esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ground-Truth Data and Methods for Satellite-Sunphotometer Comparison
### Method for Data Matchup  
The spatiotemporal collocation between satellite and CE318 measurements follows the method of the Multi-sensor Aerosol Products Sampling System (MAPSS), in which sunphotometer data with \(\pm\)30 min of satellite overpass are compared with satellite data within 25 km radius of the sunphotometer [(32)]. Minimum requirements for a matchup are at least two observations from AERONET and 5 pixels from the satellite. The CE318 AOD at 550 nm and at VIIRS blue (488 nm) and red (672 nm) bands are interpolated from 440 nm, 675 nm, 870 nm and 1020 nm by using an established fitting method [(33)]. The results for comparison of AOD values between VIIRS (VIIRS_EDR and VIIRS_ML) and ground CE318 observations are presented with various statistical parameters, including the number of matchup data (N), the mean bias (MB), root mean squared error (RMSE), correlation coefficient (R), and the percentage of data within the expected error 0.05 + 0.15 AOD (%EE) which is used as the MODIS AOD expected uncertainty over land [(34)], the slope (Slope) and intercept at y-axis (y-int) of linear regression.
# Synthetic Medical Imaging Generation with Generative Adversarial Networks for Plain Radiographs
## 4 Discussion
### Further Research  
Limitations for GAN evaluation scores like the FID have been noted in the literature [27]. The quantitative analysis is not sufficient alone for the purpose of evaluating model performance. Because of this, manual inspection was required, and the \"gan train gan test\" approach was used to supplement the quantitative analysis. Metrics such as the FID score are generally very useful but did not capture the artifacts present in the generated  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Approach** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{3}{*}{Inception} & Base & 78.36 & 78.43 & 78.37 & 78.35 \\  & Gan train & 65.87 & 68.78 & 65.87 & 64.49 \\  & Gan test & 74.03 & 76.56 & 74.04 & 73.41 \\ \hline \multirow{3}{*}{Resnet101} & Base & 82.69 & 83.00 & 82.69 & 82.65 \\  & Gan train & 69.71 & 73.77 & 69.71 & 68.36 \\  & Gan test & 65.86 & 69.94 & 65.86 & 64.02 \\ \hline \multirow{3}{*}{VGG19} & Base & 82.21 & 83.10 & 82.21 & 82.09 \\  & Gan train & 68.27 & 73.21 & 68.27 & 66.48 \\ \cline{1-1}  & Gan test & 59.61 & 63.54 & 59.61 & 56.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation results.  
images. This could be due to expressed limitations such as the FID score's high bias, and medical image datasets often are smaller in size and can lead to over-estimation of the FID score [28]. A way to mitigate this issue could be to include an extrapolation approach which would render an \"unbiased FID\" [28]. The field could be improved by expanding existing metrics and developing more robust new metrics that can easily catch artifacts. This would in turn decrease the need for human-in-the-loop inspection.  
The fact that the blind test participant could not distinguish between \"real\" and \"fake\" images is significant as it indicates the images are realistic and therefore potentially useful. Since this pipeline was created with the intention of supporting clinical AI with synthetic datasets, the natural next step is to test the performance of machine learning classifiers trained on these synthetic data and tested on real data to see if the synthetic images are useful from a machine learning perspective. The \"gan train gan test\" portion of the pipeline is an initial step in this analysis.  
It is the goal that clinicians and data science practitioners who are not experienced with deep learning can use the current investigation's pipeline to more easily create synthetic images for their clinical AI. While the current pipeline is limited to two-dimensional images, there is already research into architectures for generating three-dimensional images, like CT-scans from CT-SGAN, which could broaden the scope of a future pipeline [29].
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 2 Highly Adaptive Reconfigurable Receiver Front-End (HARRF) for 5G and Satellite Applications  
A highly adaptive reconfigurable receiver front-end (HARRF) for 5G and satellite applications is presented. The proposed design utilizes a Single-Pole Double-Throw (SPDT) switch to toggle its functionality between X-band frequencies, primarily for radar and satellite communication, and K/Ka-band frequencies, which support wideband switchable applications, particularly for millimeter-wave (mmWave) 5G communications in Frequency Range 2 (FR2).
###### Abstract  
The estimation of the ripening state in orchards helps improve post-harvest processes. Picking fruits based on their stage of maturity can reduce the cost of storage and increase market outcomes. Moreover, aerial images and the estimated ripeness can be used as indicators for detecting water stress and determining the water applied during irrigation. Additionally, they can also be related to the crop coefficient (_Kc_) of seasonal water needs. The purpose of this research is to develop a new computer vision algorithm to detect the existing fruits in aerial images of an apple cultivar (of Red Delicious variety) and estimate their ripeness stage among four possible classes: unripe, half-ripe, ripe, and overripe. The proposed method is based on a combination of the most effective color features and a classifier based on artificial neural networks optimized with genetic algorithms. The obtained results indicate an average classification accuracy of 97.88%, over a dataset of 8390 images and 27,687 apples, and values of the area under the ROC (receiver operating characteristic) curve near or above 0.99 for all classes. We believe this is a remarkable performance that allows a proper non-intrusive estimation of ripening that will help to improve harvesting strategies.  
ripeness estimation; apple segmentation; agricultural video processing; neural networks; genetic algorithms; color classification +
Footnote †: journal: Accepted: 12 February 2019; Published: 14 February 2019
## 2 Signal Transmission and Reception in MIMO-WCDMA Networks
### Theoretical Background for the Calculation of Mean BER in MIMO-WCDMATtopologies  
In this section, the Gaussian approach is described for the calculation of mean BER in a MIMO-WCDMA network. In this context, an arbitrary user (e.g., the \(k\)th) is considered as the desired one, and MAI from the rest \(K\)-1 users as well as ISI are expressed in terms of the desired user signal. With respect to Equation (10), then [19]:  
\[E\Bigg{\{}\sum\limits_{l=1}^{L}\left\|\mathbf{r}_{k,l}\mathbf{H}_{k,l,T_{k^{ \prime}}}\mathbf{t}_{k^{\prime}}\right\|_{F^{\prime}}^{2}d\Bigg{\}}\rightarrow \sum\limits_{l=1}^{L}\Big{\{}\left\|\mathbf{r}_{k,l}\right\|_{F}^{2},d\Big{\}}p _{k^{\prime},d},1\leq d\leq dim \tag{12}\]  
The term {\(\mathbf{A}\),\(\times\)} indicates the xth line of matrix\(\mathbf{A}\). In addition:  
\[E\Bigg{\{}\sum\limits_{l=1}^{L}\left\|\mathbf{r}_{k,l}\mathbf{H}_{k,l,T_{k}} \mathbf{t}_{k}\right\|_{F^{\prime}}^{2}d\Bigg{\}}\rightarrow\sum\limits_{l=1}^ {L}\Big{\{}\left\|\mathbf{r}_{k,l}\right\|_{F}^{2},d\Big{\}}^{2}p_{k,d},1\leq d \leq dim \tag{13}\]  
The Signal to Interference plus Noise Ratio (SINR) for the \(k\)th user will be given by [19]:\[SINR_{k,d}\approx\frac{\sum\limits_{l=1}^{L}\left\{\|\mathbf{r}_{k,d}\|_{F}^{2},d \right\}p_{k,d}}{(K-1)\left(\frac{1}{6\Gamma G}\right)+\left(\frac{L-1}{L} \right)\left(\frac{1}{4\Gamma G}\right)+N_{o}},1\leq d\leq dim \tag{14}\]  
where \(PG\) is the system's processing gain [1]. In Equations (13) and (14), \(p_{k,d}\) is the transmission power per active user and mode, subject to:  
\[\sum\limits_{d=1}^{dim}p_{k,d}=p_{t,k} \tag{15}\]  
The desired user signal can be expressed as:  
\[\gamma_{k,d}=\sum\limits_{l=1}^{L}\left\{\|\mathbf{r}_{k,l}\|_{F}^{2},d\right\} p_{k,d} \tag{16}\]  
Hence, for BPSK modulation, BER will be given by [18]:  
\[BER=\frac{1}{dim}\sum\limits_{d=1}^{dim}\int\limits_{0}^{\infty}Q\bigg{(}\sqrt{ SINR_{k,d}(\gamma_{k,d})}\bigg{)}p_{\gamma_{k,d}}(\gamma_{k,d}) \tag{17}\]  
where \(p_{\gamma_{k,d}}(\gamma_{k,d})\) is the probability density function (pdf) of the desired user signal of Equation (16), while the \(Q\) function is given by:  
\[Q(\mathrm{x})=\frac{1}{\sqrt{2\pi}}\int_{\mathrm{x}}^{\infty}\exp\bigg{(}- \frac{u^{2}}{2}\bigg{)}du \tag{18}\]  
The pdf of the desired user signal can be evaluated by taking samples from a sufficient number of independent channel realizations. Afterwards, \(\gamma_{k,d}\) is calculated for each transmission mode. For a \(4\times 2\) MIMO network with six multipath components, the pdf functions of the two transmission modes follow the Gamma distribution (\(\Gamma(\mathrm{x})\) is the Gamma function):  
\[f_{1}(\mathrm{x})=\mathrm{x}^{27}\frac{e^{-\mathrm{x}/0.11}}{0.11^{28}\Gamma (28)} \tag{19}\]  
\[f_{2}(\mathrm{x})=\mathrm{x}^{18}\frac{e^{-\mathrm{x}/0.09}}{0.09^{19}\Gamma (19)} \tag{20}\]  
where \(\mathrm{x}=\gamma_{k,d}\). Note that the expressions in Equations (19) and (20) were derived after \(10^{7}\) channel realizations with the help of Matlab(r).
## 2 Materials and Methods
### Understanding the Data: Model-Based Method  
The data are grouped using K-Means cluster algorithms developed by [PERSON] (1993). Cluster methods aggregate similar or interrelated data into the same cluster ([PERSON] and [PERSON], 2007). Cluster methods are generally classified into four groups: partitioning, hierarchical, density-based and model-based methods. Within the scope of the present study, model-based methods were chosen because the data groups could be formed according to appropriate \"management levels\" as suggested in the literature. Our method counts every cluster as pertaining to a model; hence, the data are grouped with respect to the related models, as explained by [PERSON] et al. (2015). The general scheme of the system is illustrated in Figure 3. This system contains log files that include who viewed the specific functional features of the hospital; for example, the medical operations performed that day. These log files were examined and the words in the files were identified according to our BoW method, as explained in the following subsection. These words are defined as the data. Then, we applied the K-Means clustering algorithms to these words to find the predefined strategic, tactical and operational clusters. These clusters are considered as the information. Finally, a hospital management report is generated. This report indicated that this hospital is managed at the operational level 73.98% of the time, at the tactical level 19.81% of the time, and at the strategic level 6.21% of the time. Based on this report, the hospital CEO can make hospital-management decisions. Insight and focus of each level of the organization was taken into consideration when clustering the data and identifying the clusters themselves.  
Figure 3: The general scheme of the system.  
Figure 2: Framework used for mining at the decision-making Level.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 2 Related Work
### Loss Function
#### 2.1.3 Margin Loss  
In general, the goal of the CE loss function is to distinguish between different classes. This leads to a problem that possibly two embeddings of two different classes at the same edge may be more similar than the embedding of the same class in different directions. To solve this problem, it is necessary to maximize the inter-class distance and minimize the intra-class distance at the same time. So, a series of loss methods have been derived to expand the inter-class interval for better distinguishing different classes. The current popular margin loss is AM-Softmax loss and AAM-Softmax loss.  
AM-Softmax: [PERSON] et al. [31] proposed the AM-Softmax loss function based on the A-Softmax [10] loss function, with the specific modification of normalizing \(x_{i}\) so that the extracted embedding is mapped to a hyper-sphere. Additionally, the A-Softmax also has to calculate the angle \(\theta\) by the inverse cosine function. At the same time, the AM-Softmax only needs to subtract the cosine value directly from the margin. The advantage of this is that the computation cost is significantly reduced. Finally, the corresponding index value is multiplied by a weight s to scale up the corresponding class probability when performing softmax. By controlling both the hypersphere and the class gap, the AM-Softmax [9] achieves excellent results. The specific formula is as follows:  
\[\mathcal{L}_{\mathrm{AM}}=-\frac{1}{n}\sum_{i=1}^{n}\log\frac{e^{s^{-}\left( \cos\theta_{y_{i}}-m\right)}}{e^{s^{-}\left(\cos\theta_{y_{i}}-m\right)}+\sum _{j=1,j\
eq y_{i}}^{C}e^{s\cos\theta_{j}}} \tag{5}\]  
The AM-Softmax loss function has become one of the most popular loss functions in the field of face recognition and speaker recognition and has been used in recent papers in the field of speaker recognition (e.g., [11; 32; 33]), and all of them have demonstrated superior performance.  
AM-Softmax: Inspired by several previous margin losses, [PERSON] et al. [34] proposed Additive Angular Margin (AAM) loss, which normalizes the feature vector and weights, adding the angular interval \(m\) to \(\theta\). The angular interval has a more direct effect on the angle than the cosine interval. Geometrically, there is a constant linear angle margin. Unlike AM-Softmax loss, which maximizes the classification interval in cosine space, AAM-Softmax maximizes the classification interval directly in angle space. It has the following equation.  
\[\mathcal{L}_{\mathrm{AAM}}=-\frac{1}{n}\sum_{i=1}^{n}\log\frac{e^{s\left(\cos \left(\theta_{y_{i}}+m\right)\right)}}{e^{s\left(\cos\left(\theta_{y_{i}}+m \right)\right)}+\sum_{j=1,j\
eq y_{i}}^{C}e^{s\cos\theta_{j}}} \tag{6}\]
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 2 Object Detection
### One-Stage Object Detection Algorithms
YOLOv8's efficiency ensures that the system can process video streams swiftly, contributing to the overall speed and responsiveness of the quality control process. Advanced Feature Extraction Capabilities:  
YOLOv8 boasts advanced feature extraction capabilities, allowing it to discern intricate details within images. This is particularly advantageous in quality control, where the identification of subtle defects or components with nuanced characteristics is paramount. The algorithm's ability to extract relevant features contributes to the system's overall precision. Holistic View of Images:  
YOLO's architecture processes images as a whole, avoiding the need for a two-step approach involving region proposals and subsequent classification. This holistic view allows YOLOv8 to contextualize information about classes and their appearance, enhancing its capability to precisely detect objects within the image. This is advantageous in scenarios involving complex assembly processes. Generalization and Adaptability:  
YOLOv8's architecture and training processes enable effective generalization, meaning it can perform well in various scenarios and environments. In the context of industrial quality control, where the system may encounter diverse manufacturing conditions, YOLOv8's adaptability enhances its overall performance. Proven Performance in Real-world Applications:  
The choice of YOLOv8 is substantiated by its proven performance in real-world applications. The algorithm has been successfully employed in diverse domains, including industrial settings, showcasing its reliability and effectiveness. This track record supports its suitability for the proposed quality control system. The strategic selection of YOLOv8 for this study is grounded in its advancements in accuracy, its suitability for near-real-time processing, advanced feature extraction capabilities, holistic view of images, generalization, and proven performance in real-world applications. These specific advantages make YOLOv8 well-suited for the intricacies of industrial quality control, providing a robust foundation for a proposed mixed reality (MR)-based quality control system.
# Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques
## 7 Experimental Findings and Results
### Accuracy Analysis per Class  
Classification was performed on the sequence segmentation sections using the above model. The accuracy values of the sections corresponding to the original data were synthesized, and the average value was used to define the class. Learning and validation data were conducted by mixing data from the entire dataset and dividing it into 85:15 ratios. Figure 21 shows the accuracy of the developed fusion model, demonstrating high accuracy in classifying the four core emotions. As shown in Figure 22, the GDS is applied to remove data that do not affect learning. Its accuracy was analyzed using test data in which the same data selection was applied. The total number of data points was not affected, and as the number of sequence segmentation sections corresponding to each data point decreased by 15%, the learning time decreased, while the accuracy remained similar.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Solver** & **Training Rate** & **Epoch** & **Batch Size** \\ \hline Training hyper-parameter & Adam & 0.01 & 20 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training hyper-parameter.  
Figure 20: Change in the number of sequence segmentations after Gaussian data selection (GDS).  
Figure 21: Custom YAMNet-VGGish late-fusion network—classification accuracy.  
Figure 22: Classification accuracy with Gaussian data selection.  
Table 5 shows the evaluation of the classification performance before and after applying the GDS. The results indicate a similar classification accuracy with and without GDS. However, the training time demonstrates a reduction from approximately 90 min to 70 min, resulting in a 22% decrease in time and memory consumption. Following the classification, various explainable models were applied to the network to analyze the focused areas. This analysis aimed to identify the sections used for correct classification and those leading to incorrect classifications. Such insights allow model users to understand the model behavior and facilitate efforts to improve its performance in the future.  
Figure 23 compares the performances before and after applying the GDS to the designed fusion model and existing transfer learning models, VGGish and YAMNet, in terms of accuracy, recall, and F1 score. Recall is an evaluation indicator representing the ratio of what the model predicts as positive to actual positives. The F1 score is the harmonic average of the model precision and recall and is an indicator of the evaluation by considering the two. YAMNet shows the lowest performance, and VGGish outperforms YAMNet. The designed model shows improved performance in terms of all evaluation indicators compared to the existing transfer learning model. There is only a small difference before and after applying the GDS.
# DDC Control Techniques for Three-Phase BLDC Motor Position Control
## 2 System Modelling and Preliminaries
### Experimental and Simulation Data Comparison  
To control the motor, an Arduino Nano V3.0, a PWM shield, a PWM driver, motor drivers, capacitors, and MOSFETs are used. In addition, H-bridges are required to control the voltage to the motor and to make sure the back-EMF does not destroy the Arduino or PWM shield. Coding on the Arduino is done in C++. To implement all the devices compactly, a Printed Circuit Board (PCB) is designed and experimental data is used to compare the experimental and simulation results. The simulations are performed in MATLAB using Math works in MATLAB 2014b installed on a Dell precision m3800. In this section, only a basic experimental and simulation test is performed to check if the output of the simulation model corresponds to the measured output. However, this article only discusses the simulation results.  
During open-loop operation, the system is operating as expected. Putting a ramp as input resulted in the movement of the motor as would be expected. In Figure 6, the open-loop measurement of a decreasing ramp as input is shown. This shows that the system is working as desired, and the closed-loop algorithm can be implemented.  
After implementing the closed-loop control algorithm, without a controller, on the Arduino and testing it on a single motor, severe unstable behavior is seen. Therefore, a PID controller, which is commonly used in the industry, is proposed [18]. The control scheme for a BLDC motor can be seen in Figure 7. In this scheme, \(\theta_{m,r}\) is the desired mechanical angle, while \(\theta_{m}\) is the actual measured angle. Since a PID controller is used, anti-windup compensation ensures no integral windup will occur. Note that, normally, cascade control is used to control the position of a PMSM.  
Figure 6: Open-loop measurement of a decreasing ramp.  
Figure 7: Control scheme to control a motor.  
A controller is manually tuned to test the control algorithm and is then implemented on the motor with the same controller values. The closed-loop step responses for the motor are shown in Figure 8. The PID controller has the values of 0.15, 9, and 0.0025 for the P, I, and D, respectively. As can be seen, the step trajectory is roughly followed by the system.  
After manual tuning, the Ziegler-Nichols closed-loop tuning method is adopted instead of a trial-and-error method. The Integral and Derivative are neglected in this method, and it is used only as a gain of control. After implementing the PID controllers, a step response for the BLDC motor is shown in Figure 9. As can be seen in the Figure 9, the angle is still vibrating with huge overshoot. In the next section, the simulation and experimental results of both the open-loop and closed-loop are compared.
## 2 Materials and Methods
### Theoretical Considerations
#### 2.2.3 Determining the IFD for Emission Classes JBE and J2B  
In the following paragraph, the case of a JBE/J2B signal that uses the upper sideband is analysed, considering the case of coherent detection (the carrier generated at the receiver is perfectly aligned in phase with the carrier generated at the transmitter). Therefore, the signals belonging to these emission classes can be expressed as in Equation (10):  
\[s_{J3E}(t)=(1/2)x(t)\cos\omega_{p}t-(1/2)\,\hat{x}(t)\sin\omega_{p}t, \tag{10}\]  
where \(\hat{x}(t)\)--represents the Hilbert transform of the modulation signal \(x(t)\).  
The signal power at the input of the demodulator is as follows in Equation (11):  
\[P_{Sinp}=\overline{s_{J3E}^{2}(t)}=(1/4)\overline{x^{2}(t)}, \tag{11}\]  
At the output of the product detector (with the same efficiency \(\eta_{d}\) as in the previous case), a signal proportional to the amplitude of the phase component is obtained in Equation (12):  
\[s_{out}(t)=\eta_{d}[(1/2)x(t)+n_{1}(t)\cos\pi Bt-n_{2}(t)\sin\pi Bt], \tag{12}\]  
such that the output signal power is expressed in Equation (13):  
\[P_{Sout}=(1/4)\eta_{d}^{2}\overline{x^{2}(t)}, \tag{13}\]  
At the demodulator input, \(P_{Zin}\) is expressed according to Equation (3), and \(P_{Zout}\) is given by Equation (8). Therefore, IFD can be deducted as mentioned below in Equation (14):  
\[\rho_{J3E}=(P_{Sout}/P_{Zout})\,/\,\big{(}P_{Sinp}/P_{Zinp}\big{)}=\Big{[}(1/ 4)\eta_{d}^{2}\overline{x^{2}(t)}/\eta_{d}^{2}N_{0}B\Big{]}/\Big{[}(1/4)\eta_{ d}^{2}\overline{x^{2}(t)}/N_{0}B\Big{]}=1, \tag{14}\]