esa-sceva/satcom-chunk-collection huggingface dataset:
# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
## 2 Materials and Methods
### Dataset  
To ensure a comprehensive evaluation and proper comparison with previous studies, this study leverages three benchmark datasets, namely, Antif_DS1, Antif_DS2, and Antif_DS3, which have been widely used in the literature [17; 18; 19; 20; 21]. These datasets, outlined in Table 1, encompass distinct characteristics and composition. In Antif_DS1, Antif_DS2, and Antif_DS3, the positive samples originate from the data repository of antimicrobial peptides (DRAMP) [20], while excluding sequences containing unnatural amino acids (BIJOUX). However, the negative samples in each dataset differ. Antif_DS1 negatives comprise active antimicrobial peptides, while Antif_DS2 negatives are randomly generated from SwissProt. Notably, the maximum peptide length in these three datasets is 100. On the other hand, Antif_DS3 encompasses peptides with lengths ranging from 5 to 30. Positive samples in Antif_DS3 were collected from CAMP [21], DRAMP [22], and StarPep [23; 24] databases, whereas negatives were randomly generated from the Swiss-Prot database. As shown in Table 1, all three datasets are balanced (equal number of positive and negative samples). They are also normally distributed.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
\begin{tabular}{l} The non-AFPs were \\ random from the Swiss-Prot \\ database, and antimicrobial \\ peptides other than \\ Swiss-Prot database. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics and properties of the datasets employed in this study, namely, Antifp-DS1, Antifp-DS2, and Antifp-DS3.  
Figure 1: The overall architecture of AFP-MVFL. The AFPs prediction pipeline consists of three modules: (i) feature extraction module using iFeature; (ii) feature selection using random forest; (iii) classification module for the prediction task.
## 3 Test Site and Field Testing
### Working Design  
The test working faces are W2708N (920 m long) and W2708S (830 m long). The width of the working face is 140 m. The bottom gas tunnels are under the middle of the working faces. The Yutiao anticline, \(F_{\text{w-7-70}}\) fault (normal fault, offset of 0.4 m) and \(F_{\text{w-7-71}}\) fault (normal fault, offset of 1.7 m) are in the coal seam. The working faces and roadways are shown in Figure 5a. First, the coal seam is divided into tectonic regions and nontectonic regions based on the presence of geological structures. Next, a construction plan is designed according to Section 2. The W2708S working face is a comparison working face. To ensure the effect of permeability enhancement, SDHFT was used on the W2708S working face (the effect of SDHFT is approximately 1.8 times that of traditional HFT [29]). Then, the effective radii of HOST and SDHFT were tested and found to be 5 m and 60 m, respectively. To ensure that the entire coal seam was enhanced, the distance between the fracturing boreholes was 110 m, the distance between the slotting boreholes was 10 m, and the distance between the extraction boreholes was 5 m. The details are shown in Figure 5b.  
Figure 5: Diagram of the working face design: (**a**) Diagram of the working faces, roadways, and geological structure; (**b**) Diagram of the fracturing boreholes and slotting boreholes.
## 2 Related Works
Despite the continuous development of technology in the world, it is not always possible to take full advantage of its capabilities, especially in the absence of Internet access. A proposal to solve this problem was presented in the paper [42] by [PERSON] et al. They proposed the use of an on-scene digital communication system aimed at optimizing the limited resources available at the disaster site by using sub-GHz frequency bands and a Delay/Disruption Tolerant Networking (DTN) system developed for long-distance communication. In the ID Victim system described here, the problem of lack of Internet access is solved by using a mobile application that also works offline. It allows data to be collected without the need for a constant Internet connection, and also allows photos of victims of the event to be easily collected using the camera, without the need for an external device. All collected data can then be analyzed in detail and processed using also neural network algorithms, under more favorable conditions, after the first phase of the DVI procedure has been completed. In addition, the system provides the ability to determine the geographical location of objects or bodies and monitor changes in their position. For this purpose, the system uses RFID tags, which are used in many areas of life such as commerce, industry and security. This technology uses radio waves. Its application allows a significant speed-up of the identification process and to automate reading and transmitting data. Table 1 compares the main functionalities of the ID Victim system with the KMD PlassData DVI [40] and Bonaparte [41] systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **ID Victim** & **KMD PlassData DVI** & **Bonaparte** \\ \hline Integration with DVI procedures & Yes & Yes & Yes, but focused on the \\  & & & comparison of DNA profiles \\ \hline Automation of the identification process & AI module automates the initial comparison of PM and AM data & Automation of dental and DNA data comparison & Automation of DNA comparison, \\  & & & data comparison \\ \hline Identification technology & Image comparison neural network using deep metric learning & Comparison of data records stored in the database, e.g., dental & DNA matching algorithms \\  & & & codes comparison \\ \hline Image data handling & Yes, handling and automation of the comparison process & Yes, but without automatic analysis & No, it focuses on genetic data \\  & & & analysis \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the main functionalities of the developed system with other available solutions.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 7 Conclusions  
To assure the reliability of the weld structure, it is critical to inspect and assess the quality of the weld joints from the radiographic films according to NDT test. The evaluation of films performed by certified operators is time-consuming, subjective, and error prone. Therefore, many researchers have tried to automate the evaluation using computer assistance. This article introduced available practices in weld defects inspection from digital radiographic images. The technologies were introduced by stage in weld defect inspection. This review supplied a comprehensive literature review on automatic models based on traditional computer vision techniques falling into three categories: image pre-processing, feature extraction and classification. The paper firstly found that some image pre-processing tools like morphological operation and thresholding operation which improved the quality and segmented the defect region. Following this, it concluded three aspects: feature extraction, selection and classifier and pointed out the limitations of these traditional methods. Lastly, the advantage of sparse representation and deep models were analyzed and their applications in weld defects inspection were introduced. It was concluded with the analysis of achievements of automating weld defect detection and challenges summarized as open questions for future research in the field.  
[PERSON], [PERSON] and [PERSON] conceived and designed the field survey; [PERSON] and [PERSON] processed and analyzed the data; [PERSON] and [PERSON] contributed to the survey and reviewed the submitted manuscript. All authors have read and agreed to the published version of the manuscript.  
This project was funded by National Natural Science Foundation of China (No. 51805006 and No. 51675005).  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.  
Figure 5: Impact of class imbalance on classification performance of minority class [67].
# Evaluating the Impact of the COVID-19 Pandemic on Residential Energy Use in Los Angeles
## 2 Materials and Methods  
### Feeder and Data Selection  
This study used Los Angeles Department of Water and Power (LADWP) municipal electrical substation 4.7 kV customer distribution feeder net loads (reported in hourly average kW load values) servicing a designated geographic territory within the city of Los Angeles to provide serviced population load data. Comparison baselines were created from composites of 2018 and 2019 load data from individual feeders, while the evaluation period was initiated with the California SIP order on 19 March 2020 and continued through the end of reporting on 31 December 2020 [31; 32]. For comparison as needed an evaluation period beginning on 1 January 2020 is used to report energy use change with respect to calendar year. To compare heatwave-related events, period to period comparisons between single years were used. From a pool of all available feeders the authors performed a two-tier screening process. The first tier selected for feeders that serviced primarily residential customers (greater than 90% residential customers with largely negligible pre-pandemic observable commercial loading patterns). The second tier selected for diversity across the city considering community location, community income, and community building types. Feeders were then excluded for substantial service interruptions or for major changes from 2018 through 2020 rendering those periods non-comparable, including substantial changes in customer base, major new construction, building demolition, or zoning changes. Feeders were also excluded for exhibiting great heterogeneity of income across neighborhoods served by the same feeder, with the exception of Feeder A providing service to Section 8 subsidized low-income housing in Watts. Four feeders were ultimately selected for the current analysis, serving distinct communities across Los Angeles with a total residential customer base of approximately 6603 combined residential customers covering areas with a range of median incomes (see Table 1). Feeders were evaluated across the period of investigation from 2018 through 2020. Customer construction permit records indicate <6% mid-day solar load contribution total, with slow growth, and 2017 motor vehicle records showed <5% average customer EV penetration average across all primary evaluated feeder service areas (see Table S1). Both factors suggest a low overall impact such that the change between the pandemic and pre-pandemic periods for the evaluated feeders and accordingly the differential impact from solar and EV loads are treated as negligible. The majority of the building types represented were a mixture of single-family homes and small multi-family properties hosting several units, with a smaller proportion of low-rise apartment complexes. The communities assessed represented two microclimates: the Los Angeles Basin and San Gabriel Valley. The California Energy Commission designates two of these feeders (A and
## 3 Materials and Methods
### Data Collection  
Reliability assessment studies are mainly dependent on failure rates data. The more reliable and valid the data, the better the reliability can be assessed. Specifically, in this case, it is necessary to have a large sample size to investigate random failures of a particular component, unlike other components where failures are not random. To satisfy this point, the components failure rates are extracted from the offshore and onshore reliability data (OREDA) [16] project, which is a large scale project under similar conditions in the same industry. For the remaining components, more valid samples were taken from one of the largest oil and gas operation companies in the Middle East. Some of the components have high reliability and failure rarely happens. For these components, using historical data from a truck unloading system was not feasible, instead, the data were taken from OREDA. These data include the number of tested units, number of failures and duration for a number of items such as ball valve, gate valve, pressure relief valve, flow control valve, differential pressure transmitter, pressure transmitter, level switch, and air eliminator. Table 3 shows the number of units tested, number of failures of tested units, test duration, and the mean time to failure (MTTF) for each component. Unloading arms, positive displacement pump, check valve, flow meter (positive displacement meter), plug valve, and strainer, where investigated under identical truck unloading system, were included, while the remaining were taken from the OREDA project. On the other hand, lambda (\(\lambda\)) and mean time to failure (MTTF) were calculated using Equations (1) and (2), respectively. Mean time between failures yielded clear figures on the random failure behavior of a particular component and ultimately it was essential for reliability calculations.  
\[\lambda=\ \frac{Number\ of\ failures}{Number\ of\ Units\ Tested\ \times\ Duration\ of\ Test} \tag{1}\]  
\[MTTF=\ \frac{1}{\lambda} \tag{2}\]  
Figure 2: Unloading system reliability block diagram.
## 5 Model Evaluation
### Comparative Analysis of Single, Ensemble, and Deep Learning Models  
Figure 8 summarizes the comparison of the prediction performance of single, ensemble, and deep learning models. It can be seen that among the single models, DT performs outstandingly, with a test R\({}^{2}\) of 0.94, showing good trend-capturing ability, and KNN is similar to it but slightly inferior overall. Among the ensemble models, Gradient Boosting performs the best, is comparable to DT in terms of RMSE and R\({}^{2}\), and significantly outperforms other integration methods, such as RF and Bagging. On the contrary, AdaBoost's performance declines significantly, with both RMSE and MAE showing large errors. Among the deep learning models, DNN shows the strongest predictive power among all models, with the lowest RMSE (263) and a test R\({}^{2}\) of 0.95, which is much better than most ensemble models. ResNet showed a relatively weak performance with an RMSE of 335.7, indicating a failure to learn effectively during training. Overall, DNN leads in prediction accuracy; ensemble models perform differently, and a single model can still work well in a given situation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**MSE**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} & \multicolumn{2}{c}{**CoV**} \\ \cline{2-10}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline DNN & 85.4 & 263 & 7287.6 & 69,173.2 & 50.7 & 112.7 & 0.99 & 0.95 & 0.244 & 0.26 \\ ResNet & 111.7 & 335.7 & 12,466.9 & 112,717.9 & 59.7 & 107.7 & 0.98 & 0.91 & 0.226 & 0.238 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Predictive performance metrics of the deep ML models.  
Figure 7: Comparison of predicted values with experimental values for each RCSW specimen obtained by deep ML models.
# FIDO2 Facing Kleptographic Threats By-Design
### Legal and Technical Background
#### 1.1.2 FIDO2 Initiative  
Given the diversity of authentication schemes and their lack of interoperability, it is desirable to create a common framework that would enable the use of authentication tokens of different vendors within a single ecosystem. This necessity gave rise to the establishment of the Fast Identity Online (FIDO) Alliance created by a large group of leading tech companies including, among others, Google, Amazon, Apple, Intel, Microsoft, and Samsung. The flagship result of the FIDO Alliance is a set of protocols called FIDO2. Its target is an interoperable and convenient passwordless authentication framework implementable on a wide range of user devices offered by different vendors.  
From the cryptographic point of view, FIDO2 is based on three fundamental concepts:  
* Creating at random separate authentication keys for each service;
* Authentication based on a simple challenge-and-response protocol, where the challenge is signed by the authenticator;
* Separating the authenticator device from the network.  
From the user's point of view, there are the following actors:  
1. Remote _servers_ offering services and requesting authentication;
2. A _client_--a web browser run on the user's local computer;
3. An _authenticator_--a device like a hardware key in the hands of the user.  
The interaction between these three actors is sketched in Figure 1. As one can see, authentication is based on the secret keys (one per service) stored in the authenticator and the corresponding public keys shared with the corresponding servers.  
The first step of a user \(U\), when getting in touch with a server \(S\) for the first time, is to create a dedicated key pair \((pk_{U,S},sk_{U,S})\) associated with user identifiers \(uid\) and \(cid\) (see Figure 2).  
If \(U\) has an account in a server \(S\) associated with a public key \(pk_{S}\) and user ID \(uid\), \(U\) can authenticate themselves by letting the client interact with the authenticator holding the corresponding key \(sk_{S}\). Simplifying a little bit, the authentication mechanism is based on signing a hash of a random challenge received from the server (see Figure 3). A full description with all (crucial) details is given in Section 2.  
Figure 1: FIDO2 system from the point of view of a user.  
Figure 2: FIDO2 registration of a user in a server.  
FIDO2 is an example of a design that may significantly impact security practice: it creates a simple self-organizing distributed ecosystem. FIDO2 also addresses privacy protection issues nicely by separating identities and public keys used for authentication in different systems. Last but not least, it is flexible: the fundamental security primitives--hash function and signature scheme--are used as plugins.
# Miniaturized PIFA for 5G Communication Networks
## 2 Long-Term Evolution Advanced Mobile Service  
A Planar Inverted-F Antenna (PIFA) is about half the size of general dipole antenna and has a conductor layer in it as the ground layer. The relationship between broad band width characteristics and the thickness of the material can be determined by the following Equation.  
\[BW=\frac{Ah}{\lambda_{0}\sqrt{\varepsilon_{r}}}=\sqrt{\frac{W}{L}} \tag{1}\]  
for \(A=180\):  
\[\frac{h}{\lambda_{0}\sqrt{\varepsilon_{r}}}\leq 0.045 \tag{2}\]  
for \(A=200\):  
\[0.045\leq\frac{h}{\lambda_{0}\sqrt{\varepsilon_{r}}}\leq 0.075 \tag{3}\]  
for \(A=220\):  
\[0.075\leq\frac{h}{\lambda_{0}\sqrt{\varepsilon_{r}}} \tag{4}\]  
And  
\[D\cong 0.2W+6.6+10\log(\frac{1.6}{\sqrt{\varepsilon_{r}}})dB. \tag{5}\]  
where \(BW\) is a broad band width, \(A\) is the area of the dielectric material, \(h\) is the thickness of the dielectric material, \(\lambda_{0}\) is the wavelength, \(\varepsilon_{r}\) is the dielectric permittivity, \(D\) is the directivity of the antenna, \(W\) and \(L\) are the width and length of the radiating element in a PIFA respectively [12]. As Equation (1) shows, the frequency band for a broad band width is proportional to the thickness of the material and is inversely proportional to the dielectric permittivity. Moreover, Equation (2) shows that the directivity of the antenna decreases as the dielectric permittivity increases. In designing the multi-band internal antenna, as the thickness of the dielectric material increases, the broad band characteristic of the antenna is improved. However, this also makes the width of the microstrip line increase, which means that the unwanted radiation occurs from the feed. To solve this problem, this paper suggests the structure of the multi-band internal antenna, as shown in Figure 1, and the designed multi-band internal antenna is shown in Figure 2.
# Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
## 7 Conclusions  
Optical networks are deployed across the world delivering higher-scale connectivity and huge data transmission. Network component failures particularly fiber cuts are frequent, whereas disaster-based failures are occasional, however, they disrupt the network severely. Large-scale disasters contribute towards geographically correlated failures that can damage a large portion of optical networks. Network survivability emerged from protection and restoration mechanisms to disaster-resilient network survivability techniques to mitigate geographically correlated large-scale disaster impacts. Another factor is network congestion that occurs due to the rerouting of disrupted paths or traffic fluctuations or growth. Network congestion is an inevitable problem like disasters. It is direly needed for network operators that solutions should be designed not only to overcome described network problems, but also eradicate the issues like cascading failures and network congestion. In this paper, we critically studied the solutions for small-scale (network component) failures and large-scale (disaster-based) failures. A framework based on different approaches may provide better protection and survivability from disasters with capacity optimization. This will assist the fault tolerance management to improve network robustness against disasters.
# Background and Recent Advances in the Locata Terrestrial Positioning and Timing Technology
## 4 Locata Applications
### Deformation Monitoring  
Later, the performance of the 2 nd generation Locata technology for structural deformation monitoring was assessed using a LocataNet that was established at the UNSW Sydney campus for a long-term static test and a simulated deformation movement test [41]. In [35], a series of deformation trials using the Locata technology were carried out at the Tumut Pond Dam (Cabramurra, Australia). These analyses confirmed that the Locata technology could generate positioning results with millimetre-level accuracy. [PERSON] et al. [57] further demonstrated Locata's performance in a five-day monitoring trial, comparing it against GNSS-RTK.
## 2 Materials
### Satellite Data
#### 2.2.1 Sentinel-1 (S1)  
S1 is a synthetic aperture radar (SAR) satellite mission with a C-band (5.6 cm) sensor. The advantage of S1 lies in its sensitivity to SM content [65]. There are two identical satellites in the S1 mission, S1a, and S1b. Each satellite has a temporal resolution of 12 days, resulting in an average of a six-day repeat cycle. Unfortunately, in December 2021, S1b failed data dissemination and became space junk. Since then, S1a has been providing data alone, and its temporal resolution depends on the area, with a minimum orbit repeat cycle of six days in Europe and 12 days in other areas. ESA is planning to launch S1c in the first half of 2023 to continue the dual satellite constellation.  
This research used the ground range detected (GRD) 10-meter spatial sampled data processed by ESA. The data we have selected has vertical transmission-vertical received (VV) and vertical transmission-horizontal received (VH) polarizations.  
In this study, all S1 passes between 31 December 2017 and 01 January 2021 were included for each station of ISMN. In the data processing step, 50 m \(\times\) 50 m region of interest was defined around each station to calculate the mean value of S1 GRD backscatter signals. The mean backscatter signals were converted from logarithmic scale to linear scale. Additionally, the VH/VV ratio was added as a feature to the dataset.