esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Quality Evaluation of the CNC Milled Surfaces  
3.2.1. Analysis of the Primary Profiles  
The primary profiles include both roughness and waviness components. The profiles reveal the presence of kinematic waviness, a typical feature of CNC milling, primarily generated by the stepover parameter. To investigate the influence of stepover and tool geometry on surface quality, primary profile graphs were generated for all combinations of these parameters (T1 with helical cutting edges, and T2 with straight cutting edges) and cutting depths (1 mm and 3 mm). Although similar observations were made for the 3 mm cutting depth, Figure 5 includes only the comparisons for the 1 mm depth.  
Each graph includes the profile of the shaved surface, which served as the reference for high-quality finishing. A black center line is provided in all graphs to help interpret the data, acting as a baseline for evaluating the depth and distribution of surface irregularities. The primary profiles are color-coded: red represents the shaved surface, navy blue corresponds to milling with a 5 mm stepover, magenta to milling with a 7 mm stepover, and light blue to milling with a 9 mm stepover.  
Figure 5: Comparison between the primary profiles of milled surfaces for stepovers of 5, 7, 9 mm, at 1 mm cutting depth and those of the shaved surfaces: red—shaved surface, navy blue—stepover 5 mm, magenta—stepover 7 mm, light blue—stepover 9 mm; \(x\)-axis—profile length; \(y\)-axis—profile height.  
For maple and oak, both milling tools enhanced the surface waviness, as well as the fuzziness, as the stepover value increased (Figure 5). The profiles corresponding to the 5 mm stepover were relatively close to the reference profiles (red), especially for oak, but the deviations grew more pronounced with the 7 mm and, especially, the 9 mm stepover.
# Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System
## 4 Experimental Results and Analysis  
### Experimental Set-Up  
**Dataset**. The proposed method needs a large number of training samples that cover a wide range of color distributions and image illumination features. We use the Gehler-Shi dataset [38] and the Cube+ dataset [15; 39], as the two datasets contain modern images indicative of real world images and illumination. The number of all original raw-RGB images is 2275 in total. The training and testing procedure follows the standard 3-fold cross validation commonly in the literature of illuminant estimation. To this end, the whole image dataset is randomly split into three sets, and each time two sets are used for training while the remaining set is used for testing. Parameters used for all experiments are selected based on the first two sets and then are fixed for the third set.  
**Implementation details**. Our MATLAB implementation, as we show in [[https://github.com/yunhuiluo/AnfisIllest](https://github.com/yunhuiluo/AnfisIllest)]([https://github.com/yunhuiluo/AnfisIllest](https://github.com/yunhuiluo/AnfisIllest)) (accessed on 22 October 2021), requires approximately 0.34 s computing the CDFs and IIFs for an image with 2601 \(\times\) 1732 pixels, 48 bits depth, and PNG format in the training dataset. Once the dimensionality of the CDFs is reduced with PCA, the illuminant estimation process takes an average of 0.65 s; this process includes weight computation, ANFIS prediction, and the final blending output. All the reported runtimes were computed on an Intel Core i5-2450M @ 2.50 GHz computer. Our method requires 5.6 MB to store the PC coefficient matrix, CDF/IIF clustering centroids, ANFIS model parameters, and other necessary settings, using single-precision floating-point representation. For default experiments, unless clearly stated, we choose the cluster numbers of \(k_{1}=2\), \(k_{2}=2\), and use the combination of Gehler-Shi and Cube+ as the training and testing dataset. We set fall-off factors \(\sigma_{1}=\sigma_{2}=0.25\) for weight computation. Due to every time k-means starts to iteration from random initial cluster centroids, the results for every experiment with the same settings might be slightly different.
## 4 Evaluation
### Discussion
#### 4.3.2 Operational Efficiency and Security  
The claim of reduced operational downtime in our framework is supported by the predictive maintenance capabilities of the proposed framework, which leverages FL to enhance model accuracy (95%) and generalization. High accuracy ensures timely detection of anomalies and faults, enabling preemptive interventions before failures occur. The integration of blockchain-based smart contracts automates the execution of maintenance tasks, reducing manual intervention delays. While specific downtime metrics are not measured in this study, the framework's architecture inherently supports more proactive and efficient operations by enabling real-time detection and response. This is consistent with the documented advantages of predictive maintenance systems in reducing unplanned interruptions in industrial environments.  
From the security perspective, blockchain's decentralized architecture and immutable ledger ensured robust protection against unauthorized access and tampering. This feature is especially critical in maintaining trust among stakeholders in industrial environments.  
Figure 6: Model’s update impact from each client node.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
## 3 Simulation and Analysis
### Computational Costs  
This section analyzes the computational complexity of the three algorithms (Refs. [11; 19] and ours) and compares the simulation time of the three algorithms under different radiation source conditions.  
Suppose the prior PRI range is \([PRI_{min},PRI_{max}]\), where \(PRI_{min}\) indicates the minimum PRI value; \(PRI_{max}\) indicates the maximum PRI value; \(K\) is the number of PRI bins, determined by estimation accuracy and PRI range; \(N\) is the number of pulses; \(H\) indicates the stagger number [11]; \(L_{1}\) indicates the length of the converted period sequence, determined by estimation accuracy and observation time; \(\phi(q)\) is the Euler function; \(\beta\) is determined by PRI range and observation time [19]; \(L_{2}\) indicates the length of the SSWI function, determined by estimation accuracy and observation time; and \(M\) is the number of points used for matched filter. The computational complexity of the three de-interleaving algorithms is shown in Table 4.  
A comparison of the execution time among the three methods is shown in Table 5, MATLAB 2018b is utilized for computation time statistics.  
The simulation shows that the computational efficiency of the algorithm proposed in this study is much higher than the algorithms in Refs. [11; 19] for multiple radiation source scenes. Table 4 shows that the computational efficiency of the proposed algorithm in this study mostly depends on the observation time and the number of matched filter points. The computational efficiency is little affected by the number of radiation sources and also unaffected by the PRI range. Therefore, the proposed algorithm has obvious advantages for the multiple radiation sources scene and wide PRI range in computational efficiency.
# A Comparative Study of 3D UE Positioning in 5G New Radio with a Single Station
## 3 3D Positioning Algorithms  
### Subspace-Based Approach  
The subspace-based signal classification is a widely used approach for angle estimation of the signal source. Most of the works can be traced back to the multiple signal classification (MUSIC) algorithm [17; 18]. a similar approach is also applied on the multiple carrier signal for signal delay measurement [19]. The subspace approach is also used for joint 2D delay-angle estimation of the radio source be using the spatial-time manifold [20; 21]. In this paper, we extend the manifold to the 3D spatial-time searching space \(\mathbf{u}(\theta,\phi,\tau)\), as shown in Equation (10). To performance the 3D position estimation, we first calculate the auto-correlation matrix:  
\[Ryyy=E[\mathcal{Y}\mathcal{Y}^{*}] \tag{11}\]  
where \(E[\cdot]\) denotes expectation and \(\mathcal{Y}^{*}\) is the conjugate transpose of \(\mathcal{Y}\). We take the eigenvalue decomposition of \(Ryyy\) to obtain the eigenvalue vector \(\mathbf{\lambda}=[\lambda_{1},\lambda_{2},...,\lambda_{MNL}]\) and eigenvector matrix \(\mathbf{e}=[e_{1},e_{2},...,e_{MNL}]\). The eigenvalues \(\lambda_{i}\) in \(\mathbf{\lambda}\) are in ascending order,and \(e_{i}\) corresponds to \(\lambda_{i}\). Assume there are \(\mathbf{K}\) sources. We can define the noise subspace as Equation (12):  
\[E_{n}=[e_{1},e_{2},...e_{MN-(K+1)}] \tag{12}\]  
With the noise subspace, the 3D angle-time spectrum can be defined as:  
\[P(\theta,\phi,\tau)=\frac{\mathbf{u}^{*}(\theta,\phi,\tau)\mathbf{u}(\theta, \phi,\tau)}{\mathbf{u}^{*}(\theta,\phi,\tau)E_{n}E_{n}^{*}\mathbf{u}(\theta, \phi,\tau)} \tag{13}\]  
The azimuth angle \(\theta\), elevation angle \(\phi\), and time delay \(\tau\) that are associated with the peak values in the 3D space \(P(\theta,\phi,\tau)\) determine the estimated signal source position.
## 3 System Model
### Proposed Hexagonal GFDM Model
The transmitted signal becomes as:  
\[\mathbf{x}(n)=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d^{(k,m)}\mathbf{g}(n-mK)e^{j2 \pi\left[k+\frac{1}{2}\text{mod}(m,2)\right]\frac{1}{K}n} \tag{3}\]0  
Write with polyphase components:  
\[\mathbf{x}(n)=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d^{(k,m)}\mathbf{g}(n-mK)e^{j2 \pi\left[k+\frac{1}{2}\text{mod}(m,2)\right]\frac{1}{K}n} \tag{3}\]1  
\[\mathbf{x}(n)=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d^{(k,m)}\mathbf{g}(n-mK)e^{j2 \pi\left[k+\frac{1}{2}\text{mod}(m,2)\right]\frac{1}{K}n} \tag{3}\]2 The transmitter part of the hexagonal multi carrier lattice structures. and we know that \(l=aK+b\) where \(b=0,1,\ldots K-1\) and \(a=-\infty\ldots\infty\) in general terms. Thus, we can write \"\(a\)\" instead of \"\(l\)\" on summation operation. Thus, replacing with convolution, the estimated signal at receiver is obtained:  
\[\mathbf{x}(n)=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d^{(k,m)}\mathbf{g}(n-mK)e^{j2 \pi\left[k+\frac{1}{2}\text{mod}(m,2)\right]\frac{1}{K}n} \tag{3}\]3  
The proposed receiver model of the hexagonal GFDM is given in \[\mathbf{x}(n)=\sum_{m=0}^{M-1}\sum_{k=0}^{K-1}d^{(k,m)}\mathbf{g}(n-mK)e^{j2 \pi\left[k+\frac{1}{2}\text{mod}(m,2)\right]\frac{1}{K}n} \tag{3}\]4 Again, we utilize to IDFT/DFT transformations by using polyphase decomposition to implement the transceiver. Here, the received signal is split to the \(M\) subsymbols. Then, the filtering and DFT transformations are done, respectively.
## 1 Introduction
In particular, the spatial distribution of the main molecular constituents of bacterial cells, such as membrane proteins and DNA components, were monitored using a Raman microscope, without any immunological staining or chemical labelling. _E. coli_ ATCC 8739 was used for this purpose as a Gram-negative representative bacterial model, and its chemical Raman fingerprint was taken. First, a comparison between a freeze-dried and an air-dried sample was performed at single-cell level to compare the preservation state of the cell structure in the case of a sudden thermal shock or not. Raman hyperspectral imaging maps of the cell were acquired and analyzed using multivariate statistical analysis of the images. Exploratory principal components analysis (PCA) was used for dimensionality reduction and then spectral unmixing was performed by multivariate curve resolution-alternating least squares (MCR-ALS). MCR-ALS is a method useful to solve the mixture analysis problem, that is, it is able to provide a chemically meaningful additive bilinear model of pure contributions from the sole information of an original data matrix including a mixed measurement [7]. From the Raman chemical map, the DNA segregation of a replicating bacterial cell was detected at a sub-micrometer level.
# WiFi Positioning in 3 GPP Indoor Office with Modified Particle Swarm Optimization
###### Abstract  
With the start of the Fourth Industrial Revolution, Internet of Things (IoT), artificial intelligence (AI), and big data technologies are attracting global attention. AI can achieve fast computational speed, and big data makes it possible to store and use vast amounts of data. In addition, smartphones, which are IoT devices, are owned by most people. Based on these advantages, the above three technologies can be combined and effectively applied to navigation technology. In the case of an outdoor environment, global positioning system (GPS) technology has been developed to enable relatively accurate positioning of the user. However, due to the problem of radio wave loss because of many obstacles and walls, there are obvious limitations in applying GPS to indoor environments. Hence, we propose a method to increase the accuracy of user positioning in indoor environments using wireless-fidelity (Wi-Fi). The core technology of the proposed method is to limit the initial search region of the particle swarm optimization (PSO), an intelligent particle algorithm; doing so increases the probability that particles converge to the global optimum and shortens the convergence time of the algorithm. For this reason, the proposed method can achieve fast processing time and high accuracy. To limit the initial search region of the PSO, we first build an received signal strength indicator (RSSI) database for each sample point (SP) using a fingerprinting scheme. Then, a limited region is established through a fuzzy matching algorithm. Finally, the particles are randomly distributed within a limited region, and then the user's location is positioned through a PSO. Simulation results confirm that the method proposed in this paper achieves the highest positioning accuracy, with an error of about 1 m when the SP interval is 3 m in an indoor environment.  
indoor positioning; wireless-fidelity (Wi-Fi); fingerprinting; fuzzy matching; particle swarm optimization (PSO) +
Footnote †: journal: Applied Sciences  
1
## 3 Results and Discussions
### XPS Analysis  
Figure 4 shows the XPS spectra of O 1s for the Ce-W-Ti catalysts. Two major peaks of different surface oxygen species could be obtained by fitting the curves. The peaks located between 529.6 eV and 530.1 eV could be ascribed to the lattice oxygen (O\({}_{\text{lat}}\)), while the peaks at 531.2 eV to 531.6 eV belonged to the surface-adsorbed oxygen (O\({}_{\text{ads}}\)) [22]. The relative concentrations of O\({}_{\text{ads}}\)/(O\({}_{\text{ads}}\) + O\({}_{\text{lat}}\)) are calculated and presented in Table 1. The Ce-W-Ti catalysts possessed higher relative concentrations of O\({}_{\text{ads}}\)/(O\({}_{\text{ads}}\) + O\({}_{\text{lat}}\)) in the range of 21.0% to 33.8% compared to the Ce\({}_{0.4}\)Ti (17.4%) and W\({}_{0.4}\)Ti (16.0%) catalysts, while the highest O\({}_{\text{ads}}\) ratio was observed over the Ce\({}_{0.2}\)W\({}_{0.2}\)Ti catalyst. The conventional SCR reaction cycles indicated that redox properties were crucial for the reaction performance at low temperatures. [PERSON] et al. reported that the interactions between Ce and W species could enrich the amount of Ce\({}^{3+}\) and surface oxygen vacancies, and that these two species were directly correlated with the surface oxygen species [35]. Surface-adsorbed oxygen (O\({}_{\text{ads}}\)) is more chemically reactive due to their higher mobility on the catalyst surfaces compared to the lattice oxygen (O\({}_{\text{lat}}\)) species, which was beneficial for the oxidation of NO to NO\({}_{2}\), one of the initial steps of NH\({}_{3}\)-SCR [24; 36]. The order of O\({}_{\text{ads}}\) was in good agreement with the sequence of the NH\({}_{3}\)-SCR performance.
###### Abstract
Various studies focusing on numerical and experimental analyses of self-centering systems have been conducted in recent years. Pioneer studies about self-centering structures have been conducted by various researchers [37; 38; 39]. [PERSON] et al. [40] carried out a parametric study focusing on the inelastic displacement spectra of SC-CR systems subjected to far-field and near-field ground motions. They have also developed new functional forms. [PERSON] and his co-workers also studied the inelastic response spectra of self-centering systems [39; 41]. [PERSON] et al. [42] studied a self-centering hysteretic device system to develop an iterative calculation method for residual displacement. [PERSON] et al. [43] investigated the application of machine learning and a probabilistic estimation method to forecast the nonlinear displacement ratio of structures. Many researchers have concluded that the force-displacement hysteretic relations of these structures are usually characterized by the flag-shaped (FS) hysteresis model [44; 45; 46; 47]. A very recent research study conducted by [PERSON] et al. [48] presented the results of a number of analyses on peak and residual displacement ratios of the self-centering coupled SDOF system and two predicting formulas for peak and residual displacement ratios. All these mentioned studies provide the results of research on fixed-base systems; in other words, soil-structure interaction or soil flexibility is almost always ignored. To the best of the author's knowledge, only one study--by [PERSON] et al. [49]--presented the findings of a parametric analysis to assess the impact of soil-structure interaction on the inelastic displacement ratios of self-centering systems that exhibit hysteretic behavior in the shape of a flag. In their analyses, they used synthetic ground motions, and the effect of the strength reduction factor on the response was considered. Soil-structure interaction effects on residual displacement have not been considered, yet this study is intended to focus on the effects of soil flexibility on both constant-ductility inelastic displacement ratios and residual displacements. In addition to the above-mentioned parametric studies, there have been some other experimental studies focusing on the effect of load on displacement behavior, and they will help us gain more insight [50; 51; 52]. The main objective of this paper is to investigate the seismic response of self-centering systems with flag-shaped hysteretic behavior subjected to near-fault ground motions, considering soil-structure interaction. To achieve this aim, SDOF systems of a given ductility (\(\mu\) = 2, 3, 4, 5, 6) are numerically analyzed under 56 pulse-like near-fault ground motions for strain hardening ratios of \(\alpha\) = 0, 5%, and 10%, energy dissipation ratios of \(\beta\) = 0, 0.25, 0.5, and 1.0, and aspect ratios of h/r = 1, 3, and 5. These considered values correspond to the parameters frequently encountered and used in the literature.
## 3 Results and Discussions
### Alfacrux Digital Twin Pilot
#### 3.2.1 Attitude Reconstruction and Dynamic Analysis
With the obtained quaternions, the mean observation vector can be computed as well as the measurement vector, innovation covariance matrices and the cross-correlation matrix:  
\[\hat{\mathbf{y}}_{k}^{-}=\frac{1}{n+\lambda}\Bigg{\{}\lambda\boldsymbol{\gamma }_{k}(0)+\frac{1}{2}\sum_{i=1}^{2n}\boldsymbol{\gamma}_{k}(i)\Bigg{\}}, \tag{7}\]where \(\gamma_{k}(i)=\mathbf{h}[\chi_{k}(i),k]\)  
\[P_{k}^{yy}=\frac{1}{n+\lambda}\Bigg{\{}\lambda[\gamma_{k}(0)-\hat{\mathbf{y}}_{k} ][\gamma_{k}(0)-\hat{\mathbf{y}}_{k}]^{T}+\frac{1}{2}\sum_{i=1}^{2n}[\gamma_{k} (i)-\hat{\mathbf{y}}_{k}][\gamma_{k}(i)-\hat{\mathbf{y}}_{k}]^{T}\Bigg{\}}, \tag{8}\]  
\[P_{k}^{vv}=P_{k}^{yy}+R, \tag{9}\]  
\[P_{k}^{xy}=\frac{1}{n+\lambda}\Bigg{\{}\lambda[\chi_{k}(0)-\hat{\mathbf{x}}_{ k}^{-}][\gamma_{k}(0)-\hat{\mathbf{y}}_{k}]^{T}+\frac{1}{2}\sum_{i=1}^{2n}[ \chi_{k}(i)-\hat{\mathbf{x}}_{k}^{-}][\gamma_{k}(i)-\hat{\mathbf{y}}_{k}]^{T} \Bigg{\}}. \tag{10}\]  
The innovation is computed as the difference between the sensor observations and the predicted output:  
\[\mathbf{v}_{k}\equiv\hat{\mathbf{y}}_{k}-\hat{\mathbf{y}}_{k}^{-}=\hat{\mathbf{y }}_{k}-\mathbf{h}(\hat{\mathbf{x}}^{-},k). \tag{11}\]  
The state vector and error covariance matrix are updated following the equations:  
\[\hat{\mathbf{x}}_{k}^{+}=\hat{\mathbf{x}}_{k}^{-}+K_{k}\mathbf{v}_{k}, \tag{12}\]  
\[P_{k}^{+}=P_{k}^{-}-K_{k}P_{k}^{vv}K_{k}^{T}. \tag{13}\]  
The attitude-error from \(\hat{\mathbf{x}}_{k}^{+}\) is transformed into a quaternion, and it is used to update the global attitude representation, which is the new attitude estimate. Later, the attitude error in the state vector is set to zero before the beginning of the next iteration. Finally, the in-orbit data will be used as input information for the small satellite simulator available at the Laboratory of Simulation and Control of Aerospace Systems (LODESTAR) [29; 30; 31]. Moreover, a new air-bearing based simulator dedicated to the CubeSat standard was built, and it is already operational to be used for the reconstruction of dynamics characteristics of the AlfaCrux. The CAD view is shown in \[P_{k}^{-}=\frac{1}{n+\lambda}\Bigg{\{}\lambda[\boldsymbol{\chi}_{k}(0)-\hat{ \mathbf{x}}_{k}^{-}][\boldsymbol{\chi}_{k}(0)-\hat{\mathbf{x}}_{k}^{-}]^{T} \quad+\frac{1}{2}\sum_{i=1}^{2n}[\boldsymbol{\chi}_{k}(i)-\hat{\mathbf{x}}_{k }^{-}][\boldsymbol{\chi}_{k}(i)-\hat{\mathbf{x}}_{k}^{-}]^{T}\Bigg{\}}+\tilde{ Q}_{k}. \tag{5}\]0 The results of the experiments in the simulator are stored in the DT database and can be used in future simulations.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 8 Optimization Using the Modified GA Approach  
A GA is a form of heuristic search that falls under the category of evolutionary algorithms. It is used to tackle search and optimization problems. The natural evolutionary processes, such as inheritance, mutation, selection, and crossover, inspired the approach of the GA. The modified GA is a type of GA that has a different approach to clustering. At each iteration, the modified GA groups the fittest chromosomes, which then again reproduce offspring chromosomes. In the modified GA, the best solution is computed that moves closer to the optimal solution in every iteration. The next best chromosomes are selected based on selection, crossover, and mutation. After these processes, the chromosomes with low fitness values are eliminated and only high-quality chromosomes go forward to the next step, which then again act as the parent chromosomes. In crossover, the chromosomes are mixed to produce stronger chromosomes, which inherit the properties from the parents, and then, the genes of the chromosomes are mutated, which is controlled by a mutation parameter and the step size. Lastly, elitism helps prevent the loss of the best chromosomes in the current iteration.  
In the modified GA, chromosomes are called solutions and the value of the solutions is evaluated by a fitness function as either \"good\" or \"bad\". The solutions that are \"good\" can move forward to the next step and are then used to form a new set of solutions, whereas the \"bad\" solutions are terminated. This new set of solutions again reproduces a set of solutions through the same process and then has to pass the test at each iteration. This cycle is repeated until an optimal solution is found. Once the optimal solution is found, it provides us the best cost. The illustration in Figure 4. depicts the proposed modified GA approach. The important elements of the modified GA are explained further below.
## 2 Theory
### Contribution of Attenuation, Random and Scattered Coincidences  
#### 2.2.1 Attenuation  
Using a notation where \(c_{i,j}\) represents the total probability of a photon emitted from the pixel \(j\) to be detected in detector \(i\), the attenuation is naturally accounted for in all the previous equations, without any modification.