esa-sceva/satcom-chunk-collection huggingface dataset:
# A Promising Technological Approach to Improve Indoor Air Quality
## Results
### Lab and Real-Scale Photocatalytic Performance  
In order to estimate the background, experiments in the absence of the photocatalytic paint were performed. The photocatalytic experiments determine the total pollutant degradation involving both UV photolysis and photocatalysis on the photo-paint. The net photocatalytic effect is calculated by the subtraction of the background contribution from the photocatalytic experiments. More specifically, blank tests were carried out by polluting the reactor with NO and Toluene in the absence of the  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Value** & **Test Method** \\ \hline Viscosity at 25\({}^{\circ}\)C (KU) & 100–110 & ASTM D 562 \\ Density at 25\({}^{\circ}\)C (kg/l) & 1.55 & ISO 2811 \\ Fineness and Dispersion & \(<\)40 \(\upmu\)m & ASTM D1210 \\ pH at 25\({}^{\circ}\)C & 8.5–9 & ISO 787-9 \\ PVC (\%) & 66 & Calculated \\ Usage rate for a 50 \(\upmu\)m dry film thickness (m\({}^{2}\)/kg) & 5.95 & Calculated \\ \hline \end{tabular}
\end{table}
Table 1: Physicochemical Properties of photo-paint.  
Figure 4: (a,b) SEM image of TCM-1 and EDX analysis.  
photocatalyst without and with irradiation, respectively; then, the same experiments were carried out in the presence of the photocatalysts.  
Figure 5a,b present the elimination of NO and toluene under the irradiation of the photo-paint from Vis-light. It is obvious that just after the irradiation of the sample (Time 30), a sharp decrease in NO and a smaller but significant decrease in toluene concentration is observed, which demonstrate the immediate response of the photocatalytic system and provide the photo-efficiency of the paint.  
The variations on NO and Toluene concentrations during the experimental procedure of photocatalysis are presented in Figure 5a and Figure 5b, respectively. At first, the pollutant was introduced into the photo-reactor and the system remained in equilibrium for 30 min. Then, irradiation was followed and a sharp reduction in NO and significant but less intense than NO elimination in toluene concentration is observed, which stabilized after 3 h and remained stable for approximately 2 h where the irradiation stopped. The results from the laboratory tests showed that for both pollutants, the photocatalytic paint gave very promising results. It is worth to note that in the case of toluene, the degradation efficiency is significantly lower than NO. However, a reduction of VOCs even at that level could have significant effects in the improvement of IAQ. Furthermore, the lower degradation efficiency in VOCs compare with NO is in line with the results of various studies [35; 36; 37; 38]. The latter could be attributed to the low adsorption capacity of toluene molecules at the catalyst surface. The extremely hydroxylated surface of the paint material due to physically or chemically bound water, could constrain its interaction with the active radicals through surface diffusion [39; 40].
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 3 Antenna Selection Schemes for FGSM
### ML Methods
#### 3.4.3 DT  
Tree-structured classifiers can classify a dataset using internal nodes as attributes, branches as decision rules, and leaves as the inference [21; 28]. Two nodes exist in a decision tree--the leaf node and the decision node. Decision nodes make decisions and have multiple branches, whereas leaf nodes generate the output of that decision process and do not contain any additional branches. In a decision tree, an initial question is asked, and based on the answer (yes/no), subtrees are constructed. The attribute value with the highest information gain will be chosen for further branching. The probability \(p_{l}\) of a sample being owned by a specific class \(l\) is computed using  
\[p_{l}=\frac{freq(l,T)}{|T|}, \tag{31}\]  
here, \(|T|\) is the number of occurrences in \(T\). \(\text{freq}(l,T)\) represents the number of samples owned by a particular class \(l\). An entropy of \(T\) is given as  
\[EN(T)=-\sum_{l=1}^{N_{t}}p_{l}\log_{2}p_{l}. \tag{32}\]  
The dataset \(|T|\) is split into \(X\) partitions depending on the domain values of a non-class attribute \(G_{l}\). The entropy associated with this is determined using  
\[EN(G_{l},T)=-\sum_{k=1}^{X}\frac{|T_{k}|}{|T|}EN(T). \tag{33}\]  
For attribute \(G_{l}\), information gain is computed using  
\[IFG(G_{l})=EN(T)-EN(G_{l},T). \tag{34}\]  
The information gain is computed for every feature-value combination. When splitting the root node, the feature-value combination that yields the highest information gain is chosen. The process is repeated at all decision nodes. Repeat this process until the maximum depth has been reached. Since the gain will not rise beyond a certain depth, the depth of the DT is regarded as a hyperparameter in the learning process.
# Bi-Objective Optimization for Joint Time-Invariant Allocation of Berths and Quay Cranes
## 3 Description and Formulation of BACASP
### Assumptions and Constraints  
Given the complexity of the BACASP, in order to simplify the analysis and ensure the feasibility of the solution, we introduce the following assumptions:  
* Each vessel cannot berth prior to arrival time and cannot berth outside the quay;
* Once berthed, a vessel cannot interrupt its processing and change its position;
* The safety distance is included in the length of each vessel;
* The processing time of each vessel is related to the berthing position and the number of assigned QCs;
* The number of QCs assigned to each vessel remains constant during its processing, which is referred to as a time-invariant assignment;
* Each vessel has a minimal and maximal number of QCs that can be assigned;
* Each position at the terminal is occupied by one vessel, at most, at any moment;
* The QCs can move along the rail but cannot cross each other.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 5 Discussion  
The advantages of our proposed EDC-Net for point cloud edge detection are based on the design of capsule network for edge detection from point clouds to disentangle geometrical features of point clouds. Furthermore, our proposed loss function improves the training process by emphasizing the prediction of edges and boosts the training for an earlier convergence.  
The notable contribution of our work lies in presenting a novel learning framework for detecting point clouds edges, and tackling the challenges of the lack of annotated data for this task. However, there are still multiple limitations in this work--namely, large-scale point clouds require different implementations and network architectures and yet there is not any public dataset available for the edge detection of large-scale point clouds. In this work, we considered the scale of point clouds based on 1024 and 2048 point samples, which are the most common point sample scales in this community for single object point clouds. Building a framework to tackle multiple objects in a large-scale point clouds is still challenging. Therefore, for the future work, we plan to apply this technique to some large-scale scene point clouds instead of just focusing on single object CAD models. Furthermore, we plan to apply some unsupervised techniques inspired by [56] in order to train an edge detection model regardless of the ground-truth data.  
Overall, the results of our proposed method are promising and constitute the first steps towards reliable point cloud edge detection based on capsule network and weakly-supervised transfer learning approaches. Our work opens a new research path in point cloud edge detection and poses novel research questions with available trends for further improvement.  
Figure 4: (**Left**) Training loss of EDC-Net for various \(\alpha\) and \(\gamma\) during training on the ABC dataset. (**Right**) Comparison of the accuracy (\(F_{1}\)) of EDC-Net on ShapeNet dataset based on various numbers of neighbors (\(K\)) and a comparison to demonstrate the importance of WSL (weakly-supervised learning) to improving the accuracy on ShapeNet dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **EDC-Net (Ours)** & **EC-Net [21]** & **PIE-Net [17]** \\ \hline Point classification (s) & 0.3 & 0.8 & 0.5 \\ Model size (MB) & 1.8 & 9.4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Complexity analysis of EDC-Net.
# PALANTIR: An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
## 4 PALANTIR General Architecture for Threat Remediation
### Design and Operation of Security Enablers  
The SC architecture features two main modules: the Security Element Manager (SEM) and the xNF. The xNF is the recipient that contains the logic of the security enabler (e.g., a firewall), whilst the SEM connects the internal xNF and the PALANTIR components. Practical benefits of the SEM are the management of the life cycle and the configuration of the SCs, but also the exposure of the internal data collected in the xNF (e.g., for SCs detecting threats or forwarding relevant data from the client network to other PALANTIR components). When deployed, SEM and xNF are allocated together into an NS, which is the minimal deployment unit that the SO (and, in general, the underlying MANO) can understand. Finally, the NS is instantiated in a container-based SCHI, such as one deploying containers on top of K8s.  
Essentially, PALANTIR covers two types of SCs to implement detection and mitigation capabilities: (i) mechanisms collecting data and monitoring network/host traffic to detect threats and anomalous behaviours; and (ii) security enablers with capacities of mitigating threats. The first type typically encompasses Intrusion Detection Systems (IDS), Deep Packet Inspection (DPI), and Data Collectors (DCs), whilst the latter may contain the Firewall and Router (FW) and SIEM solutions (e.g., Wazuh).  
Each SC exposes interfaces against the PALANTIR platform, where the most relevant is that between the SO and the message (event) broker shared between the components in the PALANTIR platform. On the one hand, the connection with SO is used to manage the SC life cycle and trigger Day-0, Day-1 and Day-2 cloud-based configuration actions, as detailed above. To that end, each SC implements an operator container (through the Juju tool) that establishes a connection towards the xNF to execute commands and apply configurations to it. On the other hand, the SC sends relevant information for the PALANTIR components to a shared message broker. Such information is provided by the monitoring SCs (generated alerts) and via collectors' SCs (retrieving sniffed traffic by means of NetFlow data, Zeek data, etc.).
# Advancing Copper Wire Technology: Graphene/Cu Composites for Superior Conductivity and Strength
Mechanical ball milling can enhance the dispersion of graphene within the copper matrix and allow for controlled compositions, but it tends to significantly damage the graphene structure. While the PVD method allows for the production of high-purity graphene films with precise thickness control, it has poor step coverage, limited process repeatability, and high production costs. In contrast, the use of CVD to prepare high-quality graphene/copper (Gr/Cu) composite powders has emerged as a novel approach to overcome the issue of graphene aggregation [8; 25; 26; 27; 28]. Combined with powder metallurgy, this method has proven effective in fabricating high-performance Gr/Cu composite wires at an industrial scale. The fabrication process of Gr/Cu composite materials involved the use of CVD to prepare composite powders, which were subsequently processed into composite wires using powder metallurgy and annealing techniques. This study methodically examined the effects of different shapes and graphene contents in the Gr/Cu composite powders, focusing on how these variations influenced the structure, morphology, and performance of the composite wires. A key finding was that high-quality graphene not only facilitated electron transport but also strengthened the copper matrix through grain refinement and effective load transfer mechanisms. The performance outcomes of these annealed composite wires were notable, with electrical conductivity exceeding 100% IACS and tensile strength surpassing 238 MPa. These enhancements in properties make the Gr/Cu composite wires highly suitable for advanced applications, such as motor windings, high-speed train contact wires, and lead frame technologies, where superior mechanical and electrical properties are crucial. The successful integration of graphene into the copper matrix and the resulting performance characteristics provide a strong foundation for the future application and development of high-performance Gr/Cu composite materials. This sets a promising trajectory for further research and industrial use, aiming to optimize and exploit the unique properties of graphene within copper composites for more demanding technological applications.
## 3 Similar Simulation Test Analysis
### Analysis of Test Results  
#### 3.4.1 Deformation and Damage Analysis of the Overburden  
Following the completion of bottom pulling in the model, the damage patterns of the ore body and overburden rock are observed, and the collapse phenomenon is studied and analyzed. Figure 13a-h illustrate the deformation and destruction of overburden rock at various stages of bottom pulling. It is observed that after each bottom pull, a collapse space forms between the bottom pulling roadway and the ore body, disrupting  
Figure 11: Layout diagram of displacement monitoring points.  
Figure 12: Layout of measurement points and excavation sequence diagram.  
the original equilibrium among the top plate, bottom pulling layer, and bottom rock layer. This disruption leads to stress redistribution and establishes a new equilibrium as mining progresses. During this process, the ore body will experience various forms of movement, including bending, collapsing, breaking, rotating, and bulging. The test results indicate clear zoning for the collapse and destruction of the rock layer, progressing from the fall zone at the bottom, through the fissure zone, to the bending and sinking zone at the top. As the burial depth decreases, the deformation range of the overburden rock gradually expands, while the degree of destruction gradually diminishes.  
Figure 13: Deformation and damage law of overburden rock: (**a**) drawdown width 20 m, (**b**) drawdown width 60 m, (**c**) drawdown width 100 m, (**d**) drawdown width 140 m, (**e**) drawdown width 180 m, (**f**) drawdown width 220 m, (**g**) drawdown width 260 m, and (**h**) drawdown width 300 m.  
In summary, the model is excavated from the middle to both ends of the pull-down, revealing a distinct pattern in the collapse of overburden rock after each completion of the pull-down work in the physical similarity simulation test. As the pull-down space expands, the un-collapsed overburden above the collapse area transitions from an elastic to a plastic state. The increase in pull-down width results in a steady increase in lateral fissures, while the ore body in the upper part of the pull-down layer stabilizes through bubbling down. Given the two-dimensional test limitations, this study assumes isotropic material properties and aligns the vertical ore body strike with the overburden falling law along the ore body strike. A pull-down width exceeding 80 m leads to new fissure expansion in the top plate after each pull-down, with an estimated initial collapse area of the ore body at 6400 m\({}^{2}\). If the pull-down width exceeds 120 m, the ore body collapses entirely to fill the pull-down space, resulting in lateral fissures on both sides of the model and an estimated continuing collapse area of the ore body of 14,400 m\({}^{2}\). The ground pressure manifestation law of the No. 2 ore body caving method in Tongshan Mine is consistent with the ground pressure evolution law obtained from numerical simulation and similar simulation experiments.
## 3 Influence of PWM Pulse Frequency on Overvoltage Distribution
### The Influence of PWM Frequency on the Voltage Distribution from the End to the Ground
The overvoltage generated by the first pulse wave at different frequencies is the same; the maximum is 3.6 kV, which is about 1.3 times of the input voltage and is located at the end coil. When the frequency is 100 kHz, the voltage of the conductor to the ground is superimposed from the middle coil to the end coil, that is, the first pulse wave falling edge overvoltage is not attenuated completely and the second pulse wave rising edge overvoltage is superimposed. At this time, the pulse rising edge overvoltage is up to 3.7 kV, which is located at the last turn of the end coil. With the decrease in pulse frequency, the influence of the first pulse wave's falling edge overvoltage on the second pulse wave's rising edge overvoltage gets smaller and smaller, which makes the second pulse wave's rising edge to the ground voltage distribution closer and closer to the overvoltage distribution generated by the first pulse wave. In the case of 10 kHz and 50 kHz, the second pulse wave rising edge overvoltage is the same as the first pulse wave rising edge overvoltage. The maximum ground voltage is 3.6 kV, which is located at the last turn of the end coil.
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used
### Usle  
The Universal Soil Loss Equation (USLE) was developed in the USA around 1930 by the Soil Conservation Service of the US Department of Agriculture (USDA SCS), now known as the Natural Resources Conservation Service (NRCS). The USLE is the average of the annual loss of long-term soil (in metric tons per hectare and per year (ton/ha/year)), which is mathematically expressed as [17; 18; 19]:  
\[A=\ R\ K(LS)C\ P \tag{1}\]  
The parameters of \(R\), \(K\), \(LS\), \(C\), and \(P\) in Equation (1) are explained in detail, as follows.
###### Abstract  
Vehicle dynamic management (VDM) is a vehicle chassis integrated control system based on electronic stability program (ESP) and continuous damping control (CDC) that has been developed in recent years. In this work, the ideal yaw angle rate and sideslip angle of the mass center are calculated deriving an ideal monorail model with two degrees of freedom. Then, a direct yaw moment proportional-integral-differential control strategy for ESP is proposed as the foundation of VDM. In addition, a multi-objective fuzzy continuous damping control (MFCDC) is proposed to achieve comfort, handling stability, and rollover prevention. The effect of the MFCDC strategy is analyzed and verified through a sine wave steer input test, double line change test, and fishhook test. The results indicate that MFCDC-ESP has a significant advantage in preventing rollover. MFCDC-ESP can maintain the optimized distribution of damping force through its own compensation under possible instability and predict the critical stable state to some extent. MFCDC-ESP exhibits strong real-time sensitivity to the control state of the damping force of each wheel. Hence, it can ensure the comfort of passengers under good driving conditions and exert strong adaptability and control effects under extreme working conditions.  
continuous damping control; multi-objective fuzzy control; electronic stability program; sky-hook control; rollover prevention 2020; 10.7923; doi:10.3390/app10217923
# Prediction of Tooth Profile Deviation for WEDM Rigid Gears Based on ISSA-LSSVM
## 4 Tooth Profile Deviation Prediction Model Based on ISSA-LSSVM
### Model Setup and Indicators' Selection  
The results of the orthogonal experiments were used as the dataset for the model, and it was separated randomly into a training set and a test set (training set-test set = 8:2). To evaluate the performance and accuracy of the rigid gear tooth deviation prediction model based on ISSA-LSSVM, the prediction results were compared with the PSO-LSSVM, GWO-LSSVM, and SSA-LSSVM models. All four models were configured with the same basic parameters: a search interval of [0.01, 1000] for the LSSVM model parameters. The remaining parameters for each algorithm were consistent with those described in Section 2.4.  
This paper evaluates and compares the optimization results for four prediction models using three metrics: the \(RMSE\), \(MAPE\), and \(R^{2}\). The \(RMSE\) quantifies the average discrepancy between the model's predicted and actual values. The \(MAPE\) expresses the accuracy of a model as a percentage, indicating how close the predicted values are to the actual values on average. A lower \(RMSE\) and \(MAPE\) indicate better performance in model prediction. The statistic \(R^{2}\) reflects the degree of closeness between the data points and the fitted curve, with values ranging from 0 to 1. A higher \(R^{2}\) value approaching 1 indicates a more ideal fit between the model and the data.  
The formulas for each of the three evaluation indicators are shown below:  
\[RMSE=\sqrt{\frac{1}{l}\sum\limits_{i=1}^{l}\left(y_{i}-f_{i}\right)^{2}} \tag{12}\]  
\[MAPE=\frac{1}{l}\sum\limits_{i=1}^{l}\left|\frac{y_{i}-f_{i}}{y_{i}}\right| \times 100\% \tag{13}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{l}\left(y_{i}-f_{i}\right)^{2}}{\sum\limits _{i=1}^{l}\left(y_{i}-\overline{y}\right)^{2}}=\frac{\sum\limits_{i=1}^{l} \left(y_{i}-\overline{y}\right)^{2}}{\sum\limits_{i=1}^{l}\left(f_{i}- \overline{y}\right)^{2}} \tag{14}\]  
In the above equation, \(l\) denotes the number of samples, \(y_{i}\) denotes the total deviation of the actual measured tooth profile, and \(f_{i}\) represents the predicted values. In Equation (12), \(\overline{y}\) denotes the arithmetic mean of the true values.
## 3 Thermographic Techniques for Skin Cancer Diagnosis
### Dynamic Infrared Thermography
#### 3.2.3 Lock-in Thermography  
Lock-in thermal imaging (LIT), first developed in the 1990s for non-destructive testing of materials, was first used for dermatological applications by [PERSON] and [PERSON] [44]. The experimental set-up suggested by the authors foresees the use of a temperature-modulated airflow to periodically stimulate the skin surface (see Figure 3). This set-up consists of a cold air device in series with a resistive wire (whose voltage is periodically modulated) to create a temperature-modulated air flow. Moreover, thermal images were recorded by an infrared camera synchronized with the air flow modulation.  
Then, the acquired thermal frames had to be demodulated according to the digital lock-in principle. Applying this procedure yields a phase and an amplitude image; the former is a map of the phase angles between the thermal stimulus and the temperature response of the skin, while the second image is related to the dissipated power at the skin surface. The main advantage of LIT compared to steady-state and dynamic IR thermography is its ability to detect very small temperature gradients even when a noisy background occurs. In fact, this method is able to reject irrelevant thermal signals coming from the metabolic and circulatory variations occurring in the subcutaneous tissues [44]. Also, the suppression of the lateral heat spreading achievable in the phase image allows the lesion and its margins  
Figure 3: Schematic of the experimental set-up used for lock-in thermal imaging [44] (permission asked and obtained from John Wiley and Sons Ltd., Hoboken, NJ, USA).  
to be localized accurately. Note that the same accuracy is not achievable using steady-state thermography. However, the results of the application of this method were reported by the authors only for benign skin lesions.