esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
When comparing control barrier function-based quadratic programming (CBF-QP) and adaptive model predictive control (AMPC) for robotic systems, it is essential to evaluate their strengths, weaknesses, and suitability for specific tasks. CBF-QP explicitly enforces safety constraints, ensuring the system remains within a safe set, such as avoiding collisions. It is particularly effective in dynamic environments with moving obstacles and offers computational efficiency, making it suitable for real-time applications. Additionally, CBF-QP can be combined with other control strategies, such as CLFs, to achieve both safety and stability. However, CBF-QP may not always produce globally optimal trajectories, especially in complex environments, and its effectiveness depends on the design of the barrier functions, which can be challenging for highly nonlinear systems. Furthermore, the QP solver may converge to local solutions, which might not be ideal for long-term planning [12]. On the other hand, AMPC optimizes control inputs over a finite horizon, providing globally optimal or near-optimal solutions for trajectory planning. It can adapt to changes in system dynamics or environmental conditions, making it suitable for uncertain or time-varying scenarios. AMPC also considers future states and constraints, enabling better long-term planning compared to reactive methods like CBF-QP. However, AMPC's computational complexity can be a significant drawback, especially for systems with long prediction horizons or high-dimensional states. Additionally, AMPC relies on accurate system models, and inaccuracies can lead to suboptimal or unsafe control actions. The computational burden of AMPC may also limit its applicability in real-time systems with strict timing requirements [14]. In summary, the choice between CBF-QP and AMPC depends on the specific requirements of the application, such as the need for safety, optimality, and computational efficiency. While CBF-QP excels in real-time safety-critical applications, AMPC is better suited for scenarios requiring long-term planning and adaptability. A hybrid approach that combines the strengths of both methods could offer a promising solution for complex and dynamic environments, such as those encountered in smart agriculture [5].
## 3 Concepts and Models
### Network Modeling
The weight constellations model is suitable for more advanced satellites and drones, which have higher computational power, resulting in a significant reduction in single satellite computation and processing latency. The final effect presented can be approximated as \(In_{i}\ll distance(v_{i},v_{j})\), so the model can be simplified as  
\[delay(s,v_{1},v_{M})\approx\sum_{i=1}^{M-1}distance(s,v_{i},v_{i+1}) \tag{4}\]  
The delay only depends on the total length of the path. For this condition, a weighted graph with an \(N\times N\) edge matrix \(\mathcal{V}_{i,j}\) can be set for further calculation. In a time slice, \(\mathcal{V}_{i,j}\) is the latency or distance between \(v_{i}\) and \(v_{j}\) if they are mutually visible, and \(\mathcal{V}_{i,j}=\infty\) if invisible.
## 2 The Numerical Framework
### Discontinuous Galerkin Method  
ILES simulations have been performed in this work, where the unfiltered Navier-Stokes equations are solved. As reported in literature [16; 17; 18; 19; 20; 21; 22; 23; 24], DG methods are well suited for ILES, due to their dissipation and dispersion properties. The numerical dissipation introduced by the discretizaton itself, e.g., by numerical fluxes and the viscous stabilization, mainly acts at the smallest under-resolved scales and resembles a high frequency filter that mimics the role of a sub-grid-scale model. The ILES of several canonical and complex flows have been performed with the present code and can be found in [14; 26].  
An efficient order-adaptive incompressible discontinuous Galerkin (DG) method [14] has been used in this work. The incompressible DG solver relies on a modified formulation for the inviscid interface numerical flux [27], computed as the exact solution of the Riemann problem relaxed by an artificial compressibility perturbation that guarantees the necessary coupling between the discrete incompressibility constraint and the rest of the governing equations. The nonlinear system of differential algebraic equations arising from the spatial discretization is advanced in time using a high-order time integration scheme, i.e., the linearly-implicit Rosenbrock-type Runge-Kutta scheme, which overcomes the severe timestep restriction proper of explicit methods. In particular, the three-stage, order three ROS3P method [28] has been used. For a comprehensive discussion on the benefits of a high-order time integration scheme adopted in this work, see [29; 30; 31].  
The main features of the DG solver are (_i_) the local adaptation of the polynomial degree of the solution over the mesh, (_ii_) a multi-constraint domain decomposition algorithm to ensure the computational balance over the processes, (_iii_) the adaptation of the degree of exactness for quadrature rules, and (_iv_) a \(p\)-MG multilevel preconditioner for the linear solver included in the implicit time integration.
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Surrogate Modeling
#### 3.1.2 Response Surface Method  
Similarly to the Kriging model, the response surface method is an interpolation technique that primarily uses data from the FEM to construct a response surface for both known and unknown data. It employs the least squares method to derive a correlation between the input and output parameters [41; 42]. Figure 7 illustrates the torque surface based on the response surface method for the input variables rotor position and excitation current for a specific SRM motor. This surface response also qualitatively depicts the relationship between torque, rotor position, and excitation current for the SRM. Warmer tones display higher torques, while cooler tones display lower torques. This is supported by the contour lines, which display constant-torque slices in the X-Y plane.  
Figure 7: Torque surface of the response surface method for the input rotor position and excitation current.  
An analysis is performed in [14] on a 24/16 (number of stator poles/number of rotor poles) SRM motor for application in an in-wheel EV using the FEM and DoE to reduce the solution search space and capture non-linear dependencies, coupled with the RSM and DE. Design candidates are generated through the FEM in ANSYS, and the RSM is implemented via DoE to reduce the search space. Simultaneously, for the control optimization, the near-optimal turn-on, \(\theta_{on}\), and turn-off, \(\theta_{off}\), angles are determined using a sweep method in MATLAB to optimize the average torque, torque ripple, losses, and RMS current. The results indicate a five-fold reduction in the computational time, from 36 h to 7 h, achieving an 80% reduction in computational time and being about 5 times faster than conventional approaches, while maintaining satisfactory optimization outcomes.  
To the best of the authors' knowledge, no other studies have been conducted employing this method for the system-level optimization of SRMs, apart from those focused on component-level optimization. In [43; 44], the authors utilize the RSM as a surrogate model for the optimization of a synchronous reluctance machine and a Dual-Permanent-Magnet-Excited (DPME) machine. Notable results include an improvement in the rotor saliency ratio from 2.42 to 3.68, leading to an enhanced torque-to-current ratio and an increase in efficiency from 64.8% to 74.7% in the former, while the latter achieved an 8.9% reduction in permanent magnet material usage. Additionally, improvements in efficiency, a significant reduction in cogging torque, and minimized torque ripple were reported. What is particularly important to highlight is that the implementation of this methodology resulted in a substantial reduction in the computational time, while maintaining a satisfactory level of accuracy, as indicated by the coefficient of determination, \(R^{2}\). Thus, its application to SRM machines in a broader context of the SLDOM could be highly beneficial.
## 9 Performance Analysis and Results
### Time Cost for Distribution  
The time needed to distribute keys during a refreshing period is calculated based on the key management study by multiplying NG (the total number of users in DR projects with users joining or exiting) by CT (the time needed to distribute a package containing the key and related data). The distribution package size for the key and data typically did not exceed 384 bytes. Typically, the Synchronous Digital Hierarchy (SDH) network based on optical fibers was used to transmit data at a rate of 155 Mb/s, 622 Mb/s, or higher between the MS and AMI systems. Table 13 can be used to calculate the cost of this distribution. The results suggest that the distribution time does not affect the key refreshment process or network traffic distribution in AMI systems.  
Figure 13 shows the relationship between the total number of users in DR projects and the time cost of key distribution and associated data. It is shown that when the number of users increased, the time cost of the key distribution increased.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{N_{G}}\) & **1000** & **2000** & **3000** & **5000** & **10,000** \\ \hline Distribution cost (ms) & 2.48 & 4.96 & 7.44 & 12.39 & 24.77 \\ \hline \end{tabular}
\end{table}
Table 13: Time cost for key and associated data distribution.  
Figure 12: Time consumed in MS for multicast with NG and NP consideration.  
Figure 13: Distribution time consumed for the key and associated data with an NG consideration.
# Clinical Assessment of Customized 3D-Printed Wrist Orthoses
## 2 Materials and Methods
### Design Fundamentals of the 3D-Printed Brace  
The construction flow of the 3D-printed brace is shown in Figure 2. The digital forearm model generated by a hand-held optical scanner (GoISCAN50, Creaform, Quebec City, QC, Canada) was imported into the software. The expert-based brace design software had the orthopedics and rehabilitation experiments embedded to handle a sequence of design processes, including preprocessing, boundary, thickness, surface smoothing, segmentation, and accessories settings. The parameters of clearance setups between the brace and wrist were adjusted accordingly. Based on the clearance in different regions of the inner layer of the brace, we generated the thickness of the brace, divided it into the palmer and dorsal side parts, smoothed the edges, and added accessories for bundling through. For easy dressing and comfort while wearing the wrist brace, dressing windows, ulnar styloid process windows, and vents were generated in the model by Boolean operation. Finally, the brace model was sent for FDM (Fused Deposition Modeling) manufacture in a 20% filling rate with polylactic acid (PLA) material.  
Immobility is critical to patient recovery and should not be compromised while pursuing improved comfort. For immobility purposes, flexibility of the plaster cast is not allowed after cast setup. We scanned the patient's arm at a neutral position as the immobility posture. The 3D-printed brace was designed and fabricated with inflexible PLA material. The aim of the digital wrist brace design was to provide similar immobility as the plaster cast by way of tweaking the clearance. A constant allowance was set to 2 mm from palm to proximal brace boundary (Figure 3). The 3D-printed personal brace was fitted on the wrist with pressure sensors at the six ROI positions and held in place with a bandage. Pressure maps were collected using the same postures of neutral and arm sling. The trends and deviations of the pressures map from the traditional fiberglass resin brass  
Figure 1: Four postures with six ROIs pressure sensors wrapped in an arm bandage.  
and the customized 3D-printed brace were compared. If the pressure of any of the six ROIs was greater than a given value \(\beta\)[4], the process returned to the design step to refine the clearance adjustment region. Such iterations continued until the average pressure of each ROI was less than the given value \(\beta\). At this point, we obtained the clearance of the personal brace.  
Figure 2: Self-developed brace design software and processing.
## 1 Introduction
More importantly, when the temperature measurement information is not complete or there is a large measurement error, the inversion results obtained based on the gradient optimization method may deteriorate. In addition, when the number of inversion points is larger, the calculation of the gradient matrix is difficult and time consuming, which directly affects the engineering application of gradient-based optimization algorithms. Non-gradient-based optimization algorithms are usually global search algorithms and have good adaptability to overcome the difficulty of gradient-based optimization algorithms easily converging to a local minimum. Moreover, they do not involve the calculation of a Jacobian partial derivative matrix in the inversion process. However, this class of methods often has high computational costs in the search process and has a slow convergence rate in the latter stage, which limits its application in IHCPs. In particular, when the measurement information is incomplete or there is a large measurement error, the inversion results obtained according to such methods will also have some gaps. Decentralized fuzzy inference was successfully applied to invert thermal conductivity by [PERSON] et al. [2], and the accuracy and stability of the algorithm were verified. There are few reports on the application of the decentralized fuzzy adaptive PID control (DFAC) to IGPs. In this study, the finite volume method (FVM) was used to solve the forward problem, the DFAC algorithm was used to compensate for the initial guess of the estimate boundary in order to minimize the residual between the calculated and the measured values of the temperature, and the true geometry boundary was obtained. To study the effectiveness and stability of the proposed method, the effects of initial guesses, measurement points, and measurement errors on the results were analyzed and discussed.
## 3 Problem Formulation
### Example  
To illustrate how Equation (2) is applied during optimization, consider a simplified scenario involving four customer locations \(C=\{c_{1},c_{2},c_{3},c_{4}\}\). The optimization algorithm evaluates a candidate route \(R=(c_{1}\to c_{3}\to c_{2}\to c_{4}\to c_{1})\), which visits all customers and returns to the origin. For each leg \((c_{i},c_{i+1})\) in the route, the physical distance \(d(c_{i},c_{i+1})\)is computed using the Haversine formula, and the corresponding estimated travel time \(T(c_{i},c_{i+1},t_{i})\) is predicted by the LSTM model based on the expected departure time \(t_{i}\).  
Suppose the route distances in kilometers and predicted times in minutes for this candidate are as follows:  
\[d(c_{1},c_{3}) =12.4,\quad T(c_{1},c_{3},t_{1})=28.5\] \[d(c_{3},c_{2}) =9.7,\quad T(c_{3},c_{2},t_{2})=22.1\] \[d(c_{2},c_{4}) =14.3,\quad T(c_{2},c_{4},t_{3})=35.0\] \[d(c_{4},c_{1}) =11.6,\quad T(c_{4},c_{1},t_{4})=26.7\]  
With \(\gamma=0.8\), the total cost of this route according to Equation (2) is as follows:  
\[\text{Cost} =(12.4+9.7+14.3+11.6)+0.8\cdot(28.5+22.1+35.0+26.7)\] \[=48.0+0.8\cdot 112.3\] \[=48.0+89.84\] \[=137.84\]  
This scalar value quantifies both spatial and temporal efficiency. The optimization algorithm uses this value as a fitness score: routes with lower total cost are favored during selection and reproduction. In this way, Equation (2) directly guides the evolution of candidate routes toward configurations that reduce both travel distance and time.
## 2 Materials and Methods
### Classification  
After the feature vector of the pair (right and left) eardrum images was formed by concatenating the lookup table values with handcrafted features, these are classified. In order to classify a pair of images together, we aimed to collect all of the features in one vector for each pair. Thus, we could analyze the difference between classifying single images and classifying pairs as 'Normal-Normal' (N-N), 'Abnormal-Abnormal' (A-A), 'Normal-Abnormal' (N-A), and 'Abnormal-Normal' (A-N). Single image classification results were obtained after retraining the Inception ResNet-v2 pre-trained network by changing the last three layers according to 'Normal/Abnormal' classes of eardrum images and frozen the first 820 layers to avoid an overfitting of the network. The number of frozen layers was empirically determined from our previous study OtoMatch as 820. For the paired image classification, a newly created feature vector is used in the Tree Bagger algorithm.  
The Tree Bagger algorithm is the ensemble model of bootstrap aggregated decision trees. Multiple decision trees constitute resampling training data with replacement again and again, and voting the trees for majority prediction [35]. These decision trees are the classification trees whose leaves represent class labels; branches represent conjunctions of features that convey to these class labels. In our problem, the leaves are the 'Normal' and 'Abnormal' class labels, and the branches are the conjunction of the feature vector. The out-of-bag (OOB) error method [36] was utilized to measure the prediction error of boosted decision trees models to sub-sample data to train the method. The OOB error is measured by excluding a sub-sample from the training data and calculating the mean prediction error in the bootstrap sample [37]. Subsampling improves the prediction performance by evaluating predictions on observations that are not used in building the tree (defined out-of-bag).  
This study modeled the decision tree using the TreeBagger supervised machine learning function in Matlab 2019b software. Selected trees, where the observation is out of the bag, compose the class posterior probabilities' weighted mean. Accordingly, the predicted class is the largest weighted mean of a corresponding class. This is also designed to improve the model's stability and accuracy by reducing the variance without raising the bias. The optimal number of trees decided according to the out-of-bag error changes with the accumulation of trees. In our study, the number of classes (normal and abnormal) and the number of observations (150 pair endrum image) limit the number of decision trees, which is empirically selected to be five.
## 1 Introduction
[17] proposed a double-threshold energy detection [18; 19; 20; 21; 22; 23; 24] method which can effectively solve the problem where traditional energy detection algorithms are prone to missing detection when the signal-to-noise ratio is low, which may reduce the detection performance of the system. A threshold used for primary user detection is highly susceptible to unknown or changing noise levels. In co-operative sensing, users rely on the variability of the signal strength at various locations. We expect that a large network of cognitive radios with sensing information exchanged between neighbors would have a better chance of detecting the primary user compared to individual sensing [25]. Major challenges appear due to multipath fading, hidden terminal problems, shadowing, and receiver noise uncertainty. To mitigate the impact of these issues, co-operative spectrum sensing turned out to be an effective method for improving performance [26]. [PERSON] et al. [27] developed a decentralized co-operative spectrum-sensing approach that fuses the benefits of compressive sampling and consensus optimization. However, when the number of cognitive users is very large, the bandwidth for reporting their sensing results to the common receiver is very large. [PERSON] et al. employed a censoring method with quantization to decrease the average number of sensing bits to the common receiver [28]. However, it does not indicate how to judge a signal for which the energy value is between two thresholds. The main structure of the remainder of this paper is as follows: The second section introduces the channel transmission model used in this paper. The third part analyzes the traditional energy detection algorithm and principle. The fourth part introduces the collaborative spectrum detection algorithm, and the fifth part introduces the algorithm principle and implementation flow chart. The sixth section simulates and compares the algorithm of this paper with several traditional energy detection algorithms and analyzes and discusses the drawbacks of each. The seventh section summarizes the overall structure and ideas of the paper and analyzes the effectiveness of the proposed algorithm.
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 4 Performance Analysis for KPS
### Standard Point Positioning (SPP)
Furthermore, the positioning accuracy for the combined KPS+GPS was improved by 25.0%, 31.8%, and 35.0% in each component compared to that for GPS.
###### Abstract  
An efficient spectrum reconstruction algorithm based on the Tikhonov regularization for terahertz (THz) synthetic aperture radar (SAR) azimuth non-uniform sampling is proposed in this article. The high bandwidth, high azimuth resolution, and high frame rate characteristics of THz SAR contribute to its wide application prospects in both military and civilian remote sensing fields. However, the higher azimuth sampling rate also leads to the more severe non-uniform sampling issues of THz SAR. Traditional methods based on the hardware adjustment of pulse repetition frequency (PRF) and simple interpolation for azimuth resampling struggle to meet the higher imaging quality requirements. The back projection algorithm (BPA) can accurately focus non-uniformly sampled data but requires significant computational resources. The algorithm proposed in this paper, which can reconstruct the wavenumber spectrum of SAR azimuth non-uniformly sampled signals, transforms the spectrum reconstruction problem into a linear equation system and solves it using Tikhonov regularization, thereby exhibiting higher computational efficiency compared to BPA. Furthermore, the proposed algorithm is derived from precise theoretical formulations and controls the solution error by utilizing a regularization parameter, leading to a superior imaging quality compared to the azimuth resampling algorithm. In this paper, an accurate spectrum reconstruction formula of non-uniform sampling signals with a finite length is derived, the influence of noise error on the solution is analyzed, and the THz SAR azimuth non-uniform sampling signals are processed from the wavenumber domain. Finally, simulation and experimental results verify the effectiveness of the proposed algorithm.  
1 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; [EMAIL_ADDRESS] (G.Z.); [EMAIL_ADDRESS] (Z.W.)1  
27 August 2023  
Accepted: 1 September 2023  
Published: 8 September 2023
# Energy Efficiency of D2D Multi-User Cooperation
## 1 Introduction
D2D users use different spectrums to provide mesh D2D service to avoid co-channel interference. The main contributions of this paper are as follows:  
1. A D2D multi-user cooperation scheme is proposed, choosing the relaying D2D users by considering both energy availability and the SNR of each D2D user. Each relaying D2D user can be prevented from being chosen by multiple D2D users at both the same time and at different times with the help of the Kuhn-Munkres algorithm. 2. To guarantee high data rate and low delay, the outage probability of the D2D link is derived according to the Shannon Equation and the system energy efficiency is analyzed combined with the outage probability of a D2D link. 3. To achieve D2D multi-user cooperation with high energy efficiency in different communication environments, the relationships among the number of cooperative users, the length of shared data, the number of data packets and the energy efficiency are studied in the simulation.