esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Model and Methods
### Relationship between Multipath and CNR  
The signal received by GPS receiver antenna not only includes the direct signal from satellite but also includes the reflect and diffract signals from the multipath inducing surface. Thus, the received GPS signal can be modeled as follows [24]:  
\[S_{m}(t)=A_{d}\cos(\varphi\cdot t)+A_{i}\cos(\varphi\cdot t+\Delta\varphi) \tag{1}\]  
where \(A_{d}\) and \(A_{i}\) denote the amplitude of the direct signal and the indirect signal; The indirect signal means the multipath signal, which mainly consists of the reflection and diffraction signals; \(\varphi\) is the phase of the direct signal; \(\Delta\varphi\) represents the phase shift of the indirect component signal.  
Furthermore, based on the relationship between the CNR, direct signal, and indirect signal, the amplitude of the received signal can be reflected in the CNR. Considering that the CNR is positively related to the amplitude of the signal, the received GPS signal can be further modeled as follows [18]:  
\[\left\{\begin{array}{l}\Delta\varphi_{m}=\arctan\Bigl{(}\frac{A_{i}\sin \Delta\varphi}{A_{d}+A_{i}\cos\Delta\varphi}\Bigr{)}\\ A_{m}^{2}=CNR^{2}\equiv A_{d}^{2}+A_{i}^{2}+2A_{d}A_{i}\cos\Delta\varphi\end{array}\right. \tag{2}\]  
where \(A_{m}\) is the amplitude of the received signal; \(A_{d}\), \(A_{i}\), and \(\Delta\varphi\) are same as the Equation (1). Thus, from the above analysis, it is clear that the CNR is strongly related to the multipath effect. Then, the relationship between flooding and the CNR can be established by the multipath, and this indicates that it is feasible to retrieve the flood by using CNR.
# Detection of Helmet Use in Motorcycle Drivers Using Convolutional Neural Network
## 1 Introduction
[23] use in the stage where the helmet is detected a improved version of the fifth version of YOLO which is superior in terms of speed and precision to its predecessors, called YOLOv5-HD, in this work the Precision reaches 98.0% and Recall 97.2%. Another proposal by [PERSON] et al. [24], is based on the use of another deep learning algorithm called Faster R-CNN, which consists of two main modules RPN and Fast R-CNN, which, in the end, try to find the helmet in a given image, this work achieved an accuracy of 97.69%. In other works that aim to improve motorcycle safety, it is proposed to develop smart helmets. [PERSON] [25], proposes to detect the user's head with a force sensor installed in the helmet, this signal is sent from a transmitter circuit to a receiver by means of a radio frequency module in order to control the ignition of the motorcycle and produce a sound to remind you of helmet use. On the other hand, there are several ITS developed for four-wheeled vehicles that can be introduced and adapted to motorcycles, one of them is the seat belt reminder system, which can be adapted in the case of helmet use, as over the years reminder systems have played a vital role in increasing seat belt use [15]. The work presented by [PERSON] et al. [26], proposes to detect the use of the seat belt based on the analysis of images obtained with a camera inside the vehicle cabin, they generate a model based on the use of the architecture of a neural network named YOLO-Net, which classified the images into three categories, seat belt correctly fastened, seat belt fastened behind the back, and seat belt not on, reaching an accuracy of 93%. The aim of this research work is to generate an artificial intelligence model for real-time detection of helmet use in motorcyclists, in order to improve the safety of these vehicles with prevention and not with the correction of the activity (helmet use). This intelligent model is based on the use of image processing and deep learning techniques. The paper is organized as follows: Section 2 explains the materials and methods used to data acquisition through video capturing. Section 3 presents the results obtained from the proposed methodology for helmet detection in real time. Section 4, the results obtained in the previous section are discussed in order to highlight the contribution of the work in relation to the literature. Finally, Section 5 concludes and proposes future work for improvement detection oh helmet.
## 6 Results  
In this section, the ambiguity resolution performance and the attitude accuracy of the direct georeferencing system will be evaluated. For the evaluation of the ambiguity resolution, real flight test data are used. Since it is difficult to get a highly accurate attitude reference for UAV flights, a test setup has been applied for the accuracy evaluation. On this setup (Figure 4), the direct georeferencing system, the GPS antennas and the magnetometer are mounted in the same configuration as on the UAV platform. As the reference system, a navigation-grade INS (inertial navigation system) from Imar has been used, which consists of fiber optical gyros (FOG) and provides attitudes with a rate of 1000 Hz. The specifications of the Imar INS are shown in Table 1, together with the specifications of the MEMS IMU (Adis16488), which is used on the direct georeferencing system. Due to its high level of bias stability (\(<\)0.003-0.01\({}^{\circ}\)/h) and the low influence of the angular random walk (0.001\({}^{\circ}\)/\(\sqrt{h}\)), the attitudes of the Imar INS serve as an excellent attitude reference for about one hour after the measurement start.
# Estimating CPT Parameters at Unsampled Locations Based on Kriging Interpolation Method
## 3 Kriging Interpolation
### Build Weight Matrix  
After the determination of the semivariogram based on existing data, it is feasible to estimate the CPT parameters at the unsampled location. Specifically, the Kriging method assigns different weights to the sampled CPT parameters at different locations, then linearly combines these parameters with their weights to obtain the estimation as shown by Equations (5) and (6). Where \(e\) indexes the unsampled location, \(i\) indexes the _ith_ sampled location, \(Z\) is the CPT parameter (i.e., \(q_{\text{c}}\) or \(f_{\text{s}}\)), \(\lambda\) is the corresponding weight, and \(n\) is  
Figure 8: Example of the CPT data detrend.  
the number of samples. The weight \(\lambda_{i}\) could be obtained by solving marix presented in Equations (7) and (8).  
\[Z_{e}=\lambda_{1}Z_{1}+\lambda_{2}Z_{2}+\lambda_{3}Z_{3}+\ldots\ldots+\lambda_{n }Z_{n} \tag{5}\]  
\[\sum_{i=1}^{n}\lambda_{1}=1 \tag{6}\]  
\[K\lambda=D\rightarrow\lambda=K^{-1}D \tag{7}\]  
\[K=\left[\begin{array}{cccc}\gamma_{11}&\gamma_{12}&\ldots&\gamma_{1n}&1\\ \gamma_{21}&\gamma_{22}&\ldots&\gamma_{2n}&1\\ \ldots&\ldots&\ldots&\ldots&1\\ \gamma_{n1}&\gamma_{n2}&\ldots&\gamma_{nn}&1\\ 1&1&1&1&0\end{array}\right],\lambda=\left[\begin{array}{c}\lambda_{1}\\ \lambda_{2}\\ \ldots\\ \lambda_{n}\\ \mu\end{array}\right],D=\left[\begin{array}{c}\gamma_{1e}\\ \gamma_{2e}\\ \ldots\\ \gamma_{ne}\\ 1\end{array}\right] \tag{8}\]  
where \(\mu\) is Lagrange constant and \(\gamma_{ij}\) is the value of semivariogram corresponding to distance between \(i\)th and \(j\)th location. The Kriging method is unbiased implying that the expectation of the estimator obtained from the above equations is equal to the actual value (i.e., the average of the estimation errors will be zero). The final \(q_{\rm c}\) and \(f_{\rm s}\) profiles of the target location F5 obtained by the Kriging method are shown in Figure 9, accompanied by data from the other six sampled boreholes. The detailed program implementation using MATLAB is shown in Appendix A.  
Figure 9: The 2D and 3D Kriging interpolation results of the unsampled location F5: (a) 2D; (b) 3D.
# Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection
## 4 Proposed Framework
### Stage1: Input (Dataset and Features)
#### 4.1.2 Dataset 2  
The study used \"Instagram fake spammer s accounts\" dataset offered by Kaggle [47]. There were 696 users in the selected dataset. The dataset comprises an exact balance  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Feature Name** \\ \hline user\_media\_count & Total number of posts an account has. \\ userFollowerCount & The account’s follower count. \\ userFollowingCount & Following of account \\ user\_has\_profit\_pic & Whether or not an account has a profile picture. \\ user\_is\_private & Whether an account is a private profile or not. \\ userBigographyLength & The total number of characters in the account biography. \\ username\_length & The number of characters in the username of the account. \\ username\_digit\_count & The number of digits in the username of the account. \\ is\_fake & True, if account is a spam/fake account, False otherwise \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of user features in dataset 1.  
Figure 4: Conceptual framework.  
of 348 real users and 348 fake users, resulting in a meticulously balanced database. The dataset contained 10 features such as profile picture, username, number of followers, and number of accounts the user is following. The list of user features used for detection is explained in Table 3.
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 3 Experimental Results Analysis
### Influence of Plant Root Systems on Soil Shear Modulus
#### 3.1.1 Different Root Distribution Patterns  
Different root distribution patterns in the soil are shown to yield varying maximum dynamic shear moduli and dynamic shear modulus-shear strain curves, as illustrated in Figures 6 and 7. At confining pressures of 100 kPa and 200 kPa, the dynamic shear modulus-shear strain curves for upper root distribution are consistently positioned above those for uniform root distribution and lower root distribution. At a confining pressure of 300 kPa, the dynamic shear moduli for the three root distribution patterns exhibit remarkable similarity. This phenomenon arises from the electromagnetic drive system, which calculates shear modulus by generating sinusoidal torque. When roots are exclusively in the upper section of the specimen, the upper soil shows greater stiffness and strength, resulting in the specimen exhibiting more substantial restorative forces and shorter vibration cycles under dynamic excitation. Consequently, this increases the resonance frequency and dynamic shear modulus. Thus, under lower confining pressures, the upper root distribution pattern is advantageous in enhancing the soil's dynamic shear modulus. In practice, this aligns with scenarios where roots are mainly distributed at the surface of the embankment in the upper portion of the soil, contributing to an improved soil dynamic shear modulus and reduced dynamic strain amplitudes.  
Figure 6: The maximum dynamic shear modulus of soil with different root distribution patterns.  
Figure 7: \(G\sim\gamma\) relationship curves for soil with different root distribution patterns.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 4 Evaluation
### Complexity Analysis  
As concluded in Algorithm 2, the model is trained collaboratively by users. Here, we define \(K\) as the dimension of the latent factor vectors, and we use \(|R|\) and \(|M|\) to denote the size of the explicit data and the size of the implicit data, respectively.  
The computation that our method carries out is mainly two parts: (1) users locally compute gradients of \(\mathcal{L}\) with respect to \(U_{i}\) and other latent factor vectors as shown in line 8 of Algorithm 2, and (2) users update the latent factors as shown from line 9 to line 27 of Algorithm 2. The computation complexity is \((|R|+|M|)\cdot K\) for (1) and \((|R|+|M|)\cdot K\cdot d\) for (2), so the total computation complexity is \((|R|+|M|)\cdot K\cdot d\) in one iteration. Normally, we can manually set \(d\) and \(K\) as small values so the computation overhead is linear with the size of the training set. As for the communication overhead, line 21 and line 24 in Algorithm 2 show that at a certain iteration, user \(i^{\prime}\) receives public gradients \(\frac{\partial\mathcal{L}}{\partial p_{j}^{\prime}}\) or \(\frac{\partial\mathcal{L}}{\partial p_{j}^{\prime}}\) from user \(i\). Thus, the communication cost is \((|R|+|M|)\cdot K\cdot d\), which is also linear with the size of the training set. The complexity comparison with other state-of-the-art methods is listed in Table 3, we can see that we have a comparable efficiency with latest DMF model.
## 2 Background Knowledge
### Decision Science Support Mechanism and/or Tools
In the decision making process, it is imperative to strike a delicate balance between multiple competing interests and factors such as performance, risks, benefits, costs, opportunities, short-term goals, and long-term sustainability. Modern electrical utilities employ a variety of models and tools to mitigate uncertainties and better quantify risks within their asset management decision-making processes. However, it is essential to link the information and insights obtained from these quantitative models to the decision maker's needs and take into account other intangible factors that may have a significant impact on final decisions[92]. Geographic Information Systems (GIS), spatial data and maps are generally applied to better assess and control threats in the built environment. GIS has proven to be a useful tool for presenting and analyzing layers of information in a spatial manner since the 1990s. It offers decision makers with information that is simple to grasp and process. GIS-based decision-support systems promote communication between researchers and decision-makers and provide a platform for multidisciplinary research [95]. UR has been increasingly discussed and incorporated into policymaking in view of controlling hazards in cities/urban areas. Consequently, it became relevant to investigate methods for visualizing and mapping UR and to comprehend the added value deriving from these types of efforts. Previous research has shown that adaptive resilience is mapped after a disaster mostly through recovery measures, and that top-down techniques are commonly used to map inherent resilience. However, resilience maps do not examine the topic of resilience completely, resilience maps do not depict the ability of systems to adapt or evolve, nor do they reflect the systemic attribute of resilience [96]. This lends credence to the idea of strengthening urban resilience to ensure risk-aware spatial planning strategies for the built environment and key infrastructure, bringing a fresh perspective in the settings of socio-ecological reconstruction and the cultural vitality of civil society [29; 97]. The relevance of assets and risk in the context of urban sustainability and resilience is emphasized in this section, where the management of assets and asset systems will be discussed in relation to our cities' infrastructure and buildings. To this extent, decision makers require a variety of tools and approaches to improve the decision making process in order to manage these asset systems that are vulnerable to diverse risks, such as tangible or intangible, natural, or man-made disasters. To arrive at a unified interdisciplinary solution for sustainable UR, a combination of data-driven and stochastic analysis will be needed. To that aim, this study attempted to identify the current trend in UR as well as potential research prospects that should be pursued in future research initiatives. These trends and gaps were retrieved via a rigorous process that included subjective and objective assessments to produce accurate and all-inclusive results.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism  
This section gives a brief account of the Supersingular Isogeny Key Encapsulation mechanism. This mechanism relies on Supersingular Isogeny Diffie-Hellman (SIDH), a key-exchange protocol introduced by [PERSON] and [PERSON] in 2011 [2]. In particular, we follow the description provided by SIKE's specification submitted to the NIST post-quantum cryptography standardization process.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 5 Implementation Setup and Results Discussion
### Scenario 1: Same Playout Time for All Receivers  
In Figure 11, the straight line showing that the packet was played out at the same time by all receivers. Some packets were received at slightly different time instance as compared to the others as encircled in Figure 11, but the playout time is not affected by this variation because the calculation of playout time depends upon the senders provided end to end delay offset and the sent time. The distance between sent time and playout time remains same for all receivers irrespective of the received time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Serial No.** & **Parameter** & **Values** \\ \hline
1 & Number of receivers & 100 \\
2 & Sender & 1 \\
3 & Packet sending rate & 50 packet/s \\
4 & Artificial Delay & 100 ms \\
5 & End to end delay offset & 400 ms \\
6 & NTP time stamp (OWD) & 64 bit \\ \hline \end{tabular}
\end{table}
Table 2: Implementation of setup parameters.  
Figure 11: Common/same playout time for all receivers.
# The Use of Non-Destructive Testing (NDT) to Detect Bed Joint Reinforcement in AAC Masonry
## 2 Potential of Non-Destructive Tests for Detecting the Reinforcement
Density and porosity of tested concrete also affect the results. Due to wave suppression, tests are usually performed on concrete with the standard density within the range of 2400-2700 kg/m\({}^{3}\), less commonly on light concrete with a density from 350 to 1500 kg/m\({}^{3}\). Non-metallic reinforcement applied in reinforced concrete structures is usually characterised by diameters greater than or equal to 5 mm, whereas masonry structures (with standard or thin joints) usually have meshes with weaves up to 1 mm in diameter, which are more difficult to localise. Problems with detecting reinforcement in masonry structures are also caused by its location. Contrary to reinforced concrete structures, reinforcement is horizontal, that is, in a perpendicular direction to the tested area because it is usually laid in head joints of the masonry, often in thin joints. This article describes an attempt to detect the non-metallic reinforcement in the masonry wall using different diagnostic techniques. The ultrasonic tomograph with 24 exponential transducers and the GPR device equipped with antennae having a wide range of frequencies 0.2-4.0 GHz were employed in the tests.
## 6 Challenges and Future Trends in LEV Charging
### Electrical Insulation Considerations  
Another aspect that needs special attention in conductive LEV charging systems is the electrical insulation. Dielectric and electrical insulation considerations have been discussed at length for smart grid systems [132], where increase in EV penetration is mentioned as a contributing factor to changing load patterns--ultimately calling for advanced insulation monitoring techniques. The effect of DC-based EV fast charging stations on distribution transformers was explored in [13]. In this work, the impact of EV penetration level was evaluated using transformer insulation life and it was found that there was an increase in transformer ageing with increasing EV penetration.  
Measures must be taken to monitor insulation conditions in LEV charger systems, which can help evaluate subsequent equipment life. Such measures are especially beneficial for isolated topologies. High frequency transformers in isolated topologies normally require insulation material between the core and the conductors [133], which can potentially become impacted due to component ageing. The same goes for insulation and dielectrics associated with passive components, which can also be subject to thermal effects [134]. Furthermore, insulation in cables and battery-side is also prone to wear and tear due to chafing and environmental factors, which can lead to safety concerns--thus necessitating the use of insulation monitoring techniques. One such monitoring technique was described in [49] for non-isolated topologies, which used a voltage divider along with detector circuits for this purpose.  
Hence, a future possible research avenue can deal with insulation ageing in charger circuit components and monitoring methods, thus providing a perspective on remaining lifetime and improving safety.
## 4 Numerical Simulation Results and Validation
### The Influence of Different Factors on TVMS and LTE
#### 4.2.3 The Influence of Face Width on TVMS and LTE  
The influence of face width on the TVMS and LTE of internal helical gears was also studied. While the other parameters remained constant, the face width was increased from 40 mm to 90 mm in increments of 10 mm. The result change curve is illustrated in Figures 18 and 19. When the face width increased, the axial contact ratio also increased, and the ability of the gear teeth to resist deformation was enhanced. As a result, the deformation decreased, and the mesh stiffness increased. On the other hand, the LTE decreased as the face width increased.  
Figure 16: Load transmission error of different modification amounts.  
Figure 17: The fluctuation value of the load transmission error of different modification amounts.  
Figure 20 shows the variation trend in the LTE fluctuation value with face width. It can be clearly seen that a larger the face width, the smaller the fluctuation value. The variation trend in the LTE fluctuation value shows that the face width can effectively enhance the stability of the system.  
Figure 19: Load transmission error of different face widths.  
Figure 18: Time-varying mesh stiffness of different face widths.