esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Layer Structure and Chemistry  
Figure 2 shows the survey scan for a normal LDH with carbonate as the interlayer anion. It is characterized by a single Mg 2p peak at 50.1 eV, a single Al 2p peak at 74.5 eV (Figure 3), as well as a single O 1s peak at 531.6 eV. High-resolution scans of the O 1s exhibit three peaks at 530.5 eV, 531.6 eV and 532.4 eV assigned to oxygen atoms in the interlayer carbonate anion-, hydroxyl groups and interlayer water, respectively. These values are in excellent agreement with the results reported by [PERSON] and [PERSON] [45]. They also showed the Mg 2p for Mg(OH)2 at 49.3 eV, which is at a slightly lower binding energy (BE) than for LDH, as shown here, caused by the partial substitution of Mg2+ by Al3+. The LDH Al 2p peak at 74.5 eV is close to the BE observed for gibbsite, Al(OH)3 (74.4 eV), hyaerite, Al(OH)3 (75.0 eV), boehmite (73.9 eV), AlOOH, and pseudoboehmite, AlOOH (74.3 eV) [46]. [PERSON] et al. [47] observed comparable BE values for LDH with Mg 2p at 50.29 eV and Al 2p at 74.31 eV.  
Based on the high-resolution spectra, the chemical composition can be determined as the surface area under the peak is directly proportional to the amount of the element present in the sample. Table 1 provides the chemical composition in atom percent of the Mg and Al in the hydroxide layer as well as the characteristic cation in the interlayer anion as well as the Mg/Al ratio and the Al/anion ratio. In all instances, the Mg/Al ratio is close to 3, as expected based on the starting mixture used for the synthesis. The Al/anion ratios observed are also close to what theoretically would be expected. In the case of the carbonate anion, a slightly lower value is observed due to the small amount of nitrate present in the interlayer, as has been shown in earlier IR and Raman spectroscopy work [19].  
Figure 3: XPS high-resolution scan of Mg 2p and Al 2p of LDH with carbonate as the anion.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Anion** & **Mg 2p** & **Al 2p** & **Anion** & **Mg/Al Ratio** & **Al/Anion Ratio** \\ \hline Co\({}_{3}\)\({}^{2-}\) & 66.8 & 22.0 & C 1s 11.8 & 3.03 & 1.86 \\ PO\({}_{4}\)\({}^{3-}\) pH 11.9 & 64.4 & 21.5 & P 2p 14.1 & 2.99 & 1.52 \\ PO\({}_{4}\)\({}^{3-}\) pH 12.5 & 64.6 & 21.7 & P 2p 13.7 & 2.97 & 1.58 \\ SO\({}_{4}\)\({}^{2-}\) & 66.9 & 22.1 & S 2p 11.0 & 3.03 & 2.01 \\ MoO\({}_{4}\)\({}^{2-}\) & 67.0 & 22.2 & Mo 3d 11.1 & 3.02 & 2.00 \\ CrO\({}_{4}\)\({}^{2-}\) & 67.3 & 22.3 & Cr 2p 10.4 & 3.02 & 2.14 \\ Fe(CN)\({}_{6}\)\({}^{4-}\) & 70.7 & 23.6 & N 1s 5.7 & 3.00 & 4.14 \\ Fe(CN)\({}_{6}\)\({}^{3-}\) & 64.7 & 21.7 & N 1s 13.6 & 2.98 & 1.60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition in atom % for Al, Mg and interlayer anions.  
Figure 2: XPS survey scan of LDH with carbonate as the anion in the BE region between 650 and 0 eV. The region between 1200 and 650 is not shown, as no peaks are observed there.
## 3 Difficulties in Computing Reliability for Optimum Inspection Planning  
If the state function is explicitly formulated, the reliability (\(p_{s}\)) and probability of failure (\(p_{f}\)) can be computed using several methods, including the MCS method [35], the importance sampling (IS) method [36], the Latin hypercube sampling (LHS) method [37], the first-order reliability method (FORM) [38], the second-order reliability method (SORM) [39], and the directional simulation method [40]. Software programs based on these methods can effectively compute the probabilities of \(p_{s}\) and \(p_{f}\) using a closed-form definition of the state function.  
When the probabilities of \(p_{s}\) and \(p_{f}\) are used to formulate the objectives and solve the optimization problem for the inspection planning of deteriorating structures, several difficulties may be encountered. For example, (a) the state function may be in a non-closed form, (b) the formulation of the state function considers multiple probabilistic conditions, (c) a low probability of failure (\(p_{f}\)) can affect the optimum inspection planning, and (d) a high computational cost is required to compute \(p_{f}\) for the optimization process based on non-closed forms of the state function and objective functions. The state function used in reliability-based optimum inspection planning can be formulated considering probabilistic damage propagation prediction, probability of damage detection, probability of maintenance application, the effect of inspection and maintenance on service life extension, and reliability improvement. In this case, the state function can be formulated in a non-closed form through preprocessing tasks, as indicated in Figure 2. The accuracy of optimum inspection planning depends on the accuracy of the computed \(p_{f}\). Furthermore, the cost for computing the \(p_{f}\) of a structure formulated by a non-closed state function is higher than that of a structure formulated by a closed-form state function. Additional computational costs are required when an optimization problem formulated with the non-closed state function and objective function is solved using GAs.
## 4 Research Methodology
### Security-Based Approaches
#### 4.9.3 Confidentiality  
[PERSON] et al. designed a secure architecture that leverages information hiding in encrypted graphics in which the clients' data are protected and prevented from unauthorized access [83]. This project decreased computer costs as well as security concerns. A guided alternative quantum walk based on image encryption was introduced by [PERSON] et al. to secure healthcare images in IoT from eavesdropping [84]. Simulation and numerical analysis were used to illustrate the system's effectiveness and security. To solve resource limits while still meeting storage, security, and privacy requirements, the authors developed a hybrid IoT-fog-cloud system that distributes fog between IoTdevices and the cloud [85]. Additionally, the system combined fine-grained ingress control with keyword-searchable encryption.
## 3 Proposed Vehicle Detection and Tracking Scheme
### Vehicle Tracking with Virtual Lines and Hot Zones  
The input of the Deep SORT algorithm is the bounding box calculated by the YOLOv5 for tracking the vehicles. The Kalman filter is used for identifying the current frame of the vehicles based on the previous frame knowledge prediction. Then, the Hungarian algorithm uses cascade matching and IOU matching. Finally, the Kalman filter updates the predictions. The traffic flow is calculated in a lane by drawing the virtual lines. If the vehicle passes through the hot zone and virtual lines, it is counted once, and if any vehicle changes into another lane in the same direction while passing through the virtual lines, it is also only counted once.
## 2 N-Way GPR Target and N-Way Detection
### N-Way GPR Target  
To understand the N-way GPR target works, we will first explain N-way interference. Consider a channel with matrix \(\mathbf{C}\) as follows:  
\[\mathbf{C}=\left[\begin{array}{ccc}0&c_{-1,0}&0\\ c_{0,-1}&c_{0,0}&c_{0,1}\\ 0&c_{1,0}&0\end{array}\right] \tag{1}\]  
If we send a serial one-dimensional (1D) signal through the channel \(\mathbf{C}\), the interference affecting both sides of the current symbol, also known as a two-way interference, is represented by the coefficients \(c_{0,-1}\) and \(c_{0,1}\). If we send a 2D signal through the channel, the interference affecting the signal will instead be on four sides of the current symbol represented by the coefficients \(c_{0,-1}\), \(c_{0,1}\), \(c_{-1,0}\), and \(c_{1,0}\). This is referred to as four-way interference. For three-way interference, we look at the data edge of the 2D signal (as shown in Figure 1). At the data edges, the interference coefficient is equal to 0 at one of the four-way interference paths, because there is no signal at that interference coefficient. For example, for the data at the left edge, the interference coefficient \(c_{0,-1}\) becomes zero, and at the top edge, coefficient \(c_{-1,0}\) is equal to zero.  
To estimate the coefficient of the N-way interference, we utilize its associated N-way GPR target. In Figure 2, we summarize the N-way GPR target.  
The first example illustrates the interference from the left and right side of the current data. In this case, it is referred to as two-way interference and a two-way GPR target. In the second example, the interference is from the left, right, and upper (or lower) sides of the data. This is referred to as three-way interference and a three-way GPR target. In the final one, we have interference coming from the left, right, upper, and lower sides of the data. This is referred to as four-way interference and a four-way GPR target.
###### Abstract  
As the number of internet users increases so does the number of malicious attacks using malware. The detection of malicious code is becoming critical, and the existing approaches need to be improved. Here, we propose a feature fusion method to combine the features extracted from pre-trained AlexNet and Inception-v3 deep neural networks with features attained using segmentation-based fractal texture analysis (SFTA) of images representing the malware code. In this work, we use distinctive pre-trained models (AlexNet and Inception-V3) for feature extraction. The purpose of deep convolutional neural network (CNN) feature extraction from two models is to improve the malware classifier accuracy, because both models have characteristics and qualities to extract different features. This technique produces a fusion of features to build a multimodal representation of malicious code that can be used to classify the grayscale images, separating the malware into 25 malware classes. The features that are extracted from malware images are then classified using different variants of support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), and other classifiers. To improve the classification results, we also adopted data augmentation based on affine image transforms. The presented method is evaluated on a Malimg malware image dataset, achieving an accuracy of 99.3%, which makes it the best among the competing approaches.  
Keywords:malware; malicious code; convolutional neural network; deep features; feature fusion; transfer learning; image augmentation +
Footnote †: journal: Applied Sciences
## 3 Medicinal Value
### Anticancer Properties  
Over the last few decades, cancer has remained one of the few life-threatening diseases on Earth. Recent reports have confirmed that the polysaccharides extracted from some mushrooms inhibit cancer cell lines because of their significant anticancer activity [84]. Polysaccharides from _A. bisporus_ have significant action against cancerous cells via en hancing cellular immunity. According to the Canadian Cancer Society, increased use of _A. bisporus_ enhances immune functions as it exhibits effectiveness against some deadly diseases [85]. _A. bisporus_ contains a large amount of several important polysaccharides, which have anti-tumor and strong immune-stimulatory activity in vitro and in vivo [86].  
[PERSON] et al. [87] reported three principal polysaccharides in _A. bisporus_, namely galactomannan, \(\alpha\)-glucan, and \(\beta\)-glucan, contributing about 55.8%. [PERSON] and [PERSON] [88] reported that the low \(\beta\)-contents in this species are beneficial in enhancing mucosal immunity. [PERSON] et al. [68] stated that a significant dietary fiber level speeds up the secretion of immunoglobulin A.  
[PERSON] et al. [89] reported that lectins and phenolic compounds in _A. bisporus_ suppress tumorous cells. The study was carried to show the effect of bioactive compounds extracted from _A. bisporus_ on cancer line cells. The results showed that proliferation of HL-60 leukemic cells was inhibited by _A. bisporus_ extract as it stimulated programmed cell death [90]. Similarly, _A. bisporus_ extract suppressed the growth of prostate cancer PC3 and DU145 cells of mice [91; 92].  
_A. bisporus_ extract containing lectins was effective against lung cancer cells by strengthening the cellular mechanism of antioxidant defense and inhibiting the growth of cancerous cells [68]. Arginine from basidiocarp of _A. bisporus_ retards the growth of tumor cells. The study recommended that persons particularly cancer patients should use this particular mushroom regularly as dietary supplements. ABP-1 and ABP-2 are polysaccharide fractions that suppressed the growth of breast cancer cells (MCF-7), but their effect on gastric cancer, prostate cancer, sarcoma, and colorectal carcinoma was not statistically significant [67; 93].  
Selenium (Se) is an essential trace element for animals, including humans. According to [PERSON] et al. [94], Se contents are found more in mushrooms compared to the fruits and vegetables. The _A. bisporus_ is a valuable source of trace minerals that contributes to its health benefits. Se has a crucial role in the chemoprevention of cancer. [PERSON] et al. [95] reported that the Se makes the immune system stronger to fight against some deadly diseases including cancer. [PERSON] et al. [96] stated that the higher doses of Se significantly alleviate the chances of cancer. Major types of cancer including lung, liver, colon, and prostate can be prevented by the antioxidants present in _A. bisporus_.
# Experimental Study on the Features of Infrasonic Waves of Sandstone under Shear Load
## 4 Analysis of Experiment Results
### Effect of Central Axial Pressure on Infrasound Features in Shear Destruction  
From the perspective of mechanics, with increased axial pressure, the shear force on the rock increases linearly (Figure 12). The hardness of sandstone is related to its  
Figure 11: Relationship of number and average excellent frequency of infrasound events with water saturation.  
Figure 10: Relationship of shear resistance strength and infrasound energy with water saturation.  
mineral composition, structure and weathering degree. According to engineering rock mass classification standards (GB50218), the hardness coefficients of sandstone are F4, F8, F10 and F15, wherein the hardness increases from F4 to F15. As the sandstone is hard, its internal friction angle and cohesion hardly change, and its failure conforms to the Mohr-Coulomb strength curve.  
We can see from the experimental results (Figure 13) that with increased axial pressure, the number of infrasound events increases linearly, and the average excellent frequency of infrasound events increases exponentially. When the axial pressure is small, the failure process is simpler, the process of generation, propagation, and running-through of microcracks in rock is short, the shear surface is relatively flat and straight (Figure 14a), the slip distance at the instant of failure is longer, so the corresponding infrasound is lower in frequency, and infrasound events are fewer. With increased axial pressure, the caving generated during the shear process increases in range and number (Figure 14b-e), and the infrasound events caused by caving also increases in number, the affected axial pressure, the broken surface of the sample is serrated, and the shear surface become increasingly rough, making the running through the slip distance shorter, so the corresponding infrasound generated is higher in frequency. With increased axial pressure, the energy released by rock failure increases, but the experimental results show the total energy of infrasonic signals generated by rock failure did not increase accordingly and had no regularity (Figure 12). This is probably because the samples in the experiments were small (all with a side length of 50 mm). Furthermore, the elastic wave generated during rock failure is higher in frequency, even exceeding the range of the infrasound frequency band. Hence, it is recommended to use sound emitting and infrasound means jointly in future studies to obtain a deeper understanding of the effect of axial pressure changes on sound wave energy in the process of shear loading on rock.  
Figure 12: Relationship of shear resistance strength and infrasound energy with axial pressure.
## 4 Results and Discussion
### Verification and Discussion of Results
#### 4.2.2 Land Subsidence  
Land subsidence No. 29 is located between the farmland north of Taiping Village and the Xiaozhangtan forest area in Xinzhuang Township. The surface deformation rate ranges from \(-\)23.97 mm/y to 6.85 mm/y, with an overall downward trend. The area is approximately 0.846 km\({}^{2}\), with a relatively flat terrain and elevation between 301 and 310 m. Two profiles, AA' and BB' (as shown in Figure 7a), were selected at the subsidence point for the rate distribution profile diagrams shown in Figure 7b,c. Both profile diagrams exhibit the same pattern: the profile lines form a funnel shape, and the deformation rates show a trend of small-large-small from one end to the other, with a maximum rate of \(-\)13 mm/y. These characteristics are similar to those observed by [PERSON] et al. [34], and the lowest point appeared twice in the AA' profile, indicating the presence of two subsidence funnels in this region, as shown in Figure 7a. These funnels are elliptical in shape. The western funnel is significantly larger than the eastern one, with the long axes measuring 1.21 km and 0.67 km and the short axes measuring 0.92 km and 0.47 km, respectively. The proposed transmission towers, 5S27189G and 5S27190G, would be located between the two funnels. The deformation rates on either side of the towers would vary, leading to uneven subsidence, which poses a threat to the stability of the tower foundations.  
The area is mainly composed of cultivated soil and loess-like silty clay with numerous visible pumping wells, as shown in Figure 8a. The wells are about 2-3 m deep, as shown in Figure 8b,c, and there is periodic water extraction for irrigation. North of the subsidence  
Figure 7: Land subsidence deformation rate diagram and profile analysis diagram of the north side of Taiping Village. The stars in (**a**) represent the location of the proposed transmission towers, while in (**b**,**c**), the purple dots represent the deformation points on the profile lines.  
point is the Wei River, with a grove of trees visible between the river and the subsidence area. The terrain is slightly elevated, forming a steep bank with a height difference of about 2 m between the river and the farmland. The erosion and scouring of the river have caused severe damage to the steep bank, leading to its gradual retreat, with some larger trees now positioned in the middle of the eroded bank. Near the steep bank, several depressions were observed during the investigation. It was found that these depressions are mainly due to the slightly higher terrain of the grove, where surface water accumulates and infiltrates toward the farmland, forming erosion channels. The unique properties of loess further influence the formation of these characteristics. Therefore, the ground subsidence in this area results from the combined effects of agricultural irrigation, groundwater extraction, and the properties of loess. The uneven subsidence of the ground is likely to impact the construction of the transmission line towers.
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 5 Evaluation  
Our proposed solution comprises the whole process of distributing the query inside an RoI and the transmission of the sensed information from the vehicles inside this region to the FC. Each of these tasks requires a different technology to undertake this dissemination. On the one hand, a geocast protocol is required to spread the query message inside the RoI, and, a routing protocol capable of dealing with the features of the VANETs is also needed that assures the delivery of the messages to its destination.  
Among the existing geocast solutions, we have selected AckPBSM [32] for the query's broadcast because it is an adaptive protocol, suitable for vehicular scenarios and which have been proved to be efficient regarding the network overload and reliable in vehicular networks.  
On the other hand, for the harvesting stage, we have selected BRAVE [37], which has also obtained an excellent performance in terms of packet delivery ratio and which also provides support for Delay Tolerant-Networks (DTN) allowing nodes to carry themselves the packets until they find a suitable neighbor for their destination.  
Finally, the last aspect that we must tune is the delay function used to set the time nodes must wait before generating a new data packet with its sensed information.  
In the simulated scenario, we have specified an RoI of \(4047\times 5047\)\(m^{2}\), and we have set our FC in the center of the scenario. So, we have tuned the following parameters: \(p_{2}=2000\), which specifies the distance to point where the slope of the function is produced; and \(p_{3}=80\), which specifies the steepness of the slope. So, after setting these parameters, we ended up having the following equation  
\[y=T_{MAX}\times e^{-e^{\frac{x-2000}{80}}}, \tag{7}\]  
which corresponds to the second sort of series in Figure 9.  
TMAX, which corresponds to the \(p_{1}\) parameter, is the value of the lifetime of the response. That is, the maximum waiting time the sensing nodes have to wait until they generate a packet with their measurements. For our simulations, we have set a TMAX value of 20 s.
## 5 Discussion of Test Results
### Human Comfort and Vibration Serviceability Assessment Based on Existing Technical Documents  
The acceleration records summarized in Section 5.2 were further assessed, with respect to conventional serviceability approaches in use for the comfort assessment of pedestrian structures [21; 22; 23; 24]. According to the literature, the human body is mostly sensitive to frequencies in the range of 4 Hz to 8 Hz (i.e., typical range of most of the constructed floors), because it is associated to resonance phenomena with internal organs [18]. For this reason, several existing guideline documents for the comfort verification of human induced vibrations are based on a double evaluation check. The first one is related to the fundamental frequency of the system object of analysis (see for example [51]). For pedestrian systems with frequency in potential risk of resonance, detailed vibration analyses are commonly required, and these can take the form of refined Finite Element models or simple SDOF calculations. In both the cases, the goal is to evaluate the expected acceleration peaks (in the vertical and transverse directions), and compare them with threshold values that implicitly account for the comfort satisfaction of the occupants [37]. The potential of the technical documents recalled in this paper, see [21; 22; 23; 24], is to offer practical methodologies for pedestrian systems, based on design charts or SDOF calculation approaches for equivalent, single occupant scenarios. However, according to the literature ([51; 52; 53; 54; 55], etc.), the possible limitations of these methods still require investigations. The reliability and accuracy of existing approaches, in particular, is a major problem for footbridges with high live-to-dead load ratios like the examined system (Table 3).