esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 3 Identifying the Problem
### Case Studies of Cyber-Physical Attacks
#### 3.1.2 Stuxnet  
The Stuxnet worm, developed in 2010, had the reputation of being one of the most threatening and complex computer worms ever created at the time of its inception. Written primarily to target industrial control systems, the worm would propagate through a computer network and multiply itself without detection with the aim to reprogram PLCs and other similar industrial control system devices. Malicious instructions compiled by the hacker could then be executed on the infected systems, while the changes to the equipment programming were hidden to avoid detection by personnel operating or maintaining the equipment. Stuxnet is considered by Symantec as one of the most complex security threats ever analyzed, and dealing with it required extensive efforts from a large number of security analysts [27].  
Making use of a vast number of components to maximize the possible attack vectors, including among others, multiple zero-day exploits, antivirus evasion techniques, process injection and peer-to-peer updates, the Stuxnet worm succeeded in infecting thousands of computers around the world, in order to maximize the chances of eventually entering \"air-gapped\" control systems networks. This was made possible in particular by its ability to hide itself on removable drives. Making use of a vulnerability in the way shortcuts and.lnk files are processed on a computer (identified by Microsoft as MS10-046) [27; 28], the worm could replicate and propagate further within the computer and to external devices by itself. Since many industrial control systems were not designed with security in mind, the fact that no integrity checks were performed on messages received by these systems played a key role in the ability of the worm to infect systems without detection [8]. Once it had manifested itself on infected machines, the worm would regularly send encrypted updates to the Stuxnet command and control (C&C) servers with identification information of the infected systems, such as IP addresses, operating systems and computer names.  
The first variant of the Stuxnet worm was encountered in June 2009. This variant did not make use of any signed driver files, but in January 2010 the Stuxnet worm was found to make use of a driver that was digitally signed with a compromised certificate by the Realtek Semiconductor Corporation. In March 2010 the Stuxnet worm was found to exploit the vulnerability that was only identified as MS10-046 by Microsoft in August 2010.  
By the time Symantec issued a comprehensive analysis of Stuxnet, an estimated 100,000 hosts had been infected in over 25 countries, with infection rates of up to 6000 hosts per day [27].
## 4 Results and Discussion
The generated neural network was based on two preprocessing methods, namely, D-values and PCA. On comparing these preprocessing methods, the D-value method achieved a better accuracy of 73.4% for a noise variance of 0.04. Thus, reducing the number of features reduces the prediction accuracy. Validation and testing of the neural network were performed on the industrial loop CHEM loop1 from ISDB data.
## 4 Experimental Results
### Estimation of SoH with Regression-Based Approaches  
The regression results, which were prepared on the basis of information explained in detail, as pointed out in the Model Development section, are presented in Table 5. The results obtained using decision trees are demonstrated in Table 4. Decision trees appear to be more successful than regression models. Among these models, the ensemble bagged tree yielded the best results. Accordingly, it has been observed that other decision tree models produced similar results.
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 4 Experiments
### Error of the Method in Different Distances  
We evaluated the accuracy of the 3D PoR estimation at different distances. In the experiment, we carried out three main steps:  
(1) We firstly set up a necessary experimental environment. We fixed the gaze tracking system in an empty room, and then fixed six plane boards at six different distances. Each plane board were drawn with a cross.  
(2) We invited four people to participate in this experiment, and everyone were allowed to complete a personal calibration. Everyone had to look at each board through the gaze tracking system, and each person was allowed to look at each board 15 times.  
(3) Finally, the spatial coordinate of each board was calculated accurately by using scene cameras, and they were used to count the average errors of our method at different distances. At the same time,  
Figure 11: Placing a box in four different positions in front of the tester, and allowed this tester to watch the six visible corners of the box. The crosses in the graph represent the six visible corners of the box, and the colored grid planes show the two visible surfaces of the box. The scattered dots represent the location of the calculated points-of-regard by each fixation. This experiment shows that our method can calculate the 3D PoR coordinates when the system is used in a common scene.  
we also compared our method with another 3D PoR estimation method described in [32]. The experimental results are shown in Table 1, and all coordinate values are in the device coordinate system.  
By analyzing the experimental data of this experiment, our method is proved to be able to output the 3D PoR coordinates of the human eye when observing object at different distances with good accuracy. The error on \(Z\) is much bigger than \(X\) and \(Y\) directions. However, the results are still in line with our expectations for the proposed method.
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 3 Methods
### Fully Connected DL Architecture  
A simple, fully connected neural network performed the best with entity embeddings in the Kaggle-winning model [29]. That model outperformed more-complex approaches, including recurrent neural networks and their variants. Therefore, for simplicity, fully connected architectures were adopted for training the InSAR embeddings. Figure 4 illustrates the fully connected architecture adopted for this study. This model has ten hidden layers, with each layer made up of _Linear-ReLU-Batch Norm-Dropout_ layers. The first hidden layer starts with 1100 neurons, and in each hidden layer, the number of neurons reduces by 100. The hidden layer structure and the neuron size ratio are similar to the fully connected architecture adopted for training embeddings in [30; 31]. The output layer is a linear layer with a sigmoid activation function.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity** & **Cardinality** & **Embedding Size** \\ \hline Coherence & 1016 & 77 \\ Longitude & 248 & 35 \\ Latitude & 205 & 32 \\ Day & 30 & 11 \\ Month & 12 & 6 \\ Year & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The embedding size is calculated based on the size of the cardinality of the entities. The cardinality is the number of unique categories representing each entity.  
Figure 4: The fully connected architecture used in this study. The first hidden layer starts with 1100 neurons, and at each hidden layer, the number of neurons reduces by 100. The visualisation of the architecture is generated through the NN-SVG [46].  
Regularisation techniques such as Batch Normalisation (Batch Norm), weight decay, and Dropout have been demonstrated to be vital in aiding deep learning model generalisation and improving training performance [47]. Batch Norm is made up of a multiplicative and an additive bias-like term [48]. [PERSON] et al. (2018) [49] demonstrated the substantial regularising effect of Batch Norm. Therefore, a Batch Norm layer handles the continuous variables and is included in the hidden layers, as illustrated in Figure 4. The embedding Dropout was set to 0.04, the Dropout in the first hidden layer was 0.001, then 0.01 for the rest of the hidden layers. Finally, stochastic gradient descent with momentum was adopted for minimising the loss function between predictions and measurements. A simple loss function, namely the mean-squared error, was adopted for training. A one-cycle policy learning rate with cosine annealing was adopted with a maximum learning rate of 0.01.
## 2 Mathematical Model
### Optimal Design of Unimorph Substructure  
In many structural design problems, first, the eigen frequency often needs to be constrained to the initially given value. So, one more constraint (for a state variable--eigen frequency) is added. An optimal design problem of the cantilever vibration energy harvester can be formulated as follows: to find a vector of design variables h maximizing the objective function (12) and satisfying the equation of state (13) and constraints (14) and (15). The objective function is integral of normal strain on the layer of unimorph substructure.  
The general problem of optimal design can be formulated as follows: maximize objective function  
\[max\;\Psi_{0}(h) \tag{12}\]  
with subject to state equation  
\[K(h)\varphi=\zeta M(h)\varphi \tag{13}\]  
and constraints  
\[\Psi(\zeta,h)\geq 0 \tag{14}\]  
and  
\[\forall h>0\;;\;\forall\zeta>0 \tag{15}\]  
where:  
\(\Psi_{0}\)--objective function,  
\(h\)--vector of design variables (thickness of the cantilever in this case),  
\(K(h)\)--stiffness matrix,  
\(M(h)\)--mass matrix,  
\(\varphi\)--eigenvector of transverse vibration,  
\(\zeta\)--eigen frequency--state variable,  
\(\varphi\) and \(\zeta\) together comprise eigenmode.  
Inequality/function (14) in a general problem of optimal design is a constraint for state variables. From the mechanical point of view, it can describe constraints for eigenvalues, strains, stresses, and displacements. The mechanical meaning of the conditions (15) are that all thickness and eigen frequencies of the structure must be positive.  
The objective function for maximize normal strain in the upper layer takes the following form:  
\[Max\;\Psi_{0}(h(x))=\int_{0}^{L}\;\left(-\frac{h(x)}{2}\;\frac{\partial^{2}v( x)}{\partial x^{2}}\right)^{2}dx \tag{16}\]  
or in finite element formulation,  
\[Max\;\Psi_{0}(h)=\sum_{i=1}^{m}\;\left(-\frac{h_{i}}{2}\;[B_{i}]\{u_{i}\} \right)^{2} \tag{17}\]  
where  
\[[B]=\left[-\frac{6}{L^{2}}+\frac{12x}{L^{3}},\;\;\;\;\;\frac{6}{L^{2}}-\frac{ 12x}{L^{3}},\;\;\;\;\;-\frac{4}{L}+\frac{6x}{L^{2}},\;\;\;\;\;-\frac{2}{L}+ \frac{6x}{L^{2}}\right] \tag{18}\]  
\(i\) is finite element and i = 1,m,  
\([B_{i}]\)--beam element strain--displacement matrix,  
\(\{u_{i}\}\)--element nodal displacement vector.  
Integrand function (16) is in square due to possible eigenvector sign variation due to the solution of eigenvalue problem (13).  
The thickness vector \(h\) of unimorph substructure is a design variable and is limited by finite element thickness:  
\[h_{i}^{min}\leq h_{i}\leq h_{i}^{max} \tag{19}\]  
where \(h_{i}^{max}\) and \(h_{i}^{min}\) are the allowable maximum and minimum element thickness values.  
\[\zeta_{j}^{min}\ \leq\zeta_{j}\leq\zeta_{j}^{max} \tag{20}\]  
where  
j = 1 number of eigenfrequency,  
\(\zeta^{max}\) and \(\zeta^{min}\)--allowable interval of eigen frequency value.  
The formulated optimization problem was solved using the gradient projection state space method of finite dimensional optimal design with sensitivity analysis for the thickness shape optimization of the unimorph substructure with fixed first eigenfrequency.
## 2 Existing Algorithms
### Random Projection  
The background for RP [22] comes from the Johnson-Lindenstrauss lemma [38]. The lemma states that points in a high-dimensional space can be projected to a lower dimension space while approximately preserving the distances of the points, when the projection is done with a matrix whose elements are randomly generated. Hence, for an \(N\times M\) dataset \(\mathbf{X}\), let \(\mathbf{R}\in M\times P\) be a random matrix. Then, the random projected data matrix \(\widetilde{\mathbf{X}}\) is given by \(\widetilde{\mathbf{X}}=\frac{1}{\sqrt{P}}\mathbf{X}\mathbf{R}\). The random matrix \(\mathbf{R}\) consists of independent random elements (\(r_{ij}\)) which can be drawn from one of the following probability distributions [22]: \(r_{ij}=+1\) with probability \(1/2\), or \(-1\) with probability \(1/2\); or \(r_{ij}=+1\) with probability \(1/6\), \(0\) with probability \(2/3\), or \(-1\) with probability \(1/6\).
## 2 SPH-FEM Coupling Analysis Method
### SPH and FEM Contact Settings  
The key to coupling SPH with FEM is that the intersection can effectively transfer SPH particle information to the finite element mesh. The common method is to set up interfacial contacts to bond the SPH particles to the finite element mesh to achieve displacement coordination between the two methods in a point-surface glued contact. The contact parameters are adjusted by setting the keyword *CONTROL_CONTACT. The contact schematic is shown in Figure 2.  
Figure 1: SPH-FEM simulation of tunnel water inrush.
## 5 Results and Discussion
### Model Performance Comparision  
The commonly used machine- and deep-learning-based CF models and the proposed model were comparatively analyzed. For comparison of all models, the goodbooks-10k dataset was used, and all recommendation models were evaluated using the same evaluation metrics. The descriptions of the selected models are as follows.  
ALS: A system that uses the matrix factorization technique and square loss function [45].  
SVD: A commonly used recommendation system with matrix factorization [46].  
Fast AI Embedding Dot Bias: Fast AI is an upper wrapper of PyTorch made by [PERSON], and it supports the CF function using deep learning. The default model parameters were used in the performance comparison [47].  
A simple algorithm for recommendation (SAR): The SAR defines the similarity based on the co-occurrence of data in all input data. In addition, it measures the strength of the relationship between the items that have interacted previously in the data. In the performance comparison, the default model parameters were used [48].  
For a fair comparison, the Recommenders, an open-source library of models published by Microsoft, was used to pool all compared models. The corresponding open-source library was in GitHub. Additionally, the proposed model used pandas, NumPy, and TensorFlow libraries.  
For the comparative analysis experiment, the dataset was split randomly [49] to obtain a training set of 80%, a validation set of 10%, and a test set of 10% of the data. The training dataset was used in model training, and the validation dataset was used to tune the hyperparameters.
# Edge and Cloud Computing in Smart Cities
## 3 Enabling Technologies
### Advanced Communication Networks  
Effective communication networks are crucial for enabling seamless interaction between edge nodes, cloud resources, and end-user devices in smart cities. The performance of edge-cloud computing systems depends on low-latency, high-bandwidth connectivity to support real-time applications [78; 79; 80].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Architecture** & **Latency** & **Scalability** & **Fault Tolerance** & **Energy Efficiency** & **Computational Complexity** \\ \hline \multirow{6}{*}{Hierarchical} & Moderate. & Moderate–High. & Low. Cloud & Moderate. Edge & Low. Task \\  & Multi-layer processing & It can scale by adding cloud resources but is constrained by layer dependencies. & & & reduces energy & allocation \\  & Low. Cloud & & dependency & & consumption, & follows \\  & Low. Cloud & & resources a single point of failure, reducing reliability. & & but inter-layer & predefined \\  & Low. Cloud & & point of failure, reducing reliability. & & communication & deterministic \\  & Low. & & & & overhead & execution \\  & Decentralized execution & High. Adaptive task allocation enables individual redistribution, scalability & High. &  
First, 5G and 6G technologies play a pivotal role in improving network efficiency by providing ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), and enhanced mobile broadband (eMBB). These advancements ensure that smart-city applications can handle vast amounts of data with minimal transmission delays [81].  
To further enhance network efficiency, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enable dynamic network configuration and resource optimization. SDN decouples the control and data planes, allowing for flexible network management, while NFV virtualizes network functions, reducing hardware dependencies and improving scalability [82; 83; 84].  
Another critical factor is interference management, which affects signal quality and data throughput. High interference levels can cause network congestion and delays, impacting real-time applications. By implementing intelligent traffic routing and adaptive bandwidth allocation, communication networks can mitigate interference issues, ensuring reliable data exchange across edge-cloud infrastructures [85; 86].  
Overall, advanced communication technologies facilitate efficient data transmission in edge-cloud environments, making them fundamental to the success of latency-sensitive smart-city applications.
## 3 The Proposed Distributed SPARQL Query Processing Scheme  
In Section 3, we introduce a distributed SPARQL query processing scheme considering Spark environments to reduce join and communication costs that occurred during query processing. As volumes of the RDF graphs increase, distributed storage and an efficient query processing scheme are required. However, the existing schemes did not consider the communication and join costs incurred during query processing, entailing a large cost during query processing of a large amount of RDF. In addition, the disk I/O cost increases as data size increases. Thus, a query processing scheme is needed in consideration of the many costs incurred during query processing. We proposed a distributed stored RDF graph query processing scheme in the Spark environment. It reduces disk I/O costs through query processing in the Spark environment, which is a distributed in-memory environment. It also reduces the communication cost through grouping the divided SPARQL subqueries when a query on distributed RDF graph is processed. In addition, join, and communication costs are reduced by setting a query processing order through efficient query execution paths.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Propagation Models for Simulation
#### 3.15.5 Rayleigh Fading Model  
If there is no LOS and we have only multi-path features, the Rayleigh propagation model as shown in Figure 10 is used to simulate the scenario. Because several pathways can be combined, productively or ineffectively, this model encompasses extensive changes in received message strength [51]. The surroundings have a major impact on the amplitude, delay, and phase shift of these variables. A deterministic model in which a particular alteration is implemented is also required by the Rayleigh model.  
\[P_{r_{ Rayleigh}}(d)\sim Rayleigh(P_{r_{det}}(d)) \tag{15}\]  
This Equation (15) can also be transformed with greater detail as follows:  
\[P_{r}(d)=P_{r_{det}}(d_{o})\times 10^{PL(d)}\log(1-unif(0,1)) \tag{16}\]  
Here, the power loss factor is represented as  
\[PL(d)=-\alpha log10(\frac{d}{d_{o}}) \tag{17}\]
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### Problem Summary  
In summary, the disengagement strategy for phasing the proposed deployment architecture requires careful consideration of several interdependent factors, including the initial orbit, the disengagement maneuver and its sequence, and the differential drag control method. The initial orbit serves as a mission design variable, constrained by launch availability and mission objectives. Given a specific initial orbit, the phasing sequence must be planned to achieve the final angular configuration of the constellation.  
The desired final angular states define the constellation's operational layout. The deployment process involves determining the disengagement velocity \(\Delta v\), the angular positions \(\theta_{k,\ i_{f}}\) at intermediate stages, and the corresponding acceleration and deceleration durations \(\left(\Delta t_{a},\ \Delta t_{b}\right)_{k_{max}}\) for each satellite.  
The phasing problem is inherently recursive, as the initial states and dynamics of the preceding stage shape the final conditions at each stage. Consequently, a bottom-up methodology is adopted, beginning with the known final configuration and propagating backwards through the deployment stages. This requires identifying appropriate insertion points at stage \(k_{Max}\) and determining the conditions under which each satellite should disengage and begin its phasing trajectory. Once the initial angular states and disengagement velocities are established for each stage, the total phasing time is obtained by summing the durations across all stages.  
This formulation highlights the coupled nature of the deployment-dispersal dynamics, where each parameter influences the overall phasing performance. The following section outlines the simulation-based methodology used to assess this deployment problem.
## 2 Materials and Methods
### Strains and Cultivation Conditions  
The following reference strains were used in the study: _Staphylococcus aureus_ ATCC 29213 and _Escherichia coli_ ATCC 25922. Bacteria were inoculated on Columbia agar with 5% sheep blood (bioMerieux, Warsaw, Poland) and then allowed to grow for 24 h at 37 \({}^{\circ}\)C in an aerobic environment. EOs from all parts of _T. speciosa_, including leaves, flowers, and roots, were used for the antimicrobial activity, and we used pure thymol compound (1 \(\mu\)g/mL) as a standard antibacterial drug.  
Determination of the Minimum Inhibitory Concentration (MIC) of Essential Oils against Bacteria  
Determination of the minimum inhibitory concentration (MIC) of EOs against bacteria. The MIC of the EOs against bacteria was determined by serial dilution in Mueller-Hinton broth (MHB, Sigma-Aldrich, Darmstadt, Germany), according to the recommendations of the Clinical and Laboratory Standards Institute (protocol M07-A9) [17]. The stock solution of the tested oils was prepared with the addition of Tween 80 (1%) (Sigma-Aldrich, Darmstadt, Germany), obtaining a concentration range of 1.0 to 125 \(\mu\)L/mL. A total of 50 \(\mu\)L of sequential concentrations of the oils were added to a 96-well microplate. Then, 50 \(\mu\)L of a bacterial suspension with a concentration of 10\({}^{6}\) CFU/mL was added to each well of the microplate. After a 24-h incubation at 37 \({}^{\circ}\)C, the MIC for individual oils was determined by adding 20 \(\mu\)L of a 0.02% resazurin solution (Sigma-Aldrich, Darmstadt, Germany) to the wells [18]. A color change from navy blue to pink after 3 h of incubation with resazurin at 37 \({}^{\circ}\)C indicated the presence of bacteria. The first well in which the navy blue color persisted determined the MIC value. In addition, a positive control (MHB + bacterial suspension of _S. aureus_ or _E. coli_) and control of the purity of the medium (MHB) were performed. For each oil, the test was carried out in duplicate.