esa-sceva/satcom-chunk-collection huggingface dataset:
# Study and Investigation on 5G Technology: A Systematic Review
## 1 Introduction  
Most recently, in three decades, rapid growth was marked in the field of wireless communication concerning the transition of 1G to 4G [1, 2]. The main motto behind this research was the requirements of high bandwidth and very low latency. 5G provides a high data rate, improved quality of service (QoS), low-latency, high coverage, high reliability, and economically affordable services. 5G delivers services categorized into three categories: (1) Extreme mobile broadband (eMBB). It is a nonstandalone architecture that offers high-speed internet connectivity, greater bandwidth, moderate latency, UltraHD streaming videos, virtual reality and augmented reality (AR/VR) media, and many more. (2) Massive machine type communication (eMTC), 3 GPP releases it in its 13 th specification. It provides long-range and broadband machine-type communication at a very cost-effective price with less power consumption. eMTC brings a high data rate service, low power, extended coverage via less device complexity through mobile carriers for IoT applications. (3) ultra-reliable low latency communication (URLLC) offers low-latency and ultra-high reliability, rich quality of service (QoS), which is not possible with traditional mobile network architecture. URLLC is designed for on-demand real-time interaction such as remote surgery, vehicle to vehicle (V2V) communication, industry 4.0, smart grids, intelligent transport system, etc. [3].
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 4 Discussion and Results
### End-to-End Latency  
End-to-end latency in the network is the actual time when packets are transmitted inside the network. In this work situation, we took the latency as one, ensuring the packet forwarding and sending delay inside the network. In Figure 7, the distance of AODV shows higher hop counts than the previous section, so the latency is also greater than 7. This shows the complete ratio, explaining that CBDRP shows smaller latency than AODV. At a longer distance, the AODV and CBDRP are prone to establishing links among the nodes. On the other hand, the proposed BBSF approach shows the lowest latency of these techniques, which shows that the proposed approach effectively provides the simulation results for latency values as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No.** & **Parameter** & **Value** \\ \hline
1. & Simulation time & 1000 s \\ \hline
2. & Simulation area size & Region 1 & Region 2 \\ \cline{2-3}  & & 1000 \(\times\)1000 m & 2500 \(\times\) 2500 m \\ \hline
3. & Number of vehicles & 0 in Region 1 \\  & & 100 in Region 2 \\ \hline
4. & Vehicle time for a drive & 150 s \\ \hline
5. & Wireless area (Single RSU) & 200 m \\ \hline
6. & Cache values & 0 s to 200 s \\ \hline
7. & Radio & 802.11b \\ \hline
8. & Road condition & New entry (One-way road) \\ \hline
9. & RSUs & 10 \\ \hline
10. & RSU broadcast time interval & 50 s \\ \hline
11. & Breakdowns duration for vehicles & 120 s \\ \hline
12. & The number of simulations run & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters and their values for the proposed methodology.  
Figure 7: Latency of the network with distance.
## 3 Research Design
### Spatial Analysis Framework for Visual Attention
#### 3.3.4 Boundary Method Validation  
To empirically validate the effectiveness and limitations of our AOI boundary definition approach, we conducted a comparative analysis between the Extremum Points (EPs) method and the Convex Hull (CH) method based on actual gaze data from the five AOI regions identified in our study.  
Area Over-Coverage Quantification  
The results show that the EP method consistently produces AOI boundaries that are approximately 33.3% larger in area than those produced by the CH method. This over-coverage is systematic and predictable across all five regions, as shown in Table 3.  
Figure 6: K-means clustering analysis results: (a) Raw fixation point distribution, (b) Segmented visual regions with different colored boundaries for cluster differentiation, (c) Constructed fixation envelopes with cluster-specific boundary delineation. Different colors are used solely for visual distinction between cluster groups.  
Such regularity suggests that the EP method introduces a conservative buffer zone rather than random distortion.  
Relationship Strength Comparison  
Despite the increased area, the EP-based boundaries preserve the core structure of inter-AOI visual relationships:  
* The correlation between EP- and CH-based relationship strengths is \(r~{}=~{}0.957\).
* The top three strongest inter-AOI connections are identical across both methods.
* The average strength values using the EP method are approximately 18.5% higher, reflecting consistent, controlled inflation.  
These findings confirm that while the EP method leads to conservative boundary definitions, it does not substantially alter the structural interpretation of the data.  
Methodological Justification  
The EP method was selected for its computational efficiency (4 vertices vs. 8 for CH on average), parameter-free operation, and reproducibility. Furthermore, in the context of design cognition, the over-coverage serves practical functions:  
* It accommodates transitional fixations that cross AOI boundaries.
* It captures exploratory gaze patterns typical in aesthetic viewing.
* It maintains spatial coherence for visual network interpretation.  
\begin{table}
\begin{tabular}{l c c c} \hline
**AOI Region** & **Fixation Points** & **EP Area (px2)** & **CH Area (px2)** \\ \hline Region 0 & 50 & 7441.0 & 5580.8 \\ Region 1 & 35 & 13,137.5 & 9853.1 \\ Region 2 & 25 & 3775.1 & 2831.3 \\ Region 3 & 60 & 10,434.8 & 7826.1 \\ Region 4 & 40 & 5616.5 & 4212.4 \\ \hline Average & 42 & 8080.8 & 6060.7 \\ \hline \end{tabular}
\begin{tabular}{l c c} \hline
**AP areas exceed CH areas by 33.3\%; EP: 4 vertices, CH: 8 vertices.**  
\end{table}
Table 3: Comparison of EP and CH boundary metrics for five AOI regions.  
Figure 7: Illustration of the extremum points classification algorithm.  
Although the EP method introduces systematic bias, its analytical impact is measurable and controllable. Future developments may include a hybrid boundary approach or adaptive method selection based on fixation density or task context.
# The Application of Numerical Ductile Fracture Simulation in the LBB Evaluation of Nuclear Pipes
## 2 Materials and Methods  
Due to its excellent corrosion resistance, good weldability, low-carbon design, and outstanding mechanical properties, SA312 TP304L stainless steel has been widely used for heat transport piping in nuclear power plants. Fracture resistance tests conducted on CT specimens and full-scale pipes to evaluate the J-R curves of TP304L pipes with a mean diameter of 168 mm and thickness of 18 mm were introduced in detail in our previous studies [29]. Figure 1 shows the geometry of a 0.5T-CT specimen, which was precracked to a/W = 0.55 with a 10% side groove on both sides. The J-R curves of CT specimens were obtained following the procedure listed in ASTM E 1820 [4], as shown in Figure 2.  
Figure 1: Geometry of 0.5T-CT specimen.  
The loading condition in full-scale pipe tests is shown in Figure 3, with the inner loading span (2L\({}_{1}\)) of 300 mm and the outer loading span (2L\({}_{2}\)) of 2800 mm. The pipes were precracked under fatigue load to 20 = 60\({}^{\circ}\), 120\({}^{\circ}\), and 180\({}^{\circ}\). Figure 4 shows the full-scale pipe test results. The J-R curves were calculated according to the equations given by [PERSON] and [PERSON] [29; 30], in which the J-integral is determined by superposing the elastic term \(J_{e}\) and the plastic term \(J_{p}\) as follows:  
\[J=J_{e}+J_{p} \tag{1}\]  
Figure 4: Full-scale pipe test results: (**a**) load–CMOD curves; (**b**) J-R curves [29].  
Figure 3: Loading condition for full-scale pipe tests [29].  
Figure 2: J-R curves of 0.5T-CT specimens [29].  
The elastic term \(J_{e}\) can be derived from the elastic solution of the GE/EPRI J-estimation scheme handbook as follows:  
\[J_{e}=\frac{\overline{R}^{2}\pi a\hat{F}^{2}\left(\frac{a}{b},\frac{\overline{R} }{\hat{T}}\right)M^{2}}{EI^{2}} \tag{2}\]  
where \(\overline{R}\) is the mean pipe radius, \(t\) is the pipe wall thickness, \(a\) is the half crack length, \(b=\pi\overline{R}\), \(E\) is the elastic modulus, \(I\) is the second moment of area, \(M\) is the bending moment, and \(\hat{F}\) is the geometry function of the elastic stress intensity factor.  
The elastic term \(J_{p}\) can be obtained by  
\[J_{p}=2\int_{0}^{\delta_{p}}\left[\eta(\beta)P\right]d\delta_{p}+\frac{2}{ \overline{R}}\int_{a_{0}}^{a}\left[\gamma(\beta)J_{p}\right]da \tag{3}\]  
\[\eta(\beta)=-\frac{\partial^{2}P_{L}}{\partial A}\frac{1}{P_{L}} \tag{4}\]  
\[\gamma(\beta)=\frac{\partial^{2}P_{L}/\partial a^{2}}{\partial P_{L}/ \partial a} \tag{5}\]  
where \(\delta_{p}\) is the plastic displacement, \(P\) is half of the total applied load in a four-point bend test, \(a_{0}\) is the initial half crack length, \(\beta\) is the half crack angle, \(\eta\) and \(\gamma\) are the factors obtained from the first and second derivatives of the function with respect to \(\beta\), and \(P_{L}\) is the limit load.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 4 From van Eck Phreaking to TEMPEST Profiling
### Shielding and Zoning Standards  
The United States and NATO have SDIP standards for shielding and zoning. SDIP stands for SECAN doctrine and information publication. SECAN stands for security and evaluation agency. The SDIP standards are shown in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Standard**} \\ \hline \multirow{2}{*}{SDIP-27} & LEVEL A: Compromising emanation laboratory test standard. \\  & LEVEL B: Laboratory test standard for protected facility equipment. \\  & LEVEL C: Laboratory test standard for tactical mobile equipment \\ \hline SDIP-28 & NATO zoning procedure. \\ \hline SDIP-29 & Installation of electrical equipment for the processing of classified information \\ \hline SDIP-30 & Installation of electrical equipment for processing classified data \\ \hline \hline \end{tabular}
\end{table}
Table 6: SDIP standards.  
The SDIP-27 shielding standard is divided into three levels of protection requirements according to the following zones: Zone 0--an environment where an attacker is expected to have almost immediate access (up to 1 m distance); Zone 1--an environment where an attacker is expected to be unable to approach within approximately 20 m or an environment where building materials are assumed to provide attenuation equivalent to 20 m; and Zone 2--an environment where an attacker must deal with an equivalent free space attenuation of 100 m or equivalent attenuation through building material (see Table 7).  
SDIP-28 defines attenuation measurement methods for dividing individual areas into zones within a security perimeter and specifies the standards required for installed equipment processing sensitive data. SDIP-29 defines installation requirements such as grounding and cable spacing. SDIP-30 describes how to install electrical equipment for sensitive data processing [88]. Shielded devices and rooms with metal materials are a reliable method to reduce emissions. Achieving complete protection is difficult and expensive, especially when it concerns rooms or buildings.
## 4 Analog Image Signal Relay Transmission (AISRT)
### Transmission System of Analog Image Signal Based on VLRC  
As shown in Figure 2, in the system, a NTSC camera is used for in-pipe information acquisition. It possesses a 170-degree wide angle and 2.8 mm lens. The collected analog image signals are added with DC bias and then transmitted to the LED driver module. In this way, the image signal can be loaded onto the optical signal. Since the performance of LEDs has a great influence on the transmission quality of the system, a special XLamp XHP 70 LED array with 3930 lm output is selected as a suitable light source for the experiment [28]. When the image signal is transmitted to the channel in the form of the light beam, as mentioned in Equation (2), the beam has a divergence angle during the optical signal transmission. In order to increase the receiving power, at the first relay node, a convex lens with a diameter of 63 mm and a focal length of 150 mm is used to concentrate light so that the light can illuminate onto the photodetector at the focal length of the lens to avoid the divergence of light. Then we select Si PIN fixed gain detector PDA10A as the photodetector for each receiver in the system [29]. The wavelength response range of this detector is from 200 nm to 1100 nm, the active area is about 0.8 mm\({}^{2}\). The signal bandwidth can reach almost 150 MHz and the maximum photoelectric conversion efficiency can even reach 0.44 A/W. After the conversion of the signal, the signal is amplified with a ZHL-6A-S+ amplifier and loaded onto the LED again [30]. Through the second signal relaying, the final receiver can receive the optical signal. After photoelectric conversion, the signal is adjusted by a KPATT2.5-90/IS-2N attenuator and eventually connected to the computer via a video capture card, so that the image captured by the camera is displayed on the computer screen in real time [31].
# Detecting Colluding Inter-App Communication in Mobile Environment
## 4 Experimental Analysis
### Results  
In this section, we present the results of the evaluation. To measure the performance of our approach we considered four different metrics: Accuracy, Recall, Measure F, and Accuracy.  
With precision, we calculated the proportion of the examples that really belong to class X compared to all those that have been assigned to the class. It is characterized by the ratio of the number of relevant records retrieved to the total number of irrelevant and relevant records retrieved:  
\[\textit{Precision}=\frac{tp}{tp+fp}\]  
in the formula, we can see some values, such as: _tp_, which indicates the number of true positives and _fp_, which indicates the number of false positives.  
The recall has been calculated as the proportion of examples assigned to class X, among all the examples that effectively belong to the class, i.e., how much part of the class was captured. Recall considers the relationship between the number of relevant records retrieved to the total number of relevant records:  
\[\textit{Recall}=\frac{tp}{tp+fn}\]  
here, we can see the value _fn_, which indicates the number of false negatives.  
The F-Measure is used to measure the accuracy of a test. This score can be interpreted as a weighted average of the precision and recall:  
\[\textit{F-Measure}=2*\frac{\textit{Precision}*\textit{Recall}}{\textit{ Precision}+\textit{Recall}}\]  
The Specificity is used to measure the proportion of negatives that are correctly identified. It is computed as follows:  
\[\textit{Specificity}=\frac{tn}{tn+fp}\]  
The Accuracy is the fraction of the classifications that are correct and it is computed as the sum of true positives and negatives divided all the evaluated samples (true positive, false negative, false positive and true negative):  
\[\textit{Accuracy}=\frac{tp+tn}{tp+fn+fp+tn}\]  
as already specified in the parenthesis _tn_ indicates the number of true negatives.  
In Table 10 we show the obtained results.  
From the experimental analysis results shown in Table 10, we obtain an accuracy and a specificity equal to 1, showing that the proposed method is able to correctly classify all the colluding applications without any misclassifications with malware not performing colluding actions and legitimate samples. In particular, we were able to correctly classify colluding applications because the name of the resource shared is the same between the \(GET\) and the \(PUT\) application because i.e., a _push_ action.
# Enhanced Enzymatic Degradability of Livestock Blood Pretreated with Ultrasonic Technique
###### Abstract
One of the recently proposed approaches to improve enzyme degradation is the use of ultrasound as a technology that can accelerate enzymatic reactions [19; 20]. Since blood cells contain more proteins than plasma, access to these proteins within the blood cells should be facilitated. The blood cell structures can hinder the hydrolysis of blood cell proteins. This problem can be addressed through ultrasonic treatment, which weakens the resistance of blood cell structures, to release the proteins within the cells into the plasma, thereby facilitating the degradation of Livestock blood proteins into amino acids. Ultrasound induces a cavitation phenomenon, and the frequency of ultrasound has a significant impact on the formation of cavitation bubbles. In particular, destructive cavitation effects are more pronounced in the Lower-frequency range compared with the higher-frequency range. Furthermore, intense ultrasound applied at Lower frequencies (below 40 kHz) can effectively destroy biological cells and facilitate the elution of soluble organic materials [21; 22]. A study on the ultrasonic treatment of sludge demonstrated that among the intracellular components, proteins exhibit particularly high solubility [23]. Similarly, high Levels of depolymerization have been observed when treating high molecular weight substances with ultrasound [24; 25]. Based on these research findings, it can be inferred that the ultrasonic treatment of blood aids in the solubilization of blood cell proteins and destroys various bacteria that may be present in the blood. The aim of this study was to determine the feasibility of utilizing Livestock blood as a high-quality amino acid fertilizer for agricultural purposes. Ultrasonic pretreatment was employed to enhance the solubilization of Livestock blood proteins and improve the efficiency of enzymatic hydrolysis. Moreover, the optimal conditions for protein enzyme hydrolysis, both when applied individually and in combination based on enzyme type, were determined to achieve the highest possible amino acid conversion efficiency.
# Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment
## 3 Preliminaries
### Fuzzy String Matching  
The fuzzy string-matching algorithms used in the proposed model in this study, namely, Jaro-Winkler, Jaccard similarity, Longest Common Subsequence (LCS), and Levenshtein, are presented in this subsection.
## 4 Discussion
Moreover, a previous report mentioned that the participant's response is related to the activity of gamma waves between the cerebral cortex and spinal cord [28]. In the current study, there was no statistically significant change in the gamma waves according to the electrical stimulation variables. However, gamma waves were reduced in each group after IFC intervention, and more stable results were confirmed in the occipital lobe. Theta waves appear during deep sleep and are characterized by slow, large amplitude vibrations in different sleep states; the pattern turns into predictable characteristic waveforms in various awake states [47]. Theta waves are not related to emotional anxiety or deep sleep but are related to internalized and quiet physical, emotional, and thinking activities [48; 49]. However, the appearance of theta waves during wakefulness can be a sign of the brain being in an abnormal state, and asymmetrical waves in the stable state can be a sign of brain dysfunction [50]. Our ANCOVA results revealed significant differences in the interaction between group and time in the Fp2, F3, F4, C3, C4, and P4 regions. Disorders of the endogenous pain control system represent a potential mechanism underlying increased pain [7]. When pain-controlled electrical stimulation is applied to alleviate severe pain, high-level pain catastrophizing is inversely related to the brain's electrical activity in specific regions of the cortex, including an inhibitory network associated with top-down pain control [51]. Previous studies suggest that HF-HI electrical stimulation leads to descending pain control via the secretion of endogenous opioids within the central nervous system [52]. Besides, receptor-based skin pressure stimulation and skin stretching showed the highest sensitivity at frequencies of 0.3-3 Hz and 25-400 Hz, respectively, and produced a variety of stimuli such as sensation, movement, and pain [53]. The current study provided supporting data needed in the use of therapeutic methods to improve cognitive ability, muscular stability, and bodily self-awareness when simple attention is required. Some limitations should be noted. First, First, baseline differences between the groups require the results to be treated with some caution. It was difficult to detect differences over time within each group due to differences in baseline values for each EEG measurement. Second, this study did not fully reveal the cortical potential changes for each frequency of each cortical region involved in motor activity. Third, since the state of brain development and age may affect EEG signals, future research should also examine electroencephalography changes according to age groups. Lastly, it will be necessary to standardize the electrical stimulation types with relevance to neurologic changes or pain control in future studies.
## 5 Limitation and Future Research  
As with every other study, our study had a limitation. The coir fibers used in this study were sourced from one point. There was a possibility that the index properties and chemical constituents of the coir fibers determined may not be representative of the coir fibers from other locations within the country. The index properties and chemical constituents of the coir fibers may influence the properties of the geopolymer composite produced. Due to this limitation, further research needs to be conducted with coir fibers from other locations across the country to bring in newer insights.  
This research is just the first step in studying the effect of coir fiber reinforcement in metakaolin-based geopolymers. There is still a need to investigate the durability performance of this composite in aggressive and corrosive environments such as acids, chlorides, and sulfates. Furthermore, the impact of high-temperature regimes on the fire resistance properties of the CFRMG composites should be explored.  
Conceptualization, O.A., E.B. and A.P.O.; methodology, O.A., E.B., A.P.O., A.A.M. and N.L.B.; formal analysis, O.A., I.I. and T.T.S.; investigation, O.A.; resources, O.A., A.M. and A.P.O.; data curation, O.A.; writing--original draft preparation, O.A.; writing--review and editing, O.A., N.L.B., A.P.O. and E.B.; visualization, O.A., I.I., A.A.M. and T.T.S.; supervision, A.P.O., E.B. and H.S.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Pan African Materials Institute (PAMI) Abuja under the World Bank African Center of Excellence (ACE) Program (Grant No. AUST/PAMI/2015 5415-NG), African University of Science and Technology (AUST), Abuja, Federal Capital Territory, Nigeria.  
Not applicable.