esa-sceva/satcom-chunk-collection huggingface dataset:
# RobustQuote: Using Reference Images for Adversarial Robustness
## 4 Experiments
### Robustness Evaluation
According to the definition in [11], we argue that RobustQuote does not prevent the attacker from obtaining useful gradients, as the attack can often be successful when the reference images are the same in attack generation and inference, according to Figure 4. Nevertheless, we evaluated RobustQuote under the various gradient obfuscation benchmarks to understand its limits. Firstly, as shown in Section 3, we employed standard known operations in accordance with [10], suggesting that all operations should be differentiable and kept in a usable range. The first test consists of gradually increasing the perturbation budget \(\epsilon\) until the performance approaches zero. We report the results in Table 2. When \(\epsilon\leq 16/255\), RobustQuote maintained the performance, while we observed an accuracy drop to 2.84% when \(\epsilon=128/255\). This reveals that RobustQuote does provide useful gradients for generating stronger perturbations, taking advantage of a larger budget \(\epsilon\). This result supports our discussion above that RobustQuote does not obfuscate the gradients. We performed experiments with a decision-based black-box attack, i.e., square attack [39]. The number of iterations was set to 1000, with a perturbation budget of \(\epsilon=8/255\). We observe in Table 3 that the square attack fails to reduce adversarial performance compared to the white-box attack (i.e., PGD-20 attack). According to the claim in [10], we again demonstrate that RobustQuote does not obfuscate the gradients. In Table 4, we examine the behavior of RobustQuote under a PGD-based transfer attack. We employed both DeiT3-T without adversarial training and DeiT3-T with FSR [17] as proxy models. We utilized the PGD-20 attack on proxy models with a perturbation  
Figure 4: Performance of adversarial attacks when the attackers do not know and then know the references used during inference while generating the perturbation \(\delta\). budget of \(\epsilon=8/255\), and then directly applied the crafted adversarial examples to the model with a RobustQuote block. The results in the last row of Figure 4 show similar behavior to the black-box attack, showing again that gradient-based attacks still outperform transfer-based attacks. Finally, we evaluated the performance of the targeted adversarial attack, namely APGD-t [21]. This attack was remarkably more efficient than the default PGD attack, confirming our suspicion that guiding the attack toward a single class can effectively focus on a single reference. In Table 2, we observe that APGD-t can achieve better results than the PGD-20 attack, but RobustQuote can still defend against this adaptive adversarial attack. In sum, our results show that RobustQuote exhibits one of the symptoms (PGD-20 attacks perform better than PGD-100) attributed to gradient obfuscation, and we do not strictly adhere to the definition. Based on these results, we believe that RobustQuote offers a brand-new defense strategy that is different from the existing ones.
# A New Criterion for the Splashing of a Droplet on Dry Surface from High-Fidelity Simulations
## 4 Results and Discussion
### Modes of Droplet Impact  
In our simulations, various modes of droplet impacting are observed, including spreading, fingering, receding breakup, partial spreading splash, splash, partial prompt splash, and prompt splash, as shown in Figure 5.  
Due to the primary focus of this study being on the criterion for droplet splashing, in order to obtain more precise splashing thresholds, the control parameters \((We,Re)\) are set around the vicinity of the droplet-splashing threshold. This allows us to reveal new modes of splashing from the simulation results. Definitions of splashing in previous works have been vague. To better identify the modes of droplet behavior after collision, we propose the following precise definition of droplet splashing based on the simulation results.  
The generation of secondary droplets before the droplet reaches its maximum diameter is defined as splashing, as shown in Figure 5c-f. Based on the distribution, morphology, and generation time of the secondary droplets, splashing can be further categorized into partial spreading splashing (late generation time of secondary droplets, relatively large droplet diameter, fewer in number and asymmetric distribution), spreading splashing (late generation time of secondary droplets, relatively large diameter, fewer in number and symmetric distribution), prompt splashing (short time interval for secondary droplet generation after collision, small droplet diameter, more in number, and symmetric distribution), and partial prompt splashing (short time interval for secondary droplet generation after collision, small droplet diameter, more in number, and asymmetric distribution). Crown splashing was not observed in our simulations. Note that we distinguish between spreading splashing and prompt splashing according to the difference described above. However, since prompt splashing is not clearly defined in the literature, it is possible that both spreading and prompt splashing modes here belong to the prompt splashing mode in the literature.  
In our simulations, both prompt splashing and partial prompt splashing occur only in cases where the contact angle exceeds 120\({}^{\circ}\), while spreading splashing and partial spreading splashing do not occur for large contact angles (\(CA>\) 120\({}^{\circ}\)). This implies that large contact angles (hydrophobic surfaces) would suppress spreading splashing and promote prompt splashing, while small contact angles (hydrophilic surfaces) would suppress prompt splashing and promote spreading splashing.
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 2 Theorical Background
### Nonlinear Fatigue Damage Accumulation  
Regarding the nonlinear fatigue damage accumulation, [PERSON] and [PERSON] [20] presented a damage curve model. Then, based on it, [PERSON] and [PERSON] [21] developed the first nonlinear load dependent damage accumulation model as,  
\[D=\sum\left(\frac{n_{i}}{N_{i}}\right)^{C^{i}} \tag{5}\]  
where \(n_{i}\) represents the number of the cycles of applied load at a given stress level \(\sigma_{i}\), \(N_{i}\) are the number of cycles to failure at \(\sigma_{i}\), \(C^{i}\) is the effect of the load sequence and \(D\) s the total damage; after that, much research work has been performed [22; 23; 24; 25] where the damage curve approach proposed by [PERSON] and [PERSON] [17; 26] explains very well the effects of load sequences under two-level loading conditions, and their theory proceeds on the basis that the crack growth is the major evidence of damage [27]. The crack length growth is expressed by  
\[a=a_{0}+(0.18-a_{0})\left(\frac{n_{a}}{N_{f}}\right)^{(\frac{2}{3})N_{f}^{0.4}} \tag{6}\]  
where \(n_{a}\) represents the applied cycles to reach a crack length of \(a\), \(N_{f}\) represents the number of cycles required to reach the fracture and \(a_{0}\) is the characteristic defect length ofthe material when \(N_{a}/N_{f}=0\). Thus, the cumulative damage \(D\) is given by the cycle radio and the crack length.  
\[D=\frac{1}{0.18}\left[a_{0}+(0.18-a_{0})\left(\frac{n_{a}}{N_{f}}\right)^{\frac{ \left(\frac{2}{3}\right)N_{f}^{0.4}}{2}}\right] \tag{7}\]  
By applying Equation (7) in a connection of two levels sequence loading \(A\) and \(B\), it is obtained,  
\[D_{A}=\frac{1}{0.18}\left[a_{0}+(0.18-a_{0})\left(\frac{n_{1}}{N_{f1}}\right)^ {\frac{\left(\frac{2}{3}\right)N_{f1}^{0.4}}{2}}\right] \tag{8}\]  
\[D_{B}=\frac{1}{0.18}\left[a_{0}+(0.18-a_{0})\left(\frac{n_{2}}{N_{f2}}\right)^ {\frac{\left(\frac{2}{3}\right)N_{f2}^{0.4}}{2}}\right] \tag{9}\]  
Now, considering equal damage for the two load levels based on the theory of elasticity and the properties of the material [28], the equivalent damage cycle radio is,  
\[\left(\frac{n_{1}}{N_{f1}}\right)=\left(\frac{n_{2}}{N_{f2}}\right)^{\frac{ \left(N_{f2}\right)}{\left(N_{f1}^{0.4}\right)}^{0.4}} \tag{10}\]  
Consequently, the damage curve is given by the power law equation,  
\[D_{i}=\left(\frac{n_{i}}{N_{if}}\right)^{\frac{\left(N_{if}\right)}{\left(N_{ if-1}^{0.4}\right)}^{0.4}} \tag{11}\]  
where the exponent 0.4 represents a material constant characteristic cause-effect of deformation with cycles applied, and it determines the crack length growth from a microscopic perspective [23]. Now that the sequence is considered, let us present the proposed method where the random vibration PSD load effect is included in the nonlinear cumulative damage analysis.  
Now, the random vibration PSD load effect is included in the nonlinear cumulative damage analysis.
## 2 State of Art
### Limitations and Weaknesses of the Analyzed Models  
The analyzed models present several limitations that affect their ability to provide generalizable and accurate predictions of CO2 emissions in diverse contexts. The DL-DTCEM model [15] focuses on light-duty diesel trucks, using specific data from PEMS and GPS systems, but its generalization is limited to predefined routes and specific vehicle configurations. It does not account for other vehicle types or more complex road conditions (urban, mixed highway routes, etc.), restricting its applicability. The MLP, XGBoost, and SVM models [23] rely on macroeconomic variables (energy consumption, GDP, population, etc.) and perform well only in scenarios where the input data are consistent and homogeneous. However, these models fail to capture local and temporal traffic dynamics, and their dependence on macroeconomic aggregates makes it challenging to use them for predictions at a micro level, such as individual road segments. The random forest model [26] effectively identifies emissions trends based on urban governance factors (city size, economic development, etc.). Still, it is unsuitable for fine-grained predictions at the road segment level due to the absence of specific traffic-related parameters such as traffic flow or speed. Finally, the BiLSTM model [3] effectively models static time series by capturing temporal dependencies. Still, it cannot incorporate spatial variations or interactions between road segments, limiting its accuracy in dense urban environments where the road network structure plays a crucial role.
In addition, they have been built with a technology readiness level (TRL) 1-4, which does not mention the solar tracking precision achieved. As for the STSs with a TRL 5-7, which consist of the prototype demonstration in a relevant environment and/or an operational environment, currently there are only a limited number of experimental or industrial designs and control systems. In [20], three pilots of dual-axis STSs for a parabolic dish concentrator with a closed-loop and open-loop control are presented. These systems were programmed in different control units:a microcontroller, a personal computer and a programmable logic controller. After a comparative study, the authors recommended a system with a cost of EUR1300 and a solar tracking error (STE) of <0.2\({}^{\circ}\). The authors in [21] proposed an open-loop solar tracker to move a Fresnel lens that concentrates the Sun's rays on a Stirling engine's heating head. The reported system reached a temperature of 900 \({}^{\circ}\)C, but the total STE was not measured. Similarly, the design and evaluation of a microcontroller-based smart solar tracker was proposed in [22]. This system employs LDRs to give feedback to a closed-loop control system with an on-off control. The tracker increased the energy production of a PV system, but the STE was not calculated. It is important to mention that none of the aforementioned works have performed the characterization of solar tracking by means of the IEC 62817 standard of design qualification of solar trackers, which defines test procedures for both key components and for the complete tracker system [1]. This standard allows for evaluating and comparing the performance of solar tracking systems using standardized methodologies. In this sense, some works on the implementation of these test procedures for single- and dual-axis STSs have been reported. For example, [PERSON]. [23] performed an experimental evaluation of the mechanical testing of the IEC 62817 standard in a dual-axis solar tracker, and the authors identified several mechanical testing improvements in the standard. Furthermore, [PERSON]. [24] implemented environmental testing of the IEC 62817 standard in a solar tracker driver to validate the tracker design under varying conditions. In addition, the usefulness of the environmental tests of the standard was confirmed. In the same way, the authors in [25] evaluated the general performance of a horizontal single-axis tracker through this IEC standard. They also identified improvements to consider for the next standard editions. Table 1 summarizes some of the reported work on STSs. In this table, the various differences between cost, tracking accuracy, TRL level and characterization methodologies can be seen; therefore, it is not feasible to compare these systems with the proposed STS, taking only one parameter as a reference.
## 4 Prediction Model of the Leakage Current and Example Analysis  
### Prediction Model  
The purpose of this study is to predict the leakage current of insulators based on weather and environmental data using artificial intelligence methods. Due to the complex relationship between leakage current and weather conditions, artificial intelligence methods open up new possibilities for addressing this issue. These algorithms learn from the data and continuously improve their performance. In order to predict the leakage current of the test insulator string at the test station, this study employs time series data modeling. The initial step involves using leakage current and weather data (temperature, relative humidity, wind speed, ultraviolet, and air pressure) from June 2016 to December 2018 (a total of 30 months). The data are then filtered to remove any anomalies during the measurement process. Subsequently, the processed data are utilized for modeling with SVR, GBR, and LSTM methods. Once the models are established, real-time measured weather parameters, including temperature, relative humidity, wind speed, and ultraviolet, can be employed to estimate the leakage current of the insulator. The proposed model is validated using collected measurement data from 2019 at the test station. The output results are evaluated using _MSE_, _MAE_, and _EVS_. Finally, this paper introduces a cloud monitoring platform based on estimated leakage current and weather parameters to assess the pollution level of the insulator. The pollution levels are categorized as Normal, Warning, and Danger, distinguished by colors. The flowchart of the analysis and algorithm modeling is presented in Figure 10.
## 5 Experimental Results and Analysis
### Experiments of Object Detection Algorithm
#### 5.3.2 Comparison and Analysis of the Actual Detection Effect of the Models  
In order to verify the usability of the improved model, this experiment used YOLOv5s, improved YOLOv5s and other versions of the YOLOv5 network model to determine the model's performance regarding the object detection of vehicles in pictures and videos, and judge the actual detection effect before and after optimization. A total of 750 internet vehicle pictures were randomly selected for the actual performance test. The actual detection speed of each network model is shown in Table 7, and the detection effect is shown in Figure 12; the number above each detection box represents the accuracy of the model when detecting the object, and the closer to 1, the more accurate the detection. As we can see from the experimental results, the improved YOLOv5s can detect more vehicles with higher accuracy and a lower detection delay (the average detection speed of each image is 10.9 ms, second only to YOLOv5n), and the average performance is the best. However, except for YOLOv5n, the width and depth of the network of the other models are greater than that of improved YOLOv5s, which means that the volume of the model generated training is also increasing. This suggests that the model contains more parameters and the accuracy of the entire model is further improved, but the detection speed of the model is sacrificed. The truck detection effect of the improved YOLOv5s presented in this paper is slightly worse than that of larger-model-volume models of YOLOv5m, YOLOv5l, and YOLOv5x, which is not very different from that of the original YOLOv5s model and belongs to the normal error range.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**YOLO Algorithms** & **\#Params (M)** & **FLOPs (G)** & \(\text{AP}^{val}_{50.95}\) **(\%)** & \(\text{AP}^{val}_{50}\) **(\%)** \\ \hline YOLOv5s & 7.2 & 16.5 & 35.9 & 56.3 \\ YOLOv8s & 11.2 & 28.6 & 38.8 & 55.8 \\ Improved YOLOv5s & 7.4 & 16.5 & 41.7 & 65.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection performance of the improved YOLOv5s and other models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline YOLOv5l & 0.6 & 30.3 & 1.5 & 31.8 \\ YOLOv5m & 0.7 & 20.2 & 1.5 & 22.4 \\ YOLOv5n & 0.7 & 5.2 & 1.3 & 7.2 \\ YOLOv5s & 0.7 & 10.5 & 1.5 & 12.7 \\ YOLOv5x & 0.6 & 31.5 & 1.5 & 33.6 \\ Improved YOLOv5s & 0.6 & 9.4 & 0.9 & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average detection speed per image (in ms).
# Simple Synthesis Method and Characterizations of Aggregation-Free Cysteamine Capped PbS Quantum Dot
## 2 Materials and Methods
### Synthesis of Aggregation-Free Cysteamine Capped PbS Quantum Dot  
Cysteamine capped PbS quantum dot was synthesized in aqueous solution with sodium docusate as a stabilizing agent. A 0.24 mmol (0.105 g) of sodium docusate salt was dissolved in 50 mL of double distilled water with stirring for 30 min on a magnetic stirrer, then a 3 mL of 0.04 M lead chloride aqueous solution was added to the solution. Since the solubility of lead chloride in water (10.8 g/L, 20 \({}^{\circ}\)C) is poor, it is recommended to keep the stock solution of lead precursors above 40 \({}^{\circ}\)C. After stirring for 10 min, a 0.6 mL of 0.2 M sodium sulfide aqueous solution was added dropwise to the solution and stirred for 10 min. When the sulfur precursor was added, the color of the solution turned dark brown immediately. For ligand exchange, a 1 mL of 0.6 M cysteamine aqueous solution was added into PbS quantum dot colloids and stirred for 10 min. After adding the cysteamine to PbS quantum dots, the texture of suspension was changed. Lastly, a 1 mL of 0.6 M 2-mercaptoethanolethanesulfonate aqueous solution was dropped into cysteamine capped PbS quantum dot and stirred for 10 min, to prevent the aggregation of quantum dots. A brief description of the synthesis method is shown in Figure 1.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 2 Cross-Domain Fusion Constellation Design Method
### Basic Constellation Design
#### 2.2.3 Remote Sensing Constellation Generation Method  
The remote sensing constellation generally focuses on its coverage area and observation resolution. Assuming that the satellite orbit height is \(h\), the Earth's radius is \(R_{e}\), the angle of view of the satellite payload is \(\omega\), the ground coverage width is \(L\), and \(a\) is a semi-major axis, the formula for calculating the satellite coverage is as follows.  
\[\beta=a\sin(\frac{(h+R_{e})}{R_{e}}\sin(\omega)). \tag{7}\] \[L=2R_{e}(\beta-\omega). \tag{8}\]  
\(L\) is the area covered by a single satellite to the Earth. To achieve a single global coverage, the sum of all satellite coverage areas in the constellation is generally twice the total area of the ground [16]. Therefore, the coverage area of the remote sensing payload needs to be doubled. The spatial resolution of remote sensing payload, \(E_{rsl}\), is related to the instantaneous field of view and orbit height, as shown in (9).  
\[E_{rsl}=\frac{\omega h}{\cos\omega}. \tag{9}\]  
In (9), \(\omega\) is related to the sensor size and load focus, and its performance quality is related to the components. As can be seen from Equation (9), the lower the orbit, the higher the resolution that can be obtained. The field angle of remote sensing payload is constrained within the range of \([\omega_{\min},\omega_{\max}]\), and the coverage constraint is shown in (10). Therefore, the optimization of the number of satellites and the orbit altitude are also involved.  
\[R_{\mathrm{cov}}^{rem}\geq 95\%. \tag{10}\]  
The constraint of formula (10) represents the coverage and detection resolution of remote sensing constellation.
# Study of Partial Discharge Inception Voltage in Inverter Fed Electric Motor Insulation Systems
## 2 Literature Review
### Lusuardi Model
\[A(V_{\text{apply}},t_{\text{r}})=aV_{\text{apply}}{}^{b}ct_{\text{r}}{}^{d} \tag{14}\]  
where \(a=1.038\ \times 10^{-4}\), \(b=7.542\), \(c=211.4\), and \(d=-1.149\) with a goodness \(R^{2}\) fitting of 0.9409 based on 100 ns data. \(V_{\text{apply}}\) and \(t_{\text{r}}\) are in kV and ns, respectively. The term \((ct_{\text{r}}{}^{d})\) is a correction function that equals 1 when the rise time is 100 ns. In order to introduce the impact of the pulse width, the authors conclude that the critical number of electrons necessary to trigger the PD is relatively stable regardless of the pulse width and rise time. Hence, the number of critical initial electrons generated can be described by the Equation (15). The term \((V_{\text{inc}}t_{\text{r}}/0.8V_{\text{apply}})\) is negligible compared to the pulse width (in \(\upmu\)s). Thus, based on Equations (14) and (15), the relationship between PDIV, pulse width \(t_{\text{w}}\) (in \(\upmu\)s) and rise time \(t_{\text{r}}\) (in ns) can be defined as in Equation (16):  
\[N_{o}(t_{\text{w}})=A\left(t_{\text{w}}-\frac{V_{\text{inc}}t_{\text{r}}}{0.8V _{\text{apply}}}\right) \tag{15}\]  
\[\text{PDIV}=\left(\frac{N_{o}(t_{\text{w}})}{ac}\right)^{\frac{1}{b}}t_{\text {w}}{}^{-\frac{1}{b}}t_{\text{r}}{}^{-\frac{d}{b}} \tag{16}\]Intending to simplify the model, the authors also propose an alternative considering a reference value (Equation (17)), being \(n=-0.1142\) and \(m=0.01818\) with a goodness \(R^{2}\) fitting 0.9484. \[\text{PDIV}=\text{PDIV}_{\text{t}_{\text{w}}=100\text{u}\text{s},t_{\text{r}}= 120\text{ns}}\bigg{(}\frac{t_{\text{w}}}{100}\bigg{)}^{n}\bigg{(}\frac{t_{\text{ r}}}{120}\bigg{)}^{m} \tag{17}\]  
This model is suitable only for cases in which there is neither an overvoltage in the applied voltage waveforms nor space charge accumulations. Indeed, the authors mention that the space charge distribution can be different depending on the rise time, due to the fact that the rise time can impact the electron generation rate. Moreover, as the model is developed for a specific insulation enamel material at room temperature, the constant values (\(a,b,c\), and \(d\)) would possibly not fit other insulation enamels and temperatures, so new experiments would be necessary to adjust the model. The temperature will most likely change the \(A\) values, as \(V_{\text{inc}}\) would be reduced with the increase in temperature. Nonetheless, the main benefit of this model is that it offers a good approximation for the estimation of PDIV in a relatively simple way, considering rise time and pulse width at ambient temperature for a specific enamel.