esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Vacuum Central Drum Seed Metering Device
## 6 Conclusions  
In this paper, a vacuum central drum seed-metering device was designed by hybrid rice direct-seeding requirements. The main research conclusions are presented as follows:  
1. A vacuum central drum seed-metering device including double drums, ventilation housing, agitating device, and cleaning devices was designed. The main structural parameters of the seed-metering device were determined through theoretical analysis and experimental research. There are two areas of positive and negative pressure that are opened inside the ventilation housing; The inner drum is provided with 8 groups, 5 rows of flow channels with and a length of 340 mm. The outer drum has a diameter of 135 mm and a length of 350 mm. The outer drum is nested with the inner drum. Different types of shaped holes are opened on the outer drum, concentric with the inner shaped holes, and can be replaced according to different seed requirements.
2. The performance of the seed-metering device was tested by single-factor and multi-factor tests. The results show that the primary and secondary order of influencing the qualification rate index was A > C > B, and A\({}_{1}\)B\({}_{1}\)C\({}_{2}\) was the best combination for improving the qualification rate; the primary and secondary order of influencing the missed seeding rate index was A > C > B, and A\({}_{2}\)B\({}_{3}\)C\({}_{2}\) was the best combination for improving the missed seeding rate; the primary and secondary order of influencing the replay rate index was A > C > B, and for improving the missed seeding rate. The analysis of improving the replay rate showed that A\({}_{3}\)B\({}_{1}\)C\({}_{1}\) was the best combination. The uniformity test of seed-metering in each row showed that the seed-metering in the row that was close to the negative pressure inlet was higher than that in the row that was far away. The average seed-metering in the three groups was 76.72 g, the standard deviation was 4.19, and the coefficient of variation was 5.45%.
3. The trajectory of seeds that were dropped was studied by means of high-speed camera technology. The 5 mm \(\times\) 5 mm grid coordinate plate recorded the dropping coordinates at different times, using EXCEL software to draw the seed dropping trajectory, carried out fitting equations, drew the three-dimensional structure model of the seed-metering tube through the equation of the dropping trajectory, and printed the seed-metering tube with 3D technology to ensure better uniformity of seeds in furrows.
## 1 Introduction
For intraoperative navigation, [PERSON], [PERSON] et al. used deep residual networks for cavity segmentation of ureteroscope images [36]; in terms of stones, [PERSON], [PERSON] improved HybResUNet for multi-class segmentation in lithotripsy surgery to assist in decision-making [37]; [PERSON], [PERSON] et al. used residual networks for multi-classification of the appearance and core of stones to distinguish different types of stones [38]; and [PERSON], [PERSON] et al. used images from holmium laser surgery for the same task training, achieving good practical results [39]. On the other hand, [PERSON] classification detection is also performed through CT images [40]; [PERSON], [PERSON] et al. attempted intra- and extracorporeal stone classification using Inception v3 and achieved good results [41]. For the task of ureteral orifice recognition, [PERSON] et al. first proposed improvements using the SSD model by adding three convolution modules as additional feature layers to increase the output of detected anchor boxes [42]. In subsequent enhancements, [PERSON] et al. [43]. fused the improved algorithm with the CSRT tracking algorithm, using a conventional ureteroscope with Wolf (brand) F8/9.6 (F is the Fr unit, 8 is the finest outer diameter, and [PERSON] is the coarsest diameter) for image collection, conducting universality experiments on videos from various platforms. The aforementioned studies still have considerable room for further exploration in terms of algorithm parameters, complexity, robustness, accuracy, and the platforms used for image collection. On one hand, in the clinical scenario of ureteroscopy, although research has compared the accuracy of target detection standards, there has been no related improvement or progress in the complexity and computational cost of models. On the other hand, traditional target detection methods perform well on SOTA datasets but still require corresponding fine-tuning for better adaptation to downstream tasks. Research on ureteral orifice recognition is still at a relatively early stage, with the possibility of further research. In response to the above situation, this paper collected clinical ureteroscope images to create a dataset, improved and trained on the classic YOLO network, and ultimately achieved effective monitoring of ureteral openings. The main innovations of this paper are:  
1. Combining practical scenarios, using ConvNextV2 to improve the feature extraction backbone of the detection network. 2. Introducing the SCConv convolutional structure on the basis of mitigating the impact of redundant features through spatial and channel enhancement to optimize the network structure. 3. Introducing attention mechanisms in the feature fusion stage to improve detection performance. 4. Replacing the original loss function to better suit the usage scenario and improve detection performance.
## 3 Results and Discussion
### Effect of Tool Wear on Milling Force  
Figure 7 shows the measurement results of the cutting force in the milling CFRP with the two styles of cutting tools. It can be seen from the figure that for both styles of the cutting tools, the value of the axial cutting force was much smaller than that of both the feed cutting force and the radial cutting force. The stress of the rhombic milling cutter was more desirable, and its value was relatively small. However, the cutting force of the staggered helical milling cutter fluctuated in a larger range because of the larger impact effect.  
Figure 8a shows the comparison of the cutting forces in the milling carbon fiber composites with the two styles of cutting tools. It can be learned from the figure that with the aggravation of the tool wear, the three-directional cutting forces of the two cutting tools tended to change almost identically, that is, they all increased to a certain extent. Therein, the feed cutting force (Fx) and the radial cutting force (Fy) increased by a larger margin, while the axial cutting force (Fz) increased by a smaller margin. The cutting state analysis of the two milling cutters is shown in Figure 8b. When the milling length was 12 m, the three-directional cutting forces from the staggered helical milling cutter were all more than the ones from the rhombic milling cutter. That is because the cutting edge of the rhombic milling cutter consisted of multiple segments, which reduced the cutting load per unit of the cutting-edge length. As a result, the chip removal was smoother, and the friction between the chips and flank face largely decreased, and thus the cutting force obviously decreased. At this point, the axial forces produced by the two styles of milling cutters were both smaller. For one thing, the helical angle of the cutting edge took a decomposing role, and for another, the sinistral and dextral cutting edges produced a certain degree of counter action. When the milling length reached 44 m, the three-directional cutting forces of the staggered helical milling cutter increased more obviously. Here, the tool wear of the staggered helical milling cutter was more serious; its coating fell off in a large area and its cutting edge was obviously blunted. In addition, its cutting mechanism transformed into extrusion from the original shearing, which greatly increased the resistance in the cutting process.  
Figure 7: The measurement results of cutting force in milling carbon fiber reinforced plastic (CFRP), (**a**) machining with the rhombic milling cutter and (**b**) machining with the staggered helical milling cutter.
# RIS-Assisted Cooperative Time-Division Multiple Access
## 2 RIS-Assisted Cooperative Time-Division Multiple Access
### Proposed Algorithm  
Table 1 shows the pseudocode for the RC-TDMA algorithm. In Step 1, the related variables are initialized, in which \(T\) is the total number of time slots, \(K\) is the total number of UEs, and \(Count_{1}\) and \(Count_{2}\) are used for counting the number of weak channel-conditioned and strong conditioned UEs, respectively. Note that \(T=Count_{1}+Count_{2}\). \(\mathbf{W}_{weak}\) and \(\mathbf{W}_{strong}\) are the matrix to save the transmit beamforming vectors for the weak-channel-conditioned and strong-channel-conditioned UEs, respectively, where each column index of each matrix indicates the time slot index. \(\mathcal{T}\) is a set of the indexes of time slots selected as cooperative time slots in Step 2. \(\mathbf{H}_{sort}\) is the channel matrix consisting of the channel vector from BS to each UE, where each vector is sorted in increasing order based on each channel gain. \(\mathbf{G}_{sort}\) is the channel matrix consisting of the channel vector from RIS to each UE, where each vector is sorted corresponding to \(\mathbf{H}_{sort}\). Note that the time slots are assigned sequentially to UEs according to the increasing order of each UE's channel gain. Thus, each UE has a dedicated time slot, and the index of UE is the same as the index of the dedicated time slot. From lines 5 to 14, the number of weak-channel-conditioned UEs, \(Count_{1}\), and the number of strong-channel-conditioned UEs, \(Count_{2}\), are calculated based on the comparison between the required SINR (i.e.,\(\gamma_{req}\)) and the SINR calculated using Equation (2). When calculating \(Count_{1}\) and \(Count_{2}\), the transmit beamforming vectors are determined using Equation (9) under the assumption that each UE is served in a non-cooperative time slot. After the cooperative time slots are determined in Step 2, the transmit beamforming vectors associated with the cooperative time slots are updated. In line (15), \(N_{c}\) is the number of cooperative time slots, which can be determined as a minimum value of either \(Count_{1}\) or \(Count_{2}\).
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 4 Code Design and Advantages
### Additional Advantages
Hence, in comparison to binary coding, the per-bit decoding complexity is (at least) increased by a factor of \(Q\). In practical application, the decoding algorithm can adopt Log-FFT-BP. First, a large number of convolution operations are converted into multiplication operations in frequency domain, and then multiplication operations are converted into addition operations in log domain, which can effectively reduce the decoding complexity.
## 4 Practical Algorithms for Action Masking
### Composite Objective Algorithm  
The On-PIAM algorithm aims to improve performance of the masked policy, and the Off-PIAM algorithm focuses on optimizing the original policy. A question naturally arises, can we optimize two policies concurrently? The solution is to design a composite objective, making a compromise between the performance of the masked policy and the original policy. In the following, we present the Composite Objective Invalid Action Masking (CO-IAM) algorithm, which is actually an on-policy algorithm with an auxiliary optimization objective. Inspired by Theorem 1, we design the composite optimization objective  
\[L_{\text{CO}}(\theta)=J(\pi^{\varnothing}_{\theta})-c_{\theta}\Phi(\pi_{ \theta}), \tag{31}\]where  
\[J(\pi_{\theta}^{\alpha}) =\mathbb{E}_{s_{0}\sim\rho,\mu_{t}\sim\pi_{\theta}^{\alpha},s_{t+1} \sim\mathcal{P}(\cdot|s_{t},\mu_{t})}\left[\sum_{t=0}^{H}\gamma^{t}r_{t}\right], \tag{32}\] \[\Phi(\pi_{\theta}) =\mathbb{E}_{\tau\sim\pi_{\theta}^{\alpha}}\left[\sum_{t=0}^{H} \sum_{a_{i}\in\mathcal{A}_{\text{inv}}(s_{t})}\pi_{\theta}(a_{i}|s_{t})\right]. \tag{33}\]  
The first objective item \(J(\pi_{\theta}^{\alpha})\) is the cumulative reward following the masked policy \(\pi_{\theta}^{\alpha}\). The second objective item \(\Phi(\pi_{\theta})\) is the cumulative probability sum of all invalid actions. \(c_{\phi}\) is the coefficient that balances two items. The second objective item aims to reduce the probability of invalid actions when \(c_{\phi}>0\). In short, the composite objective aims to optimize the cumulative reward and adjust the probability of invalid actions concurrently.
## 3 Process for Training a Neural Network
### Neural Network Structure  
The structure of the neural network affects the final training results, so it should be carefully and reasonably designed. The network was fully connected, as shown shown in Figure 3. The input layer was composed of \((m+1)\) neurons, which was represented as the vector  
\[z_{0}^{in}=[d_{1}^{i}\;d_{2}^{i}\dots\;d_{m}^{i}\;\phi^{i}]^{\top}. \tag{9}\]  
Since the data values may vary in rather different ranges, a rescaling step was performed at the input layer such that the output of the input layer was computed as the vector  
\[z_{0}^{out}=[d_{1}^{i}/d_{max}\;d_{2}^{i}/d_{max}\dots\;d_{m}^{i}/d_{max}\;\phi ^{i}/360]^{\top}. \tag{10}\]  
This rescaling step adjusted the input values to a relatively similar level, overcoming the potential slow convergence problem, which would occur at the large-scale dimension.  
As shown in Figure 3, the model included \(k\) hidden layers in the dashed red box, and the \(i\)-th hidden layer had \(r_{i}\) units. The output of the \(i\)-th layer was a vector \(z_{i}^{out}\in\mathbb{R}^{r_{i}}\) and was transferred to the input \(z_{i+1}^{in}\in\mathbb{R}^{r_{i+1}}\) of the \((i+1)\)-th layer as  
\[z_{i+1}^{in}=W_{i+1}z_{i}^{out}, \tag{11}\]  
where \(W_{i+1}\in\mathbb{R}^{r_{i+1}\times r_{i}}\) is a weight matrix. An activation function \(f_{i,j}(\cdot)\) was applied before the network output of each layer, which is defined as  
\[z^{out}=f_{i,j}(z^{in})=\text{RReLU}(z^{in}+b_{i,j}), \tag{12}\]  
where \(\text{RReLU}(\cdot)\) is called a randomized rectified liner unit function defined as  
\[\text{RReLU}(x)=\begin{cases}\;x,\;x\geqslant 0,\\ ax,\;\text{else}.\end{cases} \tag{13}\]  
In Equation (13), coefficient \(a\) is randomly sampled from a uniform distribution. The function \(\text{RReLU}(\cdot)\) serves is that of a nonlinear factor to solve problems that cannot be solved by linear models. Compared to other activation function such as Sigmoid, it saves computation load during the backward updating process of the neural network.  
The output corresponds to the possibility of each label, so the input vector of the output layer \(z_{k+1}^{in}\in\mathbb{R}^{n}\) is processed by  
\[z_{k+1}^{out}=g(z_{k+1}^{in}), \tag{14}\]  
Figure 3: The structure of the neural network model.  
where \(g(\cdot)\) is a softmax function defined as  
\[g(x)_{i}=\frac{e^{x(i)}}{\sum_{j=1}^{n}e^{x(j)}}. \tag{15}\]
## 5 Results and Discussion
### Multicollinearity Analysis  
Before the regression model analysis, the independent variables must pass the multicollinearity test. The independent variables with collinearity are screened by the Pearson correlation coefficient and variance expansion factor to ensure the effect of passenger flow prediction. Through correlation analysis, when the Pearson correlation coefficient is greater than 0.8, it shows that there is multicollinearity between the independent variables. After  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types of** & **Moran’s I** &
\begin{tabular}{c} **Expectation’s** \\ **Index** \\ \end{tabular} & **Variance** & \(Z\) **Score** & \(p\) **Value** \\ \hline \hline \multicolumn{6}{c}{**Dependent variable**} \\ \hline \(Y\) & 0.149558 & \(-\)0.006061 & 0.001300 & 4.315885 & 0.000016 \\ \hline \multicolumn{6}{c}{**Independent variable**} \\ \hline \(X_{1}\) & 0.365038 & \(-\)0.006061 & 0.001325 & 10  
the multicollinearity test, the correlation coefficients between variables such as servicing businesses (\(X_{3}\)), public services (\(X_{5}\)), government and corporate offices (\(X_{6}\)), commercial housing (\(X_{7}\)), and the transportation hub (\(X_{8}\)) are high (show Figure 5 below, where \"*\" denotes significant correlation, and \"***\" denotes highly significant correlation), indicating that there is multicollinearity between them.  
In addition to considering multicollinearity, the independent variables that have a significant impact on the dependent variables are also selected. In this paper, the stepwise regression method is used to optimize the model by gradually increasing or decreasing the independent variables to find the most effective set of independent variables for the prediction of dependent variables. Finally, five independent variables are obtained: average house price, government and corporate offices, commercial housing, floor area ratio, and accessibility.
## 4 Performance Evaluation
### Simulation Setup  
To analyze the performance of our algorithm, a rectangular monitoring region of size 200 \(\times\) 300 \(m^{2}\) is taken and the algorithm is simulated using ns 2.33. The number of nodes deployed over the said monitoring region varies from 250 to 500 including normal, beacon and anchor nodes. In our simulation, \(80\%\) to \(90\%\) of the total number of deployed nodes is taken as the normal nodes without location information and rests are considered as the nodes with known location (beacon and anchor nodes). In our simulation, IEEE 802.15.4 MAC is considered as the medium access mechanism. Communication range of all normal nodes is fixed at 40 m and value of path loss exponent is set to 2. Each beacon node transmits beacon packet in an interval of 2 ms and the initial energy resource of each sensor node is considered as 50 joules, which is decreased by 0.3 joules in each transmission. Besides, Shadowing Model is used in our simulation to simulate the shadow effect of obstructions between the transmitter and receiver. In the simulation, the shadowing deviation of 10 dB and path loss \((\beta)\) is taken 3, which are suitable for the shadowed outdoor environment [23, 24].
## 1 Introduction
The existing models are insufficient to reliably estimate coconut yields because of their inability to handle complex environmental factors and non-linear connections. Furthermore, the influence of environmental conditions on coconut yields has not been taken into account properly by the earlier models. This study presents a reliable and accurate coconut yield prediction model to support informed decision-making in coconut farming and agricultural research by filling these gaps and accomplishing the research objectives. Based on the results of this study, Hyperparameter Optimization (HPO)-based classification is recommended. Further, automated HPO is designed to minimize human effort and improve classification. The concise contributions are as follows:* To perform crop yield prediction, continuous data from coconut yields is collected using IoT devices. The gathered data includes information about the past 10 years from 2011 to 2021. * Further, redundant attributes in the collected data are removed by using LASSO before performing the prediction. * The BILSTM classifier uses historical and future data to estimate the coconut yield while the LFSOA optimizes hyperparameters. The BILSTM offers an effective prediction by using the information from both the backward and forward directions. * The incorporation of LF into conventional SOA provides a means to effectively navigate complex search spaces and identify optimal results, leading to improved handling of the control search and helping in achieving a better set of hyperparameters. * The developed LFSOA-based hyperparameter tuning helps to perform reliable prediction by avoiding overfitting risk. The proposed BILSTM-LFSOA predicts coconut yields in terms of statistical and classification measures. The preexisting literature has suffered from ineffective data processing during the prediction process, a failure to analyze complex and non-linear dependencies, and risks of overfitting. This research seeks to fill this research gap in the following manner: IoT sensors are used to obtain field data, thus overcoming the problem of reliable data. Meanwhile, the usage of past and future data in the BILSTM model captures complex and non-linear dependencies and therefore enhances the predictive power of the model. Finally, hyperparameter tuning using the LFSOA reduces the overfitting risk. The structure of the paper is arranged as follows. The related works about the forecast of agricultural output are included in Section 2. Section 3 provides detailed information about the BILSTM-LFSOA, while the results and comparisons are given in Section 4. Finally, Section 5 presents the conclusion.
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 4 Experimental Results
### Performance Evaluations and Comparisons
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **DHA Dataset** & **MSR-Action3D** & **UTD-MHAD** \\ \hline DAAL [27] & - & 92.3\% & - \\ DSTIP+DCSF [32] & - & 89.3\% & - \\ SDM-BSM [35] & 89.5\% & - & - \\ DMM+CL-HOG [36] & - & 89.7\% & 83.5\% \\ DMM-MV [36] & - & 91.9\% & 85.3\% \\ DMM-LOGP [36] & - & 84.2\% & 88.4\% \\ VD-DMM [38] & - & - & 85.1\% \\ WDMM-HOG [39] & - & 91.9\% & - \\ EGSA [41] & - & 89.5\% & 82.8\% \\ TGSA [41] & - & 89.8\% & 76.2\% \\ LP-DMI-HOG [42] & 91.9\% & 84.4\% & 85.1\% \\ LP-DMI-VGG [42] & 84.4\% & 91.9\% & 81.9\% \\ ACG [43] & - & - & 87.7\% \\ VCDN [45] & 79.8\% & - & - \\ VCA [47] & 80.9\% & - & - \\ VCA-Entropy [47] & 82.6\% & - & - \\ SeMix [47] & 82.7\% & - & - \\ LSTM [48] & 67.7\% & - & - \\ \hline
**Ours (\(\mathbf{\varphi}\))** & **92.2\%** & **93.1\%** & **93.4\%** \\
**Ours (\(\mathbf{\psi}\))** & **92.9\%** & **94.1\%** & **94.4\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons of the DHA, MSR-Action3D, and UTD-MHAD datasets with state-of-the-art methods. metric having \(k=24\) to describe the individual class results. Figure 8 depicts the confusion chart for the DHA dataset. From Figure 8, it can be said that the proposed method does work well for most of the actions except for two actions, '_LegCurl_' (70%) and '_LegKick_' (60%), which are performed by the leg. The proposed method misdirects by detecting '_LegCurl_' as the '_LegKick_', with an accuracy of 30% and vice-versa. We achieve 100% accuracy for most of actions, such as '_Bend_', '_Jack_', '_Jump_', and '_Kick_'. Likewise, for the DHA dataset, we provided the confusion chart for the best results of the MSR-Action3D dataset, as shown in Figure 9. The lowest recognition result is about 78.6% for the '_DrawCross_' action. This action is equally misclassified as '_ForwardPunch_', '_HandCatch_', and '_HorizontalArmWave_' actions, with an accuracy of 7.1%. On the other hand, most of the actions are recognized correctly, with an accuracy of 100% in the MSR-Action3D dataset. Figure 10 visualizes the confusion chart for the UTD-MHAD dataset in which the minimum accuracy is observed for '_Throw_', '_ArmCurl_', and '_Clap_' actions of approximately 68.8%, 81.3%, and 81.3%, correspondingly. The '_Throw_' is recognized as '_Catch_', '_DrawCircle (CLW)_', '_Knock_', and '_SwipeLeft_' actions. On the other hand, the '_ArmCurl_' action is misclassified as the '_ArmCross_' action with approximately 18.8% accuracy, which is the highest misclassification rate.