esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development of the Strength Model
### Base Model Optimisation and Ensemble  
Following the determination of base learner combinations, model optimisation becomes a critical step for enhancing ensemble performance. To improve search efficiency and avoid local optima entrapment, this study employs the aforementioned LARO method for optimisation.  
During the experimental process, the primary optimisation parameters for SVM include the penalty parameter (C) and the kernel parameter (GM) of the radial basis function. The penalty parameter controls the model's tolerance for training errors, while the kernel parameter determines the influence range of the radial basis function. As illustrated in Figure 5a, during the initial optimisation phase of the SVM model (first 10 iterations), the fitness value rapidly decreases from an initial level of 0.385 to approximately 0.365, demonstrating LARO's efficient optimisation capability. In subsequent iterations, the fitness curve exhibits pronounced fluctuation characteristics, reflecting the effects of the Levy flight mechanism in the LARO method, which introduces random perturbations to prevent premature algorithm convergence to local optimal solutions. Within the 15-40 iteration interval, the curve fluctuation amplitude gradually diminishes, indicating the algorithm's entry into the fine search phase. After approximately 50 iterations, performance indicators stabilise, with the final determined parameter combination being: C = 47.7399 and GM = 0.0270.  
XGBoost model optimisation involves more parameter dimensions, primarily including the number of estimators (NE), learning rate (LR), subsample ratio (SA), and regularisation parameter (RL). Figure 5b presents the performance optimisation curve for XGBoost. Compared to SVM, the XGBoost fitness curve exhibits more intense fluctuations  
Figure 5: LARO optimisation convergence curves for base learners: (**a**) SVM model; (**b**) XGBoost model.  
in the initial phase, with fitness values oscillating dramatically within the 0.358-0.374 range, reflecting the complex characteristics of its parameter space. During the first 15 iterations, the algorithm experiences multiple alternating processes of local search and global jumps. Throughout the 15-70 iteration period, the fitness curve maintains relatively stable fluctuation states within the 0.364-0.369 interval. During the 70-80 iteration period, fitness values exhibit a significant decline, rapidly decreasing from approximately 0.365 to below 0.360, subsequently stabilising. The final parameter configuration obtained is NE = 158, LR = 0.0982, SA = 0.4179, RL = 0.3003.  
The optimised base models and the MLP meta-learner together constitute the ensemble architecture LARO-EnML, developed in this study for predicting the strength of MICP-treated sand. Overall, this modelling strategy integrates intelligent optimisation with model diversity, demonstrating favourable scalability and practical value while providing a methodological foundation for similar geotechnical engineering prediction problems.
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 2 Irregular Behavior Detection Dataset of Substation Personnel
### Gamma Correction Preprocessing  
The images from the IBD dataset of substation personnel were various and diverse, as they were shot at different times, in different environments, and at different angles. Thus, problems with the images in the dataset including uneven illumination, low contrast, etc. had to be accounted for. Due to the above-mentioned problems, gamma correction [18] was used to preprocess the images to reduce irrelevant information, improve image contrast, and speed up the training process of the object detection model.  
Gamma correction is a method for performing non-linear tone editing on an image based on the gamma curve, detecting the dark and light parts in the image signal, and adjusting their ratio, thereby improving the contrast of the image. Moreover, in the field of computer graphics the gamma curve [19] is a transform relationship between the screen output voltage and the corresponding brightness. Assuming a single substation operator image used as input, each pixel in the image was gamma corrected according to the following steps:  
1. Normalization: the value of each pixel in the substation personnel irregular behavior image was converted into a range between 0 and 1 according to the equation \(I_{N}=(I+0.5)/256\), where \(I_{N}\) is the value of normalized pixel and \(I\) is the value of original pixel.
2. Pre-compensation: the pre-compensated value \(I^{\prime}_{N}\) was obtained based on the equation \(I^{\prime}_{N}=I^{1/\gamma}_{N}\), where \(\gamma\) is the parameter of gamma correction.
3. Denormalization: the pre-compensated value \(I^{\prime}_{N}\) was converted back to the original range, between 0 and 255.  
Through the above three steps, a preprocessed image with gamma correction can be obtained; the effect is shown in Figure 1. Moreover, when \(\gamma<1\), images become brighter with decreasing \(\gamma\), while when \(\gamma>1\), images become darker with increasing \(\gamma\). When \(\gamma\) tends to 0 or 1, the image is extremely bright or extremely dark, and excessive correction results in low image contrast.
# A Multilevel Switched Capacitor Inverter with Reduced Components and Self-Balance
## 5 Simulation and Experimental Analysis  
### Simulations  
To validate the correctness of the proposed switched-capacitor multilevel inverter, the 13-level inverter model was built and simulated in the MATLAB/Simulink environment. The simulation parameters are listed in Table 5. In the simulation model, the input voltage \(V_{\mathrm{dc}}\) is 25 V, the load is 80 \(\Omega\) or 80 \(\Omega+15\) mH, the output frequency is 50 Hz, and the value of \(M\) is 0.9.  
Figure 8 shows the corresponding results of the proposed topology. The waves of the output voltage and current under resistive-inductive load is shown in Figure 8a. The proposed inverter outputs 13 levels and the peak voltage is close to 150 V, which also indicates a voltage gain of 6. The load current is sinusoidal, so the inverter can provide a channel for reverse current. Figure 8b presents the waves of the output voltage and current under pure resistive load. Figure 8c gives the waves of all capacitors voltage. It can be seen that the voltage of the capacitors fluctuates slightly within the allowable range during charging and discharging.  
The THD of the inverter is approximately 10.37%, as shown in Figure 9, which indicates that the modulation method used can effectively reduce the harmonic content and improve the quality of the output voltage. In addition, due to the carrier frequency being 3 kHz, the 60 th harmonic component is the highest compared to the others. Moreover, the low THD can be further reduced by smaller filters or optimized modulation strategies.
## 1 Introduction
### Literature Review
conducted a comparative analysis between EnergyPlus simulations and empirical consumption data in ongoing research into Seasonal Coefficient of Performance (SCOP) optimization, which has led to the development of dynamic evaporator temperature control mechanisms [32]. [PERSON] et al. investigated the performance of a novel heat recovery VRF system designed to optimize energy conservation while ensuring precise indoor temperature and humidity control [33]. [PERSON] et al. conducted a comprehensive review addressing the inherent modeling challenges of VRF systems, considering dynamic load variations and transient operating conditions [34]. Complementary research by [PERSON] et al. introduced a variable evaporating temperature control strategy to enhance VRF efficiency [35]. Additionally, [PERSON] et al. proposed a machine-learning-driven, catalog-based performance estimation model to improve the predictive accuracy of VRF system operations [36]. Furthermore, research has explored the application of heat pumps (HPs) for energy recovery in ventilation systems, including an examination of how calibrating Variable Refrigerant Flow (VRF) systems can significantly enhance energy efficiency in several climates and highlighting various HP technologies and their integration into building ventilation systems [29; 37; 38]. They underscore their crucial role in reducing energy consumption and promoting sustainability. This study builds on these advancements by evaluating the real-world SCOP of VRF systems in university buildings during the winter season, benchmarking their performance against conventional natural gas-fueled boiler systems. The findings underscore the transformative potential of VRF systems in meeting sustainability targets, achieving significant energy savings, and advancing nearly Zero-Energy Building (nZEB) objectives. The insights presented here contribute to support optimizing HVAC strategies for high-performance, energy-efficient buildings, reinforcing the critical role of VRF technology in global climate change mitigation efforts.
# Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault
## 5 Analysis of Short-Circuit Fault Simulation Data  
### Electromagnetic Torque Analysis of Short-Circuit Fault  
The simulation results for the above four short-circuit faults are shown in Figure 10, for the maximum transient electromagnetic alternating torque.  
Figure 9: Characteristic curve of three-phase short circuit fault.  
It can be seen that the transient electromagnetic alternating impact caused by a two-phase short-circuit fault at the generator terminals is the largest. The transient alternating impact value of the three-phase short-circuit fault on the grid side is the smallest. The transient electromagnetic alternating impact caused by generator terminal short-circuit is greater than the short-circuit fault in the power grid. At the same fault location, the transient electromagnetic impact torque caused by the two-phase short-circuit is greater than that caused by a three-phase short-circuit.  
We extracted four short-circuit fault time domains' (0.2-0.25 s) alternating torque frequencies; as shown in Table 5, the frequency fluctuation range is about 50 Hz (power frequency). In the short-circuit fault analyzed above, the frequency of the transient electromagnetic impact torque precludes the natural frequency of the torsional vibration of shifting. Therefore, in the excitation time domain of four short-circuit faults, the resonance of the torsional vibration of shafting will not occur. After the short-circuit impact fault, the system still needs a period of small-amplitude alternating torque fluctuation to return to the steady state. After the fault is removed, the residual torque fluctuation in the system affects shaft excitation.
## Main Design of ERF-IMCS  
The logical scheme of ERF-IMCS to perform topological navigation for a mobile robot is shown in Figure 1. ERF-IMCS consists of three stages, named topological mapping, global localization, and path planning, respectively. The purpose of the topological mapping stage is to search the keyframes to represent the environment discriminatively. These keyframes described by CNN features represent visually different nodes of the topological map in the environment. In each topological node, the keyframes provide semantic visual information for the robot to perform navigation more flexible and more robust. In the global localization stage, an image-based Monte Carlo localization is adopted to estimate the robot pose, integrating the image retrieval mechanism into particle filter. The image retrieval is responsible for calculating the importance weight for the resampling of samples. Besides, with respect to the image retrieval, we employ the coarse-to-fine mechanism and hash index technology to perform the process of image matching robustly and efficiently. The final stage is path planning, in which Dijkstra algorithm is employed to search the shortest graph-based path from current topological node to the target topological node. Path planning is performed by traversing a sequence of keyframe images in the topological map.
# Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features
## 1 Introduction
In recent years, CNN networks have inputted the medical field, which has high capabilities to identify many diseases, including distinguishing between normal and blasted blood cells. CNN automatically learns the hierarchy of spatial features from the input images. CNN has millions of parameters, biases, weights, and connections that can be learned and adapted while training the data. The weights and parameters are adjusted by minimizing the difference between the actual and expected data through the backpropagation of the network. The main motivation of this work is to develop effective microscopic blood slide analysis models for the diagnosis of ALL. The medical dataset lacks huge images, making it difficult to train the CNN network from scratch so that pre-trained CNN models can extract features from the C-NMC 2019 and ALL_IDB2 datasets. To improve the performance of the proposed systems, PCA was used to select only important features, combine features of CNN, and feed them to RF and XGBoost algorithms. The main major contributions of this study are as follows:  
* Enhancement of microscopic blood images by two consecutive filters;
* Applying the active contour algorithm to only extract WBC regions and feed them to CNN models;* Analysis and diagnosis of ALL images using hybrid techniques of CNN-RF and CNN-XGBoost;
* Selection of highly representative features and removal of redundant ones using PCA;
* Fusion of deep feature maps of CNN models to obtain hybrid deep feature map vectors for DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet models and their classification by RF and XGBoost classifiers. The rest of the paper is organized as follows: Section 2 analyzes previous studies' techniques for diagnosing ALL and summarizes their findings. Section 3 explains proposed strategies for analyzing microscopic blood images to diagnose ALL. Section 4 summarizes the results of the proposed methods. Section 5 discusses the performances of all systems and compares them. Section 6 concludes the study.
## 4 Maximization of Weighted Sum Rate Based on ZF Scheme  
In this section, we investigate the maximization of the weighted sum rate of the proposed MU-PhC-MIMO VLC system based on the analysis in Section 3.2, using the ZF scheme. We begin by outlining the problem statement and endeavor to transform it into an equivalent convex problem. Then, we summarize the entire algorithm's process,analyze its computational complexity, and attempt to consider reasonable simplifications for the algorithm.
# Automatic Classification of Web Images as UML Static Diagrams Using Machine Learning Techniques
## 4 Analysis of Graphical Characteristics of Diagrammatic Images
### Detection of Line Pixels Against the Background  
A pixel belongs to a line if it has a horizontally or vertically adjacent pixel with a gray intensity at least 80 levels above, in a scale from 0 (black) to 255 (white), and diagonal lines are not considered for efficiency [31]. Note that this rule does not classify pixels into two categories: there is not a unique threshold for the whole image that distinguishes black/white pixels. Instead, it only identifies line pixels by comparison with their immediate environment. That means that a given gray level could be interpreted as a 'line pixel' in a certain area of the image, and as a 'background pixel' in a different area. In this context, 'background' means light pixels either outside or inside the UML shapes.  
Let us consider the example in Figure 1. It contains a fragment of a rectangle border in dark gray (four pixels wide), surrounded by background in light gray, with a difference of more than 80 gray levels between dark and light gray. The leftmost dark pixels are identified as border pixels because they have adjacent light pixels on the left. This is the same for the uppermost dark pixels, and also for the pixels belonging to the inner border. On the contrary, those dark pixels inside the dark gray area are not identified as border pixels because they do not have light adjacent pixels, that is, a dark line that is wide enough (more than two pixels wide) can produce more than one line of border pixels. We will observe what happens with the \"inner borders\" later in Section 4.3.  
Our work presents a novel approach, with respect to the traditional way, of identifying thresholds and borders, which are not identified in two separate steps (see Section 4.2), but simultaneously in a single processing of the image in order to improve efficiency. Therefore, we do not use a fixed threshold for the whole image, but a dynamic comparison depending on the environment of each pixel. After analyzing the gray histograms in the set of images, a heuristic value of 80 levels was chosen as a good discriminator; adjacent pixels with a difference less than 80 belong to the same area, otherwise, the darker pixel is identified as a line pixel.  
Figure 1: Detection of border pixels by comparison with their immediate environment.
## 2 Hardware Settings
### Pneumatic Control Module  
The overall operating sequence of the pneumatic control module is shown in Figure 3. Initially, the air passes through a three-stage filtration system consisting of an air filter, dust filter 1, and dust filter 2. The air filter removes impurities from the air drawn into the pressure generation unit, while dust filter 1 and dust filter 2 control the particle size of airborne dust particles to 0.3 um and 0.01 um, respectively. The filtered air is then drawn in through the pressure relief 3-port valve, and the pressure within the system is regulated to fall within the range of 0.3\(\sim\)0.5 MPa using a pressure sensor. The supplied air is utilized for vacuum generation and supply pressure control. The vacuum generation process is as follows. To provide consistent pressure from irregular air pressure, the pressure of the pneumatic regulator (ARM10 series, SMC) is manually set to 0.35 MPa, and the pressure generator, i.e., the ejector (ZX2 series, SMC), creates a vacuum state. The electro-pneumatic regulator (EPR) adjusts the pressure for the vacuum within the range of \(-\)100\(\sim\)0 kPa, generating precise vacuum pressure. A pressure sensor senses the accuracy of the vacuum pressure, and it is then connected to the 3-port solenoid valve 3 to switch the vacuum state for use. The supply pressure control process is separate from the vacuum generation process. The supplied air pressure is regulated within the range of 0\(\sim\)0.9 MPa using the EPR device. A pressure sensor senses the numerical value of the pressure, and it is connected to the 3-port solenoid valve 1, increasing the internal air pressure. Finally, the 3-port solenoid valve 2 is connected to the syringe. The operating principle of the 3-port solenoid valve is as follows. When the control switch attached to the syringe dispensing system is activated, it increases the internal air pressure within the syringe through the path from 1 to 2, causing the ejection of the liquid contained in the syringe. When the control switch is not activated,  
Figure 2: Hardware for food 3D printer and syringe dispensing system.  
the supplied air is blocked, and the air is exhausted from 2 to 3. At this time, the internal state of the syringe becomes a vacuum, causing the cessation of liquid discharge.
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 3 Fast Multiple Transcoding and Encryption
### RTSP Streaming  
Real-time transmission protocol (RTSP) is a network control protocol designed for use in entertainment and communication systems to control streaming media servers. The protocol is used to establish and control communication sessions between endpoints. The transmission of data online is not the task of RTSP. Most RTSP servers use real-time transmission protocols in combination with real-time control protocols to distribute streaming media. However, some vendors implement proprietary transmission protocols. More information about RTSP is extensively explained in [25; 26].  
To provide live streaming services from CCTV camera to clients, the proposed method is a server-client model using RTSP protocol. This model consists of two part:  
1. RTSP server side: Provides multiple real-time RTSP streams at the same time. Each RTSP stream is transcoded and encrypted by transcoder box.
2. RTSP client side: Customer's devices or control center uses computer, smartphone or tablet to decrypt/decode RTSP stream.  
Figure 11: Output encrypted HEVC stream—Type 2.  
Figure 10: Output encrypted HEVC stream—Type 1.  
As shown in Figure 12, RTSP server functionality was merged into transcoding to avoid overhead of computational complexity of system while adding more functionalities. Client-side consists of two main functionalities: decryption and decoding RTP stream and rendering video. Regarding multiple live streaming sessions, an optimized RTSP based FFmpeg library was implemented to support multiple streaming simultaneously.
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 2 Topology and Principles
### Principles of Topology  
First, the capacitor power supply is pre-charged to 30 V, and the mechanical switch of the capacitor power supply is turned on. Subsequently, the STM32 microcontroller sends a control signal to activate the capacitor power supply switch and conduct the steady-state branch while turning off the power electronic branch in the transfer branch and short-circuit circuit. At this point, the system voltage of the circuit breaker is established, and the rated current flows through the steady-state branch to supply power to the load. After a brief delay, the short-circuit circuit conducts, causing a short-circuit fault in the system, leading to a rapid increase in fault current in the steady-state branch. Under the suppression of the auxiliary current-limiting branch, the growth rate of the fault current slows down, providing more time for the circuit breaker to handle the short-circuit fault. When the fault current in the steady-state branch is about to reach its carrying limit, the STM32 microcontroller issues a command to conduct the power electronic branch and turn off the steady-state branch. The power electronic branch, with a large carrying capacity, further processes the fault current. When the current in the power electronic branch  
Figure 1: **(a)** The topological structure; **(b)** circuit breaker hardware topology.  
approaches the threshold, the power electronic branch is turned off, and the fault current surges into the buffer circuit composed of capacitors, resistors, and diodes in series and parallel. When the buffer capacitor charges to the activation voltage of the varistor, the metal oxide varistor (MOV) begins to process the energy stored in the system inductors, ultimately dissipating the fault current and disconnecting the system. Simultaneously, post-fault reclosing simulation experiments were conducted. Given that overhead line faults in flexible DC systems can be transient or permanent, after the fault is cleared, there is a deionization period of 200 to 500 milliseconds [23] before automatic reclosing. In this small-scale experiment, the deionization time was set to 2 to 2.5 milliseconds. After deionization, the state of the short-circuit circuit is controlled to simulate transient and permanent fault types. Facing different fault types, the microcontroller will choose to restart the system or disconnect it.