esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Additionally, generative adversarial networks (GANs) have been widely applied in anomaly detection tasks [23, 24], where the generator learns the distribution of normal data, and the discriminator is used to distinguish between normal and anomalous samples. By training the generator to produce samples similar to normal data, the model can detect anomalies during testing based on the discriminator's output or the generator's reconstruction error. In recent years, methods based on contrastive learning have also gained considerable attention, learning robust feature representations by maximizing the similarity between normal samples and minimizing the similarity between anomalous samples [25, 26]. This approach leverages data augmentation techniques to generate positive sample pairs and optimizes the model through a contrastive loss function, enabling the learning of more discriminative features in an unsupervised manner. Furthermore, time-series modeling methods, such as long short-term memory transformers [11] and selective state space model [27], have been widely used to capture temporal dependencies in audio signals, thereby improving the accuracy of anomaly detection [28, 29]. These models effectively handle long-term dependencies in audio data and detect anomalies by predicting future frames or reconstructing input sequences. In practical applications, data augmentation techniques and multimodal fusion methods have also been introduced to enhance the generalization capability of models. For example, combining spectral features (such as mel spectrograms) and visual features of targets can more comprehensively capture anomalous patterns [30]. Additionally, some studies have explored clustering-based methods, where normal samples are clustered into the same category, and samples far from the cluster centers are labeled as anomalies [31]. Despite the impressive performance of these methods on various public datasets, several challenges remain, such as robustness to complex environmental noise, efficiency in on-site experiments, and generalization to unknown anomaly types. Moreover, with the proliferation of edge computing and IoT devices, achieving efficient anomaly detection on resource-constrained devices is also an important research direction. To summarize, traditional methods, supervised learning-based methods, and unsupervised learning-based methods each have their own advantages and disadvantages. Traditional methods rely on expert knowledge and exhibit weak robustness but offer unique advantages in certain scenarios. Supervised learning-based methods require labeled data, provide high diagnostic accuracy, and are suitable for diagnosing known fault types. Unsupervised learning-based methods do not require labeled data and are suitable for practical application scenarios but still need improvement in detecting specific anomaly types.
## 3 Linear Streaking Measurements at Free-Electron Lasers
### Measurement resolution
For a TOF operating in a single-electron mode, it is sufficient to determine the time-of-flight from the rising edge of this signal, which can be determined with high accuracy, depending mostly on the temporal resolution of the ADC that samples the pulse. In panel b, the MCP response for multiple distinct single-electron events are depicted. This situation is typical in the case of experiments at synchrotrons, where, on average, less than one electron per X-ray shot is detected. The accumulated arrival time distribution of the single photoelectron events gives the photoelectron spectrum. The time resolution is mostly determined by the rise time of the MCP signal and the resolution of the digitizer. In contrast, in measurements at FELs, where the entire photoelectron spectrum must be recorded in one FEL shot, the sum of the individual electron signals is collected and the detector acts as a current amplifier, rather than a discriminator for counting single-electron events. The measured photoelectron spectrum is then a convolution of the actual signal and the width of the single-electron signal. Therefore, to increase the resolution of single-shot TOF measurements, the pulse width of a single-electron event must be reduced. To achieve this, MCP amplifiers with a smaller diameter can be used; however, this would reduce the collection efficiency. Independent of TOF detector issues, the THz streaking strength determines the degree of broadening of the photoelectron spectra and thus directly influences the achievable temporal resolution. The FEL pulse can be retrieved with the highest resolution, if it is overlapped at the zero crossing of the streaking pulse vector potential. At this overlap, the maximum streaking strength is present and given by [101]:  
\[s=\frac{\partial(\Delta W)}{\partial t} \tag{7}\]  
The average streaking speed is given by the ratio of the peak-to-peak amplitude of the kinetic energy shift to the rise time of the streaking vector potential:  
\[s_{\mathrm{avg}}=\frac{\Delta W_{\mathrm{max-min}}}{\Delta t_{\mathrm{rise}}} \tag{8}\]  
With these formulas, a measure of the temporal resolution of the streaking measurement can be written as:  
\[\tau_{\mathrm{res}}=\frac{\sigma_{\mathrm{field-free}}}{s}, \tag{9}\]  
where \(\sigma_{\mathrm{field-free}}\) is the width of the photoelectron spectrum without streaking field. Clearly, the temporal resolution can be increased by applying steeper streaking fields. Such fields can be achieved by scaling the THz generation to higher peak intensities, or alternatively by using higher frequency THz pulses with shorter streaking ramps.
## 3 Wireless Body Area Network (WBAN)
### PPG sensors  
A photoplethysmography (PPG) sensor is a device that measures changes in peripheral blood vessel blood volume. It detects variations in the absorption or reflection of light caused by blood flow using light sensors. PPG sensors are commonly found in wearable devices such as fitness trackers and smartwatches to estimate blood oxygen levels and monitor pulse rates. The sensor emits light onto the epidermis and measures the intensity of the light that is reflected, producing a photoplethysmography waveform. This waveform can provide insight into various health parameters and is frequently employed in applications related to healthcare and wellness [47].  
PPG is a common optical monitoring method used to track heart rate at a low cost. PPG does not require any incisions. A PPG sensor measures changes in blood flow by placing a light source and a photodetector on the skin's surface. The PPG signal has recently attracted the attention of several academics interested in extracting further data from it [48]. The PPG signal's second derivative wave carries crucial diagnostic information. Therefore, this waveform analysis can aid in identifying many cardiovascular problems. Placement of the PPG sensor on the patient's finger, earlobe, or forehead is the norm. Researchers are exploring several anatomical zones in search of more convenient measurement points.
## 4 A Case Study
### Test Cases with a Single Dynamic Obstacle  
Test cases with a single dynamic obstacle involving three different scenarios, head-on, overtaking, and crossing, were conducted in this section. All the mentioned cases are the most basic ship encounter situations. Furthermore, the research on the three basic cases is of great significance for ship collision avoidance in complex scenarios.
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 4 Experimental Evaluation  
### Datasets  
**NTU-RGB+D 60**. The NTU-RGB+D 60 [10] dataset is a highly prevalent dataset in the domain of skeleton behavior recognition. The Rose Lab of Nanyang Technological University published it in 2016. The dataset comprises 56,880 action clips, amounting to a total of 4,000,000 frames. The data modalities consist of RGB video, depth information, and skeletal data. There are a total of 60 classes included in this dataset. Every frame may accommodate a maximum of two individuals. The collection contains the three-dimensional coordinates of 25 important spots for each subject. The Kinect v2 depth-sensing camera captures the  
Figure 5: The architecture of the ACE-GCN.  
Figure 6: The architecture of the temporal convolution module.  
coordinates of these important spots. The dataset contains two assessment benchmarks: (1) Cross Subject (X-Sub): The training set contains 40,320 skeleton sequences from 20 volunteers, and the test set contains 16,540 skeleton sequences from other volunteers. (2) Cross View (X-view): The training set contains 37,920 skeleton sequences from two camera views, while the test set contains 18,960 skeleton sequences from another camera view.  
**NTU-RGB+D 120.** NTU-RGB+D 120 [11] is an expanded iteration of NTU RGB+D60. This dataset offers a greater number of samples pertaining to environmental circumstances, human traits, and changes in camera view angle. It is presently the most extensive dataset for recognizing actions in indoor settings. This dataset offers data in three distinct modalities: RGB video, depth video, and skeleton sequence. The NTU RGB+D 120 dataset consists of 120 action classes, which encompass 82 daily actions, 12 health-related acts, and 26 interactive actions. Each action was demonstrated by 106 volunteers for a total of 114,480 videos. Similarly, the NTU-RGB+D 120 dataset is set up with two evaluation benchmarks: (1) Cross Setting (X-Set): In the 32 camera position settings, if the setting parameter ID is even, the samples captured by these cameras are classified as the training set. Conversely, if the setting ID is odd, the samples captured by these cameras are classified as the test set. (2) Cross Subject (X-Sub): The 106 volunteers who perform the action demonstrations are divided into a training group and a test group, each consisting of 53 volunteers.  
**Northwestern-UCLA.** The Northwestern-UCLA dataset [12] is a multi-view dataset for 3D action recognition proposed by [PERSON] et al. in 2014, specifically designed for evaluating and developing deep learning-based action recognition models. The dataset contains actions from 10 different classes, which were performed by 10 different actors in three different Kinect v1 camera views. The 3D skeleton data for each action was recorded in the dataset, including the 3D coordinate information of 10 joints for each subject. This study follows the cross-view setting in [12]: the data acquired by the first two cameras is used as the training set data, and the data acquired by the other camera is used as the test set data.
# Loss Research and Thermal Analysis of BLDC Hollow-Cup Motor Under Reactor Suppression
###### Abstract  
In order to avoid overheating of a BLDC permanent magnet (PM) motor at high speeds, this paper focuses on the loss reduction of a 90 W 47,000 r/min BLDC hollow-cup motor. It is proposed to provide an optimizing method for the series reactors and the parameterization of reactors in the motor system. The finite element method (FEM) is used to calculate and analyze the time harmonic of air-gap magnetic flux density, stator core loss, and rotor eddy current loss in two cases: with a series reactor and without a reactor. By parameterizing the inductance value, the optimal resistance value is determined to minimize motor loss. In addition, an electromagnetic-thermal coupling analysis is conducted, and the results show that the temperature distribution of the stator core, winding, and rotor are improved under reactor suppression. Finally, an experimental platform is built to verify the temperature increase and the efficiency of the motor load operation. A clear reference for the research and optimization analysis of motor loss reduction is provided.  
BLDC hollow-cup motor; time harmonics; reactor; finite element analysis; optimization of losses; temperature field Article
## 4 Ground Roll Attenuation via NA-MEMD  
This three-component geophones record two horizontal components and one vertical component of the incident seismic data, which are an oscillation stack of different frequency sub-bands. The ground roll, which is located in the low-frequency range, is different from the reflection wave. The alignment of frequency sub-bands can be obtained from different channels of the multivariate signal by inserting them into noisy channels via the NA-MEMD method. As a result, the IMFs were aligned according to their frequency content. Then, we can utilize the low oscillation property to separate the ground roll from the reflection wave.  
Attenuating the ground roll based on the NA-MEMD method includes two steps: The first step is to use NA-MEMD to decompose multicomponent seismic data into a set of IMFs. The IMFs of ground roll are present in high-index IMFs, and their amplitude spectra are computed. A complete estimate of IMFs is reduced to identify these high-index IMFs in that the ground roll is dominant. In the second step, we remove these high-index IMFs and reconstruct the remaining low-index IMFs to obtain the seismic data without ground roll. In this workflow, the key parameter is the number of IMFs of the high-index ground roll. The number choice is a trade-off between less damage to the reflection waves and more ground roll suppression.
## 2 Background
### Terms and Definitions  
A **Knowledge Representation** (KR) is a branch of Artificial Intelligence (AI) that deals with the question of how knowledge can be represented in such a way that a machine can draw conclusions from it, derive further knowledge, or find a solution to a given problem [23; 24].  
Following the definition presented by [PERSON] et al. [25], an **Ontology** is an explicit specification of a conceptualization, which means that ontology provides well-defined concepts, relations, and other objects that specify the knowledge of a domain in an explicit way. Within knowledge-based applications, ontologies aim to overcome misinterpretation and thus optimize communication between humans and machines as well as optimizing interhuman and intermachine communication [26].  
**Design Automation** (DA) is an evolutionary step in computer-aided engineering achieved by knowledge-based engineering, which represents a merging of object-oriented programming, artificial intelligence, and computer-aided design technologies [4; 27]. Generally, design automation in engineering design requires deep insight into the design process to be able to capture and formalize the principles in the design domain [28].  
**Mass Customization** (MC) is the process of designing and manufacturing products that are customized to meet the user's needs but at speeds and costs equivalent to those used in mass production [3]. Customization in the mechanical domain is a sequence of design adaptations, which are defined as modifications of the sizing, shaping, and positioning of geometric elements as well as the modification of material or manufacturing specifications [29].  
**Design Rationale** (DR) is considered important knowledge for the future generation of product development systems [30]. In general, it refers to the explanation of why an artifact is designed the way it is [31]. Without a careful record of design rationale, a significant increase in time and effort is necessary to search for relevant answers [5].
## 4 Materials and Methods
### Synthesis and Characterization of PLGA-PEG-PLGA Copolymers  
PLGA-PEG-PLGA triblock copolymers were synthesized from LA, GA, and PEG with catalyst Sn(Oct)\({}_{2}\) by ring-opening copolymerization. In brief, calculated amounts of monomers LA, GA, initiator PEG, and Sn(Oct)\({}_{2}\) as the catalyzer were added in a glass tube under vacuum. And a nitrogen filling-exhausting process was repeated for 3 times to remove air and moisture residues from the reactants. The reaction was carried out at 150 \({}^{\circ}\)C in oil bath for 12 h with argon protection. Afterwards, the tube was cooled at room temperature, and the as-obtained products were dissolved in dichloromethane, precipitated in large amounts of cold methanol and filtered for purification. The final PLGA-PEG-PLGA triblock copolymers were collected after vacuum-dried at 40 \({}^{\circ}\)C for 2 days. \({}^{1}\)H NMR (Varian 400 spectrometer, Santa Clara, CA, USA) spectra was conducted to characterize the chemical structure of the copolymers by dissolving samples in CDCl\({}_{3}\). To determine the molecular weight as well as molecular weight distribution of the copolymers, we used GPC (Agilent 110 HPLC, Palo Alto, CA, USA) at a flow rate of 1 mL/min, with tetrahydrofuran (THF) as the eluent. Molecular weights were calibrated according to standard polystyrene samples.
## 2 Existing Technologies and the Position of the Present Research
### Position of the Present Research  
This paper presents a message communication protocol for detecting stray nodes in an ad-hoc network for scuba divers. Protocols that rely on CSMA/CA only cannot avoid contention sufficiently with carrier sensing because the propagation delay of ultrasonic communication is extremely long. A feature of the proposed protocol is that it avoids message collisions arising from this contention by using the application layer to stagger message occurrence time.  
Communication via a single channel is assumed. A feature of underwater communication is that the higher the signal frequency, the greater the signal is attenuated, rendering the usable bandwidth extremely narrow. If multiple channels are used for data transmission, the bandwidth of each channel becomes so narrow that the amount of data that can be sent becomes small, which in turn reduces transmission speed. Protocols relying on the media access control (MAC) layer described in Section 2.2 cause such a long transmission delay that it is difficult to avoid contention. Therefore, they cannot achieve efficient underwater communication in an ad-hoc network. This paper proposes a contention avoidance method that relies on the application layer. This method decreases channel contention by varying message transmission timing according to the message type. An ideal protocol for use in scuba diving is one that permits communication with stray nodes so that these nodes will be able to rejoin the diver group they belong to. Therefore, multi-hop message transmission is used. Figure 1 shows the position of the proposed transmission medium multi-access contention avoidance technology.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth Position Mapping and Transformer Integration  
Inspired by the success of Transformer architectures in capturing long-range dependencies and modeling global relationships [29], we introduce the Depth Position Mapping (DPM) module as a key component of our framework. The DPM module is designed to address the challenges of integrating spatial and depth cues in monocular 3D object detection. Unlike traditional methods that rely solely on local visual features or predefined depth priors, DPM leverages a depth-guided mapping mechanism to explicitly encode positional depth information into the feature space. By embedding depth positions into learnable queries within an encoder-decoder structure, the module enables a precise alignment of spatial and depth representations. This design enhances the understanding of scene-level depth geometry while improving the quality of feature fusion for downstream 3D attribute prediction. The DPM module thus provides a robust and efficient solution for bridging the gap between depth estimation and object localization in monocular 3D object detection.  
To further enhance these depth-sensitive features, we introduce the DepthFusion Transformer (DFT), which leverages self-attention to capture long-range spatial dependencies. By integrating local depth cues with a global spatial perspective, the model achieves more accurate depth reasoning, ultimately improving 3D object localization. This approach helps the model handle common challenges in real-world monocular 3D detection, such as occlusion and variable distances, by focusing on pertinent spatial regions and depth information. The encoder-decoder design within the DepthFusion Transformer (DFT) is pivotal in capturing long-range interactions between depth and spatial features. In the encoder, spatial features are processed via self-attention, while the decoder employs cross-attention to refine depth features against spatial context. This structure ensures themodel can effectively capture global interactions throughout the entire image, boosting object localization even in scenarios with occlusion or diverse depth scales.
# Numerical Study on Hydrodynamic Performance of a New Semi-Submersible Aquaculture Platform
## 3 Numerical Model
### Net System
#### 3.2.2 Equivalent Simulation  
As shown in Figure 7, the entire nets are divided into multiple subparts in the vertical direction, and the cables within each subpart are assumed to form a new single cable. For instance, the cables within the dashed box are replaced by a new cable positioned in the middle of the dashed box. The equivalent substitution follows two principles: (1) The weight of the new cable equals the total weight of the original cables; (2) The hydrodynamic force of the new cable equals the total hydrodynamic forces of the original cables. The same approach is used for equivalent substitution in the horizontal direction [44].  
Based on the Morison equation, the equivalent diameter \(D\), equivalent drag coefficient \(C_{D}\), and equivalent inertia coefficient \(C_{M}\) of the new cable are calculated as follows [44; 45]:  
\[D=d\sqrt{n} \tag{15}\]  
\[C_{D}=C_{d}^{\prime}\sqrt{n} \tag{16}\]  
\[C_{M}=C_{m}^{\prime} \tag{17}\]  
where \(d\) represents the diameter of a single cable; and \(n\) is the number of cables used to form the equivalent cable.  
In this study, 95 cables in the vertical direction are consolidated into 1 equivalent cable, and 60 cables in the horizontal direction are consolidated into 1 equivalent cable. The relevant equivalent coefficients of nets based on Equations (15)-(17) are presented in Table 5.  
Figure 7: The schematic diagram of net equivalent substitution.