esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Some studies did not provide sufficient details on the experimental methodology. For example, one study erroneously stated that N95 respirators did not provide 95% FE when tested with no leakage points [21; 46; 47]. Certified masks are subjected to a standardized testing procedure to ensure their ability to filter out 95% of aerosols. The NIOSH testing procedure uses a TSI Respirator Fit Tester to test twenty filters. With extensive testing procedures, masks that receive a certified N95 rating have a high-accuracy standard and excellent consistency between the samples. Real-time particle sizing instruments can significantly simplify and expedite the test procedures. An increasing number of studies use real-time particle sizes for calculating collection and transmission aerosols [48; 49; 50; 51], for sensor calibration studies [52; 53; 54; 55; 56], and recently became a well-accepted method for testing the filtration efficiency, e.g., refs [57; 58; 59; 60; 61; 62; 63]. Environmental conditions and breathing rates affect local relative humidity (RH) in the fiber matrix. In a high RH environment, hygroscopic fibers absorb water and swell [64], affecting the material's porosity and pressure drop [65]. Few studies have examined the effects of humidity on the overall performance of textiles. The \"quality factor\" index can be used to parameterize this effect; it describes a mask's performance based on the ability to filter out particulates and the breathability [11]. This paper aims to complement previous studies describing the FE of common mask materials while adding new insight into the effects of RH on a material's filtration performance. In our study, materials were challenged with polydisperse sodium chloride (NaCl) aerosols in a size range of \(\sim\)0.3 to 2.5\(\mathrm{\SIUnitSymbolMicro m}\). The face velocity through the material was varied at 0.05 m/s, 0.10 m/s, and 0.19 m/s to simulate the variability in the breathing rate [15]. RH was varied in the 30% to 70% range. The FE and the pressure drop were measured to determine the quality factor. The certified materials by NIOSH outperform other textiles by a factor of three. Additionally, it is found that humidity has minimal effects on both the FE and the breathability of the materials in the tested RH range.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 3 Experiment Result and Analysis
### Post-Processing Experiment
It can be seen that the positioning error in the east-west component is much smaller than that in the south-north and up components, both for the code and for the carrier-phase measurements. This is mainly because of the distribution of the current CAPS constellation, including the 4 GEO satellites and 1 IGSO satellite in view, as the 4 GEO satellites are located in the east-west direction (See Figure 2). It can be seen from Figure 7 that there is an obvious slow fluctuation in the differential positioning result based on the code measurement. This is caused by the multipath error. Although we chose, to the extent that was possible, an open area when placing the antenna, the effectsof the multipath still could not be avoided completely. This error would be reduced with the addition of anti-multipath processing in our CAPS receiver in future research. Moreover, it can be seen from Figure 8 that the distribution of the positioning error is not zero-mean and that it may have occasional jumps. The non-zero-mean of the positioning error is due to the integer bias on the ambiguities which is caused by the multipath error. The reason for those jumps may could line in a twice cycle slip that may have occurred but failed to be detected during the period. This result infers that we must take care of the ambiguity of the CAPS carrier-phase due to the short wavelength compared with that of GPS or BDS. The root mean square (RMS) of those positioning errors is calculated using the result during the experiment period, shown in Table 2. The accuracies of the CAPS relative positioning based on the code measurement are about 0.66, 1.07 and 1.8 m in the east-west, north-south and vertical components, while they are about 1.35, 2.49 and 3.15 cm for the result based on the carrier-phase. Following our experience, the precision of the differential CAPS using the code and carrier-phase measurements is almost at the same level as current BDS and GPS [18]. In addition, we are able to calculate the STDs of the relative positioning error based on the carrier-phase measurement in three different components by removing the averaged value of the positioning errors, and they are about 0.33, 0.46 and 0.81 cm for the three components, respectively. This may likely be regarded as the precision of the relative positioning with the carrier phase when the ambiguities are correctly fixed. Figure 8: Time series of the positioning error in the east (**left**), north(**middle**) and up (**right**) components for the CAPS static relative positioning based on the carrier-phase measurement with a sampling rate of 4 Hz. Figure 7: Time series of the positioning error in the east (**left**), north (**middle**) and up (**right**) components for the CAPS static relative positioning based on the code measurement with a sampling rate of 4 Hz.
## 3 Methodology
### Track Management and Zero-Shot ReID Integration
#### 3.3.2 Data Association  
Data association is essential in MOT, aiming to match detected objects in the current frame with existing tracks. We employ the Hungarian method, a combinatorial optimization algorithm, to efficiently solve the assignment problem. Afterward, unassigned detections may signify new objects entering the scene, prompting the initialization of new tracks. Conversely, unassigned tracks that lack matches are terminated based on the number of consecutive frames without valid matches.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 3 Results
### Comparison of the Proposed Model with Different Classification Models  
To investigate the competence of the proposed method, we compared it with four different classifiers: k-NN, LDA, SVM, and CNN (see Table 4). The first three classifiers are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epoch** & **Accuracy (\%)** & **Training Time (s)** & **Testing Time (s)** \\ \hline
0.1 & 77.9 \(\pm\) 0.18 & 250 & 0.18 \\ \hline
0.2 & 63 \(\pm\) 0.22 & 213.45 & 0.18 \\ \hline
0.3 & 76.85 \(\pm\) 0.18 & 125 & 0.2 \\ \hline
0.4 & 94.8 \(\pm\) 0.25 & 96.25 & 0.32 \\ \hline
0.5 & 98.75 \(\pm\) 0.46 & 75 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the proposed method with different epoch lengths and selected channels.  
Figure 9: Classification results for the subjects in the PhysioNet dataset (test data) for subject-independent classification. (**a**) Confusion matrix. The model achieves 100% accuracy for right fist MI. (**b**) Precision–recall curve. (**c**) ROC curves. (**d**) Zoomed-in view of the upper left corner of the ROC curve plot. Precision–recall curve and ROC plot also validate these results.  
binary classifiers, and hence we must combine the classifiers in a one-vs-one manner. CNN can be carried out with a multiclass classification. We used the Scikit Learn library in Python to classify using the first three classifiers. TensorFlow was used for classification using CNN. Next to the proposed model, SVM has better classification accuracy and computation time. However, the computational cost increases as we must carry out a series of binary classifications in a one-vs-one manner. Although CNN carries out a multiclass classification, its accuracy is significantly compromised as we tried to train and classify 1D-data with 2D-CNN.
# Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks
## 2 Methods
### Assembling of Multiple Kernel Representations  
Given the kernel function in Equation (11) providing a metric for a frequency band, the knowledge from each band can be merged into a single metric using a multiple kernel learning (MKL) approach.  
For the set of matrices \(\{\mathbf{K}_{b}\in\mathbb{R}^{N\times N}\}_{b=1}^{B}\), the linear combination in Equation (19) corresponds to a weighted concatenation of the Hilbert space features reproduced by each kernel [24].  
\[\mathbf{K}_{\mu} =\sum_{b=1}^{B}\mu_{b}\mathbf{K}_{b} \tag{19}\] \[k_{\mu}(\mathbf{x}_{n}(t),\mathbf{x}_{m}(t)) =\sum_{b=1}^{B}\mu_{b}k(\mathbf{x}_{nb}(t),\mathbf{x}_{mb}(t)|\mathbf{W}_{b}) \tag{20}\]  
Aiming to preserve the positive definiteness of a kernel function, the vector of weights \(\mathbf{\mu}=\{\mu_{b}\}_{b=1}^{B}\) must belong to subset of positive one-valued norm vectors, that is, \(\mathbf{\mu}\in\{\mathbf{\mu}\in\mathbb{R}^{B}:||\mathbf{\mu}||_{2}=1,\mathbf{\mu}\geq\mathbf{0}\}\). Finding the optimal \(\mathbf{\mu}\) is posed as the maximization of the CKA cost between the kernel matrices \(\mathbf{K}_{\mu}=\{k_{\mu}(\mathbf{x}_{n}(t),\mathbf{x}_{m}(t))\}_{nm=1}^{N}\) and \(\mathbf{K}_{y}\) while constraining the weight values as follows:  
\[\max_{\mathbf{\mu}\in\mathbb{R}^{B}} \frac{\left\langle\overline{\mathbf{K}}_{\mu},\overline{\mathbf{K}}_{y} \right\rangle_{F}}{||\overline{\mathbf{K}}_{\mu}||_{F}||\overline{\mathbf{K}}_{y}||_ {F}} \tag{21}\] \[s.t. ||\mathbf{\mu}||_{2}=1\] \[\mathbf{\mu}\geq\mathbf{0}\]  
The optimization problem in Equation (21) is rewritten as a straightforward quadratic optimization problem with linear constraints [24]:  
\[\min_{\mathbf{\
u}\in\mathbb{R}^{B}} \mathbf{\
u}^{\top}\mathbf{M}\mathbf{\
u}-2\mathbf{\
u}^{\top}\mathbf{a} \tag{22}\] \[s.t. \mathbf{\
u}\geq\mathbf{0}\]  
where vector \(\mathbf{a}=\left\{\left\langle\overline{\mathbf{K}}_{b},\overline{\mathbf{K}}_{y}\right\rangle _{F}\right\}_{b=1}^{B}\in\mathbb{R}^{B}\) and matrix \(\mathbf{M}=\left\{\left\langle\overline{\mathbf{K}}_{b},\overline{\mathbf{K}}_{b^{\prime }}\right\rangle_{F}\right\}_{bb^{\prime}=1}^{B}\in\mathbb{R}^{B\times B}\) account for the alignment with the supervised information and between the input kernels, respectively. Lastly, the solution weighting vector is achieved by normalizing \(\mathbf{\
u}\) as:  
\[\mathbf{\mu}=\frac{\mathbf{\
u}}{||\mathbf{\
u}||_{2}} \tag{23}\]  
Therefore, the weights given by Equation (23) rank each frequency band according its similarity with the provided labels, so that the smaller the value, the lesser the contribution to build the kernel in Equation (19). Further, the introduced MKL gathers the band-wise covariance matrices from different Riemannian manifolds into a single supervised reproduced kernel Hilbert space favoring separability of mental states.
###### Abstract  
Background: Until now, there have been many studies on the postoperative stability of orthognathic patients treated with traditional fixed orthodontic appliances. Recently, the use of clear aligners as orthodontic appliances has increased in orthodontic treatment for aesthetic and patient convenience. The aim of this preliminary study was to investigate the stability and characteristics of patients undergoing orthognathic surgery using clear aligners. Patients and Methods: This study was performed on patients who underwent orthognathic surgery by one surgeon at Pusan National University Dental Hospital from April 2017 to August 2021. A comparative study was conducted on five patients treated with clear aligners during orthodontic treatment and ten patients treated with traditional fixed appliances as a control group. Postoperative skeletal changes and recurrence were evaluated by cone beam computed tomography and lateral cephalometric radiographs taken two days postoperatively and six months postoperatively. Several measurement variables were used to confirm the presence or absence of recurrence, preoperative and postoperative orthodontic treatment period, and the number of extracted teeth. Results: Postoperative stability for six months after surgery was not significantly different between the clear aligner group and the traditional fixed appliance group. The preoperative orthodontic treatment period was also shorter in the clear aligner group, and the number of extracted premolar teeth and impacted teeth were also fewer in the clear aligner group, but there was no significant difference. Conclusions: Orthodontic treatment using clear aligners continues to develop, and it is believed that there is no limit to what can be accomplished during orthodontic surgery accompanied by clear aligners.  
Orthognathic surgery; clear aligner; Invaslign; stability; preoperative orthodontic treatment; CAD-CAM; 3D printer
# Performance Comparisons of Broadband Power Line Communication Technologies
## 2 Structures of PLC
### Hpgp  
A detailed description of HPGP is found in [7]. In this section, a brief description for the physical layer of HPGP is given for the sake of completeness. In Figure 7, a block diagram for the physical layer of HPGP is shown. The information bits are provided in physical block (PB) units. For payload symbols, PB136 and 520 are used, which have 136 and 520 bytes in the block, respectively. For FEC, a  
Figure 5: Diversity mapper: (**a**) number of padding bits and tones in use; and (**b**) locations of the copied bits.  
Figure 6: OFDM symbol.  
rate 1/2 turbo convolutional code is used and a double binary RSC encoder with a constraint length of 4 is used for the constituent encoder. The turbo convolutional code of HPGP is similar to the CTC of IoT PLC. However, the generator and feedback polynomials of the constituent coder are different. At PB136, there are 1088 input bits. Thus, the size of each input block \(N\) is 544. For PB520, \(N\) is 2080.  
The encoded bits go to the channel interleaver. The channel interleaver uses an interleaver matrix. In HPGP, information blocks and parity blocks are interleaved separately, unlike in IoT PLC. Two \(1040\ \times\ 4\) matrices are used for PB520, while two \(272\times 4\) matrices are used for PB136. The row-wise 4-bit output of each matrix is shuffled once more through subblock switching. The robust OFDM (ROBO) interleaver copies the input bits for the output. The function is essentially the same as the diversity mapper of IoT PLC. HPGP has three ROBO modes: standard (STD), high speed (HS), and mini (MINI). The number of copies for the input bits for STD, HS, and MINI modes is 4, 2, and 5, respectively. STD and HS modes use PB520, whereas MINI mode uses PB136.  
HPGP uses OFDM for the transmission technique. For modulation, only coherent QPSK is used. The number of IFFT points is 3072. CP is also used to mitigate the effect of ISI. However, CS is not used in HPGP. There are 372 samples of RI at both ends of the symbol. For the pulse shaping, raised-cosine windowing is used. The sampling frequency \(f_{s}\) is 75 MHz. It is noted that the length of the guard interval (GI) depends on the ROBO modes. The length of GI is the length of CP minus RI. In STD and HS modes, 417 samples are assigned to the GI, whereas 567 samples are assigned to the MINI mode. The OFDM symbol period is (3072 + GI) samples long, which corresponds to (40.96 + \(\tau\)) \(\mu\)s. For STD and HS modes, \(\tau=5.56\ \mu\)s, whereas \(\tau=7.56\ \mu\)s for MINI mode.
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 1 Introduction
Compared with the previously explained two former topologies, CHBMC requires fewer components in the circuit design, which makes it convenient for physical layout and packaging. The major disadvantage of CHBMC is that it cannot perform properly under imbalanced conditions without feeding from isolated DC sources supplied from multi-winding transforms, which gives rise to the same problems associated with the use of the line-frequency transformer [8; 9]. To overcome the previously mentioned problems, a new modular multilevel converter-based STATCOM (MMC STATCOM) is recommended for medium and high voltage applications. The MMC STATCOM is characterized by its availability, compact and modular construction, generation of least harmonics, etc. Moreover, the MMC STATCOM is able to exchange both active and reactive power via the common DC-bus. Accordingly, the active power, which is redistributed in the internal circuits, is utilized for the purpose of negative sequence balance. Consequently, the MMC STATCOM can operate under three-phase imbalance without any intermission. In this paper, an optimization problem is introduced for the optimal design of the multilevel converter and for determining the optimal parameters of the DC voltage controller. The minimization of total harmonic distortion (THD) in the power system under consideration is introduced as the objective function while keeping the ripple voltage within the allowable ranges and obtaining minimum circulating current. Novel optimization algorithms, namely Atom Search Optimization (ASO) and Harris Hawk optimization (HHO) that have not been reported in the literature for tackling the aforementioned problem of optimization of the values of the capacitors of the MMC STATCOM have been applied. Moreover, to ensure the effectiveness of the proposed optimization techniques, the results obtained from the application of these methods have been tested under two different case studies, nonlinear load and three-phase imbalance. The paper is organized as follows: in Section 2, a brief review of MMC topologies is introduced in detail; Section 3 introduces the Model of the Modular Multilevel Converter (MMC); the Modular Multilevel Converter-based STATCOM (MMC STATCOM) is introduced in Section 4; Section 5 presents the problem formulation; Section 6 presents the Harris hawk's optimization (HHO) and atom algorithm in detail; the result simulation presents in Section 7; and the conclusion is stated in Section 8.
## 2 Materials and Methods
### Chemical and Structural Characterization of Fe\({}_{3}\)O\({}_{4}\)-Ag Decorated  
The Fe\({}_{3}\)O\({}_{4}\)-Ag decorated nanoparticles underwent comprehensive characterization using scanning electron microscopy (SEM) with a Hitachi model 8230 microscope. Various electron detection techniques were employed to obtain a thorough understanding of the morphology and chemical composition. High-resolution images acquired through High-Angle Secondary Electron (SE-HA) microscopy highlighted the presence of Fe\({}_{3}\)O\({}_{4}\) and Ag phases, while Secondary Electron (SE(U)) imaging provided detailed surface topography. High-Angle Backscattered Electron (HA(T)) analyses discerned chemical element distribution, exploiting the technique's sensitivity to atomic number. Composite images revealing both morphology and elemental composition were generated using SE + HA + OFF by combining secondary and backscattered electron detectors. Additionally, chemical analysis via Energy-Dispersive X-ray Spectroscopy (EDS) mapped the distribution of iron (Fe), oxygen (O), and silver (Ag) in the nanoparticles, confirming the presence and uniformity of Ag decoration on the Fe\({}_{3}\)O\({}_{4}\) surface. SEM operational parameters included a magnification of \(\times\)500k, a working distance of 3.7 mm, and an acceleration voltage of 3.0 kV, ensuring precise and accurate results from EDS mappings conducted in representative areas.  
Furthermore, the magnetic nanoparticle characterization involved utilizing an X-ray diffractometer (Rigaku Ultima IV, Tokyo, Japan) equipped with Cu-K\(\alpha\) radiation, employing parallel-beam geometry and 20 scans spanning from 15 to 80\({}^{\circ}\). Magnetic analyses were conducted using a vibrating sample magnetometer (VSM) at 300 K.
## 3 Results and Discussion
### Chromatography Analysis  
The graph shows the significant influence of the carbonization process for sorption. The greatest influence is demonstrated by nitric acid (75% of the ASA initial amount was adsorbed) and the lowest by the silver nitrite (sorption of ASA amounted to 29%). The adsorption capacities of the carbonized materials are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Specific Surface** & **Total Pore Volume** & **Micropore** & **Average Pore** \\  & **Area SBT** [m\({}^{2}\)/g] & **V\({}_{\text{TOT}}\) [cm\({}^{3}\)/g]** & **Volume [cm\({}^{3}\)/g]** & **Diameter [nm]** \\ \hline EGDMA + St + 4L & 0.55 & 0.0046 & 0.0011 & 40.68 \\ ECDMA + St + 4L-C & 12.41 & 0.0112 & 0.0064 & 3.89 \\ EGDMA + St + 4L-P1 & 4.21 & 0.0216 & 0.0008 & 19.78 \\ EGDMA + St + 4L-P2 & 21.29 & 0.0403 & 0.0013 & 7.19 \\ EGDMA + St + 4L-S & 299.04 & 0.1492 & 0.0960 & 2.00 \\ EGDMA + St + 4L-N & 1.02 & 0.0054 & 0.0006 & 22.12 \\ EGDMA + St + 4L-Ag & 28.46 & 0.0309 & 0.0091 & 4.45 \\ \hline \end{tabular}
\end{table}
Table 7: Parameters of the porous structures of the studied copolymers.  
Figure 11: Sorption of Acetyl salicylic Acid on the studied sorbents.  
The largest ASA sorption has the EGDMA + St + 4L + N copolymer. This carbon has a low specific surface area. The sorption is due to the interactions between the functional groups present on carbon and ASA. In order to determine the chemical characterization of groups present in the carbonized materials, the ATR-FTIR analysis (Figure S4) and pH determination were performed. The pH for EGDMA + St + 4L-N is 8, for EGDMA + St + L-Ag and EGDMA + St + 4L-C is 7, and for the rest within 5-6. The material which is the most effective represents a slightly alkaline character. Analyses for the copolymer EGDMA + St + 4L + N showed that the presence of absorption bands in the area of 1720 cm\({}^{-1}\) correspond to stretching vibrations of lactones and carboxylic groups. The signal at 1600 cm\({}^{-1}\) is related to C=N group and about 700 cm\({}^{-1}\) to out of plane ring deformation. Acetylsalicylic acid has an ester and carboxyl group and an aromatic ring. Therefore, the basic interactions that should be considered are hydrogen bonds (between functional groups) and \(\pi\)-\(\pi\) electron interactions between the aromatic rings of salicylic acid and sorbents and van der Waals interactions. The proposal scheme of sorption is presented in Figure 12[43].