esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating Anti-Evasion Malware Triggers Using Automated Sandbox Reconfiguration Techniques
## 5 Results  
For our study, a total of 251 malware samples were tested using the MORRIGU environment, consisting of 10 different malware classifications and 49 different malware families. As discussed previously, samples were drawn from recent CIS reports, to ensure that we are considering current threats that are being reported in real-world operations. Within our testing environment, 173 of the 251 samples (68%) were deemed as \"active\", i.e., they would trigger some malicious response within the testing environment such as connecting to an IP address or causing significant activity within the VM such as encryption or destruction of data. Samples were only confirmed as being 'active' if the behaviour was observed at least three times under repeatable testing conditions. Of the active samples, 133 samples were found to perform malicious behaviours when used in a typical virtualised environment (i.e., where \"no changes\" are made to the testing environment). These samples therefore do not exhibit any evasive capabilities. There may well be good reason for this, for example, malware that is designed to corrupt any data may not necessarily be concerned about being evasive and may wish to simply execute whenever possible, rather than being targeted to specific machine configurations. We found that all 10 malware classifications (Adware, Banking Trojan, Bot Net, Cryptocurrency Miner, Disk Wiper, Domain Generating Algorithm (DGA), Ransomware, Remote Access Trojan (RAT), Trojan Downloader and Trojan Infosteeler) included samples that were non-evasive.  
As a result of this initial testing phase, 40 samples were identified using MORRIGU that exhibit no malicious behaviour when used in a typical virtualised environment (i.e., where \"no changes\" are made); however, they do perform malicious behaviours when anti-evasion configuration changes are made. While this represents only 16% of our original malware sample dataset, it provides an initial step towards curating evasive malware datasets for further research. To the best of our knowledge, there are currently no publicly available dataset for evasive malware, and so we see this as a valuable contribution to the research community.
## 3 Results
### Detailed Analysis of Developments in Selected Specialties
#### 3.7.3 eHealth Development  
The evolution of eHealth in Poland encompasses user acceptance, digital literacy, telemedicine solutions, and inequities, reflecting the pandemic and demographic challenges. Studies have examined internet usage and eHealth literacy among elderly populations [94; 95]. An 8-item eHealth literacy scale validation enabled reliable measurements [96]. Research has explored eHealth acceptance among patients with chronic diseases [97] and machine translation of telemedicine data [98]. Studies have assessed user satisfaction during COVID-19 [99; 100], while surveys have examined demographic factors affecting older adults' engagement [101]. Geographic disparities were analyzed using the Theil Index [102]. Research has examined telemedicine for individuals with intellectual disabilities [103] and eHealth service use factors in Central and Eastern Europe [104]. The Polish eHEALS validation supported literacy measurement [105]. A virtual patient collection blueprint enhances clinical reasoning [106]. Studies have analyzed pandemic-driven eHealth usage [107; 108] and digital health definitions [109]. Advanced analytics has been evaluated in omics research [110]. Studies have established adoption hurdles and validated literacy assessment tools. COVID-19 has accelerated adoption, while research has addressed inequities and vulnerable groups through inclusive design.
## 4 Discussion
### Screening of the Selected Repurposed Drugs
#### 4.3.2 Benztropine Mesylate  
Benztropine's main indication is for Parkinson's disease, and the pharmacological effects come from its anticholinergic activity. It is a centrally acting antihistamine and dopamine reuptake inhibitor. To date, no studies on the MCF-7 cell line can be found. In 2017, extensive research regarding this drug's efficacy on a triple-negative breast cancer cell line (MDA-MB-231) was performed. The inhibitor function of benztropine for CSCs in vivo and in vitro was described. This inhibitory effect was mediated by acetylcholine receptors, dopamine transporters/receptors and/or histamine receptors--CSCs, for example, cause increased acetylcholine receptor expression levels [24]. Recently, [PERSON] et al. showed, by inhibiting the dopamine transporter SLC6A3 and reducing the signal transducer and activator of transcription, that benztropine suppresses tumor growth, circulating tumor cells and metastasis [38]. The study clarified that the antitumor effect does not involve dopamine or dopamine receptors and suggested the involvement of dopamine transporter SLC6A3. This transporter is usually genetically amplified in several types of cancers and involved in a poor prognosis, and therefore, is susceptible to active dopamine transporter (DAT) inhibitors such as benztropine [38].  
Benztropine displayed a statistically significant decrease in cell viability at all the concentrations tested, both at 24 h and 48 h (Figure 14). Moreover, in comparison with the study published by [PERSON] et al., the IC50 calculated for other cell lines was 16.5 \(\upmu\)M (Figure 15), which is very close to the value obtained in our experiment (15.84 \(\upmu\)M for 48 h). All of this makes this drug a strong candidate for repurposing and combination.
## 2 Data and Methods
### Variable Selection  
Multicollinearity among predictor variables (i.e., remote sensing and environmental variables) was assessed using the Variance Inflation Factor (VIF) step (_vifstep_) function of usdm package. Variables above the threshold (greater than 10) were considered as collinear and excluded from further processing [78]. This test aids in the selection of predictor variables by assessing their relative importance [79].
## 3 Results  
### Total Phenolic and Flavonoid Analysis  
The MeOH extract of the shoots of _M. sinica_ exhibited the highest total phenolic content (259.7 \(\pm\) 0.57 mg GAE/g powder), followed by the MeOH extract of leaves (171.2 \(\pm\) 1.68 mg GAE/g powder). On the other hand, crude extract from the stem exhibited the lowest total phenolic content (96.5 \(\pm\) 2.98 mg GAE/g powder) (Figure 1A). Similarly, crude extracts of the shoots and leaves exhibited the highest total flavonoid content (69.5 \(\pm\) 1.07 mg and 57.7 \(\pm\) 0.57 mg QE/g powder, respectively), while crude extract from the stem exhibited the lowest total flavonoid content (Figure 1B).  
Methanol Extract of the Shoots of _M. sinica_ Selectively Perturbed Cell Survival of Breast Cancer MDA-MB-231 Cells  
The cytotoxicity methanol, ethyl acetate, and chloroform crude extracts prepared from the stem, leaves, and shoots of _M. sinica_ in human liver and breast cancer cell lines is shown in Table 1. The data indicate that crude extract prepared from the stem did not affect cell proliferation of HepG2 cells, and induced weak cytotoxicity in MDA and MCF7 cells. Crude extract from the leaves showed weak cytotoxicity (IC50 values \(\geq\) 300 ug/mL) in HepG2 and MCF7 cells, but no cytotoxicity was observed in MDA-MB-231 cells. Similarly, the methanol and ethyl acetate extract of the shoots of _M. sinica_ did not exhibit cytotoxicity in HepG2 cells, and exhibited very weak cytotoxicity in MCF7 breast cancer cells. However, methanol, ethyl acetate, and chloroform extracts of the shoots of _M. sinica_ demonstrated strong cytotoxicity in MDA-MB-231 cells with IC50 values of 114.32, 148.93 and 143.98 ug/mL, respectively. _M. sinica_ was not toxic to normal cells, except the methanol extract of the leaves, which affected the cell survival of HUVEC with an IC50 value of 245.34 ug/mL. Doxorubicin is a known anti-cancer agent that was used as positive control in this evaluation. Doxorubicin infected the cell survival of all cancer cell lines with IC50 values less than 10 \(\upmu\)M, and also showed strong toxicity in normal HUVEC cells with IC50 values of 3.14 \(\upmu\)M (Table 1).  
Figure 1: Total phenol and flavonoid content. (**A**). The shoots exhibited the highest phenol and flavonoid contents, and the total phenolic contents of shoots, leaves, and stem extracts were 259.7 \(\pm\) 0.57, 171.2 \(\pm\) 1.68, and 96.5 \(\pm\) 2.98 mg gallic acid equivalents (GAE)/gm. (**B**) Total flavonoid contents of shoots, leaves, and stem extract were 69.5 \(\pm\) 1.07, 57.7 \(\pm\) 0.57, and 35.9 \(\pm\) 2.57 mg quercetin equivalent /g of the extract.
# Meta Surface-Based Multiband MIMO Antenna for UAV Communications at mm-Wave and Sub-THz Bands
## 2 Materials and Methods
### Simulation
#### 2.1.2 The Proposed MIMO LWA and Incorporation of Neutralization Network and MTS Cells
An MTS often comprises a thin layer containing regularly spaced scatterers within a base material. It exhibits remarkable electromagnetic characteristics like elevated surface impedance, coherent reflection, negative refractive indices, and propagation constants with spatial gradients. When dealing with a finite-sized MTS connected closely to a conductive ground and a primary source, using characteristic mode analysis offers advantages in system assessment and facilitating the development of metasurface antennas (MAs) [40; 41]. The resonant modes of the proposed antenna and the resonant mode of the coffee bean unit cell are amalgamated, followed by an investigation into their characteristics. In the design phase of the proposed unit cell, emphasis is placed on ensuring that the lattice period is approximately 0.2 of the wavelengths at the resonance of the operating mode, thus inducing a local TM\({}_{01}\) resonance for each unit cell. Moreover, integrating the MTS and antenna modes enhances the antenna's performance, resulting in wider bandwidth, higher gain, and improved radiation efficiency. Initially, the radiation and impedance characteristics of a single-element coffee bean metasurface (depicted in Figure 12d) are evaluated. The reflection and transmission coefficient results are presented in Figure 12a, showcasing passbands at the desired frequencies. Additionally, Figure 12b,c depict the permittivity and permeability results of the single-element metasurface. The negative index results of the metasurface element shown in Figure 12 demonstrate its capability to enhance the proposed antenna's radiation characteristics. The results of integrating the metasurface cells with the proposed antenna will be discussed later in the text. Figure 11: The electric field of the proposed two ports MIMO antenna at different frequencies (**a**) 8.5 GHz, (**b**) 25 GHz, (**c**) 75 GHz, (**d**) 115 GHz, and (**e**) 120 GHz.
# Effect of Diagonal Pattern Training on Trunk Function, Balance, and Gait in Stroke Patients
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classification** & \multicolumn{2}{c}{**Experimental Group (n = 21)**} & \multicolumn{2}{c}{**Control Group (n = 21)**} & \multicolumn{1}{c}{**F**} & \multicolumn{1}{c}{\(p\)} \\ \hline  & Before & After & Before & After & & \\ BBS (score) & 38.48 \(\pm\) 4.17 & 40.43 \(\pm\) 4.50 [1, 2] & 38.43 \(\pm\) 3.90 & 39.05 \(\pm\) 4.32 [1] & 16.345 & 0.01 *a \\
10 MWT (s) & 25.00 \(\pm\) 5.24 & 22.48 \(\pm\) 5.50 [1, 2] & 24.48 \(\pm\) 5.03 & 23.48 \(\pm\) 4.32 [1] & 22.750 & 0.01 *a \\ \hline \hline \end{tabular}  
* \(p<\) 0.05, 1 There was a significant difference between before test and after (\(p<\) 0.05), 2 The experimental group improved more than the control group (interaction),
* Analyzed by two-way repeated measures ANOVA, BBS: Berg balance scale, 10 MWT: 10 meters walk test, Experimental group: diagonal pattern training group, Control group: single plane training group.  
\end{table} 
Table 5: Comparison of BBS, 10 MWT in before and after. experience difficulty in separating the thorax and pelvis during gait; additionally, the anteroposterior and mediolateral trunk movements increase [11]. As the trunk coordination and trunk motion depend on the walking speed [11], trunk control through diagonal movement was also connected in walking. In addition, the diagonal pattern training composed of repetitive weight movements [27] can be regarded as having increased the walking speed by contributing the most to maintaining the balance of the trunk located on the pelvis during walking. Consequently, diagonal pattern training improved the gait speed by separated movements of the thorax and pelvis. The patients who participated in this study had mild and moderate physical impairment and thus it is not representative of all stroke patients. Additionally, daily activity level of hospitalized stroke patients was not controlled.
# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 2 Materials and Methods
### FA Scoring System  
The UWF FA score was determined by grading FA images based on an FA scoring system previously reported [7]. This FA scoring system consists of a total of 40 points summarized from nine parameters of FA signs: optic disc hyperfluorescence (maximum score 3 points); macular edema (4 points); retinal vascular staining/leakage (7 points); capillary leakage (10 points); retinal capillary nonperfusion (6 points); neovascularization of the optic disc (2 points); neovascularization elsewhere (2 points); pinpoint leaks (2 points); and retinal staining/pooling (4 points). We used four of the nine parameters (optic disc hyper fluorescence, macular edema, retinal vascular staining/leakage, and capillary leakage, for a total maximal score of 24 in grading the FA vascular leakage, according to the previous report [21]. One uveitis specialist determined the FA score using late-phase FA images before and after treatment in six patients through this system.
## 6 Applications of RF Energy Harvesting
### Biomedical and Wearable Devices  
The biomedical field has witnessed remarkable innovation through the integration of RF energy harvesting into wearable and implantable medical devices. Wearable health-monitoring devices, such as continuous glucose monitors and cardiac patches, have incorporated flexible RF harvesters capable of collecting energy from ambient Wi-Fi and cellular signals to extend battery life or even achieve full autonomy [95]. Studies have reported the development of skin-conformal harvesters that maintain operational efficiency during motion, bending, and stretching, making them ideal for body sensor networks (BSNs) [96]. A notable example is the InfiniWolf smart bracelet, which combines thermal and solar energy harvesting to achieve self-sustainability. This device demonstrates the feasibility of integrating multiple energy-harvesting methods in wearable electronics [44]. In implantable applications, RFEH addresses a critical challenge by enabling pacemakers, cochlear implants, and neural stimulators to operate without repeated surgical battery replacements. On-chip micro-reetennas have been designed to power implants from ambient RF sources, achieving sufficient energy delivery while minimizing tissue heating and maximizing biocompatibility [97]. Researchers in [30] reported the practical demonstration of an RF-powered implantable simulator, tested in vivo, which maintained wireless charging efficiency above 70% when under tissue layers. Their findings support the feasibility of RF harvesting in powering next-generation medical implants with clinical reliability. These breakthroughs significantly improve patient outcomes, reduce healthcare costs, and open new avenues for personalized medicine.
## 3 Results and Discussion
### Comparison of Grinding Characteristic between Untreated and Pre-treated Converter Steel Slag
#### 3.3.4 Particle Morphologies of Ground Untreated and Pretreated Steel Slag  
The particle morphologies of two kinds of ground steel slag powder after 50 min grinding are shown in Figure 8. It can be seen from the figure that steel slag particles are mainly spherical in shape, and fine particles adhere to the larger particles. By comparison, the overall particle size of pretreated steel slag powder is smaller than that of untreated steel slag, and particle uniformity in size is also superior to that of untreated steel slag. To some extent, both the ground untreated and pretreated steel slag powders show some agglomeration phenomenon.
## 5 Result
### SELMWK Algorithm Effect Comparison Experiment  
#### 5.3.1 Algorithm Verification  
This experiment mainly verifies the proposed SELMWK algorithm and conducts three sets of comparative experiments. The first set of comparative experiments compares the SELMWK algorithm's recognition accuracy with three traditional supervised learning algorithms (SVM-linear, SVM-rbf and ELM) [31; 32] and a semi-supervised learning algorithm SSELM algorithm. The first dataset of this set of experiments selected Batch10 data in dataset 1, and the second dataset selected dataset 2. Figure 4 shows the test results of five different algorithms on two datasets:  
Figure 4a shows the effect of 5 different algorithms on Batch10. Take 1000 samples in the dataset as the test set, and test the recognition accuracy of different algorithms  
Figure 4: The effect of 5 algorithms on different datasets. (**a**) Algorithm effect on Batch10; (**b**) Algorithm effect on Dataset 2.  
when the number of labeled samples = {6,12,24,48,96,192}. As can be seen from Figure 4a, in this dataset, two SVM algorithms have the lowest recognition accuracy, while another supervised learning algorithm, ELM, has achieved good results. This is because the random hidden layer of the ELM algorithm maps the data to extract the features in the data well. When observing two semi-supervised algorithms, when there is a small amount of labeled data, the recognition effect of the SELMWK algorithm used in this paper is better than that of SSELM.  
Figure 4b shows the performance of 5 different algorithms on dataset 2. Take 100 data as test samples, and test the recognition effects of different algorithms when the number of labeled samples = {4,8,16,32,64,128}. The same conclusion can be drawn from the figure; the two semi-supervised learning algorithms have better results when there is only a small amount of labeled data. Among them, the SELMWK algorithm proposed in this study is based on the SSELM algorithm, obtains a stronger mapping ability through the weighted kernel, and maps the data from multiple angles. The SELMWK algorithm can extract features from data from multiple high-dimensional spaces, resulting in better recognition results.
## 4 Discussion
's (2021) paper, which used the extreme gradient boosting method to predict vegetation growth [20], this research employed SVM for forest detection. Despite the different methods and applications, both studies underscored the versatility of machine learning and vegetation indices in environmental studies. [PERSON] and [PERSON] (2019) emphasised the selection of suitable Sentinel-2 bands and vegetation indices for crop classification [21], which aligned with the present study's use of Sentinel-2 multispectral bands and various vegetation indices. However, this research expanded on this by demonstrating the effectiveness of these tools in a different context, namely, forest detection. While all these studies demonstrated the power and versatility of machine learning algorithms in environmental studies, they differed from this paper in their focuses and methodologies. This paper's unique emphasis on binary classification for SVM forest detection set it apart. It augmented the progressively increasing academic research on the application of machine learning algorithms in forest management. Furthermore, this study's use of the SVM algorithm for forest detection was not limited to a few focal species and did not require high-resolution imaging spectroscopy data. The flexibility of the SVM algorithm allowed it to handle a broader range of forest types and conditions, making it a more versatile tool for forest detection. Furthermore, this study's second focus on defining and optimising the SVM parameters added another layer of precision and adaptability to the model. This approach contributed to the field and facilitated new possibilities for future research. Compared to the studies above, the present research offered several distinct advantages. It demonstrated the effectiveness of machine learning and remote sensing in a large-scale, real-world application, providing valuable insights for environmental resource management. The achieved high classification accuracy underscored the potential of the used approach. Furthermore, the methodology and findings could be applied to other regions and ecosystems, contributing to the preservation and conservation of forest ecosystems globally. Although the study's findings are promising, it is essential to note that they are specific to the study area and datasets used. Generalising these findings to other regions and datasets should be performed cautiously, considering the variability of vegetation types and environmental conditions. Future research could explore the application of the proposed methodology to larger study areas and different types of ecosystems. Additionally, integrating other data sources, such as LiDAR or hyperspectral imagery, could further enhance the accuracy and detail of vegetation classification. Evaluating the performance of other machine learning algorithms and comparing them with SVM could also be beneficial for identifying the most suitable approach for specific classification tasks.
# Pursuing High Quality Phase-Only Liquid Crystal on Silicon (LCoS) Devices
## 2 High Resolution and High Pixel-Per-Inch (PPI)  
Achieving higher resolution with higher pixel density and smaller pixel pitch is a continuous goal of LCOS micro-displays, especially for near-eye VR display applications [18]. For holographic AR, higher diffraction, or larger image projection, a high-resolution panel is also required [19; 20; 21]. The small pixel size enables a large diffraction angle, and a large panel with high resolution empowers finer details of the projected image. The current resolution density in pixel per inch (ppi) and the pixel pitch of commercial LCoS-SLM panels are summarized in Figure 4. In particular, panels with high ppi (>5000) 4K2K or 8K4K LCoS possess a low input frame rate (\(\leq\)60 Hz) or limited driving voltage (<3.3 V). In general, it is difficult to maintain a frame rate of 60 Hz in a 4K2K panel [22].  
Several approaches to achieve high frame rates in high-resolution panels (\(\geq\)4000 ppi) have been realized recently. [PERSON] et al. demonstrated 4000 ppi in a 0.55-inch 1920 \(\times\) 1080 IC backplane with a pixel pitch of approximately 6.4 \(\upmu\)m [23]. [PERSON] et al. demonstrated a digital drive LCoS micro-display with a pixel pitch of 3 \(\upmu\)m and a high fill factor of 93.5% in resolutions from 1080p to 4K [24]. Such a small panel size can be easily integrated into an optical machine or an embedded system. The advantage of a panel with small pixels is that a larger diffraction angle can be obtained. In addition, two commercial products listed in Figure 4--the 0.26 inch 1280 \(\times\) 720 resolution (Holoeye HED2200) and the 0.37 inch 2048 \(\times\) 1024 resolution IC back panels (Himax Display; Tainan, Taiwan R.O.C.)--are suitable for panels with a large diffraction angle, while maintaining a high picture frame rate.