esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The present work aimed to assess the resilience of shallow circular tunnels in soft soil areas exposed to earthquakes based on derived fragility functions. The following conclusions can be obtained through the above analyses:  
1. The exceedance probabilities, direct seismic losses, and seismic resilience indexes of the examined tunnels are thoroughly evaluated with various earthquake intensity measures (_IMs_), i.e., _PGA_ or _PGV_, which is beneficial for the post-earthquake strategic recovery planning.
2. It is revealed that an increase in the earthquake intensity measure (_IM_), i.e., _PGA_ or _PGV_, for different damage states will lead to a significant increase in recovery time and direct seismic loss, which will also affect the tunnels' functionality.
3. The effects of different fragility functions on tunnel resilience are identified and the importance of selection of appropriate fragility functions in resilience assessments of tunnels is highlighted accordingly.
4. The evolution of tunnel seismic resilience demonstrates the important role of adequate seismic design of tunnel structures and rapid recovery measures at the post-earthquake event for the various seismic intensities.
5. The results of this study also highlight the requirements of seismic resilience assessment for better evaluation of seismic risk, earthquake emergency management, and recovery strategy planning for tunnel structures.  
The proposed seismic resilience evaluation framework can also be extended to other types of underground structures by using more realistic restoration functions and further considering the effects of aging phenomenon [34] and potential multiple hazards [35]. Moreover, it is noted that the present work only considers horizontal earthquakes in the seismic analysis. Further studies could also consider the effect of the vertical components of earthquakes so as to obtain more accurate seismic fragility and resilience assessment results.  
This research was funded by the National Natural Science Foundation of China (52108381, 52090082, 51978517), the Shanghai Science and Technology Committee Program (21 DZ120060, 20 DZ1201404), the National Key Research and Development Program of China (2021 YFF0502200), and the China Postdoctoral Science Foundation (2021M702491).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The author would like to thank the editors, reviewers, [PERSON], and [PERSON] for their helpful and constructive comments that greatly contributed to improving the quality of the paper.  
The author declares no conflict of interest.
## 2 Hydrogen Value Chain
### End-Use on Hydrogen Value Chain
#### 2.1.3 Power-to-Industry Configuration  
P2I refers to hydrogen usage in modern industry; presently, it is an essential chemical agent for the production of ammonia, methanol, various polymers, and many other compounds and materials in oil refining and also as a process byproduct in some sub-sectors of the inorganic chemical industry (many more applications can be identified with a wide-level range of technological maturity) [27].  
Looking at Figure 7, which depicts a generic scheme of P2I, hydrogen has primarily the potential to replace natural gas as a heat source in industry, in processes where electrification is not possible or is economically inefficient--such as sectors that use high temperatures (e.g., steel and cement)--which may require the adaptation or replacement of equipment but do not need highly pure hydrogen [28; 29].
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 5 Results and Discussions
### Comparative Methods
#### 5.3.3 Comparative Analysis of M5 Forecasting Dataset Based on Delay  
The UDAF model's effectiveness in demand forecasting is evaluated by comparing it with other methodologies using MAE, MSE, RMSE, and RMSLE, in line with the delay of 25.  
According to Figure 6a, the UDAF model surpasses NARX in demand forecasting, with an MAE of 2.69.  
Figure 5: Comparative analysis of demand forecasting dataset based on K-fold: (**a**) MAE, (**b**) MSE (**c**) RMSE, (**d**) RMSSE, and (**e**) R-squared.  
Furthermore, Figure 6b demonstrates the UDAF model's proficiency in demand forecasting through MSE, outperforming NARX by 5.56% with a 6.97 MSE for a delay of 25.  
Regarding demand forecasting, Figure 6c showcases the superior performance of the UDAF model compared to NARX. It achieves a 2.94% improvement with an RMSE of 0.026 for a delay of 25, surpassing previous techniques.  
Similarly, in demand forecasting, as shown in Figure 6d, the UDAF model exhibits superior performance compared to NARX, achieving a 6.17% improvement with an RMSSE of 0.76 for a delay of 25, surpassing previous techniques.  
Figure 6: Comparative analysis of M5 forecasting dataset based on delay: (**a**) MAE, (**b**) MSE, (**c**) RMSE, (**d**) RMSSE, and (**e**) R-squared.  
Regarding demand forecasting, Figure 5(e) showcases the superior performance of the UDAF model compared to NARX. It achieves a 6.17% improvement with an R-squared of 0.81 for a delay of 25, surpassing previous techniques.
## 3 Results and Discussion
### Wear Resistance of the A-CF/EP/FEP Composites
#### 3.3.2 Mechanistic Analysis  
Low strength is a common problem of hydrophobic surfaces and limits the application of hydrophobic products under actual working conditions. Why the A-CF/EP/FEP composites possess excellent hydrophobicity and wear resistance at the same time shall be explained from the structural and compositional perspectives. On the one hand, the combination of A-CF and EP/FEP provides micro-nano structures for the formation of hydrophobic surfaces (Figure 4a). On the other hand, the FEP offers low surface energy to contain fluorine, and the combination of FEP and EP provides good adhesion (Figure 4) to the filler (A-CF). Moreover, the good combination of the filler and resins is the premise of excellent properties of composites. The surface morphology of the A-CF/EP/FEP composites after wearing was analyzed to explain the reason for their continuous hydrophobicity. After 60,000 wear cycles, some epoxy resins were damaged, and the composite surface remained relatively intact (Figure 6a). In addition, there is no obvious gap between the filler (A-CF) and resins (EP/FEP), indicating that A-CF has good interfacial adhesion. When the wear cycle increased to 120,000 times (Figure 6b), severe continuous friction aggravated the damage to EP and subjected a large number of carbon fibers to exposure and fracturing (Figure 6c,d). At the same time, the remaining debris and nanoparticles attached to the carbon fiber (Figure 6d) ensure the micro-nano structure required by the hydrophobic surface. Both intact and broken carbon fibers can provide low friction resistance in the friction process, which is related to the high wear resistance of carbon materials and the slender morphology of fibers.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 5 Actual Damage Detection and Quantification of a Twin-Screw Compressor Body  
### Description of the Detection of a Twin-Screw Compressor Body  
An actual 22 kW twin-screw compressor body (218A) is introduced for further verification of the effectiveness of the damage detection technique. The body is made of grey cast iron (No.35) by casting and has rough dimensions of 370 \(\times\) 230 \(\times\) 180 mm\({}^{3}\), as shown in Figure 12. The upper and lower parts of the body are connected by positioning pins and bolts. There is a strict demand for the quality of the body because any small crack in the body may cause an extremely horrible production accident under high temperature and high-pressure working conditions. However, in practice, it is easy to produce sand inclusions and the resulting fatigue cracks into the body during the casting process. Using the proposed technique and the experimental setup proved in this study, the damage on the connection transverse plane was detected and quantified. Images of the actual damage are shown in Figure 13. Both images were taken with a digital microscope by KEYENCE (VHX-100). Figure 13a is a global view of the damage and contains both the sand inclusions and fatigue cracks. The magnification is 25 times the original size. Figure 13b is a local enlarged picture of Figure 13a. The area containing a fatigue crack in Figure 13a was extracted and enlarged to obtain a closer look at the damage. The magnification is 100 times the original size.  
Figure 11: Damage visualization by the NNPE values: (**a**) Damage width: 0.05 mm; (**b**) damage width: 0.1 mm.  
Figure 12: The twin-screw compressor body.
## 2 Materials and Methods
### Determination of Antioxidant Activity  
NEO's antioxidant activity was compared to BHA and BHT by carrying out in vitro tests, including peroxide, thiobarbituric acid value, 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging, and \(\beta\)-carotene bleaching assays. The NEO, BHA, and BHT were added individually at the concentration of 200 mg/L to cold-pressed sunflowers oil, acquired from the local market. An equal quantity of cold-pressed sunflowers oil without any additives was taken as a negative sample for peroxide and thiobarbituric acid value assays.
###### Abstract  
In this work, a novel electronic lock that can encode and decode optical signals, modulated using Morse code conventions, was developed to build a smart home security system based on the Internet of Things (IoT). There are five topics of interest in this research: (1) optical Morse code encoder; (2) optical Morse code decoder; (3) ambient light sensor circuit; (4) fuzzy controller; (5) cloud monitoring system. We take advantage of the light-emitting components as the encoder, which are readily available in hand-held mobile devices (e.g., Smart phones) and photoresistors and a microcontroller as the decoder. By Wi-Fi transferring, even without a personal computer, real-time information about this lock can be uploaded to the cloud service platform, and helps users to ensure home safety on the remote monitoring system. By using the ambient light sensor and fuzzy controller in this novel optical Morse code-based electronic lock, experimental results show that the reliability of this system is much improved from 65% to 100%. That means that it is highly resistant to different illumination conditions in the work environment, and therefore all functions, including coding, emitting, receiving, decoding, uploading and cloud monitoring, can work well. Furthermore, besides the convenience and cost reduction, by incorporating traditional keys into smart phones, as a consumer electronics, our proposed system is suitable for users of all ages because of a user-friendly operation interface.  
Internet of Things; optical Morse code; ambient light sensor; fuzzy control 1  
A Note: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Road-Side Unit Anomaly Detection
## 2 State of the Art
### Clustering Algorithms
#### 2.3.3 Artificial Neural Network (ANN) Algorithms  
Unlike the previous algorithms, the algorithm which uses an artificial neural network cannot handle a dataset immediately but needs to be trained beforehand. Once trained, it can efficiently handle larger amounts of data than the previous algorithms with higher accuracy. SOM (self-organized map) [30] is an artificial neural network trained to produce a low-dimensional and discrete representation of the data. This makes it a strong visualization tool for high-dimensional data.  
In our study, the collected data have to be injected into a classification method. We have chosen the K-Medoids algorithm in order to build clusters. We intend to use the resulting clusters in order to be able to distinguish between proper behavior and improper behavior. To the best of our knowledge, there is no study about the automatic detection of anomalies on C-ITS devices, even though some recent studies focus on collaborative learning of communication routes [31] and selfish behaviors [32].
# Mechanism and Equivalence of Single Event Effects Induced by 14 MeV Neutrons in High-Speed QDR SRAM
## 3 Results and Analysis  
The main contents of this section are: (1) revealing the 14 MeV neutron-induced SEE characteristics in 65 nm high-speed and high-capacity QDR SRAM, focusing on the MCU probability, shape, and inner mechanism; (2) comparing the experimental results of 14 MeV neutrons, the high-altitude test and spallation source test.
# An Improved Robust Fractal Image Compression Based on M-Estimator
## 5 Experimental Results
Further optimization for this case is required in the future. Table 1 shows the simulation comparative results on [PERSON]'s image with salt and pepper noise of 5%, 10%, 15%, 20%, and 30% noise levels, respectively. Both these algorithms are with full search schema. As we discussed in Section 4, the most time-consuming part of the original HFIC is the eight times of iterated IRLS algorithm for one pair of range block \(r\) and domain block \(d\). To encode the noisy [PERSON] image in Figure 1b with the full search HFIC method, requires 9704 s. By eliminating the unnecessary calculations on the original HFIC algorithm, the encoding time of the proposed RMFIC-H (using Huber M-estimator) was reduced almost 87.5%, and its relative reconstructed image quality is improved since the quadtree partitioning method is adopted. Even when the noise level is up to 30%, the proposed RMFIC-H method still shows a better quality by PSNR. RMFIC-TB (using [PERSON]'s bisquare M-estimator) also shows considerable robustness, compared to the original FIC algorithm with normal least squares regression. Compared to the existing robust fractal-based algorithms, such as HFIC and LAD-FIC, RMFIC-TB shows a similar performance on the reconstructed image from the noisy image. However, the encoding time of RMFIC-TB is not optimized enough, and further work is still required in the future. Figure 1: Retrieved images by Full search FIC-LS, RMFIC-H, and RMFIC-TB for Lena image corrupted by 10% salt and pepper noise. (**a**) Original image, (**b**) Corrupt image, (**c**) FIC-LS 23.31 db/9.70 s, (**d**) RMFIC-H 28.10 db/973.45 s, (**e**) RMFIC-TB 26.46 db/5670.20 s.  
Figure 2: Retrieved images by Full search FIC-LS, RMFIC-H, and RMFIC-TB for Boats image corrupted by 10% salt and pepper noise. (**a**) Original image, (**b**) Corrupt image, (**c**) FIC-LS 22.68 db/9.156 s, (**d**) RMFIC-H 25.57 db/1100.34 s, (**e**) RMFIC-TB 24.56 db/5832.00 s.  
For the bell-shaped noises, such as zero-mean Gaussian noise and Laplace noise, the original HFIC algorithm does not show significant robustness under them. We take a simple examination for zero-mean Gaussian noise and the Lena image of 10% noise level to test, by the baseline FIC (LS). And the reconstructed image can obtain PSNR of 26.48, by RMFIC-H algorithm, the PSNR is 26.40 and by RMFIC-TB, this value is 26.43.
## 4 Methodology
### Blockchain and Motifs' Graphs  
Blockchain data can be directly represented through an address-transaction graph (Figure 1). In this graph, vertices represent addresses and transactions, while directed edges (arrows) between addresses and transactions indicate incoming relations, and directed edges between transactions and addresses correspond to outgoing relations. Each directed edge can also include additional information such as values, time-stamps, etc.  
According to Bitcoin.org [43], each single user has the responsibility for adopting good practices in order to protect his/her anonymity and remain private. The basic recommendation is to use a new Bitcoin address for any new payment, and additionally use multiple wallets for different purposes. These suggestions create new dynamics in the network increasing the complexity of user behavior detection. Nevertheless, over time, these same concepts were used to define several heuristic properties with the aim to find related addresses for subsequent address clustering. This process is used to find all addresses that belong to a certain user, and this allows us to introduce the _entity_ concept. In particular, we refer to an entity as a physical person or organization related to one or multiple public key addresses belonging to one or more wallets. Using these clusters of addresses and the relation among transactions, it is possible to create the entity-transaction graph starting from the address-transaction graph.  
Heuristic properties have been developed and presented in different studies such as [20; 23; 44]. However, in this study, we do not need to apply heuristic assumptions since, as indicated in Section 3, the Bitcoin blockchain data were combined with labelled data gathered from WalletExplorer, such that relations among addresses (i.e., clusters) are known.  
Figure 1: Address-transaction graph computed with one block of the Bitcoin mainnet.  
The motifs graph used here is usually applied in bioinformatics, specifically in metabolic network analysis [45]. However, prior studies such as [11; 32] have successfully translated the concept of motifs to Bitcoin analyses.  
According to [10], a \(N\_motif\) graph is a graph composed by paths from the entity-transaction graph with length \(2N\) that starts and ends with an entity. Let \((e_{1},..,e_{M})\in E\) be a class of entities and \((t_{1},..,t_{N})\in T\) be a class of transactions, with \(M\leq N+1\), then:  
\[N\_motif=(e_{1},t_{1},...,t_{N},e_{M}). \tag{1}\]  
From this particular graph, it is possible to extract information concerning the relations among entities, but also concerning the topology that such relations create among entities. If two different entities are connected through one transaction, the topology is called Direct Distinct. If one entity is connected with itself (again through one transaction) the topology is called Direct Loop (Figure 2).  
Starting from the results presented in [10], in the present study, only the \(1\_motif\) and \(2\_motif\) graphs were used.
## 2 Experimental
### Powder X-ray Diffraction, Thermal Analysis and Chemical Analysis  
Structural characterization was carried out by powder X-ray diffraction (PXRD, Huber G670 Guinier camera, Cu \(K_{\alpha 1}\) radiation, \(\lambda\) = 1.54056 A, \(\Delta 2\theta\) = 0.005\({}^{\circ}\), HUBER Diffraktionstechnik GmbH & Co., KG, Rimsting, Germany). Data were collected at room temperature using LaB\({}_{6}\) (NIST, 660a) as an internal standard. An experimental position of each reflection was corrected by comparison with standard, and lattice parameters of the \(\beta\) phase were calculated from least-squares refinement using WinCSD software [29]. Absence of C, N, O impurities was verified by elemental analysis (TCH600, C200, Leco, St. Joseph, MI, USA) and inductively coupled plasma optical emission spectroscopy (ICP-OES 5100, Agilent Technologies, Santa Clara, CA, USA). Thermogravimetry and differential thermal analysis experiments (TG-DTA: STA 449F3, Al\({}_{2}\)O\({}_{3}\) crucibles; DTA: STA 409C/CD, SiO\({}_{2}\) ampoules, Netzsch GmbH & Co., Selb, Germany ; heating rate: 5/10 K\(\cdot\)min\({}^{-1}\)) were performed to identify phase transitions and to detect Ag\({}_{2}\)Te impurities.
# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 3 Methodology
### Analyzing Text in the Visible Image  
Extracting text from images is integral to obtaining location data beyond mere landmarks. This dual approach offers a thorough understanding of the location-specific information presented in the media.  
We employ the Tesseract OCR engine [51], renowned for its precision in converting diverse image formats into machine-encoded text for text extraction from video frames. Alternatives such as ABBYY FineReader PDF [64] or Adobe Acrobat Pro [65] delivered good results but are challenging to integrate into an automated processing pipeline. Google Cloud Vision API [66], Microsoft Azure Computer Vision API [67], and Amazon Textract [68] allow such integration but are not free to use or are not as flexible and customizable as Tesseract OCR due to their proprietary nature. While Tesseract OCR is robust in handling various image qualities, special attention is given to preprocessing and normalizing the images to mitigate the challenges posed by diverse video formats and resolutions. This normalization includes adjusting contrast and brightness and applying noise-reduction techniques tailored to optimize OCR performance across different video qualities. Once preprocessed, the frames are fed into the Tesseract OCR engine, which scans, detects, and converts text regions into digital text which is subsequently stored in our data structure at the corresponding time frame.  
The unstructured text can be analyzed using NLP methods. NER can examine the text for entities such as persons, organizations, and locations and aims to extract the specific names or entities in the text to understand the meaning and structure of the text.  
Figure 2: Workflow diagram illustrating the analysis of the visible image.  
We iteratively perform NER for every frame to process all the contents of the extracted text. The complete process is visualized in Figure 3.  
In the next step, geocoded coordinates are added to these extracted locations. The geocoding of locations plays a crucial role in spatial analysis. A detailed spatial analysis can be performed by assigning geographic coordinates to identify patterns, trends, and relationships between locations. In addition, geocoding enables the visualization of information on maps. The entities of the type Location recognized in the previous step are geocoded by Nominatim [69] based on OpenStreetMap [70] data. If geocoding is successful, a point coordinate pair consisting of latitude and longitude is added to the data structure.