esa-sceva/satcom-chunk-collection huggingface dataset:
# An ANN-Based Adaptive Predistorter for LED Nonlinearity in Indoor Visible Light Communications
## 2 Preliminary Work  
### System Model  
It is thought that the computational complexity of DCO-OFDM is lower than that of ACO-OFDM for a fixed symbol rate [15]. However, DCO-OFDM signals would suffer from more serious LED nonlinearity than would ACO-OFDM signals because DCO-OFDM converts real-value bipolar signals to polar ones by adding a DC bias, a distinction which suggested the consideration of adaptive predistortion in the context of DCO-OFDM-based VLC systems in this study. Figure 2 is a block diagram of a DCO-OFDM VLC system with an adaptive predistorter. The input of the DCO-OFDM modulation block is represented as \(\mathbf{X}=[X_{0},\ X_{1},\ \cdots,\ X_{N-1}]\), where \(X_{k}\) denotes the \(M\)-ary quadrature amplitude modulated (QAM) complex data signal on the \(k\)-th subcarrier and \(N\) is the size of the IFFT. Moreover, \(X_{k}\) in \(\mathbf{X}\) should have Hermitian symmetry in order to have a real time-domain signal, and should be followed by adding a DC bias for IM-DD optical wireless systems [6; 7]. Using the IFFT, the time-domain signal corresponding to \(\mathbf{X}\) is given by \(\mathbf{x}=[x_{0},\ x_{1},\ \cdots,\ x_{N-1}]\), where the \(n\)-th sample is given by:  
\[x_{n}=\frac{1}{N}\sum_{k=0}^{N-1}X_{k}\exp\biggl{(}\frac{j2\pi nk}{N}\biggr{)}, \tag{1}\]  
and the bipolar real signal \(\mathbf{x}\) is converted to a positive DCO-OFDM signal \(\mathbf{x}^{\rm(DCO)}=\left[x_{0}^{\rm(DCO)},\ x_{1}^{\rm(DCO)},\ \cdots,\ x_{N-1}^{\rm(DCO)}\right]\) by adding a DC bias \(x_{DC}\) to \(\mathbf{x}\). The optimal DC bias is related to the number of subcarriers and the modulation order, and for the convenience of using different levels of modulation, a fixed DC bias was adopted in this study. Signal \(\mathbf{x}^{\rm(DCO)}\) is then predistorted and digital-to-analog (D/A) converted to a continuous electrical  
Figure 1: Illustration of the application of an adaptive predistorter to compensate for LED time-variant nonlinearity.  
waveform \(x(t)\). The LED, serving as an optical modulator, transforms \(x(t)\) into an optical signal, and the resulting signal is transmitted through the optical channel. Usually, the VLC channel is a line-of-sight (LOS) channel and the dominant shot noise is modeled as additive white Gaussian noise (AWGN) [16]. The received light intensity \(r(t)\) is perceived by utilizing a PD in the receiver. The analog signal is analog-to-digital (A/D) converted and down-sampled to be \(\mathbf{x}^{\text{(DCO)}}\) and processed by the demodulation block to attain estimates of \(\mathbf{X}\), which is denoted as \(\mathbf{X}\).
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 3 Background
### Gradient Boosting  
Decision trees have several important characteristics for forecasting tasks. However, decision trees' individual predictive power can be enhanced by forming a prediction committee, which is the central idea of boosting techniques.  
Boosting is a generic strategy to improve the performance of any learning algorithm. The method was proposed to deal with pattern classification problems, with the introduction of the AdaBoost [50]. Subsequently, several generalizations emerged from the original strategy, among them, the gradient boosting algorithm [51], which can be applied to both classification and regression problems, using any differentiable objective function.  
The so-called boosting methods are based on the principle of minimizing a cost function through the aggregation of multiple weak models (weak learners) to build a more robust model using the gradient method as a procedure, which comprises a systematic strategy for the construction of forecast committees [51].  
In a boosting method, the performance evaluation of the models is performed sequentially. The criteria in the next step can be changed according to the evaluation in the previous step: if the first evaluator indicates a low score for a criterion, this score will be considered by the next evaluator. Thus, the final assessment is more accurate, and the process is more dynamic [52].  
In the gradient boosting method, errors are minimized by the gradient descent algorithm. The proposal avoids local minimum points so that the second-best criterion receives the best rating by adjusting the criteria to capture the best performance [53].
## 3 Overvoltage Simulation Model Including Variable Capacitance
### Overvoltage Simulation Model Including Variable Capacitance
#### 3.3.3 Arc Resistance and Variable Capacitance Calculation Module  
The arc resistance and capacitance calculation module is designed to simulate the impedance variation during the arcing process. As shown in Figure 9, by inputting the parameters of the arc differential formula, the differential expression of the arc conductance with respect to time can be obtained. The signal \(C_{1}\), which is output from the pantograph-catenary distance control module, is the first integration control signal. When the pantograph dropping process is completed, \(C_{1}\) is set to zero to indicate that the conductance is infinitely small and enters the arc extinction state. \(C_{2}\) is output from the arc burning detection module, which is the second integration control signal. When the condition of arc extinction is satisfied, the output result is equal to the calculated value of the real-time pantograph-catenary impedance. If the condition is not met, the differential calculation module will output a resistance value (\(10^{20}\,\Omega\)). In order to reduce the amount of calculation in the simulation process, a selector is set to limit the arc resistance to \(2.5\times 10^{12}\,\Omega\) when arc extinction occurs.
# Experimental Investigation on Jet Vector Deflection Jumping Phenomenon of Coanda Effect Nozzle
## 3 Results and Discussion
### Active Flow Control Results
The calculation formula of phenomenon correlation coefficient \(R\) is:  
\[R=\frac{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)\left(y_{i}-\overline{y} \right)}{\sqrt{\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}\sum_{i=1}^{n }\left(y_{i}-\overline{y}\right)^{2}}} \tag{14}\]  
Figure 18: Pressure change curve under the condition of the jet transient detachment process condition. (**a**) Adding mode PSD frequency pulse jet in the separation bubble area; (**b**) pressure differential change curve in the separation bubble area. where: \(x_{i}\) and \(y_{i}\) are the coordinates of the appropriate point \(i\), respectively, and \(\overline{x}\) and \(\overline{y}\) are the average of \(x_{i}\) and \(y_{i}\), respectively. Figure 20a shows the dynamic pressure change cloud diagram in section S1 under the original working condition. When T = 0, the jet is completely attached to the wall. The blue low pressure represents the structural scale of the separation bubble. It can be observed from the figure that the separation bubble scale gradually grows with time, and disappears at a particular moment. This phenomenon describes the viscous tension generated when the main jet vector deflects the separation bubble structure. When the separation bubble bears enough tension, it will break instantly and cause a jump. Figure 20b shows the dynamic pressure change cloud diagram on S1 section under the 78.16 Hz working conditions. The figure shows that the blue low\(-\)pressure range decays linearly with time, and does not disappear instantaneously. This phenomenon indicates that the addition of a pulsed jet with the same frequency as the vortex structure in the separation bubble can enhance the structural strength of the separation bubble. After the separation bubble structure is subjected to the viscous tension generated by the vector deflection of the main jet, the separation bubble can avoid cracking, at the cost of reducing its volume. Figure 19: Vector force variation curve under the jet transient vector detachment process condition. (**a**) Comparison of original condition, 6.2 Hz condition, and 78.16 Hz condition; (**b**) vector force differential variation curve. Figure 20: Nephogram of dynamic pressure change on S1 section. (**a**) Original working condition; (**b**) adding 78.16 Hz pulsed jet. To sum up, the 78.16 Hz working condition has reached the hypothetical goal of this paper. The DMD method accurately obtains the characteristic frequency required in active flow control. The third\(-\)order modal results obtained by DMD in the separation bubble region characterize the vortex structure in the separation bubble structure. The active flow control method of a 78.16 Hz pulsed jet can effectively suppress the jump phenomenon and make the vector deflection angle velocity of the jet change linearly.
## 3 Experimental Results and Analysis  
This section first introduces and analyzes the structure and evaluation index of SPEED, and analyzes the characteristics of data label pose distribution. In terms of the experiment, the influence of different numbers of codecs on the experimental effect is compared, the optimal number of codecs is determined, and the details related to the inference speed of the model are analyzed. Finally, the model performance is measured using the pose estimation accuracy and inference speed.
###### Abstract  
Components assembly and disassembly are fundamental tasks in manufacturing and the product service industry. Job instructions are required for novice and inexperienced workers to perform such tasks. Conventionally, job instructions may be presented via printed manual and video display. Augmented reality (AR) device has been one of the recent alternatives in conveying such information. This research compared the presentation of job instruction via AR display, video display, and a printed manual in performing computer component assembly and disassembly tasks in terms of efficiency, quality, and usability. A Microsoft(r) HoloLens 2 device and a laptop computer were adopted to present the job instruction for the AR and video conditions, respectively. A total of 21 healthy adults, including 11 males and 10 females, participated in the study. Our findings were that AR display led to the least efficiency but the best quality of the task being performed. The differences of the overall usability scores among the three job instruction types were insignificant. The participants felt that support from a technical person for the AR device was significantly more than the printed manual. More male participants felt the AR display was easier to use than their female counterparts.  
augmented reality; job instruction; assembly and disassembly; usability Article
## 3 Materials and Methods
### DLCTC Model
#### 3.2.1 Character Embedding  
The aim of this layer is to map characters in a word to a low-dimensional vector representation by using FastText embeddings; this technique uses skip-gram, which helps in handling out-of-vocabulary words [23]. In this way, the word \"I-95\" can be broken down into the characters \"I\", \"-\", \"9\", and \"5\". A character-level embedding model would capture these characters and their patterns. The sequence \"I-\", followed by a number, is a common format for road or highway designations. The model recognizes that \"I\" typically stands for \"Interstate\", and the number represents a specific highway by integrating a knowledge base to map abbreviations to their full forms and synonyms to their related terms. This can provide additional context and improve accuracy. Figure 2 shows the character embedding diagram using FastText.
## 3 MPPT Techniques
### Off-Line MPPT Techniques
This approach makes use of the temperature and irradiance-dependent, roughly linear relationship between the open circuit voltage (\(V_{OC}\)) and MPP voltage (\(V_{MPP}\)) [99]. \[V_{MPP}=K_{OC}\times V_{OC} \tag{9}\]  
where \(K_{OC}=0.72\)-0.8. In both FSCC and FOCV techniques, only one sensor is necessary (current or voltage) [101]. The proportionality factor between the V\({}_{OC}\) and I\({}_{SC}\) of the PV module has an effect on the accuracy of both methods. The advantages of off-line MPPT techniques are that they are simple and fast-tracking techniques. In addition, they do not suffer from fluctuations around the MPP. The disadvantages of off-line MPPT methods are as follows:  
* They are not suitable for high-efficiency operations. * No real-time adjustment is made. * It is noticeable that with full day operation, the irradiance and temperature vary; hence, intermittent measurement offline parameters (V\({}_{OC}\), I\({}_{SC}\)) are required. This intermittent/periodic measurement causes a power loss. * These approaches never operate at accurate MPP, and hence are not suitable for efficient systems. * Not suitable for environmental changing conditions. A comparison of the various offline MPPT techniques is provided in Table 1.
# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
## 2 Materials and Methods
### Image Analysis
#### 2.3.2 Image Sampling  
Four independent datasets were generated: one for each type of DME and one for the tissue that is not associated with any fluid accumulation (not necessarily healthy) as were the labels defined by the experts. To sample each image, it is divided into overlapping windows. These samples are, then, assigned with a label based on the ground truth established by the experts. To assign the label, it is first evaluated whether the sample is centered within a defined label region. If this is the case (i.e., the center pixel of a sample has a label that can be non-fluid, CME, DRT or SRD), then that sample is directly considered as belonging to that class.  
On the other hand, if a sample does not have a defined category in the central pixel (as may be the case when it falls into one of the previously defined black doubtful regions), it is assigned to the class with the largest area within the sample. An example of this case can be seen in Figure 5, where the sample would have been assigned to the healthy class, as it is the one with the most  
Figure 4: Diagram of the different relevant regions considered in the OCT image: the two limiting layers of the ROI, the _vitreous humor_ and the _choroid_.  
surface within the candidate sample limits. This way, we ensure that the detections are adjusted to the samples, while avoiding any confusion in the system if the pathological fluid accumulations have a minimal amount of non-pathological tissue inside. This strategy represents a trade-off balance between reducing the amount of patterns of non-ROI regions and directly losing the ROI border patterns by not considering these samples.  
Once all the samples have been labeled, 30 random samples were selected for each class from the total available in the image (giving a theoretical sum of 120 samples per image). In case this quota of 30 samples per class/image was not covered in an OCT image, the debt is accumulated progressively in successive images, trying to extract from them a greater number of random samples until the sample debt for that class was satisfied and the base extraction quota was enough.  
Finally, as samples extracted in borderline retinal regions could contain undesired patterns from any of the external regions and it is important to preserve the spatial relationship of the pixels, the biggest rectangle from each sample that only contains pixels inside the ROI was extracted. This way, three balanced datasets were generated using only the set of 284 training images. The CME dataset was composed of 16,980 samples, the DRT dataset was composed of 16,980 samples and the SRD dataset, being less prominent in our dataset and with a smaller surface area when present, the methodology has only been able to extract 16,620 samples.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
###### Abstract  
Computational radio frequency identification (CRFID) sensors are able to transfer potentially large amounts of data to the reader in the radio frequency range. However, the existing EPC C1G2 protocol is inefficient when there are abundant critical and emergency data to be transmitted and cannot adapt to changing energy-harvesting and channel conditions. In this paper, we propose a fast and reliable method for burst data transmission by fragmenting large data packets into blocks and we introduce a burst transmission mechanism to optimize the EPC C1G2 communication procedure for burst transmission when there are critical and emergency data to be transmitted. In addition, we utilize erasure codes to reduce Acknowledgement (ACK) delay and to improve system reliability. Our results show that our proposed scheme significantly outperforms the current fixed frame length approach and the dynamic frame length and charging time adaptation scheme (DPCA) and that the goodput is close to the theoretically optimal value under different energy-harvesting and channel conditions.  
caption =1
## 2 Temperature Variation in Particle Flow Modeling Based on Energy Analyses
### Damping Effect  
In the particle flow modeling, the damping is required to dissipate the kinetic energy associated with the seismic waves [47], so the dissipated kinetic energy will contribute to the production of heat. The damping can be considered at either the normal or shear directions on the contact between two particles, with the normal or shear critical damping ratio. The definition of the damping model is introduced in [51] with more detailed descriptions and equations.  
Consequently, similar to the friction energy, the damping energy \(E_{\mathrm{d}}\) on contact can also work on the particles to cause a temperature rise. In particle flow modeling, damping energy is also defined as a type of cumulative energy, and it is updated as Equation (8) [51]:  
\[E_{\mathrm{d}}:=E_{\mathrm{d}}-F^{\mathrm{d}}\cdot\left(\dot{\delta}\Delta t\right) \tag{8}\]  
where \(F^{\mathrm{d}}\) is the damping force, \(\dot{\delta}\) is the relative velocity, and \(\Delta t\) is the time step.  
It is assumed that the work done by the damping on the particles is all converted into heat \(Q_{\mathrm{d}}\):  
\[Q_{\mathrm{d}}=E_{\mathrm{d}} \tag{9}\]  
Thereafter, the temperature change \(\Delta T_{\mathrm{d}}\) caused by the damping effect can be obtained according to the heat calculation formula:  
\[Q_{\mathrm{d}}=C_{\mathrm{p}}m\Delta T_{\mathrm{d}} \tag{10}\]  
where \(C_{\mathrm{p}}\) is the specific heat capacity at constant pressure and \(m\) is the mass of the particle. It should be noted that the heat \(Q_{\mathrm{d}}\) is assumed to be distributed equally between the two contacted particles.
# Model-Robust Estimation of Multiple-Group Structural Equation Models
## 3 Simulation Study 1: Unmodeled Residual Error Correlation
### Results  
In Table 1, the bias and the relative RMSE for the factor correlation \(\phi_{12}\), the factor loading \(\lambda_{11}\), and the residual variance \(\psi_{11}\) are presented.  
It turned out that in the condition of no unmodeled residual error correlations (i.e., \(\delta=0\) and no model misspecification), no biases occurred for all parameters. However, there were slight efficiency losses with RME for \(N=500\), which were particularly pronounced for the powers \(p=0.25\) and \(p=0.5\) for the residual variance. The efficiency loss for the factor loading for the robust estimation methods was slightly smaller than for the residual variance. Finally, there was almost no efficiency loss for the robust methods for the factor correlation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Bias**} & \multicolumn{4}{c}{**Relative RMSE**} \\ \cline{4-13}  & & & & \multicolumn{4}{c}{**RME with \(p=\)**} & & & & & & & & & & & \\ \cline{4-13}
**Par** & \(\delta\) & \(N\) & **RegML** & **0.25** & **0.5** & **1** & **ULS** & **ML** & **RegML** & **0.25** & **0.5** & **1** & **ULS** & **ML** \\ \hline \multirow{6}{*}{\(\phi_{12}\)} & & 500 & \(-0.01\) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 102 & 102 & 101 & 101 & 100 & 101 \\  & 0 & 1000 & 0.00 & 0.00 & 0.00 & 0.00 &  
In the conditions with model errors (i.e., in the case of unmodeled residual correlations), ML and ULS were severely biased for the factor correlation. The bias for ML and ULS turned out to be slightly smaller for the factor loading and the residual variance. RegML and RME, using the powers \(p=0.25\) or \(p=0.5\), were unbiased, while RME with \(p=1\) had a small bias for the latent correlation. Regarding efficiency, RME with \(p=0.25\) was superior to \(p=0.5\).  
In general, RME with \(p=0.25\) performed quite competitively to RegML. Given the fact that RMSE is computationally much less demanding than RegML, one could prefer model-robust SEM estimation in the presence of unmodeled residual error correlations using the \(L_{p}\) robust loss function in practical applications.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
## 3 Results and Discussion
In this case, some yeast by-products are represented by main fusel alcohols, i.e., 2-methyl-1-propanol, 3 and 2-methyl-1-butanol. During yeast fermentation, many medium- and long-chain fatty acids are also formed via the fatty acid synthesis pathway from acetyl-CoA, while acetates and esters are resulted from the equilibrium reaction between an alcohol and an acid [3]. The major fermentation compounds, such as alcohols, ethyl esters and fatty acids constitute a main and common part of the wine flavor and are considered to play a positive role in wine fruity notes [29]. _Alcohols._ The most abundant fusel alcohols in all wines analyzed were 3 and 2-methyl-1-butanol, whose content reached a maximum for LV1, while CY3079 had the lowest concentration. 2-Phenylethanol had a significantly higher content in QA23 compared to the control, while D47 showed the lowest content. A strong statistically significant difference (\(p<0.001\)) was observed for 2-methyl-1-propanol, whose content for QA23 was the lowest, while VL1 had the highest content, with an almost 4 times higher content. On the contrary, VL1 showed the lowest level for linalool, while QA23 had the highest one. Non-significant differences were observed for 1-butanol, 1-hexnaol and \(\alpha\)-terpineol. Linalool and \(\alpha\)-terpineol are terpene alcohols obtained by the metabolism of mevalonic acid and are responsible for the typical floral aromas of Muscat and Gewurztraminer wines [3]. _Esters._ The major compound belonging to this class was ethyl lactate, whose content statistically significant changed according to the yeast strains used. The lowest content was observed for QA23, while CY3079 and VL1 had more than double the concentration of QA23. All volatile compounds were significantly affected by the yeast strain used. Generally, with a few exceptions, esters had higher content for QA23 and lower content for D47. Diethyl ester of butanediolic acid had a different trend, with the highest concentration