esa-sceva/satcom-chunk-collection huggingface dataset:
# The Emerging Role of Long Noncoding RNAs in Sorafenib Resistance Within Hepatocellular Carcinoma
### Secondary Resistance
#### 1.3.2 Exosomes  
Exosomes are intercellular information carriers and help in regulating the tumor microenvironment. They play a key role in drug resistance by RNA molecules and protein transportation. In HCC and RCC, lncRNAs contribute to various aspects of cancer progression, including tumor initiation, progression, metastasis, recurrence, and drug resistance [72]. They are also packaged and sorted into exosomes, acting as messengers in intercellular crosstalk. Several lncRNAs have recently been shown to regulate exosome biogenesis and secretion in HCC and RCC [73; 74; 72]. For example, lncRNAs such as HEAIH and HOTAIR facilitate exosome secretion by enhancing the transport of multivesicular bodies toward the plasma membrane in HCC and other cancers. The lncRNA LINC00511 promotes an invasive phenotype in HCC and RCC by increasing exosome secretion. While some lncRNAs have been reported in HCC and RCC, many remain unknown. Exosome-derived lncRNAs may serve as diagnostic and prognostic biomarkers in HCC, RCC, and various other cancers [72; 74].  
The HCC cell-derived exosomes exerted their functions by increasing the level of proteins related to sorafenib resistance, protecting tumor cells from sorafenib-induced apoptosis, activating the HGF/c Met/Akt pathway in vitro, and targeting the HGF/c-Met/Akt pathway, which may help improve treatment efficacy in liver cancer [75; 76]. A study by [PERSON] and [PERSON] demonstrated that sorafenib could promote HCC release of exosomes by enhancing Rab27a activity. Thereby, the secreted exosome promoted the behavior of recipient hepatoma cells and activated the AKT signaling pathway, resulting in decreased sensitivity for chemotherapy [37; 77].
## 4 Discussion
### Practice, Policy, and Future Research  
#### 4.4.1 Practice  
This review highlights opportunities for advancing eye-tracking applications in reading. Integrating advanced machine learning models, like CNN-LSTM architectures,into commercial eye-tracking software can enhance the real-time analysis of reading behaviours, benefiting education, workplace productivity, and healthcare.  
The adoption of synthetic eye-tracking data frameworks, such as SP-EyeGAN, offers a cost-effective way to pretrain machine learning models, reducing reliance on time-intensive data collection while expanding accessibility.  
Mobile eye-tracking systems also present opportunities for improvement. Developing lightweight algorithms and adaptable hardware can enhance accuracy in resource-constrained environments, such as classrooms and workplaces, by mitigating environmental challenges like lighting variations and head movement.
## 3 Methodology
### The Frequency-Wavenumber (F-K) Analysis  
In the last decade, array-based techniques have become popular and are widely used for the analysis of ambient vibrations. The concept of configurations in arrays, with simultaneous ambient vibration recordings, has proven its reliability and efficiency through several studies and investigations [16; 46; 47]. One of the most applied techniques for array processing is frequency-wavenumber analysis, commonly known as F-K analysis [27; 28; 29; 30]. This technique is performed in the frequency domain and allows for the estimation of the back azimuth and the slowness of the seismic wave sources recorded by the array [47].  
The theoretical aspect of this technique is based on two main assumptions. The first one is that the wavefront propagation is on the vertical plane, and the process is stationary in the other two planes. The second assumption is that the process is stationary in time. The microtremor wavefield is a superposition of seismic waves propagated from several distant sources [28]. The F-K analysis exploits the stationary and stochastic character of these propagated waves to construct the frequency-wavenumber power spectral density function, which holds information on the power as a function of the frequency and the velocity of travelling waves. The power spectral density can be estimated using the following two following methods: The beam-forming method (BFM) [29; 30], and the maximum likelihood method (MLM) or the high-resolution method (HR) [27; 28; 48]. In this study, we used the beam-forming method to calculate the frequency-wavenumber power spectral density and thereby retrieve the velocity and the directions of the seismic propagating waves. The HR method provides a higher resolution in the frequency-wavenumber plane than the BFM method. However, the latter is less sensitive to measurement errors since it uses less computations [27]. [PERSON] [16] calculated the shear-wave velocity using both the BFM and the HR methods and the obtained dispersion curves were almost similar.  
The ability of these methods to identify the directional properties of the noise wavefield is highlighted by [PERSON] [46].
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 2 Materials and Methods
### Automatic Meter Reading Pipeline  
The pipeline proposed for the automatic digital meter reading system (Figure 3) is divided into three modules:  
* Screen detection is composed of two steps: firstly, a model that detects a box containing approximately the screen of the equipment (screen bounding box detection); followed by a model to detect keypoints that correspond to the corners of the equipment's screen (screen keypoint detection) is employed. The detected screen is then projected onto the normal plane.
* Text detection, where the regions that contain text are detected. Considering that the objective of this system is to extract specific values, the selection of important information is performed by using an equipment template with its expected position, in a template-matching step.
* Text recognition, in which the text from the important regions is extracted.  
All experiments were conducted on a virtual machine (VM) with two AMD EPYC 7302 and six virtualized CPU cores, 16 GB of RAM, and one shared NVIDIA A16-8C with 8 GB of VRAM available per user.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### Cicids-2017  
This dataset was created by the Canadian Institute for Cybersecurity at University of Brunswick in 2017. The purpose of CICIDS-2017 was intrusion detection, and it consisted of several attack scenarios. In this dataset, the attack profiles were produced using publicly available tools and codes. Six attack profiles were implemented, such as brute force, heartbleed, botnet, DoS, DDoS, web attack, and infiltration attack. The realistic background traffic was generated, using a B-Profile system [52]. The B-Profile system extracted 25 behaviors of users based on several protocols, such as HTTP, HTTPS, FTP, SSH, and SMTP. The network traffic features were captured with CICFlowMeter [53], which extracted 80 features from the pcap file. The flow label included SourceIP, SourcePort, DestinationIP, DestinationPort, and Protocol. Mixed methods are used, incorporating CICIDS-2017 to detect specific attacks such as DoS attack [54] by using feature reduction, web-attack detection [55], and anomaly web traffic [56] with ensemble architecture and feature reduction. Others are improving the AdaBoost-based method [57] to counter the imbalance of the training data [58], and combining feature selection and information gain to find relevant and significant features and to improve accuracy and execution time.
# Novel Small-Molecule Treatment and Emerging Biological Therapy for Poriasis
## 2 Small Molecules
### Corticosteroids
#### 2.1.4 Cubosomes  
Cubosomes are distinct nanoscale vesicular systems made up of biodegradable lipids and water, which organize into a bicontinuous cubic liquid crystalline structure. They possess an exceptional capacity to solubilize and encapsulate molecules with varying properties, including hydrophilic, hydrophobic, and amphiphilic compounds, alongside showcasing excellent biocompatibility and strong bio-adhesive characteristics. The researchers loaded powerful corticosteroid betamethasone dipropionate (BD) and salicylic acid (SA) into cubosomes and modified the adhesive and rheological properties of cubosomes by incorporating sodium carboxymethyl cellulose, thereby extending the contact time of the formulation with the scalp. The change in ear thickness of psoriatic mice treated with BD/SA cubosomes was 11 \(\times\) 10\({}^{-2}\) mm, while that of the commercial BD/SA lotion was 17 \(\times\) 10\({}^{-2}\) mm, which indicates that BD/SA cubosomes exhibit superior anti- psoriaticitic efficacy [45].
# Design of a Predictive RBF Compensation Fuzzy PID Controller for 3D Laser Scanning System
## 5 Simulation
### Step Response  
The sampling time is 0.01 s, and the controller parameters are set according to the range of the system error and error change rate, which are shown in Table 7.  
In Table 7, careful selection of flexibility coefficient is needed. The larger the coefficient of flexibility \(\mu\) is, the stronger the anti-interference ability is. However, too large a coefficient results in system overshoot or oscillation. The step signal with a target value of 50 degrees is set. Three kinds of controller response curves are shown below.  
Figure 9a shows the control result of FPID when the unknown friction of the system is ignored. The result, which has favorable response performance, indicates the feedback compensation can eliminate the nonlinear factors to obtain great control performance. However, the above result was simulated in the case of zero friction. When the system has the unknown and nonlinear influence factors, FPID cannot achieve a satisfying control effect. In Figure 9b,c the comparison of the three controllers shows that the response speed of FPID is very slow while the response speed of RFPID and PRFPID is significantly accelerated after RBF compensation. The error of PRFPID is the smallest in Figure 9d, and it indicates that the predictive control effectively reduces the steady-state error in RFPID. Moreover, it can be seen from Table 8 that PRFPID has the fastest response speed and the smallest steady-state error. Hence, the overall system performance has been improved by the RBF neural network and the predictive control algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **K\({}_{\text{u}}\)(V/V)** & **K\({}_{\text{m}}\)(N=m/A)** & **R(\(\Omega\))** & **G(N)** & **r(m)** \\ \hline
**Value** & \(11\) & \(0.6\) & \(7.77\) & \(11\) & \(0.058\) \\
**Parameters** & J(kg**s**m\({}^{2}\)) & F\({}_{\text{m}}\)(N**m**) & F\({}_{\text{c}}\)(N**m**) & k\({}_{\text{v}}\)(N**m**s**/\({}^{\circ}\)) & \(\alpha\)(\({}^{\circ}\)/s) \\
**Value** & \(0.00662\) & \(0.2\) & \(0.15\) & \(0.02\) & \(0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: System parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Type**} \\ \cline{2-4}  & **FPID** & **RFPID** & **PRFPID** \\ \hline k\({}_{1}\), k\({}_{2}\), k\({}_{3}\), k\({}_{4}\), k\({}_{5}\) & 0.1, 0.1, 0.5, 0.5, 1 & 0.1, 0.1, 0.5, 0.5, 1 \\ RBF nodes &
## 4 Results and Discussions
### Influence of Nozzle Outlet Pressure on Rock Breaking Effect  
Figure 16 shows the trend of area, depth, volume loss, and cumulative volume loss of crushed granite by SPWJ and CWJ under the outlet pressure of six nozzles. It can be clearly observed from Figure 16 that the size of the erosion pits produced by SPWJ and CWJ is similar. More specifically, the erosion depth of the two jets first increases and then decreases with an increase in nozzle outlet pressure, whereas the erosion area generally increases with an increase in pressure. However, there are certain singularities in the erosion area, which are due to the chaos of granite itself, and granite will occasionally crack and break on a large scale in the erosion process, forming a large area of erosion pit. Similarly, with an increase in pressure, the volume of granite spalling tends to increase first and then decrease. This is because the dimensionless standoff distance, \(S\), in this experiment was fixed at 100. With the increase in pressure, the velocity nuclei of the two jets will obviously increase, and the optimal dimensionless standoff distance of the jet will move backward. Therefore, with the increase in nozzle outlet pressure, the loss of depth, area, and volume of granite crushed by the two types of jet erosion first increases and then decreases. In addition, with the increase in pressure, the cumulative spalling volume of granite increases continuously.  
Furthermore, Figure 16 shows that at an initial low pressure, the erosion pit, erosion area, and spalling rock sample volume caused by the SPWJ were smaller than those of the CWJ. However, as the pressure increased, the erosion depth, area, and spalling volume of the SPWJ became greater than those of the CWJ. These characteristics have a strong relationship with the water cushion utility of the CWJ and the optimal target distance. For the CWJ, as the output pressure increases, the optimal dimensionless standoff distance also increases. This leads to an increase in the negative water cushion effect and a weakening of the stress wave effect produced by the jet during the erosion process. However, the water wedge ability of the CWJ is enhanced. At a certain drilling depth, the water wedge effect of the continuous water jet is dominant, which gives priority to releasing energy and cracking granite. Therefore, with an increase in the pressure of the CWJ, the volume and area of the broken granite increase, whereas the depth decreases. In contrast, the water cushion effect is further weakened for the SPWJ. At 70 MPa, the depth, area, and spalling volume of the crushing pit are better than those of the CWJ, which occurs because the SPWJ is intermittent.  
Figure 15: Mechanism of SPWJ decomposition of granite.  
Intermittent spraying leads to attenuation of water cushion effect. The jet head can generate a water hammer shock wave at high frequency at the granite contact surface, stretch the granite repeatedly, and further crack it by using its water wedge effect. It can be seen from Figure 16e that the erosion volume of SPWJ is higher than that of continuous water jets under unit input pressure.
# 5G/B5G Service Classification Using Supervised Learning
## 2 Related Work
When KQIs are included, the ML algorithm becomes even more complicated; due to the multiple interdependencies throughout the E2E route, the measurement of service quality is not a trivial issue, even when it is limited to objective quality rather than subjective experience. KQI offers a framework that can reflect service performance and quality in an objective way, from an E2E perspective and these indicators can be obtained through direct testing and statistical analysis of the network [12]. In the literature [9; 10; 11], the classification carried out was considering only KPI parameters. We propose improving the classification (identification) of the services considering (besides KPI) KQI parameters. We estimated that the incorporation of KQIs into the classification of 5G/B5G services would improve the QoS and QoE, and would make the determination and compliance of SLAs more precise. The requirements for establishing SLAs in 5G based on the services provided and the infrastructure of the provider are known as Service Level Objectives (SLOs). It is therefore imperative to consider both the KPIs and KQIs, among others [3], since the QoS, which forms the basis for establishing the SLA, is a function of the applications, the network, and subjective factors such as user experience (QoE) [6].
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Nigella sativa L.
#### 3.3.7 Cardioprotective Potential  
In a randomized, double-blind, placebo-controlled clinical trial, individuals with coronary artery disease who took 2 g of black cumin oil for 8 weeks experienced significant reductions in both systolic and diastolic blood pressure [112]. [PERSON] et al. [113] also observed the hypotensive effect of black cumin oil in their randomized, double-blind, placebo-controlled clinical trial. They assessed the effectiveness of black cumin seed oil as an adjunct treatment for hypertension. They found that hypertensive individuals who received 2.5 mL of black cumin seed oil twice daily for 8 weeks experienced significant reductions in systolic and diastolic blood pressure. Additionally, levels of total cholesterol, low-density lipoprotein, malondialdehydedehyde, and fasting glucose decreased, while increases in glutathione reductase and HDL levels were noted [113]. [PERSON] et al. [114] showed that in individuals with cardiovascular disease risk factors, taking two 500 mg capsules of black seed oil daily for two months led to a significant increase in flow-mediated dilation (FMD), plasma nitrite, and nitrate (NOx) levels compared to the placebo group. No significant differences were observed between groups in the levels of vascular cellular adhesion molecule-1 (VCAM-1) and intracellular adhesion molecule-1 (ICAM-1) [114]. However, a twice higher dose of black cumin oil (2 g per day) used for 8 weeks in patients with coronary artery disease significantly reduced the levels of adhesion proteins sVCAM-1 (soluble vascular cell adhesion molecule-1) and sICAM-1 (soluble intercellular adhesion molecule-1) and oxidative marker MDA; moreover, it increased total antioxidant capacity. This suggests that black cumin oil may have a beneficial effect on endothelial function by reducing inflammation and oxidative markers [115]. Further studies confirming the beneficial effects of black cumin, its underlying pathways, its potential uses, and dosage as a therapeutic or complementary agent in the treatment of cardiovascular diseases are still important.
## 3 Implementation
### Questions  
Our main survey consisted of 14 main questions and 72 sub-questions. For the initial questions Q1 and Q2, as seen in Table 1, participants were presented with two example scenarios, one for each question. Each scenario was made up of three pictures, and we asked if they would share the content publicly if it was theirs. The questions consisted of a 5-P Likert scale entry in combination with an open-ended sub-question. Because such reflective questions can be influenced by later questions in the survey [5], we ask these questions first. For Q1, participants were presented with Example 1 consisting of three images from a video where someone shows the surroundings of a rural area that can be assumed to be their home, as seen in Figure 2. The video title: Wild Oklahoma Weather, indicates that the video is about an upcoming severe storm. The participants were asked to consider if they would share the video if the depicted house was theirs. For Q2, participants were presented with Example 2, consisting of three social media postings, as seen in Figure 3, and we ask them similarly if they would share the images publicly if they depicted their own property and surroundings. Question Q3, as seen in Table 2, asks the participants about their privacy perceptions on the various data types that are detectable from the social media postings detectable in Example 2 according to the OSINT analysis method proposed by [PERSON] [6]. Several other key sensitive data types, like date of birth (Q3.3), blood type (Q3.6), and social security number (Q3.7), are also included. Questions Q4 and Q5, as seen in Table 3, ask participants about their perception of various privacy guidelines they practice currently (Q4) and in the future (Q5). The questions Q4 and Q5 differ only in their usage, namely, Q4-current and Q5-future. As the guidelines are the same, they are best represented in joined Table 3. The purpose of Q5 is to see to what extent participants had their perceptions influenced by participating in this survey. Questions Q6 and Q7 are about social media usage (Tables 2 and 14). Questions Q8-Q14, which are about demographic values, as seen in Table 3, were implemented. We use these demographic data to organize respondents into various sub-group filters. The abbreviations on the filters used for each subgroup are listed and explained in Table 4. Besides these active responses, the survey provider also collected the start and end timestamps for each survey. These start and end times allow us to calculate the time spent on the survey. Figure 2: Shows the pictures presented to participants in first question Q1 and Q1.1. Subfigures (**a**–**c**) show different scenes from the video. (**a,b**) combined hints the shape of the backyard, whereas (**c**) depicts a smartphone with a weather app showing an incoming storm and the current position as a blue dot. The pictures were taken from Kutschera [6] and [34], respectively.
###### Abstract  
In order to solve the problems of a low target recognition rate and poor real-time performance brought about by conventional infrared imaging spectral detection technology under complex background conditions or in the detection of targets of weak radiation or long distance, a kind of infrared polarization snapshot spectral imaging system (PSIFITS) and a spectrum information processing method based on micro-optical devices are proposed in this paper, where the synchronous acquisition of polarization spectrum information is realized through the spatial modulation of phase with a rooftop-shaped multi-stage micro-mirror and the modulation of the polarization state of light with a micro-nanowire array. For the polarization interference image information obtained, the infrared polarization spectrum decoupling is realized by image segmentation, optical path difference matching, and image registration methods, the infrared polarization spectrum reconstruction is realized by Fourier transform spectral demodulation, and the infrared polarization image fusion is realized by decomposing and reconstructing the high- and low-frequency components of the polarization image based on the Haar wavelet transform. The maximum spectral peak wavenumber error of the four polarization channels of the polarization spectrum reconstruction is less than 2 cm\({}^{-1}\), and the polarization angle error is within 1\({}^{\circ}\). Ultimately, compared with the unprocessed polarization image unit, the peak signal-to-noise ratio is improved by 45.67%, the average gradient is improved by 8.03%, and the information entropy is improved by 56.98%.  
infrared imaging; polarization snapshot; information processing; polarization spectrum reconstruction; polarization image fusion +
Footnote †: journal: _sciences_  
_Applied_  
_sciences_
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 3 Bounded-Error Sensor Data Compression (BESDC)
### EB-Hc  
In conventional HC, as the length of code depends on the frequency of the data which appear, we try to count the frequency of same data value appearance. Different from HC, EB-HC can merge all data within the given bounded error range into one code. In this paper, we proposed a greedy algorithm to find the \"largest group\" of sensor data which can be represented by the same value within the given bounded error. Considering the same example of sensor data sequence (22.2, 22.6, 22.8, 23.1, 23.5, 23.7, 24.2, 23.6, 23.3, 22.8) in Figure 4, our EB-HC algorithm sorts the upper bounds and the lower bounds of the bounded error bars to obtain scan lines EB1 to EB17. For each scan line, the related counter represents its \"bump-to-bar\" counting result to indicate the number of sensor data that can be represented by one single value. We have the related counters EB\(i\) = 1, 2, 3, 4, 5, 5, 7, 7, 7, 65, 4, 2, 1 for \(i=1\) to 17. By greedy, we select EB8 to represent seven data including {22.8, 23.1, 23.5, 23.7, 23.6, 23.3, 22.8}. These seven data can be represented by the same value 23.2. Then, {22.2, 22.6} can be represented by 22.7. The final result is a bit sequence 0011110111 with the codebook (\"1,23.2\", \"01,22.7\", \"00,24.7\").