esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Key Parameters of Typhoons and Probability Distribution Models  
When the linear trajectory model is employed for typhoon simulation, individual typhoon events are characterized by a set of key parameters: annual occurrence rate (\(\lambda\)), central pressure deficit (\(\Delta p\)), translational velocity (\(V_{T}\)), bearing angle (\(\theta\)), and minimum approach distance (\(D_{min}\)). The optimal distribution for each parameter was determined through Kolmogorov-Smirnov (KS) goodness-of-fit tests at the 5% or 1% significance level. Parameters that failed statistical validation were modeled via empirical distributions.  
The annual occurrence rate \(\lambda\), defined as the annual frequency of typhoon impacts on the target location, is quantified by counting typhoon tracks intersecting the 250 km simulation circle. This parameter controls the annual typhoon count generated through Monte Carlo simulations. Common distributions include Poisson, binomial, and negative binomial distributions.  
The central pressure deficit \(\Delta p\) represents the difference between the typhoon's minimum central pressure and the ambient atmospheric pressure at the outermost closed isobar. Following the recommendations of [PERSON] [29] for the Northwest Pacific region, the peripheral pressure is set to 1010.0 hPa. The candidate distributions include log-normal, gamma, and three-parameter Weibull distributions.  
The translational velocity \(V_{T}\) is calculated as the ratio of the displacement distance to the time interval between consecutive records (6-hourly or 3-hourly) in the dataset. Potential distributions include log-normal, normal, and gamma distributions.  
The movement direction \(\theta\), measured clockwise from true north (0\({}^{\circ}\)) within the range [\(-\)180\({}^{\circ}\), 180\({}^{\circ}\)], is evaluated via normal, bimodal, and von Mises distributions.  
The minimum distance \(D_{min}\), defined as the perpendicular distance from the target location to the typhoon track (positive values indicate left-side passages relative to the movement direction), is modeled with uniform, trapezoidal, and linear distributions.
# Motion Simulation and Human-Computer Interaction System for Lunar Exploration
## 2 Materials and Methods  
### System Architecture and Interactive Operating Platform  
To achieve high-fidelity lunar exploration simulation and meet training needs, the core of the system is the creation of lunar scenes and the motion simulation of a rover. The physical effects of objects on the moon are different from those on the earth. We built a simulation environment with Unity3D, adding elements such as gravity and interactions between objects to form a physical system.  
As shown in Figure 1, the system includes an interactive operating platform and a lunar surface simulation environment.  
The interactive operation platform is composed of a human-computer interaction module, a master computer, and a platform control module. The master computer receives data from the interactive operation platform and sends the posture information of the rover to the platform control module. The distributed controller controls the motion of the 6-DOF platform and simulates the tilt, vibration, acceleration, and deceleration of the rover.  
The interactive operation platform can accurately reflect the motion state felt by astronauts. The hardware component includes a 6-DOF motion platform at the bottom and a console at the top. The 6-DOF platform is shown in Figure 2.  
The size of the 6-DOF motion platform is 1000 \(\times\) 1000 \(\times\) 740 mm, which can carry a load of 400 kg. Six hydraulic cylinders drive the platform to move. The pitch, roll, and yaw angles of the platform can reach up to 26\({}^{\circ}\); the maximum displacement in the \(X\) and \(Y\) directions is 280 mm, and in the \(Z\) direction is 300 mm. We choose an LJ-EC-80-300 electric cylinder, which has a displacement accuracy of 0.1 mm and an angular accuracy of 0.1\({}^{\circ}\). The platform has a response speed of 0.01 s, which can provide astronauts with a high-fidelity and continuous driving experience.  
Astronauts use a force feedback handle to control the forward, backward, acceleration, deceleration, steering, and braking of the rover. We designed and implemented the force feedback method. The handle generates feedback force according to the slope of the terrain and generates damping according to the resistance received when turning. The above  
Figure 1: Schematic diagram of the system.  
Figure 2: 6-DOF motion platform.  
functions can provide astronauts with intuitive feedback on the terrain, allowing astronauts to clearly know the terrain conditions around the rover.  
As shown in Figure 3, the operating platform is built on a 6-DOF platform. We designed a vision system to provide real-time wide-angle vision and driving information such as speed and path planning. To improve the interaction efficiency, we designed a touch screen, buttons, and voice modules to form a multi-channel human-computer interaction module.
# Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR
## 3 Contrastive Learning-Based Intrusion Detection Model for Industrial Control Systems
### Training Contrast Learning Models
#### 3.2.4 Contrast Loss Function  
The contrast loss function is a loss function with the self-discovery property of difficult negative samples, which is essential for learning high-quality self-supervised representations, and a loss function without this property can significantly deteriorate the performance of self-supervised learning.  
The loss function used is InfoNCE Loss, which is calculated as shown in Equation (10):  
\[L_{i}=-\log\left(\exp\bigl{(}S\bigl{(}z_{i},z_{i}^{+}\bigr{)}/\tau\bigr{)}\,/ \sum_{j=0}^{K}\exp\bigl{(}S\bigl{(}z_{i},z_{j}\bigr{)}/\tau\bigr{)}\right) \tag{10}\]  
where \(S\bigl{(}z_{i},z_{i}^{+}\bigr{)}\)denotes the similarity of the feature vectors of the same sample after data enhancement, that is, the degree of similarity between positive examples. \(S\bigl{(}z_{i},z_{j}\bigr{)}\) denotes the similarity of the feature vectors of one data-enhanced sample to the feature vectors of other enhanced samples. \(\tau\) denotes the temperature coefficient, which is a hyperparameter that controls how well the model discriminates between negative samples. A small temperature coefficient will make samples that are more similar to this sample separate, that is, be able to be classified more evenly. \(S\) is the calculation of similarity, which is usually calculated by using the dot product operation after the L2 norm or by using cosine similarity. The calculation formula is shown in Equation (11):  
\[S\bigl{(}z_{i},z_{i}^{+}\bigr{)}=z_{i}^{T}z_{i}^{+}/\left(\|z_{i}\|_{2}\|z_{i} ^{+}\|_{2}\right) \tag{11}\]
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 2 Background
### Comparative Analysis and Innovation Highlights  
The field of cyber threat intelligence (CTI) sharing has witnessed significant advances in recent years, yet several limitations persist in the existing frameworks. A comparative review of the related work reveals key challenges that have guided the design of the TrustShare framework:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
* **Limited Support for Fine-Grained Access Control:** Many existing blockchain-based CTI systems lack attribute-based access policies, instead relying on static role-based models. This restricts flexibility in cross-organizational sharing and fails to meet the compliance needs for highly regulated sectors.
* **Inadequate Alignment with the GDPR and Data Sovereignty Requirements:** The ability to enforce consent-driven auditable access to sensitive data is often missing in prior work. Systems without privacy-preserving encryption or explicit access constraints risk breaching regulatory obligations.
* **Absence of Integrated Trust Models for Data Providers and Consumers:** Most platforms assume all nodes are equally trusted or use binary trust evaluations. This oversimplifies real-world collaboration, where entities have varying reputations and behavior patterns. In response, TrustShare introduces the following innovations:
* **CP-ABE-Based Fine-Grained Encryption:** By integrating Ciphertext-Policy Attribute-Based Encryption (CP-ABE), TrustShare allows data owners to define expressive access control policies directly embedded in the encrypted payloads, ensuring only authorized recipients can decrypt shared intelligence.
* **Hybrid Trust Scoring Model:** We propose a novel combination of direct and indirect trust metrics to assess participant reliability over time. Trust scores influence access and endorsement privileges, thereby promoting collaboration only among reputable parties.
* **Smart Contract-Enforced Policy Compliance:** Smart contracts codify organizational and regulatory policies (e.g., minimum trust thresholds and data handling restrictions), enabling automated verifiable enforcement and audit logging on the blockchain. Table 5 summarizes the differences between TrustShare and the existing frameworks across key functional dimensions. These innovations collectively enable secure, compliant, and trust-aware CTI sharing suitable for deployment in multi-stakeholder environments such as national CERTs, healthcare consortia, and financial threat sharing alliances.
## 3 Results and Discussions
### The Effects of Single Uncertainty  
The single uncertainty of the physical property of nanofluid has been analyzed in the last section. Thus, another type of uncertainty is considered in this section. As the manufacturing error constantly happens, the uncertainty in geometric parameter of the microchannel needs to be taken into account. To demonstrate the effect of manufacturing error, the uncertainty in the depth of the groove is considered. A deviation of 20% in the depth of the groove is chosen. Comparisons between the results of IM and those of SM are summarized in Table 5. A good agreement can be observed in Table 5, which proves the accuracy of IM. The uncertain temperature, velocity, and local Nusselt number solutions are shown in Figures 5 and 6. Different from the effects of uncertainty in \(\varphi\), the depth of the groove seems to have significant influence only on the variables of the local region while the solid volume fraction can influence the variables of the global region. The bound of temperature and velocity in Figure 5 is narrower than that in Figure 3, which means the depth of the grooves has a lesser impact on the temperature and velocity of nanofluid than that of the solid volume fraction of nanofluid. The depth of the grooves just arouses obvious variation of velocity and the local Nusselt number where the groove occurs. In addition, for the local Nusselt number, the upper wall presents a more obvious variation than the lower wall. In general, the uncertainty in the depth of the groove has a less impact on the thermal performance and flow behavior than that of the solid volume fraction of nanofluid.
## 4 Experiments and Discussion
### Training Performance  
During the training episodes, the TD3 approach is executed for 400 episodes each for env1 to env4. The resulting converged models are named TD3-env1, TD3-env2, TD3-env3, and TD3-env4, respectively. For the FL-TD3 approach, we enable four UGVs to operate simultaneously in env1 to env4. After 400 episodes, we can obtain a global model. Similarly, we train the FGRL approach with four UGVs running in parallel in the same way. After 400 training episodes, the global model for FGRL is also obtained.  
In order to evaluate the training performance, we record the average rewards per round for both of them across env1 to env4. Figure 6 presents the training curves of three approaches, where the \(x\)-axis denotes the number of rounds and the \(y\)-axis denotes the average reward. The solid line and shaded regions depict the mean and standard deviation, respectively, derived from ten independent runs. In terms of the convergence of the average reward, FL-TD3 exhibits similar performance to TD3 in env1, env2, and env3 but outperforms TD3 in env4. Comparatively, our FGRL demonstrates the best performance across all four environments. As for the convergence speed, TD3 achieves convergence in approximately 150 episodes for env1 and 200 episodes for env2, env3, and env4. Conversely, both FL-TD3 and our FGRL converge in approximately 100 episodes.
## 5 Experimental Results and Analysis
### Comparative Results and Analysis of Machine Learning Model Performance  
In the study of traffic black spot recognition for each road intersection, it was found that, among the four models, namely SVM, GBDT, RF, and fully connected neural network, the random forest model was the most effective for traffic black spot recognition, with an accuracy rate of 87%, and at the same time the value of the loss was relatively small, and the results of the evaluation of the various models are shown in Table 23, which demonstrates that it is possible to utilize the characteristics of the built environment of the roads at each  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1\_Score** & **Loss (Mean)** & **Loss (Max)** & **Loss (Min)** & **Loss (Std)** \\ \hline SVM & 81.14\% & 74.12\% & 63.08\% & 41.86\% & 6.513 & 7.346 & 5.658 & 0.269 \\ \hline GBDT algorithm & 85.58\% & 85.02\% & 74.32\% & 63.99\% & 4.636 & 5.795 & 3.741 & 0.430 \\ \hline Random forest algorithm & 87.19\% & 86.12\% & 80.07\% & 75.06\% & 3.725 & 4.928 & 2.738 & 0.335 \\ \hline Fully CNN & 84.54\% & 83.04\% & 73.57\% & 68.09\% & 4.884 & 5.973 & 3.634 & 0.421 \\ \hline \hline \end{tabular}
\end{table}
Table 22: Different model evaluation metrics and loss function values.  
road intersection to conduct the study of traffic black spot recognition by means of the results of the above experiments.  
The results of the above models show that, in the case of only considering the built environment of urban roads with only a single intersection, the random forest model is the most effective, with an accuracy rate of about 87%, while the precision rate and the recall rate both reach more than 80% at the same time, and the accuracy rates of the other models reach more than 80% as well. The above information fully exemplifies the feasibility of using street view images to identify the traffic black spots at road intersections.  
However, all of the above machine learning models only recognize the road intersections in each study area as an independent individual, and have not yet considered the influence of the interconnections between the road intersections on the recognition of traffic black spots; therefore, in the next section, we will analyze the spatial correlation of the traffic black spots to show that there is a clustering effect between the traffic black spots.
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
## 3 Scenario-Based Probabilistic Modeling of Reliability Solutions with ESS
Use the scenario reduction method [10; 21] on both sets obtained from Step 2 and merge them to obtain the representative scenario set of DG output (\(d\)) and load (\(l\)); \(SC_{d,l}=\left\{SC^{st}_{d,l^{\prime}},P^{st}_{sc,dgl^{\prime}},P^{st}_{sc,dgl^{ \prime}},P^{st}_{sc,dgl^{\prime}},P^{st}_{sc}\right\}\). Here, \(sn\) represents a scenario number representing the particular DG output \(P^{sn}_{sc,dgl}\) and \(P^{st}_{sc,dgl^{\prime}}\), and load level \(l^{sn}_{sc}\). \(\pi\) represents the probability of a scenario under consideration. Two types of renewable resources (Photovoltaic and Wind) are denoted with \(dg1\) and \(dg2\). The output of all DGs for a scenario (SC) can be combined to get DG output \(P^{st}_{sc,dgl}\). 4. Incorporate ESS in the scenario set obtained above as follows: 1. Evaluate the power required from ESS, for a scenario \(sn\); \(P^{ES,req}_{sn}=(l^{sn}_{sc}-P^{sn}_{sc})\) 2. Evaluate \(chk_{p}=P^{ES}_{\pi\omega}-P^{ES,req}_{sn}\). The available power of ESS (\(P^{ESS}_{\pi\omega}\)) depends on hardware availability and rated power. 3. If \(chk_{p}<0\), assign \(\pi^{sn}_{ES}=\pi^{sn}_{sc}\), else; \(\pi^{sn}_{ES}=\pi^{sn}_{sc}\)\(e^{-(D^{ESS}_{\pi\omega}/r^{\prime}_{l})}\) where \(r^{\prime}_{l}\) is the average outage time for load point \(l\) due to contingency without considering ESS operation. Discharge duration available (\(D^{ESS}_{\pi\omega}\)) for a scenario \(sn\) is given by \(D^{ES}_{\pi\omega}=\max\left(\frac{\eta\times SOC_{\pi\omega}+\frac{P^{ES}_{ \pi\omega}+\frac{T^{ES}_{\pi\omega}}{\min\left(P^{ES,req}_{\pi\omega}\right)}, 0\right)}}{\min\left(P^{ES,req}_{\pi\omega},P^{ES}_{\pi\omega}\right)}\), where \(\eta\), \(T^{ES}_{rated}\), and \(SOC_{ini}\), respectively, denote efficiency, discharge duration at rated ESS power, and SOC of ESS (expressed in per unit). * Form an event tree considering all possible events (\(Ex\)) following an occurrence of \(C^{x}\). It incorporates the status of fault isolation and presence of mitigation measures, hardware availability, capacity state of ESS, different scenarios of DG output level and load level, as shown in Figure 4.
* Evaluate the probability of events, \(P(Ex)\), where \(x\) is event number ranging from 1 to a total number of events (\(n\)). * Pick an event (from (\(E1\)) to (\(En\))) from an event tree and execute Algorithm 2 to find the probability of reliability event (\(RE\)) not being mitigated for a load point as given in (13). \[P_{l,RE}^{c,x}(Ex)=P(Ex)\times\pi_{ES}\] (13)
* Repeat Step 2-Step 3 until all scenarios of the event tree are covered, and update (14). \[P_{l,RE}^{c,x}=\sum_{x=1}^{n}P_{l,RE}^{c,x}(Ex)\] (14)
* Modify frequency/duration indices in (1)-(4) as represented by (15)-(16). \[F_{l,RE}^{c,x}=F_{l,RE}^{c,x}\times P_{l,RE}^{c,x}\quad\forall\ RE\in\{{\it{ DSE},\ MI,\ SI}\}\] (15) \[U_{l,SI}^{c,x}=U_{l,SI}^{c,x}\times P_{l,SI}^{c,x}\] (16)
## 4 Results and Discussion
### Acoustic Characteristics of Different Configurations
#### 4.2.3 Aerodynamic Noise Directivity  
To reveal the difference in aerodynamic noise directivity for different configurations, the pantograph is considered as the sound source and sound monitoring points arranged every \(10^{\circ}\) on a circle with a radius of 7.5 m are used. As a result, 36 monitoring points (\(0^{\circ}\leq\theta\leq 360^{\circ}\)) are set in X-Y plane, and 19 monitoring points (\(0^{\circ}\leq\theta\leq 180^{\circ}\)) are set in X-Z and Y-Z planes, respectively. The overall sound pressure level (OASPL) in the three planes is calculated and compared in Figure 16. It can be observed that in the X-Y plane, the noise directivity of cases 1 and 3 is similar, and distinct variations that the OASPL first decreases and then increases appear between \(50^{\circ}\leq\theta\leq 90^{\circ}\), which might be caused by the asymmetry of insulator 1. When the pantograph is installed on the sinking platform, the noise directivity is different from the other two cases, and its variation range is within \(60^{\circ}\leq\theta\leq 90^{\circ}\). In the X-Z plane, as shown in Figure 16b, the OASPL within \(90^{\circ}\leq\theta\leq 180^{\circ}\) is higher than within \(0^{\circ}\leq\theta\leq 90^{\circ}\) for all cases, probably due to the closer proximity to the insulators in this region. In the Y-Z plane, the noise directivity of the three cases is almost symmetrical, and the higher OASPL is mainly distributed above the pantograph, which is \(80^{\circ}\leq\theta\leq 100^{\circ}\).  
Of note, in the planes of Y-Z and X-Z, for all monitoring points, the highest OASPL is found for the pantograph alone on the roof, followed by the configuration with the sinking platform on the roof, and the lowest OASPL is found for the pantograph with the fairing. This performance signifies that the fairing effectively reduces the noise induced by the pantograph itself. Meanwhile, the pantograph fairing could play a certain sound barrier role, as well. However, as mentioned above, the noise caused by the fairing itself makes a great contribution and should be considered in a practical scene.
# Secure Speech Content Based on Scrambling and Adaptive Hiding
## 2 Imitation Property of Speech Signals  
The imitation property of speech signals was presented by first time in 2012 by [PERSON] and [PERSON] [8], with the hypothesis that any speech signal may seem similar to another speech signal if its wavelet coefficients are sorted. In the second approach with this property (2014), the ability of adaptation was used as a scrambling mechanism for obtaining private messages [11]. The authors analysed that the obtained scrambled signal is unconditionally secure in terms of [PERSON]'s theory. A generalization of the imitation property of speech signals was proposed by [PERSON], [PERSON] and [PERSON] in 2016, with the hypothesis that a speech signal with intelligible content can imitate a Gaussian noise signal if the entropy of both signals is similar [12]. Unlike the first hypothesis of imitation, it can be applied in the time domain and, on the other hand, it is feasible that a speech signal imitates not only another speech signal but also a Gaussian noise signal.
## 2 Integrated Dynamics Model of the Helicopter Engine/Transmission System/Rotor
### Turboshaft Engine Modeling  
A turboshaft engine is a standard power source for helicopter power systems. It is designed to maintain a consistent output speed during its operation and to swiftly respond to load changes without surpassing its maximum torque limit. When there is a change in the power demand of the main rotor, the fuel injection is adjusted to ensure a consistent rotor speed across various flight conditions [24]. A general gas engine model is adopted to adjust the relationship between the torque \(T_{e}\) and rotational speed \(\Omega_{\rm{e}}\) by using the fuel flow signal \(Fue\). The physical fuel flow signal input specifies the normalized engine torque. The demand power \(P_{e}\) of the engine is a function of the output speed \(\Omega_{\rm{e}}\), namely \(g(\Omega_{\rm{e}})\), At steady state, \(P_{e}(\Omega_{\rm{e}},Fue)=Fue\times g(\Omega_{\rm{e}})\). When the engine speed is in the operating range, the engine power is continuously output, assuming that at \(\Omega_{\rm{e0}}\), the maximum output power is \(P_{\rm{emax}}\), defined \(w=\Omega_{\rm{e}}/\Omega_{\rm{e0}}\), \(g(\Omega_{\rm{e}})=P_{\rm{emax}}\times p(w)\), and \(p(1)=1\), \({\rm{d}}p(1)=0\), as follows:  
\[T_{e}=\frac{P_{\rm{emax}}}{\Omega_{\rm{e0}}}\times\frac{p(w)}{w} \tag{1}\]  
The function \(p(w)\) takes the form of a cubic polynomial, as follows:  
\[p(w)=p_{1}\times w+p_{2}\times w^{2}-p_{3}\times w^{3} \tag{2}\]  
\[p_{1}+p_{2}-p_{3}=1 \tag{3}\]  
\[p_{1}+2p_{2}-3p_{3}=0 \tag{4}\]  
The relationship between engine demand torque \(T_{e}\) and output speed \(\Omega_{\rm{e}}\) is shown in Figure 5.  
The boundary is defined as: \(0\leq\Omega_{\rm{emin}}/\Omega_{\rm{e0}}\leq w\leq\Omega_{\rm{emax}}/\Omega_{ \rm{e0}}\). At the minimum rotational speed, the engine power \(p(w_{\rm{min}})\geq 0\), and the following holds.  
\[p(w_{\rm{min}})=p_{1}\times w_{\rm{min}}+p_{2}\times w_{\rm{min}}^{2}-p_{3} \times w_{\rm{min}}^{3}\geq 0 \tag{5}\]  
Figure 4: General model for a helicopter’s main power reduction system.  
Figure 5: Typical engine power demand curve.  
Similarly, the engine power at the maximum speed must be non-negative: \(p(w_{\max})\geq 0\), and the following holds.  
\[p(w_{\max})=p_{1}\times w_{\max}+p_{2}\times{w_{\max}}^{2}-p_{3}\times{w_{\max}}^ {3}\geq 0 \tag{6}\]