esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Aero-Hydro-Servo-Elastic Model
### Tower Modelling
From these experimens the two weighting parameters can be determined by  
\[\alpha_{d}=\zeta_{1}\omega_{1}\quad\text{and}\quad\beta_{d}=\frac{\zeta_{1}}{ \omega_{1}} \tag{15}\]  
where \(\alpha_{d}\) and \(\beta_{d}\) can be calculated to be 0.270 and 0.002, respectively. The Rayleigh damping model is an approximation of the damping matrix and is often used for its ease of implementation. Furthermore, the mass proportional term will dampen the low frequency motions heavily, while the stiffness proportional term will dampen the high frequency motion heavily. In the simulations, a non-physical high frequency motion can occur, the stiffness proportional damping will also help dampen these [31]. To describe the motion of the tower structure, the local elements must be assembled into a global formulation. The local system matrices are converted into global system matrices, which give the global governing equation for the tower  
\[\mathbf{M}_{tow}\mathbf{\ddot{Z}}+\mathbf{C}_{tow}\mathbf{\dot{Z}}+\mathbf{ K}_{tow}\mathbf{Z}=\mathbf{F}_{ext} \tag{16}\]  
where \(\mathbf{\ddot{Z}}\in\mathbb{R}^{6N}\), \(\mathbf{\dot{Z}}\in\mathbb{R}^{6N}\) and \(\mathbf{Z}\in\mathbb{R}^{6N}\) is the global acceleration vector, velocity vector and position vector, respectivly, N is the number of nodes used for the tower and wind turbine discretization, which in this paper is 22. \(\mathbf{M}_{tow}\in\mathbb{R}^{6N\times 6N}\) is the global mass matrix, \(\mathbf{C}_{tow}\in\mathbb{R}^{6N\times 6N}\) is the global damping matrix, \(\mathbf{K}_{tow}\in\mathbb{R}^{6N\times 6N}\) is the global stiffness matrix. \(\mathbf{F}_{ext}\in\mathbb{R}^{6N}\) is the global load vector on the tower structure. At the top two elements, which is the wind turbine, the mass and inertia were found by usage of the 3D-model developed in the 3D CAD software Inventor [34]. The stiffness of the nacelle is assumed to be from the rectangular plate, the stiffness is therefore modeled as a rectangular plate of aluminum. The damping is assumed to be the same as used for the tower.
## 6 Training and Simulation Results
### Cross-City Generalization: Seattle Crime Pattern Analysis and RL Integration  
To test transferability, we trained DDQN and PPO on the Washington DC dataset and evaluated performance on Seattle (2008-2024). Despite differing urban topology, refer to Figure 7, DDQN retained 86.3% of reward performance, while PPO retained 91.2%, demonstrating strong generalization. PPO exhibited higher adaptability due to policy-based exploration, validating its use in multi-city deployments.  
To further support this claim, we analyzed patrol routing behaviors using the Seattle KDE maps and graph overlays. PPO agents trained in DC adapted to Seattle's high-density crime areas such as Pioneer Square, Belltown, and the waterfront, dynamically prioritizing high-risk hotspots. DDQN agents also retained coverage but displayed more static patrol loops. The reward consistency under the Seattle environment was confirmed via episodic logs and reward trends. (Figure 4). The Seattle testbed also validated map generalization: PPO maintained low detour rates despite Seattle's grid-agnostic urban geometry.  
Technically, node and edge embeddings from the DC-trained models were reused without retraining. Dynamic incident rates from Seattle crime heatmaps were embedded into graph traversal probabilities. This demonstrated the RL agent's ability to generalize across urban morphologies using KDE-transformed cost functions, spatiotemporal normalization, and dynamic reward re-scaling, refer to Table 4.
## 5 Conclusions  
In this paper, the bending performance of reinforced RC beams with a new type of high-strength and high-toughness epoxy mortar thin layer was studied with the bending test, and the feasibility and effect of the reinforced material were verified. Based on the flat section assumption, the bending capacity of the reinforced RC beams was calculated. The main conclusions are as follows:  
1. The contrast beams exhibited the typical bending failure modes. The failure mode of the reinforced beam was the yielding of the tensile reinforcement of the original beam and then fracture damage of the new epoxy mortar-reinforced thin layer.
2. No debonding phenomenon was observed between the reinforced thin layer and the original concrete, and no visible cracks appeared before the tensile failure occurred in the thin layer.
3. Compared with the corresponding contrast beams, the cracking load of the reinforcement beams A1 and B1 increased by \(14\%\) and \(23\%\) respectively, the yield load by \(32\%\) and \(40\%\), respectively, and the peak load by \(18\%\) and \(17\%\), respectively; the number of cracks developed slowly, but the crack width and spacing decreased significantly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Beam ID** & **Experimental Value/kN** & **Calculated Value/kN** & **Deviation/\%** \\ \hline A1 & 168.72 & 151.00 & \(-11.73\) \\ \hline B1 & 101.61 & 106.00 & 4.14 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of experimental and theoretical values of flexural capacity.  
* The stiffness of the reinforced beam, especially after cracking, was significantly higher than that of the unreinforced contrast beams, but the deformation ductility coefficient was significantly reduced.
* For the calculation method of the epoxy mortar thin layer reinforcement RC beam proposed in this study, the calculated values showed a good agreement with the experimental values, with errors at \(-\)11.73% and 4.14%, respectively, which can provide support for related research on such reinforcement methods.
* In this study, only two-point flexural tests were carried out on beams without initial stress, and the influence of load holding or immediate damage on the reinforcement effect was not considered. The follow-up plan is to carry out the relevant experimental research to provide a basis for the practical engineering application of the new epoxy mortar thin layer reinforced RC beam.  
Methodology, W.L.; Writing--original draft, X.H.; Writing--review & editing, T.W., L.L., X.L. and C.J. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Guangxi Science and Technology Planning Project (GuikeAD21159010), Doctoral Foundation of Guangxi University of Science and Technology (No: 21Z26) and Guangxi University young and middle-aged teachers research basic ability improvement project (No: 2020 KY08021).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 2 The Proposed Methodology
### Feature Selection Using Significance Scores  
The GA evaluates subsets of features or SMART attributes in high-dimensional spaces. Given the conclusions drawn in previous studies, such as [16], which is summarized in Section 1, it is  
Figure 3: The flow of the proposed feature selection algorithm using a genetic algorithm and feature significance scores.  
plausible to assume that these features might behave well as a group, i.e., lead to easily distinguishable clusters in the feature space when considered together. However, individually, they might not hold considerable significance in determining a drive's failure. Hence, a simple mechanism is proposed to individually evaluate each feature selected by the GA by calculating its significance score, which provides a crude measure of the contribution of that feature towards the failure of disk drives. For a given feature, \(x_{i}\), first its mean values for both healthy and failed disk drives are determined, denoted as \(\overline{x}_{i}^{(h)}\) and \(\overline{x}_{i}^{(f)}\), respectively. Then, the significance score \(\Psi\) is determined by calculating the frequentist probability of the event, when the distance of a sample of feature \(x_{i}\) from \(\overline{x}_{i}^{(f)}\) is smaller than its distance from \(\overline{x}_{i}^{(h)}\), as given in Equation (11):  
\[\Psi=P(\|x_{i}-\overline{x}_{i}^{(f)}\|<\|x_{i}-\overline{x}_{i}^{(h)}\|) \tag{11}\]  
According to Equation (11), a feature is considered significant for predicting a disk failure if most of its values for failed drives lie closer to the mean value of that feature for the failed disk drives.
## 2 System Model and Theoretical Analysis  
### CFSOC System Model with SLAO  
The architecture of the typical CFSOC system with SLAO is illustrated in Figure 1. At the transmitting terminal, the laser source is modulated to generate a carrier signal. At the receiving terminal, the received optical signal is mixed with local oscillation to generate an intermediate frequency signal. Then, the proper demodulator and digital signal processer are used to further process and complete the subsequent processing. During the transmission of the laser through the atmospheric link, the wavefront is distorted by atmospheric turbulence. The SLAO system is introduced in the receiving terminal to compensate the influence of atmospheric turbulence and improve the quality of the optical signal.  
The schematic of the SLAO system composed of a beam steering unit (BSU) and a high-order aberration correction unit (HCU) is shown in Figure 2. In BSU, by quickly capturing and tracking the beam, large skew of the laser beam, such as tilts and jitters, is corrected through an FSM. In HCU, the laser carrier signal falls on the deformable mirror (DM) and then is divided into two beams by a beam splitter (BS). A high-speed camera (HSC) is used to capture speckle images, and the current performance index of the system is obtained by calculating the energy concentration rate of the images. Then, the selected optimization algorithm is executed in the high-order aberration correction computer (HCC) and generates a voltage control signal of the DM according to the performance index. Finally, the high-voltage amplifiers are used to amplify the signal to a suitable voltage range and control the DM to correct the distorted wavefront. After SLAO correction, the performance of the CFSOC system can be improved with higher ME and lower BER. In this study, we only consider the high-order aberration correction [9; 16].  
Figure 1: Typical CFSOC system with SLAO.
## 2 Methodology
### Structural Modeling and Analysis
#### 2.2.2 Implementation Details of the Structural Model in ParOptBeam  
The Timoshenko beam model realized in the current work (ParOptBeam [28]) is capable of carrying out a 3D analysis of tall buildings subjected to wind forces. A linear (geometric and material) model is used for the structure, and the time domain dynamic problem is solved with a _generalized_ - \(\alpha\) time integration scheme. Mode shapes (two sway modes and torsion) are uncoupled.  
Analysis in the Modal Coordinates in the Framework  
The structural solver is capable of computing time-dependent responses in the modal coordinates as well. Such computations help to reduce the time required for analysis, as one needs to solve a smaller set of equations (which comprise a series of scalar solves). This can be seen as useful if the number of DoFs of the system is very large, and if the system is mainly influenced by a few of the modes. The capabilities are implemented for the structural model. The responses computed in transformed coordinates are thereafter transferred back to the initial system of reference.  
The Capability of Stepping  
In many designs in practice, it so happens that the stiffness values are reduced along the height of the structure towards the top. This is realized by different zones (intervals) having changing prescribed stiffness and mass values. Constant stiffness (material and geometric) and mass (density) are assumed within an interval.  
Modeling of Outriggers  
In practice, outriggers are generally designed in-between changes of mass and stiffness. These are mostly used to help reduce the bending moment at the core. The mass and stiffness contributions of outriggers are added to the corresponding matrices.  
Variations in the model regarding mass and stiffness considered are shown in Figure 2 and are as follows:  
* Continuous--constant distribution over height;
* With intervals--varying (but constant on intervals) distribution of these over height (three intervals considered);
* With outriggers--additional inclusion of outriggers (at the two changes between intervals).
## 4 Results and Discussion
### Experiments under Heatwave Conditions
(**a**) Mean absolute percentage error (MAPE); (**b**) Root mean square error (RMSE). The correlation coefficients of the DNN model were 0.914 and 0.887 for heatwaves longer than five and seven days, respectively, which indicates an excellent agreement with the actual yields under unexpected extreme weather conditions (Figures 8 and 9). The remaining five models showed more deviation from the 1:1 line and significant overestimation. Similar to the drought prediction results, the other five models were unable to deal with the unexpected heatwave cases. However, the corn yields predicted by our DNN model were very close to the actual corn yields, which indicates that this model was constructed appropriately for such extreme weather events. In Figure 10, the Illinois counties outlined in black correspond to the case of heatwaves for more than five consecutive days. The map shows that the counties under heatwave conditions had lower corn yields than the other counties, but the predicted yields were very closely simulated to the actual yields.
# Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
## 5 Application Framework
### Phylogenetic Tree Analysis Using EATT  
Recently, AIT techniques were successfully applied [59] in constructing the phylogenetic tree of RNA viruses. This research was conducted in the context of understanding the SARS-CoV-2 coronavirus responsible for the ongoing pandemic. Studying the genetic information by means of these computational tools can shed light on the rapid spread of SARS-CoV-2 and whether its evolution is driven by mutations, recombination or genetic variation. This information can then be applied for the development of diagnostic tools, effective antiviral therapies and in the understanding of viral diseases in general.  
The diversity of viruses prevents reconstruction of evolutionary histories as they lack any equivalent set of universally conserved genes on which to construct a phylogeny, such as eucaryotes. Viruses differ significantly in their genetic material, RNA or DNA, and configurations (double or single stranded), as well as the orientation of their encoded gene. The research analyzed this genetic information by means of the Kolmogorov complexity and Shannon information theories. In the first case, the normalized information distance and the normalized compression distance is estimated using the zlib compressor. In the second, a statistical approach is adopted by constructing histograms for the relative frequency of base triplets and interpreted using entropy, cumulative residual entropy and Jensen-Shannon divergence.  
The results indicate clearly the superior performance of the approaches based on the Kolmogorov complexity. The clusters are easily distinguishable and a relation is observed between the new SARS-CoV-2 virus and some CoV found in bats and pangolin, which are the most likely intermediate host from which the pandemic spread to humans.  
This type of methodology may help to study how an animal virus jumped the boundaries of species to infect humans, and pinpoint its origin knowledge can help to prevent future zoonotic events. The statistical and computational techniques allow different perspectives over viral diseases that may be used to grasp the dynamics of the diseases. These methodologies may help interpreting future viral outbreaks and to provide additional information concerning these infectious agents and understand the dynamics of viral diseases.
# An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction
## 2 Basic Theory and Algorithm  
Based on the three main problems experienced: the feature correlation measurement standard in the feature extraction and calculation process, the computational complexity in the predictive modelling process, and the generalization ability of the prediction model, this section lists the relevant theories that can be used to solve these problems.  
First, the initial reference degradation indicator subset \(F^{*}\) was screened by the fast correlation-based filter (FCBF) solution and approximate Markov Blanket to construct an initial subset of reference degradation feature indicators that can characterize the bearing degradation process. Secondly, the maximum information coefficient (MIC) was used to measure the correlation between features and features, as well as the correlation between features and bearings degradation state, to construct the initial reference degradation indicator subset \(F_{F-R}\) with maximum correlation between features and real RUL and the subset \(F_{F-F}\) with minimum redundancy between features. Thirdly, cross-operation was adopted for the initial reference bearing degradation indicator subsets to reduce the computation load, shorten the training time of prediction model, and reduce the computational complexity of the prediction modelling process. The reason for choosing different correlation measurement methods to construct the bearing degradation indicator subsets was to avoid the single correlation measurement method being affected by outliers in the data set, resulting in bias of the constructed bearing degradation indicator set and affecting the prediction accuracy.  
The results obtained using different correlation measurement methods were cross-operated to retain the effective indicators to the maximum extent. Finally, the AdaBoost algorithm was used to enhance the prediction model of LSTM neural network, and multiple weak predictors were assembled into a strong predictor to predict the bearing remaining useful life.
###### Abstract  
This article proposes a dual-band, frequency- and polarization-selective surface. Multiple resonant modes are introduced using the U-shaped resonator with a ground via to achieve dual-band responses and polarization selectivity. Two symmetrically grounded U-shaped resonators are coupled through electrically coupled apertures in a common ground, resulting in a passband with two transmission zeros per polarization. A general design flowchart and additional examples at the S, X, and K-bands are presented as well. A prototype at X-band is analyzed, fabricated, and measured, showing the passband center frequencies of 9.68 GHz and 10.73 GHz, fractional bandwidths of 3.45% and 3.48%, and insertion losses of 0.9 dB and 1.1 dB, respectively. Due to the high selectivity, small frequency ratio, low profile, and stable performance under oblique incidence, the proposed designs have application potential in wireless communication systems.  
Keywords:dual-band; frequency selective surface; polarization selective selectivity; wireless communication; duplex system; meta-surface +
Footnote †: journal:
## 2 Methods
### Reliability Analysis and Model Fit Test  
Reliability analysis was performed using [PERSON]'s alpha value to assess the internal consistency of the variables for work environment, work-family conflict, musculoskeletal pain, sleep-related problems, and burnout in the KWCS raw data. The SEM was validated through model fit and convergent validity analysis.  
Model fit was measured by the \(\chi^{2}\), \(p\)-value, goodness-of-fit-index (GFI), root mean square residual (RMR), root mean square error of approximation (RMSEA), normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), comparative fit index (CFI), and Tucker-Lewis index (TLI). The RMR [49; 50; 51] and RMSEA [51] were evaluated as good if less than 0.05 and acceptable if less than 0.08. The GFI [52], NFI [52; 53], RFI [54], IFI [55], CFI [50; 52; 56], and TLI [52] were evaluated as highly suitable when above 0.9 and acceptable up to 0.85 [57].  
The reliability of the SEM was analyzed using average variance extracted (AVE), composite reliability (CR), and correlation coefficients between variables. AVE was evaluated as appropriate when it was greater than 0.5 and CR was greater than 0.7 [58]. The statistical packages used for statistical analysis were SPSS 27.0 and AMOS 22.0.
# Two-Party Quantum Private Comparison Protocol Based on Rotational Encryption
## 2 Rotational Encryption  
The encryption and decryption mechanisms are fundamental to our protocol. In this approach, the classical bit \"0\" is encoded as state \(|0\rangle\), while the classical bit \"1\" is represented as state \(|1\rangle\). The rotational encryption operator can be expressed as  
\[R_{y}(\theta)=\begin{pmatrix}\cos\frac{\theta}{2}&-\sin\frac{\theta}{2}\\ \sin\frac{\theta}{2}&\cos\frac{\theta}{2}\end{pmatrix} \tag{1}\]  
Equation (1) can be interpreted as a unitary operation that involves rotation around the Y-axis on Bloch sphere [41,42]. The symbol \(E_{k}[|\psi\rangle]\) denotes the encryption process applied to the photon \(|\psi\rangle\) using a rotation angle \(k\in[0,2\pi)\), which serves as the encryption key. Similarly, let the symbol \(D_{k}[|\psi\rangle]\) denote the decryption process. We assume the classical binary message \(M=0\), which can be encoded as \(|\psi\rangle=|0\rangle\), and the encryption key is \(k=\theta\). After the encryption, the resulting state \(|\psi\rangle^{\prime}\) is given by  
\[\begin{split}&|\psi^{\prime}\rangle=E_{k}[|\psi\rangle]=R_{y}( \theta)|\psi\rangle=\left(\begin{array}{cc}\cos\frac{\theta}{2}&-\sin\frac{ \theta}{2}\\ \sin\frac{\theta}{2}&\cos\frac{\theta}{2}\end{array}\right)\left(\begin{array} []{c}1\\ 0\end{array}\right)\\ &=\left(\begin{array}{c}\cos\frac{\theta}{2}\\ \sin\frac{\theta}{2}\end{array}\right)=\cos\frac{\theta}{2}|0\rangle+\sin\frac{ \theta}{2}|1\rangle\end{split} \tag{2}\]  
This means that applying the rotational encryption operator to the quantum state can achieve the encryption of that quantum state. The decryption process to obtain the original state \(|\psi\rangle\) is regarded as rotating the state \(|\psi\rangle^{\prime}\) with an angle \(\theta\) in the opposite direction. That is,  
\[\begin{split}& D_{k}[|\psi^{\prime}\rangle]=R_{y}(-\theta)| \psi^{\prime}\rangle=\left(\begin{array}{cc}\cos\frac{\theta}{2}&\sin\frac{ \theta}{2}\\ -\sin\frac{\theta}{2}&\cos\frac{\theta}{2}\end{array}\right)\left(\begin{array} []{c}\cos\frac{\theta}{2}\\ \sin\frac{\theta}{2}\end{array}\right)\\ &=\left(\begin{array}{c}\cos\frac{\theta}{2}\cos\frac{\theta}{2}+\sin\frac{ \theta}{2}\sin\frac{\theta}{2}\\ -\sin\frac{\theta}{2}\cos\frac{\theta}{2}+\cos\frac{\theta}{2}\sin\frac{ \theta}{2}\end{array}\right)=\left(\begin{array}{c}1\\ 0\end{array}\right)=|\psi\rangle\end{split} \tag{3}\]