esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissemination of Enterococcuscal Genetic Lineages: A One Health Perspective
## 4 Enterococcal Genetic Traits: Dissemination in the One Health Continuum  
According to the WHO [49], around 75% of emergent human infectious diseases originate in animals, due to the interdependence between humans and animals, changes in ecosystems, intensification of agriculture, urbanization, travel, and commerce [50]. To  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Antibiotic Class** & **Cellular Target** & **Species** & **Type of** **Resistance** & **Mechanism of Resistance** & **Associated Antimicrobial Resistance Genes** \\ \hline Tetracyclines & Protein synthesis (30 s) & _E. facetum_, _E. facetalis_ & Intrinsic/acquired & Target-site modification & _pextA_ \\ \cline{3-6}  & & & - & Antibiotic efflux & _tet (K), tet (L)_ \\ \cline{3-6}  & & & - & Target-site protection & _tet (M), tet (O), tet (S)_ \\ \hline Macrolides & Protein synthesis (50 s) & Most enterococci & Acquired & Ribosomal & _ermA, errnB, errC,_ \\  & & & & methylation & _ermS, errV_ \\ \hline Oxazolidinones & Protein synthesis (23 s) & _E. facetum_ & Intrinsic/acquired & Target-site protection & _pextA, optrA_ \\ \cline{3-6}  & & & & Target-site modification (point mutations) & - \\ \hline Phenicols (Chloramphenicol) & Protein synthesis & _E. facetum_, _E. facetalis_ & Acquired & CAT-encoding enzymes & _catA, catB_ (mostly) \\ \hline StreptoGramins & Protein synthesis (early/late stage) & _E. facetalis_, _E. gailinarum_, _E. casselflaws_ & Intrinsic & Antibiotic efflux & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._tackle this increasing issue, the \"One Health\" concept was coined over two decades ago, aiming to define an integrated, multispectral, and sustainably balanced health approach that recognizes the interconnection between human, animal, and environmental health [51]. As a result of enterococcal duality and transversality to different ecosystems, rapid adaptation to the host, and propensity to acquire virulence and resistance traits, enterococci are fit to be used as indicator organisms for tracking and monitoring the transmission of pathogenicity-related genetic determinants in a One Health perspective [52]. Accordingly, antimicrobial resistance and the dissemination of resistance genes through interconnected ecosystems have been considered quintessential One Health issues [53]. To face this problem, many authors [18; 36; 54; 55] have been performing comparative genomic analyses of _Enterococcus_ spp. as indicator bacteria, to study the transference of ARGs, virulence genes, and MGE determinants across the One Health continuum.  
Accordingly, considering the extensive number of studies addressing the dissemination of genetic determinants within each sector (human health, animal health, environmental health, and food safety), the present review will focus on the dissemination of genetic traits across sectors of the One Health triad. However, considering that HiRECCs are associated with hospital settings and healthcare-associated infection, human-related studies are discussed in more detail, to assure understanding of the topic under review.
## 3 Results
### Comparison of Pre-Trained Models  
The prediction performances of the five pre-trained BERT models are listed in Table 4. The model with the highest performance is the Umls BERT model, with a very slight difference from the Bio BERT model, and shows an AP score of 0.7872, F1 score of 0.7603, and AUC of 0.9863 based on the micro-average setting. Unlike other models, base BERT is a trained model that uses a general corpus rather than a medical corpus. Therefore, as explained in Section 2.3.2, the performance of the tasks in this study was low. Additionally, the overall performance of the Medical Character BERT model was lower than that of the other word-level BERT models, as shown in Table 5. However, its performance on minority data was better than that of the other word-level BERT models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{3}{c}{**AP**} & \multicolumn{3}{c}{**F1**} & \multicolumn{3}{c}{**AUC**} \\ \cline{2-9}  & **Micro** & **Macro** & **Micro** & **Macro** & **Micro** & **Macro** \\ \hline \multirow{5}{*}{**Single Model**} & * Base & 0.7854 & 0.5718 & 0.7570 & 0.3210 & 0.9860 & 0.7565 \\  & Umls & 0.7872 & 0.5815 & 0.7603 & 0.3383 & 0.9863 & 0.7575 \\  & Medical & 0.7836 & 0.5786 & 0.7584 & 0.3333 & 0.9854 & 0.7564 \\  & Bio & 0.7895 & 0.5815 & 0.7592 & 0.3369 & 0.9863 & 0.7557 \\  & * MC & 0.7751 & 0.5445 & 0.7495 & 0.2965 & 0.9853 & 0.7566 \\ \hline \multirow{2}{*}{**Aggregated Model**} & Medical + * MC & 0.7898 & 0.5891 & 0.7604 & 0.3578 & 0.9867 & 0.7580 \\  & Umls + * MC & **0.7937** & **0.5959** & **0.7622** & **0.3643** & 0.9876 & **0.7587** \\  & Bio + * MC & 0.7922 & 0.5905 & 0.7593 & 0.3637 & **0.9877** & 0.7583 \\ \hline \hline \end{tabular}  
* Base = base BERT: pre-trained with general corpuses,
* MC = Medical Character BERT, AUC = area under the curve.  
\end{table}
Table 4: Comparison of models.
# An Innovative Adaptive-Dimension Accordion Honeycomb for Circumferential Deformation
## 2 Variable-Dimension Honeycomb Design
### Honeycomb Geometry  
Figure 2 illustrates the geometry of the novel honeycomb structure, comprising unit cells arranged along the orthogonal directions of the polar coordinate system. In this work, we denote \(\{m,n\}\) and \((i,j)\) as the configuration of the honeycomb and the arrangement index of a particular cell, respectively. For example, \(\{m,n\}\) indicates the number of unit cells in the radial and circumferential directions, while \((i,j)\) signifies the \(i\)th cell in the radial direction and the \(j\)th cell in the circumferential direction. Evidently, the honeycomb structure exhibits the following properties:  
1. All unit cells in the honeycomb are similar.
2. The unit cells located in the same circumferential direction are congruent.  
To describe the geometry of the honeycomb, point \(A_{ij}\) (as shown in Figure 1) is utilized to identify the position of the \((i,j)\) cell. \(\left(r_{ij},\theta_{ij}\right)\) and \(l_{ij}\) represent the polar coordinates and the length of the outside cell wall, respectively. According to (i) and (ii), \(l_{ij}\) and \(r_{  
At this point, the geometric model of the variable-dimension honeycomb is accurately described by Equations (10) and (14), and the overall dimensions \(\Theta\) and \(L\) can be expressed by  
\[\Theta=n\theta \tag{15}\]  
\[\begin{array}{ll}L&=\sum_{i=1}^{m}l_{i}\\ &=r_{m+1}-r_{1}\\ &=r_{1}\big{(}(q+1)^{m}-1\big{)}\end{array} \tag{16}\]  
According to Equations (12) and (14), we can obtain the wall length \(l_{i}\) of the unit cells located in the radial direction using the following:  
\[l_{i}=qr_{1}(q+1)^{i-1} \tag{17}\]  
Since \(q\) is a constant, \(l_{i}\) changes exponentially about \(q+1\), and this is the reason for the variable-dimension characteristics of the honeycomb. From Equation (11), it can be seen that \(q\) increases with an increase in \(\theta\) and decreases with an increase in \(p,\phi\). As a result, we can let \(q\) tend to 0 by decreasing \(\theta\) and increasing \(p,\phi\), thereby weakening the variable-dimension characteristics of the honeycomb.
## 6 Discussion  
### Six Stages of Curing  
The stages of curing, outlined in Table 2, are justified due to the following reasons.  
* Stage I is obviously the start of the curing reaction. This stage exists directly after the mixing of the components. Even if measurements would be possible it cannot be distinguished from early Stage II measurements. For all practical reasons, it is the OBR zero strain state.
* In Stage II, the epoxy is still liquid and not gelated. Thus, no mechanical strain is acting upon the optical fiber. However, the ongoing exothermic reaction leads to a rising temperature as can be seen in the left graph in Figure 7. This in turn leads to a positive OBR signal which is to be interpreted as temperature and not strain (cf. Figure 4).
* Stage III is the point of gelation. In the temperature measurements, no change in behavior is indicated by reaching this point. However, if the OBR-signal is interpreted as temperature before reaching this point, the OBR values start to deviate from the temperature values determined with independent temperature probes after passing this point. In accordance with the theory, this happens at approximately 60% conversion.  
Figure 8: Time and location dependent temperature of the epoxy in the 60 cm mold. For this graph, it was assumed that each temperature probe covers the area halfway to the next sensor, hence the differently sized columns where the fiber enters the epoxy due to more sensors in this area. The distance between the the majority of the temperature sensors was 2–3 cm. An exception are the three sensors at the entry point of the fiber into the epoxy. These were placed with a distance of ca. 1 cm between them. The first sensor was placed 5 mm away from the wall of the mold. The gap between 35 cm and 39 cm is due to two malfunctioning probes around this position. At the end of the vessel, three centimeters were not covered with a probe, hence it is left blank in the graph.  
* Stage IV is defined by the still rising temperature due to the ongoing (and probably accelerating) curing reaction.
* Stage V distinguishes itself from the previous stage by the fact that the temperature reaches a maximum and stays at this value. The conversion advances but the chemical reaction is slowing down.
* In Stage VI, the conversion is so high that all ongoing reactions are so slow that just minute amounts of heat are released and the whole regime is governed by the cool down of the sample. For all practical purposes, the epoxy can be seen as \"fully cured\".  
Since the temperature plays a defining role in the chemical reaction, it is worth mentioning that, from a technical point of view, the duration of all these stages are somewhat controllable, as discussed in the Introduction. For example, in thin samples, all exothermic heat can be transported away fast enough or by external heating it can be kept at a constant temperature. That means that Stage IV will be skipped and 100% conversion can be reached during Stage V.
## 3 Results  
The means and SD values for the fracture resistance (N) in the study groups are displayed in Table 3 and Figure 4.  
The highest mean fracture resistance value was observed at the FRC post-FRC resin core study group (\(3620\pm 470\)) and the lowest mean fracture resistance value was observed at the FRC post-CR study group (\(2420\pm 1010\)) (Table 3 and Figure 4). Three study groups evidenced more fracture resistance than Control group (\(3290\pm 830\)): Elastics Post-CR study group (\(3510\pm 730\)), FRC post-FRC resin core study group (\(3620\pm 470\)) and Elastic Post-FRC resin core study group (\(3520\pm 730\)), as if they could be increasing the fracture resistance of the specimens (Table 3 and Figure 4). The paired Tukey test revealed statistically significant differences between the mean fracture resistance values between FRC post-CR and Elastic FRC Post-CR (\(p=0.043\)) and between Elastic FRC post-CR and CR (\(p=0.008\)) (Figure 5). Also, there was statistically significant difference between FRC post-CR and FRC post-FRC  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **n** & **Mean** & **SD** & **Minimum** & **Maximum** \\ \hline Conventional FRC post-CR Core & 10 & 2420 * & 1010 & 1190 & 4010 \\ Elastic FRC post-CR Core & 10 & 3510 & 730 & 2010 & 4230 \\ Conventional FRC post-FRC CR & 10 & 3620 & 470 & 2800 & 4020 \\ Elastic FRC post-FRC CR & 10 & 3520 & 730 & 2070 & 4020 \\ CR Core & 10 & 2560 * & 570 & 1690 & 3400 \\ FRC CR & 10 & 3040 & 1080 & 1310 & 4070 \\ Control: intact teeth without & & & & & \\ MOD cavity, nor endodontic & 10 & 3290 & 830 & 1830 & 4930 \\ treatment nor restorations & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics of the fracture resistance values (N) of the study groups after bending test. * \(p\) value < 0.05. (One-way ANOVA test for comparing the means between multiple groups).  
The scale distribution parameter (\(\eta\)) of Weibull statistics showed statistically significant differences between CR core study group and control group (\(p\) = 0.0065), however, there were not statistically significant differences between the mean fracture resistance values of the study groups. There were also statistical significant differences at the shape distribution parameter (\(\beta\)) between FRC post-FRC resin core study group and control group (\(p\) = 0.0161), however, there were not statistical significant differences between the mean fracture resistance values of the study groups (Table 4 and Figure 7).
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 3 Proposed Low Sampling Rate Design
### Signal Model  
In the proposed scheme, the time domain signal transmitted at the \(m\)-th antenna is still obtained by (2) and the received signal at the \(k\)-th user is also obtained by (3), except \(x_{m,n}\) is generated in a different way.  
With the sampling frequency as \(f_{\rm s}=V/T\), the ADC at the \(k\)-th user output \(V=N/K\) samples during the period \(T\). The samples could be expressed as:  
\[\tilde{y}_{k}(v)=\frac{1}{\sqrt{N}}\sum_{m=1}^{M}\sum_{n=0}^{N-1}\tilde{H}_{k, m}^{n}x_{m,n}\mathrm{e}^{\mathrm{i}2\pi\frac{v}{b}v}+\tilde{z}_{k}(v),\quad v=0,1, \cdots,V-1, \tag{17}\]  
where \(\tilde{z}_{k}(v)\sim\mathcal{CN}(0,\sigma^{2})\). The matrix form of (17) is written as:  
\[\tilde{y}_{k}=\mathbf{D}\sum_{m=1}^{M}\mathrm{diag}\big{(}\tilde{\mathbf{H}}_{k,m} \big{)}\mathbf{x}_{m}+\tilde{z}_{k}, \tag{18}\]  
Figure 2: The proposed scheme with a low sampling rate.  
where \(\tilde{\mathbf{y}}_{k}=(\tilde{y}_{k}(0),\tilde{y}_{k}(1),\cdots,\tilde{y}_{k}(V-1))^{ \text{T}}\) and \(\tilde{\mathbf{z}}_{k}=(\tilde{z}_{k}(0),\tilde{z}_{k}(1),\cdots,\tilde{z}_{k}(V-1))^ {\text{T}}\). \(\tilde{\mathbf{H}}_{k,m}\) is the same as in (8). Similar to the conventional OFDM system in Figure 1, we also assume that each user could perfectly estimate its channel \(\{\tilde{H}_{k,m}^{n}\,\forall n,m\}\) and feed back the measurements to the BS. Note that the low sampling rate of the receiver does not mean that we encounter difficulties in channel estimation. In fact, due to the sparsity of the channel responses, the channel estimation in real OFDM systems is generally performed on a subset of subcarriers [35].  
The matrix \(\mathbf{D}\in\mathbb{C}^{V\times N}\) in (17) is obtained by:  
\[\mathbf{D}=\frac{1}{\sqrt{N}}\left(\begin{array}{cccc}1&1&\cdots&1\\ 1&\text{e}^{\frac{2\pi(N-1)}{V}}&\cdots&\text{e}^{\frac{2\pi(N-1)}{V}}\\ \vdots&\vdots&\ddots&\vdots\\ 1&\text{e}^{\frac{2\pi(V-1)}{V}}&\cdots&\text{e}^{\frac{2\pi(V-1)(N-1)}{V}} \end{array}\right). \tag{19}\]  
Note that \(\mathbf{D}\) is known beforehand by the BS.
# Weak Multiple Fault Detection Based on Weighted
## 3 The Proposed Algorithm  
### WOGS Model  
Fault signal \(x\) is assumed to be non-sparse, but it is approximately sparse under the Morlet wavelet transform. Fault signal \(x\) can be expressed as:\[x=\Phi\theta, \tag{13}\]  
where \(\Phi\) and \(\theta\) represent the Morlet wavelet dictionary and the sparse representation coefficient. In order to solve the regularization inverse problem, that is, recover \(x\) from the observed vibration signal \(y\), the sparse optimization problem can be formulated as:  
\[\theta^{opt}=\arg\min_{x}\left\{F(\theta)=\min_{\theta\in\mathbb{R}^{N}}\frac{1 }{2}\|y-\Phi\theta\|^{2}+\lambda G(\theta)\right\}, \tag{14}\]  
where \(G(\theta)\) is a regularized penalty function for inducing sparse solutions. Penalty term \(G(\theta)\) depends on the structural form of coefficient \(\theta\). In the multiple fault signal of rolling bearings, only a small number of coefficients have large amplitudes, and the rest are close to zero. The OGS regularization term in Equation (3) is used to characterize the overlapping group sparsity of \(x\). Therefore, the regularization function based on the Morlet wavelet transform coefficient \(\theta\) is defined as  
\[G(\theta)=\sum_{i}\left[\sum_{j\in J}|\theta(i+j)|^{2}\right]^{1/2} \tag{15}\]  
for a two-dimensional (2-D) vector \((\theta)\) of size \(N_{1}\times N_{2}\) with a group size of \(K_{1}\times K_{2}\), and then \(I\) and \(J\) are set to:  
\[I=\{(i_{1},i_{2}):0\leq i_{1}\leq N_{1}-1,0\leq i_{2}\leq N_{2}-1\},J=\{(j_{1},j_{2}):0\leq j_{1}\leq K_{1}-1,0\leq j_{2}\leq K_{2}-1\}. \tag{16}\]
# Airborne Quantum Key Distribution Performance Analysis under Supersonic Boundary Layer
###### Abstract  
Airborne quantum key distribution (QKD) that can synergize with terrestrial networks and quantum satellite nodes is expected to provide flexible and relay links for the large-scale integrated communication network. However, the photon transmission rate would be randomly reduced, owing to the random distributed boundary layer that surrounding to the surface of the aircraft when the flight speed larger than Mach 0.3. Here, we investigate the airborne QKD performance with the BL effects. Furthermore, we take experimental data of supersonic BL into the model and compare the airborne QKD performance under different conditions. Simulation results show that, owing to the complex small-scale turbulence structures in the supersonic boundary layer, the deflection angle and correspondingly drifted offset of the beam varied obviously and randomly, and the distribution probability of photons are redistributed. And the subsonic and supersonic boundary layer would decrease ~35.8% and ~62.5% of the secure key rate respectively. Our work provides a theoretical guidance towards a possible realization of high-speed airborne QKD.  
Keywords:boundary layer; quantum key distribution; airborne +
Footnote †: journal:
# Panchromatic and Hyperspectral Image Fusion Using Ratio Residual Attention Networks
## 4 Results  
### Experimental Environment  
The traditional fusion methods discussed in this paper were run on an Intel i7-8700 processor with a clock speed of 3.2 GHz. The deep learning-based methods were executed in a computing environment equipped with an NVIDIA GeForce GTX 1080 Ti graphics card to accelerate both model training and inference processes. The deep learning frameworks used in the experiments were TensorFlow v2.8.0 and PyTorch v1.12.0, and the operating system was Windows 10. All experiments were conducted under this configuration. This experimental setup is capable of fully meeting the computational demands of both traditional methods and deep learning approaches for remote sensing image fusion tasks.
## 1 Introduction
### Motivation and Contributions  
In summary, the aforementioned schemes are limited by permanent authorization, difficulty of changing the password that is kept in the smartcard, the inability of a round-oriented smartcard to verify simultaneously the need for health information in multiple places, and poor computing efficiency during medical treatment.  
In order to solve these problems, this investigation proposes a user-authentication and key-agreement scheme that complies with HIPAA security regulations by using enhanced Chebyshev polynomials. Many recent studies showed some mathematical models and theorems can be applied to information systems and have good computing performance [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30]. Enhanced Chebyshev polynomials exhibit semigroup and commutative properties and provide the Logarithm problem and the Diffie-Hellman problem. Additionally, recent investigations have established that cryptosystems that are developed using extended Chebyshev chaotic maps are more efficient than those developed using modular exponentiations and scalar multiplications on elliptic curves. Therefore, the efficiency of the scheme that is proposed in this investigation is enhanced by using extended Chebyshev chaotic maps. In the mutual-authentication and key-agreement phase of each session, different authorizations are generated to be compliant with HIPAA privacy regulations and security regulations. The contributions of this work are summarized as follows:  
1. A secure and efficient authentication and key-agreement scheme that is based on extended Chebyshev chaotic maps is proposed by using lightweight extended Chebyshev chaotic maps and hash operations.
2. The proposed scheme solves the security problems of previous schemes, which do not include updated passwords, patients' authorization and patients' control, and cannot resist password-guessing attacks, impersonation attacks, replay attacks and stolen verifier attacks.
3. The proposed scheme is compliant with HIPAA privacy and security regulations.
## 3 Methodology
### Image Preprocessing
#### 3.2.3 Data Augmentation  
Data augmentation methods are employed on the images to expand the dataset size and prevent overfitting. The images undergo various transformations such as rotation, flipping, scaling, and translation to augment the dataset. This method is effective for expanding the dataset size by generating new samples from existing samples. This can help to prevent overfitting and enhance the generalization capability of an ML model. For a pixel intensity \(f(x,y)\) at \((x,y)\) position in an input image, the mathematical representations for these data augmentation techniques are presented below.  
* Rotation: The equation for rotating an image by an angle \(\theta\) is given as \[f^{\prime}(x,y)=f(xcos(\theta)-ysin(\theta),xsin(\theta)+ycos(\theta))\] (6) where \(f^{\prime}(x,y)\) here is the new pixel intensity in the rotated image at the same position.
* Flipping: The equation for flipping an image horizontally or vertically is given below: \[\text{Horizontalflip}:f^{\prime}(x,y)=f(x,-y),\ \ \text{Verticalflip}:f^{\prime}(x,y)=f(-x,y)\] (7) where \(f^{\prime}(x,y)\) here is the new pixel intensity in the flipped image at the same position.
* Scaling: The equation for scaling an image by a factor of \(s\) is given as \[f^{\prime}(x,y)=f(sx,sy)\] (8) where \(f^{\prime}(x,y)\) here is the new pixel intensity in the scaled image at the same position.
* Translation: The equation for translating an image by a displacement \((dx,dy)\) is given by \[f^{\prime}(x,y)=f(x+dx,y+dy)\] (9) where \(f^{\prime}(x,y)\) here is the new pixel intensity in the translated image at the same position. These equations provide a general framework for performing data augmentation on images, but the specific implementation details may vary depending on the software and hardware used. Figure 5 shows the dataset samples which are augmented with the above data augmentation techniques.