esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Validation by Finite-Dimensional Model  
The asymptotic solution was validated against numerical results, relevant to the discretized version (Equations (37) and (38)) of the nonlinear continuous model (Equation (8)). The space interval was divided in \(N=128\) subintervals, and numerical integration carried out, via commercial routines, for given initial conditions. Regardless of these latter, after a transient had been exhausted, the motion stabilized either on a limit cycle or comes back to the trivial state. In order to compare the transient motion \(v_{j}\left(t\right)\) to that predicted by the MSM via the amplitude \(a\left(t\right)\), an initial deflection shape coincident with the first natural mode of the undamped bridge was chosen.  
Four typical time-histories are shown in Figure 5 for different wind velocities. It was observed that: (a) when \(u<u^{*}\) (Figure 5a) the motion stabilized on the trivial solution \(a=0\); (b) when \(u^{*}<u<u_{c}\) (Figure 5b) the motion stabilized either: (I) on the trivial solution \(a=0\), if the initial condition was sufficiently small, or (II) on a limit cycle of amplitude \(a\
eq 0\), if the initial condition was sufficiently large; (c) when \(u>u_{c}\) (Figure 5c) the motion stabilized on a limit cycle of amplitude \(a\
eq 0\). All the time-histories were compared with the numerical integration of the bifurcation Equation (27). It was seen that this latter captured at the best extent the exact (numerical) solution.  
To compare the numerical to the asymptotic limit-cycles, the numerical amplitudes were extracted by the recorded time-histories for different wind velocities, and bullets superimposed to the asymptotic bifurcation diagram (see Figure 5d), showing an excellent accordance. Note the power of the perturbation methods in providing accurate predictive formulae (as for the galloping velocity, Equation (29) and for the amplitudes of the limit-cycles, Equation (30)).  
Figure 4: Bifurcation diagram for the case study: steady amplitude of the limit-cycle \(vs\) the wind velocity.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 5 Discussion
### KMeans++ vs. GA-KMeans++  
The main differences between KMeans++ and GA-KMeans++ are their starting procedures. KMeans++ uses smart initialization to choose centroids far apart, improving convergence but perhaps resulting in poor solutions. The GA with KMeans++ uses the initialization approach to balance and evenly distribute centroids, which speeds convergence and improves clustering. The GA with KMeans++ relies on genetic algorithms to improve centroids through selection, crossover, and mutation. Better cluster assignments and clustering quality result from this method. In contrast, KMeans++ does not use genetic processes, which may decrease convergence and reduce clustering performance.  
The GA using KMeans++'s evaluation metrics demonstrate its benefits. It has an excellent silhouette score, Davies-Bouldin score, and Calinski-Harabasz score, showing enhanced inter-cluster separation and cluster compactness. KMeans++ performs worse in these metrics, implying poorer clustering. The GA with KMeans++ has better clustering performance, resilient initialization, and efficient search space exploration. It outperforms KMeans++ and other genetic algorithm-based clustering methods due to these advantages.
# Spread Spectrum Image Watermarking Through Latent Diffusion Model
## 3 Experiments
### Imperceptibility Test  
Table 1 presents the comparison of image quality with various schemes derived from deep learning models, while Figure 3 displays the visual comparisons. As Table 1 and Figure 3 show, HiDDeN performs the best in terms of overall image quality. However, it is very limited in terms of robustness, as shown in Table 2, especially when facing attacks with global modification. For our method, the loss of image quality is mainly due to the reduced dimension of the latent space, while it is still comparable to other deep learning watermarking schemes. In Figure 3, it can be seen that the proposed method embeds watermarks in the edge details of images, thus leading to better imperceptibility. Additionally, the proposed method modifies fewer pixels of the original image, as illustrated by the comparison of residual images in Figure 3. In contrast to Stable Signature, which is also based on the diffusion model, our training strategy in the noisy latent space achieves less reconstruction loss and thus leads to better quality of watermarked images in terms of all the three metrics.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & **COCO** & \multicolumn{3}{c}{**DiffusionDB**} \\ \hline
**Methods** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS \(\downarrow\)** \\ \hline HiDDeN & 31.70 & **0.93** & **0.02** & 31.42 & **0.94** & **0.02** \\ Stegastamp & 28.73 & 0.89 & 0.07 & 28.05 & 0.89 & 0.07 \\ SSL & **32.07** & 0.87 & 0.11 & **32.11** & 0.88 & 0.10 \\ Stable Signature & 26.43 & 0.75 & 0.06 & 25.78 & 0.75 & 0.06 \\ Proposed & 30.85 & 0.90 & 0.05 & 30.49 & 0.90 & 0.05 \\ Proposed (64) & 28.28 & 0.88 & 0.10 & 28.63 & 0.89 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of quantitative image quality among different methods. The best result is marked in **bold**, while the second-best is underlined.  
Figure 3: Visual quality comparison with various methods. From left to right are clean images, HiDDeN, Stegastamp, SSL, Stable Signature, and proposed. The second and fourth rows are the residual images.
# Seven-Phase PMSM Drives Operation Post Two Types of Faults
## 3 Operation of the Seven-Phase Drive after Losing Phase 'A'  
### The Stator Current Distribution in the Seven-Phase PMSMs after Losing Phase 'A'  
When the seven-phase PMSM is running without faults, the currents will be equal in magnitudes and phase shifted by an angle equals \(2\pi/7\) as shown in Equation (5) and Figure 4a,b [16; 17; 18; 19; 20; 44]:  
\[\begin{bmatrix}i_{A}\\ i_{B}\\ i_{C}\\ i_{D}\\ i_{E}\\ i_{F}\\ i_{G}\end{bmatrix}=\begin{bmatrix}I_{m}*\cos(wt)\\ I_{m}*\cos(wt-2\pi/7)\\ I_{m}*\cos(wt-4\pi/7)\\ I_{m}*\cos(wt-6\pi/7)\\ I_{m}*\cos(wt-8\pi/7)\\ I_{m}*\cos(wt-10\pi/7)\\ I_{m}*\cos(wt-12\pi/7)\end{bmatrix} \tag{5}\]  
where \(I_{m}\) is the peak value of the phase current.  
As mentioned in the introduction, one major advantage of the seven-phase motor drive is that it is inherently fault-tolerant. This means that if one phase is lost due to the open-circuit fault in the seven-phase motor, the motor will be capable to keep on its operation with good dynamic and steady-state performance using a proper FTC technique. The FTC target is to maintain the MMF post the fault pure sinusoidal without harmonics. This can be completed by controlling the healthy currents of the seven-phase motor post the fault according to new predefined reference currents. These new predefined reference currents can be optimized to achieve other goals besides maintaining the MMF. Such as: equalizing current amplitudes after fault, minimizing copper losses, and maximizing torque/power under a given current limit [16; 17; 18; 19; 20; 44].  
Figure 4: Current distribution of seven-phase motor under normal operating condition (**a**) Phasor; (**b**) sinusoidal.  
According to that, if phase 'A' is lost due to the open-circuit fault, the stator reference currents should be modified according to Equation (6) and Figure 5a,b to maintain the MMF and at the same time minimizing the torque ripple [16; 17; 18; 19; 20; 44].  
\[\begin{bmatrix}i_{A}\\ i_{B}\\ i_{C}\\ i_{D}\\ i_{E}\\ i_{F}\\ i_{G}\end{bmatrix}=\begin{bmatrix}0\\ 1.585~{}I_{m}*\cos(wt-41.6~{}^{\circ})\\ 1.152~{}I_{m}*\cos(wt-94.4~{}^{\circ})\\ 1.188~{}I_{m}*\cos(wt-202^{\circ})\\ 1.188~{}I_{m}*\cos(wt-158~{}^{\circ})\\ 1.152~{}I_{m}*\cos(wt-265.8~{}^{\circ})\\ 1.585~{}I_{m}*\cos(wt-318.4~{}^{\circ})\end{bmatrix} \tag{6}\]  
To maintain the MMF and at the same time minimize the copper losses in the seven-phase motor drive when phase 'A' is lost, the stator reference currents should be modified according to Equation (7) and Figure 6a,b [16; 17; 18; 19; 20; 44].  
Figure 5: Current distribution of seven-phase motor under minimizing torque ripple optimization. (**a**) Phasor; (**b**) sinusoidal.  
Figure 6: Current distribution of seven-phase motor under minimizing copper losses optimization. (**a**) Phasor; (**b**) sinusoidal.  
It can be noticed from the results in Figure 7 that the current in the remaining phases after losing phase 'A' are equal in magnitude based on the minimizing of the inverter current optimization technique. In this paper, the minimizing inverter current optimization technique will be considered only.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 3 Methods
### Heuristic Compound Sampling Strategy
#### 3.2.3 Hard Sample Mining  
We classify sports video images into two types, easy and difficult, according to the complexity of the sports competition scene. Simple refers to a clear boundary between the ball and the background, which can be easily distinguished and has the least classification difficulty, as Figure 2a shows. Difficulties include different factors such as occlusion, motion blur, and background blending. Occlusion means that part of the ball is covered by the player, but the complete position of the ball can still be inferred based on the remaining part, as shown in Figure 2b. Motion blur refers to the fact that when the ball is moving at high speed, and due to shooting reasons, the ball has a relatively long shadow, no obvious boundaries, and the most severe deformation, as shown in Figure 2c. Background integration means that the ball is completely integrated into a similar background, such as billboards, auditoriums, etc., as shown in Figure 2d. This article uses simple samples as the benchmark when constructing the training data set. For each difficult situation, resampling is performed according to the resampling factor. The samples marked as difficult are repeated multiple times to form a new training fine-tuning data set. In this way, we perform difficult sample mining to improve ball detection capabilities in complex motion situations.
## 2 System Model  
As shown in Figure 1, the IIoT scene includes many kinds of targets for monitoring by sensor deployment optimization. In an industrial Internet information collection system, the deployment strategy of sensor network nodes needs to consider three issues: node location selection, communication range settings, and energy allocation and management. Node location selection is the primary consideration in node deployment. Choosing appropriate locations for nodes can maximize the coverage of the target area, reduce communication distance, and lower energy consumption. The communication range between nodes directly affects the network connectivity and stability. An excessively large communication range may lead to energy waste, while an insufficient range may result in nodes being unable to communicate properly. Therefore, setting an optimal communication range is another critical issue in node deployment. Energy is one of the most valuable resources in wireless sensor networks, making effective energy allocation and management crucial. The energy allocation for each node should be based on its role and importance within the network to ensure the stable operation of the entire network.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 4 Security Analysis  
Under the semi-honest attack model [23], if \(OT_{N_{k}+\Delta}^{N_{k}}\) is secure, the SQRT scheme is secure. The proof process is specified as follows.  
Proof.: Let the protocol with formal security proof be \(\Pi\)[24]. Note that during the implementation of the protocol, the two views of \(UE_{k}\) and DS are \(\left\{\text{VIEW}_{k}^{\Pi}(q_{i},\left\{d_{1},d_{2},\ldots,d_{m}\right\}) \right\}_{k=1,2^{\prime}}\), including four parts of messages: {secret input, random number, messages transmitted from the other party, and output}. Based on the two views, \(UE_{k}\) and \(DS\) can obtain the views as follows, respectively,  
\[\left\{\text{VIEW}_{1}^{\Pi}(q_{i},\left\{d_{1},d_{2},\ldots,d_{m}\right\}) \right\}=\left\{q_{i},s_{a},\mathbf{B}_{k},S_{ij}^{\prime}\Big{|}_{i,j=1,\ldots,n, i\
eq j}\right\}\Bigg{\{}\text{VIEW}_{1}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{i} \Bigg{|}_{i=1,\ldots,m},r_{2})\Bigg{\}} \tag{5}\]  
\[\left\{\text{VIEW}_{2}^{\Pi}(q_{i},\left\{d_{1},d_{2},\ldots,d_{m}\right\}) \right\}=\left\{d_{1},d_{2},\ldots,d_{m}\right\}M_{k}\Bigg{\}}\Bigg{\{}\text{VIEW}_{2}^{OT_{N_{k}+\Delta}^{N_{k}}}(z,r_{i} \Bigg{|}_{i=1,\ldots,m},\lambda)\Bigg{\}} \tag{6}\]  
Figure 4: \(N_{k}+\Delta\)-out-of-\(N_{k}\) oblivious transfer model.  
From the definition of security [25], only the probabilistic polynomial time algorithm \(S_{1}/S_{2}\)[26] needs to be constructed, so that \(P_{1}/P_{2}\) can be constructed on the premise of known input/output: \((q_{i},r_{2})/(\{d_{1},d_{2},\ldots,d_{m}\},\lambda)\), i.e., \(\{S_{1}(q_{i},r_{2})/\{S_{2}(\{d_{1},d_{2},\ldots,d_{m}\},\lambda)\}\). It is computationally indistinguishable from the views obtained in the process of real protocol execution, i.e., it meets the requirements  
\[\{S_{1}(q_{i},r_{2})\}\stackrel{{ c}}{{=}}\left\{\text{VIEW}_{1}^{ \Pi}(q_{i},\{d_{1},d_{2},\ldots,d_{m}\})\right\} \tag{7}\]  
\[\{S_{2}(\{d_{1},d_{2},\ldots,d_{m}\},\lambda)\}\stackrel{{ c}}{{=}}\left\{\text{VIEW}_{2}^{\Pi}(q_{i},\{d_{1},d_{2},\ldots,d_{m}\})\right\} \tag{8}\]  
Therefore, we need only to construct \(S_{1}\) and \(S_{2}\). Next, we will prove the security of the SQRT scheme in two cases.
## 2 Background
### Memory Compression  
Memory compression is widely used in servers, smartphones, and other computer systems to save memory space [9]. In a memory compression system, virtual memory is compressed and stored in physical memory or secondary storage devices (HDDs or SSDs). Compressed memory blocks are set to inaccessible states, subsequent access to the data triggers page faults and invokes the swap system to decompress and restore it. Compression is compute-intensive. Software memory compression schemes [7; 10; 11] are often bottlenecked by the computation overhead. Other memory compression schemes [9; 12; 13; 14; 15; 16; 17; 18] are performed by a dedicated processor between the CPU cache and physical memory, which is called hardware memory compression.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 2 System Model
\begin{array}{cccc}h_{v}^{l,1}&&&\\ &h_{v}^{l,2}&&\\ &&\ddots&\\ &&&h_{v}^{l,l}\end{array} \right], \tag{2}\]  
where \(h_{v}^{l,j}\) represents the channel gain between user \(v\) and resource \(j\) at the \(l\)-th block, and \(\mathbf{H}_{v}^{l}\) is a diagonal matrix. Thus, at the receiver, the \(l\)-th received symbols can be described as  
\[\mathbf{y}_{l}=\sum\
olimits_{v=1}^{V}\mathbf{H}_{v}^{l}\mathbf{s}_{v}^{l}+\mathbf{n}_{l}, \tag{3}\]  
where \(1\leq l\leq L\), \(\mathbf{y}_{l}=\left\{y_{l,1},y_{l,2},\cdots,y_{l,l}\right\}^{T}\), and \(\mathbf{s}_{v}^{l}=\left\{s_{v}^{l,1},s_{v}^{l,2},\cdots,s_{v}^{l,J}\right\}^{T}\). \(\mathbf{n}_{l}=\left\{n_{l,1},n_{l,2},\cdots,n_{l,J}\right\}^{T}\) is a complex additive white Gaussian noise (AWGN) vector with the mean vector \(\mathbf{\theta}\) and covariance matrix \(N_{0}\mathbf{I}\), and \(N_{0}\) denotes the variance of Gaussian noise. At the receiver, the received symbols \(\mathbf{y}_{l}\) are decoded by the MPA detector first. The output extrinsic messages of the MPA detector are in the form of log-likelihood ratio (LLR), and de-interleaved to \(\mathbf{z}_{v}=\left\{\mathbf{z}_{v,1},\ldots,\mathbf{z}_{v,L_{c}}\right\}\) for user \(v\), where \(\mathbf{z}_{v,k}=\left[ \begin{smallmatrix}1&0\\ 0&k\end{smallmatrix} ,\ldots, \begin{smallmatrix}2&N\\ 0&k\end{smallmatrix} \right]\). Then, as the a priori information, \(\mathbf{z}_{v}\) is feed into the coupled polar decoder. The output extrinsic messages \(\mathbf{L}_{v}^{v}\) is then interleaved and entered into the MPA detector as a priori information. After several iterations, the receiver converges and outputs each user's decoded information bits \(\left\{\hat{\mathbf{u}}_{1},\hat{\mathbf{u}}_{2},\cdots,\hat{\mathbf{u}}_{V}\right\}\), where \(\hat{\mathbf{u}}_{v}=\left\{\hat{\mathbf{u}}_{v,1},\ldots,\hat{\mathbf{u}}_{v,L_{c}}\right\}\). The information-coupled polar coding is described in Section 3. In addition, the CRC is used not only for SCL decoding, but also for constructing extrinsic messages and iterative reception. The detailed algorithm is given in Section 4.
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 2 Overview of Existing KF RAIM
### Limitations of Existing Approach and Modifications  
The limitations of the described KF RAIM algorithm are as follows:  
1. SKF does not estimate user acceleration needed for moderate to high dynamics.
2. Time constants of all time-correlated errors are assumed to be 100 s. More appropriate error models having different time constants for different error sources must be adopted.
3. \(a_{\ell 1}\) entries of \(\boldsymbol{\Psi}_{\text{sub}}^{i}\) are calculated with modeled values of measurement error standard deviations. By definition, they should be obtained with true error standard deviations, which are not known. A more accurate model of \(\boldsymbol{\Psi}_{\text{sub}}^{i}\) would therefore account for uncertainties in the knowledge of actual \(a_{\ell 1}\).
4. In the beginning of the receiver operation, relatively large transient position errors of the navigation filter can be handled by including artificial white noise in the carrier-smoothed pseudorange measurement error model of the SKF. While this can be easily incorporated in the SKF, the \(\boldsymbol{\Psi}\) matrix of \(\Delta\boldsymbol{\tilde{\rho}}^{*}\) does not model white noise.  
Thus, \(\boldsymbol{\Psi}\) needs to be appropriately modeled, considering more realistic error models, initial artificial white noise, and uncertainties in the knowledge of \(a_{\ell 1}\). The next two sections discuss the adopted error models in the current work and appropriate boundsof the \(\mathbf{\
abla}\) matrix. SKF formulation also estimates error in the a priori user acceleration from truth. \(N\) is adaptively calculated the same way as before with \(\mathbf{B}\) matrices. \(M\) is also calculated as before, but with \(\mathbf{B}^{\prime\prime}\) matrices, where \(\mathbf{B}^{\prime\prime}_{k}\)(1:2:5:,) = \(\mathbf{B}^{\prime}_{k}\) = \(\mathbf{C}^{\mathrm{tr}}_{e}\mathbf{B}_{k}\)(1:3:7,:), and \(\mathbf{B}^{\prime\prime}_{k}\)(2:2:6:,) = \(\mathbf{C}^{\mathrm{tr}}_{e}\mathbf{B}_{k}\)(2:3:8,:). \(\mathbf{B}_{k}\)(1:3:7,:) has rows of \(\mathbf{B}_{k}\) corresponding to the position states of the filter, and \(\mathbf{B}_{k}\)(2:3:8,:) has rows for velocity states. This is found to reduce \(M\) and hence PLs. The effect of overbounding the pseudorange rate (or precise range rate) error covariance is also included, and a tighter value of \(\Gamma\) in Equation (11) is determined.
## 4 Evaluation
### Routing Fairness under Heterogeneous Buffer Capacity
#### 4.2.3 Impact of FLARoute and Choice of Parameters  
A. Impact of FLARoute  
The main aim of FLARoute is to reduce unfairness in the utilization of handheld user device resources and increase throughput in the presence of strong spatial locality in urban environments. When incorporated into PRoPHET in the Skudai scenario, the additional fairness provided by FLARoute is able to: (i) increase the number of central nodes by 77%; and (ii) achieve 3.8 times CCAF's energy distribution, thereby distributing energy consumption more evenly among the central nodes and the other nodes. As shown in Figure 28, FLARoute also reduces the maximum energy consumption in the network. These results signify increased user satisfaction among the different sets of nodes. In terms of throughput, FLARoute also provides an additional 8.8% as compared with CCAF. This is mainly because: (i) FLARoute considers spatial locality inherent to user movement when selecting relay nodes and dropping messages; and (ii) more nodes are actively involved in routing, thereby increasing the network's buffer availability to reduce chances of message loss. As compared with CCAF, FLARoute achieves more fairness without incurring significant increase in the energy consumption per node, and delivers more messages within a TTL of 1 day. As compared with using the PRoPHET routing protocol alone, FLARoute: (i) increased fairness by 2.2%; (ii) increased energy distribution by 153.2%; (iii) increased throughput by 30.2%; (iv) reduced transmission overhead by 73.5%; and (v) reduced average energy consumption by 65.6%.  
Figure 28: Energy consumption (in Joules) on the five most popular nodes.  
FLARoute's performance under conditions in which other solutions are unable to guarantee fair treatment and improve throughput leads us to believe that it is more suitable for sensed data collection in real-life scenarios. Apart from its awareness of spatial locality inherent to user movement demonstrated in the Skudai scenario, results in the Helsinki scenario suggest that it can also cope with changing data traffic, heterogeneous buffer allocations on user devices, and dynamic user behaviour.
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 4 LoRaWAN v1.1 Security Structure Analysis  
According to the National Institute of Standards and Technology (NIST)'s recommended key management guideline [25], the secret key lifecycle consists of the following four phases: (1) pre-operational phase, (2) operational phase, (3) post-operational phase, and (4) destroying phase. The key generation and distribution processes are parts of the pre-operational phase. Key storage, usage, backup, and recovery are parts of the operational phase. Key expiry and updating are taking place in the post-operational phase. Finally, the key revocation takes place in the destroying phase. To analyze LoRaWAN v1.1, the following sub-sections examine each stage of the key management lifecycle.