esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Data Processing  
#### 2.2.1 Carbon Flux Observed Data  
The carbon flux data used in this paper were from the Multi-Scale Observation Experiment on Evapotranspiration for heterogeneous land surfaces, 2012 of the Heihe Watershed Allied Telemetry Experiment Research (HiWATER-MUSOEXE) [29; 30; 31]. Carbon flux data with a time interval of 30 min were obtained from nine flux tower sites from May to September in 2014, which is considered to be the growing season in the study area, and were used to study the spatial representativeness of GPP in this paper. The locations of these carbon flux towers are shown in Figure 1. The underlying surface types of the eddy covariance systems include Populus, crops (maize), grass, shrubs, wetland, Gobi and desert. The steps required to process the datasets included time delay corrections, density fluctuations, spectral loss, secondary coordinate rotation, sonic virtual temperature conversion, gaps filled, and the selection of high-quality carbon flux data [3; 30; 31; 32; 33; 34; 35]. The daily carbon flux was the summation of the 30-min data for one day. We calculated the difference in the estimated ecosystem respiration (\(R_{eco}\)) and observed net ecosystem carbon dioxide exchange (NEE) [36; 37; 38] as follows:  
\[GPP=R_{eco}-NEE \tag{1}\]
## 7 Discussions and Clarification of Comparison Results  
The performance comparison results and CLA configurations are discussed in this section. The experimented protocol configurations are ranked first according to their performance evaluation results. Then, a comprehensive discussion of the CLA operation and performance comparison is provided to clarify why they performed differently.  
The investigated DTN protocols and configurations are roughly ranked in Table 4. according to their goodput performance at all three BERs. As observed, LTP LTP PRIM (i.e., running LTP over both link segments of the primary relay path) is ranked on top among all four protocol configurations, followed by LTP TCP SEC, then by the transmissions involving UDP (i.e., UDP LIP PRIM and LTP UDP SEC).
## 4 Role of sRNA in Antibiotic Resistance
### Activation of Efflux Pumps  
The tripartite efflux pump systems in _E. coli_--AcrAB-TolC, MacAB-TolC, MdtABC-TolC, and EmrAB-TolC--play crucial roles in multi-drug resistance by expelling a variety of toxic compounds [200] (Figure 3a). The AcrAB-TolC system consists of AcrA, a periplasmic adapter protein that bridges the inner and outer membranes, AcrB, a Resistance-Nodulation-Division (RND) transporter responsible for recognizing diverse substrates such as antibiotics and bile salts, and TolC, an outer membrane channel [201]. The _acrAB_ genes are organized within an operon, while _tolC_ is located separately on the _E. coli_ chromosome within an operon that includes _ygiB_ and _ygiC_. Both the _acrAB_ and _tolC_ promoters are regulated by the MarA, SoxS, and Rob transcriptional regulators and are upregulated in response to superoxide stress and exposure to heavy metals, bile salts, and antibiotics, reflecting the efflux pump's role in exporting these harmful substances from the cell [202] (Figure 3a). In addition to AcrAB-TolC efflux pump, TolC was found to be associated with MacA-MacB and MtdA-MdtB-MdtC to form MacAB-TolC [200,203] and MdtABC-TolC efflux pumps, respectively [204]. The MacAB-TolC pump exports macrolides like erythromycincin and secretes virulence factors, whereas the MdtABC-TolC system confers resistance to bile salts and heavy metals, aiding bacterial colonization in the gut. Lastly, the EmrAB-TolC system, part of the Major Facilitator Superfamily (MFS), uses a proton/drug antiporter mechanism to resist hydrophobic antibiotics [205] (Figure 3a). Collectively, these efflux pumps are vital for bacterial survival in hostile environments and contribute significantly to antibiotic resistance. Recent works have deciphered the regulations of these multi-drug efflux pumps by sRNAs, as discussed in the following paragraphs.
## 2 Materials and Methods
### Data Processing
#### 2.2.3 Distance Calculation  
The RSSI distance measurement uses the logarithmic distance path-loss model, used in several studies using BLE signals such as [17; 18]:  
\[L_{s}=(-10n)log_{10}(RSSI_{s})+L_{0}\]  
where \(L_{s}\) is the distance in meter based on the RSSI value of sensor \(s\), \(n\) is the broadcasting power value (2 in our case) and \(L_{0}\) is the measured RSSI power at 1 m. In our case, \(L_{0}\) is equal on average to \(-62\) dB, but it is mandatory to keep in mind that this value can drastically change depending on the environment (such as the size of the room studied) and thus this constant must be measured in all new environments.
## 4 Performance Evaluation
### Selection Criteria for Doppler Value Intervals  
The algorithm that we developed is capable of handling a wide range of Doppler values, from hundreds of Hz to several thousand Hz. We categorized these values based on the target speed as follows:  
* Low-Doppler targets: This category includes various types of drones, such as consumer drones, racing drones, and some types of helicopters. Consumer drones typically have speeds ranging from 14 to 20 m/s, with advanced models reaching up to 30 m/s. Racing drones, designed for speed and agility, can exceed speeds of 45 m/s. Some types of helicopters, which are slower and used for specific tasks, also fall into this category. Consequently, the Doppler values for this category range from 100 Hz to 2000 Hz [21; 22].
* Medium-Doppler targets: This category covers fixed-wing aircraft and commercial airplanes. Their average speeds range from 100 m/s to 350 m/s, resulting in Doppler values between 2000 Hz and 7000 Hz [22; 23].
* High-Doppler targets: This category is dedicated to military aircraft, which have speeds ranging from 500 m/s to 750 m/s. The corresponding Doppler values for this category range from 10,000 Hz to 15,000 Hz [24].  
To cover the full Doppler interval from 0 to 15 KHz, we choose the following Doppler ranges:  
* Low-Doppler Range: 100-2000 Hz;
* Medium-Doppler Range: 2000-10,000 Hz;
* High-Doppler Range: 10,000-15,000 Hz.
# Malware Detection Using Memory Analysis Data in Big Data Environment
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Authors** & **Model** & **Dataset/Repository** & **Samples** & **Accuracy (\%)** \\ \hline
2014 & [PERSON] et al. [10] & NB & VrHeaven & 200 b & 98.90 \\
2015 & [PERSON] et al. [11] & SVM & AutoMall & 115,157 & 99.22 \\
2016 & [PERSON] et al. [5] & SGD & VrHeaven & 100 b & 96.00 \\
2016 & [PERSON] et al. [12] & XGBoost & BIG 2015 & 21,741 & 99.80 \\
2017 & [PERSON] and Jaidhar [13] & RF & VrHeaven SourceForge & 4500 b & 99.55 \\
2017 & [PERSON] et al. [14] & RF & Virushaer & 3130 m & 91.40 \\
2017 & [PERSON] et al. [27] & DCNN & Malimg & 9339 & 98.62 \\
2018 & [PERSON] et al. [29] & MLP & OpenMalware & 1984 m & 95.20 \\
2018 & [PERSON] et al. [18] & DNN & MalRec & 66,301 m & F-Score: 94.20 \\
2018 & [PERSON] et al. [15] & FF & MalRec & 9000 m & 99.65 \\
2018 & [PERSON] et al. [28] & Hashing + CNN & BIG 2015 & 10,805 m & 99.26 \\
2019 & [PERSON] et al. [30] & CNN & VirusTotal & 10,000+ & 90.50 \\
2019 & [PERSON] et al. [31] & RF+ MLP & OpenMalware & 27,000 & 96.90 \\
2019 & [PERSON] et al. [8] & SVM & VirusTotal & 1200 m & 98.50 \\
2019 & [PERSON] et al. [20] & CNN + LSTM & BIG 2015 & 10,868 m & 99.31 \\
2019 & [PERSON] et al. [19] & Word2 Vec + LSTM & BIG 2015 & 10,868 m & 97.59 \\
2019 & [PERSON] et al. [21] & LSTM & VirusDataset & 1430 m & 96.70 \\  & & & VirusTotal & 1352 b & 939 m & 98.23 \\  & & & Malimg & 9339 m & 98.23 \\  & & & Big 2015 & 10,868 m & 98.46 \\
2021 & [PERSON] et al. [34] & DenseNet & MaleVis & 13,183 & 98.21 \\  & & & Malicia & 9670 m & 89.48 \\
2020 & [PERSON] et al. [22] & fastText + LSTM & BIG 2015 & 10,868 m & 96.76 \\
2021 & [PERSON] et al. [33] & SMO & Dumpware10 & 608 b & 96.39 \\
2021 & Parker and Nissim [23] & REKNN & VirusTotal & 21,800 & 98.70-99.90 \\  & & & Malimg & 9339 & 99.14 \\
2021 & [PERSON] et al. [32] & DenseNet201 + ShuffleNet + & MaleVis & 13,760 & 95.01 \\  & & & ECG-SVM & Virus-MNIST & 51,880 & 86.36 \\  & & & Dumpware10 & 4294 & 96.62 \\
2021 & Diaz and Bandala [24] & LSTM + LightGBM & SoReL-20M & & 91.73 \\  & & & BIG 2015 & 10,868 m & 99.86 \\
2022 & [PERSON] and [PERSON] [35] & CNN & Dumpware 10 & 3686 m & 99.60 \\  & & & & 608 b & 99.273 \\
2022 & [PERSON] et al. [26] & XGBoost & & 29,273 & 88.70 \\
2022 & [PERSON] and [PERSON] [25] & TextCNN & SoReL-20M & 10,260 m & 98.18 \\  & & & BIG 2015 & 3759 m & 93.46 \\
2022 & This Study & LR & CIC-MalMem-2022 & 29,298 m & 99.97 \\ \hline \hline \end{tabular} Note: m—malware, b—benign.  
\end{table} 
Table 1: Comparison of other works on Malware Detection.
## 7 Experiments
### Results and Discussions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & \begin{tabular}{c} **Total Stack** \\ **Memory** \\ \end{tabular} & \begin{tabular}{c} **Relative** \\ **Execution Time** \\ \end{tabular} \\ \hline \begin{tabular}{c} Estimate the frequency and \\ apply the RF phase compensation \\ \end{tabular} & 128 B & 2.94\% \\ \hline \begin{tabular}{c} Calculate the covariance matrices \\ \end{tabular} & 112 B & 7.67\% \\ \hline \begin{tabular}{c} Compute the noise covariance matrix \\ \end{tabular} & 440 B & 5.27\% \\ \hline \
\begin{tabular}{c} Compute the companion matrix \\ \end{tabular} & 64 B & 0.93\% \\ \hline
\begin{tabular}{c} Find the polynomial roots \\ \end{tabular} & 224 B & 83.17\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stack memory consumption and relative execution time of principal operations. Figure 9: RMSE of the accuracy over the SNR run in a nRF52840 SoC. Table 4 shows the memory footprint, execution time, and energy consumption. From these values, the implemented solution satisfies the memory requirements for an nRF52, nRF53, and EFR32 BG22 SoC series, as can be verified in [56; 57; 58]. The execution time is 16.2 ms. By comparison, the standard MUSIC implemented in [59] takes about 18 ms to 133 ms with a median of roughly 31 ms to estimate a single DOA of one ULA in the same SoC (nRF52840). That means that even though the implemented solution estimates DOAs from two ULAs, it is still almost two times faster than the median of the standard MUSIC. A 2D standard MUSIC was implemented in C programming language based on parallel computing using a Digital Signal Processor of 1 GHz and an L-shaped antenna [22]; however, despite the parallelization and a much more powerful processor, it takes about 190 ms. Nonetheless, the implemented solution could be much slower than the modified 2D Unitary TLS ESPRIT in [34]. Its 1D version was developed in the same SoC, and it takes about 0.855 ms using the same precision format (single-precision floating-point). Hence, we can roughly estimate that its 2D version for L-shaped arrays could be two times that value, that is, 1.71 ms if it were developed. Coin batteries are used for small electronic devices [60], including constrained IoT ones. We found that the capacity of such batteries ranges from 1 mAh to 2000 mAh [61] in a well-established global distributor of semiconductors and electronic components. That means, considering the implemented solution as the only source of energy consumption, the nRF52 series can run from 16,574 to more than 33 million times approximately. Therefore, the implemented solution can be used for battery-powered small embedded devices. However, it is worth mentioning the experiment did not measure the RF front-end, since we did not employ a real array of antennas. Therefore, in practice, we considered that the measurement was evaluated after IQ sampling.
## 3 Results and Discussion
### Chloride Ion Diffusion Coefficient Recovery Rates of Mortars  
The chloride ion diffusion coefficients of the mortar after standard curing for 28 days are shown in Table 9. The chloride ion diffusion coefficient of CCM0 was 16.99 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s. After adding 4% cement weight of WM1 or WM2 to the mortar, the chloride diffusion coefficients of CCM1 and CCM2 were 14.68 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s and 15.77 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s, respectively. Compared with CCM0, the chloride ion diffusion coefficients of CCM1 and CCM2 decreased by 13.6% and 7.2%, respectively. This is because some small pores appear inside the mortar after molding, and adding the appropriate dosage of microcapsules can fill some pores, increase the compactness of the mortar, reduce its chloride ion diffusion coefficient and thus improve the impermeability of the mortar [33]. Since the particle size of WM1 is smaller than that of WM2, it can better fill the pores and microdefects inside the mortar, hinder the flow transmission of chloride ions and increase the compactness of mortars, which leads to a more obvious improvement in the impermeability of the mortar [34].  
Figure 12 shows the chloride ion diffusion coefficient recovery rates of mortars after different self-healing periods. After 1, 4, 7 and 14 days of self-healing, the chloride ion diffusion coefficient recovery rates were 52.6%, 59.1%, 72.7% and 72.9% for CCM1, while the chloride ion diffusion coefficient recovery rates for CCM2 were 55.1%, 62.4%, 79.3% and 79.8%, respectively. This result is due to the different microcapsules incorporated in the mortar. From the experimental results in Sections 3.1 and 3.2, it can be seen that WM2 had a higher core content and less weight loss after 60 days of placement, which means that more E-44 epoxy resin is involved in the self-healing reaction. Therefore, more healing products can be produced, reducing the channels for chloride ion transport in the mortar, resulting in a higher chloride ion diffusion coefficient recovery rate for CCM2 than CCM1.  
Figure 11: Pore size distribution of mortars after 14 days of self-healing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mortars** & **Chloride Ion Diffusion Coefficient (after 28 Days of Standard Curing)** \\ \hline CCM0 & 16.99 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s \\ CCM1 & 14.68 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s \\ CCM2 & 15.77 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s \\ CCM60 * & 44.02 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s \\ \hline \hline \end{tabular} \({}^{*}\)CCM60: the control mortar under 60%\(a\) pre-load.  
\end{table}
Table 9: Chloride ion diffusion coefficients of mortars.
## 5 Flying Base Station Capacity Gain Benefit
### Capacity Gain in Half-Duplex Case  
If the flying base station does not have full-duplex capability and either transmits or receives at a time, the transmission bandwidth is halved from the channel bandwidth, as described in Section 4.2.2.  
**Theorem 5**.: _If the flying base station has half-duplex capability, it can be beneficial and provide greater capacity if_  
\[P_{\mathbf{F}}>P_{\mathbf{S}}\cdot 2^{-\gamma}(\mathrm{SNR}_{SU}+2) \tag{9}\]  
Proof.: \(C_{FU}>C_{SU}\) using Equations (6) and (4) and \(d_{FU}=0.5d_{SU}\) yields \(\eta>2^{-\gamma}(\mathrm{SNR}_{SU}+2)\), which is equivalent to \(P_{\mathbf{F}}>P_{\mathbf{S}}\cdot 2^{-\gamma}(\mathrm{SNR}_{SU}+2)\). We do not include the algebraic steps and derivations in this proof.  
**Corollary 1**.: _If the flying base station has half-duplex capability, then the flying base station is beneficial and provides greater capacity if the stationary base station's SNR is less than \(2^{\gamma}\cdot\eta-2\), i.e.,_  
\[\mathrm{SNR}_{SU}=\frac{P_{\mathbf{S}}\ \alpha\ (d_{SU})^{-\gamma}}{W \cdot N_{0}}<2^{\gamma}\cdot\eta-2 \tag{10}\]  
Corollary 1 defines the received SNR threshold of the stationary base station, given that the flying base station transmission power is fixed relative to the stationary base station's. The left-hand side of Equation (10) is the received SNR when the stationary base station transmits to the user, and the right-hand side is the threshold increasing with the channel condition \(\gamma\) and with the power ratio \(\eta\). As the user moves away from the stationary base station, the flying base station becomes more beneficial. Such a received SNR-based approach is analogous to the handover across stationary base stations and cells. Similar to a mobile user moving from one stationary base station to another, we can implement a handover from a stationary base station to the flying base station, although the concrete handover protocol between the stationary and flying base stations is left for future research.
## 2 Materials and Methods
### Data Acquisition  
A FLIR SC3000 digital thermal camera was used in the experiment. It is characterized by a Focal Plane Array of 320 \(\times\) 240 Quantum Well Infrared Photodetectors, sensitive to the thermal radiation in the 8-9 \(\upmu\)m band. The temperature sensitivity/noise equivalent temperature difference of the thermal camera is 0.02 K. The thermal camera was blackbody-calibrated to factory specifications by means of periodic calibration performed by the Quality Management Systems of FLIR, which is certified to comply with ISO 9001:2008. The process of blackbody calibration is described in [37]. Such a process is useful to remove noise-effects related to the sensor drift/shift dynamics and optical artefacts. In accordance to literature [39], cutaneous emissivity was considered as \(\epsilon\approx\) 0.98. The thermal camera was placed 1.5 m distant from the hands' dorsum and it was placed perpendicular to the analyzed region [40].  
The thermal images acquired were used to feed the DCNN models. In detail, the thermal image corresponding to the central part of the baseline period was chosen as representative of the resting condition for each participant. The whole CCP including the rewarming condition was instead represented by a sub-sample of thermal images constituted by extracting an image every three minutes.
## 1 Introduction
Positive effects have been observed in alleviating sleep problems, often associated with anxiety and depression [25]. Improvements in sleep quality have also been reported in individuals with schizophrenia [26], where EEG NFB was shown to alleviate symptoms of varying degrees of schizophrenia, whereas medications are effective only in the initial stage. Additionally, EEG NFB has been effective in alleviating panic attacks that occurred during the COVID-19 pandemic [27] and has shown promising results in the treatment of depression [28] and attention disorders [29]. All these studies emphasize that therapy needs to be tailored to the individual, as a uniform approach does not work the same for all patients. A study by [PERSON] et al. [27]. demonstrated the durability of EEG NFB's effects with follow-up measurements conducted six months after the treatment. Participants reported sustained improvements in well-being, speech, muscle control and organizational abilities. All these studies conducted in recent years suggest that EEG NFB can be a very effective method in alleviating various health issues. Furthermore, the effectiveness and application of this method are also evident in the field of sports, for example, to improve the reaction time of judokas [30]. Participants in the experimental group showed a significant improvement in results compared to the control group, confirming the effectiveness of the method in different situations and among different participants. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Brain Waves** & **Frequency [Hz]** & **Training Protocols** \\ \hline Delta & 1–4 & Reduce pain, relieves headache, traumatic brain injuries, improve sleep. \\ \hline Theta & 4–8 & For treatment of depression, anxiety and attention disorders. \\ \hline Alpha/theta & 7–8.5 & To reduce stress, depression, addiction and anxiety. \\ \hline Alpha & 8–13 & Relief pain, reduce stress and anxiety, improve memory and motor performance. \\ \hline Low alpha & 8–10 & Relief stress and anxiety \\ \hline High alpha & 10–13 & Deep relaxation, reduction of stress, sleep improvement, relief of symptoms of depression, increase attention and concentration. \\ \hline SMR (sensorimotor rhythm) & 13–15 & Reduce anxiety, anger and fear. \\ \hline Beta & 15–20 & To improve attention and concentration. Improve reading skills. In the treatment of anxiety, obsessive compulsive disorder, alcoholism and insomnia. Reduce fatigue and stress. \\ \hline High beta & 20–32 & To treat anxiety and stress. \\ \hline Gamma & 32–100 & To stimulate cognition, reducing the migraine attacks. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of neurofeedback training protocol. The table describes which NFB protocols are used to alleviate certain health problems (summarized from [10; 11; 24]).
## 5 Conclusions  
In this study, we investigated SDBD using time-resolved imaging and optical emission spectroscopy. We characterized the SDBD plasma generated using two types of reactors, one with a planar electrode and the other with a cylindrical electrode, both powered by sinusoidal AC voltage under identical parameters. Our main findings are as follows:  
* There are differences in plasma morphology, particularly in the evolution of pseudo-Trichel plumes during the falling half-cycle of the voltage waveform and formation of positive streamers during the rising half-cycle;
* Both positive streamers and plasma plumes are more intense in the planar electrode configuration, while the cylindrical electrode significantly suppresses streamer formation;
* The plasma front velocity remains approximately constant for both reactor types and discharge modes;
* The emission spectrum of the SDBD plasma is dominated by the first and second positive nitrogen bands;
* Despite morphological differences, the plasma thermodynamic properties (rotation, translation, vibration, and electron temperatures) are similar for both reactor types;
* The non-uniform electric field distribution around the planar electrode favors the formation of the plasma onset spots with higher electric field intensity, while the field distribution around the cylindrical electrode lacks such regions, which affects the plasma formation process.  
We believe that our research contributes to filling the gap in understanding SDBD. The results presented in this paper may be useful for studying the role of electrode configuration in the SDBD plasma devices.  
Conceptualization, M.T., A.B. and J.P.; methodology, M.T. and A.B.; validation, M.T., J.P. and J.M.; formal analysis, M.T., J.P. and A.B.; investigation, M.T. and A.B.; resources, J.M.; data curation, M.T. and J.P.; writing--original draft preparation, M.T.; writing--review and editing, J.P.; visualization, M.T. and J.P.; supervision, M.T.; project administration, J.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflicts of interest.