esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion
## 2 Materials and Methods
### Machine Learning Models
#### 2.2.2 Convolutional Neural Network  
CNN is widely used in image related regression and classification tasks [14; 15; 16; 17]. The state-of-the-art image classification algorithms utilize some form of CNN [30; 31]. There is also a lot of research for it being used with hyperspectral images [10]. The CNN algorithms we used are described in Tables 3-6.  
In the first experiment with grid search (Table 3), we varied the shape of two convolutional layers, the size of the convolution kernel, and size of the max-pooling [32] window. The resulting network (Table 4) had two one-dimensional, convolution layers with convolution window of size 12, 32, and 64 filters, respectively. After each convolution, there were one-dimensional, max-pooling with pool size of two. Both convolutional layers have rectified linear unit (ReLU) [33] activation. After the last max-pooling layer, there was a flattening layer and three dense layers (ReLU activation), dropout of 50%, and an output layer. The first experiment is referred to in the results and the discussion as CNN.  
In the second experiment, we varied the amount of convolutional layers, the convolution kernel size, and size of the max-pooling window (Table 5). The resulting network (Table 6) consisted of a one-dimensional, convolutional neural network, max-pooling, two one-dimensional, convolution layers, another max-pooling, flattening layer three dense layers, dropout of 50% [34], and output layer. All dense and convolutional layers except output layers utilized ReLU activation. Every convolutional layer had a convolution window of size 15, and the max-pooling pool size was two. The second experiment is referred to in the results and discussion as CNNV2.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Values** \\ \hline Convolution filters & (64 and 128), (128 and 256), **(32 and 64)** \\ Convolution kernel size & 6, 9, **12** \\ MaxPooling pool size & 1, **2**, 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grid search parameters for the first convolutional neural network experiment, abbreviated as CNN in the results and discussion. The best parameters are bolded.  
The used optimizer was an Adam optimizer [35] in both experiments. The learning rate was 0.001, \(\beta_{1}=0.9\), \(\beta_{2}=0.999\) and \(\epsilon=10^{-7}\), which were not included as the optimization parameters as the focus was on the architecture of the network. The loss score was mean square error (MSE).
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs  
Here, in this section, we categorize the FSSs on the basis of array element, structure design, and application. Figure 4 gives the taxonomy of the FSS presented in this article.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Phase 3: SIMD Homomorphic Image Processing  
The computational phase leverages the parallel nature of image processing operations through sophisticated SIMD techniques. When an operation like brightness adjustment is applied, it affects all pixels simultaneously (see Algorithm 5). As demonstrated in Algorithm 5, our implementation includes careful input validation and noise budget monitoring to ensure reliable results. This parallelism, inherent in the CKKS scheme's slot-packing design, transforms what would otherwise be computationally prohibitive operations into tractable procedures.
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequence**} & \multicolumn{2}{c}{**Illuminated**} & \multicolumn{2}{c}{**No Light**} \\ \cline{2-5}  & **Reflector** & **Background Layer** & **Reflector** & **Background Layer** \\ \hline A combination of image completion & 0.91563 & 0.90875 & 0.90897 & 0.89745 \\ and illumination & & & & \\ \hline Static Video Reflection Removal Method [33] & 0.86982 & 0.87431 & 0.86232 & 0.86968 \\ \hline Black box video reflection removal [34] & 0.89236 & 0.86624 & 0.88746 & 0.86364 \\ \hline Removing reflections from multiple polarized images with different exposure times [16] & 0.90365 & 0.90369 & 0.89541 & 0.89796 \\ \hline An edge-guided removal of single image & 0.89677 & 0.90623 & 0.88965 & 0.88943 \\ reflections [10] & & & & \\ \hline The removal of specular reflection from & & & & \\ non-dielectric surfaces based on multi-image & 0.88972 & 0.89748 & 0.87644 & 0.89656 \\ optimization [20] & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._area. A future research direction will focus on the design of a reflection region removal decision system that can reduce time delays under the condition of complete reflection region removal. Conceptualization, J.N. and Y.X. ; methodology, S.D. ; software, Y.X., S.D. ; validation, S.D., X.K. and S.S.; investigation, S.D. ; resources, Y.X. and S.D. ; data curation, Y.X., S.D. ; writing--original draft preparation, S.D. ; writing--review and editing, Y.X. and S.D. ; supervision, Y.X., S.D. and J.N. ; project administration, Y.X. ; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China under Grant 51905320 and 52102465, the Natural Science Foundation of Shandong Province under Grant ZR2022 MF230, Experiment technology upgrading project under Grant 2022003, and the Shandong provincial programme of introducing and cultivating talents of discipline to universities: research and innovation team of intelligent connected vehicle technology. Not applicable. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
# Evaluation of Tooth Movement Accuracy with the F22 Aligner System: A Retrospective Study
## 2 Materials and Methods
### Analysis of Digital Models  
For each patient, maxillary and mandibular models of the initial malocclusion (pre-treatment model), of the final step of digital planning (reference model) and of the end of the initial prescribed series of aligners (pre-finishing model) were acquired in STL format using the appropriate setup software. Thus, a total of 720 models were investigated. Each model was imported into Onyxceph 3 TM software (Firma Image Instruments GmbH, Chemnitz, Germany) in order to perform the various digital superimpositions at the reference unmoved first and/or second molars in each quadrant (Figure 2) [14; 34].  
Figure 2: Process of digital matching between the pre-treatment model, reference and post-treatment model, respectively, using Onyxceph 3 TM software (Firma Image Instruments GmbH, Chemnitz, Germany). The matching was achieved using two posterior teeth, which were not moved, as reference landmarks after the segmentation.  
After the segmentation of the teeth and digital model superimposition, using best-fitting at the posterior teeth with no movement planned, a specific algorithm made it possible to acquire the following data:  
1. Prescribed movements (T1): the difference between the pre-treatment and reference model;
2. Achieved movements (T2): the difference between the pre-treatment and pre-finishing model (Figure 3) for each movement investigated (inclination, angulation and rotation) and for each tooth (where present, third molars were excluded).  
Prescribed movements (T1) \(\leq 2^{\circ}\) and the respective achieved movements (T2) were omitted in order to take into account the imprecision of the measurement of the method used due to the very small size of the prescription; after the application of this cut-off, in no case were there T1 \(-\) T2 pairs that had opposite directions of movement.  
The same models were imported into Orthoanalyzer 2017 software (3 Shape, Copenhagen, Denmark) in order to measure the following intra-arch linear distances in pre-treatment (T0), reference (T1) and pre-finishing (T2) models:  
* Inter-canine distance (Ic): measured between the tips of the canine cusps;
* Inter-premolar distance (Ip1): measured between the vestibular cusps on the first premolars;
* Inter-premolar distance (Ip2): measured between the vestibular cusps on the second premolars;
* Inter-molar distance (Im): measured between the mesiovestibular cusps on the first molars (Figure 4).  
Both angular and linear measurements were imported into Excel (Microsoft, Redmond, USA) for further analysis.  
Figure 3: Synthesis of the digital matching process between pre-treatment (**A**), reference (**B**) and post-treatment model (**C**), and use of a dedicated algorithm to quantify planned movements (**T1**) and those achieved (**T2**).
## 4 Experiments
### Rating-Based Recommendation  
To test the IACF's effectiveness regarding sparsity, we used a larger dataset Movies 10M ([[https://grouplens.org/datasets/movielens/10m/](https://grouplens.org/datasets/movielens/10m/)]([https://grouplens.org/datasets/movielens/10m/](https://grouplens.org/datasets/movielens/10m/))) than the Movies 1M used in [1]. MovieLens 10M contains 10 million ratings given to 10,000 movies from 72,000 users. The ratings are given on a five-star scale; 90% of the ratings are kept as a training set, and 10% are kept as a testing set.  
The evaluation metric adopted is RMSE as defined below. Statistically, RMSE is the standard deviation of the residuals. It represents the square root of the second sample moment of the differences between predicted values and observed values, so it is extensively used in measuring the accuracy of prediction. Clearly, the smaller the value is, the better the prediction is.  
\[RMSE=\sqrt{\frac{1}{n}\sum_{(u_{i},v_{j})\in TestSet}(r_{i,j}-estimated\_rating)^{2}}.\]  
For this scenario, we only compare IACF directly with ACF to see the improvement brought about by the dimension reduction in the initialization phase. The comparison results in Table 1 indicate that the IACF runs much faster than the ACF with better RMSE points. IACF is faster because much fewer types of pheromones are assigned due to the clustering procedure in the initial phase. The users are clustered into K clusters according to their rating pattern, i.e., the desired number of types of pheromones. Typically, we have \(k<<\#users\). For the batched training phase, the time complexities for both ACF and IACF are \(O(K\times\#ratings)\). For the online update, the update complexity is only \(O(K)\). In the recommendation phase, for the rating-based recommendation, the time complexity is \(O(\#users+\#items)\), but the computations could be significantly reduced if the user neighbors and item neighbors are explicitly maintained in memory.  
Additionally, the statistical difference is examined. The average RMSE for ACF and IACF algorithms over 5 runs is 1.853, with a standard deviation of 4.32 and 1.012, respectively. Our alternative hypothesis is that IACF does not improve the performance regarding RMSE. A value of 0.05 is used for alpha, which means the results are significant if the \(p\)-value is below 0.05. The z-score and consequent \(p\)-value produced based on these measurements are \(-\)0.4353 and 0.3316, respectively. Based on the \(p\)-value, the null hypothesis can be rejected.
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Beidou-2**} \\ \hline
**Satellite** & **PRN** & **SVN** & **Type** & **Notes** & **Launch Date** \\ \hline G01 & C01 & C003 & GEO & 140.0\({}^{\circ}\)E & 17 January 2010 \\ G02 & C02 & C002 & GEO & Not in operation & 15 April 2009 \\ G03 & C03 & C004 & GEO & 84.0\({}^{\circ}\)E & 2 January 2010 \\ G04 & C04 & C006 & GEO & 160.0\({}^{\circ}\)E & 1 November 2011 \\ G05 & C05 & C011 & GEO & 58.75\({}^{\circ}\)E & 25 February 2012 \\ G06 & C02 & C016 & GEO & 110.5\({}^{\circ}\)E & 25 October 2012 \\ G07 & C17 & C017 & GEO & In orbit test & 12 June 2016 \\ I01 & C06 & C005 & IGSO & 122\({}^{\circ}\)E & 1 August 2010 \\ I02 & C07 & C007 & IGSO & 119\({}^{\circ}\)E & 18 December 2010 \\ I03 & C08 & C008 & IGSO & 120\({}^{\circ}\)E & 10 April 2011 \\ I04 & C09 & C009 & IGSO & 96.5\({}^{\circ}\)E & 27 July 2011 \\ I05 & C10 & C010 & IGSO & 92.5\({}^{\circ}\)E & 2 December 2011 \\ I06 & C15 & C017 & IGSO & 95\({}^{\circ}\)E & 29 March 2016 \\ M01 & C30 & C001 & MEO & Not in operation & 4 April 2007 \\ M03 & C11 & C012 & MEO & & 30 April 2012 \\ M04 & C12 & C013 & MEO & & 30 April 2012 \\ M05 & C13 & C014 & MEO & Not in operation & 18 September 2012 \\ M06 & C14 & C015 & MEO & & 18 September 2012 \\ \hline \hline  & & \multicolumn{4}{c}{**Beidou-3**} \\ \hline
**Satellite** & **PRN** & **SVN** & **Type** & **Notes** & **Launch Date** \\ \hline I1-S & C31 & C101 & IGSO & & 30 March 2015 \\ M1-S & C33 & C102 & MEO & & 25 July 2015 \\ M2-S & C34 & C103 & MEO & & 25 July 2015 \\ I2-S & C32 & C104 & IGSO & & 29 September 2015 \\ M3-S & C35 & C105 & MEO & In orbit test & 1 February 2016 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BeiDou navigation satellite system status (as of August 2016). accuracy was approximately 20 cm and several meters for MEO/IGSO and GEO satellites in RMS, respectively. The SLR residuals were better than 10 cm for MEO/IGSO satellites and several decimeters for GEO satellites. Precise orbits are a basic requirement needed to expand the global scientific application for the BDS global system, but there are difficulties and limitations in the precise orbit determination of BeiDou-3 satellites. Compared to BeiDou-2 satellites, there are fewer and worse distributed tracking stations. Meanwhile, the optimal dynamic model for BeiDou-3 satellite POD, particularly the SRP model, must be further studied. In this study, we aim to precisely determine the orbit of BeiDou-3 satellites based on a tracking network operated by the International GNSS Monitoring and Assessment Service (iGMAS) in China and investigate the achievable accuracy. The paper is organized as follows: in Section 2, the data collection for BeiDou-3 satellites is introduced. In Section 3, the methods and strategies for BeiDou-3 satellite POD are described, and a POD experiment is conducted. The results of the orbit overlap comparison and SLR validation are presented in Section 4. Finally, some conclusions and discussion are provided in Section 5.
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 3 Results and Discussions
### Global Electrophilicity and Local Site Reactivities of Carbonyls
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Compound** & **Structure** & **E\({}_{neutral}\) (eV)** & **E\({}_{cation}\) (eV)** & **E\({}_{anion}\) (eV)** & **\(I\) (eV)** & **\(A\) (eV)** & **\(\mu\) (eV)** & \(\eta\) (eV)** & \(\omega\) (eV) \\ \hline \multicolumn{10}{c}{simple carbonyl} \\ formaldehyde & \(-3116.84\) & \(-3108.89\) & \(-3118.93\) & 7.95 & 2.10 & \(-5.02\) & 2.93 & 8.62 \\ acetaldehyde & \(-4187.26\) & \(-4179.67\) & \(-4189.01\) & 7.59 & 1.75 & \(-4.67\) & 2.92 & 7.46 \\ benzaldehyde & \(-9405.86\) & \(-9398.58\) & \(-9408.46\) & 7.27 & 2.60 & \(-4.94\) & 2.34 & 10.43 \\  & & & \(\alpha\),\(\beta\)-unsaturated carbonyl & & & & & \\ acrolein & \(-5223.81\) & \(-5216.24\) & \(-5226.45\) & 7.58 & 2.64 & \(-5.11\) & 2.47 & 10.57 \\ _trans_-2-hexenal & \(-8434.11\) & \(-8426.68\) & \(-8436.18\) & 7.42 & 2.07 & \(-4.75\) & 2.68 & 8.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Global reactivity descriptors (\(I\), \(A\), \(\mu\), \(\eta\), and \(\omega\)) calculated using density functional theory.
## 2 Load Frequency Control and DoS Attack Models Based on ADES
### Dynamic Model of Load Frequency Control Considering DoS Attacks  
When power grids need ADES to provide frequency control service, hackers may launch DoS attacks against the communication network. DoS attacks take up a large amount of malicious communication network resources and block the transmission of remote state information between subsystems [50].  
We assume that \(\delta_{ji}\in\{0,1\}\) is the indicator variable of the state data packet loss caused by DoS attacks. At this time, the input of the ADES controller is \(u_{i}(k)=\varphi(\mathbf{x}_{i},\delta_{ji}\mathbf{x}_{N^{\prime}(i)})\). \(\delta_{ji}\) at each sampling moment satisfies the Bernoulli probability distribution as follows.  
\[\delta_{ji}=\left\{\begin{array}{ll}1,P\{\delta_{ji}=1\}=1-\eta_{ji}\\ 0,P\{\delta_{ji}=0\}=\eta_{ji}\end{array}\right. \tag{7}\]  
where the probability \(\eta_{ji}\) of packet loss satisfies the following constraint.  
\[\sum_{i=1}^{N}\sum_{j\in\mathcal{N}(i)\cap\mathcal{N}^{\prime}(i)}\eta_{ji} \leq\xi \tag{8}\]  
The packet loss probability \(\eta_{ji}\) is proportional to the intensity of DoS attacks [51]. \(\xi\) as a fixed constant is the sum of packet loss probabilities between all communication connections.  
**Note.** The above assumptions are reasonable. The communication network resources that attackers can use are limited. Communication network resources limit the attack intensity of DoS attacks. Therefore, the sum of the attack intensity of DoS attacks is considered to have a fixed upper bound, which means the sum of \(\eta_{ji}\) is also considered to have a fixed upper bound.  
They cause the ADES controller to lose some remote information \(\mathbf{x}_{j}(k)\) within a certain time period. The loss of remote information \(\mathbf{x}_{j}(k)\) may increase the control error, reduce the frequency error elimination speed, and even cause the frequency deviation of power grids to diverge, thereby, making the control strategy completely invalid.  
Thus, in order to study the power grid frequency control strategy under DoS attacks, the following discrete dynamic model is established for multi-machine power grid frequency involving DoS attacks.  
\[\mathbf{x}_{i}(k+1)=\mathbf{A}_{i}\mathbf{x}_{i}(k)+\mathbf{B}_{i}u_{i}(k)+\mathbf{E}_{i}\varpi_{ i}(k)+\sum_{j\in\mathcal{N}(i)\cap\mathcal{N}^{\prime}(i)}\delta_{ji}\mathbf{B}_{ji} \mathbf{x}_{j}(k) \tag{9}\]
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 1 Introduction
In remote areas, inspection of long-distance transmission lines takes several weeks or even months, and the data are usually transmitted in a delay-tolerant mode. Robots can work in an autonomous mode and delay-tolerant communication can also meet the control requirements. Moreover, the inspection data satisfy the delay-tolerant requirements. The  
Figure 1: Inspection robot project running in Jilin, 2016–2017. (**a**) Power tower and additional device; (**b**) Inspection robot; (**c**) Mobile Monitoring Station (MMS). evaluation of transmission lines can be conducted based on the collected data such as the images or videos of loose or broken lines, and the corrosion or displacement of on-line equipment. In order to solve the problems existing in the intelligent monitoring of a smart power grid, Robot Delay-Tolerant Sensor Network (RDTSN) is proposed in this research. The RDTSN is a trade-off solution of efficiency, cost and reliability. It combines the technical advantages of a WSN-based robot inspection system and monitoring system as well as the advantage of DTSN. It deploys a small number of robots for inspection and the static nodes for real-time monitoring. The system setup is shown in Figure 1. RDTSN aims to achieve the remote and dynamic monitoring of transmission lines. In summary, a Robot Delay-Tolerant Sensor Network (RDTSN) is proposed to monitor the state of transmission lines for discontinuous and non-real-time network applications. The main innovations are as follows:  
1. Integrate advanced robot technology, WSN technology and DTSN technology to generate RDTSN. Communication nodes, along a ground wire in the air, would not be restricted by topographic situation; RDTSN can also reduce the number of fixed nodes with a small number of robots, which can balance network performance and decrease deployment costs. 2. A network model for a smart grid is established, in order to better understand the interactive relationship between power grid architectures, network performances and routing algorithms. Different RDTSN scenarios are explored to determine the optimal parameters, for the balance in network performance and financial cost. 3. A data delivery strategy is introduced to ensure communication security. Additionally, we try to solve the problem of availability of data traversing smart grid environments. This paper is organized as follows. Section 2 presents some previous related work for this kind of network. The proposed method and its detailed algorithm are presented in Section 3. Section 4 shows and analyzes the simulation results of the proposed method. Section 5 provides the corresponding discussions. Conclusions are drawn in the last section.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 3 Materials and Methods
### Fuzzification Analysis
#### 3.2.1 Fuzzy Rule Base and Membership Functions  
To capture system uncertainty and enable adaptive decision-making, a Mamdani-type Fuzzy Inference System was employed. The input variables are fuzzified into linguistic terms, and a rule base is defined to relate input conditions to control outputs.  
Input Variables:  
* Queue Length (QL): Short, Medium, Long
* Server Status (SS): Idle, Busy, Broken
* Arrival Rate (AR): Low, Moderate, High
## 10 Conclusions  
In conclusion, this paper has presented a high-efficiency DC-DC converter utilizing the interleaved configuration of the MHSLCS with an MSC and MHSLCP. The primary objective of achieving exceptionally high voltage gain in photovoltaic applications has been successfully validated through the incorporation of the MHSLCS with an MSC interleaved with the main switch, effectively doubling the voltage transfer gain. The inclusion of the MHSLCP as interleaved components, integrated in parallel with an auxiliary switch, not only optimizes voltage transfer gain but also ensures low voltage stress across the auxiliary switch, achieved by combining the main and auxiliary switches with the MSC. Furthermore, the series connection of each pair of inductors, with matching voltage and current, serves to reduce the number of passive elements and enhance the overall performance of the proposed converter. The inductors of the MHSLCP operating in DCM (discontinuous conduction mode) contribute to significant stress reduction in power diodes and switches at high output voltage, thereby improving overall efficiency. The converter's operation at high efficiency is particularly pronounced when the inductors of the MHSLCP operate in DCM, leading to a substantial reduction in voltage stress across power switches and diodes during high-power applications. Moreover, the use of only one input capacitor eliminates pulsations in input current at both low and high duty ratios. The implementation of a dual PI controller as the control strategy has proven effective, demonstrated by the converter achieving a high efficiency of 96.2% at a low duty cycle and a 500V output voltage. The comprehensive analysis of power losses underscores the significance of employing SiC diodes and flat wire inductors, contributing to an overall efficiency improvement. The converter's ability to operate at a low duty cycle and step up low input voltage to high output voltage highlights its efficiency, emphasizing the crucial role of key components in reducing power losses and enhancing overall performance.  
Conceptualization, X.W.; Software, A.F.A.; Formal analysis, A.F.A.; Writing--original draft, A.F.A.; Writing--review and editing, A.F.A.; Supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.