esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Alternative Silicon Materials to Avoid Boron-Oxygen (B-O) Light-Induced Degradation (LID)
### Additional Non-Boron Related Doping During Crystal Growth  
Other modifications can be made during the crystallisation process to reduce the extent of B-O related degradation. These modifications include the use of germanium or carbon co-doping [22; 65]. In the case of germanium doping, the doping is believed to enhance the diffusivity barrier for O\({}_{\text{i}}\), or form Ge-O complexes, therefore reducing the availability of interstitial oxygen species to form B-O defects [65]. However, the required germanium doping in the order of \(1\times 10^{20}\) cm\({}^{-3}\) to prevent LID is probably not desirable for industrial-scale production due to the increased ingot growth costs and tendency to induce the nucleation of multi-crystalline silicon growth [65; 66]. For carbon co-doping, carbon is believed to compete with boron for the formation of complexes with oxygen [67]. There are also uncertainties as to the impact carbon has in reducing the lifetime of silicon. For example, recent work has identified the formation of strong recombination active carbon-oxygen-hydrogen complexes in n-type silicon [68]. Therefore, the co-doping of boron-doped ingots with germanium or carbon does not appear to be a viable solution for completely avoiding B-O related degradation, without introducing other performance limiting defects.  
In summary, the methods described above are all capable of reducing B-O LID. However, they all come with either economic or technological drawbacks, typically resulting in a less cost-effective production as compared to Cz-Si with optimal doping level before B-O LID. It is therefore unsurprising that, even though the alternatives are known for many years, only the use of multi-crystalline silicon material is widespread in the industry. Furthermore, p-type Cz silicon solar cells are going from strength to strength with continual improvement in solar cell efficiencies. Since 2012, record efficiencies on industrial p-type Cz solar cells have increased from 20.3% to 22.7% [69; 70]. As a result, p-type Cz silicon is likely to continue to have a significant market share well into the future, with the most recent International Technology Roadmap for Photovoltaics (ITRPV) predicting a market share of approximately 30% over the next ten years [5]. However, the continued use of p-type Cz will rely on processes to mitigate B-O related LID. With such processes, more heavily boron-doped substrates can be used, resulting in reduced lateral resistive losses in PERC solar cells.
# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 3 Results
### Experimental Results of the Improved YOLOv5 Model  
#### 3.2.1 Dataset and Experimental Environment  
The 624 GPR images were divided into the training set, validation set, and test set according to 8:1:1. The training set contains 499 images, the validation set contains 62 images and the test set contains 63 images. In the training stage using SAHI, it is necessary to preprocess the training and validation sets using SAHI to slice the original image according to the width size of 640 and the height of the original image, and the overlap rate between slices is set to 0.4. Finally, the training and validation sets were sliced into 958 and 118 slices each.  
To verify the effect of GPR images generated using DCGAN in the training dataset and slicing the dataset using SAHI in the training stage on target recognition, four training datasets were created, as shown in Table 3.  
The four datasets were fed into the model separately for training. The computer used to train YOLOv5 was the same as the one used to train DCGAN, and all experiments were validated using consistent hyperparameter training. The number of iterations was set to 500, the SGD optimizer was implemented, the initial learning rate was 0.01, the batch size was set to 16, and the other settings were the defaults for YOLOv5.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
## 3 Lead-Time Prediction Based on NHPSO-JIVAC-SVM
### Data and Preparation
#### 3.1.3 Parameter Setting  
As shown in Figure 6, for comparison with the proposed algorithm NHPSO-JTVAC, we also applied other meta-heuristic algorithms such as PSO, BA, GA, and GOA [38; 39; 40; 41] and compared the performance of each algorithm with the others.  
Figure 5: Flow chart of feature selection.  
In all the algorithms, the population size was unified and set to 20, and the number of iterations set to 500. The search space dimension of each algorithm was set to 3, which represented the three parameters (\(C\), \(\epsilon\), \(\sigma\)) of the SVM. The search range of \(C\) was set to \([10^{0},10^{3}]\), \(\epsilon\) was set to \([10^{-3},\,10^{0}]\), and \(\sigma\) was set to \([10^{-2},\,10^{1}]\). The remaining parameters of the NHPSO-JTVAC were set as listed in Table 3. Furthermore, the initial parameters of PSO, GA, BA, and GOA were set as listed in Appendix A (Table A1).
## 6 Conclusions and Future Work  
This paper introduces a framework designed to automatically identify GUI display issues in software screenshots through an enhanced Faster R-CNN object detection algorithm. In this modification, the conventional VGG-16 network is replaced with ResNet-50 as the feature extraction network, while the inclusion of a feature pyramid network (FPN) and enhanced multi-scale attention (EMA) enhances detection accuracy. Additionally, to improve the network's generalization ability, ROI-Align is employed in place of ROI-Pooling. Experimental results demonstrate that the improved Faster R-CNN algorithm achieves an average accuracy of 87.3%, marking a notable 7% enhancement over the original algorithm. This signifies the effectiveness of the improved algorithm in enhancing the accuracy of GUI display issues. Given the absence of a publicly available dataset for GUI display issues, a training data generation algorithm, based on the Rico dataset, is developed to automatically generate realistic screenshots featuring GUI display issues, mitigating the need for extensive manual collection.  
In summary, this research approach offers valuable insights for software automation testing with broad applications, not only in the industrial sector but also for various terminal devices employing GUI displays. However, it is essential to note that this research is at an early stage. Future work includes expanding the focus from four to a more extensive range of GUI display issue categories, refining the current algorithm for generating training data, and optimizing it further. Additionally, while the Faster R-CNN algorithm has been improved in this study, there is a need for further research to enhance other types of object detection algorithms and identify the most optimal solution.  
Conceptualization, X.P. and Y.L.; methodology, X.P. and Y.L.; software, Z.H. and Y.C.; validation, Z.H.; investigation, X.P. and Y.C.; writing--original draft preparation, X.P.; writing--review and editing, Z.H. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Discussions
### Limitations and Future Works
#### 4.5.3 Regulatory and Compliance Challenges in Blockchain Storage Solutions  
Blockchain-based storage solutions offer enhanced security, transparency, and decentralization; however, regulatory and compliance challenges remain significant barriers to widespread adoption, particularly in highly regulated sectors such as healthcare and finance. The primary concerns are data sovereignty and jurisdictional compliance. Regulations such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States impose strict requirements on data storage, access, and privacy, which may conflict with blockchain's immutable nature [115; 127].  
In healthcare, ensuring compliance with patient data protection laws while leveraging blockchain for secure electronic health records (EHRs) presents a challenge. While blockchain enables data integrity and tamper-proof record-keeping, its decentralized nature raises concerns regarding data retrieval rights and the right to be forgotten (as stipulated  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Consensus Mechanism** & **Energy Consumption** & **Security Level** & **Scalability** & **Sustainability Impact** & **Examples** \\ \hline Proof-of-Work (PoW) & High [45; 88] & Very High [92] & Low [101] & High carbon footprint [106] & Bitcoin, Ethereum (pre-merge) [21; 22] \\ \hline Proof-of-Stake (PoS) & Low [94] & High [103] & High [108] & Energy-efficient [99] & Ethereum 2.0 [22], Cardano \\ \hline Delegated PoS (DPoS) & Very Low [85] & Medium [88] & Very High [92] & Sustainable [101] & EOS, TRON [190] \\ \hline Proof-of-Authority (PoA) & Very Low [106] & Medium [94] & High [99] & Sustainable [108] & VeChain, BSC [103] \\ \hline \end{tabular}
\end{table}
Table 14: Comparative Energy Consumption of Blockchain Consensus Mechanisms.  
in GDPR) [122]. Similarly, in the financial sector, compliance with anti-money laundering (AML) and know-your-customer (KYC) regulations requires adaptable access control mechanisms that balance security with regulatory requirements [126]. To address these challenges, hybrid blockchain models that incorporate off-chain storage for sensitive data while using on-chain mechanisms for verification and auditability are being explored as potential solutions. Additionally, zero-knowledge proofs (ZKP) and confidential smart contracts offer promising methods for ensuring compliance while maintaining privacy [170].  
Future research should focus on developing regulatory frameworks tailored for blockchain-based storage systems, ensuring that decentralization and security advantages do not come at the expense of legal and ethical obligations [123].
## 2 Experiments and Methods
### Rainfall Evapotranspiration Comparison  
Evapotranspiration is considered to be the prominent parameter in the water balance equation, which is expressed as:  
\[P=Q+\text{ETo}+\Delta S, \tag{3}\]  
Figure 3: The basic structure of (**a**) long short-term memory neural networks (LSTM) and (**b**) bidirectional LSTM.  
where \(P\) is precipitation, \(Q\) is runoff, ETo is evapotranspiration, and \(\Delta S\) is represented as storage in soil. Over the longer periods, changes in water storage for a particular region may be neglected [31] and precipitation is balanced by runoff and ETo only. Furthermore, in agricultural, the land runoff has minor effects on the water balance equation because of higher infiltration rates of soils. Soils of Prince Edward Island are sandy loam and exhibit negligible surface runoff and soil water retention. The finding of a study by [PERSON] [32] suggested that the majority of soils in agricultural production are sandy loam with relatively less percentages of clay in comparison with sands. Therefore, rainfall and ETo were compared on the province scale without considering the effect of runoff and change in soil storage. The potato growing season (June-November) was considered to compare rainfall with ETo, as the agriculture is not possible in winter season [33] in Prince Edward Island because of the historical snow and colder weather conditions. Furthermore, the winter season soil water storage (\(\Delta S\)) should not be neglected as it may influence the water balance even for longer periods because of snow accumulations during winter and snow melt during early summer. Due to the above said reasons, the limitation of the present study is that the rainfall and ETo analysis used in this study is valid only for potato production season (June-November).
## 3 Results and Discussion
### Results
#### 3.1.7 Aquatic Environment  
Figure 10: Key modeled rate terms associated with the aquatic environment. (**a**) The plot represents the modeled time series, as indicated in the legend, across the 20 year simulation time frame. These data are summarized and presented as a box and whisker plot (**b**). Note that the \(y\) axis in log transformed in the upper plot, but is linear in the lower plot.  
The discussion of the aquatic environment focuses on mobilization and immobilization of N, as well as flushing. Particulate export of N dominates the model results in the aquatic region, with simulated values of 1.73 g/m\({}^{2}\)-year. A value of 2.53 g/m\({}^{2}\)-year was estimated by [30] for a particular year. The next largest rate term in the aquatic environment, DON export, is 0.024 g/m\({}^{2}\)-year, and because of the approximately two orders of magnitude difference, particulate export has not been included in Figure 10. Results indicate net immobilization of DIN (3.42 mg/m\({}^{2}\)-year) occurs in the aquatic region, consistent with [47] for a somewhat larger stream in HJA. The dynamic model results also indicate, however, that during the growing season, the instream biomass can function as a source of N, primarily because DIN limitation caps the growth rate, yet DIN mobilization is treated with first order dependence on the aquatic biomass, and so proceeds at a relatively higher rate during periods of low DIN availability (Figure 10). Nevertheless, even excluding simulated particulate export, model results indicate that more nitrogen is lost from the stream environment on a yearly basis in dissolved forms (24.8 mg/m\({}^{2}\)-year) that is retained within it by the aquatic biomass (3.42 mg/m\({}^{2}\)-year).
## 2 Materials and Methods
### Literature Review
#### 2.1.3 Topic Modeling and ANT  
There are a few cases of publications that have combined topic modeling and ANT, particularly in analyzing networks of actors and emerging topics in various contexts. For example, [PERSON] et al. [22] suggested an actor-topic modeling approach to examine literary narratives. The proposed Actor-Topic Model (ACTM) was developed to analyze social relations within literary texts by combining topic modeling with some of the key principles of network analysis. This approach identifies actors (characters) in a narrative and maps their interactions based on the topics emerging from their dialogues. Following a topic modeling logic, it treats dialogues as distributions over topics and links actors based on the topical similarity of their conversations. By doing so, it constructs a network of relationships between characters without requiring any preliminary annotated data. The ACTM method exemplifies how topic modeling can uncover hidden relationships and patterns within networks, aligning with ANT's focus on tracing connections between human and non-human actants [22].  
Figure 1: A visual representation of the Latent Dirichlet Allocation (LDA) topic modeling algorithm in the context of the present study. LDA acts on a corpus of text documents to identify the number of topics defined by the researcher. The topic outcomes include a list of the most frequent words associated with a given topic and the list of documents that are most highly associated with it. Each topic (set of words and associated documents) needs to be further interpreted to define its overall meaning in the context of the specific research project.  
[PERSON] and [PERSON] [23] provide another example of the integration of ANT and textual analysis by using topic modeling to analyze actor-topic networks in online debates on the former Twitter (current X) social platform. They examine co-emerging topics and actors to visualize and understand the dynamics of online discussions. By mapping how topics are distributed among different actors and how these actors interact within the network, this method aligns with ANT's emphasis on the interplay between material (e.g., tweets) and semiotic (e.g., topics) elements in forming networks.  
Studies such as the ones by [PERSON] et al. [22] and [PERSON] and [PERSON] [23] demonstrate how topic modeling can complement ANT by providing a computational means to identify and analyze the relationships between actors and the topics they engage with, thus enriching the understanding of emerging network dynamics.
## 4 Results  
### Quantifying the SZA Effect for Typical MOD10A1 Data  
Figure 2 shows the snow cover distribution determined from the MOD10A1 and HJ-1A/B data, and their corresponding SZA distributions for four consecutive days in the study area within the Tarim River Basin (red box in Figure 1). The corresponding SCAs for this four-day period are shown in Figure 3a, and the maximum, minimum, and average SZA values are shown in Figure 3b. A greater amount of detail on the snow cover information is missing from the MOD10A1 dataset on 15 and 17 May than that on 14 and 16 May (top row, Figure 2). For example, the SCAs for 15 and 17 May are more expanded than those for 14 and 16 May because of the presence of a blurred margin for 15 and 17 May. As shown in the second row of Figure 2, the disagreement area between HJ-1A/B and MOD10A are also larger for 15 and 17 May. The bottom row of Figure 2 shows the corresponding SZA maps. It is evident that their values are elongated in a north-south direction each day, and that the absolute SZA values changed drastically each day. The SZA values on 15 and 17 May were higher than those on 14 and 16 May. Figure 3a,b further verifies this areal contrast/overestimation phenomenon. The overall SCA of the sample area was \(\sim\)8000 km\({}^{2}\). The reference SCA determined from the HJ-1A/B data changed slightly within this four-day period, having an average area of 5900 km\({}^{2}\). However, the SCA calculated from the MOD10A1 data during this period was not only higher than that derived from the HJ-1A/B data, but it also showed an oscillatory variation in amplitude by up to 200-300 km\({}^{2}\), accounting for 5% of the overall SCA. On 15 and 17 May, the average SZA values were clearly higher than those for the other two days; thus, the graphical and areal comparisons were successfully used to reveal this unusual expansion of the SCA during snow-melting periods, which was confirmed to be associated with an increase in SZA.  
Figure 2: Comparison of snow cover maps produced by MOD10A1 and HJ-1A/B satellite data, and corresponding sensor zenith angle (SZA) values in a typical area of the Tarim River Basin on 14–17 May 2011.
## 3 Pre-Ie-Fe
### The Governing Equations for PRE-IE-FE
#### 3.2.2 Assembly of the Global Stiffness Matrix  
Combining the equations above, the potential energy generalization of the thin finite element can be expressed as follows:\[ \begin{array}{l}\Pi^{e}_{P}=\frac{1}{2}\int_{S}\varepsilon^{T}\sigma dS=\frac{1}{2} \int_{S}\varepsilon^{T}D\varepsilon dS\\ =\frac{1}{2}\int_{S}\frac{1}{t^{2}}(B_{a}a^{e}+B_{\gamma}\gamma^{e})^{T}D(B_{a} a^{e}+B_{\gamma}\gamma^{e})dS\\ =\frac{1}{2}\int_{S}\frac{1}{t^{2}}a^{eT}B_{a}^{T}D\textbf{{B}}_{a}a^{e}dS+ \int_{S}\frac{1}{t^{2}}a^{eT}B_{a}^{T}D\textbf{{B}}_{\gamma}\gamma^{e}dS+\frac {1}{2}\int_{S}\frac{1}{t^{2}}\gamma^{eT}\textbf{{B}}_{\gamma}^{T}D\textbf{{B}} _{\gamma}\gamma^{e}dS\\ =\frac{1}{2}a^{eT}\textbf{{K}}_{a}^{e}a^{e}+a^{eT}\textbf{{K}}_{a\gamma}^{e} \gamma^{e}+\frac{1}{2}\gamma^{eT}\textbf{{K}}_{\gamma}^{e}\gamma^{e}\end{array}  \tag{18}\]  
where \(\textbf{{K}}_{a}^{e}\), \(\textbf{{K}}_{a\gamma}^{e}\), and \(\textbf{{K}}_{\gamma}^{e}\) are the finite element stiffness matrix, the finite element stiffener element coupling stiffness matrix, and the stiffener element stiffness matrix in the thin finite element, respectively, of the following form:  
\[\textbf{{K}}_{a}^{e}=\int_{S}\frac{1}{t^{2}}\textbf{{B}}_{a}^{T}D\textbf{{B}}_ {a}dS \tag{19}\]  
\[\textbf{{K}}_{a\gamma}^{e}=\int_{S}\frac{1}{t^{2}}\textbf{{B}}_{a}^{T}D\textbf{ {B}}_{\gamma}dS \tag{20}\]  
\[\textbf{{K}}_{\gamma}^{e}=\int_{S}\frac{1}{t^{2}}\textbf{{B}}_{\gamma}^{T}D \textbf{{B}}_{\gamma}dS \tag{21}\]  
Then, the total potential energy generalization, \(\Pi_{P}\), of the interface element between the finite element and the rigid body element in the system can be expressed as the sum of the potential energy generalizations of the individual thin-layer elements, as follows:  
\[ \begin{array}{l}\Pi_{P}=\sum\limits_{e}\Pi^{e}_{P}\\ =\frac{1}{2}a^{T}\sum\limits_{e}\left(\textbf{{G}}_{a}^{T}\textbf{{K}}_{a}^{e} \textbf{{G}}_{a}\right)\textbf{{a}}+\textbf{{a}}^{T}\sum\limits_{e}\left( \textbf{{G}}_{a}^{T}\textbf{{K}}_{a\gamma}^{e}\textbf{{G}}_{\gamma}\right) \gamma+\frac{1}{2}\gamma^{T}\sum\limits_{e}\left(\textbf{{G}}_{\gamma}^{T} \textbf{{K}}_{\gamma}^{e}\textbf{{G}}_{\gamma}\right)\gamma\end{array}  \tag{22}\]  
where \(\textbf{{G}}_{a}\) is the transformation matrix of the finite element nodal displacement array, \(\textbf{{a}}\), of the system and the finite element nodal displacement array, \(\textbf{{a}}^{e}\), of the finite element, i.e., \(\textbf{{a}}^{e}=\textbf{{G}}_{a}\textbf{{a}}\), and \(\textbf{{G}}_{y}\) is the transformation matrix of the rigid body form-centered displacement array, \(\gamma\), of the system and the rigid body form-centered displacement array, \(\gamma^{e}\), of the finite element, i.e., \(\gamma^{e}=\textbf{{G}}_{\gamma}\gamma\). Since the total potential energy of the system satisfies the principle of minimum potential energy, it is necessary to determine the stationary value of the total potential energy functional. According to the variational principle, the condition for the functional to attain a stationary value is that its first variation vanishes.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Improved Spatial Pyramid Matching (SPM) Model
#### Vector Quantization of Image Features  
The low-level features extracted above may contain some redundancy and noise. In this paper, to improve the robustness and distinctiveness of feature representation, an efficient feature vector quantization method is needed to encode the low-level features. In general, the vector quantization method of image features can be divided into two main steps [18]. First of all, the visual codebook is obtained by clustering the descriptors of the image training set and then the features extracted from the image are encoded according to the obtained visual codebook. Different visual codebook or different feature encoding methods will lead to different performance during the quantization procedure.  
In general, there are several clustering algorithms for obtaining sparse or dense visual codebook from the image training set, of which the most widely used is the K-means clustering algorithm. It is fast, straightforward and still can maintain high efficiency and scalability when the data volume is large [22]. Therefore, the most classical partition-based K-means clustering method is first used to generate different visual codebooks for the two types of features extracted from the image training set, which can be used to comprehensively express different types of image information, therefore, improving the quality and robustness of the results.  
Furthermore, to effectively organize the feature vectors and reduce the reconstruction error of image features, the LLC (locality-constrained linear coding) is used to encode local image feature descriptor. In comparison with VQ used in traditional SPM, LLC encodes and reconstructs low-level features on a local manifold by adding locality constraints [23], as shown in Figure 6. In addition, the output image feature representation based on LLC is nonlinear and satisfactory results can be obtained by using a linear classifier.