esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Silicon-based solar cells (Si-SCs) remain the dominant technology in the photovoltaic industry, despite their theoretical efficiency limitations imposed by the Shockley-Queisser limit. While substantial progress has been made in achieving commercial efficiencies approaching 29% under standard test conditions (STCs), significant performance losses persist due to a combination of factors, including optical reflection, thermalization, and recombination losses [1, 2].  
A substantial portion of incident solar radiation is reflected at the air-silicon interface owing to the mismatch in refractive indices. This reflection loss, typically around 35%, significantly reduces the overall efficiency of Si-SCs. To mitigate these losses, various anti-reflection coating (ARC) strategies, such as surface texturing, single-layer ARCs (SLARCs), and multi-layer ARCs (MLARCs), have been implemented [3, 4, 5, 6]. However, the inefficient absorption of ultraviolet (UV) radiation by silicon, leading to approximately 10% energy loss, necessitates additional approaches to broaden the spectral response of Si-SCs [7, 8, 9].  
Traditional ARCs, such as SiO\({}_{2}\) and Si\({}_{3}\)N\({}_{4}\), have been widely employed to enhance light absorption and minimize reflection in Si-SCs. These materials offer compatibility with standard fabrication processes, cost-effectiveness, and proven performance across a broad spectral range. To further augment light absorption, nanocrystalline quantum dots (NQDs) have emerged as promising candidates. By downshifting high-energy photons to wavelengths more efficiently absorbed by silicon, NQDs can broaden the spectral response of Si-SCs [10, 11, 12]. However, the stability and durability of NQDs under environmental conditions necessitate protective measures. Lithium fluoride (LiF), with its high transparency and passivation properties, serves as an ideal encapsulation layer for NQDs, ensuring their long-term performance and stability [13, 14].  
Building upon these advancements, the integration of micro-/nano-patterned structures within MLARCs offers a promising avenue for further enhancing light management and device performance. By precisely controlling the dimensions and distribution of these patterns, it is possible to optimize light trapping and reduce reflection losses, leading to improved solar cell efficiency [13, 14].  
To address these challenges and enhance light absorption in Si-SCs, this study investigates the potential of patterned MLARCs integrated with NQDs to enhance Si-SC performance by significantly reducing reflection losses. By combining experimental characterization and numerical modeling, we aim to optimize the design and performance of these structures. The incorporation of NQDs within the MLARCs offers the potential to improve UV light absorption through downshifting while enhancing light trapping through the creation of intricate patterns. By carefully optimizing ARC design, material selection, and device configuration, this research seeks to achieve substantial improvements in the overall efficiency of silicon solar cells.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Feature Selection and Correlation Matrix  
Feature selection is the process of selecting the most effective variables (features) in the dataset to increase the accuracy of the model and reduce unnecessary information. The correlation matrix is used to analyze the linear relationship between variables. This is an important tool for both feature selection and understanding the relationships that may affect the performance of the model. Correlation expresses the direction and strength of the linear relationship between two variables and takes values between \(-1\) and \(+1\). Within the scope of the online dataset study, which consisted of 129 rows and 40 attributes, feature selection was made with the correlation matrix, and in the outputs realized in the Phyton/Pandas environment, the attributes with high relationship strength with the target variable were taken into account in the model performance analysis.
## 3 Results
### Emotional Regulation Domains and Burnout  
The relationship of emotional regulation with burnout revealed a similar correlation pattern among interns and undergraduate dental students. Mean scores of both cohorts for all two subscales were \(4.8+1.3\); \(4.0+1.3/4.6+1.3\); \(4.3+1.1\) respectively (Table 1). A negative correlation existed between burnout and emotion regulation, i.e., a higher burnout score is associated with lesser cognitive reappraisal and more expressive suppression (CBI--personal burnout r = \(-0.251\); r = \(-0.220\) respectively) among dental students, whereas interns managed expressive suppression as seen in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Burnout Measure** & \multicolumn{2}{c}{**Interns**} & \multicolumn{2}{c}{**Undergraduate Students**} & \multicolumn{1}{c}{\(\mathbf{t}\)} & \multicolumn{1}{c}{\(\mathbf{\boldsymbol{p}}\)**Value**} & \multicolumn{1}{c}{**Cohen’ s d**} \\ \hline  & **Mean** & **SD** & **Mean** & **SD** & & & \\ \hline CBI—personal burnout & 56.7654 & 13.82950 & 57.9513 & 16.53423 & \(-0.404\) & 0.687 & 0.085751 \\ \hline CBI— work-related burnout & 56.1096 & 12.75079 & 57.3735 & 16.70977 & \(-0.434\) & 0.665 & 0.099123 \\ \hline CBI— patient-related burnout & 44.8049 & 19.84173 & 42.3082 & 21.29829 & 0.645 & 0.520 & 0.125831 \\ \hline ER- cognitive reappraisal & 4.8255 & 1.39224 & 4.6046 & 1.31913 & 0.891 & 0.374 & 0.158665 \\ \hline ER—expressive suppression & 4.0021 & 1.35632 & 4.1384 & 1.16144 & \(-0.605\) & 0.546 & 0.100493 \\ \hline IRI—perspective-taking & 2.1452 & 0.84826 & 2.1606 & 0.76250 & \(-0.106\) & 0.916 & 0.018155 \\ \hline IRI—fantasy & 1.9950 & 0.68877 & 2.0222 & 0.80135 & \(-0.190\) & 0.850 & 0.039491 \\ \hline IRI—empathic concern & 2.3730 & 0.77528 & 2.2105 & 0.81539 & 1.091 & 0.277 & 0.201896 \\ \hline IRI—personal distress & 2.0506 & 0.68195 & 2.0966 & 0.60637 & \(-0.396\) & 0.693 & 0.067454 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of burnout, emotional regulation, and social cognitive scores among interns and undergraduate dental students.
## 1 Introduction
### Skin Cancer and Infrared Thermography  
Active infrared thermography (IRT) is a non-invasive diagnostic tool that uses infrared radiation to measure the temperature of the skin. Thermography can detect temperature changes and distribution on the surface of the human body [10]. Infrared emissions from human skin at 27 \({}^{\circ}\)C are in the wavelength range of 2-20 \(\upmu\)m and peak at 10 \(\upmu\)m. Body infrared rays, a narrow wavelength range of 8-12 \(\upmu\)m, is used for medical applications [11]. Infrared thermography can be categorized into two distinct subtypes: passive thermography and active thermography. Passive thermography involves the examination of the subject in its natural thermal equilibrium without the imposition of external thermal stress. In contrast, active thermography employs a deliberate thermal excitation to enhance thermal contrast. Specifically, in the context of skin cancer detection, active thermography involves cooling the skin cancer lesion to capture its subsequent reheating pattern and rate. More background can be found in previous research [4]. It has been used in medical applications for decades, and it is particularly useful for identifying skin cancer due to the increased metabolic activity and angiogenesis present in malignant cells [12]. Melanoma, specifically, is thought to have a higher temperature than surrounding healthy skin, making it a prime target for infrared imaging [4]. Other skin tumor types, such as basal cell carcinoma, may have a different thermal signature, indicating that thermography can be used to differentiate between various types of skin cancer [13]. Studies have shown that thermal infrared imaging can detect new blood vessels and chemical changes associated with tumor development and growth [14; 15]. Infrared thermography boasts a distinct advantage over alternative imaging methods: its ability to discern the rate of skin rewarming, facilitating precise assessment of both the boundaries and depth of a skin lesion as outlined in [16]. Additionally, this technique eschews the use of ionizing or detrimental radiation, relying solely on the body's intrinsic heat for imaging purposes.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 3 Deep Learning Techniques
### Deep Neural Networks  
_Deep neural networks (DNNs)_ were developed to parametrically approximate functions that map a vector of input data to an output vector (e.g., an image to the percentage probability that it is in each of five classes labels) [24; 29]. Expanded from original simple perceptron systems, deep neural networks are feed-forward systems that use many hidden layers and internal functions to approximate non-linear relationships between input and output. Each DNN model uses gradient descent to minimize a cost function (mean square error, maximum likelihood) in an optimization and training process called \"back-propagation\". Cost functions for 5G networks could also include minimizing operating costs, latency, or downtime. Within 5G networks, deep learning has already been applied to themes such as traffic classification, routing decisions, and network security [30].  
_Convolutional neural networks (CNNs)_ are specialized DNN models to appropriately handle large, high-resolution images as inputs [24; 31]. CNNs exploit the relationship in nearby data (such as pixels in an image or location-based measurements). CNNs use mathematical \"convolutions\", linear operations that compute a weighted average of nearby samples, and \"pooling\" to summarize the data in a region typically with max-pooling or a similar operation depending on the aim [24]. CNNs have been used in 5G networks to predict mobility based on traffic flows between base stations [32] and for object classification applicable also to 5G-enabled industry robotics [33].  
_Recurrent neural networks (RNNs)_ scale the DNN models for function approximation to handle temporal sequences, where the output from a previous time step influences the decision the network makes for the next step [24]. RNNs require the use of a \"memory\" to recall information learned in previous time steps in addition to the current input to the model. Performing the gradient descent-based training on RNNs caused issues with \"exploding gradients\", which was corrected by new ML models called long-short term memory (LSTM) models with additional information flow structures called \"gates\". LSTM models have proven useful and accurate in solving traffic prediction [34] and mobility [35] problems within communication networks.
## 2 Materials and Methods
### Stage 3: Efficiency Determination Using DEA  
The data envelopment analysis is a non-parametric tool used to evaluate the relative efficiency of decision-making units (DMUs). It was first developed by [PERSON]. [29] in their seminal work \"Measuring the efficiency of decision making units\". Due to the few assumptions considered in DEA, it is an appropriate instrument for evaluating warehouse order-picking systems and policy sets. The warehousing literature lacks strong hypotheses about the warehousing production function and there is no reliable production function specification test [30].  
For our problem, we have a set \(O=\{O_{1},\ O_{2},\ldots,\ O_{n}\}\) of \(n\) policy sets. Assuming variable return to scale, the efficiency of the \(lth\) policy set in a warehouse can be defined as follows. For the sake of simplicity, we replace \(C_{d}^{wij}\) by \(C_{d}^{wl}\), by changing index \(i\) and \(j\) to \(l\). \(l\) represents routing policy \(i\) given the technology \(j\):  
\[\min_{C_{d}^{wl},\beta^{wl}}C_{d,SL}^{wl}+C_{d,P}^{wl}+C_{d,E}^{wl}+C_{d,F}^{wl} \tag{10}\]  
\[\text{St}: \tag{11}\]  
\[C_{d,SL}\geq\sum_{l}\sum_{w}\beta^{wl}C_{d,SL}^{wl} \tag{12}\]  
\[C_{d,P}\geq\sum_{l}\sum_{w}\beta^{wl}C_{d,P}^{wl} \tag{13}\]  
\[C_{d,E}\geq\sum_{l}\sum_{w}\beta^{wl}C_{d,E}^{wl} \tag{14}\]  
\[M\leq\sum_{l}\sum_{w}\beta^{wl}M^{wl} \tag{15}\]  
\[L\leq\sum_{l}\sum_{w}\beta^{wl}L^{wi} \tag{16}\]  
\[A\leq\sum_{l}\sum_{w}\beta^{wl}A^{wi} \tag{17}\]  
\[\sum_{l}\sum_{w}\beta^{wl}=1 \tag{18}\]The linear programming seeks for a set of weights (\(\beta^{val}\)) that minimize the cost function in Equation (10). If it is not possible to further reduce the cost function \(C_{d}\), in other words, if the minimized cost is equal to the actual cost, the policy set \(O_{l}\) is efficient. Otherwise, it is considered to be inefficient. Equations (11)-(14) make sure that the virtual cost is less or equal than the actual cost, while Equations (15)-(17) restrain the virtual output to be greater or equal than the actual output. Equation (18) deals with the return to scale assumption, and assumes variable returns of activities.
## 3 Method
### Adaptive Wide-Kernel Multichannel CNN (Ada-WMCNN)  
As we have presented and shown in our previous work, a wide-kernel deep multichannel CNN (WDMTCNN) [11] is able to better process and classify signals due to its separate feature extraction between the different time series dimensions. The model is fed by separate inputs each representing a univariate time series. These time series are processed at the same time by the separate CNNs, which are concatenated in the last stages of the model, e.g., before the fully connected layers. This approach leads to completely independent feature representations of the separate time series, which has proven to perform better in [11]. One main issue with the multichannel approach is that the model is less  
Figure 2: **Left**: A-WCNN model utilizing the adaptive wide-kernel layer on two time series. **Right**: Ada-WMCNN model that processes two times series in separate channels.  
scalable than the non-multichannel versions, since the number of separate CNNs increases with every additional feature in the multivariate time series, resulting in a complex model with many more parameters. In many cases this leads to longer processing times while performance gains are not always evident.  
We adapted the WDMTCNN by only implementing a separate CNN structure in the first wide-kernel layer. This optimized model is still able to extract these specific characteristics of every individual time series, e.g., features from low-frequency bands, but concatenates directly after, resulting in less parameters and is therefore less computationally heavy. To compare its performance properly with our previous multichannel model (WDMTCNN) that runs completely separate CNNs, which is called the _A-WMCNN_ in this paper, we experimented with both model settings to show its behavior across multiple datasets and multiple experiments. In addition, our adapted multichannel model also exploits local average pooling throughout every convolutional layer, reducing the vector size with steps of two. A complete overview on Ada-WMCNN can be seen on the right side of Figure 2.
# Correlations Between Phenolic Composition and Perceived Astringency of Wines
## 3 Results and Discussion  
### Color Characteristics  
The visual appearance and color indices of wines made from the different breeding lines are presented in Figure 1 and Table 2, respectively. The color intensity (I) reflects the overall visual appearance of a wine [13]. In the red wines, therefore, the overall color intensity value was proportional to the darkness of the color of the red wines. Differences in color intensity were also proportional to the concentration of anthocyanin in wines as anthocyanin contributes greatly to the color of red wines. The color intensities of white wines were quite negligible compared with the red wines. The wine hue indicates the development of an orange color and can be estimated using the ratio of yellowness and redness of wines. Thus, wine hue values were higher in white wines containing no or trace amounts of anthocyanin, which is responsible for the red to violet color of red wines [18]. The contribution of pigments is expressed as a percentage of each pigment (yellow, red and blue).  
Interestingly, the color intensity of red wines was proportional to the %Yellow pigments rather than the %Red pigments. The contribution of blue pigment was low (7-14%) regardless of the wine type.  
The development of the wine color and variations in the color are associated with the composition and concentration of phenolic compounds, their oxidative transformation and the direct condensation of anthocyanins with other compounds during aging [19; 20]. In the present study, variations in the color indices of the wines tested were likely to have been related to the compositional differences in the phenolic compounds rather than the other factors (i.e., oxidative transformation and condensation of anthocyanins) because all of the wine samples were similarly aged. The correlation coefficients between the total anthocyanin content and the color indices supported this. A strong positive correlation (\(r=0.8231\), \(p<0.0002\)) was observed between the total anthocyanin content and color intensity (I). Furthermore, the total anthocyanin content showed a strong positive correlation (\(r=0.7375\), \(p<0.0017\)) with %Red pigment but a strong negative correlation (\(r=-\) 0.7546, \(p<0.0012\)) with %Yellow pigment. Our results were in agreement with previous studies [21], showing a strong positive correlation between color intensity and variables associated with the total anthocyanin content in grapes.  
Figure 1: Visual appearances of wines made from different breeding lines.
## 1 Introduction
### Organization of the paper  
The remainder of this paper is organized as follows: First we give some basic definitions and preliminaries. In the following sections we discuss one problem per section: dominating set, connected dominating set, independent set, and finally vertex cover. For each problem we prove our lower bounds for approximation ratios of local algorithms with arbitrary locality distance. We also give our algorithms for unit disk graphs and unit line graphs. For our dominating set algorithm on unit disk graphs we give an example which shows that the analysis of the approximation ratio is tight.
## 3 DNN-Based Synchronization and the Proposed Receiver  
Although the DNN model can learn from many of the received samples and can estimate the desired time-varying Doppler, the large number of parameters prevents its application if the DNN's input is the whole-packet waveform or the DNN's output is time-varying time offsets. Two pieces of expert knowledge were used to reduce the dimension of the DNN for synchronization: the ambiguity function of the LFM and the spline reconstruction of the motion displacement, whose functional modules were integrated before and after the DNN, respectively. Using the DNN for the timing offset bias correction, the receiver was then fully rebuilt with lower complexity and better performance than the traditional adaptive equalizer.
# Environment Perception with Chameleon-Inspired Active Vision Based on Shifty Behavior for WMRs
## 6 Experiments
### Target Tracking Experiment of WMRs on Rigid, Flat Ground  
In this experiment, the robot was located indoors on rigid, flat ground to search and track a red target in front of it, as shown in Figure 12.  
Target search stage: first, the robot was initialized with \((\phi_{b},\varphi_{l},\varphi_{r},\theta_{l},\theta_{r})=(0,0,0,30,30)\), and the camera entered the short-focus mode. Then, at the 11 th second and 15 th second, respectively, the robot performed a rapid scan of the environment using the chameleon-inspired binocular negative-correlation-motion target search model.  
\[(\phi_{b},\varphi_{l},\varphi_{r},\theta_{l},\theta_{r})=\left\{\begin{array}[] {l}(20,36,-36,30,30),q=1\\ (-20,36,-36,30,30),q=2\end{array}\right. \tag{12}\]  
Subsequently, the four acquired images were compared and analyzed using the selective attention algorithm, and it was determined that when the neck and right camera were rotated \((\phi_{b},\varphi_{r},\theta_{r})=(20,-36,30)\), respectively, the area located at the coordinate \((248,77)\) of the image had the highest saliency. The horizontal and vertical orientation of the target was derived by the PTZ rotation equation as \((-27.1^{\circ},28.0^{\circ})\). As shown in Figure 13a, we first used a histogram-based selective attention model to identify the targets [46], which uses histograms for image color statistics, first quantizing color images in RGB space and then performing a color-space smoothing operation, which is more suitable for mobile robot environment scenes. As shown in Figure 13b, we used superpixel segmentation [47] to improve the accuracy, which is more desirable in terms of execution speed, the compactness of the generated superpixels, and contour preservation.  
Target alignment stage: first, in the 24 th second, the robot body and vision system rotated by \((\phi_{b},\varphi_{l},\varphi_{r},\theta_{l},\theta_{r})=(0,0,0,28.0,28.0), \ \omega=-27.1^{\circ}\), respectively, aligning with the target, and the cameras moved together to realize stereo vision. Then, in the 41 st second, the camera zoomed and entered the long-focus mode to obtain high-resolution pictures of the target to achieve precise target alignment.  
Target tracking stage: starting from the 41 st second, the robot started to track the target. The experimentally collected rotation angles of each axis of the vision system and the target tracking error are shown in Figure 14. The tracking error curve showed that although the tracking error fluctuated due to the vibration problem of the robot itself, it always fluctuated around the 0 axis and was basically between plus and minus 15 pixels, indicating that the target tracking was achieved.  
Figure 12: Target tracking process of WMRs on rigid, flat ground.  
Figure 13: Image processing during target search stage: (**a**) saliency map; (**b**) superpixel segmentation process.
## 2 Materials and Methods  
### General  
Reverse-phase HPLC was performed at ambient temperature with a Shimadzu LC-6 AD liquid chromatograph equipped with a UV-Vis detector (220 nm and 260 nm, Shimadzu SPD-10 AVvp, Kyoto, Japan) using an Inertsil WP300 C18 column (GL Science, 250 mm \(\times\) 4.6 mm or 250 \(\times\) 20 mm). MALDI TOF mass spectra were obtained on an Autoflex T2 (Bruker Daltonics, Billerica, USA) in linear/positive mode with \(\alpha\)-cyano-4-hydroxycinnamic acid (\(\alpha\)-CHCA) or 3-hydroxypicolini acid (3-HPA) with diammonium hydrogen citrate as the matrix. The UV-Vis spectra of DNA-conjugated peptides were measured at 260 nm using a Jasco V-630 (JASCO Corporation, Tokyo, Japan) with a quartz cell (S10-UV-1, GL Science). An mRNA coding mCherry fluorescent protein (mCherry mRNA, \(\sim\)1 kb) with a 3\({}^{\prime}\)-polyadenylic acid (poly(A)) tail was purchased from OZ Biosciences (Marseille, France). RNase A was purchased from Nacalai Tesque (Kyoto, Japan). All other reagents were obtained from a commercial source and used without further purification. Deionized water of high resistivity (\(>\)18 M\(\Omega\) cm) was purified using a Millipore Purification System (Milli-Q water, Merck Millipore, Burlington, USA) and used as a solvent.  
Figure 1: Schematic of synthesis of dT\({}_{20}\) modified \(\beta\)-annulus peptide (dT\({}_{20}\)-SS-\(\beta\)-annulus) and formation of an artificial viral capsid by co-assembly of \(\beta\)-annulus peptide and dT\({}_{20}\)-SS-\(\beta\)-annulus hybridized with mRNA.