esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Phase, Bidirectional, 7.7 kW Totem Pole On-Board Charging/Discharging Infrastructure
## 2 Brief Detail and Parameter Design
### Bidirectional DC-DC Converter Design
#### 2.4.1 Charging Mode  
In charging mode, DC-link is connected as an input of bidirectional converter and EV batteries are connected as a load on the output side. Hence, to achieve the voltage level of batteries (\(V_{batt}\)) at the output side, BSBD converter operates in buck mode with components as an inductor (\(L_{buck}\)) and capacitor (\(C_{buck}\)). The values of (\(L_{buck}\)) and (\(C_{buck}\)) depend upon the corresponding voltage and current rating of the converter. These values are calculated as follows:  
\[L_{buck}=\frac{(V_{DC}-V_{batt})D_{buck}}{\Delta I_{batt}f} \tag{6}\]  
\[C_{buck}=\frac{(1-D_{buck})V_{batt}}{8L_{buck}\Delta V_{batt}f^{2}} \tag{7}\]where \(\Delta I_{L}\) and \(f\) are the ripple current and switching frequency for the buck mode, respectively.
# Feasibility Study of Integrated Precision Abrasive Machining of Shaped Surfaces
## 2 Materials and Methods  
### Design of the Dual-Tool Grinding Head  
The subject of analysis was a dual-tool grinding head protected in a 2018 patent [32]. The head has two functional zones, the first of which will be used for rough grinding with a ceramic grinding wheel, while the second zone will be used for finishing grinding with a prone wheel.  
Such a solution allows the workpiece to be machined in a single fixture, thus reducing the total time for process preparation and the manufacture of the workpiece. A conceptual diagram of the dual-tool grinding head is shown in Figure 1.  
The first stage of the work was to proceed with the design of a prototype dual-tool grinding head. During the design of the head, several changes were made with respect to the one in the patent. Among other things, the method of mounting the ceramic grinding wheel was changed, the number of springs was increased, and the method of extending the head was changed (the location of the bearing was changed from seating it on the head to actuators). The designed prototype head consisted of three basic components: an outer body, an inner body and a mounting adapter. All the components were connected using springs, bolts and nuts to mount a ceramic grinding wheel on the outer body and a prone grinding wheel on the inner body. Figure 2 shows a technical drawing of the designed prototype of the dual-tool grinding head.  
Figure 1: Conceptual diagram of a dual-tool grinding head (1—grinding head body; 2—grinding wheel body; 3—ring-shaped bonded grinding wheel; 4—flexible abrasive disc holder; 5—flexible abrasive disc; 6—spring; 7—coolant feed channels; 8—coolant feed nozzle; 9—pneumatic actuator; 10—thrust bearing; 11—workpiece): (**a**) in the rough grinding setting; (**b**) in the finishing grinding setting [32].  
Figure 2: Technical drawing of a prototype of a dual-tool grinding head (1—ER14 adapter, 2—outer body, 3—inner body, 4—O110 grinding wheel, 5—O50 3M Trizact® grinding wheel, 6—0.8 \(\times\) 7.7 \(\times\) 25 pressure spring, 7——centering shaft, 8—prone grinding wheel spacer, 101—M8 bolt, 102—M5 bolt): (**a**) isometric view; (**b**) top view; (**c**) A-A cross-section.  
The following figures show a digital 3D model of the designed head. Figure 3a shows an isometric view of the head with a view of the outer body. In this body, the coolant supply channels to the machining zone are also designed. Figure 3b, on the other hand, shows the head with a view of the outer body, where you can see the springs and the way the ceramic grinding wheel is mounted.  
Next is a cross-sectional view of the digital 3D model of the grinding head in two positions. This is shown first in the position for coarse grinding with a ceramic grinding wheel (Figure 4a), and then in the position for finish grinding with a prone wheel extended (Figure 4b).
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 4 Discussion
Once an outbreak has been detected, fine-scale imagery could be collected from UAVs to regularly monitor an infection front across an area of interest in a cost-effective way. Data collection from UAVs is considerably cheaper and can be collected in a greater range of weather conditions than satellite data. Our research shows that the multispectral sensors studied are compatible and can be used in a complementary manner. As a result, the UAV data can be used to augment the data collected from the satellite platforms and enhance our understanding of the spatial distribution of disease expression across forest ecosystems. Our findings also show that data from the RapidEye satellite can be used to expand the range of physiological stress monitoring that can be achieved using a UAV sensor. However, the RapidEye sensor offers lower sensitivity as it was only suitable for detecting stress in larger clusters. When used in combination these two technologies offer a solution for both widespread and fine-grained monitoring of plantation forests. This system can be deployed to track physiological stress and to put in place management activities to mitigate stress and increase forest productivity. The knowledge developed through this experiment can be useful for the designers of multi-scale detection systems for tracking biosecurity incursions. Intensive, high-resolution UAV surveys could be deployed over high-risk sites and larger, less sensitive, satellite methods could be used across wider areas. In combination, an approach of this type could provide an extremely practical tool as both an early warning system and in monitoring the spread of an incursion.
# A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring
## Appendix A Data Collection  
Peshawar (34.015\({}^{\circ}\) N, 71.52\({}^{\circ}\) E) is a city located in Khyber Pakhtunkhwa, Pakistan, situated at an elevation of 340 m above sea level. Peshawar covers an area of 1257 km\({}^{2}\) and has a population of 1,218,773 making it the biggest city in Khyber Pakhtunkhwa. Peshawar is predominantly hot during summer (May-Mid July) with an average maximum temperature of 40 \({}^{\circ}\)C followed by monsoon and cold winter.  
Local vehicular emission, fossil fuel energy plants and industrial processes are the significant sources of air pollution in Peshawar. Wind direction and wind speed also play a crucial role to observe transboundary pollution build-up. Furthermore, at this site, the distribution and dispersion of air pollution are further impacted by the nearby buildings, and its proximity to Grand Trunk Road, creating a built-up street canyon environment, generated primarily from nearby, increasing traffic pollution.  
The air quality monitoring sensor (AQMS) was installed at the University of Peshawar's Physics Department Building (see Figure 11) at 6 m height from the ground surface level. It is described as an urban background site.  
Sheffield (53\({}^{\circ}\)23' N, 1\({}^{\circ}\)28' W) is a geographically diverse city located in county South Yorkshire, UK, built on several hills thus situated at an elevation of 29-500 m above sea level. Sheffield covers a total area of 367.9 km\({}^{2}\) with a growing population of 582,506. Sheffield is claimed to be the \"greenest city\" in England by the local city council. Sheffield enjoys a temperate climate with July considered the hottest month, with an average maximum temperature of 20.8 \({}^{\circ}\)C.  
The air pollution in the city is primarily due to both road transport and industry, and to a lesser extent, fossil fuel-run processes, such as energy supply and commercial or domestic heating systems (for example, wood burners).  
The AQMS is installed at 2.5 m height from the elevated ground surface level at the playground of Hunter's Bar Infants School (see Figure 12), which lies in close proximity to a busy roundabout, and at the intersection of Ecclesall Road, Brocco Bank, Sharowe Vale Road and Junction Road; thus, traffic is the primary source of pollution. It is also described as an urban background site.  
In our case, the AQMSs are commercially low cost sensor nodes AQMesh. They have been deployed at the two sites in Peshawar and Sheffield. A \"black box\" post calibration is applied to the data by the manufacturer to eliminate the impact of humidity and temperature on the sensor and to eliminate cross sensitivity. The data are aggregated and sampled every 15 min. The data collected from these nodes are transferred to the cloud-based AQMesh database via standard GPRS communication integrated. The data are then accessed through the dedicated API.
## 4 RISs Controlled by AI
### Reinforcement Learning
Furthermore, the maximization of end-to-end data rates in multiple RIS-aided multi-hop cooperative networks has been explored by applying the PPO algorithm with exceptionally good long-term results [44]; the study emphasized the long-term benefits of PPO for maximizing data rates in IoT applications. By combining DDPG and PPO, satisfactory power efficiency, while addressing communication quality, has been achieved in wireless power transfer systems and RIS-assisted communication to IoT devices and Unmanned Aerial Vehicles (UAVs) [41]. Table 4 indicates that the studies presented so far explore a variety of deep RL approaches, including value-based (DDQN), policy-based (DDPG, PPO), and hybrid (TD3) methods, offering a comprehensive toolkit for RIS optimization. Each algorithm is tailored to specific challenges, such as network security [38], trajectory optimization [38], and URLLC [40]. It is worth noting that all these algorithms are model-free; thus, they do not require explicit knowledge of the system model. This is particularly advantageous in dynamic and uncertain wireless environments. Moreover, algorithms like PPO [44] balance long-term system performance with computational feasibility, indicating readiness for real-world applications. However, deep RL methods require extensive training, as highlighted in [40; 43], which may limit their adaptability to fast-changing environments. In addition, value-based algorithms like DDQN may suffer from overestimation issues, while PPO and TD3 demand fine-tuning and involve greater computational complexity. As regards the performance of the aforementioned approaches, all studies have reported notable superiority over conventional methods, highlighting the effectiveness of RL approaches in RIS applications. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Ref.** & **Problem Description** & **Algorithm Used** & **Achievement** \\ \hline [PERSON] et al., 2022 [40] & Phase shift design for RIS-aided URLLC systems & Deep RL with TD3 & Reduced overestimation of the action-value function that comes with DDPG \\ \hline [PERSON] et al., 2022 [44] & Optimization of multi-hop RIS-aided cooperative networks & Deep RL with PPO & Maximization of data rates in IoT applications \\ \hline [PERSON] et al., 2022 [41] & Wireless power transfer and RIS-assisted communication with IoT and UAVs & Deep RL with DDPG and PPO & Maintenance of power efficiency while addressing communication quality in dynamic environments \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._While the approaches presented so far show promising results, most of them focus on relatively small-scale scenarios; thus, their scalability for larger, more complex networks and/or higher numbers of RIS elements remains a challenge.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 4 Experimental Evaluation and Analysis
### Phase 1
#### 4.2.1 GANs Generative Adversarial NWs
As the generator produces more synthetic samples, \(a\) increases, leading to a corresponding increase in \(\partial\). This dynamic adjustment of \(\partial\) allows the loss function to adapt, starting with a relaxed constraint and gradually tightening it as more synthetic data becomes available. This is particularly beneficial for early ransomware detection, where initial data is often limited. Consequently, the GAN discriminator's constraints transition from lenient to stringent as the volume of generated synthetic attack patterns grows. To dynamically adjust the upper bound of loss estimation in the Minimax loss function, we introduced the coefficient \(\partial\). This adaptation is crucial during the pre-encryption phase of ransomware attacks, where probability distributions are highly variable. Initially, with few synthetic instances, the discriminator's term in the loss function (Equation (6)) has minimal influence, allowing the generator's term to dominate and produce viable attack patterns. When the discriminator's term is minimized due to this imbalance, misclassification of synthetic patterns as real is more likely. However, as the number of generated synthetic attack patterns increases, the discriminator's influence in Equation (6) grows, while the generator's influence diminishes. This shift empowers the discriminator to more accurately distinguish synthetic instances from real ones.
## 5 Importance of Ubiquitous Cellular Coverage in Hospitals  
Hospitals are dynamic and complex environments where seamless communication is essential for delivering high-quality patient care and ensuring operational efficiency. Cellular coverage plays a pivotal role in enabling the functionality of medical technologies, supporting telemedicine, and facilitating real-time data transmission. This section explores the critical importance of robust cellular coverage in hospital settings, highlighting its impact on medical systems, telemedicine, and proposed solutions for coverage improvement [17].
## 7 Towards a Simplified Optical Transceiver
### Simplified Coherent Optical System  
In an effort to offer economical solutions to support the short-reach links and the associated applications, several simpler transmission schemes that are based on direct detection systems have been presented [2; 18; 44; 46]. Instances of such simplified solutions are the SCOH schemes that are alternative solutions to conventional Co-Rxs. With the SCOH implementation, the complex-valued single-polarization optical field can be fully reconstructed without an LO by leveraging the direct detection scheme [19].  
In this context, the CW tone and the modulated signal are transmitted concurrently to ensure COH detection with a single-ended detector per polarization [19]. Based on this, linear propagation impairments can be effectively prevented with the aid of standard DSP techniques [18]. Furthermore, with SCOH detection, there is no need for polarization tracking. This is due to the fact that copropagation of the LO and the signal makes them experience the same polarization rotations. Based on this, as long as higher-order polarization mode dispersion is negligible, both the LO and the signal stay polarization-aligned [19]. However, the scheme presents notable limitations regarding the power budget and the required frequency gap. For instance, as a significant fraction of the launched power has to be assigned to the LO, the power budget happens to be less satisfactory [18]. Furthermore, it is worth noting that as balanced detection is inappropriate for SCOH approaches, either a high CSPR or iterative DSP algorithms are required for SSBN cancellation. Moreover, the requirement for a high CSPR hinders the receiver sensitivity and subsequently the reach. Instances of the SCOH schemes are the Stokes vector-based and KK-based schemes [8; 19; 44; 46; 49; 51], as discussed in Sections 5 and 8, respectively.
## 4 Reproducibility and Scalability
### Scalability Considerations  
Scaling the proposed implementation to accommodate larger ones involves several considerations and strategies. The technical architecture is modular, allowing individual components to be scaled independently. For instance, the data indexing, retrieval, and processing modules can be distributed across multiple servers and vectorized databases to handle increased loads from more than one data storage. Additionally, each document type from a different source could follow different preprocessing stages, allowing enhanced configuration based on the document's type.  
Moreover, utilizing the LLM orchestration framework enables the creation of multiple RAG solutions based on various sources, with modular prompt configuration, accessing each user's needs in e-governance implications. Additionally, using multi-agent solutions, a more detailed search can be done based on needs, creating efficient tools for specific tasks and allowing the conversational multi-agent virtual assistant to identify the tool that should be used based on user queries.  
Lastly, the proposed framework could run with local LLMs or with API providers and cloud-based solutions to be used for scaling based on the solution demand. Utilizing cloud services such as AWS, Azure, or Google Cloud provides on-demand scalability, offering tools for automatic scaling, resource management, and monitoring, enabling the system to adapt dynamically to changing demands.
## 1 Introduction
Task 3: Application in Different Scenarios  
The training data are only collected in the same experimental scene (experimental scene 1), but the positioning model should be able to be used in different actual scenes, and the established positioning model can be applied to different scenes. The 10 sets of data in annex 3 are collected in the following experimental scenario 2 (the first 5 sets of data signals have no interference, and the last 5 sets of data signals have interference). Use the positioning models established above to perform precise positioning (3-dimensional coordinate). Experimental scenario 1:  
Target (Tag) range: 5000 mm \(\times\) 5000 mm \(\times\) 3000 mm  
Anchor position (unit: mm):  
A0 (0, 0, 1300), A1 (5000, 0, 1700), A2 (0, 5000, 1700), A3 (5000, 5000, 1300)  
Experimental scenario 2:  
Target (Tag) range: 5000 mm \(\times\) 3000 mm \(\times\) 3000 mm  
Anchor position (unit: mm):  
A0 (0, 0, 1200), A1 (5000, 0, 1600), A2 (0, 3000, 1600), A3 (5000, 3000, 1200)  
Task 4: Classification Model  
The above positioning model is established under the condition of known signal interference or no interference. However, when the UWB collects data, it is unknown whether the signal has interference or not. Therefore, judging whether the signal has interference is the focus and difficulty of precise UWB positioning. Use the data processed by task 1, establish a mathematical model (or algorithm) to distinguish whether the data is collected from the signal with or without interference, and explain the validity of the established classification model (or algorithm). The classification model (or algorithm) of judging that the 10 sets of data provided in annex 4 (these 10 sets of data are also collected in the experimental scene 1) are collected from the signal with or without interference. Task 5: Movement Track Positioning  
Movement track positioning is one of the important applications of UWB. Using the static point positioning model and the target's own movement law, the movement track of the dynamic target point can be obtained. Attachment 5 is the data collected on dynamic target points (multiple sets of data collected continuously over a period). When collecting these data, there will be random signal interference. Accurately position this movement track, and finally draw this movement track map (data collection is from experimental scene 1). For this study, the following assumptions are made as follows:  
1. Abnormal data with missing data contain less useful information. 2. When collecting information, the position and ranging performance of the anchor point are the same at continuous time, and the anchor point is repeated measurement with equal precision. 3. The ranging error of the anchor point includes only the measurement error and the interference error. A description of the symbols in this paper is shown in Table 1.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 3 Technical Background  
The considered OCN aims to transmit multi carrier signals over long distance, with minimum dispersions effects. We implement FFE and DFE equalization techniques to control the effect of high order dispersion effects in long distance and multi carrier transmission over SMF.
## 4 Implement and Discussion of Intelligent Dynamic Offloading Model
### Offloading Based on Edge Computing  
As described above, the functions offloaded to the edge node are divided into two core technologies: (1) data-intensive offloading and (2) computation-intensive offloading [32]. Figure 1 shows that it has two functions (1) Data and (2) Task.  
Data-intensive edge node: As shown in Figure 2(a), the data is collected in real-time from the end node stored in the edge node without sending it to the cloud after data pre-processing [33], i.e., only the necessary data submitted to the cloud. Cloud computing compensates for network bandwidth limitations and minimizes latency. [34] With data offloading that pre-processes and filters the sensed data function, only sensitive data is stored, transmitted and sent to the edge node. Data offloading pre-processes and filters the data collected at the edge node close to the location of the device where the data generated. That has the advantage of optimizing cloud computing systems [35] by reducing network traffic (burden) that occurs when sending data sensed from edge devices to the cloud.  
Task-intensive edge node: Figure 3b shows that offloading computing works for high-performance tasks such as analyzing and processing data from the cloud to the edge node. It leads to an improvement in the quality of calculations, including the delay and power consumption of the device [36]. The advantage is that the real-time response to the situation reaction is more guaranteed [34] by avoiding the significant delay caused by minimizing the network delay time. To date, energy consumption has increased rapidly, like two in the end node and the cloud is unloaded at the border node and centralized. Also, there is a high probability of overloading equipment located on edge. There is a problem in that the processing speed of the calculation task reduced. Therefore, the problem that occurs when two tasks occur at the same time should be considered.
# Automatic Fabric Defect Detection Method Using PRAN-Net
###### Abstract  
Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.  
fabric defect detection; deep learning; Faster R-CNN; extreme and tiny defects; priori anchor +
Footnote †: journal: Applied Sciences