esa-sceva/satcom-chunk-collection huggingface dataset:
# Far Off-Resonance Laser Frequency Stabilization Technology
## 1 Introduction
It seems feasible to monitor the wavelength in real time using a wavelength meter for far off-resonance laser frequency stabilization. However, the low frequency stability and the high cost of the wavelength meter seriously limit the practical application of this method. This review presents comprehensively the far off-resonance laser frequency stabilization technologies. The remaining part of the review is structured as follows: Section 2 reveals principles and applications of PDH technique. Section 3 discusses large detuning laser frequency stabilization by using the Faraday effect. In Section 4, far off-resonance frequency locking systems based on transfer cavity are presented. Conclusions and outlook are presented in Section 5.
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 1 Introduction  
Recent developments in microelectronics and additive manufacturing have enabled miniaturization of systems and the implementation of distributed architecture, thus leading to the development of constellation and mega-constellation of mini and nanosatellites [1]. These new concepts imply the utilization of a standard, low cost satellite bus and the reduced reliability and decreased lifetime become acceptable considering that the huge number of orbiting satellites can overcome problems due to single satellite failure. This new paradigm in conceiving space missions sees a growing role for the reliability analysis in spacecraft design. Besides traditional design sciences, risk management and reliability engineering are becoming increasingly more relevant in the space domain. As for aviation, the future of space safety presents even a greater challenge about providing models and methods for supporting decision-making, to free decisions from hidden goals, biases, and subjective emotions [2].  
This paper discusses the development and the application of a technique for the analyses of failure modes in a nano-satellite environment. More specifically, the paper provides an approach basedon the well-established Failure Modes, Effects and Criticality Analysis (FMECA), for determining a three-dimensional risk indicator. The paper presents a structured coding system for labeling failure modes and associating them with the CubeSat's items. An extended case study is presented to clarify the application of the proposed method, showing the benefits and lessons learnt, and specifying how the method affects the design phases in real case scenarios.  
The remainder of the paper is organized as follows. Section 2 provides an overview of the development and application of FMECA in space operations, detailing the current state of research and industrial practice, with particular reference to CubeSat architectures. Section 3 details the proposed method, clarifying the definition of specific FMECA coefficients valuable for the case under analysis. Section 4 presents an extended case study related to the application of the proposed risk management process for the purpose of managing risks of a university CubeSat. Finally, the conclusions summarize the contributions of the paper, providing potential paths for future research.
## 2 Materials and Methods
### Pipelines Setup
#### 2.2.2 Machine Learning Algorithm  
We employed a combined approach of three robust models: XGBoost, Random Forest, and neural networks. XGBoost is a sequential model that iteratively corrects errors from previous predictions, thus enhancing the predictive accuracy. Random Forest, containing multiple decision trees, excels in handling intricate datasets, offers resistance to overfitting, and highlights essential features. Neural networks are adept at deciphering complex patterns using interconnected layers of neurons, making them suitable for high-dimensional datasets. Our ensemble approach taps into the distinct strengths of each model, ensuring a comprehensive and efficient prediction mechanism.  
XGBoostXGBoost is an ensemble gradient-boosting algorithm abbreviated from eXtreme Gradient Boosting. This method has proven effective in handling geophysical data, especially with small sample sizes. We use XGBoost to build a regression model for the production prediction in this paper. The algorithm begins by training a weak learner, typically a decision tree. This tree starts with a root node encompassing all training data, and then it optimally splits the dataset based on features. The dataset partitioning continues until the data are correctly classified. Successive learners iteratively refine prediction errors from their predecessors using gradient descent on the loss function's second-order Taylor expansion. The final model aggregates these weak learners' results. For in-depth mathematical insights, we have provided a brief introduction based on the work of [PERSON] et al. [46].  
Given a dataset \(D=\{(x_{i},y_{i})\}_{i=1}^{n}\), the tree ensemble model in XGBoost is represented by a set of \(K\) functions \(\{f_{k}(x)\}_{k=1}^{K}\) from an input space to the output space. Each \(f_{k}\) corresponds to a tree structure \(q\) that maps an instance to the corresponding leaf index, yielding a predictive value from the set of leaf weights \(w\). Specifically, an instance \(x_{i}\) is mapped as follows:  
\[\hat{y}_{i}^{(t)}=\sum_{k=1}^{K}f_{k}(x_{i})=w_{q(x_{i})} \tag{3}\]The objective function to be minimized during the training of XGBoost is given by the sum of a differentiable convex loss function \(l\) and a regularization term \(\Omega\). Formally,  
\[\text{Obj}(\Theta)=\sum_{i=1}^{n}l\Big{(}y_{i},\hat{y}_{i}^{(t)}\Big{)}+\sum_{k= 1}^{K}\Omega(f_{k}) \tag{4}\]  
For the loss function, a second-order Taylor expansion provides an approximation:  
\[l\Big{(}y_{i},\hat{y}_{i}^{(t)}\Big{)}\approx l\Big{(}y_{i},\hat{y}_{i}^{(t-1) }\Big{)}+g_{i}\Big{(}w_{q(x_{i})}-\hat{y}_{i}^{(t-1)}\Big{)}+\frac{1}{2}h_{i} \Big{(}w_{q(x_{i})}-\hat{y}_{i}^{(t-1)}\Big{)}^{2} \tag{5}\]  
where \(g_{i}\) and \(h_{i}\) represent the first- and second-order gradients, respectively.  
The regularization term, which penalizes the complexity of the tree ensemble, is defined as follows:  
\[\Omega(f)=\gamma T+\frac{1}{2}\lambda\sum_{j=1}^{T}w_{j}^{2} \tag{6}\]  
where \(T\) denotes the number of leaves in the tree and \(\lambda\) is the L2 regularization term on the leaf weights.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 4 Modelling and Planning
### Geospatial Analysis  
A minor part of the reviewed literature considers geospatial analysis, wherein geographic data are manipulated through clustering methods or density calculations in order to map possible CI locations. In [37], candidate locations for CSs in urban areas are obtained from the superimposition of six geospatial data layers, including spatial data of the electric power distribution network, public transport stations, residential statistics, parking areas, etc. The geographical information is then partitioned into cells and buffer zones (effective areas for hosting CS around a point of interest), which can be used by urban planners to design future CI. The methodology enables the urban planners to rank the layers according to their importance. The authors of [3] use a hexagon overlay method to allocate CI, based on factors such as destination types, categories of residential areas, and distinguishing between night-time (potentially slow home charging) and day-time charging. Other factors include socio-economic parameters, such as average income, the presence of tourism, and geographical factors (e.g., presence of slopes). A more complex but similar approach is implemented in [66]: in this case, 15 criteria are used to allocate CSs in Ankara, Turkey. These criteria range from environmental factors (such as distance to vegetation, water, slope, and earthquake risk) to economic factors (land cost, EV ownership, and power cuts), as well as criteria concerning urbanity (proximity to main road, junction, petrol station). Once defined, these criteria are prioritized through a fuzzy analytical hierarchy process, as in [131]. The most important factors assessed are EV ownership, distance to power cut, and vegetation. Finally, the process ranks geographical locations by similarity to the ideal solution in terms of Euclidean distance. However, ref. [40] uses a graph analysis to extract reasonable links, which are a set of links that are feasible for site FCSs, and primary nodes. Through the calculation of a distance matrix and the quantification of the daily traffic flow on each link, CSs are finally allocated and sized. In [47], the authors develop an algorithm to locate and size charging stations for drayage trucks, both in the short-term and the long-term, using daily truck activities and geospatial analyses of truck yards locations within budget and grid constraints.
## 4 Result Analysis of Detection Model
### Runtime Cost Analysis  
In this section, we analyze the runtime of our proposed optimized YOLOv8 model.  
Figure 12 depicts the runtime analysis of the experiments, as mentioned earlier. Investigating runtime complexity is vital to this study as it refers to the model's efficacy in processing data and making predictions within a suitable timetable. In this research, we trained our object-detection model for 100 epochs, where each epoch needed 75 s to be completed. This research utilized an AMD Ryzen 5 5600X 6-core Central Processing Unit (CPU) and 16 GB of RAM for all the experiments. It is paired with a Graphical Processing Unit (GPU) named ZOTAC GAMING GeForce RTX 3060 Twin Edge OC GDDR6 with 12 GB video RAM (VRAM). It was revivably stated that, for a single epoch, the device required 75 s; for 100 epochs, the total time stands at 7500 s, just exceeding 2 h. Since we conducted the experiment on a good number of images, this shortest training duration shows the efficiency of the proposed model. Moreover, optimizing the model facilitates convergence while maintaining the model's mAP, which exhibited consistent improvement with each epoch. In addition to the hardware setup, the optimal configuration of this model played a significant role in enhancing the efficiency of our research. Also, it highlights the possibility of timely and precise object detection for practical applications.
## 2 Materials and Methods
### The Decoder  
The decoder part decodes the features of three levels step by step, and its inner architecture is illustrated in Figure 2. The input for it comprises three distinct components: first, the high-order multi-scale features output by GCASPP; second, the underlying features produced by the backbone network.; and third, the characteristics of intermediate fusion generated by AFFM. We introduce the Attention Modulation Module (AMM) [33] in the low-level feature to rearrange the distribution of feature importance from the channel-spatial sequential perspective, in order to better extract the detailed features of water body. AMM can enhance the secondary features and suppress the most sensitive and least sensitive features. AMM emphasizes the direct extraction of easily overlooked areas through secondary regions, which is crucial for water segmentation tasks. AMM consists of two parts: channel attention modulation and spatial attention modulation. Firstly, the input feature map will pass through the channel attention modulation stage, which utilizes average pooling and convolutional layers to explicitly capture and model the interdependence between channels. Subsequently, in order to capture internal spatial relationships more deeply in the spatial dimension, the feature map will be further modulated through spatial attention, which cascades the channel information. Additionally, AMM cleverly introduces a Gaussian modulation function, which enhances secondary features while suppressing the most and least sensitive features. This mechanism emphasizes the use of sub-important regions to directly extract important information that is easily overlooked, which is crucial for segmentation tasks. In short, AMM improves the performance of segmentation tasks by integrating attention modulation into both channel and spatial dimensions, and optimizing feature processing using Gaussian modulation functions.  
The decoder first concatenates the intermediate fusion features and multi-scale advanced features output by the encoder and performs upsampling by 2 times to obtain the fusion features. Then, a 1 \(\times\) 1 convolution operation is performed on the low-level features output by AMM to reduce channel redundancy features, and then these features are concatenated with the fused features. After fusion, we apply a 3 \(\times\) 3 convolution to refine the features and subsequently achieve the final water extraction results through upsampling by 4 times and bilinear interpolation.
## 2 Refined Numerical Modelling Approach
### Base Model Parameters  
A three-span CPCR bridge with spans of 150 m + 270 m + 150 m, constructed in Guangdong Province, China, in 1996, was selected as the base model for this study. The box-girder section was utilized and its height varied along the span direction. Specifically, the section height was 14.8 m at the end of the main span and decreased to 5.0 m at mid-span while maintaining a constant width of 15.0 m throughout the span length. For convenience, this bridge will be referred to as \"Bridge A\" hereafter. The main span of Bridge A was constructed using segmental assembly technology, with the entire bridge comprising a total of 131 segments and 63 segments forming the main span. These segments have been expertly assembled to create an integrated bridge structure. The site picture of Bridge A and its numerical segmental model are shown in Figure 1.
# Design and Application of a New Visible Light-Curing 3D Printing Platform
## 3 Results  
### Visible Light-Curing 3D Printing Platform Test  
The visible light-curing 3D printing platform encompasses several modules: a light source, control, leveling, conveying, time relay, and heat dissipation. Upon assembly of all components, the completed visible light-curing 3D printing platform is depicted in Figure 5.  
We conducted tests on various components: the peristaltic pump, the three-axis stepper motor, the time relay, and the light-curing lamp. First, we carried out the peristaltic pump flow rate test. Second, we carried out the swing test on the single cantilever. Third, we observed whether the time relay provided the interrupt signal in time. Finally, the working state of the light-curing lamp was observed. Deionized water was introduced into the system through the funnel, and its flow was managed via the peristaltic pump and connected piping. The operational state of the pump is captured in Figure 6. By executing the pre-set G-code, we monitored the movement of the single cantilever, illustrated in Figure 7. Upon activation, the time relay was set to halt after 58 s; its functioning is demonstrated in Figure 8. Lastly, when powered, the light-curing lamp emitted illumination as captured in Figure 9.  
Figure 5: Visible light-curing 3D printing platform.  
Figure 6: Peristaltic pump text.  
In Figure 6, the peristaltic pump effectively manages the deionized water from the funnel, providing a consistent and steady discharge, confirming its operational efficiency. Figure 7 showcases the single cantilever moving exactly as pre-set, verifying its proper functionality. The time relay when activated, as depicted in Figure 8, starts its countdown from 53 s, confirming its successful time management capability. Lastly, the regular light emission from the light-curing lamp, as seen in Figure 9, attests to its optimal working condition. After the energetic slurry is added into the feeding funnel, the energetic slurry will flow along the pipe under the action of the peristaltic pump. At this time, the sprinkler head located on the X-axis pulley is controlled by the control motherboard. When the sprinkler head moves to the housing position, the computer software controls the peristaltic pump to put a certain amount of energetic slurry into the tube shell through extrusion. The first control channel of the time relay starts to control the work of the transducer, the transducer energizes the ultrasonic oscillator to generate ultrasound, and the ultrasound is transmitted to the slurry in the shell through the platform and the housing, so that the energetic slurry is smooth, the first control channel of the time relay cuts off the power, and the ultrasonic oscillator stops working. The second control channel of the time relay is activated, the control curing lamp provides enough visible light radiation to cure it, and the curing of a layer of energetic slurry is completed. Through the above process, the curing of the energetic slurry is completed layer by layer into the housing.
## 1 Introduction
Despite advancements in SLAM algorithm precision and robustness, addressing this degradation issue in particle filtering continues to be a subject of ongoing research and development [9; 10; 11]. To overcome the limitations of existing algorithms, this paper proposes a novel FastSLAM method integrated with particle swarm optimization (PSO) [12]. Each particle in the PSO algorithm navigates through the solution space, updating its velocity and position by tracking both individual and group historical best positions. With its simplicity, efficiency, and ease of implementation, the PSO algorithm has achieved notable success in optimization problems across engineering, machine learning, and other domains [13; 14]. Our primary contributions include the proposal of a new particle optimization strategy that enhances the performance of particle filtering by designing a fitness function set to optimize the PSO algorithm. This set of fitness functions considers not only the contribution of particles to the current estimation but also their diversity to prevent particle degradation. We have designed a series of experiments to validate the effectiveness of the proposed method and, through performance comparison with existing SLAM algorithms, demonstrate the advantages of our approach. A series of experiments have been meticulously designed to validate the efficacy of our proposed method. These experiments not only confirm the merits of our approach but also provide a comparative analysis against existing SLAM algorithms, demonstrating the superior performance of our PSO-enhanced FastSLAM method. The structure of this paper is as follows: The first section introduces the background and research motivation of SLAM technology; the second section reviews the fundamental principles of relevant SLAM and PSO algorithms; the third section elaborates on the design and implementation of the proposed PSO-optimized FastSLAM algorithm in detail; the fourth section presents experimental results and performance evaluation; the final section summarizes the paper and proposes prospects for future research directions.
###### Abstract  
This study aimed to investigate the effects of online high-intensity interval training (HIIT) in abdominally obese women experiencing health complications due to COVID-19.  
**Methods:** Sixteen participants were enrolled and divided into the HIIT group (\(n=8\)) and moderate-intensity continuous training (MICT, \(n=8\)) group. The HIIT group underwent 20 min of exercise consisting of 20 s of high-intensity (85-90% HRmax) exercise followed by 30 s of exercise at 60% HRmax using only body weight. The main exercise program for the MICT group included 40 min of stationary bike pedaling at 65-70% HRmax. Exercise was performed three days a week for eight weeks using a smart device and application that enables bidirectional communication. **Results:** The HIIT group showed reduced body fat (\(p=0.036\)), BMI (\(p=0.021\)), and visceral fat (\(p=0.003\)) compared to the MICT group. Further, the HIIT group also had reduced insulin (\(p=0.021\)) and LDL levels (\(p=0.024\)), increased grip strength (left \(p=0.012\), right: \(p=0.002\)), and a substantial drop in total stress index (\(p=0.004\)) compared to the MICT group. **Conclusions:** Thus, online HIIT is a useful means to reduce abdominal fat, improve blood lipid profile and muscle strength, and relieve stress caused by COVID-19.  
high-intensity interval training; exercise therapy; COVID-19 exercise; abdominal obesity; sedentary lifestyle
## 4 Algorithm Design
### Hybrid Ant Colony Optimization Algorithm  
The ant colony optimization algorithm has been widely demonstrated as effective for solving VRP and EVRP [36; 37; 38]. Its core idea simulates the foraging behavior of ants, reinforcing high-quality paths through pheromone evaporation and updates to iteratively converge toward the optimal solution.  
In this study, considering the capacity constraints of CI, as well as constraints such as battery level limits, vehicle capacity, and maximum route duration, we design a feasible node access strategy under heterogeneous energy replenishment modes and incorporate problem-specific heuristic information. Furthermore, by integrating the conflict resolution mechanism for energy replenishment resources proposed in Section 3.1, we develop the HACO algorithm for the EVRP-HERI-CC.  
[PERSON] et al. [39] employed a biased and randomized greedy randomized adaptive search procedure (BR-GRASP) algorithm with a penalty function to solve the GVRP with private capacity-constrained AFSs, achieving solutions comparable to exact algorithms on benchmark instances and demonstrating higher efficiency on large-scale instances. In the IEEE CEC-12 2020 competition on the electric vehicle routing problem (the CEC-12 competition on the electric vehicle routing problem ([[https://mavrovouniotis.github.io/EVRPcompetition2020/](https://mavrovouniotis.github.io/EVRPcompetition2020/)]([https://mavrovouniotis.github.io/EVRPcompetition2020/](https://mavrovouniotis.github.io/EVRPcompetition2020/)) (accessed on 2 March 2025)), simulated annealing (SA) and genetic algorithms (GAs) were the winning algorithms [40], highlighting their effectiveness in addressing the EVRP. We also observed that the adaptive gaining-sharing knowledge (AGSK)-based algorithm was a winning method in the IEEE CEC 2020 competition [41], while its canonical form, GSK, has demonstrated strong performance in high-dimensional continuous optimization problems [42]. Although both GA and GSK belong to swarm intelligence algorithms, the latter distinguishes itself by employing a knowledge management strategy that enhances exploration in the junior phase and exploitation in the senior phase. Inspired by this mechanism, we propose integrating GSK principles into GA's mutation phase to further validate its efficacy. Specifically, we adapt the core GSK formulation to determine the number of individuals in each GA generation that should apply the junior mutation operator (significant path perturbation) versus the senior mutation operator (slight path perturbation). This allocation is governed by Equation (37), where \(popsize\) represents the population size, \(G\) indicates the current generation number, \(GEN\) corresponds to the maximum allowed generations, and \(k\) denotes the knowledge rate.  
\[D(junior\_mutation)=round(popsize\times(1-\frac{G}{GEN})^{k}) \tag{37}\]  
Based on the above analysis, this study adopts the BR-GRASP, SA, GA, and GA-GSK algorithms as baseline algorithms to solve the same problem and compares their results with those obtained using HACO.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Protocol Operation  
Another way to classify routing protocols in WSNs is to classify them according to the protocol operation. This category can be divided into five broad subcategories depending on protocol procedures. These subcategories are multipath, query, negotiation, QoS and coherent-based routing protocols. Each of these classes is detailed in the following.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 5 Methods
### TATR Encoder and Decoder
In addition, the inclusion of position-encoded spectral features aids the subsequent decoder in accurately locating the signal position.