esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
However, 65.10% were aware that the time required for CBCT scanning is less than conventional CT, 38.4% were aware that CBCT can be used to detect dental infections or abscesses at an earlier stage as compared to the periapical radiographs, 40.1% were of view that CBCT a better diagnostic aid than radiographs and RVG for detecting supernumerary teeth in children, 41.4% were aware that CBCT can be used in pediatric patients having malocclusions and craniofacial anomalies, 48.9% were aware that CBCT can be used for detecting salivary stones, 42.20% were aware that CBCT can be used in pediatric patients having cleft lip and palate, 48.9% were aware that CBCT can be used in pediatric patients for correct determination of the position of the un erupted teeth, especially for maxillary canines, 76.5% did not know about the about SEDENTEXCT international guidelines used for CBCT and 44.4% believed that CBCT should be preferred for 3D imaging in dentistry. Only 26.1% preferred CBCT for analyzing root resorption and root fracture, whereas 30.8% still believed in the use of IOPAR. Only 30.4% believed that the CBCT technique is best for analyzing dental trauma, and 30.6% believed lateral CEPH to be best for analyzing airway space. Only 28.2% believed CBCT to be best for analyzing the periodontal status, whereas 31.7% still believed in the use of IOPAR \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Questions** & \begin{tabular}{l} **IOPAR/Lat** \\ **Ceph** \\ \end{tabular} & **OPG** & **CT** & **CBCT** & **All** & \
\begin{tabular}{l} **\(p\) Value** \\ \end{tabular} \\ \hline (17) & Which of the following technique & & & & & & & \\  & will you prefer for analyzing root & 143 (30.8\%) & 86 (18.5\%) & 47(10.10\%) & 121 (26.1\%) & 67(14.40\%) & 0.001 (Sig) \\  & resorption and root fracture? & & & & & & & \\ (18) & Which of the following technique is & & & & & & \\  & best for analyzing dental trauma & 155 (33.4\%) & 107 (23.1\%) & 30 (6.5\%) & 141 (30.4\%) & 31 (6.7\%) & 0.001 (Sig) \\ (19) & Which technique is the best for & & & & & & \\  & analyzing the Periodontal status & 147 (31.7\%) & 51(11.0\%) & 68 (14.7\%) & 131 (28.2\%) & 67 (14.4\%) & 0.001 (Sig) \\ \hline Question & Lateral Ceph & OPG & CT & CBCT & & & \\ (20) & Which technique should be used for & & & & & & \\  & analyzing the airway space & 142 (30.6\%) & 127 (27.4\%) & 34 (7.3\%) & 132 (28.4\%) & 29 (6.2\%) & 0.001 (Sig) \\ \hline Question & MST & CT & CBCT & All & & & \\ (21) & Which technique has the least & & & & & & \\  & radiation exposure? & 110 (23.7\%) & 81 (17.5\%) &
\begin{tabular}{l} 179 \\ (38.6\%) \\ \end{tabular} & 94 (20.3\%) & 0.001 (Sig) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Choice of diagnostic methods.
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 4 Simulations and Results  
In the simulation section, the case study is the communications among the nodes in the smart grid IEEE 39-Bus system. IEEE 39-Bus is a very general network dynamical system which has been widely used for research such as synchronisation, stability, optimal sampling, etc. [37; 38; 39]. This network is composed of 10 generator buses (nodes) and 29 loaded buses. When there is electricity consumption at some of the loaded buses, the angular speed under these buses will change from the standard \(2\pi\cdot 60\) rad/s, and then the cascaded effects will make all the angular speeds under all buses changes. In the meantime, 10 generator buses are used to maintain the angular speed back to the standard value, i.e., \(2\pi\cdot 60\) rad/s. These two constitute the time-varying networked dynamics (i.e., the speed deviations) of the IEEE 39-Bus system. For our GLS work, the goal here is to use the time-varying speed deviations under buses (nodes) to encrypt the wireless data transmitted among them. Here, such networked dynamical speed deviations are irrelevant with the communications (e.g., channels and RSS). For encryption and communication process, we at first compute the relay nodes and coefficients, i.e., \(\boldsymbol{\alpha}^{(Tx,Rx)}\) for any two pairs of (Tx,Rx) node, using Algorithm 1 in the off-line mode. Then, information is encrypted and transmitted from Tx using Equation (3), and then passed through and processed by relay nodes and Rx using Equation (5). It is noteworthy that the relay computation result for this case study is specific, and will be recomputed for different dynamical models.
## 3 The 6G Technology
### Sixth-Generation Requirements
#### 3.2.2 Scalability  
As envisioned, the 6G network will handle a very high volume of users and devices. It is required to be highly scalable. It is developed to accommodate M2M applications, which will launch massive connectivity of devices to the network. Scalability will ensure continuous optimal user experience even as more users connect to the network. Some contributing components to a highly scalable 6G network include network slicing, software-defined networking (SDN), hybrid cloud, and heterogeneous networks [15].
## 2 Background Research
### Unrestricted Adversarial Examples  
As mentioned in [16] by [PERSON] et al. (2018), there have been various studies on generating unrestricted adversarial examples where some specific norm constraint does not constrain the generated adversarial examples. More specifically, this paper focuses on [PERSON] et al. (2018) [20], where the authors used a generative model to create adversarial samples from scratch. Their method is based on searching for these adversarial samples in the latent space of the model at hand. Unlike other methods, which generally add small perturbations, generating unrestricted adversarial examples through an AC-GAN performs a search that opens new possibilities for discovering adversarial samples that standard algorithmic attack methods cannot create. The authors demonstrated the legitimacy of their results through human evaluation on datasets such as MNIST, SVHN, and CelebA.  
To further explain the generative model process, [PERSON] et al. (2018) [20] formulated the objective functions of the Generator and Discriminator in an adversarial example generation context as follows in Equations (3) and (4):  
\[\min_{\theta}-E_{z\sim P_{z},y\sim P_{y}}[d_{\varphi}(g_{\theta}(z,y))-logc_{ \psi}(y|g_{\theta}(z,y)))] \tag{3}\]  
\[\min_{\varphi,\psi}E_{z\sim P_{z},y\sim P_{y}}[d_{\varphi}(g_{ \theta}(z,y))]-E_{x\sim P_{x}}[d_{\varphi}(x)]-E_{x\sim P_{x},y\sim P_{y|x}}[ \log c_{\psi}(y|x)] \tag{4}\]  
The equations were derived from the original [PERSON] GAN [58] and AC-GAN [27] formulations and customized to an adversarial example generation scenario. Equation (3) provides a minimization function for generating unrestricted adversarial examples. According to the equation, with \(c_{\psi}\) being the auxiliary classifier, it can be seen that the Generator's objective is to minimize the loss over the classification of the generated images. On the other hand, in Equation (4), the Discriminator is trying to minimize the loss between the output generated samples \(g_{\theta}(z,y)\) and the output of the original samples, \(x\). The last term of the equation is a gradient penalty term that encourages the discriminator's gradient to have a norm of 1.  
Based on the above equations, our approach modifies this, aiming to primarily benefit adversarial training by allowing for the generation of adversarial samples for better robustness. The architecture was changed due to the constraint implemented when generating adversarial examples. Removing this constraint and implementing different loss functions can improve adversarial training coverage; this can be seen through the proposed methodology and experimental results in the following sections.
# Effect of Amphiphilic Polymer/Nano-Silica Composite on Shale Stability for Water-Based Muds
###### Abstract  
Research on using nanotechnology to solve shale instability problems in drilling engineering has been increasing. The combination of amphiphilic polymer and silica nanoparticles may be a new way to improve shale stability. Herein, an amphiphilic polymer/nano-silica composite (poly(styrene-methyl methacrylate-acrylamide)/nano-SiO\({}_{2}\)) was introduced as a novel shale stabilizer SMA/SiO\({}_{2}\) for water-based muds, which possessed the advantages of both physical plugging and chemical inhibition during the drilling operations. The SMA/SiO\({}_{2}\) was prepared and characterized by Fourier transform infrared spectra (FT-IR), nuclear magnetic resonance (\({}^{1}\)H-NMR), transmission electron microscope (TEM), particle size distribution (PSD) and thermogravimetric analysis (TGA) experiments, which confirmed that SMA/SiO\({}_{2}\) was regularly spherical with nano-scale and showed good high-temperature resistance. To evaluate the plugging capacity of SMA/SiO\({}_{2}\), the pressure transmission test and BET analysis were applied. The results indicated SMA/SiO\({}_{2}\) was capable of effectively plugging the pores and fractures in shale. To evaluate the hydration inhibition capacity of SMA/SiO\({}_{2}\), the rolling dispersion experiment and contact angle test were adopted. The results demonstrated that SMA/SiO\({}_{2}\) could reduce the tendency of shale hydration, which was better than potassium chloride (KCI) and polymeric alcohol (JHC). In addition, SMA/SiO\({}_{2}\) only created slight variations on the rheological parameters of the water-based muds (WBMs) and showed a significant filtration control performance. Due to the outstanding performance of physical plugging and chemical inhibition, SMA/SiO\({}_{2}\) was expected to be a novel shale stabilizer to solve shale instability problems.  
shale stabilizer; amphiphilic polymer; nano-SiO\({}_{2}\); plugging ability; hydration inhibition +
Footnote †: journal: Applied Sciences
## 3 Background
### Iot Protocol Stack Development
#### 3.1.3 Link Layer Protocols Comparison  
There are three main link layer protocols present for IoT applications, IEEE 802.15.4 for 6 LoWPAN and ZigBee, IEEE 802.11 for Wi-Fi. and IEEE 802.15.1 for Bluetooth. A brief analysis of the above mentioned protocols is given below:  
IEEE 802.15.4 is a low-power wireless network standard developed by the Institute of Electrical and Electronics Engineers (IEEE). It has a communication range of at least 10 metres and up to 100 metres. The 802.15.4 category is the dominant standard for low data-rate and security in Wireless Personal Area Networks (WPAN).  
IEEE standards for wireless communications WLANs/Wireless Fidelity (Wi-Fi) are 802.11 networks with communication ranges varying from 40 to 90 metres. Wi-Fi (IEEE 802.11) is inefficient in terms of battery life, does not cover a vast area, and does not support a large number of end devices.  
Bluetooth, i.e., 802.15.1, is another WPANs standard specified by the 802.15 group, with a communication range of 10 metres to 50 metres. It is more power efficient but less encrypted compared to Wi-Fi [37].  
From the comparison it is determined that IEEE 802.15.4 is the best fit for the proposed CSFSA framework because it is compatible with 6 LoWPAN, provides more security, consumes less power and has a higher communication range compared to the other protocols.  
Based on the comparison of several protocols at different layers, the IoT protocol stack for proposed CSFSA framework is presented in Figure 2.
## 3 Methods
### Fitness Landscape Analysis
#### 3.3.1 Fitness Landscape Diameter  
Table 2 compares the diameters of fitness landscapes for the various mutation operators. Except where noted, we use the metrics identified above. The diameter of both swap and insertion landscapes is \(n-1\). The maximum distance for swap occurs for a single \(n\) element cycle. The maximum case for insertion occurs when one permutation is the reverse of the other. For reversal mutation, we use the exact diameter for a reversal edit distance rather than relying on the surrogate distance measure identified earlier. The diameter of a reversal landscape is \(n-1\), proven by [PERSON] and [PERSON] [73], the proof of which is well beyond the scope of this article. The diameter of a scramble landscape is simply 1 since every permutation is reachable from any other via a single scramble operation.  
The diameter of a Cycle(\(\alpha\)) landscape is 2, which is the diameter of the space of permutations for cycle edit distance (Section 3.2.2). When \(kmax\leq 4\), the diameter of Cycle(\(kmax\)) is max\(\{[n/2],\,[(n-1)/(kmax-1)]\}\), which is the maximum \(k\)-cycle distance (Section 3.2.3).  
We previously saw that \(k\)-cycle distance does not satisfy the triangle inequality if \(kmax\geq 5\). Although computing a Cycle(\(kmax\)) edit distance for \(kmax\geq 5\) is too costly for our purpose, it is straightforward to compute its diameter. Recall the examples illustrating that \(k\)-cycle distance violates the triangle inequality, and specifically that two cycle edits of length \(k\) can transform a set of cycles with a total of \(k\) elements into \(k\) fixed points. Thus, the maximum case of \([n/2]\) cycles of length 2 can be transformed to \(n\) fixed points with approximately \(2n/kmax\) applications of the Cycle(\(kmax\)) operator when \(kmax\geq 5\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mutation Operator** & **Diameter** \\ \hline Cycle(\(kmax\)), \(kmax\geq 5\) & \(\approx 2n/kmax\) \\ Cycle(\(kmax\)), \(kmax\leq 4\) & max\(\{[n/2],\,[(n-1)/(kmax-1)]\}\) \\ Cycle(\(\alpha\)) & 2 \\ Swap & \(n-1\) \\ Insertion & \(n-1\) \\ Reversal & \(n-1\) \\ Scramble & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fitness landscape diameter for cycle mutation and common permutation mutation operators.
###### Abstract  
The goal of this study was to propose and validate a method for predicting cropland suitability based on biophysical variables and machine learning according to an FAO land suitability standard using soybean (_Glycine max_ L.) as a representative crop, aiming to provide an alternative to geographic information system (GIS)-based multicriteria analysis. The peak leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) from PROBA-V/Sentinel-3 data were calculated according to ground-truth soybean agricultural parcels in continental Croatia during 2015-2021. Four machine learning regression algorithms, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), as well as their combination, were evaluated for predicting the peak LAI and FAPAR on the entire agricultural land in the study area, with RF producing the highest prediction accuracy with an R\({}^{2}\) in the range of 0.250-0.590. The translation from K-means classes to the FAO land suitability standard was performed using a relative-based approach, ranking five resulting classes based on their relative mean sums of LAI and FAPAR values. The results of the proposed approach indicate that it is viable for major crops, while cropland suitability prediction for minor crops would require higher spatial resolution, such as vegetation indices from Sentinel-2 imagery.  
PROBA-V; Sentinel-3; leaf area index (LAI); fraction of absorbed photosynthetically active radiation (FAPAR); random forest; FAO land suitability standard +
Footnote †: journal: Applied sciences  
Article
###### Abstract  
To ensure the safety of excavations such as large section tunnels of the Guangxi Liubin Highway Tunnel Project, we implemented a simulation analysis of 3D tunnel models based on finite software for different construction methods. Different from the traditional simulation, this paper uses various construction methods to compare, study, and analyze the advantages and disadvantages of different excavation methods by combining them with the excavation. The feasibility of different construction methods was analyzed by studying the mechanical characteristics and settlement changes using the Cross Diaphrag (CRD), Center Diaphragm (CD), and full-section methods while building a large section tunnel. The arch perimeter deformation and surface settlement during the initial support and secondary lining proved that the CRD excavation method is the most favorable and causes the least damage to the stability of the surrounding rock. For the analysis of surface subsidence, the \"V\"-shaped surface subsidence curve excavated by the CRD method is the closest to the subsidence curve. However, we used the adjusted peak formula to fit the prediction formula for the surface settlement curve of the deep-buried two-lane tunnel with a large cross-section applicable to this project, which will provide an important reference for similar projects.  
Keywords:CRD method; CD method; full-section method; surface settlement; large-section tunnel +
Footnote †: journal: Applied Sciences
## 3 Test Results and Discussion  
### Analysis of Strength Variation and Failure Characteristics in Jointed Red Sandstone  
The red sandstone specimens used in the experiments include regions with filled joints, and when compared to intact red sandstone, these samples exhibit varying degrees of changes in their mechanical characteristics and deformation properties due to the influence of joints. An analysis of the uniaxial compression test results for these samples reveals noticeable differences in axial stress variations and failure modes among specimens with different joint sizes.  
Figure 6 depicts the trends in axial stress over time under uniaxial loading for red sandstone with different joint sizes. The experimental results indicate that, in comparison to red sandstone without joints, the compressive strength of the jointed red sandstone samples No. 1-1 to 1-4 decreased by 9.86%, 45.76%, 40.24%, and 51.68%, respectively. The presence of filled joints results in a reduction in the rock's load-bearing capacity. Additionally, the increase in joint size to some extent contributes to the decreased load-bearing capacity of the rock. Sample No. 1-4 exhibits the most significant decrease in compressive strength.  
Figure 4: Acoustic emission layout.  
Figure 5: Schematic diagram of the test system.  
In the jointed red sandstone, tensile failure is the primary mode of failure, accompanied by localized shear failure in the jointed regions, as depicted in Figure 7. Samples No. 1-1 to No. 1-3 all display tension cracks that penetrate the samples at the filled joint ends, propagating perpendicular to the loading direction. In the case of No. 1-4, the filled joint extends across the entire sample, and under the influence of applied stress, the jointed portion of the sample experiences sliding shear failure along the section. The failure modes of the samples indicate that, due to size effects, No. 1-2 initially develops cracks on the specimen's end face during loading, resulting in significant reductions in peak strength due to localized splitting and tensile failure.
# Partial Discharge Wideband Full-Band High-Gain Resonant Cavity UHF Sensor Research
## 4 Antenna Performance Simulation Analysis
### Gain Direction Diagram  
The gain directional map is a parameter used to describe the directionality of the antenna and the antenna efficiency, measuring the degree of concentration of the radiant energy of the antenna in free space and the degree of effectiveness of the antenna in converting wave energy into high-frequency current or energy [18; 35; 36]. The higher the gain, the better the directionality, the more concentrated the energy, and the higher the efficiency. At the same time, the higher the gain means the higher the receiving sensitivity of the antenna, and the more favorable to the processing of PD signal. Therefore, the antenna has high gain performance is the common requirement of receiving antenna and transmitting antenna. There are already many methods to evaluate antenna radiation, such as near-field to far-field transformation techniques [37; 38].  
The gain direction plots of the designed Fabry-Perot resonant cavity monopole antenna at six frequency points of 0.7 GHz, 1.0 GHz, 1.4 GHz, 2.0 GHz, 2.4 GHz, and 3.0 GHz are shown in Figure 14. As can be seen from the figure, the antenna gain direction shows symmetrical characteristics, 0.7 GHz and 1.0 GHz in the low-frequency band have the maximum gain at 0\({}^{\circ}\) in the axial direction; the antenna is inverted \"8\" at 1.0 GHz, with good bi-directional radiation characteristics. The gain direction map starts to show fission and depression at 1.4 GHz, which may be caused by too much cross-polarization due to the complexity of the current path.  
Figure 12: VSWR diagram of resonant cavity monopole antenna.  
Figure 13: Resonant cavity monopole antenna measured VSWR diagram.  
Figure 14: Gain direction diagram at different frequency points: (**a**) 0.7 GHz; (**b**) 1.0 GHz; (**c**) 1.4 GHz; (**d**) 2.0 GHz; (**e**) 2.4 GHz; (**f**) 3.0 GHz.  
The HFSS simulation shows that the average gain of the designed resonant cavity monopole antenna is 4.92 dBi in the frequency band of 0.3 GHz-3 GHz, and the maximum gain at each frequency point obtained from the simulation is given in Table 3. As shown by the simulation results, the peak gain at the main resonant frequency of 1.0 GHz is 7.16 dBi, and the gain is relatively high in the whole frequency band, which has a high gain performance. This advantage allows the Fabry-Perot resonant cavity monopole antenna to detect the PD signal to a larger area in the surrounding area, achieving higher sensitivity and better signal-to-noise ratio.
# Reputation and Trust Approach for Security and Safety Assurance in Intersection Management System
## 3 Trust and Reputation Approach Description
### Truth, Trust and Reputation Models
#### 3.1.3 Trust  
As mentioned above, the function of assessing the _Trust_ value is a function of two parameters--the value of \(R\) for the preceding time moments and the _Truth_ value at the current time moment--and is calculated according to Equation (10).  
\[Trust_{e_{i_{t}}}=f(R_{e_{i_{t-1}}},Truth_{e_{i_{t}}}) \tag{10}\]  
The overall task of assessing the _Trust_ of the agent-object boils down to the comparison of the _Trust_ value against the given threshold and is determined accordingly (Equation (11)).  
\[Trust_{e_{i_{t}}}\geq\alpha_{trust} \tag{11}\]  
If the condition in Equation (11) is met, the behavior and the data of the agent-object are assessed by the agent-subject as correct. The function of calculating the reputation value can be represented as a function built on weights. In this case, the values of _Truth_ and \(R\) are taken into account when calculating the _Trust_ value with some coefficients characterizing the effect on the calculated value of each indicator. In a generalized form, this function can be represented according to Equation (12).  
\[Trust_{e_{i_{t}}}=\gamma Truth_{e_{i_{t}}}+(1-\gamma)R_{e_{i_{t-1}}},\gamma\in[ 0,1], \tag{12}\]  
where \(\gamma\) is the system reactivity coefficient.  
In general, the reputation is formulated as a function that depends on trust calculated at previous time moments. In this case, the reputation is used as the agent's major characteristic for further actions. Consequently, the trust value has an indirect impact on the decision-making regarding the behavior of the agent. With the linear function of reputation, the data transmitted by the agent can be estimated incorrectly, as, during a long observation period, the value of reputation does not change quickly enough with a sharp change of the trust value. To address this challenge, we have introduced the _Truth_ indicator and reputation decrease with an exponential function.