esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Energy Demand  
The effect of the VRR and the transmembrane pressure on the energy demand is presented in Figure 2. The increase in the pressure led to an increase in the energy demand (\(p<0.05\)) at all VRRs. The lowest value was observed at 0.2 MPa and VRR 1 (20.37 kWh/m\({}^{3}\)), and the highest was at 0.5 MPa and VRR 5 (127.31 kWh/m\({}^{3}\)). The pressure rise leads to an increase in the power required by the pump, and thus the energy consumption increases [22]. The same authors reported that the ultrafiltration process consumed more energy at higher operational pressures which is in accordance with our results. According to [18], the pressure (0.465 and 0.672 MPa) had no significant effect on the energy consumption at both temperatures studied (10 and 50 \({}^{\circ}\)C). The same authors found that only temperature significantly affected (\(p<0.01\)) the total energy requirement and this effect was higher at 10 \({}^{\circ}\)C than at 50 \({}^{\circ}\)C. An explanation for this consists of the fluid viscosity which decreases with the temperature rise. Our investigation showed a significant difference (\(p<0.05\)) in energy demand at different pressures. This could be explained by the different  
Figure 1: Effect of volume reduction ratio on the permeation flux during ultrafiltration of sweet buttermilk. Different lowercase letters (a, b, c) show a significant difference between the permeate flux at 0.2, 0.35, and 0.5 MPa (\(p<0.05\)).  
levels of pressure investigated in this research. It is also well known that at high transmembrane pressure, the concentration polarization is more pronounced, and flux starts to form a plateau with increasing pressure [20]. This phenomenon is probably reflected in energy consumption at high pressures. The energy demand was also influenced by the volume reduction ratio. A higher level of ultrafiltration concentration increased the processing time and energy consumption of the pump. Moreover, the fluid viscosity increases when the VRR increases because of the rise in the dry matter content. This was confirmed in [37] where the authors established higher values of density and viscosity of retentates from goat's milk obtained with three ultrafiltration membranes of 10, 25, and 100 kDa.
###### Abstract  
The object of the study was to assess the effect of different finishing and polishing systems on the roughness and gloss of a 3D-printed permanent restorative material. One 3D printable Permanent material was selected for the study. Squared-shaped specimens (14 mm\({}^{2}\); 5 mm thickness) were obtained by designing and printing. Eighty specimens were produced and randomly assigned (n = 10) to 8 finishing and polishing methods: Sof-Lex(tm) Spiral Wheels (SW), Sof-Lex(tm) YT Pop-on Disc (SD), Identoflex Lucent no paste (Ln), Identoflex Lucent + paste (Lp), Resin Nitrogen polymerized (NG), Optiglaze (OG), OptiStep (OS), and HiLusterPLUS (HL). Surface roughness and gloss were then measured by a roughness meter and a glossmeter, respectively. For roughness, statistically significant differences were found (\(p\) < 0.001), with NG(a) > SD(b) = OG(b) = Lp(b); Lp(b) = Ln(bc); Ln(bc) = OS(cd); OS(cd) = SW(de); and SW(de) = HL(e). For gloss, statistically significant differences were also identified (\(p\) < 0.001) with NG(a) > SD(b) > Lp(c) = OS(c) = OG(cd); OG(cd) = Ln(d) > HL(e) = SW(e). The nitrogen chamber polymerization showed better results for both roughness and gloss. Multi-step finishing/polishing systems were able to produce smoother surfaces than 1-step and 2-step systems.  
3D printing; finishing; polishing; gloss; roughness Article  
Effect of Different Finishing Systems on Surface Roughness and Gloss of a 3D-Printed Material for Permanent Dental Use  
2024147289[[https://doi.org/10.3390/app14167289](https://doi.org/10.3390/app14167289)]([https://doi.org/10.3390/app14167289](https://doi.org/10.3390/app14167289))
## 4 Description of the Physical System
### Mesh Convergence Study  
The mesh convergence study was done for all the cyclone models. The pressure drop is the comparison parameter calculated in three different grids for each geometry. Table 3 shows the number of elements and pressure drop in each mesh implemented.  
Note that the percentage change between pressure drop of the coarse and medium meshes, as well as the medium and fine, do not exceed 5%. Therefore, the medium mesh is suitable to solve the calculations in this paper. Also, Table 4 shows the area-weighted average \(y^{+}\) to highlight the grid refinement near the wall.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{2}{c}{**Model A**} & \multicolumn{3}{c}{**Model B**} & \multicolumn{3}{c}{**Model C**} & \multicolumn{3}{c}{**Model D**} \\ \cline{2-13}  & \(AR\) & \(OQ\) & \(S\) & \(AR\) & \(OQ\) & \(S\) & \(AR\) & \(OQ\) & \(S\) & \(AR\) & \(OQ\) & \(S\) \\ \hline Coarse & 21.1 & 1 & 0.70 & 61.61 & 1 & 0.93 & 68.51 & 1 & 0.93 & 23.37 & 1 & 0.70 \\ Medium & 19.66 & 0.99 & 0.86 & 66.81 & 1 & 0.94 & 70.70 & 1 & 0.93 & 23.02 & 1 & 0.81 \\ Fine & 18.36 & 1 & 0.70 & 71.09 & 1 & 0.94 & 76.71 & 1 & 0.94 & 22.15 & 1 & 0.74 \\ \hline \hline \end{tabular}  
* \(AR\) is the aspect ratio, \(OQ\) is the orthogonal quality and \(S\) is the skewness.  
\end{table}
Table 2: Mesh quality parameters of cyclone models A, B, C and D.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{2}{c}{**Model A**} & \multicolumn{2}{c}{**Model B**} & \multicolumn{2}{c}{**Model C**} & \multicolumn{2}{c}{**Model D**} \\ \cline{2-10}  & **Elements** & **Pressure** & **Elements** & **Pressure** & **Drop (Pa)** & **Elements** & **Pressure** & **Drop (Pa)** & **Elements** & **Pressure** \\ Coarse & 1,020,400 & 811.63 & 1,004,665 & 785.34 & 1,080,617 & 764.26 & 977,769 & 758.23 \\ Medium & 1,225,217 & 817.81 & 1,220,779 & 791.93 & 1,218,783 & 774.70 & 1,224,120 & 766.59 \\ Fine & 1,488,747 & 823.92 & 1,445,118 & 797.95 & 1,470,052 & 782.65 & 1,479,022 & 774.93 \\ \multicolumn{10}{l}{a Percentage change} & 0.76\% & 0.84\% & 1.37\% & 1.10\% \\ \multicolumn{10}{l}{b Percentage change} & 0.75\% & 0.76\% & 1.03\% & 1.09\% \\ \hline \hline \end{tabular}  
* Percentage change between the coarse and medium mesh values for pressure drop.
* Percentage change between the medium and fine mesh values for pressure drop.  
\end{table}
Table 3: Mesh independence study for cyclone models.  
Figure 2: Meshing of cyclone models; (**a**) Model A (Stairmand) cyclone separator, (**b**) Model B cyclone separator, (**c**) Model C cyclone separator, (**d**) Model D cyclone separator.  
In this study, we used RANS equations with wall functions, a value of \(y^{+}<30\), indicates that at least 1 point is into the viscous sublayer, which is adequate to model turbulence near the wall, keeping in mind that RANS is not concerned with individual eddy behavior, therefore is not necessary that RANS node spacing be less or equal to the Kolmogorov eddies.
# Temporal Modeling on Multi-Temporal-Scale Spatiotemporal Atoms for Action Recognition
## 1 Introduction
C3D [12] and Res3D [13] were used to learn spatiotemporal information from clips with 16 and 8 frames, respectively. 2. Unordered modeling. An action can be considered as a temporal evolution of the spatiotemporal information. However, some CNN-based action recognition methods ignore the temporal evolution. The C3D and Res3D methods split the video action into clips and merged the features of the split clips by averaging and L2-normalization. [PERSON] et al. [17] attempted to fuse the CNN frame-wise spatial feature to video-wise spatiotemporal feature via stratified pooling. 3. Equal weight. For a given action, each frame, or clip contains information with different weights. Nevertheless, most of the CNN-based methods treat each frame or clip with equal weight. 4. Information broken. Some of the CNN-based action recognition methods extract deep information from clips with sparse splitting, such as non-overlapped 8-frames clips in Res3D work [13] or stacked 10-frames in the temporal stream of two-stream model [7], however, these sparse splitting would break the key spatiotemporal information across two consecutive clips. To overcome these limitations, in this paper, we propose to model the temporal evolution of multi-temporal-scale atoms for action recognition. The atoms are the action units, which capture the key semantic and characteristic spatiotemporal features and can be used to depict the video action. We illustrate the framework of our method in Figure 1. For each temporal scale, we build RGB and optical flow streams. And as illustrated in Figure 2, in each stream, we first split videos into dense video clips to preserve more key spatiotemporal information. Then, we use 3D CNN to extract the spatiotemporal information and mine the action atoms from the spatiotemporal information. Finally, we use LSTM [18] to model the temporal evolution of atoms for action recognition. In summary, this paper makes the following contributions:1. We propose a method of temporal modeling on multi-temporal-scale atoms for action recognition. 2. We create the variants of Res3D [13], a ResNet18-style [19] 3D CNN architecture, to extract spatiotemporal information in different temporal scales. To apply Res3D on optical flow, we propose some practices to transfer the knowledge learned from the RGB to optical flow field. 3. We extend the video summarization technique to action atoms mining in the deep spatiotemporal space. The proposed multi-temporal-scale atoms modeling method achieves recognition performance comparable to that of state-of-the-art methods. We also use model visualization to gain insight into our proposed method. The remainder of this paper is organized as following: Section 3 describes related action recognition studies. Section 2 presents our proposed multi-temporal-scale atoms modeling method. Section 4 provides the experiments and results. And conclusions and future work are given in Section 5.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 3 System Architecture
### Implanted Receiver
#### 3.2.2 The Digital Demodulator  
The non-coherent digital demodulator tries to detect if a '0' or a '1' was transmitted by detecting the type of edge it encounters in the digitized received modulated signal. The digitized modulated signal has a falling edge for the '0' and a rising edge for the '1'. While detecting which type of edge is present, the subsystem must take care to avoid the transition point between symbols so that it is not detected as an edge type. The digital demodulator comprises mainly of an edge detector and a reset generator to reset the edge detector before the next symbol. The edge detector consists of a rising edge and falling edge flip-flop. With AND and OR logic gates, the type and timing of the occurring edges are determined. Through a D-flip-flop, the received bits are recovered from the edge type and edge detected. The detected edge is delayed to ensure alignment before entering the recovery D-flip-flop.
## 4 Case Study and Applications
### Evaluation of Classification Accuracy Based on Neural Networks Trained with the PSO  
We carried out a validation process. We compared all the accuracy assessment points with the ground truth values. The calculated parameters are the next ones.  
* Precision: Precision is the probability value that a detected class element is valid. It is given by the following equation: \[Precision\ =\ \frac{Number\,of\,correctly\,detected}{Number\,of\,all\, detected}\ =\ \frac{TP}{TP+FP}\] (5)
* Recall: Recall is the probability value that a detected class element is detected in the ground truth. It is given by the following equation: \[Recall\ =\ \frac{Number\,of\,correctly\,detected}{Number\,of\,detected\, in\,ground\,truth}\ =\ \frac{TP}{TP+FN}\] (6)
* F1-score: This is a metric usually calculated to evaluate the performance of a binary classification model. It is given by the following equation: \[F1-score\ =\ \frac{2\times Precision\times Recall}{Precision+Recall}\] (7)  
The orange line in the \"receiver operating characteristic\" (ROC) curve (Figure 10a) represents the performance of the binary classifier, while the blue dashed line represents the performance of a random classifier.  
The points on the blue line in the \"precision-recall\" (PR) curve (Figure 10b) indicate the precision values corresponding to different recall values.This allows the changes in the precision of the classification to be observed as the recall increases. The light blue shaded area under the PR curve, also designated as Average Precision (AP), provides a unified numerical representation of the precision-recall performance. The average AP of 77% indicates that the model correctly identifies a similar percentage of positive cases in the ground truth across all recall levels.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **Relative Risk** \\ \hline weight(kg)\_Low & 0.38230844375970974 \\ weight(kg)\_Medium\_Low & 1.1138572402084614 \\ weight(kg)\_Medium\_High & 1.4722921983586552 \\ weight(kg)\_High & 1.657394753125524 \\... & \\ fasting blood sugar\_Low & 0.7625790496264635 \\ fasting blood sugar\_Medium\_Low & 0.9737980190980333 \\ fasting blood sugar\_Medium\_High & 1.0923983061205762 \\ fasting blood sugar\_High & 1.2241433259236203 \\... & \\ triglyceride\_Low & 0.38822905484291464 \\ triglyceride\_Medium\_Low & 0.825744093793402 \\ triglyceride\_Medium\_High & 1.2999795981567317 \\ triglyceride\_High & 1.7644652462095984 \\ HDL\_Low & 1.552294793364521 \\ HDL\_Medium\_Low & 1.21516619862005 \\ HDL\_Medium\_High & 0.860305547576019 \\ HDL\_High & 0.49401247683404703 \\ LDL\_Low & 1.0871634378867505 \\ LDL\_Medium\_Low & 1.1002852299103176 \\ LDL\_Medium\_High & 1.0422964148237216 \\ LDL\_High & 0.80065254077625 \\... & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Relative risk for categorical variables.
# Energy Efficiency Perspectives of PMR Networks +
## 5 Technical and Economic Considerations on Energy Efficiency of Next Generation PMR Systems  
### Business Models for Mobile Broadband PMR  
When evolving to mobile broadband communications, public safety agencies may choose from numerous business models that will support its specific needs, taking into account existing PMR network operations, available spectrum, regulatory environment and financial resources. It may contract services provided by a mobile network operator (MNO), operate or use a service from a dedicated virtual network over a mobile operator's infrastructure (G-MVNO), build a wholly owned and operated dedicated network, or use a mix of different approaches [29]. Adding LTE mobile broadband capabilities to existing PMR networks in a nondisruptive, cost-effective and energy-efficient way can be complex, with many factors to consider. These include startup costs, operating and capital expenditures (OPEX and CAPEX), expected revenues, available spectrum, existing network equipment, commercial wireless services and the political environment. The following are five possible business models that are proposed to match the trade-off between objectives and constraints.
## 2 Data and Methods
### Data
#### 2.1.4 CM SAF, CERES and Ground-Based SSR Data
The third station (rural) is located in a coastal area (35.33\({}^{\circ}\) N, 25.28\({}^{\circ}\) E) 15 km east from the city of Herakleion at the premises of the Hellenic Center for Marine Research (HCMR) on the island of Crete. The measurements come from a CM11 pyranometer that measures solar radiation in the spectral range 305-2800 nm with an uncertainty of \(\pm\)3% ([PERSON] et al., [27] and references therein). The ground-based observations from Thessaloniki and Athens used here cover the period 2005-2019 while the data from HCMR cover the period 2005-2013.
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 6 Algorithm Performance Analysis
### Time Delay Error CSI System and Rate and Bit Error Rate Analysis
\begin{split}\mathbf{R}_{\text{{k}}_{\text{g}}}=\sum\limits_{w=1}^{d _{\text{{k}}_{\text{g}}}}\text{log}_{2}\Big{\{}1+\text{SINR}_{\text{{k}}_{ \text{g}}}^{w}\Big{\}}\\ =\sum\limits_{w=1}^{d_{\text{{k}}_{\text{g}}}}\text{log}_{2} \left\{1+\frac{E\bigg{\{}\Big{(}\text{g}_{\text{{k}}_{\text{g}}}^{\text{{g}}} \Big{)}^{2}\bigg{\}}}{\left(\delta_{\text{n}}\right)^{2}+J_{\text{{k}}_{\text{ g}}}}\times E\left\{\left(\sum\limits_{x=1}^{d_{\text{{k}}_{\text{g}}}} \left|\rho_{w,x}\right|\right)^{2}\right\}\right\}\end{split}  \tag{45}\]  
For user \(k_{\text{g}}\), the average BER of M-PSK modulation can be calculated using Equation (46):  
\[ \begin{split}\mathbf{P}_{M}^{k_{\text{g}}}=\frac{\sum\limits_{w=1}^{d _{\text{{k}}_{\text{g}}}}\text{erfc}\bigg{(}\sqrt{\text{SINR}_{\text{{k}}_{ \text{g}}}^{w}}\sin\left(\frac{\pi}{M}\right)\bigg{)}}{d_{k_{\text{g}}}}\end{split}  \tag{46}\]
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 6 Design Guidelines and Recommendations  
The result and discussion of this user experience study serve as the basis for a set of design recommendations that will be useful in the future when designing virtual conference applications for collaborative purposes. According to the findings of the user experience study, there is a need for an intuitive interface to help increase the usage and ease of use of such synchronous, collaborative tools. For instance, the interface should be designed to be as close as possible to the real-world settings of a meeting or collaborative site. This would put the participants at ease as they would recognize the virtual location as being somewhat familiar to the real-world meeting site. Likewise, the tools and additional features provided by the synchronous, collaborative virtual conference application should also have been designed to encourage exploration when engaging in online collaboration activities with others.  
Another key finding from this study is the need to provide for an alternative method of representing facial expressions when communicating online. Although respondents stated that they were mostly shy or reluctant to switch on their cameras and show themselves (and their backgrounds) to others, they had also stated that they would appreciate the facial cues and reading responses from others. This study proposes the use of an avatar to achieve this. Avatars use less bandwidth to display expression than a live video feed from a web camera, which consumes a lot of data. Avatars also help to improve privacy as they are less intrusive compared to using a web camera. The virtual character should not be too distracting and can be disabled if users want to. This could help increase user engagement and awareness, as the virtual avatar can share expressions without having to show the user's actual face or background.
## 3 Results
### ML Models and Developed Ensembles Results
  
Accuracy was used to select the optimal model using the largest value. The final value used for the model was \(mtry=2\). Here \(mtry\) is the number of variables available for splitting at each tree node. The Table 4 summarizes the results of models'comparison. The numbers in the bold present the highest accuracy in the column for weak predictors, boosting models and stacking respectively. The numbers in the italic present the results of developed ensembles of machine learning models. The difference in accuracy column given the difference between model accuracy built on whole dataset and model accuracy built on selected features. The models comparison is given in Figure 9. The accuracy, specificity and sensitivity are taken into account. Therefore, the proposed ensembles show the good predictive accuracy for both classes too. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Results for the Whole Dataset** & **Results for Selected Features** & **The Difference in Accuracy** \\ \hline RTree & 0.8005724 & 0.8066330 & 0.00606 \\ ADABOOST & 0.8533677 & 0.8519529 & \(-\)0.0014 \\ LG & 0.7208754 & 0.859503 & 0.13848 \\ MLP & 0.6637374 & 0.6637374 & 0 \\ SVMLin & 0.7684848 & 0.8526936 & 0.08421 \\ SVMPoly & 0.8255219 & 0.8701010 & 0.04458 \\ SVMdial & 0.7716162 & 0.8388721 & 0.11226 \\ KNN & 0.8117845 & 0.8426263 & 0.03084 \\ RF & 0.8467677 & 0.8498999 & 0.00222 \\ _Bostral \#_ & 0.8373016 & 0.8489899 & 0.01169 \\ _bostral \#_ & 0.8656746 & 0.8501010 & \(-\)0.0156 \\ _boget CART_ & 0.8380952 & 0.848989 & 0.01089 \\ _Stacking_ & 0.8990257 & 0.9190155 & 0.01999 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Models’ comparison (accuracy). Figure 9: _Cont._  
The pairwise correlation coefficients for the mentioned sub-models are given in Table 5. The pairwise correlation is calculated for ensemble quality estimation. From this table, we can see a generally low correlation for all pairs of predictions. The two methods with the highest correlation between their predictions are Logistic Regression and kNN, with a correlation coefficient of 0.517, which is not considered high (>0.75).
# Beam Scanning and Capture of Micro Laser Communication Terminal Based on MEMS Microm mirrors
## 3 Scanning Range of MEMS Micromirrors  
As scanning devices in laser communication, MEMS micromirrors have the significant advantages of being small and giving excellent performance [15]. However, their mechanical deflection angle is relatively limited, and currently available rapid-scanning 2D scanning mirrors typically have mechanical deflection angles of around \(\pm 5^{\circ}\), rendering them unsuitable for large-field scanning. In view of this, the expansion angle optical system for the MEMS micromirror is designed to increase the scanning angle. By adjusting the distance between the lens and the micromirror, the optical system expands or reduces the optical scanning range [16].  
By designing an optical beam expanding system as shown in Figure 2, the laser beam is emitted and then collimated before being focused onto the reflective surface of a MEMS mirror by a preceding focusing lens. The mechanical deflection angle of the MEMS mirror is relatively small. After passing through an \(f-\theta\) lens, a light spot corresponding to a certain field of view for the rear lens is obtained. In this case, the field-of-view angle is designed to be \(60^{\circ}\).  
The maximum image height \(y\) on the image plane of the \(f-\theta\) lens can be expressed as:  
\[y=f_{1}\cdot\theta_{1} \tag{15}\]  
where \(f_{1}\) is the focal length of the \(f-\theta\) lens, and \(\theta_{1}\) is 2 times the maximum mechanical deflection angle of the MEMS. Finally, through a positive lens, its field of view \(\theta_{2}\) satisfies Equation (16):  
\[y=f_{2}\cdot\tan\theta_{2} \tag{16}\]  
where \(f_{2}\) is the effective image square focal length of the angle-expanding lens, and \(\theta_{2}\) is the field-of-view angle after beam expansion. Therefore, Equation (17) can be obtained:  
\[\theta_{2}=\arctan\left(\frac{f_{1}}{f_{2}}\cdot\theta_{1}\right). \tag{17}\]  
Obviously, to expand the scanning-angle range of the MEMS micromirror, \(f_{2}\) must be smaller than \(f_{1}\), which is a key condition of the optical beam expander system. Moreover, when the light emitted by the short-focus positive lens is parallel or close to parallel beams, the laser spot can be made smaller when projected from a longer distance.