esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Innovative technologies can improve user usability and satisfaction in computer-based activities in the work environment, including surgeons working in the operating room (OR). A scrubbed surgeon must ask unscrubbed staff to retrieve medical images from a surgical PACS system on a monitor in a hybrid operating room. The study aimed to check users' usability and satisfaction with the designed and developed Ortho_Kinect_OR application, which enables contactless control of access to medical images during surgery. The application also facilitates access to telemedicine applications such as intraoperative telemernoting during orthopedic procedures or tele-education. The application's usability was tested by assigning standardized tasks to surgeons for PACS and teleconferencing in the operating room. Hand gestures were recognized and translated to function like mouse buttons. Field tests conducted among orthopedic surgeons showed high usability and user satisfaction. PACS access in the operating room did not distract the orthopedic surgeon during orthopedic surgery procedures. All surgeons completed the tests and tasks without any problems. OR field test results showed high agreement among users and were very satisfactory. Junior surgeons and residents pointed out that people with average computer knowledge could use the application. It has been shown that the contactless system designed and built based on the Kinect sensor available on the shelves meets the requirements of the operating room environment and is easy to use. Touchless controller technology provides the opportunity to increase the use of intraoperative imaging previews and improve the safety of surgical patients by improving sterility and reducing unnecessary staff in the operating room. Using the Ortho_Kinect_OR application and the Kinect sensor, it is possible to provide contactless access to videoconference telemernoting and PACS in the operating room without an orthopedic surgeon's unnecessary distraction in the operating room environment.  
K Kinect; gestures; operating room; orthopedic surgery; touchless image retrieval; PACS; teleconferencing; scrubbed surgeon-computer interaction +
Footnote †: journal: Applied Sciences
# Continuous Control Set Predictive Current Control for Induction Machine
## 3 Control Structure
### Deriving the Control Algorithm for the Inner Control Loop  
By expressing the rotor current from (7) and plugging it into (2), Equation (9) that describes rotor flux dynamics is derived:  
\[\frac{\text{d}}{\text{d}t}\mathbf{\psi}_{\text{ra}\beta}=\frac{L_{m}}{\tau_{r}}i_ {\text{sa}\beta}-(\frac{1}{\tau_{r}}\mathbf{I}-jp\omega_{r})\mathbf{\psi}_{\text{ra}\beta} \tag{9}\]  
where \(\tau_{r}=\frac{L_{r}}{R_{r}}\) represents the rotor time constant and \(\mathbf{I}\) is identity matrix. Furthermore, by plugging (6) and (7) into (1) and replacing the rotor flux derivative with the expression (9), following equation for stator current dynamics is derived:  
\[\frac{\text{d}}{\text{d}t}\mathbf{i}_{\text{sa}\beta}=-\frac{R_{s}+\frac{L_{m}^{2 }}{\tau_{r}\tau_{s}}}{\sigma L_{s}}\mathbf{i}_{\text{sa}\beta}+\frac{L_{m}\sigma L_ {s}}{L_{r}}(\frac{1}{\tau_{r}}\mathbf{I}-jp\omega_{r})\mathbf{\psi}_{\text{ra}\beta}+ \frac{1}{\sigma L_{s}}\mathbf{v}_{\text{sa}\beta} \tag{10}\]  
where \(\sigma=1-\frac{L_{m}^{2}}{L_{r}L_{r}}\) represents leakage inductance factor. By arranging Equations (9) and (10) into a state-space form, following equation is derived:  
\[\frac{\text{d}}{\text{d}t}\mathbf{x}=\mathbf{A}(\omega_{r})\mathbf{x}+\mathbf{B}\mathbf{u} \tag{11}\]where \(\mathbf{x}\) is a state vector, \(\mathbf{A}\) is state matrix, \(\mathbf{B}\) is input matrix and \(\mathbf{u}\) is input vector, defined by (12). \[\mathbf{x}= \begin{bmatrix}i_{sa}\\ i_{s\beta}\\ \psi_{rs}\\ \psi_{r\beta}\end{bmatrix} \quad\mathbf{u}= \begin{bmatrix}\mathbf{v}_{sa}\\ \mathbf{v}_{s\beta}\end{bmatrix} \]  
\[\mathbf{A}(\omega_{r})= \begin{bmatrix}-\frac{R_{s}+\frac{12}{L\tau_{s}}}{\sigma L_ {s}}&0&\frac{L_{m}\sigma L_{s}}{L_{r}\tau_{l}}&\frac{L_{m}\sigma L_{s}\rho \omega_{r}}{L_{r}}\\ 0&-\frac{R_{s}+\frac{12}{L\tau_{s}}}{\sigma L_{s}}&-\frac{L_{m}\sigma L_{s} \rho\omega_{r}}{L_{r}}&\frac{L_{m}\sigma L_{s}}{L_{r}\tau_{l}}\\ \frac{L_{m}}{\tau_{l}}&0&-\frac{1}{\tau_{l}}&-p\omega_{r}\\ 0&\frac{L_{m}}{\tau_{l}}&p\omega_{r}&-\frac{1}{\tau_{l}}\end{bmatrix} \quad\mathbf{ B}= \begin{bmatrix}\frac{1}{\sigma L_{s}}&0\\ \frac{1}{\sigma L_{s}}\\ 0&0\\ 0&0\end{bmatrix}  \tag{12}\]  
In order to implement the predictive current control, the machine model should be discretized. Using forward Euler discretization (13), it is possible to predict state values of the next sample based on the values of the current sample. Since the \(\alpha-\beta\)-component values of the stator current are of interest, only first two rows of the state and input matrix will be considered. After discretization with sampling time \(T_{s}\) and state and input matrices truncation, the Equation (14) is derived and it will be used in the proposed method:  
\[\mathbf{x}[k+1]=\mathbf{A}_{d}(\omega_{r})\mathbf{x}[k]+\mathbf{B}_{d}\mathbf{u}[k] \tag{13}\] \[\mathbf{A}_{d}(\omega_{r})=\mathbf{I}+T_{s}\mathbf{A}(\omega_{r})\quad\mathbf{B}_ {d}=T_{s}\mathbf{B}\]  
\[ \begin{bmatrix}i_{sa}[k+1]\\ i_{s\beta}[k+1]\end{bmatrix}
# Spectral Analysis of Electroencephalographic Data in Serious Games
## 4 Results
### Results across Subjects  
The three types of spectrograms that characterize each specific group of subjects were discussed in previous subsections. It is desirable to compare each individual's brain activities in time intervals corresponding to the gameplay and the resting with closed eyes. The average brain activity results in calculating a single number that summarizes the power of specific brain activity in a defined time interval. For instance, let us calculate the average power of beta activity with the help of the graph in Figure 6. The average power of beta activity in some interval, e.g., during the gameplay, can be computed as  
\[\overline{\beta}=\frac{\sum_{k=0}^{L}\mathrm{PSD}_{\beta}[k]}{L}, \tag{8}\]  
where \(\mathrm{PSD}_{\beta}\) is the power of beta activity at discrete time \(k\) in the time interval with the length of \(L\).  
The \(\overline{\beta}\) value is calculated for the three game levels. Levels have increasing difficulty, so it is assumed that beta activity will rise. The change of beta activity in three levels is shown in Figure 10. The power of beta activity is normalized to the maximum value within levels to make the results comparable across subjects. Approximately half of the subjects showed an upward trend. Some of the subjects show a downward or fluctuating trend.  
The beta/alpha ratio is a valuable indicator of the brain's response to specific mental activity. The averaged power of beta/alpha ratio \(R\) is computed in the state of relaxation and during the gameplay by the following equation  
\[R=\frac{\overline{\beta}}{\overline{\alpha}}, \tag{9}\]  
Figure 10: The normalized average beta activity in the game levels.  
where \(\overline{\beta}\) and \(\overline{\alpha}\) are calculated by Equation (8). Figure 11 shows the beta/alpha ratio during the closed eyes and gameplay. According to the results, the beta/alpha ratio raised in all subjects except the last three subjects.
# I-Shaped Metamaterial Using SRR for Multi-Band Wireless Communication
## 7 Result and Discussion  
The parametric analysis for the ring gap (g) is carried out and presented in Figure 6. This analysis provides a suitable dimension for the ISMeTM design. It can be observed from Figure 6a that when the dimension of the ring gap (g) is varied, the operating band changes. It can be observed that for g = 0.5 mm and 1 mm the MeTM have the same number of resonance frequencies, while at g =1.5 mm and 2 mm the MeTM have five and six resonance frequencies, respectively. However, the MeTM has its best performance at  
Figure 5: S-parameters and effective parameters of the ISMeTM: (**a**) S-parameters; (**b**) effective parameters.  
the dimension of g = 0.5 mm. Furthermore, the variation of S21(dB) parameter with the variation of the ring gap (g) is displayed in Figure 6b. At g = 1 mm, 1.5 mm, and 2 mm, the ISMeTM has four and three resonance frequencies, respectively. Thus, when g is at 0.5 mm seven resonances are realized, making it the best dimension for optimal performance of the design.  
Figure 6: _Cont_.  
The variation of the strip length (RL) of the unit cell is presented in Figure 6c. It is observed that the most suitable dimension of RL for the ISMeTM unit cell is 2 mm. Similarly, the strip width (W) of the unit cell is varied and presented in Figure 6d. It can be studied that at W = 0.5 mm the unit cell exhibits the optimum multi-band behavior. The variation of the S21 parameter with the dielectric constant and sh are shown in Figure 6e,f, respectively. From these figures, it can be observed that the center frequencies and the frequency bands can be modified by varying the dimensional parameters of the structure. The variation of permittivity and permeability with ls is shown in Figure 6g. The variation of permittivity and permeability with sh is shown in Figure 6h. From Figure 6g,h, it can be observed that the frequency band with SNG property and the values of permittivity and permeability can be varied by changing the dimensional parameters of the structure.  
Figure 6: ISMeTM unit cell parameters: (**a**) variation of S11 with g; (**b**) variation of S21 with g; (**c**) variation of S11 with RL; (**d**) variation of S11 with W; (**e**) variation of S21 with dielectric constant of the substrate; (**f**) variation of S21 with sh; (**g**) variation of permittivity and permeability with ls; (**h**) variation of permittivity and permeability with sh.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
###### Abstract
In contrast, the latter is unconstrained by production progress and constitutes the primary algorithmic instrument for the compilation of mine gas geological maps. Nevertheless, owing to the inadequate comprehensiveness of the selected geological factors, the accuracy of gas content prediction encounters difficulties in satisfying the field operational requirements of mines characterized by intricate geological structures. The prediction method proposed in this paper considers many geological factors, such as anticlines, faults, coal seam thickness, and coal seam dip angle. Applying this prediction method to other coal mines is the same, except that the influencing factor coefficients involved in the prediction need to be determined based on the monitoring data of the predicted mine, which can be used for predicting the gas content of the mine. The proposed algorithm for forecasting coal seam gas content, as presented in this article, relies upon the quantitative analysis of the equivalent layer partitioning and the grouped influencing factors pertinent to coal seam gas content. This approach remains unhindered by the progression of fieldwork or construction activities. The mining gas prediction software (V1.0), intelligently devised upon this predictive methodology, autonomously computes and delineates the contour mapping of gas content across the entire coal seam. This facility significantly aids on-site personnel in taking preemptive and judicious gas prevention measures, thereby contributing to the safe and efficient operation of coal mines.
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 1 Introduction
This method has been applied to implement a dual-mode (de)MUX [21]. Both the density method and the GPDS method leverage the gradients of their variables to facilitate rapid convergence of the device structure and achieve high performance. However, both pixel-based and topology-optimization-based approaches can introduce holes and cracks in the device structure. These fine structures can potentially cause additional scattering and dissipation of the optical field, as well as compromise the stability of the manufacturing process. To tackle this issue, [PERSON] et al. manually removed the island parts from the main body of their designed structure, which was generated based on density method. As a result, an improvement in device performance was demonstrated, with the insertion loss reduced from 0.8 dB to 0.63 dB [27]. However, this approach increases the workload for the designer, particularly when dealing with a large number of intricate structures. In this paper, we present a compact and high-fabrication-tolerance single-connected mode (de)MUX based on the adjoint and level set method with a footprint of 9.4 um \(\times\) 2.9 um. By utilizing the level set method, only the characteristics of the graphic boundary are evolved. The level set method focuses exclusively on evolving the properties of the graphical boundary, resulting in a final structure that maintains the initial property of being simply connected. This design approach eliminates the presence of hole and crack structures, which could lead to scattering and dissipation. Consequently, it contributes to low loss and high-performance implementation, while enhancing manufacturing stability. In the wavelength range of 1500 nm to 1600 nm, our simulation results demonstrate the insertion losses (ILs) of less than 0.28 dB for TE\({}_{0}\), and 0.35 dB for TE\({}_{1}\), with crosstalk levels (CTs) not exceeding \(-\)23 dB for both TE\({}_{0}\) and TE\({}_{1}\). Experimental results validate that the ILs are below 0.89 dB and 0.44 dB for the TE\({}_{0}\) and TE\({}_{1}\) modes, respectively, within the range of 1530 nm to 1570 nm, while CTs remain below \(-\)20 dB. For deviations of 40 nm, we achievedexperimental results of 0.93 dB, 1.5 dB (ILs for TE\({}_{0}\) and TE\({}_{1}\)), and \(-13\) dB (CTs) in the wavelength range of 1530 nm to 1570 nm. The proposed device exhibits high performance, compactness, and fabrication stability, positioning it as a promising candidate for serving as a fundamental building block in the implementation of on-chip MDM technology.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 3 Gabor-Based Visual Feature Extraction
### Visual Input II: Gabor Lip Features  
In addition to using Gabor Lip Images as network input, this paper also reports experiments with Gabor Lip Features. For each target lip image, the Gabor lip features are calculated. The Python \(regionprops\) function is used to identify the closest region \(r\) to the ROI centre point as the target lip region and calculate features in the form of a lip 'patch' covering the mouth area, as shown in Figure 10. The feature extraction approach has been fully described in previous research [49], and full details can be found there.  
For each lip patch \(r\), seven feature values are generated: width, height, area, intensity, x and y values of the central point, and orientation. The box width is the inter-lip width. The height is the inter-lip height. The area is the number of pixels,  
Figure 10: Gabor Transformed ROI.  
Figure 9: 2D Gabor Lip Image (2D-GIL).  
of each pixel density value (the darker the inter-lip area, the deeper the mouth opening and the larger the sound intensity), as shown in Figure 11.  
Compared to pixel-based inputs such as face pictures or Gabor Lip Images, Gabor lip features significantly reduce the quantity of visual input data required for speech reconstruction. The original pixel-based image input, which required 155,952 inputs (one for each pixel), can be reduced to just seven Gabor lip eigenvalues, resulting in a significantly smaller network input. By utilising Gabor lip eigenvalues, which directly reflect lip changes, the number of layers in the model can be reduced by removing all CNN layers entirely, resulting in faster speech reconstruction and more lightweight models.
# A New Maximum Likelihood Estimator Formulated in Pole-Residue Modal Model
## 2 Structure of the Combined LSCF-MLE-PMM  
The ML-based technique proposed in this paper optimizes the invariants of the Pole-residue Modal Model iteratively and, therefore, it requires a good starting guess to assure convergence over the performed iterations. Though the model parameter estimates provided by any LS estimator can be used as starting guess, some particular advantages are obtained if the ML-based estimator described in the second part of the paper is combined with the LSCF identification technique formulated in Common Denominator Model (CDM). These advantages include: (1) the fact that the Pole-residue Modal Model and the CDM are closely related and, therefore, the LS multi-reference information provided by the LSCF estimator can be retained and iteratively optimized by the MLE-PMM; and (2) the possibility of combining the ML Estimator formulated in CDM (MLE-CDM) [21] with the LSCF estimator to improve even more the accuracy of the LSCF estimates used as starting guess.  
The identification process with the combined LSCF-MLE-PMM consists basically of the following steps: (1) primary identification of the physical poles with LSCF estimator from the measured FRFs and estimation of the corresponding modal residues together with the lower and upper residuals with the Least Squares Frequency Domain (LSFD) estimator [20]; and (2) optimization of the model parameters provided both by the LSCF and LSFD estimators and estimation of their respective uncertainty bounds with the MLE-PMM in the final step of the identification process. The second step of this combined technique is derived in this paper and consists of the novel ML-based estimator itself. The chart flow of the identification process with the combined LSCF-MLE-PMM is shown in Figure 1.
## 5 Experiments
### Encoder and Decoder Architecture in Experiments
#### 5.3.1 Encoder  
The encoder comprises \(L=3\) identical layers, each containing:  
* Multihead Attention (MHA);
* Feed-Forward Network (FFN):  
* Hidden dim: \(d_{\text{ff}}=512\).  
* Activation: ReLU.
* Residual Connections + Batch Normalization: Applied after each sublayer.
Both scattering and spectral features that are useful to discriminate different crop types should be used as inputs for CGANs in the context of crop classification. Despite the potential of red-edge and shortwave infrared (SWIR) spectral bands for crop classification [49; 50; 51], the generation of those spectral bands has not yet been conducted using CGANs. For example, the spatial resolution of Sentinel-2 red-edge and SWIR bands is 20 m, which is different from the visible and near-infrared (NIR) bands with a spatial resolution of 10 m. Thus, conventional CGAN models cannot be applied to all spectral bands of Sentinel-2 without scale conversion. Most previous studies focused on the generation of visible and NIR (VNIR) band images and only used backscattering coefficients as input variables [40; 43; 44; 46; 47; 48]. Furthermore, the benefit of using an additional feature derived from dual-polarization SAR data (e.g., Sentinel-1 images), such as the radar vegetation index [52; 53], has not been thoroughly investigated. From a practical perspective, the virtual optical image should be used as the input for specific tasks such as land-cover classification or change detection. However, most studies have focused solely on virtual optical image generation with improved accuracy. They have not analyzed the impacts of the virtual image on specific applications when optical imagery is unavailable at the desired time. Very few studies have been conducted where the generated virtual optical image is directly applied to classification tasks, except for [PERSON] et al. [42], where crop classification and wildfire detection were conducted. Despite the great importance of early crop mapping, to the best of our knowledge, the value of virtual optical imagery generated at the optimal date has not yet been evaluated. The objectives of this study are to generate cloud-free virtual optical images using SAR images and CGANs and explore the effects of using the generated virtual optical image for early crop mapping. More specifically, this study demonstrates (1) how well the CGAN model predicts the reflectance of spectral bands from SAR imagery using additional information and (2) how predicted virtual imagery could be helpful for early crop mapping. The main contributions of this study are summarized in the following points:  
1. Specific feature representation and extraction procedures tailored to image translation in croplands are considered in this study. Various features extracted from Sentinel-1 SAR and Sentinel-2 multi-spectral optical images are utilized for virtual Sentinel-2 image generation. 2. Unlike most previous studies that focused on virtual optical image generation itself, the benefits of virtual optical imagery for a real application, such as early crop mapping, are demonstrated. The rest of this paper is organized as follows. The study area and data sets are described in Section 2. Section 3 presents the methodological framework and experimental settings employed in this study. Experiment results of virtual image generation and early crop mapping are presented in Section 4, followed by a discussion in Section 5 and conclusions in Section 6.
## 3 Optimum Design of SOE  
### KOD II-Type SOE  
To achieve the optimum parameters of the KOD-type SOE, the following steps of optimization design process were performed:  
1. The original prism pitch of the Fresnel lens with the KOD-type SOE was 0.2 mm. The resulting optical efficiencies under various prism pitch, ranging from 0.2 mm to 1.4 mm, were calculated and compared, as shown in Figure 12a. Figure 12a shows that when the prism pitch was 1.0 mm, the optical efficiencies under normal incidence and at an incidence deviation of 1.2\({}^{\circ}\) were both the highest. Hence, the prism pitch was selected as 1 mm.
2. As Figure 12b shows, the effects of the F-number (ranging from F/2.0 to F/2.4) of the Fresnel lens and the designed incidence angle \(I_{i}\) of the KOD-type SOE were calculated and compared. The elliptical curve of the top section of the KOD-type SOE was re-calculated according to different values of the designed incidence angle \(I_{i}\). Theoretically, the designed incidence angle of the KOD-type SOE \(I_{i}\) is very similar to the calculated acceptance angle. It was found that the best optical efficiency at an incidence angle of 1.7\({}^{\circ}\) with an F-number of 2.3. Notably, the F/2.3 was the original value of the F-number, which means the focal length of the POE was unchanged.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**SOE** & **No SOE** & **KOD** & **KFFS** & **SP** \\ \hline Acceptance angle (degree) & 0.1 & 1.2 & 0.7 & 0.3 \\ Optical efficiency under normal incidence (\%) & 85 & 84.6 & 85.3 & 88.7 \\ Optical efficiency at the acceptance angle (\%) & 75.8 & 76.2 & 78.3 & 80.5 \\ Peak value of irradiance on the receiver under normal incidence (W/mm\({}^{2}\)) & 1.0557 & 0.9291 & 0.7916 & 1.0555 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of the optical performance of the Fresnel lens with three types of secondary optical elements (SOE).  
Figure 11: Optical efficiency of the Fresnel lens with three types of SOE.  
3. The optimum design of KOD is designated as KOD II in this study.  
Figure 13 and Table 3 summarize the optical characteristics and comparison of CPV unit with the KOD and KOD II SOEs. As shown in Figure 13 and Table 3, the KOD II SOE exhibits a better acceptance angle (1.7\({}^{\circ}\)) than the KOD SOE does (1.2\({}^{\circ}\)), and the optical efficiency of KOD II is 50% even under an incidence angle deviation of 2\({}^{\circ}\). The optical efficiency under normal incidence for KOD and KOD II SOEs are almost the same. Figure 14(a,b) display the irradiance distribution with KOD II SOE when the incidence angle deviations are 0\({}^{\circ}\) and 1.7\({}^{\circ}\), respectively.