esa-sceva/satcom-chunk-collection huggingface dataset:
# A Partitioned Rigid-Element and Interface-Element Method for Rock-Slope-Stability Analysis
## 2 Theory
### Equations of Rigid Element  
As shown in Figure 1b, The boundary of the rigid element consists of three parts: the stress boundary \(S_{\sigma}\), the displacement boundary \(S_{u}\), and the interface boundary \(S_{i}\). Since there is no deformation inside the rigid element, the strain is 0. According to the principle of virtual displacement, the equivalent integral weak form of equilibrium equations and boundary conditions of the rigid element can be obtained, which has the following matrix expression:  
\[\int_{S_{\sigma}}\delta u^{T}\mathrm{d}S+\int_{S_{i}}\delta u^{T}\mathrm{d}S+ \int_{V}\delta u^{T}f_{g}\mathrm{d}V=0 \tag{1}\]  
Figure 1: Schematic diagram of the PRE-IE method.  
where \(\delta\mathbf{u}\) is the virtual displacement, \(\mathbf{t}\) is the surface force on the \(S_{\sigma}\), \(\mathbf{F}\) is the surface force on the \(S_{i}\), \(f_{g}\) is the body force, and \(V\) is the integral region of the rigid element. According to the principle of minimum potential energy, Equation (1) can be expressed as  
\[\delta\Pi_{P}^{r}=0 \tag{2}\]  
where \(\Pi_{P}^{r}\) is the potential energy of a rigid element, which can be expressed as  
\[\Pi_{P}^{r}=\int_{S_{\sigma}}\mathbf{u}^{T}\mathrm{d}S+\int_{S_{i}}\mathbf{u}^{T} \mathrm{d}S+\int_{V}\mathbf{u}^{T}f_{g}\mathrm{d}V \tag{3}\]  
The displacement \(\mathbf{u}\) of any node on a rigid element can be expressed as  
\[\mathbf{u}=\left[\begin{array}{c}u_{x}\\ u_{y}\\ u_{z}\end{array}\right]=\left[\begin{array}{cccccc}1&0&0&0&z-z_{c}&y_{c}-y \\ 0&1&0&z_{c}-z&0&x-x_{c}\\ 0&0&1&y-y_{c}&x_{c}-x&0\end{array}\right]\left[\begin{array}{c}u_{x}^{c}\\ u_{y}^{c}\\ u_{z}^{c}\\ \sigma_{x}^{c}\\ \sigma_{y}^{c}\\ \theta_{z}^{c}\end{array}\right]=\mathbf{W}\mathbf{\gamma}^{c} \tag{4}\]  
where \(\mathbf{W}\) is the rigid displacement transformation matrix, \(\mathbf{\gamma}^{c}\) is the displacement of the rigid body centroid, and \((x_{c},y_{c},z_{c})\) is the coordinates of the rigid body centroid. Substituting Equation (4) into Equation (3), the potential energy of rigid element can be written as follows:  
\[\Pi_{P}^{r}=\int_{S_{\sigma}}\mathbf{\gamma}^{c^{T}}\mathbf{W}^{T}\mathrm{d}S+\int_{S _{i}}\mathbf{\gamma}^{c^{T}}\mathbf{W}^{T}\mathrm{F}\mathrm{d}S+\int_{V}\mathbf{\gamma}^{c ^{T}}\mathbf{W}^{T}f_{g}\mathrm{d}V \tag{5}\]
# Dirac Equation-Based Formulation for the Quantum Conductivity in 2D-Nanomaterials
###### Abstract  
Starting from the four component-Dirac equation for free, ballistic electrons with finite mass, driven by a constant d.c. field, we derive a basic model of scalar quantum conductivity, capable of yielding simple analytic forms, also in the presence of magnetic and polarization effects. The classical Drude conductivity is recovered as a limit case. A quantum-mechanical evaluation is provided for parabolic and linear dispersion, as in graphene, recovering currently used expressions as particular cases. Numerical values are compared with the ones from the literature in the case of graphene under d.c. applied field. In particular, the effect of  
conductance are neglected. All real life loss mechanisms, such as intra-band transitions, interaction with the substrate, etc., are subsumed by a global finite life-time.  
Considering just one-dimensional motion under the influence of a constant field, we then derive analytically the eigenvectors of the Dirac equation and find the resulting quantum current.  
A Kubo-type approach is followed, including density of states and Fermi distribution, and obtain relatively simple analytical expressions valid for fields of moderate strength.  
Numerical results are provided for the case of graphene conductivity, under d.c. condition.  
We first consider the case of applying a constant bias, that is \(V(x)=V_{0}\), in order to compare the numerical results from the present method with respect the ones obtained by implementing the Kubo-Drude (KD) formulation [33; 34]. The comparison shows excellent agreement, thus confirming the robustness of the present formulation.  
We then analyze the case of applying an electric field superposing the uniform bias, \(V(x)=V_{0}+\overline{E}x\), as shown in Figure 1. The latter situation can be analyzed only by using the present Dirac-based formulation (DF); in fact, the KD formulation can only deal with the case of applying a constant bias along the structure. Moreover, the KD formula is based on a scalar wave function, and does not provide, e.g., any information about the particles spin. Indeed, the present formulation relies on a rigorous four-component spinor description, that can be used to provide, depending on the kind of problems (electric/magnetic polarization) a proper formulation for the spin wave propagation.
###### Abstract  
The aim of the present work is the analysis of a solar-driven unit that is located on the non-interconnected island of Kythnos, Greece, that can produce electricity and green hydrogen. More specifically, solar energy is exploited by parabolic trough collectors, and the produced heat is stored in a thermal energy storage tank. Additionally, an organic Rankine unit is incorporated to generate electricity, which contributes to covering the island's demand in a clean and renewable way. When the power cannot be absorbed by the local grid, it can be provided to a water electrolyzer; therefore, the excess electricity is stored in the form of hydrogen. The produced hydrogen amount is compressed, afterward stored in tanks, and then finally can be utilized as a fuel to meet other important needs, such as powering vehicles or ferries. The installation is simulated parametrically and optimized on dynamic conditions, in terms of energy, exergy, and finance. According to the results, considering a base electrical load of 75 kW, the annual energy and exergy efficiencies are found at 14.52% and 15.48%, respectively, while the payback period of the system is determined at 6.73 years and the net present value is equal to EUR 1,073,384.  
solar energy; ORC; green hydrogen; water electrolysis; optimization
## 5 Challenges, Open Research Problems, and Future of WBANs
### Security  
The security of the sensors, the data collection, and the communication of that data to a collection point is an important and critical issue in WBANs, especially for sensitive applications. Some solutions have been proposed to protect the node-to-node communication from any attacks in WBANs. As in any secure system, fundamental security specifications are needed for WBANs which are confidentiality, integrity, availability, accountability, and access control. For assuring these security requirements, new encryption methods should be developed. For instance, in [94] elliptic curve cryptography for key distribution to decrease energy consumption is proposed. However, there is still a need for more efficient cryptography methods to meet the security requirements. Another challenging issue with the security requirements for WBANs arises when the patients are unconscious as in thatcase obtaining passwords may not be possible. In this case, biometric methods can be used for accountability as in [95]. As well as, the physiological signal based authentication method is proposed in [96]. According to the findings of [96], the proposed scheme generates identity information for mutual authentication from an electrocardiogram-like measure at two different parts of the body. Finding unique biometric features can be used for identification purposes which represent a new challenge in this issue. As well as, when sensors will become smaller and more capable, their benefits will increase. Therefore, the security of the networks they form is critical to maintain, and this will continue to be an important area of research.
## 1 Introduction
Numerous studies have shown that CNN outperforms conventional techniques in both suppressing noise and detecting weak signals [22; 23; 24]. However, CNN still faces persistent challenges in seismic layer tracking, including data scarcity, overfitting, excessive model complexity, and inadequate lateral resolution in layer identification [25; 26; 27]. Recognizing the complementary strengths of manifold learning and CNN, this study introduces, for the first time, a UMAP-CNN-coupled framework for seismic waveform feature extraction, aiming to solve the high dimensionality problem and enhance classification accuracy in seismic facies analysis. By combining UMAP with CNN, multi-scale deep features are directly extracted from seismic data and mapped to low-dimensional space to achieve waveform feature space compression and information fusion, avoiding the deviation in artificial attribute design.
## 1 Introduction
### BCI Applications  
The key medical applications of brain-computer interfaces include neurorehabilitation, assistive technology, diagnosis and monitoring, and treatment of neurological and psychiatric disorders (Table 1).  
Brain-computer interfaces have also found applications in gaming and entertainment, which enable users to control virtual environments and gaming experiences using their brain activity.  
\begin{table}
\begin{tabular}{c c} \hline
**Area** & **Application** \\ \hline \multicolumn{2}{c}{Stroke rehabilitation [11; 12]} \\ Neurorehabilitation & Spinal cord injury rehabilitation [13] \\ Traumatic brain injury rehabilitation [14] \\ Cerebral palsy treatment [15] \\ \hline \multicolumn{2}{c}{Communication devices for people with locked-in syndrome [16]} \\ Assistive Technology & Wheelchair control [9; 17; 18; 19] \\ \multicolumn{2}{c}{Prosthetic limb control [16]} \\ \multicolumn{2}{c}{Environmental control (e.g., controlling lights, appliances) [20]} \\ \hline \multicolumn{2}{c}{Epilepsy detection and prediction [21]} \\ Diagnosis and Monitoring & Sleep disorder diagnosis [22] \\ \multicolumn{2}{c}{Brain tumor detection [23; 24]} \\ Monitoring consciousness in coma patients [25; 26] \\ \hline \multicolumn{2}{c}{Neurofeedback for ADHD, anxiety, and depression [27]} \\ Treatment of Neurological and Psychiatric & Deep brain stimulation for [PERSON]’s disease and essential tremor [28] \\ Disorders & Treatment of chronic pain [29] \\ \hline \end{tabular}
\end{table}
Table 1: Key BCI medical applications.  
1. Brain-Controlled games: * Games that use brain activity to control characters or objects [30; 31]; * Neurofeedback games for relaxation and focus [31; 32; 33];
2. Virtual Reality and Augmented Reality Experiences: * Immersive and interactive experiences controlled by brain activity [33];
3. Music and Art Generation: * Creating music or art using brainwaves ([PERSON] 2021 [34]). Brain-computer interfaces can enable hands-free communication and control of external devices.
1. Hands-free typing and communication: * Spelling devices for people with severe motor impairments [35];
2. Brain-to-Brain communication: * Research into direct communication between brains [36];
3. Control of external devices: * Smart home control [20];
4. Vehicle control (e.g., cars and drones) [37; 38; 39]. Other applications of brain-computer interfaces include education and training, neuromarketing, security, and authentication.  
1. Education and Training: * Enhancing attention and focus in students [40];
2. Training pilots and surgeons using simulations [41; 42];
3. Neuromarketing: * Measuring consumer responses to products and advertisements [43];
4. Security and Authentication: * Brainwave-based lie detection [44];
5. Biometric authentication [45; 46; 47].
## 3 Natural Compounds Inhibiting De Novo Fatty Acid Synthesis and Modification
### Natural Compounds as Acetyl-CoA carboxylase (ACC) Inhibitors  
ACC catalyzes the production of malonyl-CoA, an essential substrate in fatty acid synthesis. Two ACC isoforms have been reported in mammals, i.e., ACC1 and ACC2 (also known as ACC\(\alpha\) and ACC\(\beta\)). In the past decades, ACC inhibitors have been used in various clinical treatments of human diseases, including microbial infections, metabolic syndrome, diabetes and cancers [76, 77].  
Soraphen A, a polyketide isolated from the myxobacterium _Sorangium cellulosum_, was first identified as a natural molecule with inhibitory activity against ACC1, which is overexpressed in human cancer cells [53, 78]. Further studies revealed that Soraphen A could allosterically promote the catalytically inactive conformation of ACC1 through binding to its biotin carboxylase domain with high affinity [79, 80]. Quercetin also showed selective inhibition to ACC without any detectable effect on FASN in rat hepatocytes and could reduce the synthesis of both fatty acid and triacylglycerglycerol [45]; however, the underlying mechanism remained unclear.
# Monte-Carlo-Based Estimation of the X-ray Energy Spectrum for CT Artifact Reduction
## 2 Materials and Methods  
In Section 2.1, FleXCT [22], a novel prototype industrial micro-CT scanner, is modeled by Monte Carlo simulation. In order to validate the obtained X-ray energy spectra, an experimental technique using Maximum Likelihood Expectation Maximization (MLEM) was implemented (Section 2.2). When using the MLEM method, only the estimation of the X-ray spectrum inside the detector is possible; therefore, as a first step, X-ray spectra for different tube voltages were calculated inside the detector. After validation of the modeled FleXCT system (Section 3), the X-ray spectrum was calculated in front of the FleXCT's X-ray tube, independent of the flat panel detector response (Sections 2.1.2 and 3). The proposed approach is explained in detail in the following sections.
## 2 Bayesian Networks  
Bayesian networks (BNs) are a class of probabilistic graphical models defined over a set of random variables \(\mathbf{X}=\{X_{1},\ldots,X_{N}\}\), each describing some quantity of interest, that are associated with the nodes of a directed acyclic graph (DAG) \(\mathcal{G}\). Arcs in \(\mathcal{G}\) express direct dependence relationships between the variables in \(\mathbf{X}\), with graphical separation in \(\mathcal{G}\) implying conditional independence in probability. As a result, \(\mathcal{G}\) induces the factorisation  
\[\mathrm{P}(\mathbf{X}\mid\mathcal{G},\Theta)=\prod_{i=1}^{N}\mathrm{P}\big{(}X _{i}\mid\Pi_{X_{i}},\Theta_{X_{i}}\big{)}, \tag{1}\]  
in which the global distribution (of \(\mathbf{X}\), with parameters \(\Theta\)) decomposes into one local distribution for each \(X_{i}\) (with parameters \(\Theta_{X_{i}}\), \(\bigcup_{\mathbf{X}}\Theta_{X_{i}}=\Theta\)) conditional on its parents \(\Pi_{X_{i}}\). This factorisation is as effective at reducing the computational burden of working with BNs as the DAG underlying the BN is sparse, meaning that each node \(X_{i}\) has a small number of parents (\(|\Pi_{X_{i}}|<c\), usually with \(c\in[2,5]\)). For instance, learning BNs from data is only feasible in practice if this holds. The task of learning a BN \(\mathcal{B}=(\mathcal{G},\Theta)\) from a data set \(\mathcal{D}\) containing \(n\) observations comprises two steps:  
\[\underbrace{\mathrm{P}(\mathcal{G},\Theta\mid\mathcal{D})}_{\text{learning}}= \underbrace{\mathrm{P}(\mathcal{G}\mid\mathcal{D})}_{\text{structure learning}}\cdot\underbrace{\mathrm{P}(\Theta\mid\mathcal{G},\mathcal{D})}_{ \text{parameter learning}}.\]  
If we assume that parameters in different local distributions are independent [25], we can perform parameter learning independently for each node. Each \(X_{i}\mid\Pi_{X_{i}}\) will have a low-dimensional parameter space \(\Theta_{X_{i}}\), making parameter learning computationallyefficient. On the other hand, structure learning is well known to be both NP-hard [26] and NP-complete [27], even under unrealistically favourable conditions such as the availability of an independence and inference oracle [28]. However, if \(\mathcal{G}\) is sparse, heuristic learning algorithms have been shown to run in quadratic time [29]. Exact learning algorithms, which have optimality guarantees that heuristic algorithms lack, retain their exponential complexity but become feasible for small problems because sparsity allows for tight bounds on goodness-of-fit scores and the efficient pruning of the space of the DAGs [30; 31; 32].
# An Efficient Resource Allocation Algorithm for Device-To-Device Communications
###### Abstract  
In order to solve the problem of interference and spectrum optimization caused by D2D (device-to-device) communication multiplexing uplink channel of heterogeneous cellular networks, the allocation algorithm based on the many-to-one Gale-Shapley (M21 GS) algorithm proposed in this paper can effectively solve the resource allocation problem of D2D users multiplexed cellular user channels in heterogeneous cellular network environments. In order to improve the utilization of the wireless spectrum, the algorithm allows multiple D2D users to share the channel resources of one cellular user and maintains the communication service quality of the cellular users and D2D users by setting the signal to interference and noise ratio (SINR) threshold. A D2D user and channel preference list are established based on the implemented system's capacity to maximize the system total capacity objective function. Finally, we use the Kuhn-Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. The MATLAB simulation is used to compare and analyze the total system capacity of the proposed algorithm, the resource allocation algorithm based on the delay acceptance algorithm, the random resource allocation algorithm and the optimal exhaustive search algorithm, and the maximum allowable access for D2D users. The simulation results show that the proposed algorithm has fast convergence and low complexity, and the total capacity is close to the optimal algorithm.  
D2D; interference mitigation; resource allocation; user fairness; optimization Article
## 2 Preliminaries
### Methodology  
The methodology followed in this article consists of two stages: the first one assumes the capacity and location that should have the DG and BESS into the DN. The second stage addresses the capability of the DN to face unexpected failures through the self-partition into VMs, which is the main contribution of this article.  
The placement and sizing have been addressed separately in [13], where a novel algorithm presented allows finding an optimal solution to the capacity and location for different types of DGs and BESSs, considering a time horizon longer than 24 h and basing the formulation on an AC-OPF to include the technical constraints of the network (this is represented in the upper level of Figure 1). The lower level of the figure corresponds to the optimal topology of the DN in case the power energy is insufficient to meet the electricity load. Therefore, following Figure 1, the optimal model for the VMs topology scheduling (VMTS) is executed for scenarios of 72 h, where every hour has two possible results regarding the system: the first one being, where the capacity installed, weather conditions, and the electricity load are balanced,meaning that the DN does not need to be partitioned into VMs. However, for the second result, if the electricity load is not satisfied by the power generation of the DGs, the model finds the amount of energy load that must be reduced at each bus to balance the system, where every load point possesses a range of load that can be reduced. If the load curtailment exceeds this range, the load in that point is reduced to zero and the bus is isolated, coming in a bound of VM. Thus, for each hour where these conditions appear, the model provides the scheduling of the topology that must follow the DN to continuously balance the system, sacrificing, of course, a load curtailment that is minimizing on the objective function but avoiding an outage.  
In order to make sure that the topologies found by the mathematical model are feasible for the classic AC-OPF problem, the loads associated with the isolated nodes are replaced by zeros and the buses are modified with a reduced load within its range of flexibility. The DN with the new set of loads is executed in a multi-period AC-OPF. Thus, the feasible solutions of the VMTS model are also feasible for the classic AC-OPF problem.
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 3 Proposed System
### Data Collection  
In this module, the SDN controller in the control layer will collect data from the virtual and physical switches in the data layer. This work provides preconditions for the subsequent detection of abnormal data. In the SDN environment, we use flows to define the data passing through the switch. The flows are a packet sequence passing an observation point in the network during a specific time interval. Previous works proposed that the data flow collection interval should generally be 1-5 min [17]. With the increase in network transmission bandwidth, it is feasible to reduce this time interval [18; 19]. This paper defines the collection interval as 1 s to respond to the anomalies hidden in the rapid data flows.  
After an interval \(t\), the SDN controller will send requests to switches to collect IP flow records by OpenFlow messages. After the switches receive a request message from the SDN controller, they will send IP flow records in time intervals \(t\). Figure 2 represents this process. This paper defines these IP flow records as a set \(\beta_{t}\)= {\(\alpha_{1}\), \(\alpha_{2}\),..., \(\alpha_{n}\)}, where each \(\alpha_{i}\) is a flow record.  
Figure 1: Proposed system architecture.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 1 Introduction
addressed path planning problems in 3D scene-constrained spaces, presenting a three-stage heuristic approach for constructing route networks based on minimum weighted set covering to enhance the utilization efficiency of escape exits [23]. The path planning problem for the interior of a building is a typical non-deterministic polynomial problem. That is, it is the problem of shortest path usage time formed by a fixed number of branch nodes with constant start and end nodes [24]. In the whole path planning process, it is important to control the direction and distance of the path [25]. In the Bloch Sphere Quantum Genetic Algorithm (BQGA), the entire Bloch sphere is the search range for the optimal solution, especially since there is no clear direction for the search, so the blindness and uncertainty of the search greatly reduce the search efficiency and optimality. At the same time, the size of \(\Delta\theta\) and \(\Delta\varphi\) in this algorithm usually needs to be determined according to the specific problem. If they take too small a value, they will make the optimization process slow and thus reduce efficiency. If they take too large a value, they will make the algorithm cross the global optimal solution or fall into premature convergence [26]. This paper focuses on researching evacuation path planning for large-scale buildings. To address the issues of local optima and low efficiency in path planning inside buildings, a Bloch spherical coordinate algorithm incorporating least squares theory and the Bloch Spherical Quantum Bee Colony Algorithm (QABC) are employed. Firstly, the characteristics of three-dimensional path planning are analyzed, and a linear decreasing inertia weighting approach is used to balance the global search ability of chromosomes and accelerate the search performance of the algorithm. Then, the application algorithm can generate a clear motion trajectory in the raster map. Thirdly, the least squares is used to fit the results, thus obtaining a progressive path. Finally, multi-robots path planning approaches based on QABC are discussed, respectively. The performance is verified by comparing with Ant Colony Algorithm (ACA), Genetic Algorithm (GA) and Artificial Bee Colony Algorithm (ABA).