esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Development of High-Energy Short-Pulse Lasers with Cryogenically Cooled Yb:YAG
## 4 Cryogenically Cooled Yb:YAG Lasers  
In China, research institutions such as the Chinese Academy of Sciences and the Chinese Academy of Engineering Physics have carried out research on cryogenically cooled high-power short-pulse Yb:YAG lasers, but due to the late start of the research, the reported laser power in the literature only reaches the order of tens of watts [8; 19; 20]. The world's  
Figure 5: Methods to suppress ASE: (**a**) bonding undoped YAG crystals; (**b**) absorber cladding (Cr4+:YAG crystal); (**c**) edge chamfering; (**d**) edge grinding.  
leading research institutions have achieved kilowatt-level output in both 10 Hz nanosecond lasers and 1 kHz picosecond lasers. The following sections discuss landmark achievements of cryogenically cooled high-power short-pulse Yb:YAG lasers around the globe.
## 3 Results
### Positive Augmentations
Figure 7 showcases ROC curves for the ROI model against the baseline. Upon evaluation of AUCs in the one-vs-all scheme (Figure 7), it was observed that Classes 0, 2, and 3 had marginally higher AUC scores in the Baseline Model compared to the ROI Model. Specifically, the AUC for Class 0 was 0.87 in the Baseline Model versus 0.85 in the ROI Model, suggesting that the Baseline Model was slightly more successful in distinguishing between positive and negative instances for this class. Similarly, for Class 2, the Baseline Model had an AUC of 0.84 compared to 0.81 in the ROI Model, and for Class 3, the AUC was 0.96 in the Baseline Model versus 0.95 in the ROI Model. In contrast, for Class 1, the ROI Model outperformed the Baseline Model, albeit slightly, with an AUC of 0.69 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Name** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Baseline Rotated & 0.655 & 0.621 & 0.645 & 0.618 \\ Baseline & 0.644 & 0.621 & 0.651 & 0.627 \\ ROI & 0.623 & 0.614 & 0.643 & 0.616 \\ Horizontal Split 20\% & 0.595 & 0.597 & 0.611 & 0.591 \\ Horizontal Split Flip & 0.581 & 0.518 & 0.577 & 0.531 \\ ROI Split Flip & 0.583 & 0.577 & 0.595 & 0.579 \\ Horizontal Split 20\% Flip & 0.577 & 0.578 & 0.591 & 0.570 \\ ROI Split & 0.570 & 0.533 & 0.579 & 0.546 \\ Horizontal Split & 0.560 & 0.497 & 0.563 & 0.501 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics of the model on the test set using positive/supportive base augmentations. against 0.68. For Class 4, both models performed impeccably, achieving a perfect AUC score of 1.00, demonstrating their ability to distinguish instances of this class perfectly. In summary, while the performance of both models was similar for all classes, the Baseline Model showed a slight edge in Classes 0, 2, and 3. The ROI Model only performed  
Figure 6: Confusion matrices for the test set using positive/supportive base data augmentations. Figure 7: Using a one-vs-all scheme, ROC curves are shown for the Baseline condition (**left**) and the ROI condition (**right**). marginally better in Class 1, and both models were equally successful in Class 4. Despite these differences in AUC values, it's noted that the curvature along the axis was similar between the two models. This suggestsed that the trade-off between sensitivity and specificity (true positive rate and false positive rate) was similar for both models across different decision thresholds. This similarity in shape indicated that both models had similar performance trade-offs, even if the absolute performance (as measured by AUC) varied slightly.
## 4 The Pressure-Based Coupled Solution Procedure  
In a fully-coupled velocity-pressure algorithm, the momentum and mass pressure equations are solved simultaneously. The structure of the system is as follows:  
\[\begin{array}{c}\text{Momentum Equation:}\\ \text{Mass Equation:}\end{array}\quad\begin{bmatrix}\mathbf{a}_{C}^{\mathbf{vv}}& \mathbf{a}_{C}^{\mathbf{vp}}\\ \mathbf{a}_{C}^{\mathbf{pv}}&a_{C}^{\mathbf{pp}}\end{bmatrix}\begin{bmatrix} \mathbf{v}_{i}\\ p_{i}\end{bmatrix}+\sum_{F,j}\begin{bmatrix}\mathbf{a}_{F}^{\mathbf{vv}}& \mathbf{a}_{F}^{\mathbf{vp}}\\ \mathbf{a}_{F}^{\mathbf{pv}}&a_{F}^{\mathbf{pp}}\end{bmatrix}\begin{bmatrix} \mathbf{v}_{j}\\ p_{j}\end{bmatrix}=\begin{bmatrix}\mathbf{b}_{C}^{\mathbf{v}}\\ b_{C}^{\mathbf{p}}\end{bmatrix}\end{array} \tag{12}\]  
where the subscript \(C\) appearing in Equation (12) denotes a control volume associated with the node of index \(i\), and \(F\) is any neighboring control volume to \(C\), associated with a node of index \(j\). There are, however, two categories of neighboring control volumes \(F\); the first category includes those that share an integration point with \(C\), while the other category includes those that do not share an integration point with \(C\) (Figure 4). The only tangible difference which arises between them is related to the advection fluxes. Neighboring control volumes that do not share an integration point with \(C\) are not involved in the discretization of advection fluxes. This will be highlighted in the following section. Superscripts \(*\) and \(\circ\) will denote the latest available value and the previous time step value, respectively, while the notation \(||a,b||\) will be used to indicate a maximum between two quantities \(a\) and \(b\).
# Design of Universal Code Generator for Multi-Constellation Multi-Frequency GNSS Receiver
## 3 Code Generators  
### Universal Code Generators Based on LFSR  
Reviewing the relevant literature reveals that currently available universal code generators typically exhibit the following structure:  
1. Universal Ranging Code Generator;  
2. Area-Efficient Universal Code Generator;
## 3 Methods
### Remote Sensing Data  
We combined multi-date imagery from the PALSAR-2, Sentinel-1, and Sentinel-2 sensors. Image dates were selected to be coincident with field data collection described in Section 3.3 below. All imagery was projected to the WGS 1984 Universal Transverse Mercator coordinate system, zone 36S. Images were stacked and clipped to the geometry of the Reserve boundary.
## 3 Method
### Basic Idea
This yields a precise calculation time interval [\(t_{3s}\), \(t_{3e}\)] (\(\Delta t_{3}\)). Finally, based on a short interval binary search strategy, the start -end times of the VTW are quickly calculated, thus obtaining the complete VTW. The specific calculation process of the algorithm is illustrated in Figure 6, where the same three colors of red, blue, and green represent three different calculation strategies, as shown in Figure 5. Initially, orbit segmentation is carried out based on the input information. In this phase, region expansion is performed. Subsequently, position calculation is conducted to determine whether the sub-point has reached the boundary of the expanded region. The corresponding circle and start-end times are recorded. In the coarse calculation phase, the regional boundary is uniformly discretized to realize the angle calculation of the payload's view field and the point. A large-step angle calculation is employed to roughly calculate whether the angle within the time interval is equal, and intervals that meet the requirements are recorded. In the precise calculation phase, only a certain precision is used for the binary calculation. The precision is checked to determine whether it meets the requirements, and intervals that meet the requirements are recorded as the VTW for the global mission interval. The pseudo code for the algorithm is shown in Algorithm 1. Figure 5: Principle diagram of TTIHS.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 4 Additions to Filtering
### Ellipsoid-Shaped Density Filter  
The main issue when extrapolating results from the sensitivity analysis to other design spaces is the mesh dependency of the density-based TO [19]. The shape of the optimized part is directly dependent on the density filter described in Equation (2). This is caused by the relation between the filter size and the design domain size: using the same filter size with different design domains leads to different resultant shapes.  
To address this characteristic, the original sphere-shaped filter is taken as an ellipsoid with equal semi-axes, so they can be individually varied with the three Cartesian dimensions of the initial design domain. As the used weighting factor is conic weights [38], the radii of the ellipsoid at the same latitude and longitude as the analyzed element, \(\mathrm{R_{ijk}^{fil}}\), must be obtained for calculating the weight of the neighbor elements.  
For obtaining this radii, six initial parameters are needed: \(\mathrm{a,b,}\) and \(\mathrm{c}\), which are the semi-axes' dimensions given by the user, and three filter radii, \(\mathrm{R_{x}^{min}}\), \(\mathrm{R_{y}^{min}}\), and \(\mathrm{R_{z}^{min}}\), respectively. The relative coordinates of the element being analyzed in the kernel filter with respect to the base element are denoted \(\mathrm{R_{i}}\), \(\mathrm{R_{j}}\), and \(\mathrm{R_{k}}\).  
The angles \(\alpha\) and \(\omega\) in Figure 12 are the geometric latitude and longitude, respectively, which can be obtained with trigonometry, as follows:  
\[\begin{array}{l}\alpha=\tan^{-1}(\mathrm{R_{y}}/\mathrm{R_{x}})\\ \omega=\tan^{-1}(\mathrm{R_{z}}/\mathrm{R_{x}})\\ \end{array} \tag{11}\]  
The coordinates of the ellipsoid surface at the same latitude and longitude as the analyzed element, \(\mathrm{R_{x}}\), \(\mathrm{R_{y}}\), and \(\mathrm{R_{z}}\), can be geometrically calcul  
Figure 12: Geometrical parameters of the ellipsoid for obtaining its radius from the Cartesian coordinates of the elements being analyzed.  
values with the general formula of an ellipsoid, Equation (12) is obtained for the calculation of the desired radius:  
\[\mathrm{R_{ijk}^{fil}}=\frac{\mathrm{abc}}{\sqrt{c^{2}\mathrm{cos}^{2}(\omega) \left(\mathrm{b}^{2}\mathrm{cos}^{2}\left(\alpha\right)+\mathrm{a}^{2}\mathrm{ sen}^{2}(\alpha)\right)+\mathrm{a}^{2}\mathrm{b}^{2}\mathrm{cos}^{2}\left( \alpha\right)\mathrm{sen}^{2}(\omega)}} \tag{12}\]  
The three implementations are presented and highlighted in green in the flowchart presented in Figure 13. The flowchart represents level 6 of Figure 1 and the blue block of Figure 10. The machining filter is divided into the three phases, described in Appendices A.1-A.3, and the density filter is computed using the ellipsoid-shaped neighborhood.
## 3 Results and Discussion
### Receiver Operation Characteristic Curves for PSPME-IMS Systems  
The ROC curves were developed for both benchtop and portable IMS systems when coupled with PSPME devices from the all the defined scenarios and replicates to determine the overall performance for detection of the target analytes for the different sampling and detection techniques. From a total of 360 samples for all the different replicates and the different defined scenarios, the ROC curves for the two instruments were constructed for the three target analytes using JMP (version 10) software (SAS Institute Inc., Cary, NC, USA). The results for the different scenarios were used to determine the sensitivity (TPR) and specificity (1-FPR) trade-offs for the target analytes as shown in Figure 4a and b for the portable and benchtop IMS, respectively. The area under the curves (AUC) of the benchtop IMS were greater for analyte 2,4-DNT than the portable IMS (1.0 and 0.87, respectively) with similar performance for detection of DPA (AUC of 0.81 and 85 for the benchtop and portable IMS, respectively) and a perfect ROC curve (AUC = 1.0) was determined for NG in both detector systems. The results indicate that the portable PSPME-IMS system achieved similar detection performance for DPA as the benchtop PSPME-IMS instrument because of similar sensitivity and limits of detection for the two instruments; however, increased positive alarms from the portable PSPME-IMS in the plastic containers (TPR = 0.27) in comparison to the benchtop PSPME-IMS instrument (TPR = 0.12) under the same scenario showed slightly improved performance for the portable PSPME-IMS. The overall performance of the two PSPME-IMS systems showed excellent performance, with similar or greater performance of the benchtop PSPME-IMS in comparison to the portable PSPME-IMS system.  
ROC curves were also constructed for the laboratory based SPME-GC-MS technique as shown in Figure 4c, which include only static extractions and excluding scenarios involving plastic containers resulting with a total of 140 samples. The sampling and detection technique resulted with better performance compared to the PSPME-IMS system with a perfect ROC curve (AUC = 1.0) for 2,4-DNT and excellent performance for detection of NG and DPA with an AUC of 0.97 and 0.94, respectively.  
Figure 3: Plasmagrams for dynamic PSPME sampling (1 min.) in cluttered environments from a local shipping facility. Sampling was performed in LD3 (4,500 L) containers and LD8 (6,880 L) containers as well as open air sampling of the location with indication of detection windows for NG and 2,4-DNT (drift time (t\({}_{\mathrm{d}}\)) of 3.8 ms and 5.6 ms, respectively).  
The poor sensitivity of NG in the SPME-GC-MS technique resulted in poorer performance in comparison to IMS detection systems. Overall, the SPME-GC-MS resulted with excellent performance for all the analytes of interest under the defined scenarios as expected for a sensitive, laboratory based instrument; however, PSPME-IMS offers similar non-contact sampling and detection performance to a well-established technique with the added advantage of fast detection in the field.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Results Analysis
#### 5.5.2 SSIM Performance  
Figure 5 illustrates the SSIM performance of the proposed CCB model under varying SNR constraints in SR fading channels with different parameters. Utilizing an encoder based on a swin transformer, our CCB model achieves a substantial recovery of the original image, with SSIM values approaching 1 at high SNR. In contrast, the Deep JSCC model, which relies on complex JSCC strategies, struggles to cope with high noise interference, leading to increased decoding errors and poor performance in the low-SNR environment. Moreover, our model demonstrates strong adaptability to different channel conditions, achieving performance comparable to or even exceeding that of the reference model. The results indicate that our CCB model maintains robust performance across both low- and high-SNR conditions, particularly excelling in high-SNR scenarios, where it effectively reconstructs the information conveyed by the original image.
## 3 Experimental Results and Discussion
### Plastic Rotation Angle (Link Rotation Capacity)  
The maximum bearing capacity, maximum displacement, and maximum rotation angle of the specimen can be obtained through the hysteretic curve, as shown in Table 8.  
The following conclusions can be drawn:  
1. By comparing RSL-4, RSL-7, with RSL-8, the bearing capacity and stiffness of the link with relatively larger section size are stronger when the length of the link, the spacing of the stiffeners and the yield type are the same.
2. By comparing RSL-1, RSL-3, RSL-4, with RSL-7, the link length and the stiffening distance has little effect on the bearing capacity, when the section size and yield type of the replaceable energy dissipation links are the same.
3. The minimum rotation angle of all specimens in the test was 0.16 rad, which indicated that the replaceable link has good rotational capacity.
4. By comparing RSL-2, RSL-3, RSL-4, with RSL-5, on the condition that the section size, length, and yield type of energy dissipating links are the same, the stiffening spacing of energy-dissipating links has little influence on the bearing capacity.
## 1 Introduction
Recently, some new meta-heuristics techniques are proposed such as monarch butterfly optimization (MBO) [11], slime mold algorithm (SMA) [12], moth search algorithm (MSA) [13], hunger games search (HGS) [14], Runge Kutta method (RUN) [15], and Harris hawks optimization (HHO) [16], to further minimize the fitness function by keeping the size of the population unchanged, to improve the weight adaption rate, to enhance the local searching method, to optimize the dynamic fitness function computation, to avoid the local optimal solutions and increase convergence speed, and to cooperatively search for the optimal local solution, respectively. Several security and privacy challenges in an IoT environment with their use cases are outlined in [17; 18]. The aim of federated learning is a privacy-aware collaborative learning mechanism of a shared model by keeping the data on the device. Hence, the users of federated learning will experience personalized machine learning and overcome privacy issues as well. Motivated by these highlighted issues of privacy in healthcare, in this paper, we propose a federated matched averaging with a Modified Artificial Bee Colony (M-ABC) optimization-based framework to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease. The objective of our proposed framework is to develop an overall privacy-aware decentralized learning method for heart disease diagnosis which improves the feature optimization at the client end and the communication efficient global cloud model. We chose M-ABC optimizer because it is highly flexible and user-friendly, uses fewer control parameters than other algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO), is easily hybrid with other optimization algorithms, and possesses strong robustness and a fast convergence rate. In addition, the M-ABC method can also accommodate a random cost objective function. This paper's contributions are as follows:  
* We design and propose a privacy-aware framework for the prediction of heart disease in healthcare using an improved federated learning algorithm for cloud and user sites. M-ABC optimizer is proposed at the client end for the optimal feature selection of heart disease data. This optimizer enables improved accuracy of prediction and fewer classification errors. * Federated matched averaging (FedMA)-based algorithm is explored for constructing a privacy-aware framework for a global cloud model. * We validated and tested the proposed framework with a real-world heart disease dataset. Evaluation of the performance of the proposed framework in terms of prediction accuracy, classification error, and communication efficiency is performed with state-of-the-art federated learning algorithms. The rest of the paper is organized as follows. Section 2 presents the review of related work. Section 3 explains the materials and the proposed framework. Section 4 is related to the evaluation of performance and results. The last section, Section 5, provides a conclusion and future work of the paper.
## 5 Conclusions
\begin{tabular}{l l} \(A_{r}\) & \(r\)th mesh harmonic amplitude of dynamic transmission error \\ \(A_{\text{rms}}\) & equivalent root-mean-square amplitude of dynamic transmission error \\ \(b(t)\) & comprehensive backlash \\ \(b(^{j-1})(t),b^{(j)}(t),b^{(j+1)}(t)\) & backlash of \(j-1\)th, \(j\)th and \(j+1\)th pair of teeth \\ \(c_{m}(t)\) & comprehensive damping \\ \(c^{(j-1)}(t),c^{(j)}(t),c^{(j+1)}(t)\) & damping of \(j-1\)th, \(j\)th and \(j+1\)th pair of teeth \\ \(e(t)\) & comprehensive reshing error \\ \(e^{(j-1)}(t),e^{(j)}(t),e^{(j+1)}(t)\) & meshing error of \(j-1\)th, \(j\)th and \(j+1\)th pair of teeth \\ \(f_{fa1}\) & profile form deviation of driving wheel \\ \(f_{fh41}\) & profile slope deviation of driving wheel \\ \(f_{r1}\) & number of sine periods over the profile evaluation range of driving wheel \\ \(F_{m}\) & equivalent applied load \\ \(F_{mechs}\) & total dynamic meshing force \\ \(F_{mechs}^{(j-1)},F_{mechs}^{(j)},F_{mechs}^{(j+1)}\) & dynamic meshing force of \(j-1\)th, \(j\)th and \(j+1\)th pair of teeth \\ \(I_{l}\), \(I_{2}\) & rotary inertia of driving and driven wheels \\ \(k_{m}\) & average meshing stiffness \\ \(k(t)\) & comprehensive meshing stiffness \\ \(k^{(j-1)}(t),k^{(j)}(t),k^{(j+1)}(t)\) & meshing stiffness of \(j-1\)th, \(j\)th and \(j+1\)th pair of teeth \\ \(l\) & nominal dimension \\ \(m_{e}\) & equivalent mass \\ \(N_{i}\) & the number of teeth on gear \(i\) \\ \(q\) & dimensionless transmission error \\ \(R_{b1}\), \(R_{b2}\) & radius of the base circle of driving and driven wheels \\ \(s_{i}\) & shaft order index \\ \(e\) & contact ratio \\ \(t_{z}\) & meshing period \\ \(t_{h}\) & meshing time of a pair of teeth from engagement to disengagement \\ \(T_{1}\), \(T_{2}\) & torque of driving and driven wheels \\ \(\omega_{n}\) & nominal frequency \\ \(\omega\) & meshing frequency \\ \(W\) & one-sided discrete autopower spectra \\ \(W_{m}(t)\) & comprehensive internal incentive \\ \(\overline{W_{m}}(\tau)\) & dimensionless comprehensive internal incentive \\ \(\theta_{1},\theta_{2}\) & rotation angle of the driving and driven wheels \\ \(\tau\) & dimensionless time \\ \(\Omega_{h}\) & dimensionless frequency \\ \(\zeta\) & damping ratio \\ \(\overline{\zeta}\) & damping ratio of a single pair of teeth \\ \(\overline{\beta}\) & function of the number of meshing pairs \\ \(BW\) & analysis bandwidth \\ \end{tabular}
## 2 Topology Optimization for the Synthesis of Compliant Mechanisms
### Formulation of Topology Optimization
#### 2.2.1 Design Variables and Their Stabilization
The spring stiffnesses are used as design variables and the elements kept solid and can be thus either nonlinear or linear element formulation. [PERSON] et al. [46] proposed to exclude the nonlinear strain energy terms in low density regions and further developed this approach in [PERSON] et al. [47]. A modified version of the method introduced by [PERSON] et al. [47] is used in this study originating from [PERSON] [38]. For low density elements, linear strain energy terms \(W_{L}\) are used. These are then interpolated towards a full nonlinear strain energy term \(W_{\text{NL}}\) in the solid regions,  
\[W_{i}=W_{NL}(\gamma_{i}\underline{u}_{i})-W_{L}(\gamma_{i}\underline{u}_{i}) +W_{L}(\underline{u}_{i}). \tag{14}\]  
The interpolation term \(\gamma_{i}\in[0,1]\) is defined similarly to the projection method in Equation (12) as a Heaviside function approximation,  
\[\gamma_{i}=\frac{\tanh(\beta_{1}\eta_{1})+\tanh(\beta_{1}((\tilde{x}_{i}-x_{ \text{off}})^{\theta}-\eta_{1})}{\tanh(\beta_{1}\eta_{1})+\tanh(\beta_{1}(1- \eta_{1})}, \tag{15}\]  
where \(\beta_{1}\) and \(\eta_{1}\) are step parameters, \(x_{\text{off}}\) is an additional offset parameter introduced and \(\theta\) is the penalization exponent term. The term \(x_{\text{off}}\) shifts the cutoff further toward \(\overline{x}=0\) in order to limit the number of elements in which linear strain energy is used. The results presented in this work are obtained using the parameters \(\beta_{1}=500\), \(\eta_{1}=0.01\) and \(x_{\text{off}}=0.2\).