esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 3 Research Gaps Addressed by This Study
### QE in Dynamic, Interactive Sports  
The majority of QE research has focused on closed skills, such as golf putting or target shooting. Badminton, like other racket sports, is a dynamic and interactive, requiring players to adapt rapidly to external stimuli, such as the opponent's movements or the shuttlecock's trajectory [9; 24; 25]. Our research expands the application of QE analysis to this more complex, open-skill environment, providing new insights into the role of QE in fast-paced sports.
## 5 Results and Discussion
### Implications of Regulation and Certification
#### 5.2.3 CLS Device Certification  
So far, there are no BSI requirements regarding the certification of CLS devices. However, this may be subject to further changes as it is intended that DERs are controlled by aEMPs via SMGWs, and form part of a critical infrastructure, namely the power supply. Therefore, a critical evaluation which considers the amount of device installations and the related accumulated power is needed. It has to be considered that implications of a CLS device certification highly depend on the scope of the potential certification; i.e., the level of detail assessed. In general, one argument for certification is that certified CLS devices have the potential to protect the user's privacy and increase the overall power system security and reliability. However, it has to be noted that device certification also transfers the responsibility from the solution provider to the certifying authority. Hence, certification also tends to lower the device provider's intrinsic motivation to keep the certified system up to date once the certification is reached. Finally, it has to be assumed that CLS certification would significantly slow down the development of new SMGW-based solutions which already comes with high barriers.
## 4 Conclusions  
Here, we investigate the coarse and fine tunability and cooling effect of the nonlinear interferometer method. The constructive interference islands are controllable by changing  
Figure 6: The calculated (**a,b**) and measured (**c,d**) JSIs at (**a,c**) room temperature and (**b,d**) liquid nitrogen cooled with \(\lambda_{p}=1550.92\) nm. The calculation of cooling condition is carried out with the assumption that \(\lambda_{0}\) of DSF and SMF are shifted \(-4\) nm and dispersion slopes are not changed. The x- and y-axes are shown \(\pm 40\) GHz from filter center which is equivalent to the 3-dB bandwidth of signal/idler filter.  
the properties of the nonlinear pair generation medium and linear dispersive medium, such as \(\Delta k_{\text{DSF}}\), \(\Delta k_{\text{SMF}}\), \(L_{\text{DSF}}\), \(L_{\text{SMF}}\), and the number of stages (\(N\)). We succeed in adjusting the peak frequency of constructive interference patterns into a transmission window of commercial 100-GHz DWDM filters. As demonstrated in reference 18, 19, and 20, the dominant parameter is \(L_{\text{SMF}}\). The peak frequency of a constructive interference island can be coarsely placed near a target wavelength by adjusting the length of SMF. In addition, the pump wavelength and length of DSF can be used for fine-tuning. To match the peak frequency with a DWDM filter center frequency, we should consider the phase shift induced in DSF (\(\Delta k_{\text{DSF}}L_{\text{DSF}}\)). The selection of \(L_{\text{DSF}}\) should be carefully considered as not only the peak frequency but also the pair-generation rate and pair-generation spectral bandwidth are also related to \(L_{\text{DSF}}\).  
Finally, we achieved high spectral purity by selecting an appropriate number of stages (\(N\)) and interference mode number (\(m\)). The demonstrated methods show the coarse- and fine-tuning ability to match the peak frequency of a constructive interference island and the center frequency of a commercial filter while maintaining high spectral purity. We think that the demonstrated methods in this study expand the usefulness of the NLI method. The generated photon pairs with the engineered quantum state can be an excellent practical source of quantum information processing involving quantum interference.  
Conceptualization, K.P. and H.S.; measurement, K.P.; calculation, D.L.; validation, K.P., D.L. and H.S.; formal analysis, K.P., D.L., and H.S.; writing--original draft preparation, K.P.; writing--review and editing, K.P., D.L. and H.S.; supervision, H.S.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Foundation of Korea, NRF-2019 MBE4A1079780; Korea Institute of Science and Technology's Open Research Program, 2E30620-20-052; Institute for Information & communications Technology Promotion (IITP), No. 2020-0-00947; Affiliated Institute of Electronics and Telecommunications Research Institute (ETRI), 2020-080.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 7 Future Work and Limitations  
Since some notes apply to interpreting our findings, we discuss these notes by showing this study's future work and limitations.  
The proposed system's evaluation experiments were conducted under the condition that all samples were malware. The false positive rate must still be evaluated by applying the system to benign files to demonstrate detection performance accurately. Generally, collecting unbiased benign files to evaluate false positives fairly is challenging. Appropriate methods should be considered, such as using manually created files as a ground truth dataset for evaluation, while allowing for some bias, as [PERSON] et al. [25] did. If our system is implemented by applying the VirusTotal API, each anti-virus engine's false positives are shown on VirusTotal statistics based on user reports [28]. These statistics could be useful in deciding which anti-virus engine to use in practice.  
Furthermore, the detection performance reported in this study must be interpreted with caution as to whether it truly represents the accuracy of detecting unknown malware. Although the MalwareBazaar submission policy states, \"Please refrain from uploading malware samples older than 10 days to MalwareBazaar\", the freshness and correctness of the samples depend on whether the users that upload adhere to the standards. Therefore, it is necessary to re-examine the best way to collect samples for experiments in the future.  
Although this study used VirusTotal, some studies have pointed out that some scanners alter their verdict even if the same file is inspected [24; 25]. Hence, only scanners known to be stable and suitable scanners should be incorporated into the detection. For instance, [PERSON] et al. [24] removed unstable scanners by a certainty score calculated by tallying up the output labels analyzing the same application multiple times. This approach also helps prevent adversarial scanners that consistently return irresponsible results from trying to mislead the final decision in cooperative systems, even without VirusTotal.
## 5 Analysis of Experimental Results
### Prediction of the Depth of Delaminations  
The depths of delaminations are predicted by using the linear regression with the chosen values of thermal diffusivity (e.g., using Equation (12) in the case of \(\alpha\) = 7.5 \(\times\) 10\({}^{-7}\) m\({}^{2}\) s\({}^{-1}\)). The mean absolute percentage error (MAPE) is commonly used to measure the differences between two datasets [42]; thus, it was applied in this study to assess the difference between the predicted depth and the real depth of defects. The MAPE can be defined by using Equation (13).  
\[MAPE(\%)=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{Depth_{pr}-Depth_{re}}{Depth_{ re}}\right|\times 100, \tag{13}\]  
where, the \(Depth_{re}\) is the real depth, \(Depth_{pr}\) is the predicted depth, and \(N\) is the total number of assessed values.  
The MAPEs obtained from fifteen different cases of thermal diffusivity from (6.8 to 8.2) \(\times\) 10\({}^{-7}\) m\({}^{2}\) s\({}^{-1}\) are almost similar to 7.20%. MAPE less than 10% indicates a good agreement between the expected and real depths as categorized by [PERSON], as shown in Table 5 [42].  
Figure 16 shows an example of the comparison between real and expected depths with the mean value of thermal diffusivity \(\alpha\) = 7.5 \(\times\) 10\({}^{-7}\) m\({}^{2}\) s\({}^{-1}\). As depicted in Figure 16, a deeper delamination has a higher value of standard deviation of the depth than a shallower one; they range from approximately 0.34 to 0.92 cm, corresponding to depths of 1 to 7 cm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MAPE** & **The Accuracy of Forecasted Data** \\ \hline \(<\)10\% & very good \\ \(<\)20\% & good \\ \(<\)30\% & reasonable \\ \(>\)30\% & inaccurate \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification of the accuracy of forecasted data based on the mean absolute percentage error (MAPE) [42].
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 4 Results and Discussion
### Comparison  
In order to better visualize the differences between the simple CACC and the proposed model, we created the following figures. In this section, we have chosen the high relative velocity scenario to compare as it could show relevant information for both controls.  
In Figure 11, the different QPSK modulations can be seen with and without the proposed model. The continuous lines show the SRI-based CACC results. Due to the high relative velocity, the safety risk was high from the beginning even when the communication was good. The dashed lines show the simple method where the control could not stop the host vehicle in time. Following the collision, which occurred approximately 11 s into the simulation, the presented plots are no longer valid.  
Figure 10: Results from SRI-based CACC for high relative velocity (22.22 m/s = 80 km/h).  
In Figure 12, comparisons of 64 QAM modulations are presented. In this case, all of the modulations were affected by the noise. Here, the simple method could not stop the HV in time and collision with the RV occurred. Due to the high relative velocities and the degraded network, the proposed model started to decrease the HV velocity, and with this, the vehicle could adapt to the RV velocity. At around 20 s, the network started to get better even with the noise as the relative distance decreased.  
Figure 11: Results comparison for high relative velocity QPSK modulation (22.22 m/s = 80 km/h).
## 6 Important Applications
### Continuous Porous Polymers in Decontamination  
The monolithic polymers present a permanent porous architecture which allows their use in the clean-up of the fluorinated solvents. [PERSON] and [PERSON] [147] developed porous organic polymer  
Figure 14: Experimental setup for the polymerization preparation of monolith: S, 10-mL plastic syringe; N, nanoporous; VB, vacuum distribution box; H, vent hole; C, polymer substrate chip; Ci, Co, inlet/outlet connecting capillaries; UV, UV light; V, vial containing monomer mixture [146]. Reprinted with permission from the American Chemical Society.  
Figure 13: Scheme showing the sampling process from a mouse brain slice using a capillary loaded with PPM; the individual steps shown include peptide collection, a monolith rinse and peptide deposition onto the matrix-assisted laser desorption/ionization (MALDI) target for MS characterization [145]. Reprinted with permission from the American Chemical Society.  
columns for the flow-through nondestructive removal of a ''mustard gas\" simulant 2-chloroethyl phenyl sulfide (CEPS), from the fluorinated solvent HFE-7100 (a mixture of methyl nonafluorobutyl ether and methyl nonafluoroisobutyl ether). First, the monolithic polymers were synthesized by the _in situ_ copolymerization of DVB and CMS in stainless steel tubes followed by a derivatization treatment with EDA or diethylenetriamine. Figure 15 shows the synthesis and functionalization of the monolithic supports.  
Once the polymers had been functionalized, the decontamination of CEPS was carried out through nucleophilic substitution reactions between the CEPS molecule and the nucleophilic polyamino groups on the polymeric surface. The results obtained indicated that polyamine-modified supports had a high affinity for CEPS, with removal efficiencies of up to 97%.
###### Abstract  
Assessing fire behavior in shrubland/grassland ecosystems of the western United States has proven especially problematic, in part due to the complex nature of the vegetation and its relationships with prior fire history events. Our goals in this study were (1) to determine if we can effectively leverage the high temporal resolution capabilities of current remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve upon shrub and grassland mapping and (2) to determine if these improvements alter and improve fire behavior model results in these grass- and shrub-dominated systems. The study focused on the shrublands and grasslands of the Owyhee Basin, which is located primarily in southern Idaho. Shrubland and grassland fuel load dynamics were characterized using Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) datasets (both derived from MODIS). NDVI shrub and grassland values were converted to biomass, and custom fire behavior fuel models were then developed to evaluate the impacts of surface fuel changes on fire behaviors. Results from the study include the following: (1) high intra- and interannual spectral variability characterized these shrubland/grassland ecosystems, and this spectral variability was highly correlated with climate variables, most notably precipitation; (2) fire activity had a higher likelihood of occurring in areas where the NDVI (and biomass) differential between spring and summer values was especially high; (3) the annual fuel loads estimated from MODIS NPP showed that live herbaceous fuel loads were closely correlated with annual precipitation; (4) estimated fuel load accumulation was higher on shrublands than grasslands with the same vegetation productivity; (5) the total fuel load on shrublands was impacted by shrubland age, and live woody fuel load was over 66% of the total fuel load; and (6) comparisons of simulated fire behavior and spread between dynamic and static fuel loads, the latter estimates being obtained from the operational and nationwide LANDFIRE program, showed clear differences in fire indices and fire burn areas between the dynamic fuel loads and the static fuel loads. Current standard fuel models appear to have bias in underestimating the fire spread and total burnable area.  
wildfire; MODIS; LANDFIRE; NDVI; grassland; shrubland; fuel dynamics Article Assessment of Fire Fuel Load Dynamics in Shrubland Ecosystems in the Western United States Using MODIS Products
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Performance Discussion and Contribution of Phase-Based Features
#### 4.3.2 Detailed Analysis of Classification Errors and Correct Predictions  
Table 8 presents the confusion matrices obtained from an evaluation of the proposed method for both the combined dataset (DDSM, MIAS, and CSAW-M) and the CSAW-M dataset. The matrix for the combined dataset is based on 4651 test images, including 440 cancer and 4211 no-cancer cases. The CSAW-M matrix corresponds to 3006 test images, with 261 cancer and 2745 no-cancer samples. These results provide a detailed breakdown of classification outcomes, including true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs). For the combined dataset, the model correctly classified most samples, with a moderate number of false positives and false negatives. In the case of the CSAW-M dataset, the matrix shows a higher count of correct predictions for both classes, reflecting improved sensitivity and specificity. These results are consistent with the reported performance metrics and highlight the reliability of the proposed method across datasets with different characteristics.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 5 Parametric Study: Finite Element Analysis of a Nailed-Soil Slope with Varying Soil Densities
### Vertical Settlement of the Footing  
Figure 13 presents the vertical settlements of the foundation for various soil densities at different footing loads. The increase in the soil relative density decreases the footing vertical settlement. This decrease of the settlement could be attributed to the decrease of the slope face displacement. Moreover, the increase in the soil relative density is accompanied by a rise of shearing resistance, and, thus, reduction of vertical settlement of the footing. The reduction in footing vertical settlement ranged from 28.39% to 52.65%. It can be observed from the footing settlement's variation that a greater reduction of the settlements with higher soil density is found at higher footing pressure.  
Figure 11: Horizontal displacements of the slope face with various soil densities. (**a**) Loose sand. (**b**) Medium dense sand. (**c**) Dense sand.  
Figure 12: Horizontal displacements of the slope face with various soil densities (at mid-height).
## 5 Experimental Results
### User Survey
#### 5.1.2 Efficiency  
This emphasized evaluating the overall performance of the authoring system in creating and transferring 360\({}^{\circ}\) video experiences. It mainly focused on assessing our focus assistance technique. The aim of this was to find out the percentage of actual experience successfully transferred to the user by the proposed method. More than 90% similarity between their actual experience and the experience assisted by the proposed focus assistance technique was reported by 72% of participants, while 28% of participants reported more than 75% similarity. In evaluating the efficiency of the system in generating and transferring 360\({}^{\circ}\) video experiences using an HMD device, 68% of the participants strongly agreed that the system was highly efficient, 24% agreed, and 8% remained neutral (M = 4.6, SD = 0.12).  
Figure 8: Multiple region of interest (ROIs) in a single 360\({}^{\circ}\) video frame based on users’ personal interests: (a) User 1 region of interest, (b) user 2 region of interest, and (c) user 3 region of interest. For a single 360\({}^{\circ}\) video, there are three different regions of interest based on a user’s perception.
# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
## 3 Results  
In our experiment, we use the open-source toolkit Caffe to implement the proposed Siamese CNN architecture for feature extraction and matching. We perform on a workstation with Intel Core I7, NVIDIA GTX TITAN X GPU, and the operating system of Ubuntu 16.04. We utilize the Stochastic Gradient Decent (SGD) to update the parameters of each layer with the mini-batch size of 100, the momentum of 0.9, and the initial learning rate of 0.01. The training procedure is maintained until the validation accuracy remains unchanged for 10 consecutive epochs.
## 2 Non-Parameterized Time-Frequency Analysis
### Wavelet Transform
#### 2.2.1 Definition  
Wavelet transform is a time-frequency analysis method used to analyze the local characteristics of non-stationary signals. Different from Fourier transform, wavelet transform represents the signal through a set of scalable and shiftable wavelet functions and is thereby able to provide simultaneous high-resolution analysis in time and frequency domains. Wavelet transform can be divided into continuous wavelet transform (CWT) and discrete wavelet transform (DWT). The former is mainly used for signal analysis, while the latter is often used in signal compression and feature extraction.
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 5 Experimental Results
### Pose Estimation Results  
Figure 3 shows the experimental results of the proposed object pose estimation algorithm in tracking the OOI with the three templates shown in Figure 2. In this experiment, the OOI was firstly rotated around the \(y\)-axis in a clockwise direction as shown in Figure 3a-c. Hence, the 3D pose of the OOI was estimated by continuously tracking the templates from No. 1-No. 3. Next, the OOI was rotated around the \(y\)-axis in a counterclockwise direction as shown in Figure 3d-g. The 3D pose of the OOI was then estimated by tracking the template No. 3, No. 1 and No. 2, successively. Finally, the OOI was rotated back to the initial pose, and it was then rotated around the \(z\)-axis in a counterclockwise direction as shown in Figure 3h. From the 3D pose estimation result shown in Figure 3, it is clear that the value of Ry is firstly increased from \(0^{\circ}\) to about \(120^{\circ}\) and then is decreased in steps to about \(-95^{\circ}\). After that, the value of Ry is returned to about \(0^{\circ}\), and the value of Rz is then decreased from \(0^{\circ}\) to about \(-37^{\circ}\). These pose estimation results are consistent with the actual motion trajectory of the OOI  
Figure 2: Three template patches of the Object-Of-Interest (OOI) used in the experiments.  
in the experiment. Therefore, the pose estimation performance of the proposed algorithm to deal with an OOI having a multi-plane structure is validated.
## 3 Multiple Reconfigurable Features in Antenna
### Radiation Pattern and Polarization Reconfigurable Antennas
The radiation pattern can be switched from bidirectional to broadside modes and the antenna's placement above a metallic reflector improves its gain. The frequency range is 1.00-2.25 GHz with an axial ratio bandwidth of 1.5-1.9 GHz, thus suitable for global positioning system (GPS), radio-frequency identification (RFID), and mobile communications.
## 5 Application of Data Analysis Algorithm in Urban Real
### State Management
#### Spatial Data Analysis Algorithm
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Name** & **Algorithm Description** & **Application Scenario** & **Main Function** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Kriging \\ interpolation [44] \\ \end{tabular} } & A spatial interpolation method & & Predict the land value in different regions and provide a basis for real estate valuation. \\  & based on variogram, used to & & Real estate \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Spatial \\ autocorrelation \\ analysis [45] \\ \end{tabular} } & Detect the spatial relationships & & & \\  & between objects in a dataset to & & Real estate \\  & determine if there are & & \\  & & & real estate market. \\  & & & \\  & & & \\  & & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Nearest neighbor \\ analysis [46] \\ \end{tabular} } & Calculate the distance between & & & \begin{tabular}{c} Optimize the spatial layout \\ of commercial outlets and \\ improve market coverage \\ efficiency. \\ \end{tabular} \\  & each point and its nearest point and & & Commercial & & \\  & analyze the uniformity of the & & \\  & point distribution. & & \\ \hline \multirow{3}{*}{\begin{tabular}{c} Spatial clustering \\ analysis [47] \\ \end{tabular} } & The spatial data are grouped & & & \\  & wherein the objects within the same & & \\  & group have a high similarity, while & Urban Plan & & \\  & the objects between different & & \\  & groups have a low similarity. & & \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Buffer zone \\ analysis [48] \\ \end{tabular} } & Create buffers around specific & & & \\  & geographic features to analyze the & Environmental & & \\  & impact those features have on & Monitoring & & \\  & surrounding areas. & & & environment. \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Road network \\ analysis [49] \\ \end{tabular} } & Analysis based on road network & & & \\  & data is used to determine issues & & \\  & such as paths, distances, & & \\  & and accessibility. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Application of spatial statistical analysis algorithms in urban real estate management.