esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition to this, these sensors also have limited range about 6-7 m, and data can be distorted by scattered light from the reflective surfaces [5]. Due to the limitations of the 3D approach, researchers prefer to employ a 2D approach for human-action recognition from multiple views [11]. The methods based on 2D models extract features from 2D images covering multiple views. Different methods have been proposed for multiview action recognition based on 2D models. However, three important lines of work are mentioned here. The first approach handles it at feature level, achieves view-invariant action representation using appropriate feature descriptor(s) or fusion of different features [12; 13], and then action recognition is performed using an appropriate classifier. The second one handles it at a classification level by determining the appropriate classification scheme. The classification is carried out either by a single universal classifier or multiple classifiers are trained, and later on their results are fused to get the final result [14; 15]. The third one utilizes the learning-based model, such as deep learning and its variations, to learn the effective and discriminative features directly from the raw data for multiview action recognition [16; 17]. Our method falls under the first category: we learn the discriminative and view-invariant features from the human silhouette. The human silhouette contains much less information than the original image, but our approach shows that this information is sufficient for action representation and recognition. By taking advantage of this simple representation, our approach not only provides high accuracy but also high recognition speed of 34 frames per second on a challenging multiview IXMAS (INRIA Xmas Motion Acquisition Sequences) dataset. Due to its high accuracy and speed our approach is suitable for real-time applications. The rest of the paper is organized as follows. Related work is discussed in Section 2, proposed approach is described in Section 3, experimentation and results are explained in Section 4, finally discussions and conclusion are presented in Section 5.
## 5 Results
### Model Performance and Cross-Lingual Analysis  
Table 5 presents the translation performance across all model and language combinations using our optimal configuration (8-shot similarity-based selection). The results reveal substantial performance variations both across models and target languages.  
**Cross-Lingual Performance Patterns:** The results demonstrate clear performance hierarchies across target languages. GPT-4 achieved the highest scores for Tarifit\(\rightarrow\)Arabic translation (BLEU: 20.2), followed by Tarifit\(\rightarrow\)French (BLEU: 14.8) and Tarifit\(\rightarrow\)English (BLEU: 10.9). This pattern aligns with our hypothesis regarding linguistic proximity effects, where extensive lexical borrowing and shared vocabulary between Tarifit and Arabic facilitate cross-lingual transfer.  
**Model-Specific Analysis:** GPT-4 emerged as the strongest performer overall, achieving the best scores across all three language pairs. The performance gap between the best and worst-performing models was most pronounced for Tarifit\(\rightarrow\)English, with a difference of 2.8 BLEU points. All models consistently followed the Arabic > French > English performance hierarchy.  
**Linguistic Proximity Effects:** The Tarifit\(\rightarrow\)Arabic language pair consistently outperformed other directions across all models, with an average improvement of 8.4 BLEU points over Tarifit\(\rightarrow\)English. This advantage can be attributed to shared Semitic substrate influences, extensive Arabic lexical borrowing in Tarifit, and similar morphological patterns.
# A Discrete Hidden Markov Model for SMS Spam Detection
## 4 Experiment Results and Discussion
### Result Discussions  
#### 4.5.1 UCI Repository Dataset Results  
We use 2/3 dataset for training and 1/3 dataset for testing, which has the same division ratio of the paper [11]. The model classified spam and ham SMS with an excellent accuracy of 0.959, which is better than those of NB, SVM, NMF, LDA, and LSTM. In addition, the model performs even better in ham classification with precision 0.969, recall 0.983, and F1 0.976. Although the spam performance is a little under expectation with precision 0.892, recall 0.816, and 0.852, it still performs better than that of LSTM.  
The performance could be better if the HMM model is trained enough. English words have many different forms. For verbs, they have past tense, present tense, future tense, and third-person singular forms. A similar situation happens in nouns and adjectives. Therefore, compared to the many English words, the total of 9955 words are not sufficient for observation states in the experiment, and words in the training dataset are less likely to reappear in the testing dataset. As a result, the spam classification performance is affected as the model does not know how to label the untrained words.  
In addition, LSTM and CNN are very complex models that consume computer resources greatly. The HMM model proposed in this paper is relatively simple, which has fast training and predicting speed. Thus, our model is easy to implement in commercial applications to process other languages.
## 5 Discussion: Future Work
### Development of Real-Time Monitoring Systems  
For fishing vessels, it is crucial to quickly monitor the current situation due to the constantly changing stability during operations and the high-speed nature of their navigation. Establishing a real-time safety monitoring system that integrates real-time meteorological data, vessel conditions, and location information could significantly enhance risk awareness and help prevent accidents. Several foundational studies on real-time safety assessments have been conducted, as noted in previous research. If real-time data can be further integrated into meteorological warning systems, vessel stability assessments, and autonomous navigation, accident prevention could be achieved with higher precision through more refined data ([PERSON] et al., 2023 [57]; [PERSON] and [PERSON], 2009 [25]; [PERSON] et al., 2023 [55]; [PERSON] et al., 2024 [49]; [PERSON] et al., 2024 [50]; [PERSON] et al., 2019 [33]; [PERSON] et al., 2012 [29]; [PERSON] et al., 2019 [5]; [PERSON] et al., 2022 [35]; [PERSON] et al., 2023 [39]; [PERSON] et al., 2023 [44]).  
To achieve this, advanced technologies must be employed. First, Internet of Things (IoT) sensors are crucial for real-time data collection. Various sensors installed on fishing vessels (e.g., GPS, speedometers, meteorological sensors, and engine status monitoring sensors) can monitor the vessel's location, speed, direction, mechanical condition, and weather conditions, providing real-time information. Additionally, by utilizing sensor network technology, data from each sensor can be transmitted to a central system, integrated in real-time, and reflected in the monitoring system. In commercial vessels, intelligent ship collision risk assessment using video data has been attempted by [PERSON] et al. (2024) [63], and there is a need to evaluate the feasibility of this approach for fishing vessels as well. Second, a reliable network is essential. Since terrestrial communications, such as LTE or 5G. are often unavailable in marine environments, real-time data transmission is challenging. Therefore, satellite communication systems, like Iridium and Inmarsat, can be employed to enable real-time data exchange even when vessels are operating far offshore ([PERSON] et al., 2008 [64]).  
However, the integration of these technologies into fishing vessels poses challenges, as many of these vessels are aging and high-end sensors are likely to be costly. Therefore, government-level support or the development of cost-effective and efficient technologies is necessary to successfully implement these systems on fishing vessels.
## 4 Discussion  
The benefits of various smart charging mode in comparison with uncoordinated mode are shown in Table 6. The PEVs battery degradation cost in smart G2V charging mode is higher than the one in V2G charging mode. This difference exists because in V2G charging mode, PEVs are used as the electricity storage. Using V2G charging mode reduces the purchased energy cost in comparison with smart G2V charging mode, while the degradation cost is higher than G2V. It is because batteries are always in interaction with the network by trading their power, in V2G mode. The difference between the degradation cost in three charging approaches is shown in Figure 9. As illustrated by the numerical results, the proposed method has a significant effect on the total charging cost of the PEVs' owners. Thus, implementing smart charging is recommended with high penetration of PEVs.  
The simulation task is done by a PC with an Intel Core i7, 3.4 GHz CPU, and 32 GB DDR4 RAM, and computational cost for different approaches is presented in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Economic Analysis**} & \multicolumn{2}{c}{**Smart G2V Charging**} & \multicolumn{2}{c}{**Smart V2G Charging**} \\ \cline{2-5}  & **Without Wind** & **With Wind** & **Without Wind** & **With Wind** \\ \hline PEVs owner benefit & 303.10 & 303.20 & 218.92 & 224.49 \\ System operator benefit & \(-\)3.67 & 2.05 & 6.91 & 6.95 \\ Net benefit & 282.03 & 287.85 & 202.33 & 207.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Various charging mode benefits.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Smart V2G Charging**} & **Without Wind** & **With Wind** \\ \hline Purchased energy cost (\$/day) & 3818.64 & 3703.775 \\ Power loss (\$/day) & 1945.86 & 1056.24 \\ Cost of power loss (\$/day) & 11.012 & 10.96 \\ Battery degradation cost (\$/day) & 434.72 & 433.17 \\ PEVs charging cost (\$/day) & \(-\)284.534 & \(-\)288.56 \\ PEVs charging infrastructure cost (\$/day) & 23.50 & 23.50 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Power loss and costs of smart V2G charging approach.  
Figure 9: PEVs Battery Degradation Cost.
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 4 Nutritional Value of Seaweed
### Carbohydrates  
In seaweed, the carbohydrate content is high, the majority of which is dietary fiber and is not absorbed by the human body [1]. The carbohydrate of seaweed is different from the carbohydrate of land-plants. In seaweed, the storage of carbohydrates has an important function in photosynthesis and osmoregulation. The type and amount of carbohydrate content in seaweed also vary based on many factors such as the type of seaweed, and physical, chemical, and biological factors. Most of the red seaweeds contain sulfated galactan (agar and carrageenan), and some of the brown seaweeds contain sulfated fuccans and alignates, while cellulose has commonly been found in large amounts in green seaweed [41].  
Carbohydrates in seaweed are found in large amounts, including monosaccharides, disaccharides, and polysaccharides; however, their composition depends on many factors. The components of monosaccharides, such as galactose, glucose, mannose, xylose, fructose, and arabinose, were found in the total sugars of seaweed [42,43]. The most representative polysaccharides in seaweed are agar, alignates, ulvan, and carrageenan [44,45]. Total polysaccharide content ranges from 4% to 76% of dry weight (dw), with the highest content found in the genera _Ascophyllum_, _Porphyna_, and _Palmaria_. In general, green seaweed genera such as _Ulva_ also have a high content, which can be up to 65% of dw [1]. For example, the carbohydrate contents of _Ulva rigida_ and _Ulva pertusa_ are 58% dw and 56% dw, respectively [46,46]. Other studies reported high carbohydrate content in _Gracilaria fisheri_ and _Gracilaria tenuistipitata_, of 63% and 59% dw, respectively [47]. For brown seaweed, _Hizikia fisheriforme_ exhibited a very high amount of carbohydrates, with a value of 72% dw [48].
## 9 Conclusions  
1. The online ultrasonic thickness measurement platform was used to detect the rolled copper tube and the accuracy of the detection was verified. The detection results were utilized to establish a tube blank model that closely resembles the actual conditions on-site. Simulations using this model produced results that are closer to reality.
2. Through finite element simulation, the effects of the outer die taper angle, drawing speed, and friction coefficient on the maximum temperature, drawing force, wall thickness, and wall thickness uniformity during the TP2 tube drawing process with a floating mandrel were obtained. The increase in drawing speed and friction coefficient leads to higher maximum temperatures during the drawing process. As the drawing speed increases, the average wall thickness and wall thickness uniformity initially decrease and then increase.
3. The finite element simulation results are in good agreement with the RBF neural network predictions, with determination coefficients of 0.899 and 0.871 for the training and testing sets, respectively, and root mean square errors of 0.0002 and 0.0009. The use of the RBF neural network to predict wall thickness uniformity in joint drawing shows high accuracy and can effectively improve computational efficiency.
4. The optimal combination of parameters for the outer die taper angle, drawing speed, and friction coefficient obtained using the particle swarm optimization algorithm is 14.77\({}^{\circ}\), 1.0687 m/s, and 0.0384, respectively. Simulations using this optimal parameter combination resulted in a maximum drawing force of 60.4 kN, a maximum  
Figure 22: Change curve. (**a**) Drawing force changes with time; (**b**) temperature changes with time.  
temperature of 102.8 \({}^{\circ}\)C, an average wall thickness of 2.161 mm, and a wall thickness uniformity of 0.01%. The minimum wall thickness uniformity was reduced by 28%.  
Conceptualization, Z.S.; methodology, Z.S. and J.L.; software, D.C.; validation, F.Y., Z.S. and H.S.; investigation, F.Y.; data curation, Z.S.; writing--original draft preparation, Z.S.; writing--review and editing, F.Y., Z.S. and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Liaoning Provincial Department of Education (LJKMZ20220 0591) and the Foundation Project. It was also funded by the Science and Technology Plan Project of [PERSON] (CQ20220057).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Conclusions  
This study explored the influence of various parameters on the efficiency of MAPbI\({}_{3}\) perovskite solar cells (PSCs) through SCAPS-1D simulation analysis. The examined PSC featured a layered configuration of ITO/TiO\({}_{2}\)/MAPbI\({}_{3}\)/Cu\({}_{2}\)O/Au, with a focus on the electron transport layer (ETL) thickness, perovskite absorption layer (PAL) thickness, and hole transport layer (HTL) thicknesses, as well as the PAL defect density and interface defect densities between the absorber and ETL layer and between the absorber and HTL layer. Optimal conditions achieving the highest photovoltaic conversion efficiency included an ETL (TiO\({}_{2}\)) thickness of 100 nm, PAL (MAPbI\({}_{3}\)) thickness of 1300 nm, and HTL (Cu\({}_{2}\)O) thickness of 400 nm, with a PAL defect density at 10\({}^{14}\) cm\({}^{-3}\) and interface defect densities at 10\({}^{13}\) cm\({}^{-3}\) for both the absorber/ETL interfaces and absorber/HTL interfaces. These optimizations enhanced the PSC's power conversion efficiency (_PCE_) to 19.30%, a significant increase from the baseline of 13.79% by 5.51%. The findings of this study are expected to contribute significantly to the optimization and efficiency enhancement of perovskite solar cells, particularly those employing inorganic charge carrier transport layers.  
Conceptualization, C.S. and B.-S.J.; software, C.S. and H.S.; methodology, C.S. and B.-S.J.; formal analysis, C.S., H.S. and B.-S.J.; writing--original draft preparation, C.S.; writing--review and editing, B.-S.J.; supervision, B.-S.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by KNU Advanced Material Research Institute, Kyungpook National University, Daegu 41566, Republic of Korea.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 13: Comparison of the current density–voltage (_J–V_) curves for the initial and optimized structures.
# Quality Monitoring for Laser Welding Based on High-Speed Photography and Support Vector Machine
## 1 Introduction
(2017) adopted the SVM model to study the relationship between the dynamic characteristics of the arc welding process and welding results. As can be seen, the applications of SVM in the field of welding are quite extensive. However, the computational cost of the SVM algorithm is always very large, due to the strong linear correlations between the input data sets of SVM. Therefore, all the data need to be preprocessed before they are trained. Real-time monitoring system of laser welding process is the basis for realizing its production automation. The signal of laser-induced plume features can directly reflect the stability of the welding process dynamically, studies conducted on the relationship between the features of metal vapor plume and the quality of welding process in high-power disk laser welding have great significance and application value for the development of real-time monitoring. But it is of highly transient and nonlinearity, which makes it difficult to obtain analytical nonlinear model with accurate quantification. And the information sources are often mixed with lots of noise, which affects the efficiency and accuracy of the extracting feature seriously. One of the key issues that the modeling of real-time monitoring system facing is to resolve how to process and analyze such features better, in order to make it possible to reflect the quality of welding accurately. In this paper, high-speed photography was used to obtain the transient image sequence of the laser-induced metal vapor during high-power disk laser welding. The dynamic characteristic changes were captured and extracted through a color image processing method. A SVM combined with Pearson product-moment correlation coefficient was used to select the features. Then, a SVM model based on the features was employed to classify the welding quality status in order to provide experimental evidence for the welding prediction and the welding process control.
# Gyrotrons as High-Frequency Drivers for Undulators and High-Gradient Accelerators
## 5 Conclusions and Outlook  
The remarkable recent progress in the development of high-performance gyrotrons with output characteristics that are appropriate for both microwave wigglers/undulators and high-gradient accelerating structures in compact particle accelerators. Although the specific requirements for each of these two applications are different, they have much in common and can easily be satisfied using a unified approach based on the available adequate self-consistent physical models and numerical codes for computer-aided design (CAD). While currently in most of the studies and experiments the utilized gyrotrons have been initially developed for heating of fusion plasmas, we anticipate the development of dedicated (specialized) tubes for gyrotron-based microwave undulators for FEL and high-gradient accelerators.  
Another important factor that will facilitate the further development of these systems is the availability of auxiliary means for the manipulation of high-power microwaves. They include advanced RF technologies for transmission (e.g., using low-loss corrugated waveguides, oversized high-power waveguide components [67], and quasi-optical lines), mode converters, steerable reflectors, pulse compressors, LDSS, etc. All this means that nowadays not only powerful gyrotrons are at hand but also the rest of the necessary components for handling their radiation already exist. It is believed that such synergy will accelerate further the development of gyrotron-powered microwave wigglers/undulators and compact accelerating structures.  
There is at least one more application of the gyrotrons related to the charged particle accelerators, namely in the Electron Cyclotron Resonance Ion Sources (ECRIS). The current (4 th generation) of ECRIS utilizing gyrotrons can provide highly-charged ions for ion accelerators (see, e.g., [68]). This topic, however, is outside of the scope of this paper and will be discussed elsewhere.  
The authors contributed equally to the preparation of this review article. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography
## 2 Materials and Methods
### Geometric Parameter Calibration Process
#### 2.2.3 Parameter Fitting and Projection Correction  
Ideally, the midpoints of all correctly matched feature-point pairs should be located on the rotation axis. So, the line where the rotation axis can be fitted by the feature-point set composed of the midpoints of the screened feature-point pairs. In this paper, the RANSAC algorithm is used to fit the rotation axis. The deflection angle of the rotation axis and the lateral deviation of the rotation axis are then solved. Finally, an affine transformation is used to correct the rotation axis deflection angle and lateral deviation of the projection data at all angles. When both the lateral deflection of the rotation axis and the angle of deflection of the rotation axis exist (\(\eta\
eq 0,\Delta u\
eq 0\)), the correspondence between the point after the deflection and the point before the deflection is:  
\[\left[\begin{array}{c}u\\ v\end{array}\right]=\left[\begin{array}{cc}\cos\eta&\sin\eta\\ -\sin\eta&\cos\eta\end{array}\right]\times\left[\begin{array}{c}u_{0}+ \Delta u\\ v_{0}+\Delta v\end{array}\right]. \tag{6}\]  
Finally, the projection data after affine transformation is reconstructed to obtain 3D volume data without geometric artifacts.