esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Organoleptic Evaluation  
Figure 5 shows the normalized and scaled values of the scores obtained for clarity, color, aroma frankness, positive aroma intensity, aroma quality, taste frankness, positive taste intensity, taste persistence, taste quality, and overall quality attributes, evaluated by the tasters. According to this plot, wines obtained by spontaneous fermentation of the grape must showed higher values for all the attributes, except for the clarity and color. In contrast, wines obtained with AGGLO yeast showed lower scores for attributes such as aroma frankness, positive taste intensity, and aroma quality. In general, an increase in the values of all attributes is also obtained when the fermentation activator is used in both WY + Act and AGGLO + Act.  
Figure 4: Footprints obtained by multivariate data analysis (MVA) of the odorant activity values (OAV) of the odorant series. The numbers of the rays correspond to the following series: 1: fruity; 2: green fruit; 3: green; 4: creamy; 5: citrus; 6: chemical; 7: honey; 8: wavy; 9: floral. The end of the ray represents the average plus three standard deviations, with the origin being the mean minus three standard deviations. Each vertex of the polygon is the average value. Abbreviations of wines obtained by fermentation with WY: wild yeast; WY + Act: the same with the addition of an activator; AGGLO: Levulia® AGGLO yeast; AGGLO + Act: the same with the addition of an activator.  
On the other hand, AGGLO showed lower values for aroma frankness, positive taste intensity, and aroma quality. Both WY + Act and AGGLO + Act improved the values regarding clarity. This may be due to the protector effect that mannoproteins from the yeast lysis have, decreasing particle size and consequently haze formation [44].  
Differences in the scored aroma attributes should be interpreted as a consequence of the differences in the contents of the active volatile compounds quantified. However, when considering the sum of the attributes assessed for sight, smell, taste, overall quality, and total score (Table 5), no significant differences were found between the wines at the 95% confidence level (\(p\leq 0.05\)). Slight differences were observed in the scores for wine produced by spontaneous fermentation, with the activator (WY + Act) obtaining the highest total score (80 points) and AGGLO obtaining the lowest (76 points). These results are similar to those obtained by other authors who attempted to establish relationships between the sensory analysis and the content in active volatile compounds [45].
# Hardware-Based Architecture for DNN Wireless Communication Models
## 6 Experimental Results and Discussions
### Software Results
#### 6.1.2 4 \(\times\) 4 MIMO OFDM Model  
The BER performance values of 4 \(\times\) 4 MIMO OFDM models are shown in Figures 15-19. The performance of deep learning and conventional digital signal processing (DSP) models are compared.  
Figure 16: BER versus SNR for the proposed receivers with M = 4, ZF is used and the clipping level of the nonlinear PAs is 5 dB.  
Figure 15: BER versus SNR for the original receivers with M = 4.  
Figure 17: BER versus SNR for the proposed receivers with M = 4, ZF is used and the clipping level of the nonlinear PAs is 7 dB.  
Figure 18: BER versus SNR for the proposed receivers with M = 4, MLD is used and the clipping level of the nonlinear PAs is 5 dB.  
Figure 15 shows the BER performance of the conventional DSP models. For the linear and non-linear signals, the BER performances of the MLD algorithm are outstanding compared to the BER values of the ZF algorithm. The BER performances of the linear signals are slightly higher than those of the non-linear 7B signals and higher than those of non-linear 5 dB signals.  
Figures 16 and 17 show the results of comparisons between the three proposed DNN models and the conventional DSP model using the ZF signal detectors. For the linear signals, non-linear 5 dB, and 7 dB signals, the BER performances of the DNN type I and type II models are higher than those of the conventional DSP model. Meanwhile, the BER performances of the DNN type III are worse than those of the conventional DSP model.  
Figures 18 and 19 show the results of comparisons between the three proposed DNN models and the conventional DSP model using the LMD signal detectors. The same conclusions as using the ZF signal detectors, for the linear signals, non-linear 5 dB, and 7 dB signals, the BER performances of the DNN type I and type II models are higher than those of the conventional DSP model. Meanwhile, the BER performances of the DNN type III are worse than those of the conventional DSP model.  
From these results, we conclude that two DNN type I and type II models are suitable for the 4 \(\times\) 4 MIMO OFDM systems. Meanwhile, the DNN model type III cannot be applied to the 4 \(\times\) 4 MIMO OFDM systems.  
Furthermore, we can also see that the BER performances of the 4 \(\times\) 4 OFDM MIMO systems are worse than those of the 2 \(\times\) 2 OFDM MIMO systems due to the higher complexity of the 4 \(\times\) 4 OFDM MIMO systems.  
Overall, two DNN type I and II models are suitable for the 4 \(\times\) 4 MIMO OFDM systems, while DNN model type III is appropriate for the 2 \(\times\) 2 MIMO OFDM systems. In the next section, the hardware results of these models are shown to verify whether practical hardware designs are applicable.  
Figure 19: BER versus SNR for the proposed receivers with M = 4, MLD is used and the clipping level of the nonlinear PAs is 7 dB.
# Automatic Defect Detection for Web Offset Printing Based on Machine Vision
## 3 Experiment
### Performance of the Proposed Defect Detection Method  
In this subsection, two indices, called sensitivity and FPR (False Positive Rate), are employed to evaluate the performance of the defect detection method, which are defined as follows:  
\[sensitivity=\frac{\#TP}{\#TP+\#FN}\times 100\% \tag{11}\]  
\[FPR=\frac{\#FP}{\#TP+\#FN}\times 100\% \tag{12}\]  
where \(TP\) represents defects that are correctly identified as defects. \(FN\) represents defects that are incorrectly identified as non-defects. \(FP\) represents non-defects that are incorrectly identified as defects.  
The values of \(a\) and \(b\) in Equations (9) and (10) are two important parameters. If the parameter values are set too large, some defects will be incorrectly regarded as non-defects. In contrast, if they are set too small, some non-defects will be labeled as defects. In the experiment, 15 types of web offset images with different sizes were analyzed to determine parameters \(a\) and \(b\) in Equations (9) and (10). From the experiment, the best parameters are \(a=15\) and \(b=5\). Table 3 gives the performance of the proposed method for three different image sizes. As shown in Table 3, the highest sensitivity and the lowest FPR are achieved when the number of training samples is 30, which means using 30 defect-free images is the best choice for creating good template images. Moreover, the average sensitivity and average FPR are 96.03% and 1.54%, respectively, while the detection speed is less than 160 ms for each image, which verifies the effectiveness of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Number of Pyramid** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Precision & \(\pm 0\) & \(\pm 0\) & \(\pm 0\) & \(\pm 0\) & \(\pm 1\) & \(\pm 2\) & \(\pm 3\) & \(\pm 6\) & \(\pm 7\) & \(\pm 10\) \\ Times/ms & 3062.43 & 1293.86 & 591.27 & 318.24 & 185.46 & 126.46 & 106.71 & 85.18 & 80.30 & 80.98 \\ Number of correct images & 20 & 20 & 20 & 20 & 20 & 20 & 20 & 18 & 14 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results of different pyramid layers for 20 images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image Height** & **20** & **100** & **1000** \\ \hline
1164 & 1.60 & 2.05 & 7.08 \\
8192 & 9.85 & 11.85 & 38.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Executing time in different sizes of ROI (ms).
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods  
### Data Description  
The data for this experiment are scallion price data from the wholesale market in Shandong Province from Brick's Agricultural Data Intelligence Terminal. The data range from 2 January 2014 to 31 August 2023 for scallion prices is used. The unit for the price of scallions is CNY/kg. Some of the missing data have been filled in using the mean value method. The sample data were divided into three subsets, with the first 80% of the data considered as the training subset, the next 10% as the validation set, and the most recent 10% as the testing subset. The validation set is used to tune the hyperparameters of individual prediction models and prediction combinations. The prediction results are included in the test set and compared. The scallion price dataset is shown in Figure 1, and its commonly used statistics are shown in Table 1, and some price data examples of the training set, test set, and verification set are shown in Tables 2-4. From the Figure 1, it can be seen that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of \"high in winter and low in summer\".  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Min** & **Max** & **Mean** & **Standard Deviation** & **Skewness** & **Kurtosis** \\ \hline Scallion & 0.8 & 12 & 3.0915 & 1.9341 & 1.7611 & 2.8712 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for agricultural commodity future prices.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **2021-08-27** & **2021-08-28** & **2021-08-29** & **2021-08-30** & **2021-08-31** \\ \hline
**Price** & 2.67 & 2.935 & 3.2 & 2.71 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some sample data from the training set.
## 3 Research Methodology  
Due to their widespread deployment and access point availability, WLAN and WSN-based technologies are gaining interest in the domain of indoor localization and positioning services. RSS data from WLAN and WSN sources are becoming increasingly important for refining and improving the accuracy in indoor environments [13; 17], particularly when GPS signals become unreliable or undetectable. In this work, our goal consists of the implementation of a refinement of previous research, again by using a hybrid indoor positioning system in which both signals from available WLAN access points and WSN-based technologies are considered.  
In particular, the possibility of further refinement and optimization is based on two previous distinct works, in which we separately considered the possibility of weighting the WLAN and WSN source signals and the tessellation of the research space into a regular grid of square tiles. A preliminary inspection of the collected data confirmed the lack of linearity of signal intensities with respect to the relative positions inside the research area. This aspect reinforced the need to process the data by means of a machine learning approach, in which we included the approach of weighting the available signals from both WLAN and WSN with the aim of improving indoor positioning accuracy.  
The concept of tessellation of the research space is based on the fact that, for many applications, the precise localization of a signal receiver and its end-user is not required. Furthermore, any positioning algorithm presented and discussed in the literature carries an incertitude of the order of magnitude of 1 m. As a result, for most indoor positioning applications, finite-sized tiling would not become a limiting factor in precision for localization purposes, especially in large indoor environments. For this reason, this work focuses on the optimization of a regular tessellation of the research space, seeking optimal sizes of rectangular adjacent tiles and the collocation of the corner of the resulting grid.
## 4 Interference Characterization
### System Performance
#### 4.4.2 Spatial Throughput  
A specific metric in stochastic geometry [43] is _spatial throughput_ which characterizes the density of the nodes which successfully transmit at a given time (_i.e._, BSNs in the context of this paper) within a unit area. Spatial throughput considers the successful transmission probability of a BSN as well as the spatial reuse. For example, according to [43], in the case of Aloha MAC network with half-duplex transceivers, the spatial throughput is expressed as \(p\left(1-p\right)\left(1-P_{o}\right)\)[43], where \(p\) is spatial transmission probability, \(\left(1-p\right)\) is the probability that the intended receiver is listening (not transmitting) when the transmitter transmits, and \(\left(1-P_{o}\right)\) is the successful transmission probability. In a BSN where the receiver is in listening state when the transmitter is transmitting, the term \(\left(1-p\right)\) is negligible. Moreover, we utilize the intensity of interfering BSNs, e.g., \(\lambda\), instead of the spatial probability \(p\) in order to consider the ratio of contention-based traffic and contention-free traffic in IEEE 802.15.6 BSNs.  
The spatial throughput is given by  
\[S=\uplambda\left(1-P_{o}\right) \tag{8}\]  
Both spatial throughput and spatial average rate measure the overall performance of BSNs. From Equations (7) and (8), both metrics consider the outage probability \(P_{o}\), while spatial average rate does not consider the intensity of nodes. The area spectral efficiency is shown to follow the similar trend with the spatial throughput.
###### Abstract  
Background and Objectives: In lumbar spine radiography, the oblique view is frequently utilized to assess the presence of spondylolysis and the morphology of facet joints. It is crucial to instantly determine whether the oblique angle is appropriate for the evaluation and the necessity of retakes after imaging. This study investigates the feasibility of using a convolutional neural network (CNN) to estimate the angle of lumbar oblique images. Since there are no existing lumbar oblique images with known angles, we aimed to generate synthetic lumbar X-ray images at arbitrary angles from computed tomography (CT) images and to estimate the angles of these images using a trained CNN. Methods: Synthetic lumbar spine X-ray images were created from CT images of 174 individuals by rotating the lumbar spine from 0\({}^{\circ}\) to 60\({}^{\circ}\) in 5\({}^{\circ}\) increments. A line connecting the center of the spinal canal and the spinous process was used as the baseline to define the shooting angle of the synthetic X-ray images based on how much they were tilted from the baseline. These images were divided into five subsets and trained using ResNet50, a CNN for image classification, implementing 5-fold cross-validation. The models were trained for angle estimation regression and image classification into 13 classes at 5\({}^{\circ}\) increments from 0\({}^{\circ}\) to 60\({}^{\circ}\). For model evaluation, mean squared error (MSE), root mean squared error (RMSE), and the correlation coefficient (r) were calculated for regression analysis, and the area under the curve (AUC) was calculated for classification. Results: In the regression analysis for angles from 0\({}^{\circ}\) to 60\({}^{\circ}\), the MSE was 14.833 degree\({}^{2}\), the RMSE was 3.820 degrees, and r was 0.981. The average AUC for the 13-class classification was 0.953. Conclusion: The CNN developed in this study was able to estimate the angle of an lumbar oblique image with high accuracy, suggesting its usefulness.  
deep learning; angle estimation of lumbar; regression model; classification model
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 1 Introduction
Moving target shadows using passive forward scatter radar systems were studied in [39; 40; 41]. From the authors' point of view, the available literature lacks a detailed description of the 3-D geometry and kinematics of the BFISAR scenario, the signal model based on the DVB-T waveform, and the 3-D geometry of the observed object. There is no analytical structure of the object's image extraction process or its interpretation, based on which the algorithm for signal processing and image reconstruction is built. The aim of the present work was to define the geometrical and kinematical features of the BFISAR system with a transmitter of opportunity, emitting BVB-T waveforms, to synthesize the BFISAR signal model based on the DVB-T waveform, and based on the vector description of the scenario and signal model, to build an algorithm for extraction of the BFISAR image. The paper is structured in the following manner. Section 2 presents BFISAR geometric and kinematic vector equations describing the positions of the transmitter, receiver, and object. Section 3 describes stages of the algorithm for extraction of the object image. Section 4 presents the source data and graphical results of simulation experiments. In Section 5, concluding remarks are drawn.
# Unleashing the Power of Tweets and News in Stock-Price Prediction Using Machine-Learning Techniques
## 4 Model Construction
### Input Parameters Selection

Table 2: The indicators that have been investigated in the literature. \begin{table}
\begin{tabular}{l l} \hline \hline
**Variable Name** & **Definition** \\ \hline Open Price & The dollar value of the first trade since the market opened \\ High Price & The highest dollar value trade of the day \\ Low Price & The lowest dollar value trade of the day \\ Close Price & The dollar value of the last trade before the market closed \\
30-Day Moving Average & The average dollar value of one share in the last 30 days \\ Trade Volume & The total quantity of shares traded all day. \\  & \multicolumn{2}{l}{Represents the difference between the number of tweets expressing} \\ Twitter Count (TC) & positive sentiment and those expressing negative sentiment towards the \\  & \multicolumn{2}{l}{parent company over a 24-h period.} \\ News Publication Count (NC) & \multicolumn{2}{l}{Represents the total number of news publications mentioning the parent} \\  & \multicolumn{2}{l}{company over a 24-h period.} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Definitions of input parameters. accuracy and reliability, we accessed Bloomberg's database and retrieved the required information using their data-export functionality. Bloomberg's database is renowned for its accuracy, timeliness, and depth of coverage, making it a preferred choice for researchers and practitioners in the financial industry. Specifically, we employed Bloomberg's Excel API to extract the data directly into an Excel format. This API allowed us to access various financial data, including stocks' open prices, high prices, low prices, and close prices daily from 1 January 2015 to 31 May 2020. It also includes trading volume, the 30-day moving average, the number of tweets with positive sentiment, the number of tweets with negative sentiment, the number of tweets with neutral sentiment, and the news publication count. To capture the sentiments of tweets, we consider the difference between positive and negative sentiment tweets as the number of Twitter counts (TC). However, we do not involve the sentiment in the news publication count (NC) parameter and consider the total number of news publications mentioning the parent company over a 24 h period. The dataset includes 1412 data items for each stock, and the attributes were selected based on their potential significance for analyzing stock-market dynamics and sentiment and their availability within the Bloomberg database. The daily average number of tweets and news publications for the selected companies are Apple (4952 tweets, 2506 news), Amazon (3009 tweets, 757 news), Facebook (3912 tweets, 701 news), Netflix (1556 tweets, 637 news), Google (3930 tweets, 1196 news), Walmart (743 tweets, 375 news), Tesla (2218 tweets, 594 news), and Ford (192 tweets, 271 news). Furthermore, by utilizing the Excel API, we could integrate the data into our analysis workflow, facilitating further processing and analysis. Additional details regarding the collected data can be found in Table 15 in Appendix A.
## Abbreviations  
\begin{tabular}{l l} WOBAN & Wireless-Optical Broadband-Access Networks \\ ONU & Optical Network Unit \\ OLT & Optical Line Terminal \\ CO & Central Office \\ PON & Passive Optical Network \\ EPON & Ethernet Passive Optical Network \\ GPON & Gigabit Passive Optical Network \\ BPON & Broadband Passive Optical Network \\ TDMA & Time-Division Multiple Access \\ WDM-PON & Wavelength Division Multiplexing-Passive Optical Network \\ WiFi & Wireless Fidelity \\ WiMAX & Worldwide Interoperability for Microwave Access \\ \end{tabular}  
Figure 12: Percent improvement of running time in between pure genetic algorithm and the combination of backtracking and genetic algorithm based channel assignment.
## 3 Results and Discussion
### Conductivity of GO/CCs
#### 3.2.3 Effect of Temperature on Electrical Conductivity  
The GO/CC specimens with WL of 11.09% and 0.05% of GO concentration were subjected to temperature sensitivity tests. In Figure 6a, it can be seen that the trend for the current intensity of GO/CCs is basically the same as the temperature trend. The difference is that when the temperature of the specimens was increased from 10 \({}^{\circ}\)C to 25 \({}^{\circ}\)C and then stabilized, the current intensity of the specimens changed from an increase to a sudden decrease. This phenomenon was observed in all three parallel specimen tests, but not in the other heating intervals. The value of the current intensity drop, \(\rm{I_{d}=I_{E1}-I_{E2}}\), is about 0.001408 mA. It may be that polarization causes this drop. When the ambient temperature stabilizes, the current intensity of the specimen also stabilizes. When the temperature of carbon nanomaterials increases, the tunneling effect is strengthened and more GO conductive networks are formed, thus increasing the electrical conductivity [27].  
Figure 6b represents the volume resistivity of the specimen after temperature stabilization. The volume resistivity of the specimen at five different temperatures was fitted. As shown in Equation (3), the volume resistivity versus the temperature curve is a quadratic parabola, \(\rm{R2=0.97269}\). This empirical equation exhibits the volume resistivity of a GO/CC specimen (GO = 0.05%, WL = 11.9%) at different temperatures.  
\[\rm{y=66,832.5939-1963.35339\times x+15.34342\times x^{2}}, \tag{3}\]  
Figure 5: The electrical resistivity of the specimens with varying GO concentrations at different drying states: (**a**) drying state I; (**b**) drying state II; (**c**) drying state III; (**d**) drying state IV; (**e**) drying state V; (**f**) drying state VI.  
Figure 6: The plot of current intensity versus temperature for the 0.05% GO/CC: (**a**) effect of temperature on current intensity; (**b**) current intensity versus temperature change fitting curve.  
Considering that this experiment only measured the conductivity of GO/CCs with temperature for a single GO concentration and drying state, further studies are necessary to determine polarization effects and conductivity with temperature.