esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Practical Application
### Analysis of Line Currents and Powers before and after Compensation  
Table 4 shows the line currents supplied by the network on the bus, before and after compensation. Note that the line currents after compensation are fully balanced.  
Table 5 displays the total active power \(P\), total reactive power \(Q\), total positive-sequence active power \(P_{+}\), total positive-sequence reactive power \(Q_{+}\), total negative-sequence active power \(P_{-}\) and total negative-sequence reactive power \(Q_{-}\) supplied to the network on the bus, before and after compensation. The apparent power \(S\) is determined by (4). As described above, it is noted that:  
* \(P\) is constant and equal to the initial one (before compensating). \(P_{+}\) after compensating has been increased to maintain a constant \(P\).
* Except \(P_{+}\) after compensation, the remaining powers supplied by the network are zero. Therefore, the total apparent power \(S\) decreases with respect to the initial one.  
Table 6 shows the powers caused by voltages and currents of different sequences in each phase that supplies the network to the bus. Here, the powers caused by the negative-sequence currents are cancelled after compensation. This does not occur with the powers caused by the negative-sequence voltages, as an infinite power network has been considered.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(I_{a}\) (A)} & \multicolumn{2}{c}{\(I_{b}\) (A)} & \multicolumn{2}{c}{\(I_{c}\) (A)} \\ \hline
**Condition** & **Modulus** & **Angle** & **Modulus** & **Angle** & **Modulus** & **Angle** \\ \hline Before & 14.88 & \(-\)138.77 & 25.51 & \(-\)84.49 & 36.27 & 76.05 \\ compensation & 10.99 & 4.35 & 10.99 & 244.35 & 10.99 & 124.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of line currents.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(P\) & \(Q\) & \(P_{+}\) & \(Q_{+}\) & \(P_{-}\) & \(Q_{-}\) & \(S\) \\ \hline
**Condition** & **(W)** & **(var)** & **(W)** & **(var)** & **(W)** & **(var)** & **(VA)** \\ \hline Before compensation & 6664.95 & 2129.99 & 6877.42 & 4608.34 & \(-\)212.47 & \(-\)2478.36 & 6997.04 \\ After compensation & 6664.95 & 0 & 6664.95 & 0 & 0 & 0 & 6664.95 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Powers caused by voltages and currents of the same sequence.  
Table 7 displays the values of the powers caused by the voltages and currents in sequence, as well as \(S_{T}\) (total apparent power of Buchholz). As expected, \(S_{-}\) and \(S_{+}^{-}\) are zero as the negative-sequence currents are compensated by NSCC. \(S_{+}\) has reduced value due to the compensation of \(Q_{+}\) by the SVC compensator. This power compensation reduces the value of \(S_{T}\), which is delivered by the network to the bus.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### Proteus Simulation  
Proteus was used to simulate the hardware functionality of both the transmission and receiving nodes. Each node was built around an Arduino Uno microcontroller. The transmission node incorporated a DHT11 sensor to measure temperature and humidity [61] and used an XBee ZigBee module for wireless communication. The receiving node, also equipped with an XBee module, relayed the data to a central monitoring station via a GSM SIM900D module. This module sends alert notifications over the mobile network, including the fire status, its computed index, and the corresponding temperature and humidity readings [62].  
Multiclass logistic regression is used for predictive AI modeling to classify fire risk based on temperature and humidity. The categorical fire risk variable is encoded numerically (0 = Low, 1 = Medium, and 2 = High) to enable supervised learning. The model is trained using an 80/20 train-test split and achieves 99.97% accuracy, evaluated with a custom accuracy function. Decision boundaries are visualized to illustrate how the model distinguishes between risk levels. The system also supports real-time prediction for new inputs and calculates raw scores using the model's learned coefficients.  
Figure 7a illustrates the relationship between temperature (\(x\)-axis), humidity (\(y\)-axis), and fire occurrence (color scale). Each point represents a data sample, with color intensity reflecting fire likelihood on a continuous scale from 0 (no fire) to 2 (high risk). Risk levels signify the probability of fire ignition under given environmental conditions, not the direct detection of active fires. The plot shows that high temperatures and low humidity correlate with increased fire risk, highlighted by the concentration of yellow points in the lower-right region.  
As illustrated in Figure 7b, for a temperature of 32 \({}^{\circ}\)C and a humidity level of 65, the three output values (\(f_{0}\approx~{}11.45\), \(f_{1}~{}\approx~{}40.50\), and \(f_{2}~{}\approx~{}-51.95\)) are generated by the equation corresponding to the fire risk levels (Equations (10)-(12)).  
Since the highest value corresponds to equation \(f_{2}\), the resulting risk level is classified as medium, which aligns with the simulation result as shown in Figure 7. Importantly, 'High Risk' indicates conditions conducive to fire ignition (e.g., dry, hot vegetation), not active burning. Active fire detection would require additional sensors beyond temperature/humidity.
# Durability Issues and Corrosion of Structural Materials and Systems in Farm Environment
## 4 Mitigation of Corrosion
### Repair and Rehabilitation  
The repair and rehabilitation of concrete and steel farm structures is influenced by the projected service life of the building structure. The service life of a material employed in the construction of farm structures is influenced by the level of susceptibility to corrosion and degradation. Materials that have a higher risk of corrosion such as mild steel require constant repair and rehabilitation to extend their service life. In particular, concrete pipes used in farm environments are repaired using organic polymer binders, which are preferred because they do not require pipe excavation and do not induce shrinkage of the repaired structure [17]. The repaired section is protected from water by the application of water repellants such as silane or silane-siloxane or linings. On the downside, the sealants and binders might result in the accumulation of odorous and toxic substances such as H\({}_{2}\)S. Alternatively, the rehabilitation of concrete structures that are in direct contact with farm effluents can be achieved using geopolymer concretes, which are resistant to acid attacks, permeation, and abrasion. The ion exchange mechanism through which geo-polymers resist acid attacks is shown in Figure 8. The costs associated with repairs can also be mitigated by the infusion of bacteria-based self-healing materials [7], which autonomously expand and fill micro-cracks. The phenomenon reduces the need for further repairs.  
Figure 7: Impact of the ratio between the resin and the hardening agent on the mechanical strength of the epoxy [31].  
Especially for concrete, EN1504 [37] provides detailed information for the protection and repair of concrete structures (surface protection systems, structural and non-structural repair, concrete injection, chemical products like corrosion inhibitors, etc.).  
For the corrosion protection of steel structures, EN ISO 12944-5:2019 [38] provides detailed information for the use of paint systems to prevent steel corrosion.
## 2 Background
### Sequential Parameter Optimisation  
Sequential parameter optimisation (SPO) is an approach that uses regression techniques to model the behaviour of control parameters. SPO aims to be less computationally expensive than other methods of tuning. This section outlines the ideas of SPO, the details of which is presented by [PERSON] et al. [PERSON] et al. (2018).  
The process of SPO starts off by sampling an initial set of candidate configurations using Latin hypercube sampling (LHS). Each candidate is then evaluated on an instance of the meta-heuristic. Using the data of this evaluation, a stochastic regression model is set up. The best design point of the previous selection is chosen to survive for the next iteration. A new set of 'good' parameter configurations are then sampled. The quality of these configurations is determined by the expected fitness as predicted by the regression model. This new set of configurations (including the best candidate of the previous iteration) is then evaluated on a new instance of the heuristic, and the model is further refined. Thisprocess repeats for a set number of iterations, until a computational budget is depleted, or until the best configuration has been repeatedly selected for some number of iterations.  
The number of configurations to sample at each iteration is determined by the researcher. Much like iterated F-race, SPO iteratively evaluates configurations and uses existing data to sample new configurations that is predicted to evaluate to a good fitness. If the number of configurations sampled is too small, there will not be enough exploration of the search space, and the regression model will be less accurate. With more configurations, exploration is higher at the expense of computational complexity.
# Correction Strategy of Online Midcourse Guidance for High-Speed Gliding Target Interceptor
## 1 Introduction
We establish a two-body motion model of the interceptor that simplifies subsequent calculations by ignoring the weak influence of aerodynamic force. We also established the THPEA model of the target and describe the PIP point update law of the interception process, which provides terminal constraints for midcourse guidance. 2. For a single PIP, we achieve interceptor correction through velocity gain guidance based on [PERSON]'s variable trajectory theory. This method is more suitable for scenarios with a wide range of terminal position constraints than existing correction methods. 3. For continuously updated multiple PIPs, we introduce a method of setting the engine start-up threshold in stages based on the correction of a single PIP. This ensures that the interceptor meets the terminal constraints and avoids redundant maneuvering. The rest of this study is organized as follows. Section 2 introduces the motion model of the interceptor. Section 3 introduces the online dynamic correction strategy for the interceptor we designed, which mainly includes the online correction strategy for the single PIP and the strategy for the multiple PIP update. In Section 4, numerical calculations and comparative analyses were performed. In Section 5, our conclusions, contributions, and innovations are explained.
## 1 Introduction  
The most common undesirable incidents on the railway system lead to delays and subsequent disruptions. Delays or disruptions usually prevent the planned schedule from being carried out due to discrepancies in time and space. As a result, trains may arrive late and travel on different tracks, requiring changes in the train crew or the train itself. These changes in operations disrupt the planned schedule. Ultimately, this results in the loss of planned, collision-free train traffic and negatively affects the resilience of the railway system.  
One area connected to train disruption is a group of factors related to human error. Traffic disruptions can lead to behavior consisting of many minor failures, which can eventually lead to an accident. Overspeeding and maintenance errors, as forms of human error, can lead to train delays. Delays can also be caused by incorrect decisions made by signal box operators. In addition, other effects of traffic disruptions on personnel can be distinguished, such as the impact on personnel of decisions made under stress and lack of time.  
In the case of controlling train traffic, there is a wide variation in the type of work to be undertaken. It is possible to have situations with heavy traffic in a small area or a need to control traffic in a larger area that is remote. The variety of tasks in the position of the traffic controller can be contrasted with the diverse predispositions of employees. Some people get tired quickly when they experience too much stimulus. Others get tired quickly when there is too little going on and they can then become distracted. There are also people who perform their tasks accurately and without errors, but only when they do not have to communicate with another person.  
The authors have already classified the workforce in previous studies and have demonstrated the links between these categories and the safety of the rail system [1]. Given the variation in people's predispositions, it is possible to hypothesize that there is a best-fit group of people for a given type of task.  
Accordingly, the following research questions can be formulated to be answered in this paper:  
* Is it possible to match the developed types of employees with specific types of tasks?
* Is it possible to build a model to categorize an employee based on a simple questionnaire?
* Is it possible to verify the method experimentally?  
The article is divided into five sections. Section 2 presents a literature review that defines the research gap. Section 3 introduces a method based on fuzzy logic, which aims to assign an employee status category based on the answers given in a simplified questionnaire. This section concludes with a description of an experiment involving eye-tracking. Section 4 presents the results of the implemented research and a discussion on them. The article ends with conclusions, which consider account the research's critical aspects and indicate directions for further research.
## 5 Results and Discussion
### MT Application
On average, the pressure ratio of the MT system was 4.5 and 5.6 for R404A and R449A systems, respectively, and that of the LT system was 9.8 and 13.23 for R404A and R449A systems, respectively. Then, the compressor efficiencies were also influenced by the pressure ratio, benefitting the MT circuit.
###### Abstract  
This paper presents a novel approach for clustering spectral polarization data acquired from space debris using a fuzzy C-means (FCM) algorithm model based on hierarchical agglomerative clustering (HAC). The effectiveness of the proposed algorithm is verified using the Kosko subset measure formula. By extracting characteristic parameters representing spectral polarization from laboratory test data of space debris samples, a characteristic matrix for clustering is determined. The clustering algorithm's parameters are determined through a random selection of points in the external field. The resulting algorithm is applied to pixel-level clustering processing of spectral polarization images, with the clustering results rendered in color. The experimental results on field spectral polarization images demonstrate a classification accuracy of 96.92% for six types of samples, highlighting the effectiveness of the proposed approach for space debris detection and identification. The innovation of this study lies in the combination of HAC and FCM algorithms, using the former for preliminary clustering, and providing a more stable initial state for the latter, thereby improving the effectiveness, adaptability, accuracy, and robustness of the algorithm. Overall, this work provides a promising foundation for space debris classification and other related applications.  
space debris; fuzzy C-means algorithm; spectrum; polarization +
Footnote †: journal: _sciences_
###### Abstract  
In the field of medical imaging, the accurate segmentation of breast tumors is a critical task for the diagnosis and treatment of breast cancer. To address the challenges posed by fuzzy boundaries, vague tumor shapes, variation in tumor size, and illumination variation, we propose a new approach that combines a U-Net model with a spatial attention mechanism. Our method utilizes a cascade feature extraction technique to enhance the subtle features of breast tumors, thereby improving segmentation accuracy. In addition, our model incorporates a spatial attention mechanism to enable the network to focus on important regions of the image while suppressing irrelevant areas. This combination of techniques leads to significant improvements in segmentation accuracy, particularly in challenging cases where tumors have fuzzy boundaries or vague shapes. We evaluate our suggested technique on the Mini-MIAS dataset and demonstrate state-of-the-art performance, surpassing existing methods in terms of accuracy, sensitivity, and specificity. Specifically, our method achieves an overall accuracy of 91%, a sensitivity of 91%, and a specificity of 93%, demonstrating its effectiveness in accurately identifying breast tumors.  
U-Net model; spatial attention mechanism; medical imaging; breast cancer; breast segmentation +
Footnote †: journal: Medical Imaging  
1
# Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on [PERSON] Shi
## 5 Image Classification via a Three-Layer Photonic Deep Neural Network
### Energy Consumption Versus Physical Layer Impairments
In the case of adding the contribution of Layer 3, it is the result of the small error accumulation as well, for the final prediction accuracy on this 3-layer photonic neural network system. However, it is evident that the two different regions are delineated when more error is accumulated from layer to layer. In particular, for the complete 3-layer photonic neural network, the best performance condition (accuracy = 92%) is found when the energy efficiency is around 5.6 pJ/operation and the waveguide loss ranges between 1.5 and 3.5 dB/cm. However it is possible to distinguish two areas where the accuracy is already higher than 89%: (1) the total energy consumption is above 4.5 pJ/operation, irrespective of the path loss; (2) the area at the left down corner where the averaged energy consumption is around 2.8 pJ/operation and the loss covers almost the full considered range (up to 4 dB/cm). The region (1) performs well due to a higher signal power with higher power consumption on the system with smaller errors induced and accumulated. We believe the region (2) appears due to the presence of more local minima, whose presence is determined by the combination of path losses, power level and optical crosstalk. Furthermore, the level of noise present in the network  
Figure 8: Investigation performance of the PDNN on computing energy and waveguide loss. (**a**) Calculated average NRMSE from output data obtained from Layers 1–3; (**b**) Corresponding prediction accuracy when Layer 1, Layers 1,2, and Layers 1–3 are implemented with photonic neuron layers. makes it a stochastic network where the intrinsic noise is supposed to provide better accuracy. Noise might play a positive role for low power levels as a good prediction is presented. However, this behavior has to be further explored for quantification. The identification of small error regions and their slight influence on the final prediction accuracy, as well as the maximum level of error at the first layer shown in Figure 8 suggests that the PDNN might be further scaled up, with prior physical parameters and error optimization.
###### Abstract  
Wireless sensor networks (WSNs) enable large-scale data collection across wide areas but face significant challenges, including limited energy resources, unbalanced energy consumption, and inefficient data transmission, leading to reduced network lifetime and poor reliability. To address these issues, this paper proposes an energy-efficient clustering scheme that integrates unmanned aerial vehicle (UAV)-assisted data collection and wireless power transfer (WPT) to enhance WSN performance in smart irrigation applications. The proposed scheme divides the network into two regions: a central circular area containing standard nodes and an outer region divided into four clusters housing advanced nodes. It dynamically adjusts cluster formation, optimizes cluster head (CH) selection based on residual energy, minimizes transmission hops, and ensures the shortest possible UAV path, thereby reducing energy consumption. Additionally, UAV-based WPT provides continuous energy replenishment, mitigating the hotspot problem and extending network lifetime. Extensive simulations demonstrate that the proposed scheme reduces energy consumption by up to 13.16%, enhances data collection efficiency by 8.84%, and extends network lifetime by more than 9.6% compared to [PERSON]'s scheme. Additionally, the proposed scheme achieves 26% higher residual energy and a 43% increase in throughput over [PERSON]'s scheme. Moreover, in smart irrigation applications, the proposed scheme reduces water consumption by approximately 20%, demonstrating its effectiveness for sustainable precision agriculture.  
WSNs; UAV; WPT; hotspot problem; smart irrigation; water consumption Article
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 6 Discussion  
### Main Contributions  
This work makes several significant contributions to the field of urban flood management through the innovative integration of augmented reality and machine learning grouped together.  
A comprehensive and detailed 3D model of Coimbra was created using data extracted with the BLosm plugin and processed through Blender, which serves as the foundation for a mobile augmented reality application. The extraction and simplification of the 3D models, with optimised AR visualisation, results in a model which maintains accuracy to display a real-sized city model without compromising details and information about the city.  
The usage of a regression line to predict the Mondego river's water height based on two different datasets, with analysis and correlation tools, shows an innovative way of obtaining a model with the current constraints, such as the lack of open data from government agencies, and with the usage of mobile devices that are low-power for a heavier model.  
This application allows for the real-time visualisation of potential flood impacts on buildings, utilising colour-coded indicators to represent different levels of water contact. This colour-coded visual simplifies the interpretation of data and helps users to quickly grasp the severity of potential flooding. The efficacy of the system was demonstrated through simulations of various use-case scenarios, showing the application's capability for real-time, interactive flood risk assessments in outside and inside areas with a good tracking method, even with a small image target.  
The general workflow for managing interactions between various objects and software was also a key contribution, ensuring smooth operation of the AR application combined with ML and giving a starting point to people that want to develop a flooding MAR application. Overall, the integration of AR and ML in this research offers significant advancements in urban flood management, highlighting the potential to enhance flood prevention and mitigation strategies in urban areas.