esa-sceva/satcom-chunk-collection huggingface dataset:
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 4 The Proposed ABC-Based Location Estimation
If the ABC algorithm is regarded as a block diagram, the proposed positioning method can be simply presented as Figure 2. After the feasible intersections of \(U\), \(V\), and \(W\) are obtained, the proposed positioning method utilizes the ABC algorithm to estimate the MS location. Figure 2: Main procedures of the proposed artificial bee colony (ABC)-based algorithm. Honey bees use several mechanisms like the waggle dance to locate food sources and search for new ones optimally. By the behaviors of a honey bee colony, the richest food sources can be obtained in the shortest possible time. The position of the richest food source is the estimated MS location with the highest opportunity approaching the real MS location. The convergence qualification is defined as the solution that keeps the minimization of the objective function value during three cycles continuously. During iterations, the best solution can be found within the area bounded by feasible intersections of three circles and satisfies the convergence qualification with the lowest objective function value.
## 3 Methods for Suppressing the Twin-Image Effect
### Spatial-Frequency Method  
The diffraction integral (3) can be presented as a convolution of two functions, and up to the constant \(\exp(ikz)\), the complex field amplitude reconstructed from the digital hologram at a distance z from the hologram plane will take the following form:  
\[\begin{split}& U(x_{2},y_{2},z)=\iint\limits_{-\infty}^{\infty}I_{ H}(x_{2},y_{2})\cdot\frac{\exp(ikz)}{i\lambda z}\cdot\exp\Bigl{\{}\frac{ik}{ \mathbb{Z}}\Bigl{[}\bigl{(}x_{2}-x_{1}\bigr{)}^{2}+(y_{2}-y_{1})^{2}\Bigr{]} \Bigr{\}}dx_{1}dy_{1}\\ &\quad=\exp(ikz)\cdot I_{H}(x_{2},y_{2})\otimes h_{z}(x_{2},y_{2} )=(-a^{*}\otimes h_{z}^{*}-a\otimes h_{z})\otimes h_{z}(x_{2},y_{2})\end{split} \tag{9}\]  
The Fresnel convolution has the following properties [31; 32]:  
1. \(h_{z}(x,y)\otimes h_{z}^{*}(x,y)=h_{z}(x,y)\otimes h_{-z}(x,y)=\delta(x,y)\) (\(\delta\)--Dirac delta function);
2. \(h_{z_{1}}(x,y)\otimes h_{z_{2}}(x,y)=h_{z_{1}+z_{2}}(x,y)\).  
Let us use these properties to convert (9) to the following form [31; 32]:  
\[U(x_{2},y_{2},z)=-a^{*}(x_{2},y_{2})-a(x_{2},y_{2})\otimes h_{z2}(x_{2},y_{2}) \tag{10}\]In case of an opaque particle, the complex field amplitude in the plane of the real image of a particle is as follows:  
\[\mathrm{U(x_{2},y_{2},z)}=-\mathrm{a(x_{2},y_{2})-a(x_{2},y_{2})\otimes h_{2z}(x _{2},y_{2})}=-\mathrm{a(x_{2},y_{2})\otimes(\delta+h_{2z}(x_{2},y_{2}))}, \tag{11}\]  
where \(\delta\)--Dirac delta function and \(a(x,y)=\begin{cases}1,\text{ }if\text{ }(x,y)\in particle\\ 0,\text{ }if\text{ }(x,y)\
otin particle\end{cases}\)--real function.  
According to the convolution theorem, the Fourier image of the convolution of two functions is equal to the product of their Fourier images:  
\[\mathcal{F}^{+}(\mathrm{U(x_{2},y_{2},z)})=\mathcal{F}^{+}(-\mathrm{a(x_{2},y _{2})})\mathcal{F}^{+}(\delta+h_{2z}(x_{2},y_{2})). \tag{12}\]  
If we multiply this expression by the inverse value \(\mathcal{F}^{+}(\delta+h_{2z}(x_{2},y_{2}))\), we obtain the Fourier image of the complex amplitude of the field corresponding to the real particle image:  
\[\mathcal{F}^{+}(-\mathrm{a(x_{2},y_{2})})\mathcal{F}^{+}(\delta+h_{2z}(x_{2}, y_{2}))\cdot\frac{1}{\mathcal{F}^{+}(\delta+h_{2z}(x_{2},y_{2}))}=-\mathcal{F}^{+} (\mathrm{a(x_{2},y_{2})}). \tag{13}\]  
It is now quite easy to obtain a field distribution by applying the inverse Fourier transform:  
\[\mathcal{F}^{-}\big{(}\mathcal{F}^{+}(\mathrm{a(x_{2},y_{2})})\big{)}=\mathrm{ a(x_{2},y_{2})}. \tag{14}\]  
Thus, an image free from the twin-image effect is formed in the plane of a particle image reconstructed from a digital hologram as a result of the interaction of the Fourier image of the complex field amplitude with the filter, the transmission of which is determined by the relation [31]:  
\[F(u,v)=\frac{1}{\mathcal{F}^{+}(\delta+h_{2z}(x,y))}=\frac{1}{1+exp[2i\pi \lambda z(u^{2}+v^{2})]}. \tag{15}\]  
where \(u\),\(v\)--spatial frequencies.
## 3 Results and Discussion
### Main Effect Parameter Analysis
#### 3.2.2 Effect of Catalyst Concentration  
Figure 3 explains the effect of KOH catalyst concentration (varied between 0.5 to 1.5 wt.%) on the performance of biodiesel yield. Yield showed an increasing trend with the increased concentration of KOH catalyst up to 1.1 to 1.2 wt.% and thereafter the trend decreased. Excess concentration of catalyst poses a difficulty in the separation of the aqueous layer during water washing of methyl ester and dissolves the methyl ester formed in it. This effect could be due to the increased saponification reaction resulting in the formation of soap, thus reducing the biodiesel yield. The biodiesel obtained with an excessive amount of catalyst is very viscous, unstable, and readily solidified and cannot be used as fuel in CI engines. Moreover, an inadequate concentration of KOH in the reaction resulted in decreased methyl ester conversion [38,51]. Figure 3 shows the impact of the methanol-to-oil molar ratio, which showed a dominant effect over that of the catalyst concentration.
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 2 Related Works  
In recent years, substantial academic attention has been directed towards the intersection of blockchain technologies and digital twin systems, driven by the increasing need for enhanced data integrity, transparency, and security in industrial applications. The paper [15] provides a comprehensive survey on blockchain applications in industries, highlighting how blockchain's decentralized and immutable nature can address critical challenges in transparency, traceability, and security. Their work emphasizes the transformative potential of blockchain for ensuring accountability and trust in data sharing, particularly in resource-constrained environments.  
Further, Ref. [16] explores the integration of digital twins and blockchain in the context of construction projects, emphasizing how blockchain enhances accountability in information sharing, thus enabling more trustworthy decision-making and reducing risks of data manipulation. Their framework offers a novel approach to mitigating conflicts of interest in large-scale projects by leveraging the blockchain's distributed ledger capabilities to ensure traceability and access control.  
Another critical contribution is the work by [6], who conducted a systematic review on the integration of blockchain and digital twins in smart-built environments. They highlighted how blockchain enhanced the trustworthiness of digital twin systems by providing mechanisms for secure and verifiable data exchange, particularly useful in environments characterized by decentralized operations and distributed data.  
Furthermore, Ref. [4] discusses digital twin security threats and countermeasures, emphasizing the necessity for robust security frameworks in digital twins. Their findings underscore the importance of blockchain's role in mitigating security risks, such as unauthorized access and data tampering, which are especially critical in industrial applications like manufacturing processes and smart cities.  
Overall, these studies collectively underscore that the integration of blockchain and digital twins not only addresses technological challenges but also significantly enhances data security, accountability, and efficiency in industrial applications. They demonstrate that leveraging blockchain for digital twins helps to foster more trustworthy and secure systems in environments that demand high degrees of data transparency and integrity.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 5 Applicability of Fault Diagnosis Methods for the High-Speed Train  
In summary, the fault diagnosis of each key system of high-speed trains is mainly realized by fault feature extraction and fault pattern recognition, and the main methods used are shown in Figure 11. In addition, the applicability of the same method in different  
Figure 10: Fault diagnosis method of information control system: (**a**) support vector regression (SVR) and Kalman filter (KF) [97]; (**b**) train safety sensor network (TSSN) [96]; (**c**) deep belief network (DBN) [98].  
systems was categorized, as shown in Table 1. From Table 1, the Kalman filter can be applied to the fault diagnosis of the bogie system and the information control system at the same time. Secondly, support vector machines can be used not only in the bogie system (bearings, suspension system, and wheels) but also in the traction system and brake system fault diagnosis. Thirdly, the principal component analysis can enable not only the effective fault diagnosis of bogie systems (bearing and wheel) but also the fault diagnosis of information control systems. Additionally, morphological filters are mainly used in the fault diagnosis of bogie systems (bearing, cardan shaft, wheel) and information control systems. Furthermore, convolutional neural networks are mainly applied to the fault diagnosis of bogie systems and electrical systems. Therefore, the Kalman filter, support vector machine, principal component analysis, morphological filter, and convolutional neural network are widely used in train fault diagnosis and have good applicability and robustness in different systems. Moreover, the main methods used in the fault diagnosis of bogie systems are empirical modal decomposition, variational modal decomposition, wavelet transform, singular value decomposition, and support vector machines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Key Systems** \\ \hline Empirical modal decomposition & Bogie system (bearing, gear, suspension system, wheel), etc. \\ Variational modal decomposition & Bogie system (bearing, gear, suspension system), etc. \\ Wavelet transform & Bogie system (bearing, gear, cardan shaft), traction system, etc. \\ Singular value decomposition & Bogie system (bearing, gear, cardan shaft, suspension system), etc. \\ Kalman filter & Bogie system (suggension system), information control system, etc. \\ Support vector machine & Bogie system (bearing, suspension system, wheel), traction system, braking system, etc. \\ Principal component analysis & Bogie system (bearing, wheel), information control system, etc. \\ Morphological filter & Bogie system (bearing, cardan shaft, wheel), \\ Convolutional neural network & information control system, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Applicability of main fault diagnosis methods.  
Figure 11: The main fault diagnosis methods used in the key systems of high-speed trains.
## 6 Proposed Scheme
### Dynamic Smart Grid Device Addition Process  
When new SG device \(SD_{i}\) wants to register with the SG environments, the following steps must be performed and detailed steps are as follows. The main goal of this process is adding a new SG device to provide flexibility in SG environments. The detailed steps of this process are given below.  
1. First, \(TA\) chooses a new \(ID_{i}^{new}\) to the \(SD_{i}\) over secure channel. After receiving the message, \(SD_{i}\) sends \(RID_{i}\) to the \(TA\) over secure channel. Then, \(TA\) generates a random number \(a_{i}^{new},x_{i}^{new}\).
2. After that, \(TA\) computes \(RID_{i}^{new}=h(ID_{i}^{new}||a_{i}^{new})\), \(X_{i}^{new}=h(RID_{i}^{new}||K_{i}||x_{i}^{new})\), \(A_{i}^{new}=X_{i}^{new}\oplus h(RID_{i}^{new}||a_{i}^{new})\), and \(B_{i}^{new}=h(RID_{i}^{new}||X_{i}^{new})\). Finally, \(TA\) stores \(\{x_{i}^{new},RID_{i}^{new}\}\) in secure database and sends its to the \(SD_{i}\) over secure channel.
3. After receiving the message, \(SD_{i}\) computes \(C_{i}^{new}=h(ID_{i}^{new}||B_{i})\oplus a_{i}^{new}\) and stores \(\{A_{i}^{new},B_{i}^{new}\}\) in the memory.
## 4 Experimental Implementation and Results  
### Datasets  
The excavator database for activity recognition is developed from five types of earthwork operations: approach, digging, dumping, idling, and leveling, utilizing an external camera and lidar scanner. These sensors are placed at various positions and angles to collect the different perspectives of the excavators; however, the external camera provides visual data from certain angles. At the same time, the point cloud provides the edge to change the excavator's perspective after collecting it and offers the best view for better learning of the training model. A total of 495,000 labeled image frames for the activities approach, digging, dumping, idle, and leveling, are collected to develop the database for activity recognition. For this research, a total of 980 video clips are used, with each activity comprising 98 video clips from each external camera stream and the point cloud, all with the same timestamp. The dataset is developed from different construction sites under various environmental conditions and synchronized. The dataset for multi-stream was split into two parts. Seventy percent of the data, consisting of 68 video clips for each activity from each stream, totaling 680 activity video clips, was allocated for training. The remaining 30%, which includes 30 videos per activity from each stream, totaling 300 video clips for each, was used for testing. This dataset includes the four construction site video frames under different environmental conditions. The RGB camera and point cloud video frames are time-synchronized for dataset input, as shown in Figure 5. Each activity contains 15 video frames in each stream. The number of video frames for each activity in both streams is counted accurately to estimate the optical stream for both streams. An overview of the data distribution is shown in Table 3.
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 4 Load Forecasting Methodology
### Feature Selection  
#### 4.3.1 Pearson Correlation Coefficient  
Pearson correlation analysis is used in this method. The Pearson correlation coefficient captures the degree of correlation between the two variables \(X,Y\) and is calculated as follows:  
\[\rho_{X,Y}=\frac{E(XY)-E(X)E(Y)}{\sqrt{E(X^{2})-(E(X))^{2}}\sqrt{E(Y^{2})-(E(Y ))^{2}}} \tag{23}\]  
when the correlation coefficient is 0, the two variables \(X,Y\) are not correlated; when the correlation coefficient is \((0,1]\), the two variables \(X,Y\) are positively correlated; when the correlation coefficient is \([-1,0)\), the two variables \(X,Y\) are negatively correlated. The stronger the correlation between A and B, the larger the absolute value of PCC, as shown in the range of coefficients and the degree of correlation in Table 2.  
In this paper, PCC values between the characteristic sequences of influencing factors and the power load sequences are calculated, which indicates the degree of influence of these factors on the power load. The sequence of influencing factors with a high degree of correlation is used as the feature values and input into the second stage of the model in this paper for forecasting.  
In the case of the calculations undertaken in this paper, the Pearson correlation analysis heat map of the power load and the influencing factors for Place 1 is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline Coefficient range & 0–0.2 & 0.2–0.4 & 0.4–0.7 & 0.7–1 \\ \hline Degree of correlation & Weak & Moderate & Strong & Extremely strong \\ \hline \end{tabular}
\end{table}
Table 2: Range of correlation coefficients.  
Figure 4: Heat map of quantitative analysis based on PCC for Place 1.
# The Dichotomy of Neural Networks and Cryptography: War and Peace
## 3 War: Neural Computing against Cryptography
### Cryptanalysis  
Cryptanalysis is the processes of finding vulnerabilities in ciphers by studying their operation. This is usually completed with knowledge of some combination of the cipher text, plain text, and key. Though it is often associated with attacks on cryptogrphic systems,cryptanalysis can be used to audit current systems to improve them. In some cases, cryptanalysis can be considered as an aggressive activity against cryptosystems, because it can be conducted in an initial stage of an attack. There have been some attempts at creating cryptanalysis attacks to directly decrypting cipher text using NNs. [PERSON] et al. found an early cryptanalysis application for NNs reordering blocks of audio data scrambled with time segment permutation scrambler [41]. Their Hopfield network, trained with simulated annealing with a \"genetic algorithm\" approach on a small set of words, was able to meet the performance of exhaustively searching for a solution [41].  
[PERSON] et al. attempted to apply neural nets to decrypt 8 bit cipher texts from a substitution permutation cipher using the same key for the training set and the test set [42], while they did not find complete success decrypting cipher texts this way, they found it possible to decrypt 232 out of the 256 possible cipher texts with the average number of wrong bits in each erroneous decryption being 1.3.  
Other papers focused on applying NNs to determine some amounts of the key used to encrypt a cipher text, possibly reducing the amount of time needed to guess the correct key. [PERSON] and [PERSON] propose an attack specific to Feistel block ciphers using NNs to determine the key but was limited to only the cipher the neural net is designed for [43].  
[PERSON] and [PERSON] studied how effective NNs could be at cryptanalyzing S-DES ciphers [44]. In particular they attempted to determine key bits based on a given plain text and it's corresponding cipher text. Their test found that certain bits of the key were more easily determined due to problems with the substitution box being used by the cipher.  
[PERSON] et al., utilized NNs to quantify the strength of ciphers by predicting cipher text based on the plain text [45]. Using this approach only ciphers as strong as DES-3 were successfully mimicked, though the researchers suggested that using a neural net architecture that more closely reflected that of the cipher being analyzed would be more capable.  
NNs have also been used to analyze cipher operation in order to encrypt data. [PERSON] et al. tested the application of neural nets to directly replicate the functionality of a cipher by training some models on cipher, plain text pairs with and without knowledge of the key [46]. [PERSON] et al. proposed a different system, training a NN to analyze the output of multiple time-delay chaotic system in order to approximate its output and synchronize a second multiple time-delay chaotic system as a means for establishing an encrypted communication over a public channel [47].
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 2 Methodology
### Finite Element Analysis
#### 2.4.1 Viscoelastic Constitutive Model  
In fact, there is a difference between the HTL and the new leveee. On the one hand, owing to long time compaction and consolidation, the compressibility of the old leveee and old foundation is very low because the consolidation effect of the old levee and the old levee foundation have basically been completed. For the case study, the deformation of the HTL mainly comes from the rheology of backfill.  
Hence, the viscoelastic constitutive model is adopted in this study. The model can describe the viscoelastic behavior of shear and volume change. In addition, the differential settlement increases with time and finally tends to be stable. However, the viscoelastic constitutive model can describe the shear and time effect. Hence, the viscoelastic constitutive model is suitable to describe the settlement rules of the HTL.  
However, it is very difficult to determine the parameters of the existing complex viscoelastic model, and the settlement of the HTL gradually decreases with time and finally tends to be stable. Therefore, in this study, it is more appropriate to choose the merchant model to describe settlement.  
The one-dimensional merchant model is shown in Figure 5. It is composed of a Hook body and Kelvin body, as shown in Figure 5. Its model parameters include instantaneous elastic modulus \(E_{H}\), viscoelastic modulus \(E_{K}\), and viscous coefficient \(\eta_{K}\).
## 5 Numerical Analysis Results and Comparison of Strong Wind Analysis with Real Tornado Action
### Wind Speed Analysis Based on a Damaged Road Sign  
There were many objects that were also destroyed in the vicinity of the destroyed building., These were primarily cars, trees, letter boxes, and road signs. An example road sign damaged during the 2018 Georgia tornado. has been shown in Figure 16.  
The Strength of a Road Sign under Load Caused by a Tornado Was Tested in this Article. The course of the investigation, including the analysis of damage based on research documentation, the creation of a computational model, and the comparison of the values derived from a strong wind with the strength of the material, was carried out in the same way as with the previously analyzed building.  
The model of the sign was calculated in the same way as the residential building, and a wind load perpendicular to the surface of the sign was applied. The pressure maps for the analyzed object are shown in Figure 17.  
The round pipe RP 51 \(\times\) 2.9 cm was used for the calculations.  
The ensuing results are presented in Figure 18.  
Figure 16: Analyzed road sign. Photo taken after destruction [Foto made by authors of the article].  
Figure 17: Model created in the calculation program. Maps of the pressure resulting from the applied wind load (**a**) front, (**b**) back.  
The profile was checked in the calculation program for damage caused by exceeding the cross-section load capacity, buckling at a wind speed of 49.58 m/s.  
The sum of the quotients of the internal forces to the bearing capacity  
\[\frac{N_{Ed}}{N_{Rd}}+\frac{My_{Ed}}{My_{c,Rd}}+\frac{Mz_{Ed}}{Mz_{c,Rd}}=1.00 \tag{18}\]  
The results show that with wind gusts reaching a speed of 49.58 m/s, the analyzed structure works at full effort and can be destroyed at any time.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 2 Geological Setting  
The Lower-Chelif Basin comprises three main plains, the Mleta, Habra, and Chelif plains, separated by a series of plateaus and anticlines [6; 28]. The Oran region is located north of the Mleta Plain. It is bordered by the Mediterranean Sea to the north, the Murdjadjo mountain to the west, the Arzew Massif to the east, and the Great Sebkha to the south (Figure 2). In terms of lithology, the Oran region consists of Mio-Plio-Quaternary sediments lying over a Cretaceous bedrock, mainly composed of shales that outcrop to the west in Murdjadjo mountain. The sedimentary series starts with a Miocene formation composed of sandstones, limestones, and marks (e.g., [6; 29]). This formation is visible on the right flank of the Murdjadjo mountain and in the northern area of Oran city. The Miocene deposits increase significantly in thickness towards the middle of the Mleta plain [7]. The Miocene deposits are overlaid by a marine Pliocene formation consisting of sandy marks of varying thickness that increase towards the east [6; 29]. These deposits are visible to the east of Oran, south of Bir-El-Djir town (Figure 2). The sedimentary series ends with a Quaternary layer consisting of alluviums, limestone, and sands of the Pleistocene age [6; 29]. The thickness of these deposits can be determined from wide cliffs that extend across the northern part of the Oran region.  
Figure 2: A geological map of Oran and the enclosing region, modified from [6; 29]. Black filled circles are array locations. The nine available boreholes from F01 to F06 and SC01 to SC03 (blue triangles) and the seismic refraction profile PS01 show the thickness of the sediment layers and their velocity (Vs). The red lines are cross-sections.