esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results  
MATLAB/Simulink R2023b runs time-domain simulations to validate the proposed system. The model contains two operational modes, which include direct supply of a three-phase residential load without storage and supply of a single-phase consumer through an integrated storage system for power management. The grid-side converter generates three voltage signals that include a 50 Hz fundamental at 120 V RMS and a 5 th harmonic (negative sequence) at 50 V RMS, and a 7 th harmonic (positive sequence) at 25 V RMS. The first operational mode delivers 100 V RMS per phase to a balanced three-phase load, which starts at 1 kW before increasing to 1.5 kW for dynamic testing. The second operational mode uses a single-phase inverter to supply power to a 70 V RMS, 650 W load, while a bidirectional buck-boost converter connects to a battery system that can discharge at 3 A and charge at 2 A. The three-phase grid-connected converter controls DC-link voltage regulation, while the storage system provides power support for system flexibility and energy equilibrium.  
The system uses the electrical parameters that are listed in Table 1. The system parameters include inverter-side output filter components, DC-link voltage and capacitance, battery emulator specifications, and realistic line impedances. The inductance L used in the feedforward voltage term of the implemented control scheme refers to the inverter-side filter inductance and does not include the line inductance. The same parameter set was used in both the simulation studies and the laboratory experimental validation to ensure consistency and credibility of the results.  
Figure 3: System configuration with DC-Link storage integration.
# Effects of Imperfect Assembly and Magnetic Properties on the Three-Pole AMB System
## 2 System Description and Dynamic Modeling
\]  
and the uncertainty of magnetic forces caused by the effect of \((\Delta x,~{}\Delta y)\) is represented by \(\Delta f_{x},~{}~{}\Delta f_{y}\) and is given by  
\[\Delta f_{x}=c_{0}(\hat{\Phi}_{1}\Delta\Phi_{2}+\hat{\Phi}_{2}\Delta\Phi_{1}+ \Delta\Phi_{1}\Delta\Phi_{2})\approx c_{0}(\hat{\Phi}_{1}\Delta\Phi_{2}+\hat{ \Phi}_{2}\Delta\Phi_{1}) \tag{7}\]  
\[\Delta f_{y}=\frac{c_{0}}{2}(2\hat{\Phi}_{2}\Delta\Phi_{2}+\Delta\Phi_{2}^{2}-2 \hat{\Phi}_{1}\Delta\Phi_{1}-\Delta\Phi_{1}^{2})\approx c_{0}(\hat{\Phi}_{2} \Delta\Phi_{2}-\hat{\Phi}_{1}\Delta\Phi_{1}) \tag{8}\]  
where \(\Delta\Phi_{1},\Delta\Phi_{2}\) is the uncertain part of \(\Phi_{1}\), \(\Phi_{2}\), given by  
\[ \begin{bmatrix}\Delta\Phi_{1}\\ \Delta\Phi_{2}\end{bmatrix} =\frac{-1}{\hat{L}}K\!\!\left[ \begin{array}{cc}- \Delta y&\Delta x\\ \Delta x&\sqrt{3}\Delta y\end{array} \right]\!\! \begin{bmatrix}i_{1}\\ i_{2}\end{bmatrix}  \tag{9}\]  
Therefore, the dynamic Equation (5) can be represented in the state space form as  
\[\dot{x}=f(x,i)= \begin{bmatrix}x_{2}\\ c_{0}\hat{\Phi}_{1}\hat{\Phi}_{2}\\ x_{4}\\ \frac{c_{0}}{2}(\hat{\Phi}_{2}^{2}-\hat{\Phi}_{1}^{2})-g\end{bmatrix} + \begin{bmatrix}0 \\ \Delta f_{x}\\ 0\\ \Delta f_{y}\end{bmatrix}  \tag{10}\]where \(x=[x_{1}\quad x_{2}\quad x_{3}\quad x_{4}]^{T}=[x_{r}\quad\dot{x}_{r}\quad y_{r} \quad\dot{y}_{r}]^{T},i=[i_{1}\quad i_{2}]^{T}\). In what follows, the stable levitation controller will be designed based on the nominal system under the influence of the uncertainty \(\Delta f_{x},\ \Delta f_{y}\).
# Mechanical Modeling and Dynamic Characteristics of a Three-Directional Vibration Absorber
## 2 Materials and Methods
### Experimental Validation  
To evaluate the accuracy and practical relevance of the analytical and numerical models, a physical prototype of the isolator was fabricated and tested. The validation process included the following:  
1. Static loading tests, to measure force-displacement behavior and compare with model predictions;
2. Dynamic harmonic excitation tests, to evaluate amplitude-frequency response and transmissibility under small- and mid-range excitation amplitudes.  
The experimental data are used to validate the proposed model's accuracy and illustrate the real-world behavior of the isolator under operational conditions.
## 2 Information Granulation and Classification Systems
### Proposed Approach
#### 2.2.1 Embedding
In light of these observations, it is possible to define the three counterparts of Equations (1)-(3) where each given node \(u\) belonging to a given simplex \(\sigma\) is represented by its node label:  
\[\overline{\mathcal{D}}_{\text{TR}}^{\text{SC}} =\{\mathcal{L}_{v}(u),\;\forall u\in\sigma,\;\forall\sigma\in \mathcal{S},\;\forall\mathcal{S}\in\mathcal{D}_{\text{TR}}^{\text{SC}}\} \tag{4}\] \[\overline{\mathcal{D}}_{\text{VAL}}^{\text{SC}} =\{\mathcal{L}_{v}(u),\;\forall u\in\sigma,\;\forall\sigma\in \mathcal{S},\;\forall\mathcal{S}\in\mathcal{D}_{\text{VAL}}^{\text{SC}}\}\] (5) \[\overline{\mathcal{D}}_{\text{TS}}^{\text{SC}} =\{\mathcal{L}_{v}(u),\;\forall u\in\sigma,\;\forall\sigma\in \mathcal{S},\;\forall\mathcal{S}\in\mathcal{D}_{\text{TS}}^{\text{SC}}\} \tag{6}\]  
Let \(\mathcal{A}\) be the set of unique (distinct) simplices belonging to the simplicial complexes evaluated from graphs in \(\mathcal{D}_{\text{TR}}\cup\mathcal{D}_{\text{VAL}}\):  
\[\mathcal{A}=\{\sigma,\;\forall\sigma\in\mathcal{S},\;\forall\mathcal{S}\in \overline{\mathcal{D}}_{\text{TR}}^{\text{SC}}\cup\overline{\mathcal{D}}_{ \text{VAL}}^{\text{SC}}\}, \tag{7}\]  
and let \(|\mathcal{A}|=M\). The next step is to properly build the embedding vectors thanks to the symbolic histograms paradigm. Accordingly, each simplicial complex \(\mathcal{S}\) (evaluated on the top of a given graph, that is, 1-skeleton) is mapped into an \(M\)-length integer-valued vector \(\mathbf{h}\) as follows  
\[\mathbf{h}=[\text{count}(\mathcal{A}_{1},\mathcal{S}),\dots,\text{count}( \mathcal{A}_{M},\mathcal{S})], \tag{8}\]  
where \(\text{count}(a,b)\) is a function that counts the number of times \(a\) appears in \(b\). The three sets \(\mathcal{D}_{\text{TR}}\), \(\mathcal{D}_{\text{VAL}}\) and \(\mathcal{D}_{\text{TS}}\) are separately cast into three proper instance matrices \(\mathbf{D}_{\text{TR}}\in\mathbb{R}^{|\mathcal{D}_{\text{TR}}|\times M}\), \(\mathbf{D}_{\text{VAL}}\in\mathbb{R}^{|\mathcal{D}_{\text{VAL}}|\times M}\) and \(\mathbf{D}_{\text{TS}}\in\mathbb{R}^{|\mathcal{D}_{\text{TS}}|\times M}\). For each set, the corresponding instance matrix scores in position \((i,j)\) the number of occurrences of the \(j\)th symbol (simplex) from \(\mathcal{A}\) within the \(i\)th simplicial complex (in turn, evaluated on the top of the \(i\)th graph).
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms
#### 3.2.3 Machine Learning
The authors in [140; 141; 142; 143; 144; 145] use leaf images to detect visual symptoms: for this purpose, different image vision feature descriptors, such as Gray Level Co-occurrence Matrix (GLCM) [146] or Histogram of Oriented Gradients (HOG) [147], are exploited. Classic dimensionality reduction techniques such as PCA are also used [148]. Additionally, RGB and hyper-spectral images are mixed together, as in [149], to estimate phenological dates. An additional usage of machine learning models is represented by [150], where bioinformatic variables and habitat heterogeneity data are correlated to climate change. In [151], [PERSON] et al. use SVM to predict the protein sequences of candidate disease resistance genes for common beans. Meanwhile, [PERSON] et al. in [152] demonstrate the applicability of different models to predict biomarkers which are useful for detecting a specific citrus disease (Huanglongbing) on a global scale. Finally, [PERSON] et al. in [153] use different machine learning-based numerical models to make predictions between the bioactivity and chemical properties of ligands. Machine learning can be efficiently applied to data fused together from different sources, as in [154], where [PERSON] et al. exploit both RGB images and vegetation indexes as input to a multimodal fusion model.
## 3 Results and Discussion
### Kinetic Adsorption Experiments  
Sb(V) adsorption as a function of reaction time with model fit is presented in Figure 3, and the model parameters obtained by kinetic models using experimental data are given in  
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline
**Fe-CMB** & **C** & **O** & **Cl** & **Fe** & **Ca** & **P** & **Mg** & **K** & **S** & **Si** \\ \hline Before adsorption & 28.0 \(\pm\) 1.4 & 15.8 \(\pm\) 2.1 & 18.0 \(\pm\) 4.4 & 14.6 \(\pm\) 0.8 & 10.2 \(\pm\) 1.5 & 6.2 \(\pm\) 1.8 & 2.2 \(\pm\) 0.2 & 2.1 \(\pm\) 0.1 & 1.9 \(\pm\) 1.5 & 1.3 \(\pm\) 0.4 \\ After adsorption & 30.4 \(\pm\) 1.3 & 19.7 \(\pm\) 3.1 & 1.8 \(\pm\) 0.4 & 31.9 \(\pm\) 3.6 & 0.0 \(\pm\) 0.0 & 8.4 \(\pm\) 4.2 & 0.4 \(\pm\) 0.0 & 0.3 \(\pm\) 0.0 & 4.2 \(\pm\) 3.9 & 0.3 \(\pm\) 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elemental composition (weight %) of Fe-CMB obtained from energy dispersive spectrometer.  
Figure 2: Fourier transform infrared spectroscopy spectra of Fe-CMB (**a**) before and (**b**) after Sb(V) adsorption.  
Table 2. Sb(V) adsorption increased sharply within 1 h and continued to gradually increase until 6 h. A high adsorption rate was observed at the beginning of the reaction because of the availability of favorable adsorption sites, which then decreased near the equilibrium time owing to the saturation of favorable adsorption sites [29].  
The pseudo-second-order model had a higher \(R^{2}\) and lower \(SSE\) than that of the pseudo-first-order model. The \(q_{e}\) obtained from the pseudo-second-order model is closer to the experimentally obtained \(q_{e}\), 13.1 mg/g. These results indicate that the pseudo-second-order model describes the kinetic adsorption of Fe-CMB-mediated Sb(V) better than the pseudo-first-order model. The order of kinetic reactions, identified from \(n\) values of the pseudo-nth-order model, were approximately 2, which is consistent with the suitability of the pseudo-second-order model. A better description of the experimental data by the pseudo-second-order model than the pseudo-first-order model indicates that the rate of Sb(V) adsorption by Fe-CMB is mainly controlled by chemical adsorption [30].
## 4 Experimental Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cutting Direction** & \begin{tabular}{c} **Rotation Speed** \\ **(rpm)** \\ \end{tabular} & \begin{tabular}{c} **Feed per Tooth** \\ **\(f_{z}\) (mm/z)** \\ \end{tabular} & \begin{tabular}{c} \(\mathbf{a_{p}}\) \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} \(\mathbf{f}\) (KHz)** \\ \end{tabular} & \
\begin{tabular}{c} \(\mathbf{A}(\mathbf{\mu}\)**m)** \\ \end{tabular} &
\begin{tabular}{c} \(\mathbf{D_{h}}\) \\ \end{tabular} \\ \hline downward & 4000 & 0.2 & 0.2 & 30 & 5 & 7.8 \\ upward & 4500 & 0.1 & 0.2 & 30 & 5 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Single factor test parameters of helix milling with ultrasonic-assisted vibration. In Figure 11, it is clearly reflected that the changing state of \(F_{z}\) at the beginning of the cutting, the cutting force mutation with the bottom edge, and the edge begin to participate in the cutting, ultrasonic-assisted vibration generated by the impact force, which makes the cutting force fall to stable, where the cutting becomes light and enters into stable cutting, stage I. As the bottom edge exits the milling process, it can be seen from the curvature of the falling axial force that the cutting removal amount is small and falls in a uniform decreasing way. The reason is that the peripheral edge gradually cuts uniformly downward, the cutting removal is gradually reduced, and at the same time, the top cutting edge involved in the cutting removal is small, which indicates stage II. As the top cutting edge of the peripheral edge exits and the third stage begins, the cutting force is reduced to a minimum. In the fourth stage of reverse cutting, the main fiber fracture mode is changed from extrusion to cutting and rolling, the material removal and deformation are low, and the axial force is lower than the critical value of the interlayer binding force; however, there are some jump points in the figure, which are caused by fiber rebound or burr in the hole wall position and by the cutting force mutation. According to the analysis in Figure 12, it can be seen that with the increase in the number of hole makings, Fx and Fy increased by 30.27% and 22.26%, respectively, with the increase in the number of processed holes at 90, which leads to an increase in tool wear, and which makes it difficult for the tool to remove the material, leading to a gradual increase in cutting force [24]. However, in the aspect of axial force, under the ultrasonic vibration impact, the cutting becomes easy and fast, and the axial force is still at a relatively low level. After 60 holes were processed, the axial force was about 18.5 N, which was reduced by 48% compared with the reference; after 90 holes were machined, the axial force was only 23.3 N, almost equal to the axial force level recorded in the reference in the initial machining state [25], which effectively avoids the tearing of carbon fiber materials, and no obvious delamination defects occurred in the holes. Figure 11: Cutting forces collect real-time data of \(F_{Z}\).
More precisely, several of them attempt to reduce the storage size [3; 4; 5], while others investigate the privacy preservation of trajectory data [6; 7]. Nowadays, not only are storage-efficient spatio-temporal transformation schemes needed, but also secure querying on large-scale spatio-temporal data [8]. An accurate capture of a moving object trajectory usually needs a high sampling rate to collect its location data. Thus, massive trajectory data will be generated, which is difficult to fit into the memory for utilizing data-mining algorithms. A common idea is to compress the trajectory data to reduce the storage requirements while maintaining the utility of the trajectory. In the context of this work, we present the storage efficiency of dual methods and experiment on data from the SMaRT system, through which the data of moving object trajectories are generated and used as input to our methods in order to evaluate the security level they offer. As already stated, the privacy of the \(k\)-anonymity method recommended in [9] is reinforced. More specifically, we summarize the main contributions of our paper as follows:  
1. We compare the proposed methods on addressing \(k\)-NN queries on moving objects' trajectories data, which are stored both in dual and native dimensional space. Our implementation shows that the innovative method of Dual Transformation constitutes a practical solution that can provide secure \(k\)-NN queries. 2. We conduct an extensive experimental evaluation that studies various scenarios that can affect the vulnerability of the \(k\)-NN queries and proceed to a comparative analysis of the underlying methods. We prove the efficiency of our solution using real data drawn from SMaRT. 3. We recall two protocols for Pseudonyms Recovery and Registration with the aim of reinforcing the individuals' privacy in the released data. An individual cannot be re-linked to specific users with a high degree of certainty, as it is described in Section 3.7. The rest of this paper is organized as follows: In Section 2, previous related works are presented in relation to our approach. The following are described in Section 3: (a) the dual transformation methods used; (b) the problem definition; (c) the problem formulation (d) the privacy-preserving analysis; (e) the experimental environment, and source of datasets. Section 4 presents the graphical outcomes gathered from experiments, while Section 5 evaluates experimental results in relation to the pros and cons of the proposed methods. Finally, Section 6 records the conclusions in terms of the studied problem and future directions of this work.
# A Secure Median Implementation for the Federated Secure Computing Architecture
## 3 Materials and Methods
### Implementation
(2020) [PERSON]; [PERSON]; [PERSON] [PERSON] ABY--A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In Proceedings of the Network and Distributed System Security Symposium 2015, Reston, VA, USA, 8-11 February 2015; The Internet Society: Reston, VA, USA, 2020; pp. 497-511. [CrossRef]
* [PERSON] et al. (2008) [PERSON]; [PERSON]; [PERSON] [PERSON] Sharemind: A Framework for Fast Privacy-Preserving Computations. In Proceedings of the 13 th European Symposium on Research in Computer Security--ESORICS 2008, Malaga, Spain, 6-8 October 2008; [PERSON], [PERSON]; Springer: Berlin/Heidelberg, Germany, 2008; pp. 192-206. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; et al. Carbyne Stack. Available online: [[https://carbynestack.io](https://carbynestack.io)]([https://carbynestack.io](https://carbynestack.io)) (accessed on 20 August 2024 ). * [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] EasySMPC: A simple but powerful no-code tool for practical secure multiparty computation. _BMC Bioinform._**2022**, _23_, 531. [CrossRef] [PubMed]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON] _Secure Multiparty Computation and Secret Sharing_; Cambridge University Press: New York, NY, USA, 2015. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] [PERSON] The UCI Machine Learning Repository (2023). Available online: [[https://archive.ics.uci.edu](https://archive.ics.uci.edu)]([https://archive.ics.uci.edu](https://archive.ics.uci.edu)) (accessed on 20 August 2024). * Amazon Web Services (2024) Amazon Web Services. Amazon ElastiCache Pricing. Available online: [[https://aws.amazon.com/de/elasticache/pricing/](https://aws.amazon.com/de/elasticache/pricing/)]([https://aws.amazon.com/de/elasticache/pricing/](https://aws.amazon.com/de/elasticache/pricing/)) (accessed on 3 June 2024). * [PERSON] et al. (1978) [PERSON]; [PERSON]; [PERSON] Even data bases that lie can be compromised. _IEEE Trans. Softw. Eng._**1978**, \(4\), 73. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] Combining Differential Privacy and Secure Multiparty Computation. In Proceedings of the 31 st Annual Computer Security Applications Conference, New York, NY, USA, 7-11 December 2015; ACM: New York, NY, USA, 2020; pp. 421-430. [CrossRef]
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## 5 Results and Discussion  
### Data  
In the following numerical comparison, we consider a nonlinear profile from the study of [PERSON] and [PERSON] [54]. This profile has been investigated by several researchers [33; 55]. The method proposed in [55] is known as penalized profile outlier detection (PPOD). In order to conduct a direct and fair comparison, we adhered to all the settings and scenarios used in [33; 55]. The considered profile has the following functional form:  
\[y=10-20 ae^{-ax_{i}}\sin\left(\sqrt{4-a^{2}}x_{i}\right)/\sqrt{4-a^{2}}+10e^{- ax_{i}}\times\cos\left(\sqrt{4-a^{2}}x_{i}\right)+\varepsilon \tag{8}\]  
where \(x_{i}\in[0.08,\ 8]\), and \(\varepsilon\) denotes the random noise of the profile that follows a standard normal distribution with a mean of 0 and standard deviation of 1. There are a total of 100 points in each profile (i.e., \(n=100\)), evenly distributed at intervals of 0.02. Taking the coefficient \(a\) as the major source of profile variation, the competing methods are evaluated and analyzed when \(a\in\{0.5,\ 0.7,\ 1.1,\ 1.3,\ 1.5\}\). The profiles with \(a=0.5\) indicate the process is in control.  
Figure 6a illustrates the IC and OOC profiles without noise. It is hard to distinguish the IC profile from the OOC profile when random noise exists. The profiles shown in Figure 6b are almost indistinguishable. For this particular profile function, when the value of the coefficient \(a\) gets larger, the peak and valley points of the entire profile will become closer and less obvious. This phenomenon indicates that it is difficult to differentiate between an IC profile and an OOC profile.  
For clarity of presentation, we use the same notation as in [53]. Each dataset consists of \(m-m_{o}\) non-outlying and \(m_{o}\) outlying profiles where \(m=200\) and \(m_{o}\) takes on values 20, 40, or 60. The contamination rate can be defined as \(m_{0}/m\). [PERSON] et al. [55] considered a case where \(m_{o}=80\), which is probably high for many anomaly-detection problems. Thus, their results are not presented here. Suppose among the \(m-m_{o}\) IC profiles, \(m_{1}\) profiles are misidentified as OOC profiles, and among the \(m_{o}\) OOC profiles, \(m_{2}\) profiles are correctly identified. When expressed in decimal numbers, the type I and type II errors can be computed as \(m_{1}/(m-m_{o})\) and \((m_{0}-m_{2})/m_{o}\), respectively.
###### Abstract  
Background--Upper molar distalization with a clear aligner is a debated topic in the scientific literature. One of the main issues is the loss of anchorage, so Class II elastics or the application of miniscrews are recommended. The aim of this study is to evaluate, through Finite Element Method (FEM) analysis, the effects of the application of the interradicular miniscrew as an anchorage device. Methods--A maxillary arch model from the second molar to second molar, which was obtained from a Cone Beam Computed Tomography (CBCT) and 0.2 mm distalization of the upper second premolar and upper first molar, was simulated. Therefore, nine different anchorage configurations were analyzed, with a miniscrew simulated between the upper first and second molars. Results--Considering the anchorage of a Class I elastic on the first premolar, the resulting side effect was a buccal movement of the canine. When the Class I elastic was anchored on the canine, distal displacement of the canine was observed. The distalization movements of second premolars and first molars were more efficient when the elastics were parallel to the occlusal plane. A greater anterior anchorage loss was observed when the elastic was anchored on the canine, with a proclination of the upper central incisor 30% higher than in the simulation, in which the elastic was on the first premolar. Conclusion--The use of interradicular buccal skeletal anchorage could reduce issues of patient compliance, provide a consistent intraoral anchorage unit and allow the desired tooth movements, reducing unwanted movement of the anterior teeth.  
clear aligners; upper distalization; miniscrews; orthodontics