esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Singular Perturbation Decomposition and Control Law Design
### The Slow Subsystem
\begin{array}{cc}\frac{p}{\|z\|}\hat{z}_{2}&\|\hat{z}_{2}\|\geq a \\ \frac{p}{\|z\|}z_{2}&\|\hat{z}_{2}\|<a\end{array} \right. \tag{53}\]  
where, \(a\) is a positive constant. According to Equations (50) and (53), when \(\|\hat{z}_{2}\|=\frac{a}{2}\),  
\[\dot{V}_{2}\leq-\lambda_{\min}\{\mathbf{B}\}\|\mathbf{z}_{2}\|^{2}+(\frac{a}{4}-\frac{ \zeta_{0}}{2})p \tag{54}\]  
where, \(\lambda_{\min}\{\mathbf{B}\}\) is the minimum of \(\mathbf{B}\)'s eigenvalue. Obviously, when  
\[\|\mathbf{z}_{2}\|\geq\sqrt{\frac{p(\frac{a}{4}-\frac{\zeta_{0}}{2})}{\lambda_{ \min}\{\mathbf{B}\}}} \tag{55}\]  
, \(\dot{V}_{2}\leq 0\). Because \(\mathbf{V}_{2}\geq 0\) and \(\dot{V}_{2}\leq 0\), \(V_{2}\) is bounded in \(t\in(0,\infty)\). Because \(\mathbf{V}_{2}\) is a function of \(z_{2}\), \(z_{2}\) is bounded. \(z_{2}=\widetilde{x}_{2}+\mathbf{A}z_{1}\), \(z_{1}=\widetilde{x}_{1}\), and \(\widetilde{x}_{2}=\dot{\widetilde{x}}_{1}\), so \(\widetilde{x}_{2}\), \(z_{1}\), \(\dot{z}_{1}\), \(\widetilde{x}_{1}\) and \(\dot{\widetilde{x}}_{1}\) are bounded. \(\widetilde{x}_{2}=\dot{\vartheta}_{\mathrm{gd}}-x_{2}\), so \(x_{2}\) is bounded. Then according to Equation (34), \(\dot{\widetilde{x}}_{2}\) is bounded, \(\dot{\widetilde{z}}_{2}=\dot{\widetilde{x}}_{2}+\mathbf{A}\dot{z}_{1}\) is also bounded. Therefore, according to Equation (43), \(\dot{V}_{2}\) is consistent and continuous in \(t\in(0,\infty)\). Because \(\mathbf{V}_{2}\) is bounded and \(\dot{\mathbf{V}}_{2}\leq 0\), \(\lim\limits_{t\to\infty}\dot{V}_{2}=0\). Because \(\dot{\mathbf{V}}_{2}\) is a function of \(z_{2}\), \(\lim\limits_{t\to\infty}z_{2}=0\), and \(\lim\limits_{t\to\infty}\widetilde{x}_{1}=0\), \(\lim\limits_{t\to\infty}\widetilde{x}_{2}=0\). That is, the system is asymptotically stable. Otherwise, when Equation (55) is not satisfied, \(\|\mathbf{z}_{2}\|\) will converge to a small field near zero. At this time, the system's tracking errors \(\widetilde{x}_{1}\) and \(\widetilde{x}_{2}\) are also consistent and ultimately bounded. The block diagram of the control system is shown in Figure 3.
# Failure Analysis of Gantry Crane Slewing Bearing Based on Gear Position Accuracy Error
## 4 Discussion and Conclusions
### Discussion
#### 4.1.2 Analysis of Large Ring Gear Tooth Fracture  
1. This paper takes the MQ4038 type gantry crane as the research object and analyzes the causes of broken teeth of the large gear ring of the slewing bearing. This type of gantry crane has a high cylinder structure and adopts three rows of roller-type slewing bearings. By establishing the finite element model of the rotary table structure, slewing bearing, and cylinder structure assembly, the relative displacement relationship between the pinion and the large gear ring and the forced nature of large gear ring teeth are analyzed. Under the frontal load condition, the relative displacement of the pinion and the large gear ring causes the angle between the two axes, which causes the extrusion of the gear teeth at the large gear ring meshing. Long-term extrusion will accelerate the fatigue of the gear teeth and cause the large gear ring gear teeth to break.
2. According to the field gear tooth section research, the fatigue crack sprouted from the tooth root and the broken tooth was formed by crack expansion, which can be understood to indicate that the broken tooth type is a fatigue fracture [21]. As the slewing bearing teeth are subjected to additional extrusion stress in addition to the rated torque of the pinion, they are in a bad meshing situation during operation, which leads to fatigue cracking of the tooth root bending. Finally, the broken tooth defect is produced after further expansion of the crack. The dynamic data acquisition instrument and resistance strain gauge were used to test and verify the pinion and large gear ring displacements and cylinder structure stresses, respectively, and the test results were consistent with the finite element analysis results.
## 3 Results
### Membrane Distillation with SDS  
#### 3.3.1 Pe  
Figure 5 shows the average flux and salt retention of the PE membrane using different SDS concentrations. Up to 40 mg/L, the flux is unchanged and the salt retention remains sufficiently high (>99.9%). At 50 mg/L SDS, the flux increases steadily over time. During the first hour of the experiment at 50 mg/L SDS, the flux was relatively stable, at 17 kg\(\cdot\)h\({}^{-1}\cdot\)m\({}^{-2}\), while the retention already decreased from 99.9% to 98.6%. These observations indicate wetting of a few membrane pores, enabling limited salt transport through the membrane. The fraction of dry membrane pores must be substantially high, because the flux is not affected and the salt retention remains above 98.6%. During the second hour of the experiment, the flux increased rapidly from 17 kg\(\cdot\)h\({}^{-1}\cdot\)m\({}^{-2}\) up to 40 kg\(\cdot\)h\({}^{-1}\cdot\)m\({}^{-2}\), while the retention decreased drastically from >98.6% to 69.5%, which indicates severe wetting of the PE membrane at these concentrations. The SDS concentration is not further increased above 50 mg/L, because even more severe wetting problems are expected. Based on the contact angle measurements, wetting would occur between 50 and 100 mg/L, while based on the LEP measurement, no wetting is expected up to 1000 mg/L. This shows that the contact angle and surface tension are more reliable measures for prediction of membrane wetting than the LEP. The short-term exposure LEP in Section 3.2.3 did not predict membrane wetting under these experimental condition, and is therefore found to be an unreliable measure for wetting prediction.  
Figure 5: Flux and salt retention for the PE membrane for a feed composition with different SDS concentrations and 35 g/L NaCl.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 1 Introduction
The highly damped viscoelastic multilayered stiffener-based solar array (VMLSA) combined with an optimized popog pin-based HRM (P-HRM) is one of the technologies to be validated by the STEP Cube Lab-II mission. In this study, a novel high-damping deployable solar panel module with a three-pogo pin-based burn wire cutting HRM was fabricated and experimentally tested for application in STEP Cube Lab-II 6U CubeSat. The key advantages of the solar panel module proposed herein are that the panel's dynamic stresses and deflections in vibration loads can be effectively attenuated or minimized owing to the high damping characteristics accomplished by shear deformation of acrylic material of adhesive tapes with multilayered thin stiffeners. The solar panel's holding and release actions were accomplished using a three-pogo pin-based wire cutting release mechanism. The mechanism has a high loading capability, a simpler electrical system, reliable release functionality, and ease in the wire knotting process that overcomes the limitations and drawbacks of conventional burn wire triggering release mechanisms. The functionality of the proposed mechanism was validated using solar panel deployment tests under various test environments. In addition, the radiation tests of electrical components used in the mechanism, such as the total ionizing dose (TID) as well as single event effect (SEE), were performed to ensure the radiation hardness of the P-HRM. The solar panel's natural frequency and damping ratio in a rigidly mounted condition were obtained by performing free-vibration tests in an ambient room temperature environment. Furthermore, the design effectiveness and structural safety under launch loads were validated through sine and random vibration tests at the qualification level. To validate the structural safety and reliable release functionality of the solar panel module in an orbit environment, a thermal vacuum (TV) test was performed. Optical microphotographs of the solar panel side edge were taken for visual inspection after performing all the tests mentioned above. These test and inspection results validated the effectiveness of a highly damped novel deployable solar panel module for use in actual space missions.
## 4 Fractional-Order PID Adaptive Impedance Control
### Control Method Design
The expression for introducing fractional-order PID impedance control is as follows:  
\[m_{r}\Delta\ddot{x}+b_{r}\Delta\dot{x}+k_{r}\Delta x=k_{p}\Delta f+k_{i}D^{- \lambda}\Delta f+k_{d}D^{\mu}\Delta f \tag{37}\]  
where \(\Delta x=x_{d}-x_{r}\), \(\Delta f=f_{r}-f_{e}\), \(k_{p}\), \(k_{i}\), and \(k_{d}\) are the proportional, integral, and derivative parameters of the fractional-order PID controller, respectively. By selecting appropriate fractional-order PID parameters, Equation (37) will achieve a faster response, more stable force tracking control, and lower force impact when compared to Equation (4). Converting Equation (37) into the frequency domain yields  
\[(m_{r}s^{2}+B_{r}s+k_{r})\Delta x(s)=(k_{p}+\frac{k_{i}}{s^{\lambda}}+k_{d}s^{ \mu})\Delta f(s) \tag{38}\]  
When combining Equation (12) with the final value theorem, we can obtain the expression for the steady-state error of the system:  
\[\Delta f_{ss}=\lim_{s\to 0}s\Delta f(s)=\frac{s(m_{r}s^{2}+b_{r}s+k_{r})\{f_{r}( s)-k_{e}(x_{r}(s)-x_{e}(s))\}}{m_{r}s^{2}+b_{r}s+k_{r}+k_{e}+k_{p}+\frac{k_{e}k_{ i}}{s^{\lambda}}+k_{e}k_{d}s^{\mu}} \tag{39}\]  
From Equation (39), we can clearly see that after introducing the fractional-order PID controller, the expression in the numerator representing the reference trajectory is not affected; only the denominator is modified, and the addition to the denominator can reduce the steady-state error. Therefore, we can conclude that the introduction of the fractional-order PID is reasonable, as it not only reduces the steady-state error and improves force tracking control performance but also coexists with the adaptive control strategy that estimates the environment parameters online. Based on the above, we can obtain the principle block diagram of the adaptive impedance control strategy based on fractional-order PID, as shown in Figure 5.
## 2 Design Procedures
By using filters, these harmonics can be suppressed, leading to a cleaner output signal. Overall, the use of filters in microwave coupler and power divider design is a powerful tool for achieving compact size and harmonic suppression. By selectively passing or rejecting certain frequencies, filters can help to improve the performance of microwave systems in a variety of applications. The designed proposed filter consists of two parts. These are a center resonator and two side resonators. The layout and frequency response of the proposed center resonator are depicted in Figure 5a,b, respectively. This structure creates a transmission zero at 3.7 GHz and provides a 1350 MHz suppression bandwidth with a 20 dB suppression level from 3.17 GHz to 4.52 GHz. The layout and frequency response of the proposed two side resonators are depicted in Figure 6a,b, respectively. These resonators create two transmission ze  
Figure 5: Proposed center resonator (**a**) layout, and (**b**) frequency response. 2.24 GHz, which provides a 450 MHz suppression bandwidth with a 20 dB suppression level from 2.02 GHz to 2.47 GHz. By combining the proposed center resonator and two side resonators, the proposed filter can be created. The equivalent proposed filter, which is designed to be used instead of the \(\lambda/4\) and \(3\lambda/4\) transmission line branches, is shown in Figure 7. The size of the deigned filter is very small, as compared with the \(\lambda/4\) and \(3\lambda/4\) transmission line branches, which produces an overall size reduction for the proposed rat-race coupler. Figure 2.0 Proposed two side resonators: (**a**) layout, and (**b**) frequency response. Figure 2.1 _Cont._  
The phase characteristics of the proposed \(\lambda/4\) and \(3\lambda/4\) filters are depicted in Figure 2.2 As can be seen, a 180-degree phase difference between the designed \(\lambda/4\) and \(3\lambda/4\) filters, which are used instead of conventional branches, is achieved. It can be concluded from comparing Figures 7b and 8 with the conventional branch response, that the designed \(\lambda/4\) and \(3\lambda/4\) filters have correct magnitude and phase characteristics, which demonstrates that the equivalent filters correctly work instead of conventional transmission lines at an operating frequency of 950 MHz.
# Russian Companies' Motivations for Making Green Investments
## 4 Results
All obtained regression equations are significant according to [PERSON]'s F-test at a significance level of 1%. The included explanatory variables and individual effects describe 66% and 30% of the total variation in green and fixed investment, respectively. Thus, in the subjects of the Russian Federation, attraction of foreign direct investment, an increase in the volume of production of extractive industries and an increase in the collection of fees for a negative impact on the environment by 1 million rubles leads to an increase in green investments by 3 thousand rubles, 3 thousand rubles and 722 thousand rubles, respectively. An increase in emissions of pollutants into the atmosphere by 1 kiloton also leads to an increase in green investments by 2.476 million rubles. With regard to investment in fixed assets, the following factors stand out. The increase in the residual value of fixed assets by 1 million rubles ceteris paribus causes an increase in investments in fixed capital in the subjects of the Russian Federation by 28 thousand rubles, and the growth of foreign direct investment reduces investments in fixed capital by 421 thousand rubles. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Wooldridge Test for Autocorrelation in Panel Data**} \\ \multicolumn{3}{c}{**(H0: No First-Order AUTCOORRELATION)**} \\ \hline GreenInv & Inv & D(Inv) \\ F(1, 82) = 1.402 & F(1, 82) = 13.959 & F(1, 82) = 0.714 \\ Prob \(>\) F = 0.2399 & Prob \(>\) F = 0.0003 & Prob \(>\) F = 0.4005 \\ \hline \multicolumn{3}{c}{**Modified Wald Test for Groupwise Heteroskedasticity in Fixed Effect Regression Model**} \\ \multicolumn{3}{c}{**(H0: sigma(i)’2 = sigma’2 for All i)**} \\ \hline GreenInv & D(Inv) \\ \(\chi\)2 (83) = 1.6 \(\times\) 10\({}^{8}\) & \(\chi\)2 (83) = 1.5 \(\times\) 10\({}^{6}\) \\ Prob \(>\)\(\chi\)2 = 0.0000 & Prob \(>\)\(\chi\)2 = 0.0000 \\ \hline \multicolumn{3}{c}{**[PERSON]’s CD Test**} \\ \multicolumn{3}{c}{**(H0: No Cross-Section Dependence (Correlation) in Residuals)**} \\ \hline GreenInv & D(Inv) \\ CD\({}_{\rm p}\) = 3.857 & CD\({}_{\rm p}\) = 20.743 \\ Prob = 0.000 & Prob = 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tests for heteroscedasticity, for autocorrelation in panel data, for cross-section dependence in residuals. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **GreenInv** & **D(Inv)** \\ \hline C & 403.526 (2.68) ** & 903.956 (0.26) \\ FDI & 0.003 (3.25) *** & \\ IndProdMining & 0.003 (2.11) ** & \\ PayNegEnvirImpact & 0.722 (3.61) *** & \\ CO\({}_{2}\) & 2.476 (6.37) *** & \\ D(FDI) & & \(-\)0.421 (\(-\)3.05) ** \\ D(FixedAssets) & & 0.028 (2.26) ** \\ \hline Cross-section fixed & yes & yes \\ Period fixed & no & no \\ \hline Adj. R-sq. & 0.66 & 0.30 \\ F-statistic & 132,16 & 11.13 \\ Prob(F-statistic) & 0.000 & 0.005 \\ \hline \hline \end{tabular} Note: values in parenthesis are t-statistics; the result of regression with Driscoll–Kraay standard errors.  
\end{table} 
Table 5: Assessment results (*** \(p<\) 0.01; ** \(p<\) 0.05; * \(p<\) 0.10).
## 4 A New Weight Model Based on SNR
Figure 9a shows the SNR value of PRN 17, one of the satellites used for TEE data processing, where the horizontal axis is the observation time, and the vertical axis denotes the SNR values. The subsidiary graphs in Figure 9b-d illustrate the measurement noise calculated from the SNR values in Figure 9a using the sigma, exponential, and HK models, respectively, with the vertical axis indicating the measured noise. Although the sigma model in Figure 9b determines the measurement noise by distinguishing between the LOS and NLOS signals, the values have a small range between zero and 0.04. Specifically, the differences in the measurement noise between the LOS  
Figure 8: Measurement noise values from the HK model. Figure 7: \(\sigma_{SNR}\) values of the LOS and NLOS signals for PRN 16 at TEE. The straight horizontal line in cyan denotes threshold value of 1.0. The red and blue dots depict the LOS and NLOS, respectively. and NLOS signals were not noticeable; hence, data processing was barely affected. The exponential model shown in Figure 9c has a range of measurement noise that is more than ten times higher than that produced by the sigma model. However, the exponential model cannot clearly distinguish between LOS and NLOS signals. The HK model can separate NLOS signals and produce noticeably high measurement noise values for the NLOS signals. Figure 9: Time series of SNR values and measurement noises of PRN 17 for three different weight models: (**a**) SNR value of PRN 17; (**b**) the measurement noise using the sigma model; (**c**) the measurement noise using the exponential model; (**d**) the measurement noise using the HK model. The color of the dots refers to the color bar in Figure 6.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 5 Experiments and Results
### Attack Simulation  
We simulated various DoS attack scenarios and provide a detailed analysis of them. We implemented at least one representative scenario from each threat category within the HLF environment to evaluate the blockchain network's response. We analyzed their impacts on the blockchain performance using a range of metrics, including throughput, latency, and accessibility of critical HLF components. We performed a regression analysis to model the relationship between attack parameters and system performance metrics.  
The network topology used in this study is illustrated in Figure 1. The default HLF configuration was augmented with three peer nodes (two in Organization 1 and one in Organization 2), three Certificate Authority (CA) nodes (one per organization and one shared), and five nodes dedicated to the Raft consensus mechanism. This modified structure was designed to represent a realistic deployment based on the standard HLF network model. However, each attack might only affect a specific part of this architecture, such as a peer, orderer, or user within an organization. In our experiments, the transactions followed a structured workflow:* Chaincode execution: An application triggered a chaincode function installed on peer nodes, which performed credential verification and permission checks before returning a response.
* Transaction submission: Once validated, the application submitted the transaction to the ordering service.
* Consensus and block creation: The ordering service coordinated a consensus between its nodes. Successful consensus results in the transaction were added to a block, which was then propagated to peers for storage on the distributed ledger.  
For clarity, our attack diagrams focus on the transaction submission and ordering phases, omitting initial validation steps. The message flows illustrate interactions between the application, ordering service, and peers across two organizations, with a channel configuration defining the component relationships. Further details about the simulations and test bed configurations are provided in Appendix A.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
###### Abstract  
With the rapid development of cloud computing, the demand for infrastructure resources in cloud data centers has further increased, which has already led to enormous amounts of energy costs. Virtual machine (VM) consolidation as one of the important techniques in Infrastructure as a Service clouds (IaaS) can help resolve energy consumption by reducing the number of active physical machines (PMs). However, the necessity of considering energy-efficiency and the obligation of providing high quality of service (QoS) to customers is a trade-off, as aggressive consolidation may lead to performance degradation. Moreover, most of the existing works of threshold-based VM consolidation strategy are mainly focused on single CPU utilization, although the resource request on different VMs are very diverse. This paper proposes a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) for PM selection, which can be dynamically adjusted by considering future utilization on multi-dimensional resources of CPU, RAM and Bandwidth. Besides, the VM selection and placement algorithm of VM consolidation are also improved by utilizing each multi-dimensional parameter in DMT. The experiments show that our proposed strategy has a better performance than other strategies, not only in high QoS but also in less energy consumption. In addition, the advantage of its reduction on the number of active hosts is much more obvious, especially when it is under extreme workloads.  
self-adaptive VM consolidation; dynamic multi-thresholds; energy consumption; QoS; IaaS clouds +
Footnote †: journal: Future Internet
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Dataset  
For more accurate predictions of the amount of solar radiation, two types of data are fed to the input of the neural network. One source is regional data, and the other source is point-in-time data. Forecast is only for point data, while regional data are used as local areas around the point data.  
To compare our results with state-of-the-art research, data from India were used. A total of 6 regions were selected with solar collectors located on buildings and 34 regions with large solar farms.  
The entire process of downloading the dataset from NASA, as well as its preprocessing and subsequent export to CSV datasets, was ensured by a separate NASA POWER bot application. The bot application was implemented in the Julia language, which provided parallelism during download as well as data preprocessing. The CSV datasets, created in the previous step, were fed to the learning process in the Google Colab environment.  
The dataset was split into training, validation, and testing sets. When the first 80% of the dataset ordered in time was used for training, the next 10% was used for model validation, and the last 10% created the test set. This particular time-ordered distribution of training, validation, and test sets resulted in tests that aimed to predict the future and, consequently, the learning pushed the model towards forecasting.  
The biggest problem with the NASA POWER dataset is the lack of data to train the complicated Transformer model more accurately. After splitting the dataset into training, validation, and testing sets, only 11,136 examples are reserved for training, which makes training such a complicated model significantly more challenging. However, in the following years, by obtaining new data, this deficiency can be substantially compensated.