esa-sceva/satcom-chunk-collection huggingface dataset:
# Malware Variants Detection Model Based on MFF-HDBA
## 4 Data Sets and Evaluation Metrics
### Evaluation Metrics  
In order to evaluate the classification performance of our model, we utilized accuracy, precision, recall, and F1-scores as assessment metrics. These metrics have been extensively applied by the research community to provide evaluations of models [45; 46; 47]. These metrics are defined as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
\[F1\text{ - }score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Malware Type** & **Family Name** & **No.** & **Samples** \\ \hline \multirow{4}{*}{Worm} & Allaple.A & 3 & 2824 \\  & Allaple.L & 4 & 1491 \\  & VB.AT & 23 & 383 \\  & Yuner.A & 25 & 775 \\ \hline \multirow{4}{*}{Trojan} & Adialer.C & 1 & 97 \\  & Dialplatform.B & 9 & 152 \\  & Instantaccess & 12 & 356 \\ \hline \multirow{4}{*}{PWS} & Lolyda.AA1 & 13 & 153 \\  & Lolyda.AA2 & 14 & 159 \\  & Lolyda.AA3 & 15 & 98 \\  & Lolyda.AT & 16 & 134 \\ \hline \multirow{4}{*}{Trojan} & Dontovo.A & 10 & 137 \\  & Obfuscator.AD & 18 & 117 \\ \cline{1-1}  & Swizzor.genIf & 21 & 103 \\ \cline{1-1}  & Swizzor.genIf & 22 & 107 \\ \cline{1-1}  & Whitrim.BX & 24 & 72 \\ \hline \multirow{2}{*}{Bogue} & Fakerean & 11 & 306 \\ \hline \multirow{2}{*}{Backdoor} & Agent.FYI & 2 & 91 \\  & Rbotgen & 19 & 133 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of each malware family in the Malimg data set.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Family** & **Samples** \\ \hline Mining & 15,759 \\ Non-mining & 7896 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of each malware family in the DataCon data set.  
where \(TP\) represents the actual positive malware types that are correctly predicted as positive samples, and \(FP\) means the actual negative malware types that are wrongly predicted as positive samples. Similarly, \(TN\) implies the actual negative malware types that are correctly predicted as negative samples, and \(FN\) means the actual positive malware types that are wrongly predicted as negative samples. The \(TP\), \(FP\), \(TN\), and \(FN\) values can be represented as shown in Figure 6, where \(F_{i}(i=0,1,2\ldots n)\) refers to malware families.
# Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System
## 4 Determining the Voltage Ratio and Measurement Uncertainty
### Measurement Uncertainty of Voltage \(U_{2}\)  
In the PD monitoring system, the Pico Technology DrDAQ data acquisition card was used to measure the voltage. According to the technical specification, the accuracy of the voltage measurement is \(\pm 3\%\) of the measured value. The formula for the maximum limit error \(E_{U}\) of the measured voltage, which is recorded in the waveform averaging mode (at least sixteen), can be written as follows:  
\[E_{U}=\pm(0.03\cdot V_{MEAS}) \tag{11}\]  
where \(V_{MEAS}\) is the voltage value measured with the acquisition card. Assuming a uniform distribution of errors and a negligible influence of voltage measurement resolution, the standard uncertainty can be calculated using the formula:  
\[u(U_{2})=\frac{E_{U}}{\sqrt{3}} \tag{12}\]
# Novel Materials for Heavy Metal Removal in Capacitive Deionization
## 3 Materials for Heavy Metal Removing Using CDI
### Others
Carbon has been shown to be an excellent material for various HMs, including Pb, Cu, Cr, Cd, and Ni. Carbon is the most widely applied material due to its abundance in nature and facile modification methods. Table 2 presents the various materials that have been studied for heavy metal removal, such as activated carbons, carbon cloths/fiber, ordered mesoporous carbons, carbon aerogels, carbide-derived carbons, carbon nanotubes, carbon black, and graphene. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metal** & **Working Electrode** & **Counter Electrode** &
\begin{tabular}{c} **Removal Rate/adsorption** \\ **Rate/CF** \\ \end{tabular} & **Ref** \\ \hline Cr\({}^{6+}\) & Fe\({}_{3}\)O\({}_{4}\)/peanut-shell-derived & & & \\ activated carbon & PSAC & 99.9\%; 24.5 mg/g; 610 F/g & [116] \\ Pd\({}^{2+}\), Cd\({}^{2+}\) & Eggplant-derived carbon & Zn foil & 99.1\% Pd; 97.9\% Cd; 172.5 F/g; & [105] \\ Pb\({}^{2+}\) & Fe\({}_{2}\)O\({}_{3}\)@C & Fe\({}_{2}\)O\({}_{3}\)@C & 90.3\%; 830.17 mg/g; 115.6 F/g & [116] \\ Cu\({}^{2+}\) & Polypyrrole/chitosan & & 56.4\%; 99.67 mg/g; 102.96 F/g & [124] \\ Cu\({}^{2+}\) & Polypyrrole /chitosan /CNT & Graphite & 80.08\%; 16.83 mg/g; 103.19 F/g & [112] \\ Cu\({}^{2+}\) & ZnS-decorated carbon felt & & \(>\)90\%; 27.4 mg/g; 168 F/g & [128] \\ Cr\({}^{6+}\) & Bael-fruit-shell-derived & Fe wire & 100\%; -; - & [129] \\  & carbon on Cu wire & & 90\%; -; - & [130] \\ Pd\({}^{2+}\) & Graphite foil & & 95.7\%; -; - & [131] \\ Cu\({}^{2+}\) & Activated carbon on Ti net & & 95.7\%; -; - & [131] \\ Pd\({}^{2+}\), Cd\({}^{2+}\) & rGo-CNT on Ti platcocoe & Pt plate/sheet & 253.25 mg/g Cu, 241.65 mg/g & [120] \\  & MIL-53(Fe) on & & Pb; 165.22 F/g & \\ Cr & graphite paper & Graphite paper & 72.2\%; 224.7 F/g & [132] \\ Cr\({}^{6+}\) & Corncob-derived 3D porous & Pt foil & 91.85\%; 452 F/g; & [106] \\  & carbon on Ni foam & & 90–96\%; 521 mg/g Pb; 498 & [94] \\ Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{2+}\) & N-doped graphene on & Graphite rod & mg/g Cu \\  & graphite foil paper & & 90\% Zn; 88\% Ni155.6 mg/g Zn; & [119] \\ Zn\({}^{2+}\), Ni\({}^{2+}\) & Na\({}_{4}\)Mn\({}_{14}\)O\({}_{27}\) on & & 90.6\% Pb; 92\% Cu; 90.2\% Cd & [117] \\ Pb\({}^{2+}\), Cu\({}^{2+}\); Cd\({}^{2+}\) & Fe\({}_{3}\)O\({}_{4}\)/graphene & & 57.13 mg/g; 120.4 F/g; & [121] \\  & TiO\({}_{2}\)/watermoendri & Activated carbon cloth on & Activated carbon cloth on & Activated carbon cloth on stainless mesh & 81\% Pb; 78\% Cr; 42\% Cd & [133] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CDI methods for heavy metal removal.
# Interdependent Transcription of a Natural Sense/Antisense Transcripts Pair (_SLC34A1_/Pfn3)
## 5 Conclusions  
We found that the bi-directionally transcribed _SLC34A1/PFN3_ locus can be activated using epigenetic drugs such as zebularine and dexamethasone in human cell lines. The effect of the drugs depended on the cellular background. In HEK293 cells, both sense (_SLC34A1_) and antisense transcripts (_PFN3_) were maximally induced after 5 days' incubation with dexamethasone. The transcriptional induction was paralleled by changes in DNA methylation and H3K27 acetylation in the sense promoter. Co-expression of transcripts from the opposite strand could also be demonstrated by CRISPR-mediated activation of the sense and the antisense transcript, respectively. These findings indicate that the _SLC34A1_ sense transcript and the _PFN3_ antisense transcript are regulated in a concordant way. Based on these findings, we propose a model whereby transcription in sense direction induces low-level production of the non-protein coding _PFN3_ RNA. Such 'co-expression' produces comparably high levels of protein coding sense transcript and low levels of the corresponding antisense transcript. The antisense transcript may adopt regulatory roles and function as lncRNAs in a cell-specific manner. Since low-level expression and concordant regulation with the cognate sense mRNA are hallmarks of many complementary gene pairs [8], our findings may apply to other sense/antisense genes.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/ncrna8010019/s1](https://www.mdpi.com/article/10.3390/ncrna8010019/s1)]([https://www.mdpi.com/article/10.3390/ncrna8010019/s1](https://www.mdpi.com/article/10.3390/ncrna8010019/s1)), Figure S1: DNA methylation analysis of _SLC34A1_-sense (S1, S2) and _SLC34A1_-antisense (AS) promoters; Figure S2: Global histone H3 acetylation; Figure S3: PCR analysis of CRISPR edited HEK293 clones; Tables S1-S6: PCR primers.  
**Author Contributions:** Conceptualization, H.S.Z. and A.W.; methodology, H.S.Z., C.S.-L., M.A.A.-M., G.A., M.M.K., A.K. and G.C.; validation, J.M., G.C. and A.W.; formal analysis, H.S.Z. and A.W.; writing--original draft preparation, H.S.Z. and A.W.; writing--review and editing, H.S.Z., M.A.A.-M. and A.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partly funded by The Northern Counties Kidney Research Fund, Grant 18.011 to A.W. and The Higher Committee for Education Development in Iraq (H.Z.).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to thank [PERSON] for her excellent technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Validation of Reference Genes  
To confirm the stability of expression of the selected reference genes, verification experiments were carried out with samples from different temperatures and developmental stages. For testing the expression stability across different developmental stages, we quantified the relative expression of \(AK\), which have been shown to be differentially expressed at different developmental stages [41; 42]. For assessing the stability across different temperatures, we quantified the relative expression of the thermotolerance-related gene _HSP90_, which is essential for the survival of \(B\). _xylophililus_[43]. Normalization of the two target genes (\(AK\), _HSP90_) was conducted using the most stable gene combinations (_EF1\(\gamma\)/Actin_ or _UCBE_/_EF1\(\gamma\)_) and the least stable combinations (_UBQ/18S rRNA_ or _EF1\(\gamma\)/PMP-2_) as determined by geNorm and RefFinder. Relative quantification of these two target genes was calculated using the \(2^{-\Delta\Delta\)Ct method. The primers for validation of selected reference genes were as follows: _Bx-AK_-F, 5\({}^{\prime}\)-CATCTCCGAATCATCTCCAT-3\({}^{\prime}\) and _Bx-AK_-R, 5\({}^{\prime}\)-GCCTTGAACTTGTCCTTATC-3\({}^{\prime}\), _Bx-HSP90_-F, 5\({}^{\prime}\)-GAAGGTCATCAGGAAGAACT-3\({}^{\prime}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene Symbol** & **Primer Sequence(5\({}^{\prime}\) to 3\({}^{\prime}\))** & **Amplicon Length (bp)** & **E\%** & **R\({}^{2}\)** \\ \hline _Actin_ & F.GCTGCCTCCTCCTCCTCC & 156 & 99.47 & 0.9908 \\  & R.GGAGTTTGAGGTGCTCTGGATA & & & \\ _TUB_ & F.TGTGATTGTCTCAAGGCTTCC & 88 & 102.52 & 0.9936 \\  & R.GATTCTGTCTGGGTATTCTTCCG & & & \\ _UBCE_ & F.ACCGCCTGGGACTTTGATTC & 99 & 104.67 & 0.9843 \\  & R.TTGGAC  
and _Bx-HSP90_-R, 5\({}^{\prime}\)-TGTGTAACGGAAGGAAACT-3\({}^{\prime}\). Statistical analysis was performed by SPSS 22.0 (SPSS Inc., Chicago, IL, USA) using a Student's _t_-test for independent samples.
## 3 Performance Analysis  
In this section, we analyze the performance of D2D-CRS-SM, in terms of BER and SE over independent Rayleigh fading channels.
## 3 Proposed Architecture: CS-SR
### Filtration Step  
The first step where requirements and the priority for the QoS objective taken from the user. The outcome of the filtration step is (1) this step will significantly reduce unnecessary services from the list; (2) this step will help decision-makers in finding optimal service quickly; (3) the filtered candidate service are only those who fit into the user requirements; therefore, it significantly improves the efficiency of the decision maker.
That paper had a limited scope, focusing primarily on presenting a few experiments. In contrast, this article significantly extends that prior work, providing a thorough overview of the overall design, implementation and detailed validation. In summary, this article presents a blockchain-based solution with the following main contributions:  
* It includes a thorough analysis of state-of-the-art systems that make use of blockchain for document verification. * A detailed description is provided on the design and implementation of a novel blockchain-based system for AR and CV verification. * A detailed evaluation of the proposed solution is presented in terms of performance (latency, throughput and gas limit) and energy consumption, when using a traditional computer and two different low-power Single-Board Computers (SBCs). The remainder of this article is structured as follows. Section 2 reviews the state of the art of traditional and blockchain-based systems aimed at document verification and academic anti-forgery, and describes the basics of blockchain. Section 3 presents the design of the proposed system, detailing the communications architecture, as well as the main functionality of the proposed system, whose implementation is described in Section 4. Next, Section 5 presents the results of the performed experiments, which were aimed at quantifying the system performance and its energy consumption when executing it in traditional and low-power computers. Finally, Section 6 summarizes the key findings of the experiments, as well as the main open challenges and future work, while Section 7 is devoted to conclusions.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 5 GNSS Meta-Signal SPP Performance Results
The positioning standard deviation performance results for the reference value \(\text{SNR}_{\text{out}}=25\) dB were as follows (i.e., recall that the E5 nominal performance in the previous scenario was 18 cm): (i) E5, 1.15 m; (ii) L2 + L5, 69 cm; (iii) E5B + E6, 52 cm; (iv) B2A + B3, 40 cm; (v) E5A + E6, 32 cm; (vi) B2 + B3, 51 cm; and (vii) E5 + E6, 44 cm. Notice that the performance degradation was remarkable; therefore, in some safety-critical applications operating in harsh propagation conditions, meta-signals may not provide the precision needed. In conclusion, as for the time-delay estimation and regardless of the satellite geometry, the best compromise in terms of robustness, performance, and estimator behavior was provided by Galileo E5  
Figure 9: Non-nominal bad GDOP scenario SPP RMSE (m) and the corresponding CRBs (m) for the different GNSS meta-signals: (**a**) GPS L2C + L5Q, (**b**) Galileo E5B + E6B, (**c**) BeilDou B2 AQ + B3Q, (**d**) Galileo E5A + E6B, (**e**) BeilDou B2 + B3Q and (**f**) Galileo E5 + E6B. The RMSE/CRB SPP performance for Galileo E5 under nominal good GDOP conditions is shown for comparison. and the full-bandwidth E5 + E6 and B2 + B3 combinations, meta-signals providing better performance, but requiring a double bandwidth.
## 2 UWB Patch Antenna Design  
### Geometric Structure  
Figure 1 illustrates the geometry of the rectangular patch antenna design. Typically, the feed lines and radiating elements are photoetched onto a dielectric substrate. The radiating patch, like the ones in [34; 35], is square-shaped and mounted on an FR4 substrate with dimensions of 31 by 24 mm. The patch antenna was developed using a copper material with only a thickness of 0.038 mm on an FR4 substrate with a dielectric constant (\(\epsilon_{r}\)) of 4.4. To establish a feed gap between the ground and the patch, a partial ground that was 12 mm long was used with the patch. The ground was then again downsized by 0.5 mm; this increased the bandwidth of the entire antenna. The radiating patch's corners were beveled to increase its impedance bandwidth (s11\(<-10\) dB) between 3.1 GHz and 14 GHz. To improve impedance matching over the full UWB range, a notch was etched into the partial ground plane just behind the transmission line. Further, a 50 \(\Omega\) impedance with a 0.02 loss tangent was used to feed the micro-strip line.  
Figure 1: Structure of the rectangular patch (**a**) front, (**b**) side, (**c**) ground plane.
## 2 Results
### Amino Acids, Amino Acid Derivatives and Nucleotides Show Major Differences after Infection
#### 2.3.4. The Nucleotide Profile Showed Elevated CTP and GTP Levels in Infected A549 Cells  
We were able to detect the nucleotides involved in RNA and DNA synthesis together with intermediates of the purine and pyrimidine biosynthesis pathways. The nucleoside triphosphates required for RNA synthesis (ATP, UTP, GTP and CTP) were found to be the most abundant metabolites of these pathways. The amounts of ATP together with UTP and some of its sugar derivatives like UDP-glucose (UDP-GlcNAc), UDP-\(N\)-acetyl-glucosamine (GlcNAcAc) and UDP-glucuron (UDP-GlcNAc) were significantly decreased between 2 h and 6 h after infection. GTP and GDP increased 6 h after the infection together with CTP and CDP. Whereas, GTP dropped to control conditions again CTP, CDP and GDP remained elevated until 24 h (Figure 4). It is noteworthy that  
Figure 4. Fold change development of selected metabolites. Time dependent FC\({}_{\text{log2}}\) (infected vs. control) of selected intracellular amino acids, metabolites of primary carbon metabolism, nucleotides and other identified metabolites are presented in a colour coded chart. Red indicates a lower amount and blue indicates a higher amount of the metabolite in infected cells compared to control cells.  
phosphoribosyl-pyrophosphate (PRPP), a key intermediate of purine and pyrimidine synthesis derived from the pentosephosphate-pathway (PP-pathway), was found in approximately seven-fold lower amounts 6 h after the infection and remained at that low level.  
Metabolic Profiles of Infected A549 Cells in the Presence of Glutamine Analogues Indicate a Different Enzyme Activity after Infection  
In our metabolomics approach we found that CTP and GTP were elevated at time points during infection whereas ATP and UTP were decreased or rather unchanged. If increased CTP and GTP levels play a role during infection is so far unknown. In this approach, we tried to inhibit the synthesis of CTP and GTP by applying the glutamine analogues diazo-5-oxo-l-norleucine (DON) or azaserine to the infected cells. These compounds are known to inhibit glutamine transaminase reactions and glutamine is an important precursor and nitrogen donor in the de novo synthesis of purines and pyrimidines.
# The Effect of Aromatherapy on Post-Exercise Hypotension: A Pilot Study
###### Abstract  
The global prevalence of hypertension continues to rise, affecting an estimated one billion worldwide. Regular exercise is well recognized as a non-pharmacological approach for individuals with hypertension due to its blood pressure (BP)-lowering effect, largely attributed to repeated exposure to post-exercise hypotension (PEH). Recent evidence also indicates that aromatherapy can contribute to BP reduction, indicating that combining aromatherapy with exercise may enhance the overall BP-lowering effects. Therefore, this pilot study aimed to investigate the effects of aromatherapy on PEH during the recovery phase following exercise. Fourteen healthy young males (22.7 \(\pm\) 0.7 yrs) participated in this randomized crossover-designed study. All participants completed two exercise sessions per week, each lasting 30 min, at a target heart rate (HR) of 60-65%. The individuals inhaled either aroma oil or water vapor at 5, 35, 65, and 95 min after exercise. The HR, BP, blood lactate level, and arterial stiffness index were measured before and after the exercise. Our findings revealed the following. (1) PEH occurred in both groups. (2) In the aroma group, PEH was augmented compared with the control group, with the maximum reduction in BP being greater in the aroma group. (3) The reduction in arterial stiffness was greater and longer in the aroma group than in the control group. (4) The changes in the lactate levels after exercise did not differ between the groups. Our findings indicate that aromatherapy can amplify PEH, suggesting that its use after exercise may help maximize the positive effects of exercise on BP reduction.  
post-exercise hypotension; exercise; aromatherapy; vascular stiffness Article
The different monitoring characteristics such as sampling time, mechanisms for data transfer, associated software etc., were highlighted in the work but the works did not outline the current progress in the field of monitoring with regards to module-based technology. [PERSON] and [PERSON] [26] focused on examining solar PV systems in terms of the sensors employed, the controllers used, and data transmission techniques. [PERSON] et al. [27] discussed the architecture and features of various monitoring systems for solar PV power plants. [PERSON] et al. [28] explained the various monitoring systems and concentrated on addressing the major failures in solar PV systems. All the above-highlighted reviews do not consider the insights of the technological implementation of data transmission and data processing modules in the field of solar PV wireless monitoring systems in a comprehensive manner. To bridge the existing research gaps, this review unveils new contributions with a detailed investigation of the monitoring technologies of the solar PV system. The review offers the following contributions:  
* A comprehensive explanation of various data processing modules for solar PV monitoring systems is presented in terms of categories, specifications, design implementation, software platforms, results, and limitations. * The categories of the various data transmission modules for wireless communication in solar PV monitoring systems are reported, highlighting topology, data transmission rage, sampling rate, power consumption, and range. * The existing issues and challenges for the monitoring technologies of solar PV applications are covered, emphasizing data handling, security, signal interference, energy efficiency, etc. * Some constructive future recommendations are presented toward the development of an efficient and reliable solar PV monitoring system. The paper is organized into eight sections. Section 2 presents the survey methodology of this review paper. Section 3 covers an overview of the solar PV monitoring system and the classification of the modules based on the data transmission and data processing boards. Sections 4 and 5 highlight the data processing technology and data transmission technology for monitoring solar PV systems, respectively. The current issues and challenges are discussed in Section 6. Section 7 presents the discussion and future perspectives. The concluding comments are outlined in Section 8.