esa-sceva/satcom-chunk-collection huggingface dataset:
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 2 EUV-Induced Plasma
### Key Parameters of EUV-Induced Plasma
#### 2.2.2 Electron Temperature  
The temperature of the plasma species is another important parameter when describing bulk plasmas. The temperature of neutral species may be assumed to be close to room temperature under circumstances that occur in EUV-induced plasmas for lithography applications. However, electrons have a much higher average energy than they would have at room temperature. Immediately after the first ionization events, the average electron energy equals 76.6 eV, i.e., the difference between the energy of the photons used (92 eV), and the ionization energy (15.4 eV for H\({}_{2}\)). Most of the excess energy of the photoionization reactions is transferred to the electrons, since these have a much lower mass when compared to that of the ions and neutrals. Note that, under normal conditions, multilayer (MLM) mirrors and optical filters that are used in the system narrow down the spectrum of the source's EUV radiation closely around 13.5 nm. Therefore, the electron energy distribution function (EEDF) is highly non-Maxwellian just after the gas is irradiated. Typically, these electrons thermalize--again dependent on the experimental conditions--on time scales in the order of 1-100 \(\upmu\)s [18]. Figure 2 shows computer simulations of the EEDF for an EUV-induced plasma at four different moments in time after the start of the EUV pulse. Here, the dashed lines--that represent Maxwellian EEDF's--indicate that at the time the EUV intensity is highest, i.e., 0.04 \(\upmu\)s after the start of the pulse, the EEDF is far from Maxwellian shaped. When irradiation by EUV photons has vanished, i.e., 0.3 \(\upmu\)s after the start of the pulse, electrons already significantly cooled down, and--even more importantly--their EEDF has become close to Maxwellian-shaped.  
Finally, the initially produced ions have thermal energies that are close to room temperature. However, in the vicinity of solid surfaces, the plasma self-induces a space charge region--in traditional plasmas often named a plasma sheath--that creates a potential difference between the bulk plasma and the facing surface. Section 3 discusses the anisotropic acceleration of (positive) ions by the created electric field present in this space charge region and the diagnostics. Some ionic species, such as H\({}^{+}\), show an energetic tail in their energy distribution function that can be correlated to their formation mechanism. Section 3 will also further discuss this.
## 1 Introduction
### Purpose and Content  
Indirect sorting is generally divided into three steps: detection, identification and separation. Improving their quality, efficiency and coordination can comprehensively enhance actual sorting quality. Our previous work explained the problems of detection, identification, separation and coordination in the system control process [39].  
High-quality monochromatic dual-energy X-ray is generally not feasible for industrial application because it can only be obtained on synchrotron, while continuous spectrum dual-energy X-ray is relatively easy to obtain. Therefore, single-source dual-energy X-ray systems with energy-integrating detectors are more popular due to high efficiency and low cost, which also have great significance for research.  
On the basis of exciting research and problems in practice, this paper employs the single-source continuous spectrum DE-XRT to conduct the in-line identification of materials. Non-ferrous metals can be sorted according to the R_value calibration surface of \(S_{L}\)-\(x\)-\(R\) and a feasible sorting process is given. The identification quality is improved by means of effective point selection based on the judgment of overexposure and underexposure of low-energy shading and comparing the average distance with a series of calibration surfaces. In addition, the separation of multiple materials at a time can be realized, helped  
Figure 1: X-ray transmission fan-shaped effect.  
by a combination of variable gas-ejection (average pressure and/or action time) separation with in-line automatic calculation of material mass and center-of-gravity coordinates on the basis of converted and integrated low-energy shading from the thickness of the calibration surface of \(S_{L}\)-\(x\)-\(t\). Despite the high fixed cost of DE-XRT system equipment, this study can fully improve the performance and efficiency of DE-XRT identification and sorting without combining other sensors, which can reduce the system's running costs. Therefore, it is of great practical value.  
The remainder of this paper are arranged as follows: Section 2 is Materials and Methods, which briefly presents the composition and control method of the DE-XRT experimental sorting system, experimental sample materials, data acquisition and image preprocessing; puts forward the material identification methods and process, the calculation methods of the material mass and center-of-gravity coordinates; introduces the shading of low-energy to the judgement of unidentifiable materials because of overall underexposure and overexposure; selects the effective points for identifiable materials to improve identification accuracy; and theoretically analyzes the variable gas-ejection method to control the separation falling point. Section 3 is Results and Discussion, which verifies the effectiveness of the DE-XRT material identification on the basis of R_value calibration surface of \(S_{L}\)-\(x\)-\(R\) and calculation accuracy of material mass and center-of-gravity coordinates. Section 4 is the Conclusion, which summarizes the full text.
# CoSimulating Communication Networks and Electrical System for Performance Evaluation in Smart Grid
###### Abstract  
In smart grid research domain, simulation study is the first choice, since the analytic complexity is too high and constructing a testbed is very expensive. However, since communication infrastructure and the power grid are tightly coupled with each other in the smart grid, a well-defined combination of simulation tools for the systems is required for the simulation study. Therefore, in this paper, we propose a cosimulation work called OOCoSim, which consists of OPNET (network simulation tool) and OpenDSS (power system simulation tool). By employing the simulation tool, an organic and dynamic cosimulation can be realized since both simulators operate on the same computing platform and provide external interfaces through which the simulation can be managed dynamically. In this paper, we provide OOCoSim design principles including a synchronization scheme and detailed descriptions of its implementation. To present the effectiveness of OOCoSim, we define a smart grid application model and conduct a simulation study to see the impact of the defined application and the underlying network system on the distribution system. The simulation results show that the proposed OOCoSim can successfully simulate the integrated scenario of the power and network systems and produce the accurate effects of the networked control in the smart grid.  
communication networks; computer simulation; power system simulation; smart grid +
Footnote †: journal: Applied Sciences
## 2 Related Work  
The demand for underwater wireless communications is being pushed by AUV-based underwater missions. However, the design of broadband and reliable underwater wireless communications able to download Gbytes of data captured during a mission is still a challenge [16; 17; 18]. In this section, we present the different underwater communications technologies available in the state of the art and provide an overview of previous work regarding underwater delay-tolerant networks and underwater data muling, used mainly in underwater wireless sensor networks (UWSN) scenarios. Moreover, we provide some insight on how an AUV is able to navigate and localize in underwater scenarios.
## 3 Case Studies
### Battle on Neretva VR
#### 3.3.2 Application Structure  
The Battle on Neretva VR application consists of storytelling and gameplay parts. The aim of such application design is to offer the users to learn about the Battle in an amusing and immersive way. The storytelling starts with VR video of the Museum, where the curator character is telling the story about basic facts of the situation before the battle. After that, the user is placed in the VR video of a partisan headquarters, where he/she meets the commander and several soldiers. The commander recounts the situation and informs the soldiers about the orders for the battle. The user is asked a question about the strategy and, if answered correctly, the gameplay loads. The user is on the bridge over Neretva river with a task to destroy it using explosives. After successfully completing the first mission, the user is placed back at the headquarters, this time destroyed by the enemy bombs. The commander informs on [PERSON]'s orders for saving the wounded and again asks the user a question. If answered correctly, the user is tasked to destroy the enemy airplanes. After each destroyed airplane, a part of the improvised bridge is being built. The last task for the user is to transport the wounded comrade across the improvised bridge.  
Upon successful completion of the mission the user is back in the Museum, where the curator informs them of the outcome of the battle. As a reward for bravery, the Supreme Commander, comrade [PERSON], shows up and congratulates the user. The application structure is presented in Figure 6.
# Probabilistic Ship Detection and Classification Using Deep Learning
###### Abstract  
For an autonomous ship to navigate safely and avoid collisions with other ships, reliably detecting and classifying nearby ships under various maritime meteorological environments is essential. In this paper, a novel probabilistic ship detection and classification system based on deep learning is proposed. The proposed method aims to detect and classify nearby ships from a sequence of images. The method considers the confidence of a deep learning detector as a probability; the probabilities from the consecutive images are combined over time by Bayesian fusion. The proposed ship detection system involves three steps. In the first step, ships are detected in each image using Faster region-based convolutional neural network (Faster R-CNN). In the second step, the detected ships are gathered over time and the missed ships are recovered using the Intersection over Union of the bounding boxes between consecutive frames. In the third step, the probabilities from the Faster R-CNN are combined over time and the classes of the ships are determined by Bayesian fusion. To train and evaluate the proposed system, we collected thousands of ship images from Google image search and created our own ship dataset. The proposed method was tested with the collected videos and the mean average precision increased by 89.38 to 93.92% in experimental results.  
Keywords:ship detection; ship classification; ship dataset; deep learning; Faster R-CNN; autonomous ship; Intersection over Union (IoU) tracking; Bayesian fusion +
Footnote †: journal: Applied Sciences
## 3 Performance Analysis
### Primary Energy Saving  
The primary energy (PE) savings is the saved primary energy, electric, and fossil. These values are mathematically described below, in order to evaluate the primary energy consumption of a solar system and a conventional one:  
\[\text{PE}_{\text{sure}}=\Delta PE_{\text{{fossil}}}+\Delta PE_{\text{{ electricity}}} \tag{20}\]  
\[\Delta PE_{\text{{fossil}}}=\left(\frac{Q_{\text{{hatt}}\text{{fossil}},{ref}} \ -\ Q_{\text{{aux}},\text{{total}}}}{\eta_{\text{{bouiler}}}\text{{C}}_{\text{{ conf}},\text{{fossil}}}}\right) \tag{21}\]  
\[\Delta PE_{\text{{de}}}=\left(\frac{P_{\text{{el}},{ref},{tot}}-P_{\text{{el}}, {sc},{tot}}}{\text{{C}}_{\text{{can}},\text{{ele}}}}\right) \tag{22}\]  
\[\text{{Relative}}\ \text{PE}_{\text{sure}}=\frac{PE_{\text{sure}}}{PE_{\text{{ ref}}}} \tag{23}\]  
\[PE_{\text{{ref}}}=\frac{Q_{\text{{hatt}}\text{{fossil}},{ref}}}{\eta_{\text{{ bouiler}}}\text{{C}}_{\text{{can}},\text{{fossil}}}}+\frac{P_{\text{{el}},{ ref},{tot}}}{\text{{C}}_{\text{{can}},\text{{ele}}}} \tag{24}\]  
where:  
\(\eta_{\text{{bouiler}}}\) is the efficiency of auxiliary boiler 0.9;  
\(Q_{\text{{hatt}}\text{{fossil}},{ref}}\) is required heat for both space heating and DHW (Domestic Hot Water) in the conventional system (kWh).  
\(Q_{\text{{aux}},\text{{total}}}\) is the produced energy by auxiliary heater (kWh).  
\(C_{\text{{conf}},\text{{fossil}}}\), \(C_{\text{{con}},\text{{ele}}}\) are the primary energy conversion factors for heat and electricity from fossil fuel, 0.95 kWhheat, fossil/kWhPE and 0.5 kWhelect, fossil/kWhPE.
## 4 Experiments and Performance  
### Training Environment and Parameter Settings  
In the software environment of the experiments in this paper, the programming language used was Python 3.8, and the deep learning framework was Pytorch 2.0.0. Meanwhile, CUDA 11.8 and cuDNN 8.7.0 were used as the deep learning acceleration modules. Based on the Windows 10 system, the model training was completed with an Intel(r) Core(tm) i9-13900 K CPU and an NVIDIA GeForce TUF-RTX4080. Regarding the training parameters, the initial learning rate (lr0) was set to 0.01, the final learning rate (lrf) was set to 0.01 times the initial learning rate, the momentum was set to 0.937, the weight decay coefficient was set to 0.0005, and the optimizer was SGD. The maximum number of training epochs was set to 300, the batch size for training samples was set to 32, and the input size of the image was 640 \(\times\) 640 \(\times\) 3.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 6 Discussion and Conclusions  
This paper proposed a collaborative path planning and task scheduling scheme for multi-UAV and vehicle systems designed to address large-scale data collection tasks in complex environments. By developing a dynamic task processing model and designing the KGTSA optimization algorithm (which integrates K-means clustering, the Hybrid Genetic Algorithm, and Tabu Search), the scheme demonstrated excellent performance in reducing data collection time and enhancing task execution efficiency. It is particularly suited for time-sensitive applications such as emergency response, environmental monitoring, and  
Figure 19: Algorithm time consumption.  
Figure 18: Combined trajectory of UAVs.  
logistics delivery. Simulation experiments validated its advantages in terms of optimization performance and computational efficiency. However, the current research is limited to a simulation environment, and its applicability in real-world scenarios may be constrained. Future work will focus on validating the scheme in real-world conditions involving multiple vehicles and UAVs and exploring the following key research directions:  
Integrated Modeling of Heterogeneity and Task Prioritization: We will conduct in-depth research on the collaborative operation modes of multi-vehicle and multi-UAV systems, with a focus on developing a comprehensive model that accounts for the differences in payload capacity, speed, and endurance between vehicles and UAVs. Additionally, we will address task diversity and prioritization, which will further enhance the academic depth and practical value of the research.
* Large-scale Validation in Complex, Real-world Scenarios: We plan to expand the current simulation-based evaluation to field tests that include a variety of complex real-world scenarios and stochastic disturbances. We will prioritize challenging environments such as urban logistics delivery and large-scale event security. Field tests will provide a more comprehensive assessment of system performance, identify real-world challenges, and offer a foundation for further optimization.  
Conceptualization, D.H. and T.D.; methodology, Z.Y.; validation, B.J.; formal analysis, H.X. and J.X.; investigation, H.X.; writing--original draft preparation, Z.Y. and X.C.; writing--review and editing, B.J. and D.H.; visualization, D.H.; supervision, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Key Research and Development Program of Hubei Province, China, under grants 2024 BAB016, 2024 BAB031, 2023 BAB074, and 2022 BAA038.  
Not applicable.  
The data in this study are available upon request from the corresponding author.  
Authors [PERSON], [PERSON], and [PERSON] were employed by the company State Grid Hubei Information & Telecommunication Company. The remaining authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# The Extreme Function Theory for Damage Detection: An Application to Civil and Aerospace Structures
## 1 Introduction
[22] used multivariate EV Statistics and Gaussian Mixture Models on 8 performance and 4 vibrational parameters extracted from data collected from aerospace gas-turbine engines. However, the basic concepts of EVT can be easily extended to whole functions to define the so-called Extreme Function Theory (EFT, [23]). The rationale is that the same statistical tools applied commonly on scalar values can be applied on 1- or multi-dimensional functions (like the mode shapes), not differently from what, e.g., is done for Gaussian Processes (GPs) in comparison to Gaussian distributions. This was, for instance, utilised by [PERSON] et al. [24] to perform damage detection in offshore wind turbines based on their recorded power curves. Another important aspect is that, according to the Fisher-Tippett-Gnedenko theorem [25, 26], any extreme distribution on the lower or upper tail converges to only three possible extreme distributions--the Gumbel, Frechet, and Weibull distribution families, also known as type I, II and III EV distributions--which can be unified in the Generalised Extreme Value (GEV) formulation. This happens independently from the parent distribution; that is to say, the proposed approach is not limited by the assumption of a Gaussian distribution, which--while being commonly used in structural mechanics and dynamics on the basis of the well-known Central Limit Theorem (CLT) [27]--could be not always consistent with the experimental data (e.g., in case of skewed distributions). In this work, GP Regression and EFT are combined to define a \"normality\" data-driven model, fitted over the mode shapes collected from the structure \"as it is\", and then to check for damage-related deviations from this model [28]. The use of a statistical framework purposely crafted on rare events greatly reduced the number of false positives. The rest of this paper is organised as follows: The theoretical framework of the procedure is described in Section 2. The results of the investigations performed on the numerically simulated case studies are reported in Section 3. The experimental validation is described in Section 4. Section 5 (Conclusions) ends this paper.
# Faster RCNN Target Detection Algorithm Integrating CBAM and FPN
## 4 Experimental Results and Analysis
### Analysis of Target Detection Experiment Results
#### 4.3.6 Adaptability Comparison  
Compared with the other two classical target detection algorithms, CF-RCNN has a longer processing time, but meets the basic real-time requirements, and the detection accuracy is higher than the other two algorithms.  
In order to test the adaptability of the CF-RCNN algorithm to the truncation and occlusion of objects, the images with object occlusion or truncation are screened out on the PASCAL VOC 2012 dataset, and Faster RCNN and CF-RCNN are used for comparative experiments. Figure 11 shows part of the experimental results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **AR (\%)** \\ \hline Faster RCNN & 53.9 \\ CF-RCNN & 62.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of recall rate before and after algorithm optimization.  
Figure 11: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **AP\({}_{50}\) (\%)** & **Average Processing Time (ms)** \\ \hline YOLOv5 & 72.9 & 84 \\ SSD & 60.8 & 91 \\ CF-RCNN & 76.2 & 111 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison between classical target detection algorithm and CF-RCNN algorithm.  
From the comparison of the experimental results in Figure 11, it can be seen that the traditional Faster RCNN algorithm can basically meet the detection requirements of truncated or occluded objects in a simple environment. The results of object classification are not significantly different from those of the CF-RCNN algorithm, but they are obviously inferior to the CF-RCNN algorithm in object location selection. In the case of truncation or occlusion of objects with complex scenes, traditional Faster RCNN has most false detection and missed detection phenomena; the CF-RCNN algorithm makes up for such defects well, and better alleviates the unsatisfactory detection results in the case of occlusion or truncation.
## 2 Background
### Permafrost Thawing  
While southern areas of Canada may benefit from the shortening of the winter season, northern provinces will likely suffer with increased differential thaw settlement, frost heave, and a decrease in overall pavement strength due to permafrost thawing and a higher number of freeze-thaw cycles. Figure 3 shows a Canadian map that provides the classification of permafrost based on the proportion of land that is underlain by frozen layers within a given area. The southern areas in Canada are not underlain by permafrost, while northern areas are underlain by discontinuous or continuous permafrost layers. It should be noted that Figure 3 includes the most recent reliable map that could be retrieved at the time of this study, which dates back to about two decades ago. This is acknowledged as one of the limitations of the current research and underlines the need for an up-to-date map to inform future research in this area.  
The thawing of previously-permanent frozen layers can cause serious damage to pavement structures due to settlement. Soil strength from ice bonding will be reduced as the ground water thaws, and this will lead to ground instability and higher incidence of slope failure in pavements [11]. The effects of a rise in the number of freeze-thaw cycles, on the other hand, include accelerated degradation of asphalt concrete materials [12], a decrease in asphalt concrete structural capacity, such as Marshall Stability [13], and a decrease in the tensile strength ratio of the asphalt mixture as a result of moisture-induced damages [14].  
TCCA affirms that an optimum approach for designing pavements over permafrost areas is to provide a pavement and embankment thickness such that thawing does not reach the existing in-situ subgrade material. Whenever this alternative becomes uneconomical, the airport pavement design should be based on the bearing strength of the thawed subgrade soil. Therefore, thaw depth plays an important role in the design and performance of pavements located over permafrost areas. A study published by [PERSON] and Denyes recorded observations of thaw penetration depth in gravel runways in Canada [4]. Based on the observations, a relationship between the depth of thaw and the thawing index in gravel runways was established according to Equation (4) [5].  
\[X=-81.3+9.5\sqrt{I} \tag{4}\]  
where \(X\) is the depth of thaw penetration (cm) and \(I\) is the thawing index (\({}^{\circ}\)C-days), calculated as the summation of daily average air temperatures over the thawing period [3].  
Figure 3: Permafrost zones of the Canadian permafrost region [11].  
This equation was used in the current study to predict thaw penetration depths in several airports over the northern regions of Canada.