esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
COVID-19 remains a serious disease all around the world. To improve COVID-19 detection, we proposed COVID-CGAN, an approach to extend the existing small CXR COVID-19 datasets using a CGAN and to detect the disease based on the extended datasets. We proposed a customized design for the CGAN with 18 layers for the generator and 24 layers for the discriminator.  
Based on the results presented in the previous section, we derived the following findings:  
1. A CGAN has a simple and straightforward architecture, yet can produce images similar to real ones. Compared to other GAN architectures that may produce better quality images, such as the least-squares generative adversarial network (LSGAN) [52] and information maximizing GAN (InfoGAN) [53], these architectures have large computational budgets and generating images is time-consuming, whereas CGANs are simpler and do not require long computation times. They can synthesize good-quality images from the original dataset.
2. Some deep learning models are better than others in terms of detecting COVID-19 patients based on their CXR images. The experimental results showed that InceptionResNetV2 outperformed other models in detecting COVID-19 based on CXR images. This model can be investigated by other researchers to detect COVID-19 based datasets other than CXR images.
3. Some deep learning models are small in size and thus provide fast predictions, yet can achieve good results in detecting COVID-19. As shown in Figure 15, SqueezeNet, which is a small network, required only three minutes to achieve accuracy comparable to that of larger networks. Based on the present study, we suggest the following directions for future research:
4. Design generative model architectures other than CGANs and compare them in terms of their ability to synthesize high-quality images that are similar to real images.
5. Include patient information related to COVID-19 other than CXR, such as symptom datasets, in the diagnostic process.  
Conceptualization, A.A.A.-S. and J.F.A.; methodology, A.A.A.-S.; software, A.A.A.-S. and J.F.A.; validation, A.A.A.-S. and J.F.A.; investigation, A.A.; resources, J.F.A.; data curation, N.A.; writing--original draft preparation, A.A.A.-S. and J.F.A.; writing--review and editing, A.A.; visualization, J.F.A.; project administration, A.A.A.-S. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by Qassim University, Saudi Arabia.  
Figure 15: Classification accuracy versus training time for the five deep transfer learning models. InceptionResNetV2 achieved the highest accuracy but consumed the longest time. SqueezeNet and AlexNet were faster and achieved comparable accuracies.
## 2 Method Description
In Figure 3, x-axis shows sample offsets and y-axis shows correlation peaks. The upper graph is correlation result between channel 1 and the local block, the first peak of which is the full peak produced by \"fully contained\" block m, and the second peak contains two partial peaks produced by m \(-\) 1 and m + 1. Similarly, the lower one is between channel 2 and the local block, the first peak is produced by block m + 1 and the second peak is produced by m + 1 and m + 3. The two peaks in the two correlation results are not strictly consistent due to signal noise. The verification in Step 6 starts with counting the number of peaks that have the same position or relative position of N. This number is denoted by N\({}_{\text{s}}\) and defined as verification times. As there are four positions of peak preserved in Step 4, the threshold number N\({}_{\text{th}}\) is confined in set {1, 2, 3, 4}. When N\({}_{\text{s}}\geq\) N\({}_{\text{th}}\), the acquisition is over. The bigger N\({}_{\text{th}}\) is chosen, the smaller detection probabilities  
Figure 3: Correlation peaks on condition that SNR equals \(-\)10 dB, N = 4096 F = 8 and k = 200. Figure 2: Input blocks and local blocks, each with length N. Local channel 1 is composed of Block 1 and Block 2, channel 2 is 3 and 4. The offset between input block and local block is k, k is uniformly distributed in [1, N]. Block m \(-\) 1 to block m + 3 are added together term by term to produce a new block. And the new block is zero padded to 2N. and false alarm probabilities are obtained. When \(\rm{N_{th}}=1\) and \(\rm{N_{th}}=2\) or relative position of two peaks is 0, post-process is needed to determine whether the peak is produced by \"fully contained\" block or two \"partially contained\" blocks.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## 3 Results
### Method Characterization
#### 3.1.3 Photomultiplier-based System for Labeled Fibroblast Analysis  
To complete the characterization of the whole system (labeling and detection), we tested the optical behavior of HDF cells, once exposed to Ru(\(\mathrm{(pypy)_{3}}^{2+}\), by the optical system described in Figure 2 (see Materials and Methods, Section 2.5).  
Fluorescence emission data, shown in Figure 5, were acquired at three different concentrations of fluorophore (0.4, 0.7 and 1 \(\mathrm{\SIUnitSymbolMicro m}\)) and at three time points (24 h, 48 h and 96 h) of cell growth. As shown in the figure, the analysis revealed the presence of a fluorescence signal from cells that increased gradually with both fluorophore concentrations (Figure 5A) and time of exposure to the medium containing the fluorophore (Figure 5B).  
Figure 6 summarizes the fluorescence emission spectra as a function of the wavelength at three different fluorophore concentration. The plot describes, clearly, how the intensity of Ru(\(\mathrm{(pypy)_{3}}^{2+}\) emission inside the cells increases with the concentration used at a fixed time point exposure (24 h). The figure also reports the HDFs auto-fluorescence signal, shown as a black curve. The auto-fluorescence spectrum in the figure exhibits an emission peak at 550 nm (green light), which is related to ultrastructural proteins; the same signal that we observed by fluorescence microscopy.  
Figure 5: Fluorescence emission (Photoluminescence) trend of HDF cells labeled with Ru(\(\mathrm{(pypy)_{3}}^{2+}\): increase of fluorescence signal with (**A**) fluorophore concentration and (**B**) time of exposure. Fluorescence was acquired with a 405 nm laser source at 50 mW power and revealed at 620–630 nm. Fluorescence values at 620–630 nm (i.e., the Ru(\(\mathrm{(pypy)_{3}}^{2+}\) emission peak) were used to plot the data.  
Figure 6: Emission spectra of HDFs controls (black curve) and exposed to 0.4 (red curve), 0.5 (blue curve) and 1 (green curve) \(\mathrm{\SIUnitSymbolMicro M}\) of Ru(\(\mathrm{(pypy)_{3}}^{2+}\) for 24 hours. Fluorescence emission was acquired with a 405 nm laser source and revealed a peak at 620–630 nm, specific of fluorophore used, for treated samples while a peak at 550 nm was evidenced for control cells, provided by the intrinsic fluorescence of HDFs.
## 3 Results
### Analysis of PET Depolymerization by S. maltophilia PRS8
#### 3.4.2 Fourier-Transform Infrared (FTIR) Spectroscopy  
The PET flakes treated with _S. maltophilia_ PRS8 and the untreated PET flakes (control) were analyzed using FTIR. The FTIR spectra were normalized at 1410 cm\({}^{-1}\) (aromatic ring vibrations), according to the literature [27]. The characteristic peaks (722, 870, 1016, 1098, 1244, and 1713 cm\({}^{-1}\)) [28] were clearly observed (full spectra in the Supplementary Materials, Figure S3). The treated PET slightly reduced the intensity of the FTIR peaks at 722, 1098, 1242, and 1714 cm\({}^{-1}\) (Figure 5) compared to the untreated PET flakes, indicating a surface degradation by _S. maltophilia_ PRS8.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy
### Taxonomy of IFA  
IFAs in NDN can be systematically analyzed through several key dimensions, as seen in Table 1: source of attack, intensity of attack, target layer, and distinction between smart versus naive attackers. This taxonomy helps us to understand the multifaceted nature of IFAs and develop effective mitigation strategies.
# Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis
## 4 Discussion
### Limitations and Future Works  
Although we have studied the topic of team-communication modeling for NPP emergencies comprehensively, there are issues to be further considered. First, communication skill is tightly connected to other NTS such as decision-making and situation awareness [10; 14; 15]. In the research background, we explained our decision to tackle team communication first; however, through team-communication model development, especially the investigation model, we reconfirm the relationship of communication with other cognitive skills. Thus, to improve team-communication skills, other NTS must be improved as well, or rather, to train for better NTS as a whole.  
In this study, we limited the team communication to the communication between operators, both intra- and inter-team. Consequently, our models were developed to suit direct (face-to-face) communication and reliable indirect communication. We considered the use of communication tools (included as influencing and performance factors), especially those prepared for NPP emergencies, such as satellite telephones; however, in this study, we decided to exclude the possibility of communication technology's poor performance and interface-induced human errors. The addition of human-machine interaction will make the models overly complicated; however, it is necessary to consider that kind of interaction, especially for hazardous scenarios [13; 61].  
The analysis of human-machine interaction, however, will have its highest contributions when the machine/system is specific. Thus, for our next study, we are aiming to design a team-communication support system, combining telecommunication media and a decision support system [62], based on our models and case study findings, aiming to alleviate ERT challenges during multi-unit NPP accident management. We are collecting the system requirements, applicable advanced technologies (virtual reality, digital twins) [63; 64], and analysis methods (social network, activity theory) [65; 66] that match the challenges of ERT cooperation. The team-communication support system, if it is in a software form, can be used to transmit messages from ERTs in different locations. Each personnel's tasks and related information are provided to improve familiarity, and a role-changing algorithm is embedded to rapidly adapt when a new ERT becomes active.
## 3 Design Concept
### Extended Link Mechanism  
To minimize the size of actuator, while still meeting the output torque requirement, extended linkages \(\overline{\text{PR}}\) and \(\overline{\text{RQ}}\) were added with a rotary actuator installed to these linkages (Figure 2d-h). The extended linkages amplify the torque output of the actuator by the reduction ratio \(N\) of Equation (9), which is defined as the ratio of change in an actuator's rotation angle \(\angle\text{PRQ}\) (\(=\beta\)) to that of the active angle \(\angle\text{KHG}\) (\(=\alpha\)):  
\[N=\frac{d\beta}{d\alpha}=\frac{\mu\left(-\tan\frac{\beta_{0}}{2}\sin\frac{ \alpha}{2}+\cos\frac{\alpha}{2}\right)}{\cos\!\left(\sin^{-1}\!\left(\mu\left( \tan\frac{\beta_{0}}{2}\cos\frac{\alpha}{2}+\sin\frac{\alpha}{2}\right)\right) \right)} \tag{9}\]  
\[\tau_{b}=N\tau_{a}, \tag{10}\]  
where the design variable is \(\mu\equiv r_{1}/r_{2}\), \(r_{1}\) is the minimum distance between point \(P\) and \(\overline{\text{HK}}\), and \(r_{2}\) is the length of \(\overline{\text{RQ}}\). \(\beta_{0}\) is the angle of \(\angle\text{PRQ}\) when the \(\alpha\) is zero, and is set to be 17 degrees to consider the thickness of the link and the size of the bearing. These design parameters are summarized in Table 3. Finally, \(\tau_{b}\) in Equation (10) is the output torque transmitted to the center R of the extended link mechanism from the actuator.  
The simulations in Figure 4a,b were performed to show the effect of the extended linkage mechanism. Parameters in Table 4 were chosen with reference to previous work [21; 27]. Their results  
Figure 3: Kinetic analysis of the KARE-1. Contour of body assistance ratio \(\Re\) as a function of \(\theta\) and \(\alpha\) (**a**). Contour of active torque \(\tau_{a}\) as a function of \(\theta\) and \(\alpha\) (**b**). Contour of an elastic torque \(\tau_{e}\)\(\Re\) as a function of \(\theta\) and \(\alpha\) (**c**). Comparison of gas and mechanical spring forces with respect to a stroke (**d**). Red dash curves in (**a–c**) divide the \(\theta\) and \(\alpha\) domain with R at 0.8. Purple areas in (**a**,**b**) stand for the \(\theta\) and \(\alpha\) domain with \(\tau_{a}\) below 20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(L\) & \(\alpha_{0}\) & \(\beta_{0}\) & \(r_{1}\) & \(r_{2}\) \\ \hline
0.2 m & 34 deg & 17 deg & 100 mm & 51 mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Kinematics Parameters.  
show the relation between the speed-torque and generated output when each mechanism of Figure 4a,b was applied to the simple human model in Figure 4c,d. The final design with the extended linkages could achieve reduction in the stall torque requirement of the rotary actuator by 30%, relative to the model without it.  
The contours of the reduction ratio with respect to the \(\mu\) and the \(\alpha\) are plotted in Figure 4e. While the \(N\) increases with the \(\mu\), it is limited around singularities when the \(\alpha>40\) degrees and/or \(\mu>1.9\) (Figure 4e). When the \(\mu\) is set to 1.9, the \(N\) increases monotonically from 2 to 10 with the \(\alpha\).
## 2 Materials and Methods
### Workflow  
#### 2.2.1 Preoperative Phase  
First, the occlusal splint for upper teeth was fabricated using acrylic resin and was attached to the upper teeth before computed tomography (CT) scan. Using the registration body which was connected to the splint, the positions from physical space are related to the location of the CT data [22, 23] (Figure 2).  
Figure 1: End-effector and robot arm.  
After CT scan (SOMATOM Sensation 10; Siemens, Erlangen, Germany) under 120 kVp and 80 mAs, virtual 3D skull model was constructed and a Le Fort I osteotomy was conducted on the virtual skull model using Mimics 19.0 (Materialise, Leuven, Belgium). To overcome the inaccuracy of CT images due to the slice thickness and artifact and improve the image quality of teeth and teeth-bearing area, maxillary dentition optical scan data was obtained using a 3D scanner (Identica Blue, Medit, Seoul, Korea) and registered with a 3D skull model generated from the CT. First, the virtually osteotomized maxilla after Le Fort I osteotomy was exported in stereolithography interface format (STL) files. The osteotomized maxilla and optical scan data of the maxillary dentition were imported into the inspection program (GOM Inspect; GOM mbH, Braunschiweig, Germany) and set as the normal data and actual data, respectively. For better registration, the registration process consists of two alignments. The initial alignment is performed automatically, and the main alignment is achieved using the local best-fit method. In the local best-fit method, the palatal and buccal surfaces in the scan data are selected and used as the reference surfaces. The aligned scan data was exported in STL and imported to the simulation program. To determine the accuracy of the registration, the contour of the aligned scan data was compared with the contour of the dentition in CT images on the axial, coronal, and sagittal planes. The registration process was finished after merging the osteotomized maxilla and aligned scan data. Using the simulation program, the osteotomized maxilla is moved to the target position according to the surgical plan.  
To match the physical space with the CT image space, registration was performed using the registration body preoperatively (Figure 2B). The physical positions of the fiducial markers on the registration body related to the tracking tool on the end-effector were measured using a wireless tracking tool tip (Northern Digital Inc.). They were registered with their corresponding fiducial markers on the 3D CT images. The relative position between the registration body and tracking tool on the end-effector is always the same and thus can be used for other experiments without measuring the physical positions of the fiducial markers.
# An Efficient Analytical Approach to Visualize Text-Based Event Logs for Semiconductor Equipment
## 4 Experiment and Results
### ASCII vs. TDMS Comparison Result  
The EventLog is structured based on Text. Additionally, the contents saved in the log are determined by the log level designated by the user. For example, if the log level is 1, only the most basic details are saved in the log. In the highest Level 5, all fine details are saved in the log, and the log file size increases. It is not easy to store large amounts of information quickly. Further, the use of disk may be restricted due to the increase in the file size of the EventLog. Therefore, we checked if TDMS is an alternative to reducing the size when saving Text Log. First of all, we checked the type of TDMS that is configured.  
TDMS can be expressed in a total of three file structures, as shown in Figure 5. A file can be created as a Physical File Format, configured as a Logical File Format, or configured as a Perceive File Format. An EventLog consists of Time-Series, so it is usually organized in the form of a Physical File Type. However, the contents inside can be extended to either the logical type or the Perceive file type. We analyzed the format of the existing EventLog and configured Objects and Properties according to the contents. A function was made to convert to TDMS. We evaluated the compression rate and memory usage rate with the existing text-based file and the new TDMS file format.  
Figure 4: Chunk (Physical table).  
Experimental results in Figure 6, the ASCII-based EventLog increased in Loading time in direct proportion to the length of the file increase. On the other hand, in TDMS, the change in speed was insignificant even when the file size increased. This is because when storing the same amount of data, TDMS is structured compared to Ascii, and the size can be reduced. For example, in ASCII-based LogFile, time can be displayed in the year, month, day, hour, minute, second, and millisecond. In the text-based EventLog, time is configured in ASCII. However, TDMS can convert to Double. Not only time but also many numerical data stored in the EventLog can be reduced in size when converting to TDMS. Therefore, TDMS can reduce the file size and is faster compared to ASCII.  
As a result of comparing memory use under the same conditions, it was found that the longer the file length, the higher the memory use of an ASCII-based EventLog. The constant memory usage of TDMS was maintained as shown in Figure 7.  
In conclusion, as shown in Table 1, TDMS files are smaller in size, more compressed, and use less memory than ASCII files. A 22.5 min recipe was run on the equipment, and data were collected from 1582 channels at 0.1 s intervals. The collected files were collected in two types, a general ASCII file, and a TDMS file. The file size was reduced to 51% for uncompressed files compared to ASCII by TDMS and 64% for compressed files. In addition, the compression ratio of TDMS was 1% higher than that of ASCII.  
Figure 5: Form of TDMS file.  
Figure 6: ASCII vs. TDMS Loading Speed Comparison.
# A Lightweight Multi-Source Fast Android Malware Detection Model
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.5.2 A Study on the Performance of Adaptive Soft Voting Method  
As shown in Table 6, by comparing the performance of different base models and adaptive soft voting assembled with multiple base models, a single base model has a limited performance on malware detection. Still, as shown in Table 7, by assembling with the adaptive soft voting method, we achieve a maximum improvement of +5%, +13%, +16% by reaching 99.52%, 96.97%, 97.89% in three performance evaluation metrics. The adaptive soft voting method assembles multiple base models. It achieves significant performance improvements, while the performance of adaptive soft voting improves more with the increase of the number of integrated base models.  
We use different ensembles of base models to compare the performance of adaptive soft voting with static weighted soft voting, respectively. Under the condition of the dynamic weighted soft voting weight parameter \(\mu=0.5\), the ensembles using different base models improves in three performance metrics, including a maximum improvement of 3.49% in ACC, 2.58% in F1-Score, and 0.43% in AUC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Integration of Base Models** & **AUC** & **ACC** & **F1-Score** \\ \hline MLP(H) & 95.73\% & 83.54\% & 81.07\% \\ MLP(M) & 96.12\% & 88.98\% & 91.76\% \\ ASCNN(I) & 98.06\% & 88.97\% & 88.75\% \\ MLP(H) + MLP(M) & 97.66\% & 94.30\% & 95.94\% \\ ASCNN(I) + ASCNN(C) & 99.23\% & 95.69\% & 96.76\% \\ MLP(H) + MLP(M) + ASCNN(I) & 99.39\% & 96.83\% & 97.20\% \\ MLP(H) + MLP(M) + ASCNN(I) + ASCNN(C) & 99.52\% & 97.26\% & 97.89\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance between different base models and ensemble learning models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AUC** & **ACC** & **F1-Score** \\ \hline
3-layer convolutional neural network & 97.26\% & 92.57\% & 94.76\% \\
6-layer convolutional neural network & 98.74\% & 95.15\% & 95.61\% \\
3-layer adaptive shrinkage convolution neural network & 99.23\% & 95.28\% & 96.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of adaptive shrinkage convolution neural network and conventional convolutional network.  
To investigate the reason for the performance difference between adaptive soft voting and soft voting methods with static weights, we use the ensemble of two base models, MLP(I), a multilayer perceptron with Intent & Permission features as input, and MLP(M), a multilayer perceptron with MEM-PSD features as input, and their AUC, Loss, and soft voting weights during the training process is shown in the Figures 12 and 13. It can be seen from the figures that MLP(I) has more jitter during the training process, MLP(M) has less jitter but the final performance is lower than MLP(I). In the traditional soft voting method, which cannot adjust the weight of each base model, we can see from the training curve in the Figure 12, the model with more jitter even becomes noise, which leads to a negative