esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.8 RBF Neural Network in Network Measurement  
An RBF is used in the creation of an RBF neural network, which is a typical three-layer feedforward network. It can be applied to classify patterns and approximate functions. The RBF neural network has a simple structure, quick learning speed, excellent approximation, and excellent generalization ability when compared to other artificial neural networks. The RBF neural network works on the idea that the input vector can be directly transferred to the hidden space without the need for a weight connection by using RBF as the \"base\" of the hidden unit to build the hidden layer space. When the mapping relationship's center point is established, the mapping relationship itself is established.  
[PERSON] et al. [21] constructed radial basis function neural network and maximum likelihood probability neural network path loss prediction models, taking into account multi-transmitter scenarios and evaluating them. They also added a radial basis function neural network to an efficient path loss prediction. The projected path loss is the most recent measured value with a minimal error, and the RBF neural network's prediction impact is superior to that of the MLP neural network. RBF neural networks can map any complex nonlinear connection, and they have simple learning principles, making them practical for computer implementation. They also have good nonlinear fitting capabilities. It hasexcellent memory, robustness, and self-learning capabilities. However, the incapacity of RBF neural networks to describe the basis and process of reasoning is one of their most critical issues. The neural network cannot operate RBF to convert all problem characteristics into numbers and all reasoning into calculations when the data are insufficient. There must have been data loss in the outcomes.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
###### Abstract  
A state-of-the-art entity recognition system relies on deep learning under data-driven conditions. In this paper, we combine deep learning with linguistic features and propose the long short-term memory-conditional random field model (LSTM-CRF model) with the integrity algorithm. This approach is primarily based on the use of part-of-speech (POS) syntactic rules to correct the boundaries of LSTM-CRF model annotations and improve its performance by raising the integrity of the elements. The method incorporates the advantages of the data-driven method and dependency syntax, and improves the precision rate of the elements without losing recall rate. Experiments show that the integrity algorithm is not only easy to combine with the other neural network model, but the overall effect is better than several advanced methods. In addition, we conducted cross-domain experiments based on a multi-industry corpus in the financial field. The results indicate that the method can be applied to other industries.  
LSTM-CRF model; elements recognition; linguistic features; POS syntactic rules +
Footnote †: journal:  
_Future Internet_**2019**, _11_, 17; doi:10.3390/f11010017
## 6 Challenges and Future Trends in LEV Charging
### Advanced Charging Techniques  
In addition to the choice of power electronic converter, an appropriate charging control technique is imperative to ensure battery and user safety. Constant current-constant voltage (CC-CV) remains the most popular control scheme for LEV charging converters, mainly because of its ease in implementation. In this process, a constant current is applied for a finite time, until the battery voltage reaches at least near its nominal value. Following this, the control transitions to constant voltage, where the charging current decays to zero, with the voltage held constant. The variation of voltage and current according to this control scheme is shown in Figure 23.  
However, the CC-CV control approach has certain limitations such as long charging times, thermal deterioration of battery and low efficiency [63]. Hence, this approach does not lend itself to fast charging applications, which require higher currents.  
Figure 23: Charging current \(i_{\text{hot}}\) variation in (a) CC-CV charging mode and (b) Reflex charging mode.  
To overcome the limitations of the CC-CV, a new charging control approach known as reflex charging is proposed in recent publications [63; 72; 125; 126]. This approach allows for higher charging currents to be injected in pulses. The battery charging current profile also contains negative pulse periods and rest periods to momentarily reverse the charging process and neutralize the electrolyte, respectively. The negative period helps in reducing bubbles, which would otherwise increase battery resistance [72]. Thus, reflex charging allows for an improved thermal profile of the battery. However, one limitation of this control technique is the reduction in average charging current due to the negative pulse period. This can be counteracted by appropriate selection of pulse width and current amplitude. The variation of charging current according to this control scheme is shown in Figure 23.  
Furthermore, considering the significance of battery thermal performance from a safety standpoint, special emphasis should be placed on control techniques by considering the temperature. In this regard, a constant temperature (CT) control, which adjusts the charging current based on the ambient temperature, may be used. Through this process, one can carry out the charging process within a limited temperature band. The authors of [127] presented a charging control technique which dynamically sets the charging current according to the battery temperature. This work achieved a 20% lower cell temperature rise for a given total charging time, thus validating this control approach. A summary of discussed control techniques is shown in Table 5.
# A Neural Network-Based Partial Fingerprint Image Identification Method for Crime Scenes
## 2 Related Works
### Preprocessing of Partial Fingerprints  
The steps of fingerprint preprocessing at the crime scene are: fingerprint collection, fingerprint segmentation, and fingerprint enhancement. Since fingerprints at a crime scene cannot be seen directly with the human eye, investigators use fingerprint powder to make latent fingerprints visible and preserve the developed images as evidence. With the development of new materials and technologies [26], fingerprint collection technology is becoming more and more mature.  
The fingerprint image of the crime scene is divided into valid region of interest (ROI) and invalid background noise. The purpose of fingerprint segmentation is to segment valid fingerprint regions from the fingerprint background. In traditional fingerprint segmentation methods, the fingerprint image is evenly divided into local blocks. The quality of each local block is evaluated by indicators such as the mean and variance of the gray value. These methods work well for ordinary fingerprints, but do not perform well for crime scene fingerprints. [PERSON] et al. [27] proposed a latent fingerprint segmentation method based on deep UNets for crime scene fingerprints. They generated the latent fingerprints and their segmentation ground truth data for training. Their proposed method converts latent images to background and fingerprint regions through end-to-end training.  
The fingerprints at the crime scene were severely damaged, and the ridge structure of the fingerprints was not clear.  
For low-quality fingerprint identification, it is easy to extract false feature points, which reduces the accuracy of fingerprint identification. The goal of fingerprint enhancement is to restore the damaged ridge structure and improve the contrast between ridges and valleys. [PERSON] et al. [28] proposed a multi-task convolutional neural network (CNN) based method to recover fingerprint ridge structure from corrupted fingerprint images. Their method not only repairs minor damages, but also filters out some background noise. However, this method cannot restore severe and large damaged areas. Therefore, extracting features from limited fingerprint areas is an important direction for partial fingerprint identification.
# Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness
###### Abstract  
Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity--commonly referred to as the non-independent and identically distributed (non-IID) nature of client data--remains a fundamental challenge. To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. The proposed approach introduces two key innovations. First, a heterogeneity quantification mechanism is designed based on statistical feature distributions, enabling the effective measurement of inter-client data discrepancies. This metric is further employed to guide the model aggregation process through a heterogeneity-aware weighted strategy. Second, a multi-loss optimization scheme is formulated, integrating classification loss, heterogeneity loss, feature center alignment, and L2 regularization for improved robustness against distributional shifts during local training. Comprehensive experiments are conducted on four benchmark datasets, including CIFAR-10, SVHN, MNIST, and NotMNIST under Dirichlet-based heterogeneity settings (alpha = 0.1 and alpha = 0.5). The results demonstrate that the proposed method consistently outperforms baseline approaches such as FedAvg, FedProx, FedSAM, and FedMOON. Notably, an accuracy improvement of approximately 4.19% over FedSAM is observed on CIFAR-10 (alpha = 0.5), and a 1.82% gain over FedMOON on SVHN (alpha = 0.1), along with stable enhancements on MNIST and NotMNIST. Furthermore, ablation studies confirm the contribution and necessity of each component in addressing data heterogeneity.  
federated learning; data heterogeneity; heterogeneity-aware; weighted aggregation; multi-loss function +
Footnote †: journal: Applied sciences  
1
# Advanced Scheme to Generate MHz, Fully Coherent FEL Pulses at nm Wavelength
## 2 Overview of Methods
### Employing an Oscillator in Standard Seeding Schemes
#### 2.1.1 High-Gain Harmonic Generation (HGHG)  
HGHG is a method to achieve fully coherent and stable seeded radiation in high-gain FELs and was introduced in [11]. The components needed are a modulator, a seed laser resonant to the wavelength of the modulator, a dispersive section, and an FEL amplifier tuned at a harmonic of the seed laser wavelength. The seed laser is overlapped with the electron bunch in the modulator, and their interaction results in a longitudinal sinusoidal energy modulation along the electron bunch with the periodicity of the resonant wavelength. In the dispersive section placed downstream, the energy modulation is converted into density modulation that includes relevant harmonic content. The dispersive section is characterized by the \(R_{56}\) matrix element of the transfer matrix, which describes the evolution of the 6-D phase space (x, x', y, y',\(\delta_{\gamma}\), z) of the electrons. The \(R_{56}\) is closely related with the presence of longitudinal dispersion. When a correlation between the longitudinal position (z) and a relative energy offset (\(\delta_{\gamma}\)) is established in the modulator, it is possible to choose an \(R_{56}\) to rotate the longitudinal phase space, and convert the energy modulation into longitudinal density modulation. The same matrix element is responsible for the so called bunch compression in accelerators, where we exploit an electron beam with an energy-longitudinal position correlation (electron beam energy chirp) to compress it longitudinally and increase its peak current. After the dispersive section, the bunched electron beam then enters the amplifier and emits coherent radiation. In the case of an HGHG oscillator-amplifier, an optical cavity which encloses the modulator is added as shown in Figure 1. Instead of injecting a seed laser pulse for each consecutive electron bunch, the optical cavity stores a radiation pulse which acts as a seed laser source. Because, as discussed above, a certain amount of power gain is required at each pass, the modulator is longer than in a conventional HGHG scheme.
# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 3 Results and Discussion
### Data Preprocessing Analysis  
The model with both Batch Normalization layers and dropout layers was used to evaluate the impact of data preprocessing on the learning effect of the proposed algorithm. As shown in Table 6, the evaluation indicators of the model gradually increase with data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configurations** & **BN + Dropout** & **Dropout** & **BN** & **None** \\ \hline Accuracy & 96.0\% & 93.8\% & 95.7\% & 93.8\% \\ Precision & 96.4\% & 94.3\% & 96.1\% & 94.2\% \\ Recall & 96.1\% & 93.7\% & 95.7\% & 93.7\% \\ F-score & 96.0\% & 93.7\% & 95.6\% & 93.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of the algorithms with different configurations. Batch Normalization+Dropout: both Batch Normalization and Dropout were added to the network. Dropout: only Dropout was added to the network. Batch Normalization: only Batch Normalization was added to the network. None: network with neither Batch Normalization nor Dropout.  
Figure 7: Changes in the training curves of networks in the same dataset with or without Batch Normalization.  
preprocessing. The average accuracy grows from 79.4% to 83.4% when the training images were cropped to a smaller size. The same trend is also observed in terms of average precision, average recall, and average F-score, increasing from 79.2% to 83.2%, from 80.1% to 84.9%, from 79.3% to 83.2%, respectively. This is because the cropped images contain only the most valid information of the sperm head with minimal disturbance from other sperms or debris. The concise information in the cropped image is helpful for the program to capture the effective features of the objects.  
In most cases, the head contours of different sperm are not distinct enough, especially between the tapered and pyriform categories. Therefore, further preprocessing is needed to improve the performance of the classifier. In this study, the cropped images were rotated to orient the sperm head to the uniform direction. When the sperm heads were aligned to the same direction, the average accuracy, average precision, average recall and average F-score are greatly improved to 96.0%, 96.1%, 96.4%, and 96.0%. The significant improvement of the rotation processing is mainly attributed to the fact that the algorithm did not need to waste computation to estimate and differentiate the position and direction of the sperm. Therefore, the deep learning network can focus on the analysis of the head morphology without dealing with the spatial and orientation disturbance.
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 5 Challenges in Evolutionary Reinforcement Learning
### Issues Encountered by Reinforcement Learning
#### 5.1.1 Parameter Sensitivity  
The performance of reinforcement learning depends heavily on the correct setting of parameters like the learning rate, discount factor, policy network parameters, and reward function parameters. The proper adjustment and optimization of these parameters are crucial for a stable and effective reinforcement learning process. Poor parameter settings can lead to unstable training, slow convergence, or a failure to find effective policies. EAs help tackle these issues by dynamically adjusting the parameters through evolutionary operations. EAs also increase the diversity of the parameter space, making it easier to find the best combination of parameters. This approach ensures that the reinforcement learning system can perform well even in complex and changing environments. According to [4], the quality of the final results of parameter control methods for metaheuristics with reinforcement learning is highly correlated with the values of these parameters. Therefore, ref. [4] introduced the SBARL method, aiming to dynamically adjust and evolve these parameters while maintaining a static configuration. Specifically, the evaluation of PBT workers in SBARL aligns with their average reward in each training period. The less well-performing workers adopt the parameter settings of the better performing workers as a reference for optimization and evolution. Similarly to SBARL, ref. [42] dynamically selected the optimal local skills to adapt to the varying requirements of a multi-agent environment, effectively addressing the parameter sensitivity issue in reinforcement learning. The proposed MAEDyS [42] not only utilizes policy gradient methods to learn multiple local skills but also enhances the capability to handle parameter sensitivity in complex multi-agent settings through the dynamic selection and optimization of these local skills. Besides dynamic parameter adjustment, ref. [92] employed a genetic algorithm to directly search and optimize parameters. The approach explores the parameter space through the selection, mutation, and crossover operations of the genetic algorithm, enhancing the diversity of the parameter space. This methodology aids in finding the optimal parameter
# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 2 You Only Hear Once (YOHO)  
### Motivation  
In this paper, we intend to make the neural network output labels that are closer to human-readable labels. This way we make the pipeline more end-to-end. Figure 1 illustrates a comparison between segmentation-by-classification and the YOHO paradigm. For both paradigms, a mel spectrogram of shape 801 \(\times\) 64 is fed as input. In segmentation-by-classification, each time step is classified as music, speech, both, or none. Subsequently, these classifications are converted to human readable labels. However, in YOHO, each block of 0.307 s is processed through regression. One neuron detects the presence of an acoustic class. If the class is present, one neuron predicts the start point of the class and one neuron detects the end point of the class. Subsequently, during post-processing, these blocks of 0.307 s are merged to form a final prediction. Using this technique, the number of time steps is reduced from 801 to 26, which makes the network significantly faster, generalise better, and more end-to-end. More details on the implementation are given in the below subsections.
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
###### Abstract  
The airborne multi-wavelength light detection and ranging (LiDAR) system measures different wavelengths simultaneously and usually includes two or more active channels in infrared and green to acquire both topographic and hydrographic information. The reflected multi-wavelength energy can also be used to identify different land covers based on physical properties of materials. This study explored the benefits of multi-wavelength LiDAR in object-based land cover classification, focusing on three major issues: (1) the evaluation of single- and multi-wavelength LiDARs for land cover classification; (2) the performance of spectral and geometrical features extracted from multi-wavelength LiDAR; and (3) the comparison of the vegetation index derived from active multi-wavelength LiDAR and passive multispectral images. The three-wavelength test data were acquired by Optech Titan in green, near-infrared, and mid-infrared channels, and the reference data were acquired from Worldview-3 image. The experimental results show that the multi-wavelength LiDAR provided higher accuracy than single-wavelength LiDAR in land cover classification, with an overall accuracy improvement rate about 4-14 percentage points. The spectral features performed better compared to geometrical features for grass, road, and bare soil classes, and the overall accuracy improvement is about 29 percentage points. The results also demonstrated the vegetation indices from Worldview-3 and Optech Titan have similar characteristics, with correlations reaching 0.68 to 0.89. Overall, the multi-wavelength LiDAR system improves the accuracy of land cover classification because this system provides more spectral information than traditional single-wavelength LiDAR.  
multi-wavelength LiDAR system; land cover classification; normalized difference vegetation index 26 April 2017; 7, 663; doi:10.3390/app7070663
## 3 Methodology
### Parking Space Automatic Annotation
#### 3.3.2 Obstacle Vehicle Detection and Vacancy Annotation
Specifically, if the point cloud density outside the extracted boundary line is significantly lower than inside, and the difference between the length of the extracted line and the prior size of the obstacle boundary is within a certain threshold range, then the extracted line is identified as the boundary straight line of the obstacle. If these convergence conditions are not met, the extracted straight line is treated as a boundary range, and the fitted straight line is used as prior information for the next boundary straight-line fitting process. Subsequent boundary extraction will be based on this range. After the obstacle boundary extraction is completed, the boundaries of adjacent obstacles form a free space, as shown Figure 13. Calculate the coverage of the free space. If the coverage can envelop one or more parking spaces, that is, the size of the free space is proportional to the length \(size_{l}\) or the width \(size_{w}\), then mark it as a free parking space:  
\[\left\{ \begin{aligned} space_{l}\propto size_{l}\\ \text{or}\\ space_{w}\propto size_{w}\end{aligned} \right.. \tag{30}\]  
where \(space_{l}\) represents the length of the free space of the parallel parking slots; \(space_{w}\) represents the width of the free space of the perpendicular. During the driving process, it is important to note that the vehicle body and the parking space (occupied by another vehicle) are not always perpendicular to each other. In our algorithm, we have incorporated a technique: upon extracting the first straight line, we calculate its relative coordinates. Subsequent straight lines are then transformed into the coordinate system of this first straight line. Additionally, angled parking spaces are handled similarly to parallel and perpendicular spaces in reality. For angled parking spaces, the process involves rotating and translating the vehicle and other vehicle point clouds into the coordinate system of the first extracted straight line. We then calculate \(space_{w}\) and compare it with \(size_{w}\).