esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver  
[PERSON]  
1 Institute of Electronic Engineering, CAEP, Minyang 621999, China; [EMAIL_ADDRESS] (P.W.);  
[EMAIL_ADDRESS] (J.T.); [EMAIL_ADDRESS] (D.L.)  
[PERSON]  
1 Institute of Electronic Engineering, CAEP, Minyang 621999, China; [EMAIL_ADDRESS] (P.W.);  
[EMAIL_ADDRESS] (J.T.); [EMAIL_ADDRESS] (D.L.)  
[PERSON]  
1 Institute of Electronic Engineering, CAEP, Minyang 621999, China; [EMAIL_ADDRESS] (P.W.);  
[EMAIL_ADDRESS] (J.T.); [EMAIL_ADDRESS] (D.L.)  
[PERSON]  
2 Department of Electronic Engineering, Rockef Force University of Engineering, Xi'an 710025, China;  
[EMAIL_ADDRESS]  
[PERSON]  
Correspondence: [EMAIL_ADDRESS] 2  
substantial frequency band to UWB, rendering it freely available [10; 11; 12; 13; 14; 15]. This development enabled the integration of UWB into the civilian sector, with TH-PPM emerging as a vital means within this landscape in the subsequent years [16; 17; 18; 19; 20; 21]. Existing literature often assumes well-established synchronization when analyzing BER performance [22; 23]. However, due to the extremely narrow nature of TH-PPM pulses, achieving reliable synchronization proves challenging for conventional TH-PPM receivers [24; 25].  
Contributions: In this paper, we propose a novel non-coherent \(Q\)-ary TH-PPM transceiver that exhibits robust BER performance even under non-ideal synchronization conditions. Therefore, it is characterized by a straightforward implementation. The BER performance is analyzed under ideal synchronization conditions and non-ideal synchronization conditions and compared to the BER performance of conventional TH-PPM. Numerical simulations are utilized to validate the analytical framework presented in this study.  
The paper is organized as follows. Section 2 introduces the conventional TH-PPM transceiver and the new TH-PPM transceiver. In Section 3, the BER performance is analyzed under ideal synchronization conditions and non-ideal synchronization conditions, both for the conventional TH-PPM and the new TH-PPM. In Section 4, the numerical simulations of BER performance are proposed. In Section 5, we propose a brief discussion comparing the results of known work in the field of UWB. Section 6 is the summary of this paper.
# Effective Optical Scattering Range Determination Based on Ray Tracing
## 4 Discussion  
The imperfections of an optical surface lead to scattering and are an important source of stray light. However, the selection of the layout of an optical system is also critical and should be considered simultaneously. Due to the stochastic nature of surface imperfections and the diversity of optical systems, this paper discusses a method to determine the effective optical range of the scattering. The directional information of the delivered scattering is recorded. Based on the experimental data, the directional information on the one hand can determine the effective spatial frequency range, and the band-limited rms roughness can hence determine the effect of typical surface structures such as intrinsic texture, waviness, etc. The results show that the effective frequency range is different for different optical elements in a system even through the same surface is assumed. On the other hand, the effect of local defects such as a single particle or scratch is also discussed. By means of the ray tracing method discussed in this paper, the averaged angle of incidence and the range of scattered angle can be determined, which provides valuable information for scattering measurement. For example, for the mentioned system, the scattering measurement can be  
Figure 11: The scattering image of small particles on the superpolished sample (**a**) and the instrument signature (**b**).  
Figure 12: The scattering caused by local particles. (**a**) The optical path of the scattering which filled the entire detector. (**b**) The probability distribution of angular deviation from the specular direction due to small particle scattering.  
conducted at the angle of incidence of \(5^{\circ}\) and the scattered angle ranges from \(-20^{\circ}\) to \(20^{\circ}\). In this way, it is no longer necessary to conduct many anisotropic scattering measurements.  
In the future, more typical surface structure as a result of figuring, polishing and coating will be considered. The accumulation of contamination will also be considered. Furthermore, the polarized scattering can be measured and polarization ray tracing can be applied to analyze the polarization reaction of the optical system.  
Conceptualization, R.L.; Data curation, S.W.; Formal analysis, R.L.; Funding acquisition, Z.W.; Investigation, Z.C.; Methodology, Z.C.; Project administration, Z.W.; Resources, S.W. and C.F.; Software, R.L.; Supervision, C.F.; Writing--original draft, R.L.; Writing--review and editing, R.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 62075214; National Key R&D Program of China, grant number 2020 YFC2200104.  
The data in publicly available for asking.  
The authors declare no conflict of interest.
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 3 Proposed VSFCM Algorithm
### Cluster Initialization Method Induced by Cut-Off Distance  
Here is a new cluster center initialization method. The following is the corresponding flowchart (Figure 2).  
The DPC algorithm is used as the start point, where the local density \(\rho\) of the data point and its minimum distance \(\delta\) between itself and other points with higher local density still use the previous calculation formulas, namely (7) and (9).  
In this study, we put forward a new cut-off distance as follows:  
\[cd=\min((cd_{1}+cd_{2})/2,cd_{1}). \tag{12}\]  
Among them,  
\[cd_{1}=D_{position},cd_{2}=\frac{d_{\max}}{2c}. \tag{13}\]  
We arrange \(d_{kj}\) in ascending order, and we might as well write the resulting ordered sequence as \(D\) (\(d_{1}\leq d_{2}\leq\ldots\leq d_{\max}\)). Moreover, \(n\) is the number of data points, and c is the number of clusters. \(d_{kj}\) is the distance from the data point \(x_{k}\) to \(x_{j}\). The cut-off distance recommended by the DPC algorithm should make the average number of neighbors of each data point about 1-2% of the total data. So, we choose the cut-off distance according to the upper limit ratio of 2%, which can be taken as \(position=round(2\%\times M)\), where \(M=\frac{1}{2}n(n-1)\). Hence we get \(D_{position}\), namely \(cd_{1}\).This is the first factor.  
Then, the data are divided into c clusters in the form of containing the maximum distance \(d_{\max}\) as the radius. Additionally, the reference number is \(\frac{d_{\max}}{2c}\), which is recorded as \(cd_{2}\). In the actual processing, \(cd_{1}\) and \(cd_{2}\) are combined as another factor \((cd_{1}+cd_{2})/2\).  
Considering that the average number of neighbors is about 1-2% of the total data, and selecting the smaller one of the two factors, (12) is obtained. Then, naturally, we use the new local density calculation formula:  
\[\rho_{k}=\sum f(d_{kj}-cd). \tag{14}\]  
Figure 1: The idea of the proposed VSFCM algorithm.  
Next, we introduce parameters \(\tau_{j}\)\((j=1,\cdots,n)\) to calculate the initial cluster centers directly. The formula is as below:  
\[\tau_{j}=\rho_{j}\times\delta_{j}. \tag{15}\]  
The traditional DPC algorithm uses the \(\rho-\delta\) distribution map to subjectively select the cluster centers, which is easy to cause human error. In contrast, the initialization method we proposed can automatically select a more appropriate cut-off radius so that the selected initial cluster center is closer to the true value. Specifically, we calculate the parameters \(\tau_{j}\)\((j=1,\cdots,n)\) and sort them from small to large, and then select the point of the largest value of \(\tau_{j}\) as the first initial cluster center. In the next high-density point selection process, we limit the distance between the current cluster center and other selected cluster centers to  
Figure 2: The flowchart of the initial method CDCI.  
be greater than the cut-off distance, ensuring that we can choose the initial cluster centers and viewpoints more conveniently, efficiently, and accurately.
# Glass Defect Detection with Improved Data Augmentation under Total Reflection Lighting
## 2 Composition of Defect Detection System for Mobile Phone Flat Glass
### Light Source and Illumination Design of the Glass Defect Detection System  
The traditional illumination methods for glass defect detection generally include high-angle illumination, low-angle illumination, coaxial illumination, or their combination. Illumination is applied to highlight the surface features of the detected object and make it easier to distinguish the foreground and background, while traditional illumination methods fail to perform well in distinguishing the bright spot defects and dust defects of mobile phone flat glass. Considering the special optical characteristics of the reflection and transmission of mobile phone flat glass and the scattering properties of defects such as scratches, point defects, dust, and stains, this study designed a lighting device with the main light source (TR) one of total reflection lighting and the auxiliary light source (OD) one of grazing incidence lighting. The principle of the optical path is shown in Figure 3.  
Figure 2: Defect detection flow-chart of mobile flat glass.  
According to the principles demonstrated in Figure 3, under the designed combined light source, the light fails to reach the designated position on the flat glass because of the errors caused by the mutual influence of the TR and OD. In response, the following restrictions should be placed:  
1. The light of the main light source should not radiate to the surface of the flat glass of the mobile phone and instead enter from the side of the flat glass.
2. The light of the auxiliary light source should not enter the flat glass of the mobile phone and instead graze the surface of the flat glass.  
With the above limitations, a light source structure is designed as shown in Figure 4, using a light source spacer to separate the main light source rays from the auxiliary light source rays and isolate them in a dedicated outlet, with a diffuse film on the inner wall of the light channel aiming to absorb non-lateral rays to ensure that the light illumination is uniform and that the main light source rays do not irradiate on the surface of the cell phone flat panel glass. This combined light source is deployed on both sides of the flat panel of the cell phone and is not depicted in Figure 4.  
Figure 4: Schematic diagram of the light source structure.  
Figure 3: Schematic diagram of the light path of the TR-OD combined light source. (**a**) Total internal reflection lighting; (**b**) low-angle grazing lighting.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 1 Introduction
Combining spatial and temporal representation learning through a GAF-CNN-SENet-GRU architecture, our approach effectively addresses the unique challenges of dynamic load behavior and distributed task execution observed in modern test and training infrastructures. 2. We design an innovative spatiotemporal attention fusion module that adaptively learns the significance of both spatial and temporal components in the data. This fusion mechanism enhances the model's ability to generalize across diverse scenarios and operational conditions typically observed in Logical Range settings. 3. We conduct comprehensive experiments on real-world Logical Range datasets. The results demonstrate that our model outperforms state-of-the-art baselines in terms of accuracy, generalization, and robustness. Specifically, GCSG achieves an \(R^{2}\) of 0.86, a mean absolute error (MAE) of 4.5, and a mean squared error (MSE) of 34, underscoring its practical utility in supporting more efficient and responsive resource scheduling across complex range systems.
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 2 Related Works  
There are two main categories of methods used for identifying scripts in natural scene images: traditional machine learning methods that involve manually designed features for script identification, and deep learning-based methods that automatically extract script features using techniques such as convolutional neural networks and transformer-encoding blocks. In general, automatic feature extraction based on deep learning has better performance than traditional handcrafting [5]. Traditional methods can achieve high accuracy for script identification of document images but are not suitable for scene text because they usually contain less text and are more difficult to image.  
Over the past few years, there have been significant advancements in script identification technology due to the development of convolutional neural networks [6]. These networks offer an end-to-end solution that is more efficient and less time-consuming than traditional methods, and they continue to evolve and improve over time. The classic CNN network such as first AlexNet [7] to VGG [8], ResNet [9], and then to MobileNet [10], EfficientNet [11], and so on. Various pooling methods, such as overlapping pooling [12], global average pooling [13], and feature pyramid pooling [14], have been employed. Loss functions, including cross-entropy loss [15], focal loss [16], and dice loss [17], have also been used. In addition, commonly used optimization techniques include SGD [18], Adam [19], and AdamW [20]. To overcome the issue of fixed-size input images, fully convolutional networks and global pooling layers have been adopted. The ResNet residual learning framework solves the problems of degradation and precision saturation caused by deep networks and improves the identification accuracy by paying attention to the residual structure. Res2 Net is an advanced version of ResNet that enhances the fine-grained multi-scale feature and receptive field size capabilities by introducing deep residual blocks.  
Several approaches have been proposed for script identification. [PERSON] et al. [21] designed a shallow CNN based on AlexNet, which introduced the horizontal position invariant. To handle irregular text, [PERSON] et al. [22] proposed an attention-based sequence recognition and correction network. [PERSON] et al. [23] proposed a CNN-LSTM network that extracts global and local features and employs a softmax layer to calculate patch weights for attention. [PERSON] et al. [24] proposed a multivariate time series classification method composed of LSTM and FCN, which showed better results with the addition of an attention mechanism. [PERSON] et al. [25] processed similar sequence inputs with a patch aggregator and converted the number of channels to the number of script classes. Finally, [PERSON] et al. [26] used an Encoder and Summarizer to extract local features and fused them using an attention mechanism to reflect the importance of different patches.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method
### Extraction of Color Components in Airlight
#### 3.1.1 Execution of the LAB Color Space Conversion Equation  
To execute the proposed method, the LAB color space conversion equation is used to convert the entered RGB image into the CIELAB color space. The well-known LAB color space conversion equation is shown below [20,21].  
\[\begin{array}{c}L=116f\left(\frac{Y}{Y_{n}}\right)-16\\ a=500\left[f\left(\frac{Y}{X_{n}}\right)-f\left(\frac{Y}{Y_{n}}\right)\right] \\ b=200\left[f\left(\frac{Y}{Y_{n}}\right)-f\left(\frac{Z}{Z_{n}}\right)\right] \end{array} \tag{7}\]  
Here,  
\[\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=\begin{bmatrix}0.4124&0.3576&0.1804\\ 0.2127&0.7152&0.0722\\ 0.0193&0.1192&0.9502\end{bmatrix}\begin{bmatrix}R\\ G\\ B\end{bmatrix} \tag{8}\]  
\[f(q)=\left\{\begin{array}{c}\sqrt[3]{q},\qquad q>0.008856\\ 7.787q+\frac{4}{25},\qquad q\leq 0.008856\end{array}\right. \tag{9}\]  
\[X_{n}=95.047,Y_{n}=100.000,Z_{n}=108.883 \tag{10}\]  
Here, R, G, and B are the pixel values of the R, G, and B channels, respectively, and L, a, and b are the pixel values of the L, a, and b channels, respectively. Further, \(X_{n}\), \(Y_{n}\), and \(Z_{n}\) are the tricolor stimulation values of the reference white light in the CIE XYZ color space.
## 6 Recommendations and Future Directions in Maritime IoT Data Analysis  
The advent of the Fourth Industrial Revolution, characterized by IoT, AI, Big Data Analytics, and Cloud Computing, has significantly reshaped the maritime industry. As our comprehensive review demonstrates, the implications of data analysis in maritime IoT applications are vast. Here, we outline key recommendations and future directions, largely stemming from the insights of expert reviewers and emerging trends:  
1. Ensuring Data Quality:
2. Embrace routine sensor calibration and periodic maintenance.
3. Harness data validation and filtering algorithms to tackle anomalies.
4. Champion industry-wide data standardization initiatives.
5. Advocate for collaborative data sharing platforms for cross-verification.
6. Strengthening Data Security:
7. Prioritize data encryption, both during transit and when stored.
8. Mandate regular security audits and penetration testing.
9. Establish robust access controls and rigorous authentication protocols.
10. Empowering Small-scale Maritime Operations:
11. Endorse open-source data analysis platforms.
12. Integrate cloud-based solutions, sidestepping the need for costly infrastructure.
13. Promote collaborations and consortiums for cost-sharing and resource pooling.
14. Fostering Interoperability:
15. Lead the charge for universal maritime data standards.
16. Integrate versatile middleware solutions for system harmonization.
17. Catalyze joint development initiatives, uniting stakeholders.
18. Harmessing AI and Machine Learning:
19. Capitalize on AI for dynamic functions, such as predictive maintenance and cargo tracking.
20. Recognize the boundless potential in realms such as autonomous shipping and advanced threat models.
21. Upholding Ethics and Data Privacy:
22. Establish transparent data policies.
23. Instill a culture of data ethics through consistent training.
24. Stay abreast of and compliant with evolving data privacy laws.
25. Expanding Case Studies:
26. Highlight success stories such as the Port of Singapore's traffic management analytics.
27. Showcase pioneering predictive analytics models, such as those adopted by Maersk Line.
28. Anticipating Maritime Tech Evolution:
29. Prepare for cutting-edge trends, such as edge computing and digital twins in maritime.
30. Encourage tech partnerships to stay ahead in the rapidly evolving landscape.
31. Prioritizing Environmental Conservation:
32. Leverage analytics for fuel-efficient routes and reduced emissions.
33. Develop systems to manage ballast water discharge, ensuring environmental sustainability.
34. Bridging the Research Gap:
35. Venture into developing resilient AI models tailored for maritime nuances.
36. Explore quantum computing's potential in maritime logistics.
37. Delve into the socio-economic implications of IoT in maritime contexts.  
By addressing these areas and implementing these recommendations, the maritime industry can fully leverage the benefits of IoT and data analysis, propelling it towards a technologically advanced and sustainable future.
# Application of Surge Arrester in Limiting Voltage Stress at Direct Current Breaker
###### Abstract  
Hybrid DC circuit breakers combine mechanical switches with a redirecting current path, typically controlled by power electronic devices, to prevent arcing during switch contact separation. The authors' past work includes a bipolar hybrid DC circuit breaker that effectively redirects the fault current and returns it to the source. This reduces arcing between the mechanical breaker's contacts and prevents large voltage overshoots across them. However, the breaker's performance declines as the upstream line inductance increases, causing overvoltage. This work introduces a modification to the originally proposed hybrid DC breaker to make it suitable to use anywhere along DC grid lines. By using a switch-controlled surge arrester in parallel with the DC breaker, part of the arc energy is dissipated in the surge arrester, preventing an overvoltage across the mechanical switches. Based on the experimental results, the proposed method can effectively interrupt the fault current with minimal arcing and reduce the voltage stress across the mechanical switches. To address practical fault currents, tests at high fault currents (900 A) and voltage levels (500 V) are conducted and compared with simulation models and analytical studies. Furthermore, the application of the breaker for the protection of DC distribution grids is illustrated through simulations, and the procedure for designing the breaker components is explained.  
DC grid; DC circuit breakers (DCCBs); surge arrester; DC protection +
Footnote †: journal: Applied sciences
Carotenoids are classified as carotenes containing only carbon and hydrogen atoms, and xanthophyls (oxocarotenoids), which also have one or more oxygen atoms [40]. Although carotenoids constitute a much larger group of over 1200 structures [41], typical diets can provide approximately 50 carotenoids [42]. The main bioavailable dietary carotenoids are lutein, zeaxanthin, \(\beta\)-cryptoxanthin, \(\alpha\)-carotene, \(\beta\)-carotene, lycopene, phytoene, and phytofluene, which are the principal carotenoids in human fluids and tissues [42]. Some of them (\(\beta\)-carotene, \(\beta\)-cryptoxanthin, and \(\alpha\)-carotene) are essential components of mammalian diets, providing precursors for vitamin A biosynthesis [43]. In turn, lutein and zeaxanthin absorb blue visible light (400-500 nm), which could help protect the eyes and may also reduce cognitive decline [44]. Other health benefits of carotenoids can be explained by their antioxidant activity. Numerous studies proved that these compounds can reduce the risk of a variety of chronic illnesses, including cardiovascular diseases and neurological disorders, type 2 diabetes, and different types of cancer [45; 46]. The main sources of carotenoids in the human diet are fruits and vegetables [46]. Common vegetable oils are generally low in carotenoids, with the exception of sea buckthorn oil and crude palm oil, which contain 67-142 mg and 50-70 mg/100 g, respectively [47]. However, the absorption of carotenoids by the human organism occurs more effectively when they are dispersed in a lipid matrix [48]. Although no dietary reference intake values are proposed for carotenoids at the present time, dietary recommendationsfor \(\beta\)-carotene, lutein, and zeaxanthin are being discussed since vitamin A deficiency is a major public health problem in many countries [49; 50]. Considering the health-promoting properties of some components of plant oils, the aim of the current study is to characterize specific unconventional oils that are abundant in valuable fatty acids and/or selected low molecular lipophilic phytochemicals. Therefore, available scientific information on the composition of 27 plant sources (Table 1) of uncommon edible oils (listed in the article abstract) is synthetized and compared to currently known sources of phytosterols, tools, squalene, and carotenoids, and to these with valuable fatty acid composition. In the next paragraphs, the compositions of these oils are presented based on information that is up to date presented by scientists.