esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 7 Optimisation and Improvements of Results
The updated rankings and graphs confirm that our quantum random number generator (QRNG) is now the best performing quantum key generation algorithm (see Figures 8 and 9). It is important to emphasise that the comparison made should be considered a performance benchmark, which remains useful as long as traditional computers exist or, more precisely, as long as they coexist with quantum computers. This is because approaches inspired by quantum mechanics are unable to provide the true randomness achievable with pure quantum systems or quantum computing. For this reason, the use of hybridisation becomes particularly significant, especially when combined with highly efficient algorithms based on multiscale prime number generation.
## 4 Material and Methods
### Gene Expression Analysis (RNAseq and qRT-PCR)  
For the analysis of poplar _trans_-IDS gene expression, we reanalyzed the raw data (SRA accession number PRJNA516861) of an RNAseq experiment recently described in [PERSON] et al. (2017). In this experiment, eight transcriptomes of _P. trichocarpa_ leaves (four biological replicates of untreated control trees and four biological replicates of gypsy moth-treated trees) were sequenced on an IlluminaHiSeq 2500 sequencer (Max Planck Genome Center, Cologne, Germany) with 18 Mio reads per library (100 base pair, single end). Trimming of the obtained Illumina reads and mapping to the poplar gene model version 3.0 ([[https://phytoxome.jgi.doe.gov/pz/portal.html](https://phytoxome.jgi.doe.gov/pz/portal.html)]([https://phytoxome.jgi.doe.gov/pz/portal.html](https://phytoxome.jgi.doe.gov/pz/portal.html))) were performed with the program CLC Genomics Workbench (Qiagen Bioinformatics, Hilden, Germany) (mapping parameter: length fraction, 0.7; similarity fraction, 0.9; max number of hits, 25). Empirical analysis of digital gene expression (EDGE) implemented in the program CLC Genomics Workbench was used for gene expression analysis.  
Qualitative real time polymerase chain reactions (qRT-PCRs) were performed in optical 96-well plates using a Stratagene Mx3000P real-time thermocycler (Stratagene, Carlsbad, CA, USA). Brilliant SYBR Green QPCR Master Mix (Agilent Technologies, Santa Clara, CA, USA) containing a double strand intercalating dye was used to visualize the amplification progress by reading the fluorescence. ROX was used as an internal standard to normalize the fluorescence. As negative controls, samples containing water instead of cDNA were used. Five biological replicates with each three technical replicates were performed. Each reaction contained cDNA and sequence-specific primers (Table 1) in a volume of 25 \(\upmu\)L. Primer efficiency was tested using five different dilutions (1- to 64-fold) and calculated with the standard curve method ([PERSON], 1995). The thermocycler protocol was performed with an initial denaturing step for 10 min at 95 \({}^{\circ}\)C followed by 40 cycles of 30 s with 95 \({}^{\circ}\)C, 60 s with 55 \({}^{\circ}\)C, and 60 s at 72 \({}^{\circ}\)C. For primer efficiency tests, the runs were followed by a melting curve analysis consisting of one cycle with 95 \({}^{\circ}\)C for 30 s, 55 \({}^{\circ}\)C for 30 s and 95 \({}^{\circ}\)C for 30 s. For each primer pair, the amplification products were cloned into the pCR4-TOPO TA vector (Thermo Fisher Scientific, Erlangen, Germany) and sequenced. Amplification was analyzed with the MxPro QPCR Software (Agilent Technologies, Santa Clara, CA, USA). The FPP synthase gene _PtFPPS1_ was used as housekeeping gene for the qRT analysis of other _trans_-IDS genes, since the fluctuation of its Ct values (\(\sim\)0,9 Ct) was minimal between control plants and herbivore-treated plants.
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 3 Method
### Swin Transformer Branch  
As shown in the blue dashed box in Figure 1, the Swin Transformer branch is constructed in four stages, with the number of Swin Transformer Blocks varying from (2, 2, 6, 2) to (2, 2, 2, 2) at each stage. The spatial resolution of a picture with x \(\in\) RH \(\times\) W \(\times\) 3 is H \(\times\) W and has three channels. In the patch partitioning module, the image for the Swin Transformer branch is segmented into non-overlapping patches of size 4 \(\times\) 4. Consequently, the image dimensions change to [H/4, W/4, 48] from [H, W, 3]. In stage1, the channel data patch features of each pixel are mapped to the high-dimensional space by doing linear transformation through the Patch Embedding layer and establishing the positional relationship between the image patches. This alteration resizes the image from [H, W, 48] to [H/4, W/4, C], which is then provided to the Swin Transformer Block. The subsequent stages 2 through 4 replicate this process, but beforehand, the feature map undergoes a Patch Merging that modifies its dimensions from [H/4, W/4, C] to [H/8, W/8, 2C], prior to being input into the Swin Transformer Block. The feature output from each stage is directed to the AtFF module for data integration.  
The Swin Transformer Block is composed of two sets of LayerNorm (LN) layers, a Windowed Multiple Self-Attention (W-MSA) layer, a residual connection, and a two-layer Multilayer Perceptron (MLP) unit. The detailed module configuration is depicted in Figure 3, where the input vector is initially processed by an LN (Layer Norm) layer. Subsequently, the input vectors are channeled into the Windows Multi-Head Self-Attention (W-MSA) module that employs a shifted window approach, which significantly decreases the model's computational complexity. Finally, it passes through the residual structure before moving to the next LN layer. LayerNorm (LN) layers are implemented before every MSA module and MLP, with residual connections following each MSA and MLP. This shift window mechanism dictates that two successive Swin Transformer Blocks are calculated as follows (1) to (4):  
\[z^{l}=W-MSA(LN(z^{l-1}))+z^{l-1} \tag{1}\]  
\[z^{l}=MLP(LN(z^{l}))+z^{l} \tag{2}\]  
\[z^{l+1}=SW-MSA(LN(z^{l}))+z^{l} \tag{3}\]  
\[z^{l+1}=MLP(LN(z^{l+1}))+z^{l+1} \tag{4}\]  
Figure 3: The diagram of Swin-Transformer Block.
## 3 Method  
### Spectral Feature Extraction  
After the pine tree crowns segmentation of the HI from the research area, the spectral reflection for each pine tree was calculated with the average of the crown hyperspectral data. Then, a Savitzky-Golay filter with points ranging from 3 to 15 was chosen in order to smooth the pine tree's spectral reflection [33]. When the pine tree suffers from PWD, its chlorophyll, water content and the cell structure in the needles change dramatically, leading to changes in the spectral reflectance. In general, the spectral reflection curves for pine trees in different PWD infection stages (Figure 4) reach their local peaks in the green region (GR, 510-580 nm), red edge (RE, 680-760 nm) and the near-infrared (NIR, 780-1000 nm) and reach their local valleys in the red region (RR, 620-680 nm). Based on those PWD-sensitive regions, we extracted 16 spectral features shown in Table 1 from the pine tree spectral reflection data, where \(\rho\) and \(\rho^{\prime}\) are the spectral reflectance and its first-order derivative.
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 3 Multi-Scenario Physics-Based Seismic Hazard Analysis
### Peak Ground Motion Results  
Once all the input data and available information for carrying out NDSHA were defined, synthetic seismograms were computed for a set of observation points covering the study area (see Figure 4). The time histories can be directly utilized by engineers as seismic input (e.g., for designing a seismo-resistant structure or evaluating the seismic vulnerability of an existing building) or as a dataset to produce seismic hazard maps of different ground motion intensities. The NDSHA allows for the assessment of seismic hazard in areas with low seismicity and does not have a complete earthquake catalog. The peak ground motion values computed at each site are mapped and shown in Figure 7. The most significant values are concentrated in the northeastern part of the city, which has the maximum acceleration (0.15 g) and velocity (8 cm/s). The estimated high peak acceleration for the sites located in the northeast of Alexandria could be due to the proximity to seismogenic zones and associated seismicity or scenarios located along the continental margin of Egypt, as described in detail in Section 3.1.  
Figure 7: _Cont._  
Figure 7: Maps of: (**a**) peak ground displacement (Dmax); (**b**) peak ground velocity (Vmax) and (**c**) peak ground acceleration (Amax).
# Penetration Efficiency and Concentration Distribution of Nanoparticles in a Hollow Tapered Cylinder
## 2 Methods
The transport behavior of small particles and the interaction between the surrounding gas molecules can be characterized by considering the Knudsen number, \(Kn\); it is the relationship between the mean free path of the gas molecules, \(l_{g}\), and the particle diameter, \(d_{p}\). \[Kn=\frac{2l_{g}}{d_{p}} \tag{5}\]  
Figure 2: Numerical domain of calculation in the hollow tapered cylinder. For very small nanoparticles (\(Kn>>1\)), the expression for the friction coefficient of the kinetic behavior between the particle and the surrounding gas can be derived using kinetic theory of gases as follows:  
\[\varphi=\frac{2}{3}\rho d_{p}^{2}\Big{(}1+\frac{\alpha\pi}{8}\Big{)}\left(\frac{ 2\pi kT}{m}\right)^{\frac{1}{2}} \tag{6}\]  
where \(m\) is the mass of the gas molecule and \(\alpha\) is the accommodation coefficient, which is usually near 0.9 [28]. The relationship can be generalized using the following expression to cover a wider range of \(Kn\) numbers [29]. \[\varphi=\frac{3\pi\mu d_{p}}{1+\frac{Kn}{2}(2.34+1.05 exp(-0.78/Kn))} \tag{7}\]  
Obtaining the number concentration of particles at the outlet of the hollow tapered cylinder, the particle penetration efficiency, \(P_{\mathit{eff}}\), of the hollow tapered cylinder with or without the conical column can be calculated using the following equation:  
\[P_{\mathit{eff}}=\frac{\int_{R_{\mathit{ex1}}}^{R_{\mathit{ex1}}}C_{\mathit{ ex}}(r)\cdot U_{\mathit{ex}}(r)\cdot 2\pi rdr}{\int_{R_{\mathit{in2}}}^{R_{\mathit{in2}}}C_{ \mathit{in}}(r)\cdot U_{\mathit{in}}(r)\cdot 2\pi rdr} \tag{8}\]  
where \(R_{\mathit{in2}}\) represents the radius of the hollow tapered cylinder at the inlet, \(R_{\mathit{ex1}}\) is the radius of the hollow tapered cylinder at the outlet, \(C_{\mathit{in}}\) is the inlet concentration, \(C_{\mathit{ex}}\) is the concentration at different cross sections, \(U_{\mathit{in}}\) is the inlet velocity, and \(U_{\mathit{ex}}\) is the axial velocity at different cross sections. Several meshes were tested in this study to obtain an appropriate number of grids to ensure optimal grid independent solutions. Figure 3 shows the variations in the penetration efficiency of the hollow tapered cylinder without conical column for different grid sizes of 21,000, 84,000, and 336,000 at the flow rate of 2 L/min. When the number of grids was increased from 21,000 to 84,000, the numerical penetration efficiency of the hollow tapered cylinder without the conical column for 5 nm particles changed from 83.3% to 84.9%. While the number of grids was further increased from 84,000 to 336,000, the numerical penetration efficiency only changed from 84.9% to 85.1%. Therefore, 84,000 grids were used in the simulation. \[(\overrightarrow{V}\cdot\
abla)\overrightarrow{V}=-\frac{1}{\rho}\
abla P+ \gamma\
abla^{2}\overrightarrow{V} \tag{1}\]0 Penetration efficiency for different grid sizes of the hollow tapered cylinder without conical column at the flow rate of 2 L/min.
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 2 Non-Binary LDPC Codes Based on High Girth Hamiltonian Graphs
### Parity-Check Matrix and Hamiltonian Graph
The \((m-1)\) non-zero elements \(\alpha^{k_{1,0}},\alpha^{k_{2,0}},\cdots,\alpha^{k_{m-1,0}}\) in the matrix \(\mathbf{H}_{P}\) are stored to calculate each check symbol \(p_{i}\) in the described recursive steps. The method described requires less calculation and storage resources when compared with traditional encoding methods. Therefore, the NB-LDPC code with the parity-check matrix in the form shown in Equation (1) can be efficiently encoded in a recursive manner.
## 4 Results on ML/DL-Based Adaptive Modulation
### Discussion on Adaptive Modulation Techniques in SC-UWA Communication Systems
Hybrid deep learning models offer strong performance in BER but involve heavy computational complexity. RL-based methods adapt to changing conditions and show promising results in shallow water trials. However, these methods often face scalability issues and require careful validation in varied underwater environments. Together, these approaches reflect an ongoing effort to balance performance, adaptability, and efficiency in underwater communication.
# A Digital Twin-Based Approach for Detecting Cyber-Physical Attacks in ICS Using Knowledge Discovery
## 2 Background
### Digital Twins in Cybersecurity  
A digital twins is a virtual representation of any real-world asset (e.g., system or process) [10]. Historically, the idea behind the development of digital twins has been to monitor and manage the performance of physical systems in the context of Industry 4.0 and smart manufacturing. Construction of a digital twin for a physical item involves three key aspects: (1) identifying the components and parameters of the physical product in its real environment; (2) establishing a link between the physical and virtual versions of the product; and (3) integrating data and information to bridge the virtual and real worlds [11].  
In the context of cybersecurity, digital twin applications have become increasingly significant [12]. For instance, they can be integrated with cyber ranges to analyze system behavior under different cyberattack scenarios. Indeed, digital twins can be used in attack emulations and simulations to evaluate resilience metrics, ultimately aiding in the design of security and safety mechanisms for cyber (physical) systems. This aspect is crucial for testing and refining security measures in a controlled risk-free environment before deploying them in real-world systems.  
Notably, digital twins can also act effectively as honeypots, providing an early detection system for uncovering external and internal attack vectors within a network [3]. The advantage of using a digital twin as a honeypot is its ability to enhance both the level of interaction and the attraction of the \"twin\" [4].  
In this work, we leverage the concept of digital twins to safely execute and study known attacks as well as to develop and test variants without impacting actual operational systems. This not only preserves the integrity and availability of the real system but also provides a more comprehensive understanding of potential vulnerabilities and attack vectors.
## 3 Results and Discussion
### Selectivity Test  
To evaluate the selectivity, two other compounds, i.e., salbutamol (SAL) and pertubatline (TER), were selected. The reason for the choice of these compounds was that they afford many similar functional groups that can bind to MIP. Figure 7 shows that the adsorption recovery of MIP3 for SLX (92.25 \(\pm\) 1.12%) was much higher than for SAL (35.23 \(\pm\) 3.34%) and TER (28.71 \(\pm\) 2.61%). Salbutamol and pertubatline have molecule sizes smaller than salmeterol. The obtained cavity has a larger size for SAL and TER, but in molecular recognition, both functional group and size effects play a part simultaneously. Herein, slight differences between the analogue structure and SLX can affect the mechanism of the \"lock-key\" recognition rule of the molecular imprinting technique and MIP provides the highest adsorption capacity toward their template [37]. The selectivity result shows that the MIP, especially MIP3, has a higher selectivity to the target of interest than to the other substances, which shows that molecular dynamics simulations combined with the precipitation polymerization method could be used as an approach to fabricate a highly selective polymer as a sorbent to extract SLX from biological matrices in the presence of another similarly structured drug. The present polymers have better selectivity than those of the previous method.  
Figure 6: Effect of SLX concentration on % recovery: (**a**) loading using isopropanol, (**b**) loading using water.  
Figure 7: The selectivity of MISPE toward salbutamol (SAL), terbutaline (TER), and salmeterol xinafoate (SLX).
# Faster Algorithms for Mining Shortest-Path Distances from Massive Time-Evolving Graphs
## 3 A New Algorithm: queue-2 hc
### Profiling of Algorithm bidir-2 hc
In what follows, given two labelings \(L\) and \(L^{\prime}\) we use \(L\setminus L^{\prime}\) to denote the set of label entries that are in \(L\) but not in \(L^{\prime}\). **Lemma 3**. : _Let \(L\) be a minimal well-ordered 2-hop-cover labeling of a graph \(G\). Let us assume that an arc \((x,y)\) of \(G\) undergoes a decremental update and let \(G^{\prime}\) be the resulting graph. Let \(L^{\prime}\) the labeling returned by Algorithm 3. Then, \(L\setminus L^{\prime}\) does not contain any label entry that is correct for \(G^{\prime}\)._  
Proof. : Assume by contradiction a correct label entry \((h,\delta_{vh})\) is removed from \(L_{out}(v)\), that is \(L\setminus L^{\prime}\) contains, for some \(h\in V\), pair \((h,\delta_{vh})\) and \(\delta_{vh}=d_{G^{\prime}}(v,h)\). Call \(u\) the neighbor of \(v\) that induces the presence of such label entry in \(L\) which is well-ordered. Clearly we have that \(\delta_{vh}=d_{G}(v,h)=w(v,u)+\delta_{uh}\) and \(d_{G}(v,h)=w(v,u)+d_{G}(u,h)\) since there must exist a neighbor on the shortest path from \(v\) to \(h\) (which is traversed, e.g., by fs-2 hc). Now, either the shortest path \(P\) from \(u\) to \(h\) contains arc \((x,y)\) or not. In the latter case, we have that \(h\in L_{out}(u)\) and \(d_{G}(v,h)=\delta_{vh}=w(v,u)+\delta_{uh}=w(v,u)+d_{G}(u,h)=w(v,u)+d_{G^{ \prime}}(u,h)=d_{G^{\prime}}(v,h)\). Therefore, in line 13 of Algorithm 5 the label entry is not selected for removal, which is a contradiction. In the former case, as the entry is correct for \(G^{\prime}\), there must exist another shortest path, say \(P^{\prime}\), that does not contain arc \((x,y)\) and such that \(w(P)=w(P^{\prime})=d_{G}(u,h)\). Call \(u^{\prime}\) the neighbor of \(v\) on such path, i.e., \(u^{\prime}\in N_{out}(v)\) and \(d_{G}(v,h)=w(v,u^{\prime})+d_{G}(u^{\prime},h)\). Since the label entry is removed, it follows that Algorithm 5 in line 12 does not find any neighbor of \(u^{\prime}\) satisfying \(\delta_{vh}=w(v,u^{\prime})+\delta_{u^{\prime}h}\), i.e., \(u^{\prime}\) cannot have \((h,\delta_{u^{\prime}h})\) in \(L_{out}(u^{\prime})\). Therefore, the visit rooted at \(h\) has not reached \(u^{\prime}\) due to some pruning test. It follows that pair \(u^{\prime}h\) is covered (on \(P^{\prime}\)) by some other vertex, say \(h^{\prime}\), such that \(h^{\prime}<h\) and \(h^{\prime}<u^{\prime}\). Since \((h,\delta_{vh})\in L_{out}(v)\) we have also that \(h\) must be preceding \(h^{\prime}\) in the vertex ordering (i.e., \(h<h^{\prime}\)) as otherwise the visit rooted at \(h\) would have not reached \(v\) (it would have been pruned thanks to \(h^{\prime}\)). This is a contradiction since, for the pruning on \(P^{\prime}\) to happen, vertex \(h^{\prime}\) must precede \(h\) (an explanatory example of the scenario discussed in this proof is shown in Figure 6a). A symmetric argument can be used to prove that no correct label entry is removed from any incoming label \(L_{in}(v)\).
###### Abstract  
This paper focuses on generating distributed flocking strategies via imitation learning. The primary motivation is to improve the swarm robustness and achieve better consistency while respecting the communication constraints. This paper first proposes a quantitative metric of swarm robustness based on entropy evaluation. Then, the graph importance consistency is also proposed, which is one of the critical goals of the flocking task. Moreover, the importance-correlated directed graph convolutional networks (IDGCNs) are constructed for multidimensional feature extraction and structure-related aggregation of graph data. Next, by employing IDGCNs-based imitation learning, a distributed and scalable flocking strategy is obtained, and its performance is very close to the centralized strategy template while considering communication constraints. To speed up and simplify the training process, we train the flocking strategy with a small number of agents and set restrictions on communication. Finally, various simulation experiments are executed to verify the advantages of the obtained strategy in terms of realizing the swarm consistency and improving the swarm robustness. The results also show that the performance is well maintained while the scale of agents expands (tested with 20, 30, 40 robots).  
s +
Footnote †: journal: Applied sciences  
Article  
Imitation Learning with Graph Neural Networks for Improving Swarm Robustness under Restricted Communications
###### Abstract  
This paper presents a comprehensive method for analyzing prestressed concrete bridges subjected to multiple concurrent dynamic loads, incorporating soil-structure interaction (SSI) and seismic wave propagation effects. The study develops a comprehensive numerical framework that simultaneously accounts for traveling seismic waves, train-induced vibrations, and soil-foundation dynamics. Three-dimensional finite element modeling captures the complex interaction between the bridge structure, foundation system, and surrounding soil medium. The investigation considers the spatial variability of ground motion and its influence on the bridge's dynamic response, particularly examining how different wave velocities and coherency patterns affect structural behavior. Advanced material constitutive models based on damage mechanics theory are implemented to represent both linear and non-linear structure responses under dynamic loading conditions. The analysis reveals that traditional simplified approaches, which neglect SSI, train, and seismic loading combinations, and traveling wave effects may significantly misestimate the structural demands. The results demonstrate how wave passage effects can either amplify or attenuate the combined response depending on the relationship between seismic wave velocity, the frequency content of the ground motion recordings, and the local soil conditions. These findings could contribute to the development of more reliable design methodologies for prestressed bridges in seismically active regions with significant railway traffic.  
Keywords: soil-structure interaction; traveling seismic waves; multi-hazard bridge analysis; nonlinear bridge dynamics; spatial variability of ground motion +
Footnote †: journal: applied sciences