B, and WPU-g-BTA-C). The successful synthesis of WPU-g-BTAs was confirmed by FTIR. The following conclusions were drawn:  
1. The gel content of WPU-g-BTAs increased from 13.57% to 21.68% with increasing chain length from WPU-g-BTA-A to WPU-g-BTA-C. Further, increasing the chain length of WPU-g-BTAs significantly increased the crosslink density, thereby improving the thermal and mechanical properties.
2. WPU-g-BTAs improved the corrosion resistance performance of the as synthesized material. The superior inhibition efficiency of 99.972% and 31.16% was achieved by WPU-g-BTA-C after 30 min and 3 days of immersion in 3.5 wt% NaCl (aq) respectively. Hence, WPU-g-BTA coatings are considered promising materials for anti-corrosion applications.  
Conceptualization, methodology, writing--original draft preparation, software, A.B.; validation, formal analysis, investigation, E.T.; resources, data curation, visualization, Y.-X.L.; data curation, K.-C.C.; writing--review and editing, supervision, project administration, funding acquisition, J.-M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science Technology Council Taiwan grant number NSTC-111-2113-M-033-007-.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** Samples of the compounds are available from the authors.
## 3 Results
### Simulation
#### 3.1.2 Simulation Results  
We took the motion data of two cycles as the result. The three-dimensional input of the system is shown in Figure 7.  
Figure 8 shows the speed inputs of the four actuators and the curves of the three outputs of the simulation system. The tension for the active gravity balance was set to 300 N.  
Figure 6: Simulated 3D scene.  
Figure 7: Three-dimensional input of the simulation system.  
Figure 8a shows that the green curve starts to change before the other two curves in each cycle, and \(\mathrm{v_{ys}}\) reflects quickly. When \(\mathrm{v_{ys}}\) works, \(\mathrm{v_{yb}}\) starts increasing and \(\mathrm{v_{ys}}\) drops. In Figure 8b, these two speed inputs are similar to intermittent startup, which is consistent with the coefficient change logic we set. Figure 8c,d are the output curves of the system. It can be seen that the outputs of the system fluctuated near the setting values, and the system performed well.  
We compared the control methods used by other scholars in the active gravity balance test bench under the same operating conditions [6]. The simulation results show that the control method proposed in this paper had a better control effect on the experimental system in this paper. This indicates that the control method selected in this article was more suitable for this control system. The tension for active gravity balance was also set to 300 N. The specific results are shown in Figure 9.  
Figure 8: Speed inputs of the four actuators and the curves of the three outputs of the simulation system. (**a**) Speed input, \(\mathrm{v_{ys}}\) in green, \(\mathrm{v_{yb}}\) in dark red, and \(\mathrm{v_{y}}\) in purple; (**b**) speed input, \(\mathrm{v_{x1}}\) in yellow and \(\mathrm{v_{z1}}\) in green; (**c**) output F in red; (**d**) output \(\alpha\) in yellow and \(\gamma\) in green.  
Figure 9: Speed inputs of the four actuators and the curves of the three outputs of the simulation system using another control method. (**a**) Speed input, \(\mathrm{v_{ys}}\) in green, \(\mathrm{v_{yb}}\) in dark red, and \(\mathrm{v_{y}}\) in purple; (**b**) speed input, \(\mathrm{v_{x1}}\) in yellow and \(\mathrm{v_{z1}}\) in green; (**c**) output F in red; (**d**) output \(\alpha\) in yellow and \(\gamma\) in green.  
It can be seen that when other control methods were applied in this system, the measured force error of the active gravity balance reached ca. 20 N, which is greater than the 10 N state achieved by the control method in this article. The inclination error between the steel wire rope and the vertical direction was also slightly greater than the simulation results of the control method in this paper.
## 4 Recommendations and Conclusions
### Conclusions  
Indoor model tests were conducted to simulate the responses of new and existing sections of a combined sheet pile wall. The earth pressure, pile bending moment, shear force, and load-sharing ratio were evaluated under different working conditions. The following conclusions can be drawn.  
(1) The earth pressure of the existing and new piles increased with the width of the filling soil or the uniform load on the upper part. The earth pressure of the existing pile increased and then decreased with the distance under all working conditions. In contrast, the earth pressure curve of the new pile had an inflection point halfway between the pile top and the ground.  
(2) The bending moments of the new and existing piles increased and then decreased with the distance from the top of the pile under all working conditions. The maximum bending moment occurred at 0.485 and 0.9 m from the bottom of the existing pile and the bottom of the new pile, respectively. The bending moment of the existing pile was smaller than that of the new pile in the same working condition and at the same height.  
(3) The lateral displacements of the new and existing piles decreased with the distance from the top of the pile under all working conditions. Its rate of change was significantly higher in the cantilever section than in the anchored section. The lateral displacement of the top of the existing pile was 6.531 mm (>control value of 5.6 mm) in working condition 5, whereas that of the new pile was 8.713 mm (<control value of 9.2 mm).  
(4) The earth-pressure-sharing ratio of the existing pile decreased with an increase in the width of the filling soil or the upper load. When an equivalent uniform load of P to 4P was applied, the earth-pressure-sharing ratio of the existing pile ranged from 0.451 to 0.403.  
Figure 13: \(\lambda_{1}\) in the combined structure under different working conditions.  
**Author Contributions:** Conceptualization, W.Z. and X.M.; methodology, W.Z. and X.M.; software, W.Z.; formal analysis, W.Z.; resources, X.M.; data curation, X.M.; writing--original draft preparation, W.Z.; writing--review and editing, W.Z. and X.W.; supervision, W.Z. and X.W.; project administration, W.Z.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Gansu Youth Science and Technology Fund project (grant numbers 21 JR1 RA249); the Special Funds for Guiding Local Scientific and Technological Development by The Central Government (grant numbers 22 ZTYQA005); the Lanzhou Jiaotong University youth science foundation project (grant numbers 2019026). There was no conflict of interest regarding the publication of this paper.  
**Institutional Review Board Statement:** This study did not involve humans or animals.  
**Informed Consent Statement:** This study did not involve humans.  
**Data Availability Statement:** This study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adsorption of Arsenic and Heavy Metals from Solutions by Unmodified Iron-Ore Sludge
## 3 Results and Discussion
### Effect of pH on Adsorption  
The effects of pH on adsorption are shown in Figure 4. The adsorption capacity of SBC was strongly dependent on the pH of the solutions (\(\rm pH_{solution}\)). The amount of target cations adsorbed by the adsorbent (\(\rm q_{e}\); mg/g) increased remarkably when the solution pH increased, with the exception of As. This is due to a decrease in competitive adsorption between H\({}^{+}\) ions and adsorbate cations for active sites on the surface of the adsorbent. The dependence of adsorption on pH is in agreement with the pH\({}_{\rm PZC}\) and pH\({}_{\rm solution}\) in the experiment (pH = 5.5). This result indicates that electrostatic attraction was the main mechanism controlling adsorption of the target elements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\rm S_{BET}\) **(m\({}^{2}\)/g)** & **CEC (mmol\({}_{\rm c(-)}\).Kg\({}^{-1}\))** & **Authors** \\ \hline SBC & 47.4 & 75 & This study \\ Laterite & 15.365 & - & [33] \\ Laterite (Hanoi, Vietnam) & - & 66 & [34] \\ Clay & 10–20 & 15–75 & [35; 36] \\ Red mud & 30 & 37 & [19] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison specific surface area (\(\rm S_{BET}\)) and cation exchange capacity (CEC) of iron-ore sludge with other materials.  
Figure 3: Zero charge point pH\({}_{\rm PZC}\) of iron-ore sludge.  
The results also demonstrate that the adsorption tendencies of Pb, Cd, Zn, and Mn were different to those of As (Figure 4). The adsorption of As and heavy metals can be explained by the following processes. Firstly, complexation occurs at pH \(\geq\) 7 [38,39], and these complexes react with other cations in the solution, resulting in co-precipitation. Secondly, when the pH\({}_{\text{solution}}\) exceeds the pH\({}_{\text{PZC}}\) of the adsorbent, the surface is negatively charged, creating favorable conditions for cation adsorption [37]. Thirdly, as mentioned above, the increase in pH\({}_{\text{solution}}\) causes a decrease in competitive adsorption between H\({}^{+}\) ions and adsorbate cations.
## 6 Conclusions  
A novel developed chattering suppression technique for the positioning system is presented in this paper. The main issue with the SMC controller is unwanted non-linear and fast-changing controller output, which have a common name: chattering phenomena. The presented work proposes a chattering alleviation technique with the Barrier Function. In the presented results, the methodology offers promising results, efficiently improving the smoothness of the SMC controller output. The approach can be also used in the HOSMC, as well as on FSMC controllers. The complexity of the BSTA controller is increased compared to the non-adaptive version, but the computational burden is inattentive. Compared to the other adaptation, the SMC technique, BF is simpler and very efficient. The only tuning parameter is the adaptation boundary, which influences the closed-loop dynamic disturbance rejection capability and chattering alleviation directly. The boundary needs to be selected experimentally and is a trade-off between dynamic disturbance performance and chattering alleviation.  
There is a lot of possibility for improvement and adaptation. The first question is how to select the STA parameters optimally. The initial value of the STA parameters influences the convergence time of the BF adaptation. The BSTA controller can be improved with an additional adaptation procedure, which can be used for STA parameter selection based on the disturbance estimation [35]. There is also the possibility of adaptive BF boundary tuning in operation mode two. The adaptive boundary can be changed according to the change in the reference signal. When the reference signal is changed by a certain value, the adaptation boundary can be increased or decreased, with the aim of ensuring a proper closed-loop dynamic or reducing chattering phenomena. Such an adaptation approach would be appropriate for time-varying or periodic reference signals.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(RMS_{\varphi}\) & \(RMS_{\sigma}\) & \(RMS_{u}\) \\ \hline STA & 168.87 & 5.32 & 1.54 \\ BSTA & 167.3 & 5.17 & 0.92 \\ \hline \end{tabular}
\end{table}
Table 5: The RMS values of the real-time experiment with disturbance of the variables \(\varphi\), \(\sigma\), \(u\).  
**Author Contributions:** Methodology A.S. and R.S., conceptualization R.S. and D.G., validation D.G., writing A.S. and R.S., editing D.G., simulation R.S. and A.S., implementation A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Slovenian Ministry of Education, Science, and Sport under Grant number P2-0065.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# The Integration of Azure Sphere and Azure Cloud Services for Internet of Things
## System Design
### Hardware Design  
According to the designed solution, Azure Sphere MT3620 is the essential component of the proposed system. MT3620 is the first Azure Sphere certified MCU, which is embedded with tri-core  
Figure 1: System overview.  
microcontroller, one ARM Cortex-A7 core runs at up to 500 MHz as application processor and two ARM Cortex-M4F cores run at up to 200 MHz as general-purpose processor. In the proposed system, the MT3620 development kit released by Seeed Studio is used as Azure Sphere device to support real-time and security requirements when interfacing with UART, I2C, SPI and ADC on-chip peripherals. The MT3620 development kit, powered by rechargeable lithium-ion batteries, is designed to perform as direct connected device or gateway for legacy nodes. For the former scenario, sensors and actuators including temperature and humidity sensor, light sensor, sound sensor, and a relay are connected via on-board interfaces. And direct HTTP or HTTPS message transmission with Azure IoT Hub can be achieved with 2.4/5 GHz dual-band Wi-Fi module. For the latter scenario, resorting to the UART interface, the legacy IoT systems such as ZigBee, Bluetooth and other network devices can be connected to the MT3620 gateway for message processing before it is uploaded to the Azure IoT Hub. In this article, a ZigBee wireless sensor network (WSN) including ZigBee End Device and ZigBee Coordinator is established as legacy IoT system for environmental perception. ZigBee End Device is equipped with temperature and humidity sensor, light sensor, gas sensor, and passive infrared (PIR) sensor.