esa-sceva/satcom-chunk-collection huggingface dataset:
# New Orthogonal Transforms for Signal and Image Processing
###### Abstract  
In the paper, orthogonal transforms based on proposed symmetric, orthogonal matrices are created. These transforms can be considered as generalized Walsh-Hadamard Transforms. The simplicity of calculating the forward and inverse transforms is one of the important features of the presented approach. The conditions for creating symmetric, orthogonal matrices are defined. It is shown that for the selection of the elements of an orthogonal matrix that meets the given conditions, it is necessary to select only a limited number of elements. The general form of the orthogonal, symmetric matrix having an exponential form is also presented. Orthogonal basis functions based on the created matrices can be used for orthogonal expansion leading to signal approximation. An exponential form of orthogonal, sparse matrices with variable parameters is also created. Various versions of orthogonal transforms related to the created full and sparse matrices are proposed. Fast computation of the presented transforms in comparison to fast algorithms of selected orthogonal transforms is discussed. Possible applications for signal approximation and examples of image spectrum in the considered transform domains are presented.  
orthogonal transform; signal processing; image processing +
Footnote †: 2021: 11, 7433. [[https://doi.org/10.3390/app11167433](https://doi.org/10.3390/app11167433)]([https://doi.org/10.3390/app11167433](https://doi.org/10.3390/app11167433))
###### Abstract  
Unsteady excitation has proved its effectiveness in separation flow control and has been extensively studied. It is observed that disordered shedding vortices in compressors can be controlled by unsteady excitation, especially when the excitation frequency coincides with the frequency of the shedding vortex. Furthermore, former experimental results indicated that unsteady excitation at other frequencies also had an impact on the structure of shedding vortices. To investigate the impact of excitation frequency on vortex shedding structure, the Detached-Eddy Simulation (DES) method was applied in the simulation of shedding vortex structure under unsteady excitations at different frequencies in an axial compressor. Effectiveness of the DES method was proved by comparison with URANS results. The simulation results showed a good agreement with the former experiment. The numerical results indicated that the separation flow can be partly controlled when the excitation frequency coincided with the unsteady flow inherent frequency. It showed an increase in stage performance under the less-studied separation flow control by excitation at a certain frequency of pressure side shedding vortex. Compared with other frequencies of shedding vortices, the frequency of pressure side shedding vortex was less sensitive to mass-flow variation. Therefore, it has potential for easier application on flow control in industrial compressors.  
unsteady flow control; vortex dynamics; separation flow; Detached-Eddy Simulation Article
# Hyperparameter Optimization of Ensemble Models for Spam Email Detection
## 3 Methodology
### Methods and Machine Learning Classifiers
#### 3.3.4 Cross Validation  
The efficiency of both RF and XGBoost was validated through K-fold cross-validation. This technique involves splitting the training set into K numbers of a subset. The K-1 subset of the entire dataset is trained and evaluated on a subset (i.e., 1-fold). This is repeated K number of times with a different subset of K used for evaluation at each iteration [36]. The essence is to rid the models of overfitting and sample bias. In this study K = 10. The 10-fold cross-validation was applied to the tuned models whereas the train/test split was applied to the baseline models.  
The choice of random forest and XGBoost for this experimental study was informed by their high performance on similar tasks in other studies. Furthermore, the random forest  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Hyperparameter Values** \\ \hline Random Forest & \begin{tabular}{c} **Baseline Models** \\ \end{tabular} \\ XGBoost & \
\begin{tabular}{c} mtre = 500, mtry = 23 \\ max\_depth = 6, nrounds = 100, \\ objective = \"binary:logistic\", eta = 0.3 \\ **Tuned Models** \\ \end{tabular} \\ Random Forest &
\begin{tabular}{c} First run: ntree = (1000, 1500) mtry = (7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40) \\ Second run: ntree = (1000, 1500), mtry = (2, 3, 4, 5, 6) \\ max\_depth = (5, 6, 7), nrounds = (70, 140, 210, 280, 350, 420, 490, 560, 630, 700), objective = “binary:logistic”, eta = (0.20, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30), colsample\_bytes = 0.6 \\ \end{tabular}
\end{table}
Table 2: Hyperparameter settings for the algorithms.  
model uses the bagging technique which reduces variance and avoids overfitting through inbuilt out-of-bag error estimates. The outcome of several predictions is aggregated to obtain a final result. The XGBoost model uses the boosting technique which sequentially tries to optimize the loss function of a preceding CART learner through successive ones.
## 4 Simulation Results
Apart from this, another advantage ensured is the BER performance of [PERSON] has much development in the proposed scheme than that of the original CIM-SDM scheme with nearly 10 dB gains. Therefore, the proposed CIM-GSDM system provides more enhancement than the conventional CIM-SDM system in terms of security of transmission. Figure 5 shows [PERSON] and [PERSON]'s BER performances of proposed CIM-GSDM scheme employing \(N_{t}=8\), \(N_{r}=8\),\(N_{e}=8\) and QPSK. The interference matrix is \(\Lambda=diag\{e^{i\Theta_{\Lambda}}\}\) (\(\Theta_{\Lambda}=\{28^{\circ},32^{\circ},40^{\circ},45^{\circ},50^{\circ},55^{ \circ},60^{\circ},65^{\circ}\}\)). From the results, it can be observed that the theoretical results have considerable approximation to the simulation results at high SNR, which convinces that the theoretical analysis are correct. Furthermore, the performance of [PERSON] is much worse than [PERSON] at middle and high SNR even if the receivers at Eve are located near to [PERSON]'s, which means the proposed CIM-GSDM system decreases performance of [PERSON] while keeping [PERSON] unaffected. The theoretical and simulation results match better in high SNR region since the interference caused by noise relatively decreases with SNR becoming higher. As a result, the proposed CIM-GSDM system is able to enhance the security of transmission. Figure 6 exhibits [PERSON] and [PERSON]'s BER performances of proposed CIM-GSDM scheme in comparison to the traditional CIM-SDM counterparts employing \(N_{t}=8\), \(N_{r}=8\), \(N_{e}=8\) and QPSK at the same spectral efficiency. The results illustrated the proposed CIM-GSDM system enhances the BER performance at both middle and high SNR compared to the original CIM-SDM system at the same spectral efficiency. Analogously, the BER performance of [PERSON] is still worse in both the CIM-GSDM system and the CIM-SDM system even if the receivers at Bob are located near to [PERSON]'s. However, the BER performance of [PERSON] is improved obviously in the CIM-GSDM system, which has almost 12 dB gains. In short, CIM-GSDM system can improve the BER performance strongly at the same spectral efficiency. Figure 4: [PERSON]’s and [PERSON]’s BER performances of proposed CIM-GSDM scheme in comparison to the traditional CIM-SDM counterparts employing \(N_{t}=8\), \(N_{r}=6\), \(N_{e}=6\) and QPSK at the same spectral efficiency.
## 4 Methods for Improved Reliability and Robustness
### Model Improvement Techniques
#### 4.2.2 Loss Formulation  
MI techniques mitigating the lack of reliability induced by typical loss functions aim at re-formulating the training procedure through the definition of additional objective losses that take into account anatomical constraints. Many of these works rely on _shape priors_, embedding prior expertise knowledge into the segmentation model. A second set of works takes inspiration from control theory, proposing _automatic correction_ schemes that make use of high-level feedback systems.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **Regression** & **No QC Labels** & **Detection** \\ \hline [PERSON] et al. [78] & ✗ & ✗ & ✗ \\ [PERSON] et al. [79] & ✗ & ✗ & ✗ \\ [PERSON] et al. [80] & ✗ & ✗ & ✓ \\ [PERSON] et al. [81] & ✗ & ✓ & ✗ \\ [PERSON] et al. [82] & ✓ & ✗ & ✗ \\ [PERSON] et al. [83] & ✓ & ✓ & ✗ \\ [PERSON] et al. [76] & ✗ & ✗ & ✗ \\ [PERSON] et al. [77] & ✗ & ✗ & ✗ \\ [PERSON] et al. [85] & ✓ & ✗ & ✗ \\ [PERSON] et al. [86] & ✓ & ✗ & ✗ \\ [PERSON] et al. [87] & ✓ & ✗ & ✓ \\ [PERSON] and [PERSON] [88] & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Post-analysis QC methods and their three main characteristics: performing regression or classification(regression), the need of quality control labels (no QC labels) and if they detect the element causing the error within the image (detection).  
Shape Priors  
[PERSON] et al. [93] extend the well-established U-net architecture [94] through the formulation of a probabilistic framework, which allows the embedding of a cardiac shape prior, in the form of a 3D volume encoding the probability of a voxel to belong to a certain \"cardiac class\" (LV, RV, or MYO), and the definition of a loss function tailored to the cardiac anatomy. [PERSON] et al. [95] propose a loss function that measures the topological correspondence between predicted segmentations and prior shape knowledge. This is done by using the differentiable properties of persistent homology, which compares topologies in terms of their Betti numbers. [PERSON] et al. [96] enforce topology preservation by combining a segmentation network with spatial transformers and diffeomorphic displacement fields. In this way, the network learns to warp a binary prior, completing the segmentation task with the desired topological characteristics.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Image Cues Constraints
#### 2.4.1 Method Content  
[PERSON] et al. [31] train a deep convolutional neural network (CNN) to establishe the mapping relationship between the image cues and 3D human pose codes. Their coding method is finally combined with a linear matching mechanism to construct an effective disambiguating solution. The ordinal depth refers to the relative depth between joint points rather than the absolute physical depth and is extracted from images as a clue by researchers for depth learning. [PERSON] et al. [32] employ ordinal depth annotation as weak supervision to replace 3D annotation. Their end-to-end model is not affected by the reconstruction ambiguity of the 2D detector. [PERSON] et al. [33] proposed a similar approach to reduce the lifting ambiguity by using the obtained ordinal depth to rank candidate 3D pose samples generated by conditional variational autoencoder (CAVE). [PERSON] et al. [34] also use the ordinal depth information to constrain the depth between adjacent joints. As shown in Figure 6, they design a pairwise ranking CNN (PRCNN) to generate pairwise depth relationships between joints, leveraging its rich geometric features and 2D joint locations to determine an unambiguous 3D pose. [PERSON] et al. [35] propose the limb depth  
Figure 5: Illustration of [PERSON]’s approach based on kinematics analysis.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline
**Methods** & **Diz.** & **Disc.** & **Eat** & **Greet** & **Phone** & **Photo** & **Pose** & **Purch.** & **Sit** & **SitD.** & **Smoke** & **Wait** & **WalkD.** & **Walk** & **WalkT.** & **Avg** \\ \hline [PERSON] et al. [23] & 69.5 & 80.2 & 78.2 & 87.0 & 100.8 & 102.7 & 76.0 & 69.7 & 104.7 & 113.9 & 89.7 & 98.5 & 82.4 & 79.2 & 77.2 & 87.3 \\ [PERSON] et al. [24] & 43.8 & 51.7 & 48.8 & 53.1 & 52.2 & 74.9 & 52.7 & 44.6 & 56.9 & 74.3 & 56.7 & 66.4 & 68.4 & 47.5 & 45.6 & 55.8 \\ [PERSON] et al. [25] & 44.7 & 48.9 & 47 & 49 & 56.4 & 67.7 & 48.7 & 47 & 63 & 78.1 & 51.1 & 50.1 & 54.5 & 40.1 & 43 & 52.6 \\ [PERSON] et al. [26] & 36.3 & 42.8 & 39.5 & 40 & 43.9 & 48.8 & 36.7 & 44 & 51 & 63.1 & 44.3 & 40.6 & 44.4 & 34.9 & 36.7 & 43.4 \\ [PERSON] et al. [30] & 37.4 & 43.5 & 42.7 & 42.7 & 46.6 & 59.7 & 41.3 & 45.1 & 52.7 & 60.2 & 45.8 & 43.1 & 47.7 & 33.7 & 37.1 & 45.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of performance on Human3.6 Protocol #1 (MPJPE: mm).  
maps representation, associated depth values with image cues, to break the previous nested learning process's limit that simultaneously estimates (X, Y, Z).
## Characteristics of Surface Waves Propagating on Grounded Anisotropic Slab
### Infinitely Large Anisotropic Grounded Slab
By solving [PERSON]'s equations based on Equations (2)-(5), the E- and H-fields in both anisotropic slab and air regions in \(y\) and \(z\) directions are found as:  
Region I  
\[E_{y}^{\rm I}=\frac{e^{-j\beta_{x}x}\sin(k_{z}z)}{j(e_{y}\mu_{k} ^{2}k_{0}^{2}-\beta_{x}^{2})}[\beta_{x}A_{\rm I}\frac{\partial f_{e}(y)}{ \partial y}-\omega\mu_{0}\mu_{z}k_{z}B_{\rm I}f_{h}(y)], \tag{7}\]  
\[H_{y}^{\rm I}=\frac{e^{-j\beta_{x}x}\cos(k_{z}z)}{j(e_{z}\mu_{y}k_{0}^{2}- \beta_{x}^{2})}[\beta_{x}B_{\rm I}\frac{\partial f_{h}(y)}{\partial y}-\omega \varepsilon_{0}\varepsilon_{z}k_{z}A_{\rm I}f_{e}(y)], \tag{8}\]  
\[E_{z}^{\rm I}=\frac{e^{-j\beta_{x}x}\cos(k_{z}z)}{j(e_{z}\mu_{y}k_{0}^{2}- \beta_{x}^{2})}[\beta_{x}k_{z}A_{\rm I}f_{e}(y)-\omega\mu_{0}\mu_{y}B_{\rm I} \frac{\partial f_{h}(y)}{\partial y}], \tag{9}\]  
\[H_{z}^{\rm I}=\frac{e^{-j\beta_{x}x}\sin(k_{z}z)}{j(e_{y}\mu_{z}k_{0}^{2}- \beta_{x}^{2})}[-\beta_{x}k_{z}B_{\rm I}f_{h}(y)+\omega\varepsilon_{0} \varepsilon_{y}A_{\rm I}\frac{\partial f_{e}(y)}{\partial y}]. \tag{10}\]  
Region II  
\[E_{y}^{\rm II}=\frac{e^{-j\beta_{x}x}\,e^{-k_{0}z}}{j(k_{0}^{2}-\beta_{x}^{2} )}[\beta_{x}A_{\rm II}\frac{\partial f_{e}(y)}{\partial y}-\omega\mu_{0}k_{0} B_{\rm II}f_{h}(y)], \tag{11}\]  
\[H_{y}^{\rm II}=\frac{e^{-j\beta_{x}x}\,e^{-k_{0}z}}{j(k_{0}^{2}-\beta_{x}^{2} )}[\beta_{x}B_{\rm II}\frac{\partial f_{h}(y)}{\partial y}+\omega\varepsilon_{0 }k_{0}z_{\rm I}A_{\rm II}f_{e}(y)], \tag{12}\]  
\[E_{z}^{\rm II}=\frac{e^{-j\beta_{x}x}\,e^{-k_{0}z}}{j(k_{0}^{2}-\beta_{x}^{2} )}[-\beta_{x}k_{0}z_{\rm II}f_{e}(y)-\omega\mu_{0}B_{\rm II}\frac{\partial f_ {h}(y)}{\partial y}], \tag{13}\]  
\[H_{z}^{\rm II}=\frac{e^{-j\beta_{x}x}\,e^{-k_{0}z}}{j(k_{0}^{2}-\beta_{x}^{2} )}[-\beta_{x}k_{0}z_{\rm II}f_{h}(y)+\omega\varepsilon_{0}A_{\rm II}\frac{ \partial f_{e}(y)}{\partial y}]. \tag{14}\]  
Based on the field distributions and boundary conditions, the characteristics of the surface waves can be analyzed, as follows. Figure 1: Surface waves on the interface of air region and grounded anisotropic slab with infinite length in \(x\) and \(y\) directions.
## 1 Introduction
### Specific Uses of S2 Bands
Indeed, this prevents discovering redundancies between different \begin{table}
\begin{tabular}{l c c} \hline \hline
**Index** & **Formula** & **Application** & **Reference** \\ \hline \(CI_{red-edge}\) & \(\left(\frac{R\overline{J}}{R5}\right)-1\) & Chlorophyll, burnt areas & [7] \\ \(CI_{green}\) & \(\left(\frac{R\overline{J}}{R5}\right)-1\) & ” & ” \\ \(REP\) & \(705+35\frac{\frac{(h+1)}{R5}-R5}{R5-R5}\) & ” & ” \\ \(MTC1\) & \(\frac{R6-R5}{R5-R5}\) & ” & ” \\ \(NDRE1\) & \(\frac{R6-R5}{R5-R5}\) & ” & ” \\ \(NDRE2\) & \(\frac{R6-R5}{R5-R5}\) & ” & ” \\ \(TRBI\) & \(\frac{R7+R6}{R5-R5}\) & LAI estimation & [8] \\ \(NSSI\) & \(\frac{R8A-R27}{R8A+R7}\) & Non-photosynthetic & [9] \\ \(PSRI\) & \(\frac{R4-R29}{R8A}\) & Senescent vegetation & [14] \\ \(STI\) & \(\frac{R1}{R12}\) & Tillage, dry vegetation & [10] \\ \(NDWI\) & \(\frac{R3-R3A}{R8A+R4}\) & Water bodies & [15] \\ \(NDWI\) & \(\frac{R3-R11}{R8-R11}\) & ” & [11] \\ \(NDWI\) & \(\frac{R8-R12}{R8+R12}\) & ” & ” \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Spectral indices leveraging Sentinel-2 spectral bands for applications related to vegetation and water surfaces. bands. For instance, this reference excludes uses for geological and lithological mapping, such as [22; 23; 24], where the higher resolution of Sentinel-2's NIR bands is assessed for the estimation of iron oxides. We think this supports the idea of performing a purely data-driven approach over a large dataset and with an application-agnostic point of view. However, the work presented in this paper is just a modest demonstration of what could be done by exploiting the existing Sentinel-2 archive. Finally, we will stress again the fact that we do not claim that some Sentinel-2 bands do not contain useful information. We want to assess the possibility of reconstructing this information by leveraging redundancies among the complete set of spectral bands. This reconstruction will, of course, contain errors, and the goal here is to give bounds allowing informed design trade-offs for future systems.
## 3 Materials and Methods
### Food Temperature Calculation from Thermal Image  
The target food temperature is automatically recommended from previous experience in the proposed microwave oven by classifying the image, and the food item continues to be heated until it hits the target temperature. The heating is switched off automatically once the target temperature is reached. IR waves are radiated proportional to their temperature from every physical object. The temperature of the food is measured using a non-contact thermal FLIR camera [28]. The thermal camera is installed through a 4-mm opening at the outer side of the microwave oven cavity wall. The microwave hasa wavelength of about 120 mm [29] and the wavelength of the infrared is 1-750 nm [30]. Since the microwaves are long waves, they don't pass through the 4 mm hole and don't damage the camera. The IR waves can pass through the hole and therefore the FLIR camera can measure the food temperature.  
The FLIR camera in [28] contains pixels organized in \(80\times 60\) matrices. The sensor sends each pixel temperature in Kelvin multiplied by 100. Averaging all the pixel values and dividing by 100 will not give precise food temperature because some of these pixels contain plate and surrounding temperature. Therefore a technique must be developed to isolate the pixels representing only the temperature of the food.  
The microwave heats the food's water molecules more than the other molecules on the plate and in the vicinity [31]. Thus, the pixels containing food temperature will have a higher value than the other surrounding pixels. To separate the food temperature pixels from the nearby area pixels, the grayscale thermal image is converted to a binary image using a threshold. The threshold value is calculated by taking the average of the pixels around the boundary of the image. Then using Open CV library [32], contours in the binary image are calculated. If there exists at least one contour, then a mask, having the same dimensions of the binary image, is generated where the inside of the contours are filled with '1' and the outside of the contours are filled with '0'. Once the mask is created, then the average value is calculated from the grayscale thermal image selecting only the pixels where the mask value is '1'. Then the average value is divided by 100 and converted from Kelvin to Fahrenheit. If no contour exists, then a mask is created by making a small square shape at the center of the image.  
The microwave incorporates a graphical LCD that displays the food's artificially colored thermal image in real-time while it is warmed. The JET colormap [33] is applied to the grayscale thermal image to create artificial color on the thermal image, where red indicates hot and blue indicates cold. The contours are also drawn using black color on the image. Figure 4 shows a food image taken by the CMOS camera, its grayscale thermal image taken by the FLIR camera, the mask, and the colored thermal image with contour.  
Figure 4: (**a**) Food image taken by the CMOS camera; (**b**) grayscale thermal image taken by the FLIR camera; (**c**) the mask; (**d**) colored thermal image with contour.
# Compact Sub 6 GHz Dual Band Twelve-Element MIMO Antenna for 5G Metal-Rimmed Smartphone Applications
## 7 Conclusions  
The proposed compactly configured a dual-band MIMO twelve-antenna system with a metal frame comprising two sets of six-antenna units arranged in a diagonal mirror-image configuration at the edges of the two longer sides of a phone. The profile height of each of six-antenna module is only 3 mm. The design has no isolation components but is able to achieve favorable isolation and ECC performance with only 3 mm spacing between adjacent antennas. In the designing of the sub-6 GHz 5G antenna element, a metal-frame design was integrated with an inverted L-shaped feed branch and two inverted L-shaped short branch structures to form multiple resonant paths, which enabled dual-band operation in the 3300-3600 and 4800-5000 MHz 5G frequency bands. The antenna structure is simple and can be closely integrated with the metal environment. The isolation performance values for this MIMO twelve-antenna system are all above 10 dB, and the measured far-field radiation pattern ECC values are all less than 0.38. The low-frequency efficiency is 36-62%, and the high-frequency efficiency is 46-63%. This design is suitable for large-screen smartphones with a high screen-to-body ratio and metal frame.
## 3 Control Methods
### Load-Estimation-with-Fan Control  
Compared with the fixed-pressure control method, which can control simple compressor operating frequency, the cooling-load-estimation control method can better reduce power consumption. However, if the cooling-load-estimation control method can precisely control the amount of heat required for cooling, then the changes in the compressor operating frequency can be reduced, thereby further improving energy efficiency. When the rotation speed of the indoor unit fan changes according to the change in the compressor operating frequency, changes in the room temperature can be reduced, thereby improving room space comfort and system energy efficiency. The following flowchart shows the control logic corresponding to Figure 5.  
\[\Delta\mathrm{RPM}=\mathrm{KRPMp}\times\Delta\mathrm{T}+\mathrm{KRPMi}\times \mathrm{d\Delta T}/\mathrm{dt}. \tag{5}\]  
The cooling-load-estimation control method is applied to the compressor, and the additional indoor air fan is reflected for control in response to the load. Fan control in the indoor air unit is achieved through the difference between the set and room temperatures rather than through the fixed RPM control method according to the set wind volume. The RPM increment (\(\Delta\)RPM) is obtained by multiplying the \(p\) gain (KRPMp) and I gain (KRPMi) with the temperature error (\(\Delta\)T) and temperature error rate (\(\mathrm{d\Delta T}/\mathrm{dt}\)) at each control cycle, respectively. The cooling load can be controlled effectively according to the RPM controller design (control cycle, gain size, and so on) if the load-response speed is slow or exceedingly fast owing to the compressor-control cycle and 1-Hz unit control. However, if the RPM controller reacts quickly or changes to an exceedingly large value, then the air conditioner will not cool as much as the cooling load but will demonstrate a large cooling capacity or be controlled by insufficient cooling capacity.  
Figure 4: Flowchart of the cooling load estimation control logic.