esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, we performed high-throughput small RNA sequencing to detect miRNAs in five tissues (root, leaf, stem, flower, and fruit) of wax gourd. In total, 422 known miRNAs and 409 novel miRNAs were identified and their target genes were also predicted. Differentially expressed miRNAs were clustered into 20 profiles based on the expression trend among five tissues. MiRNAs in profiles 1, 4, 6, 8, 11, 13, 15, 17, and 19 had more expression in the fruit of wax gourd. MiR164-x in profile 13 had the highest expression in fruit and was proposed to regulate fruit development by forming miR164-NAC module with its targets NAC transcription factors. To our knowledge, this is the first comprehensive report of miRNAs identification in wax gourd. Combined with bioinformatic analysis and experimental validation, these results will contribute to the functional characterization of miRNAs and understanding the regulatory roles of miRNAs in different processes of wax gourd.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112110068/s1](https://www.mdpi.com/article/10.3390/app112110068/s1)]([https://www.mdpi.com/article/10.3390/app112110068/s1](https://www.mdpi.com/article/10.3390/app112110068/s1)), Figure S1: Length distribution of novel miRNAs, Table S1: Primers used for qRT-PCR analysis, Table S2: Expression profile of all known and novel miRNAs among five tissues of wax gourd, Table S3: Genomic location and hairpin structure of reference genome mapped known miRNAs, Table S4: Genomic location and hairpin structure of novel miRNAs, Table S5: Detailed information of target genes of miRNAs, Table S6: Pairwise comparison of the expression of miRNAs among five tissues. Table S7: The Spearman's correlation test between qPCR data and RNA sequencing data tissue-by-tissue.  
Conceptualization B.J. and J.Y.; data curation J.Y.; formal analysis [PERSON], M.W., W.L. and Q.P.; writing--original draft preparation B.J. and J.Y.; writing--reviewing and editing, B.J., J.Y. and X.H.; visualization J.Y.; resources D.X. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Science and Technology Program of Guangdong (2020B020220003), the National Natural Science Foundation of China (31972403, 31801851), Guangdong Basic and Applied Basic Research Foundation (2020A1515111138), Discipline team construction project of Guangdong Academy of Agricultural Sciences(202114 TD), Training plan for young and middle-aged discipline leaders of Guangdong Academy of Agricultural Sciences (R2020 PY-JG003), and the Science and Technology Program of Guangdong (2020A0505020006).
## 3 Joint Optimization Algorithm Design  
In this section, due to the binary constraint (C11) and the non-convex constraints (C6, C7, C14-C16), it is difficult to solve the formulated mixed-integer non-convex problem P1 effectively and directly. Hence, we first propose a DRL-based algorithm to preliminarily determine the UAV trajectories, mission mode, and transmit power of the buoys. Then, we designed an SMP-based association algorithm (SAA) and an UAV-OBS association algorithm (UAA) to solve the buoy-UAV and UAV-OBS association subproblem, respectively.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 3 System Model
### Radio Model  
In the WBAN, each sensor node consumes energy during the processes of data sensing, processing, and transmission, but the data transmission process consumes the largest percentage of energy, so we use a first-order radio model for the calculation. The formula is as follows:  
\[\mathrm{E_{Tx}(N,I)=E_{Tx-elec}\times N+E_{Amp}\times N\times I^{2}}, \tag{1}\]  
\[\mathrm{E_{Rx}(N)=E_{Rx-elec}\times N}. \tag{2}\]  
where \(\mathrm{E_{Tx}(N,I)}\) represents the energy consumed by sending data, \(\mathrm{E_{Rx}(N)}\) represents the energy consumed by receiving data, \(\mathrm{N}\) is the size of the data packet, \(\mathrm{I}\) is the data transmission distance, and \(\mathrm{E_{Tx-elec}}\) and \(\mathrm{E_{Rx-elec}}\) represent the energy consumption of the circuit when the node sends data and the circuit when receiving data. \(\mathrm{E_{Amp}}\) represents the energy consumed by the amplifying circuit.  
In the WBAN, the human body as a communication medium attenuates the transmission signal. Therefore, the path loss parameter is added to the energy model, and the formula for calculating the energy consumption of the node is  
\[\mathrm{E_{Tx}(N,I)=E_{Tx-elec}\times N+E_{Amp}\times N\times c\times I^{2}}. \tag{3}\]
## 4 Discussion
### Uncertainties, Errors, and Accuracies  
The concept of FPAR or FPARcanopy (the fraction of photo-synthetically active radiation absorbed by plant canopies) is employed in this studies, just like it is widely used in many classical models, such as GLO-PEM [42], 3-PG [43], MODIS-PSN [44], C-Fix [45], EC-LUE [46], _etc._ However, only the PAR absorbed by chlorophyll (FPARchl) or other photosynthetic pigments throughout the canopy is used for photosynthesis. Therefore, A few studies indicate that this widely used FPARcanopy has proved to be physiologically insufficient to calculate GPP [47, 48, 49, 50]. For all this, they utilize MODIS reflectance products to calculated FPARcanopy and FPARchl, and evaluate differences between FPARcanopy and FPARchl. By using FPARchl instead of FPARcanopy to calculate GPP, [PERSON] _et al._[49] presented that uncertainty of estimations was reduced. It indicates that FPARchl rather than FPARcanopy should be widely employed to calculate GPP in the further study.
## 3 Results and Discussion
### Micro-Section Analysis and Micro-Hardness Testing  
In the final part of the present work, micro-hardness testing and micro-section analysis were performed. For the welding joint test, a Micro Vickers hardness tester (Mitutoyo, Aurora, IL, USA) was used to measure the hardness of the welded samples. A Vickers hardness test with a load of 1.961 N was used to measure the hardness. The selected welded samples were experimented at Ia of 250 A and three EN ratios. Measurements were conducted on BM, fusion zone (FZ) and HAZ. Figure 17 shows the hardness distribution measured at a distance of 2 mm below the top of the base plate. It was found that at the same value of current, i.e., 250 A but at different EN ratios, the micro-hardness distribution was not strikingly affected. The measured average hardness of the BM was found to be 160 HV, whereas, in the FZ, the maximum hardness was found to be 213 HV. The higher hardness value was found in the FZ followed by low hardness in HAZ. The lower hardness in the HAZ is due to the fact that coarse grains appeared in HAZ, which resulted in the lower hardness in this zone. For understanding the microstructures of welded joints, microstructures of the welded joint at Ia of 250 A and 50% EN ratio were visualized. The microstructures of different zones of interest like weld metal, HAZ and FB were viewed. The microstructures, after polishing and macro-etching, the cross sections of the joints were captured with an optical microscope (AmScope, Irvine, CA, USA) coupled with an image analyzing software (AmScope, Irvine, CA, USA) to facilitate measuring of the details like cross-section areas of the faces and roots of weld metals as shown in Figure 18. The microstructure of the BM from Figure 18a revealed that grains were oriented in the rolling direction. The microstructure of the BM was composed of ferrite and pearlite. The microstructure of the BM was the layer laminated structure of distribution of ferrite and pearlite. The light regions were ferrite and dark ones are pearlite. The amount of pearlite was relatively small. From Figure 18b, microstructure of the welded joint in the weld metal showed columnar grains that are dendritic. Figure 18c shows the microstructure of the HAZ. The grain morphology in HAZ was different from the BM. The microstructure of the HAZ showed large grains. The increase of the grain size was due to high heat input and low cooling rates. Low cooling rate increased the size of the grains. Figure 18d shows the boundary between FZ and HAZ. Large grains were observed in HAZ near the FZ.  
Figure 17: Vickers hardness distribution across weld joint.
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Situ Methods for R&D Purposes
#### 3.2.1 High Temperature X-ray Diffraction (HT-XRD)
The combination of EBSD, for example, can also be used to identify the influence of the grain structure on the forming behavior [83; 84]. The difficulty in this case is to obtain good image quality even at high temperatures. Similarly, as mentioned before, the sample preparation requires a lot of effort. Furthermore, the acquisition costs of such a system are enormously high.
## 5 Description of Experiment and Analysis of Experimental Result
### Experimental Data and Parameter Values  
The test data for the calculation example are obtained using random generation. The customer demand is generated randomly within the interval [0, 3], and the \(x\) and \(y\) coordinates of the customer's position are generated randomly in [0, 100]. The service time is set to 10 min. The data pertaining to the distribution center and transit point are set according to practical significance. Tables 1 and 2 list the detailed data. The distribution center is represented by DC, the transit point by Pi (\(i\) = 1,2,3,4),and the customer point by natural numbers 1, 2, 3,..., 30. The values of the parameters in the model are: \(Q_{1}=25\) t; \(Q_{2}=8\) t; \(c_{1}=2\) yuan/km; \(c_{2}=1.5\) yuan/km; \(c_{1}^{\prime}=100\) Yuan/h; \(c_{2}^{\prime}=60\) yuan/h; when \(Q_{1}=25\) t, \(W_{1}=1.2\) kg/h, \(W_{2}=2.2\) kg/h, \(v_{1}=60\ km/h\); when \(Q_{2}=8\) t, \(W_{1}=0.8\) kg/h, \(W_{2}=1.8\) kg/h; \(v_{2}=40\ km/h\); \(P_{1}=4000\) yuan/t; \(\theta_{1}=0.003\); \(\theta_{2}=0.002\); \(\theta_{3}=9.3\) yuan/kg; \(\lambda_{1}=50\) yuan; and \(v_{3}=40\) t/h. The values of the parameters in the algorithm are: the initial population size \(N=100,gen=1\), max iteration number _maxgen_ = 500, crossover probability \(p_{c}=0.8\), and mutation probability \(p_{m}=0.1\).
## 4 Results and Discussion
### Numerical Results--SBD2D Communication Using Data Scheduling
\begin{table}
\begin{tabular}{l l} \hline _Parameter_ & _Value_ \\ \hline _Bandwidth_ (_MHz_) & 40 \\ _Area_ (_m_) & 4 \(\times\) m \\ _Pathloss exponent_ & 3.76 \\ _Noise over pathloss at 1 km (dBm)_ & 33 \\ _Amplifier efficiency_ & 0.39 \\ _Quality of Service requirements_ (_Mbps_) & 50 for at least 95\% of the user \\ _SINR Threshold_ (_dB_) & 3 \\ _Type of D2D channel model_ & Free space Propagation channel \\ _Static power (W)_ & 10 \\ _Circuit power per active user_ (_W_) & 0.1 \\ _Circuit power per BS Antenna_ (_W_) & 0.2 \\ _Signal processing coefficient_ (_mW_) & 3.12 \\ _Antenna Gain_ (_dB_) & G0 = 20 \\ _Noise power spectral density_ (_dBm/Hz_) & \(-154\) \\ _Receiver node_ (_dB_) & 7 \\ _Coding/decoding/backhaul (bit/f)_ & 1.15.10\(-9\) \\ \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. As indicated in Figure 7a,b, some parameters are to be considered without a loss of generality. The exact channel gain parameters configured as IG\({}_{1}\) and IG\({}_{2}\) are interference parameters, and \(\alpha_{1}\), and \(\alpha_{2}\) are the noise parameters of channels 1 and 2 between the corresponding beekeepers and base stations, respectively. Figure 7a,b represent the two near-to-near cells in the cellular network applied in the SBD2D communication energy efficiency model to improve the transmission of data in SBD2D communication. The impact of the base stations' transmission power on the energy efficiency in smart beekeeping and the spectrum efficiency is demonstrated by the above figures. It also represents the impact of the number of antennas by the beekeepers, where two types of channel gain parameters are set to the specified values for the simplicity of illustrating the energy efficiency and spectrum efficiency changes. The IG\({}_{1}\) = IG\({}_{2}\) = 0.1 and \(\alpha^{2}_{1}\) = \(\alpha^{2}_{2}\) = 0.1, the values of the base station transmission power are standardized from 0 to 1. According to Figure 7a, the transmission power of both base stations influences the energy efficiency. The energy efficiency non-linearly decreases when the transmission power increases. The transmission power of both the  
Figure 6: Energy efficiency with Antenna/Channel propagation and energy efficiency, including circuit power. Figure 7: (**a**) Data rate vs. power transmission. (**b**) Energy-efficient vs. power transmission. BSs has an impact on the spectrum efficiency, as illustrated in Figure 7b. The spectrum efficiency non-linearly increases if the transmission power of the base stations is increased. Additionally, the smaller the value of the transmission power, the greater the impact on the energy efficiency and spectrum efficiency. We can conclude from the figures that small cells improve the SNR (signal-to-noise ratio), because you obtain massive power for the same power transmission and an improved SNR, because you get closer to the home BS; you also get closer to the other BS. There is also an improvement in the cell-edge SINR (signal-to-interference noise ratio) and the circuit power shared.
## 3 Experimental Validation
### Comparison Results
#### 3.3.2 XYZ Point Cloud Data  
Experiments were conducted to assess the efficacy of the four methods when applied to XYZ point cloud data. The number of input points was 4096; therefore, the dimensions of the input data were [4096; 3]. Figure 7 presents the mIoU curves of the four training methods. The curves obtained from XYZ data presented more pronounced fluctuations than did the training curves obtained using XYZRGB data. This can be attributed to the fact that the XYZ dataset did not include color elements, and there were only 25% as many data point. As shown in Figure 7, the blue curve obtained using the baseline training method achieved accuracy of 86.23% (at epoch 78), which is 5% below the accuracy obtained using XYZRGB data. The green curve obtained using the balanced focus loss function achieved accuracy of 88.20% accuracy (at epoch 48), which is 2% better than baseline method. The red curve obtained using the hybrid-sampling method achieved accuracy of 90.08%, which is 4% higher than the baseline method and slightly better than the balanced focus loss function. The pink curve obtained using the hybrid-sampling method with the balanced focus loss function achieved accuracy of 92.62%, which is 6.39% higher than the baseline method, 2% higher than hybrid-sampling, and 4% higher than the balanced focus loss function.  
Figure 6: Comparison of ground truth and prediction results obtained when XYZRGB point cloud data was applied to the baseline or proposed training methods. The prediction results obtained using the baseline training method were affected more by noise than were the results obtained using the proposed method: (**a**) ground truth; (**b**) point cloud predicted using conventional training method, showing erroneous prediction on the wrench; (**c**) point cloud predicted using the proposed hybrid training method.  
Figure 7: Comparison of mIoU curves obtained using the four methods to process XYZ point cloud data.  
Figure 8a-c compare the ground truth of the point cloud with the prediction results obtained using the baseline PointNet method, and the prediction results obtained using the proposed two-stage framework when applied to XYZ point cloud data. As shown in Figure 8b, the pipe (red points) was erroneously segmented as blue points by the baseline method. As shown in Figure 8c, the proposed framework succeeded in segmenting the pipe as red points.  
Figure 9a-c present another comparison of the ground truth with the baseline training method and the proposed two-stage framework when applied to XYZ point cloud data in the case of multiple objects. As shown in Figure 9b, the baseline model was unable to predict the wrench completely. As shown in Figure 9c, the proposed two-stage framework succeeded in predicting the point clouds of both the pipe and the wrench. Again, the two-stage framework outperformed the baseline training method when applied to XYZ point cloud data.
## 1 Introduction
The work in [40] further used wavelet transform to extract effective representation from non-stationary EEG records and adopted LR as a classifier to identify epileptic and non-epileptic seizures. In [41], regularized linear LR was trained using the raw EEG signal without feature extraction to classify imaginary movements. In [42], LR with L2-penalization to avoid overfitting was trained using spectral power features from intracranial EEG (iEEG) signals for the analysis of the brain's encoding states and memory performance. The study in [43] further incorporated t-distributed stochastic neighbor embedding (tSNE) for dimension reduction of iEEG signals, and the learned L2-regularized LR classier was used for predicting memory encoding success. Despite the above studies, the potential of the LR model for EEG-based emotion recognition is still not fully explored. In this present study, we systematically introduced the logistic regression (LR) algorithm with Gaussian kernel and Laplacian prior [44; 45; 46] for EEG-based emotion recognition. Different from these LR classifiers, Gaussian radial basis function (RBF) kernel was used to enhance the data separability in the transformed space [46]. Moreover, Laplacian prior promoting the sparsity of logistic regressors was acted as L1-regularization [44]. This prior forces many components of logistic regressors to be zero. Thus, the learned logistic regressors with sparseness control the complexity of the LR classifier and consequently avoids over-specification in EEG-based emotion recognition. The logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm [45] was introduced to optimize the logistic regressors for lower computational complexity. Thus, the introduced LR method is abbreviated as LORSAL. For overall evaluation of the LORSAL classifier, various power spectral features and features calculated by combinations of electrodes were used as input for the classifiers. The conventional NB, SVM, linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) were used for comparison to evaluate the performance of the LORSAL classifier. This paper also presents an investigation of critical frequency bands [47; 48] and an analysis of the effect of extracted features for EEG-based emotion classification. The rest of this paper is organized as follows. Section 2 presents the materials and methods, including the dataset for emotion analysis using EEG, physiological and video signals (DEAP), various features extracted from the EEG signals, the introduced LR model with Gaussian kernel and Laplacian prior, and the LORSAL algorithm to learn LR regressors. The experimental results are shown in Section 3. The introduced method is evaluated in the task of subject-dependent emotion recognition in valence and arousal dimensions, and the compared methods include NB, SVM, LR_L1, and LR_L2. Section 4 gives the discussion and a further comparison of LORSAL and the DL methods. Related conclusion and future work are presented in Section 5.
## 3 Results and Discussion
### Trends in Human Water Use
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & **Withdraw million** & **Percent consumptive** & **Consumptive water** & **Consumptive water** \\  & **gallons/day** & **water** & **million gallons/day** & **mm\(\cdot\)yr\({}^{-1}\)** \\ \hline Thermo-electric Power & 94,701 & 2.5\% [46] & 2368 & 0.78 \\ Public Supply + Domestic & 23,258 & 23\% [46] & 5349 & 1.76 \\ Industrial & 9073 & 14\% [46] & 1270 & 0.42 \\ Irrigation & 7876 & 56\% [46] & 4411 & 1.45 \\ Aquaculture & 3787 & N/A [46] & N/A & N/A \\ Mining & 1106 & 21\% [46] & 343 & 0.11 \\ Livestock & 671 & 68\% [46] & 456 & 0.15 \\ Total & 140,471 & & 14,197 & 4.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Water withdrawal by water-use category for the U.S. states east of the Mississippi river for 2005 from the USGS [38]. We use the total area of the study region (\(3.57\times 10^{12}\) m\({}^{2}\)) to convert the unit from gallons/day to mm/year. Numbers in brackets are references.
# Sentiment-Aware Word Embedding for Emotion Classification
## 4 Experiments
### Experimental Results
Specifically, the performance of the method based on the Skip-Gram and that based on the hybrid word vectors proposed in this paper were compared. As is shown in the Table 5, the experimental results of hybrid word vectors as classifier input are better than the experimental results of the initial word embedding as classifier input. Among them, hybrid method 1 is slightly better than hybrid method 2. The main reason is that mixing the methods increases the difference between the word vectors of different emotions more significantly. Mixing method 2 also brings about the improvement of precision, but the increase is not significant. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Method** & **Evaluation Indicators** & **HAPPINESS** & **TRUST** & **ANGER** & **SADNESS** & **FEAR** & **DIGUST** & **SURPRISE** \\ \hline \multirow{4}{*}{Support vector machine (SVM)} & \multirow{2}{*}{Hybrid method 1} & Prec & 0.7900 & 0.796 & 0.7901 & 0.6364 & 0.6579 & 0.7273 & 0.6490 \\  & & Rec & 0.83