esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Findings
### Accuracy and Reliability  
The interviewees were asked whether accuracy and reliability were important considerations in the publication of medical information, and the degree to which they were strict about verifying that the information published was in fact accurate and non-biased. Representatives of all of the websites except one, which belonged to a small non-public organization, emphasized the importance of publishing information that is reliable and accurate. An interviewee representing a large private organization's website commented: \"...that is, if I get an article that touches on medical issues, I'll pass it on to a physician for review and... that's how I work. Since I have no medical training...\". Another interviewee noted:  
_I never just copy and paste what I receive without first checking that it [the information] is suitable, reliable and appropriate\". An interviewee representing a public organization noted: \"...our commitment is to demonstrating reliability; we stand behind every written word.__The fact that we are a government website, a government hospital... everything published must be 100 percent accurate._  
However, there is a distinction between the accuracy and reliability of health-related information and that of administrative information (e.g., visiting hours or the location of a specific clinic). In websites of large public health organizations, the health-related information is produced by physicians and other experts (dieticians, psychologists, _etc._); consequently, the interviewees were not anxious about the accuracy of the medical information: \"...about the accuracy of medical information...I have no clue. I rely on them (the experts); that's to their credit...\". The challenge the interviewees face pertains to the accuracy and reliability of administrative information, since the processes required to present accurate and reliable information are not always clear.  
In contrast, in websites of non-public organizations, the challenge is not about the accuracy or reliability of administrative information; rather, it is about presenting accurate, reliable and up-to-date medical information, particularly given the abundance of information dispersed for PR purposes, by establishments propelled by various other motives: \"...When I find materials published by pharmaceutical companies, even though the material may quote a statement made by an M.D., I can't rely on that 100 percent, because they [the persons quoted] are remunerated one way or another...\". In the words of yet another interviewee:  
_I can tell when a PR agency is trying to promote a certain product or medication, I'm very attentive so as to avoid these [on the website], because... we're trying to present a clean and objective approach/image, and not to promote any particular product or medication._
# Sign Language Interpreting System Using Recursive Neural Networks
## 2 Related Works
This platform allows the native integration of real-time tracking of hand movements from 2D images. DAKTILOS recognizes and evaluates both static and dynamic signs of the LSM alphabet the user signals. First, the system identifies 21 critical points of the hand using the MediaPipe model; next, these are dynamically compared to the target hand configurations (letters) using the FingerPose classifier. Finally, the system computes a scores and determines whether the sign was performed correctly, providing visual feedback. The platform allows for exploring 27 letters, showing the correct configuration with a 3D hand. Preliminary tests were conducted with eight subjects to evaluate the functionality of DAKTILOS. These works support the idea of using RNN to generate a common means of communication to facilitate communication between people with hearing disabilities who use sign language, with people who can hear; however, despite previous efforts, the following issues remain unaddressed:  
* Previous systems depend on the specific signs of the language, that is to say, they are language-oriented and as a consequence, there are particular design constraints. * The accuracy of most of those approaches is still low for practical scenarios. * The authors have limited the experiments they conducted on their schemes to offline tests.
# Behavior of Airport Concrete Pavement Slabs
## 3 Data Collection
### Real Strain and Shrinkage of the Concrete Slab  
The strain data measured by the static strain gauges were analyzed for the real and shrinkage strains. The strain data shown in the paper were from the positions where the strain trends at each depth were clearer than other positions even though the strain data from all the positions were reasonable. The real strain was calculated using Equation (2) [27]. The real strain develops in a concrete slab owing to environmental loads, such as temperature and moisture variations.  
\[\varepsilon_{real}=C_{e}\times\varepsilon_{i}+C_{\beta}\times\Delta_{t} \tag{2}\]  
\(\varepsilon_{real}\): Real strain (\(\times 10^{-6}\));  
\(C_{e}\): Calibration coefficient of the strain gauge;  
\(\varepsilon_{i}\): Change in the measured strain from the first strain (\(\times 10^{-6}\));  
\(C_{\beta}\): CTE of the strain gauge (\(\times 10^{-6}\)/\({}^{\circ}\)C);  
\(\Delta_{t}\): Temperature change from the final setting time (\({}^{\circ}\)C).  
The change in the measured strain from the first strain at the final setting of concrete was multiplied by the calibration coefficient of each strain gauge for the first correction of the strain in the slab. The thermal strain of the strain gauge was calculated by multiplying the CTE of the gauge with the temperature change from the final setting. The real strain was calculated by subtracting the thermal strain of the strain gauge from the corrected strain in the slab.  
The shrinkage strain was calculated by subtracting the thermal strain due to temperature variations from the real strain and is expressed as Equation (3) [27].  
\[\varepsilon_{sh}=\varepsilon_{real}-\gamma\times\Delta_{t} \tag{3}\]  
\(\varepsilon_{sh}\): Shrinkage strain (\(\times 10^{-6}\));  
\(\gamma\): CTE of concrete (9.92\(\times 10^{-6}\)/\({}^{\circ}\)C);  
\(\Delta_{t}\): Temperature change from the final setting time (\({}^{\circ}\)C).  
The CTE of concrete was obtained from a test of the concrete specimens [21]. The thermal strain of the concrete slab due to temperature variations was calculated by multiplying the CTE of concrete with the change in concrete temperature from the final setting of the concrete slab.  
Figure 8: Temperature gradient according to the slab depth at the final setting time.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 4 A 2-Port MIMO System Using Two Opposite 1 \(\times\) 4 Antenna Arrays
### Performance Comparison of Two 1 \(\times\) 4 Arrays MIMO with Previous Works  
The comparison in Table 8 highlights the performance of the proposed MIMO two 1 \(\times\) 4 arrays design against existing sub-6 GHz 5G antennas. The suggested antenna exhibits higher gain and a wider operational frequency range compared to the maximum bandwidth and gain of previously published antenna designs [21; 22; 23; 24; 25; 26; 28; 40]. Although the antenna in ref. [26] achieves the same maximum gain as our proposed design, the total antenna size is relatively higher compared with the proposed antenna. Additionally, the  
Figure 26: MIMO parameters of Two 1 \(\times\) 4 Arrays: (**a**) measured and simulated CCL, (**b**) the ECC, (**c**) the DG, (**d**) the TARC, and (**e**) the MEG.  
proposed design presents a perfect ECC value compared with previous designs, so the proposed MIMO is more effective because of a wider operating frequency range and maximum gain, with perfect MIMO performance parameters achieving maximum radiation efficiency of 97.46%.
## 3 From Recommendations to Vectors of Differences and a Graph
### From Differences to Distances and a Graph  
Table 1 represent the content of this analysis as a collection of features. Table 2 encodes these differences in recommendations as numbers of differing features between pairs of recommendations. Then, Table 3 shows the distances between the guidelines derived from Tables 1 and 2 using the _Jaccard distance_ (the percentage of different elements in two sets):  
\[d_{j}(A,B)=1-\frac{\left|\begin{array}{c}A\cap B\\ A\cup B\end{array}\right|}{\left|\begin{array}{c}A\cap B\end{array}\right|}\]  
Given two recommendation summaries \(A\) and \(B\) we compute the number of the differing feature values from Table 2 and divide it by five. For example, for the pair (AFP, ACR) we get \(3/5\). All these distances were normalized to sum to 1 and shown in Table 3 (we are not assuming that distances are always symmetric. In most cases they are, but later we will also report experiments with the search distances, which are not symmetric). The normalization does not change the relative distances, and in the comparisons with the geometry of full documents we only care about the relative distances.  
Tables 1-3 represent the process of converting the information in Figure 3 into a set of distances. These distances are depicted graphically in Figure 4, where we display both Jaccard distances between the recommendations and the number of differing features as per Table 2.  
In the following section we will create a graph representation for the full documents (Figure 4(b)). We will present our graph comparison method in Section 5. In Section 6, we will assign numerical values to the distance between the two graphs, and show that this similarity cannot be the result of chance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **AAFP** & **ACOG** & **ACP** & **ACR** & **ACS** & **IARC** & **USPSTF** \\ \hline
**AAFP** & 0 & 0.0238 & 0.0357 & 0.0357 & 0.0238 & 0.0238 & 0.0119 \\ \hline
**ACOG** & 0.0238 & 0 & 0.0476 & 0.0119 & 0.0238 & 0.0238 & 0.0119 \\ \hline
**ACP** & 0.0357 & 0.0476 & 0 & 0.0357 & 0.0238 & 0.0357 & 0.0357 \\ \hline
**ACR** & 0.0357 & 0.0119 & 0.0357 & 0 & 0.0119 & 0.0238 & 0.0238 \\ \hline
**ACS** & 0.0238 & 0.0238 & 0.0238 & 0.0119 & 0 & 0.0119 & 0.0119 \\ \hline
**IARC** & 0.0238 & 0.0238 & 0.0357 & 0.0238 & 0.0119 & 0 & 0.0119 \\ \hline
**USPSTF** & 0.0119 & 0.0119 & 0.0357 & 0.0238 & 0.0119 & 0.0119 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Normalized distances between the summarized guidelines computed using Jaccard distances from Tables 1 and 2.
## 3 Performance Analysis
### Asymptotic Outage Probability  
In this subsection, the asymptotic OP will be analyzed when the average transmitted optical power \(R_{\rm t}\) at node S and the average optical power \(P_{\rm o}\) at each relay are sufficiently large.  
When \(P_{\rm t}\) tends to infinity, the corresponding channel gain threshold \(h_{\rm th}\) will approach zero. According to the series expression of Meijer's G-function [28], we can simplify (17) to  
\[F_{\gamma_{m,1}}(\gamma_{\rm th}) \approx \exp\left(-\frac{\theta_{\rm FoV,m,1}^{2}}{2\sigma_{0}^{2}} \right)+\left[1-\exp\left(-\frac{\theta_{\rm FoV,m,1}^{2}}{2\sigma_{0}^{2}} \right)\right] \tag{21}\] \[\times \frac{\zeta_{m,1}^{2}\Gamma(\alpha_{m,1}-\eta_{m,1})b_{m,1}}{ \Gamma(\alpha_{m,1})\Gamma(\beta_{m,1})\eta_{m,1}}\left(\frac{\alpha_{m,1} \beta_{m,1}}{A_{m,1}h_{m,1}^{4}}\sqrt{\frac{\gamma_{\rm th}\sigma_{r}^{2}}{2T^ {2}P_{\rm t}^{2}}}\right)^{\eta_{m,1}},\eta_{m,1}=\min\left(\zeta_{m,1}^{2}, \beta_{m,1}\right)\]  
where \(b_{m,1}=1\Big{/}\Big{(}\zeta_{m,1}^{2}-\beta_{m,1}\Big{)}\), if \(\zeta_{m,1}^{2}>\beta_{m,1}\); and \(b_{m,1}=\Gamma\Big{(}\beta_{m,1}-\zeta_{m,1}^{2}\Big{)}\), if \(\zeta_{m,1}^{2}<\beta_{m,1}\).  
In (21), the term containing \(\sqrt{\gamma_{\rm th}\sigma_{\rm r}^{2}/\left(2T^{2}P_{\rm t}^{2}\right)}\) becomes zero when \(P_{\rm t}\) tends to infinity. Thus, the asymptotic expression of \(F_{\gamma_{m,1}}(\gamma_{\rm th})\) can be expressed as  
\[F_{\gamma_{m,1}}^{\rm bound}(\gamma_{\rm th})=\exp\left(-\frac{\theta_{\rm FoV,m,1}^{2}}{2\sigma_{0}^{2}}\right) \tag{22}\]  
Similarly, when \(P_{\rm o}\to\infty\) in (18), we can derive the asymptotic expression of \(F_{\gamma_{m,2}}(\gamma_{\rm th})\) as \[F_{7m2}^{\text{bound}}(\gamma_{\text{th}})=\exp\left(-\frac{\theta_{\text{FoV},m,2}^ {2}}{2\sigma_{\text{o}}^{2}}\right) \tag{23}\]  
Substituting (22) and (23) into (16), we can derive the asymptotic bound of \(F_{7m}(\gamma_{\text{th}})\) as  
\[F_{7n}^{\text{bound}}(\gamma_{\text{th}}) =1-\left[1-\exp\left(-\frac{\theta_{\text{FoV},m,1}^{2}}{2\sigma_ {\text{o}}^{2}}\right)\right]\left[1-\exp\left(-\frac{\theta_{\text{FoV},m,2}^ {2}}{2\sigma_{\text{o}}^{2}}\right)\right]\] \[=1-\prod_{j=1}^{2}\left[1-\exp\left(-\frac{\theta_{\text{FoV},m,j }^{2}}{2\sigma_{\text{o}}^{2}}\right)\right] \tag{24}\]  
Finally, substituting (24) into (15), we can derive the asymptotic expression of the system OP as  
\[P_{\text{out}}^{\text{bound}}(\gamma_{\text{th}})=\prod_{m=1}^{M}\left\{1- \prod_{j=1}^{2}\left[1-\exp\left(-\frac{\theta_{\text{FoV},m,j}^{2}}{2\sigma_ {\text{o}}^{2}}\right)\right]\right\} \tag{25}\]  
**Remark 1**.: _By observing (25), it can be found that when the average transmit optical power \(P_{\text{t}}\) and \(P_{\text{o}}\) are sufficiently large, increasing the optical power does not further improve the outage performance of the system. In this case, the OP of the system is only related to the number of UAV relay nodes \(M\), FoV \(\theta_{\text{FoV},m,j}\), and the standard deviation of the direction deviation \(\sigma_{\text{o}}\)._
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 1 Introduction
### Aims, Novelty, and Contribution  
The motivation for this paper is to propose a deep-learning-based approach to detect phishing attacks in blockchain transaction networks. The use of deep learning methods, such as long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), can help to identify and prevent phishing attacks in real time. Deep learning is increasingly recognized as a powerful tool for advancing big data technologies, including those related to the internet of things (IoT). However, developing effective deep learning models for IoT applications requires addressing several critical issues, such as single points of vulnerability, privacy leakage, lack of usable knowledge, and data poisoning attacks. In addition, phishing scams are a severe threat to the financial security of users in blockchain ecosystems. To address this issue, this paper proposes a systematic approach to detecting phishing scams in the Ethereum ecosystem using an improved ensemble-based LSTM architecture combining deep learning and blockchain technology. The proposed system aims to provide possible solutions for mitigating phishing scams in IoT networks by integrating ensemble learning techniques, such as convolutional and recurrent neural networks, with blockchain and deep learning.  
The novelty of this study lies in the proposal and evaluation of a deep learning-based approach to detect phishing attacks in blockchain transaction networks. Specifically, the use of long short-term memory (LSTM), bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM) for detecting phishing attacks in real-time on blockchain networks is a new and innovative application of deep learning techniques.  
The contributions of this study are summarized as follows:  
* A novel deep-learning model for detecting phish scams in blockchain transactions is presented.
* Using Bi-LSTM, CNN-LSTM, and embedded-LSTM on the Ethereum transaction network dataset is demonstrated.  
A dataset of malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset is used to evaluate the proposed approach. The evaluation results demonstrate the proposed approach's effectiveness in detecting phishing attacks, which can contribute to developing more secure and trustworthy blockchain-based systems.  
The rest of the article is arranged as follows. Section 2 discusses a literature review on the security of blockchains. The methods used to implement this study are presented in Section 3. Section 4 discussed the findings discovered, and the outcomes deduced from the implementation of the proposed system. The paper is concluded in Section 5, and future work is discussed.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 5 Discussions  
Time-frequency plots of high-precision sampled electromagnetic field observations reveal the presence of ionospheric QP emission characterized by changes in spectral morphology. This is particularly evident for targets affected by high levels of background  
Figure 11: Detection results on the June 2023 dataset using the QP-DETR algorithm, with the proton cyclotron frequency indicated by red lines. Several error detection issues were identified during testing.  
Figure 10: The QP-DETR successfully detected a selection of QP events in June 2023.  
noise interference, narrow bandwidths, short modulation periods, short durations, and low power spectral densities, which are effectively addressed by our proposed model.
## 1 Introduction
### Literature Review and Terminology
#### 1.1.6 Sustainability  
Sustainability is the ability to maintain or support an activity or process over the long term [17; 33].  
Designs for sustainability include socio-ethical sustainability and work sustainability. Socio-ethical sustainability means that the design should ensure building and improving consensus as social values change. Work sustainability means that the design should provide comfort and safety for long-term work or usage.
## 2 Results  
### Methodology for Compound Identification  
The methodology to tentatively identify MVCs is based on several steps as illustrated in supplementary Figure S1. Because of the complexity of the chromatogram of a bacterial culture, a first step consists of a mathematical processing (deconvolution) of each chromatographic peak to identify those corresponding to a co-elution and to draw mass spectra corresponding to each individual peak. At the same time, a comparison of the mass spectra obtained with the National Institute of Standard and Technologies (NIST) and the Wiley databases was performed to give a first list of identified compounds--around 240 compounds per sample. Then, the compounds linked to the decomposition of the adsorbent phases (e.g., compounds containing siloxane and polyethylene glycol units) were removed, resulting in a second list of about 60 compounds. To ensure a robust identification, mass spectrum of each compound was checked with databases, the linear retention index (LRI) was calculated and compared with the LRI of the literature (NIST Chemistry WebBook). Compounds with a maximum relative deviation of no more than 2.5% were retained leading to a third list of 19 potential bacterial metabolites.  
Finally, these metabolites were compared with the compounds identified in controlled conditions (Figure 1).  
Compounds found exclusively in bacterial culture (1-methyl-1-propylhydrazine, 3-ethyl-2,5-dimethylpyrazine, 3-methylbutanoic acid, and acetamide) were considered as being produced by _S. aureus_.  
For compounds found under both conditions, a statistical analysis of the peaks areas was performed. Area distribution was assessed using boxplots and outliers were eliminated (supplementary Figures S2 and S3). The variation of area was observed to determine if the compound was produced (observed area higher than the area of the medium) or consumed (observed area lower than the area of the medium). The significance of the area variation was assessed by a one-way Anova test with a \(p\)-value \(<\) 0.05 (Supplementary Table S1).  
Conversely, an average area for bacterial culture lower than control indicated a consumption of the volatile/semi-volatile compound by the bacterium.  
Figure 1: **(A)** Comparison of total ion chromatograms obtained with SPME extraction with CAR/PDMS phase. **(B)** Comparison of total ion chromatograms obtained with SBSE/HSSE extraction. SA: _Staphylococcus aureus_ culture condition; LB: control condition; SPME: solid phase microextraction; CAR: carboxen; PDMS: polydimethylsiloxane; SBSE: stir bar sorptive extraction; HSSE: headspace sorptive extraction.
## 3 Comparison and Analysis of Acceleration Response Results
### Comparison of Flat Ground with Adjusted Elastic Modulus
#### 3.2.2 Spectral Acceleration  
Figure 9 shows the spectral acceleration under measured at depths in the 1 \(g\) shaking table test and the spectral acceleration obtained from numerical analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**USCS** & **Description** & **Loose (MPa)** & **Medium (MPa)** & **Dense (MPa)** \\ \hline GW, SW &
\begin{tabular}{c} Gravels/Sand \\ well-graded \\ Sand, uniform \\ Sand/Gravel \\ silly \\ \end{tabular} & 30–80 & 80–160 & 160–320 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Typical values of elastic modulus for granular material [13].  
Figure 8: Part of acceleration-time history graph for experiment and analysis in flat ground: (**a–d**) Sine 8 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m; (**e–h**) Sine 10 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m.  
Figure 9: Spectral acceleration of experiment and analysis in flat ground: (**a–d**) Sine 8 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m; (**e–h**) Sine 10 Hz at depth 0.5 m, 0.3 m, 0.1 m, 0.05 m.  
Figure 9a-d exhibit the sine 8 Hz spectral acceleration. As the depth decreases, the difference between experiment and numerical analysis gradually increases. The spectral acceleration amplification obtained from numerical analyses is lower than the experimental spectral acceleration amplification. Figure 9e-h present the sine 10 Hz spectral acceleration. The spectral acceleration of ABAQUS is close to the experimental data at very deep depth, but decreases with depth. The spectral acceleration of DEEPSOIL is closer to that of the experimental data.  
Using the adjusted elastic modulus, acceleration time history and spectral acceleration all show very good results for different seismic waves. In this case, the analysis result of ABAQUS is closer to the experimental result than that of DEEPSOIL. As ABAQUS and DEEPSOIL use different constitutive models, they use different parameters. In ABAQUS, direct soil parameter values such as elastic modulus and friction angle are used. In DEEPSOIL, most of the values such as OMC and PI need to be calculated. So the analysis result of ABAQUS is closer to the experimental result than that of DEEPSOIL. This case verifies the feasibility of the adjusted elastic modulus and the accuracy of ABAQUS modeling.