esa-sceva/satcom-chunk-collection huggingface dataset:
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 5 Conclusions  
In this paper, we proposed the receiving antenna allocation scheme for the downlink MU-MIMO-OFDM transmission; the AP simultaneously transmits data frames to a combination of allocated antennas per subcarrier at each STA. The first feature of the proposed scheme is the limited CSI feedback sequence; each STA feeds back to the AP just the channel response of the antenna with maximum norm if the spatial correlation value between receiving antennas is higher than a threshold. This sequence decreases the amount of CSI feedback and computational complexity while incurring only a small performance penalty in the downlink MU-MIMO-OFDM transmission.  
The second feature of the proposed scheme is the receiving antenna decision method; the overhead to notify the allocated antenna information greatly decreases by the received power comparison.  
To clarify the effectiveness of the proposed scheme, we evaluated it in a computer simulation using channel responses measured in an indoor environment. The results showed that the proposal matched the performance of the coordinated Tx-RX BD techniques. That is, the proposed scheme can maintain the channel capacity of the downlink MU-MIMO-OFDM transmission while greatly reducing the overhead and computer complexity.  
[PERSON], [PERSON], and [PERSON] designed the proposed scheme; [PERSON] and [PERSON] performed the experiments and analyzed the results.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Closed-Form Method for Simultaneous Target Localization and UAV Trajectory Optimization
## 3 Simultaneous Target Localization and Unmanned Aerial Vehicle (UAV) Trajectory Optimization
### Simultaneous Target Localization and UAV Trajectory Optimization
#### 3.2.1 Target Localization Using EIF
Prediction update: \[\mathbf{Y}_{k|k-1}=\left(\mathbf{\Phi}_{k|k-1}\mathbf{Y}_{k}^{-1}\mathbf{\Phi }_{k|k-1}^{\top}+\mathbf{\Gamma}_{k|k-1}\mathbf{Q}_{k-1}\mathbf{I}_{k|k-1}^{ \top}\right)^{-1}\] (14) \[\mathbf{B}_{k|k-1}=\mathbf{Y}_{k|k-1}\mathbf{\Phi}_{k|k-1}\mathbf{Y}_{k}^{-1}\] (15) \[\mathbf{\hat{y}}_{k|k-1}=\mathbf{B}_{k|k-1}\mathbf{\hat{y}}_{k-1}\] (16)
2. Measurement update: \[\mathbf{i}_{k}=\mathbf{H}_{k}^{\top}\mathbf{R}_{k}^{-1}\mathbf{z}_{k}\] (17) \[\mathbf{I}_{k}=\mathbf{H}_{k}^{\top}\mathbf{R}_{k}^{-1}\mathbf{H}_{k}\] (18) \[\mathbf{\hat{y}}_{k}=\mathbf{\hat{y}}_{k|k-1}+\mathbf{i}_{k}\] (19) \[\mathbf{Y}_{k}=\mathbf{Y}_{k|k-1}+\mathbf{I}_{k}\] (20) where \[\mathbf{H}_{k}=\left( \begin{array}{cc}\frac{\partial\gamma}{\partial x_{k}} &\frac{\partial\gamma}{\partial y_{k}}\\ \frac{\partial\delta}{\partial x_{g}}&\frac{\partial\delta}{\partial y_{g}} \end{array} \right)\] (21) denotes the Jacobian matrix of \(\mathbf{h}(\mathbf{x}_{k})\) with respect to \(\mathbf{x}_{k}\) at \(\mathbf{\hat{x}}_{k|k-1}\), and \(\mathbf{\hat{x}}_{k|k-1}\) can be computed using the corresponding information matrix and information state vector as follows: \[\mathbf{\hat{x}}_{k|k-1}=\mathbf{Y}_{k|k-1}^{-1}\mathbf{\hat{y}}_{k|k-1}\] (22) Using the above procedure, the moving target can be located consistently in real-time.
# QUIC Network Traffic Classification Using Ensemble Machine Learning Techniques
## 7 Conclusions  
In this paper, five different ensemble machine learning models were investigated for the QUIC traffic classification, namely, Random Forest, Extra Trees, Gradient Boosting Tree, Extreme Gradient Boosting Tree, and Light Gradient Boosting Model. The models were trained using 15, 30, 40, 50, 60, 80, 100, and 120 packets as features on two different training and testing scenarios, i.e., holdout, and 10-fold cross-validation. From the experimental results and analysis, it was found that the LGBM outperforms the others achieving more than 99% in terms of accuracy, precision, recall, and F-1 score. In addition to that, it was found that LGBM and XGBT can be trained using a small number of features such as 15 packets and still achieve a performance score of about 92%. The models were compared to other state-of-the-art models found in the literature, and achieved the highest results, using only the traffic packets as a feature. These results indicate that ensemble learning techniques, especially LGBM and XGBT, can reveal the service from generated traffic and be a possible choice for deploying in real-world online application.  
For future work, a replication study might be conducted on a larger dataset with more QUIC services. In addition, other models could be tested and compared to the proposedmodels in this research. Finally, a prediction tool based on the proposed models could be produced for the industry. Moreover, automatic hyperparameter optimization can be applied to further investigate the performance of ensemble models.  
Conceptualization, S.A., M.A. and A.A.; methodology, M.A. and A.A.; software, M.A. and A.A.; validation, S.A., A.A. and M.A.; formal analysis, S.A., M.A. and A.A.; investigation, S.A.; resources, S.A., M.A. and A.A.; data curation, M.A. and A.A.; writing, S.A, M.A. and A.A.; writing, review and editing, S.A., A.A. and M.A.; visualization, S.A., M.A. and A.A.; supervision, S.A.; project administration, S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this paper is available at [5].  
The authors would like to acknowledge King Fahd University for supporting this research.  
The authors declare no conflict of interest.
## 3 Methods
### Combined Vegetation Index  
In HVBM, the VWC is a key variable and could aid in yield estimation and irrigation decisions. Many vegetation indices such as NDVI and NDWI (Normalized Difference Water Index) are used to parameterize the estimation of VWC [57; 58]. The equations of NDVI and NDWI are listed in Table 2[59; 60]. Although NDVI is sensitive to biomass and can effectively eliminate the effects of soil background and topographic changes, it is prone to saturation points, which can cause biomass estimation bias and reduce the accuracy of soil moisture retrieval [61]. Compared with NDVI, NDWI can better estimate vegetation moisture content, but it lacks the perception of atmospheric effects and so the inversion accuracy will be affected in soil moisture retrieval.  
In this study, a new vegetation index called combined vegetation index (CVI) is proposed to estimate the value of VWC from the optical remotely sensed data. The CVI combines the near-infrared band in the high-reflection area of the vegetation canopy, the red band in the high-absorption area of chlorophyll, and the green band in the water absorption range, taking into account the sensitivity of vegetation greenness and leaf liquid water [62], which can be expressed as follows:  
\[CVI=\left(2*R_{\mathrm{NIR}}\ -R_{\mathrm{GREEN}}\ -R_{\mathrm{RED}}\ \right)/ \left(2*R_{\mathrm{NIR}}\ +R_{\mathrm{GREEN}}\ +R_{\mathrm{RED}}\ \right) \tag{6}\]  
where, \(R_{\mathrm{GREEN}}\,\ R_{\mathrm{RED}}\) and \(R_{\mathrm{NIR}}\) are the radiances at the green, red and near infrared bands, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vegetation Index** & **Equation** & **Reference** \\ \hline NDVI & NDVI = \(\frac{R_{\mathrm{NIR}}-R_{\mathrm{RED}}}{R_{\mathrm{NIR}}-R_{\mathrm{RED}}}\) & [PERSON] et al., 1974 [59] \\ NDWI & NDWI = \(\frac{R_{\mathrm{NIR}}-R_{\mathrm{NIR}}}{R_{\mathrm{NIR}}+R_{\mathrm{ SWER}}}\) & [PERSON], 1996 [60] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The vegetation indices and related equations used in this paper.
# Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines
###### Abstract  
The vibration of the belt drive system in fresh wolfberry sorting machines significantly impacts the sorting efficiency of wolfberries. To analyze the vibration changes induced by the belt drive, a simulation model was developed using multi-body dynamics software, Recur Dyn. The lateral vibration characteristics of the grading device's belt were examined under varying initial tensions, speeds, and deflection angles. Response surface methodology (RSM) was employed to determine the relative influence of these factors on the belt's vibration characteristics. The analysis indicated the order of influence, from greatest to least, as initial tension, deflection angle, and speed. Aiming to minimize the vibration amplitude at the belt's midpoint, the optimal parameter combination was determined. The operating conditions yielding the minimum amplitude were found to be an initial tension of 520 N/mm, a drive speed of 60 rpm, and a belt deflection angle of 5\({}^{\circ}\). Concurrently, a transverse vibration modal analysis was conducted to study the system's natural frequencies and corresponding mode shapes, aiding in the identification of potential resonance issues. Finally, under optimal operating conditions, guided by the results of the belt simulation test, a 10 mm fillet was introduced at the edge of the pulley, effectively mitigating wear and vibration. Specifically, when the effective length of the transmission mechanism is set to 2200 mm and the total length of the fixed device is configured as 1600 mm, the amplitude attenuation rate achieves its peak value. This study demonstrates that the integration of theoretical analysis with simulation techniques provides a robust approach for optimizing the structural design of the grading device.  
Recur Dyn; transverse vibration; dynamics simulation; response surface method; optimal design Article
## 4 Conclusions  
E-nose, ATR-FTIR and image analysis were used as rapid and non-destructive techniques to discriminate the ripening stage (half-red or red) of strawberries cv Sabrosa, commercially named Candonga, harvested at three different times. The correlation analysis between e-nose sensor responses and VOC data demonstrated that HS-SPME/MS-e-nose experimental data contain enough information to allow the discrimination of strawberry samples based on their degree of ripening. Moreover, among the chemical indicators of ripening, TA was correlated to both ATR-FTIR and IA data. Since TA usually decreases during ripening, its assessment by ATR-FTIR or IA might provide a suitable indicator for a fast and non-destructive evaluation of the ripening stage in strawberries.  
Further investigations to improve the results obtained herein, performed using a larger sample size and cultivars as well as samples of different strawberry crop seasons, will be carried out with the aim to build predictive models of ripening, which can offer several advantages in terms of rapidity, low cost and easy-to-handle analysis for the industries of the sector. Models built considering multiple techniques (i.e., ATR-FTIR and IA) will also be considered in the perspective of improving the reliability of the models. The final goal is to obtain significant models usable in portable devices for a real-time and on-site prediction of the most suitable harvest time.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods11111534/s1](https://www.mdpi.com/article/10.3390/foods11111534/s1)]([https://www.mdpi.com/article/10.3390/foods11111534/s1](https://www.mdpi.com/article/10.3390/foods11111534/s1)), Table S1: Volatiles compounds obtained in strawberries cv Sabrosa, commercially named Candonga at two different ripening stages (half-red and red) and at three harvest times (H1, H2 and H3); Table S2: Chemical parameters measured in strawberries cv Sabrosa, commercially named Candonga, at two different ripening stages (half-red and red) and at three harvest times (H1, H2 and H3); Figure S1. Half-red (in ripening phase, fully expanded and 50% red), and Red (in ripening phase, fully expanded and 100% red) Candonga strawberries used in the experiment.  
Conceptualization, writing, revision, R.C., M.C. and B.P.; formal analysis, data processing, C.L., F.S., L.M., G.P., M.P. and A.C.; data curation, methodology, validation, writing--original draft, M.S. and R.R.; funding acquisition, project administration, M.C. and B.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project PON <<R&l>> 2014-2020--Azione II--\"E-crops--Technologies for Digital and Sustainable Agriculture\" (grant number ARS01_01136) from European Union, and the Italian Ministry of Education University.  
Not applicable.  
The authors thank [PERSON] of CNR-ISPA for the technical support in the laboratory and [PERSON] and [PERSON] of Apofruit Italia Soc. Coop. Agricola (Scanzano Jonico, Italy) for agronomy support during fruit harvest.  
The authors declare no conflict of interest.
## 3 Results
### Nutritional Profile
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Type of Beans** & **A (ug)** & \begin{tabular}{c} **B1** \\ **(mg)** \\ \end{tabular} & \begin{tabular}{c} **B2** \\ **(mg)** \\ \end{tabular} & \begin{tabular}{c} **B3** \\ **(mg)** \\ \end{tabular} & \
\begin{tabular}{c} **B6** \\ **(mg)** \\ \end{tabular} &
\begin{tabular}{c} **B9** \\ **(mg)** \\ \end{tabular} & **E (mg)** & **K (mg)** & **C (mg)** & **References** \\ \hline _Black beans_ & 17 & 2.4 & 0.42 & 1.9 & 2 & 149 & 6.7 & 0.56 & 8.63 & [27; 31] \\ \hline  & 17 & 0.9 & 0.1 & 1.9 & 0.286 & 128 & 0.21 & 0.56 & 1.32 & \\ \hline _Kidney beans_ & 768 & 0.5 & 0.03 & 0.51 & 6.7 & 130 & 0.90 & 0.56 & 2.67 & [18; 19; 20; 21; 22] \\ \hline  & 462 & 1.17 & 0.103 & 0.51 & 0.212 & 194 & 0.43 & 0.56 & 0 & \\ \hline _Navy beans_ & n.d. * & 2.2 & 1.68 & 2.188 & 4.28 & 140 & 0.02 & 0.25 & 4.5 & [19; 21; 22; 27] \\ \hline  & n.d. * & 0.431 & 0.12 & 1.181 & 0.251 & 222 & n.d. * & 0.25 & 0 & \\ \hline _Pinto beans_ & - & 4 & n.d. * & 1.174 & 4.74 & 172 & 0.21 & 0.56 & 6.3 & [19; 21; 22; 27] \\ \hline  & 818.9 & 0.33 & 0.106 & 1.174 & 0.392 & 210 & 3.51 & 0.56 & 0 & \\ \hline _Great Northern beans_ & n.d.* & 0.88 & 0.19 & 2.1 & 0.49 & 102 & 5 & 0 & 0.01 & [19; 22; 30; 50] \\ \hline  & n.d. * & 0.28 & 0.104 & 1.205 & 0.207 & 90 & 2.5 & 0.01 & 0.01 & \\ \hline _Lima beans_ & 57.2 & 0.99 & 0.2 & 1.99 & 0.49 & 300 & 2.94 & 0.056 & 4.37 & [18; 51; 52; 53] \\ \hline  & 20.9 & 0.142 & 0.1 & 1.33 & 0.204 & 34 & 1.92 & 0.056 & 0.03 & \\ \hline _Black-eyed beans_ & 12 & 0.51 & 0.2 & 1.3 & 0.3 & 133 & - & - & 0.0026 & [22; 53; 54; 55] \\ \hline  & 0 & 0.345 & 0.094 & 0.846 & 0.171 & 356 & - & 0.007 & 0.0004 & \\ \hline _Soybans_ & 421 & 0.73 & 0.39 & 3.2 & 6 & 100 & 0.12 & 277 & 47 & [22; 35; 37; 55] \\ \hline  & 0 & 0.7 & 0.25 & 2.61 & 0.57 & 305 & 0.12 & 410 & 0 & \\ \hline _Garbanzo beans_ & 67 & 0.477 & 0.212 & 1.541 & 0.535 & 557 & 0.82 & 9 & 4 & [22; 38] \\ \hline  & 27 & 0.116 & 0.063 & 0.526 & 0.228 & 172 & 0.35 & 4 & 1.3 & \\ \hline _Fava beans_ & 350 & \textless{}1 & \textless{}1 & 1.5 & \textless{}1 & 96 & 0.08 & n.d. * & 33 & [22; 40; 41; 56] \\ \hline  & 270 & 0.372 & 0.11 & 1.2 & 0.094 & 58 & 0 & n.d. * & 19.8 & \\ \hline _Mung beans_ & 10.9 & 0.5 & 0.3 & 2.2 & - & - & 12.5 & n.d. * & 10 & [18; 42; 57] \\ \hline  & 13 & 0.05 & 0.102 & 0.817 & 0.054 & 29 & 0 & 22.7 & 10 & \\ \hline _Azuki beans_ & 18 & 0.04 & 0.02 & 0.96 & 0.4 & 278 & 36.87 & n.d. * & 0 & [18; 45; 58] \\ \hline  & 17 & 0 & 0 & 2.63 & 0.351 & 622 & 12.46 & n.d. * & 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Vitamin contents of twelve bean varieties (ug or mg per 100 g dry weight at the first row and per 100 g cooked at the second row).
## 3 Results
### Question 6: What Is the Combined Effect of Individual IEQ Aspects on Overall IEQ Satisfaction?
#### 3.6.5 Other Analytical Methods  
Apart from two-step analysis methods, [PERSON]'s satisfaction model (usually used in the marketing field) was also applied to test the relationships between overall IEQ satisfaction and the IEQ indicators by [PERSON] and [PERSON] [40]. They first developed a linear regression model to prepare for the deviation between overall IEQ satisfaction and 30 dummy variables that were converted by 15 IEQ indicators. As a result, each IEQ indicator had two regression coefficients representing their impacts on overall satisfaction and dissatisfaction, respectively. Based on these regression coefficients, the 15 IEQ indicators were divided into three categories: primary factors, proportional factors, and bonus factors. Although no IEQ indicators were identified as bonus factors in their study, they did prove that [PERSON]'s satisfaction model could be used in the field of IEQ. It could help researchers to understand the influence of different IEQ indicators on overall IEQ satisfaction from a different perspective. Another exciting analytical method is artificial neural network (ANN) modeling; by employing this method, [PERSON] [89] analyzed eight different network structures and found one model with one hidden layer with 10 neurons that could successfully reflect the relationship between the occupants' symptom prevalence and the measured IEQ indicators.  
[PERSON] [51] stated that developing an IEQ model that works in all countries is impossible. The comparison results between linear and non-linear regression analyses indicated that the selection of research approaches significantly influenced the assessment results [16]. Thus, it is necessary to set a unified analysis procedure that includes the individual index calculation and the weighting approach. If the same data analysis methods are applied in future studies, then the IEQ performance in different indoor environments can be compared, and a global database can be built. By that time, for those studies where subjective data are difficult to collect, the researchers could select suitable weighting schemes from the database, based on the building type, outdoor climate, etc.
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 4 Proposed 5G-NR Decoder Architecture
### Comprehensive Decoder Architecture
Data flow is color-coded for clarity: bold black for the main data path, blue for raw data, pink for processed data, orange for layer controls (shifting data), and violet for the processed data memory. (1:Mp Demux), based on the locations of non-1 and \(-1\) entries in the base graph, to store in memory for future decoding layers. The decoder architecture requires only two principal memories. First, the MS-mem block stores the MINU outputs at the end of each decoding layer to be used for the subunit subtraction. It consists of several Mp memory subblocks. Every subblock corresponds to a layer in the base matrix. Therefore, each subblock is designed according to the weight of the convenient row. In the base graph, the first four layers' weights are dc = 19, so they require 19 sub-memories each, while the fifth layer weighs 3, so it requires 3 sub-memories. Furthermore, each sub-memory contains Z memory locations. Then, the Lay-Mem block comprises Np memory banks of Z units to store the updated data after each layer process.
## 1 Introduction
Moreover, the presented approach is less prone to consider false positives memory accesses. Regarding non-access, a false positive is considered if the data have been loaded into the cache and removed from the cache in a short period of time, which is unlikely to happen. Whereas a false positive in the traditional approach is considered whenever the data are loaded into the cache in any other round of the AES encryption, rather than the target round, or whenever these data are predicted to be used, thus are speculatively loaded into the cache, being both frequent options. In summary, the main contributions of this work are the following  
* We present a non-access attack, a novel approach to exploit information gained from the cache misses. * We show that our approach improved the performance of previous cache attacks and demonstrate its effectiveness for _Flush+Reload_, _Flush+Flush Prime+Probe_ and _Prime+Abort_. We could reduce the amount of encryptions required to derive the key between 93% and 98%. That is, if an access attack requires 100,000 samples, our approach requires fewer than 3000, performed in a similar and real experimental setup. * We extend the non-access attack to gain information from more rounds of the algorithm, introducing EBD, a practical attack implementation that provides the full 256-bit AES encryption key using cache side channel attacks. * We show that the complexity of the attack does not depend on the size of the key but on the number of rounds of each encryption. Passing from 10 rounds (AES-128) to 14 rounds (AES-256) increases the complexity by a factor of around 3. The remainder of this paper is organized as follows. In Section 2, we provide information on the relevant concepts to understand the attacks. Section 3 describes AES implementations and the attacks we performed to extract the keys. In Section 4, we provide a discussion of our results. Finally, in Section 5, we draw some conclusions.
# Ice Thickness Detection of Transmission Lines Based on Cross-Guide-UNet
## 3 Cross-Guide-UNet (CG-UNet)
### Cross-Guide Module  
Figure 2 shows a schematic of the CGM. The features extracted by the encoder are connected through crosslayers. Initially, channel attention is extracted, followed by the calculation of spatial attention. The calculated spatial attention utilized to weigh the decoder features guides the decoder's image recovery process. Furthermore, the features extracted by the decoder undergo the same operation, and the cross-weighted features are concatenated to generate edge images.  
\[Y_{en}=\Sigma_{i=1}^{n}A_{de}^{i}\cdot X_{en}^{i} \tag{1}\]  
Equation (1) is the calculation formula for encoder weighted attention. \(A_{de}^{i}\) represents the weight calculated by the decoder through the attention module, and \(X_{en}^{i}\) is the input feature of the encoder. The attention weights of all features were dot-multiplied with the feature vectors and combined to obtain the final encoder output feature \(Y_{en}\).  
\[Y_{de}=\Sigma_{j=1}^{n}A_{en}^{j}\cdot X_{de}^{j} \tag{2}\]  
Equation (2) is used to calculate the weighted attention of the decoder. \(A_{en}^{j}\) is the feature attention weight of the encoder, and \(X_{de}^{j}\) is the input feature of the decoder. Similarly to Equation (1), point multiplication is performed to obtain the output feature \(Y_{de}\) of the decoder.  
Figure 1: CG-UNet model structure.