esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Ablation Studies
#### 5.1.1 Impact of Convolution Blocks  
A series of ablation investigations were performed on the Myogym dataset utilizing IMU and EMG sensor data to investigate the impact of using convolution blocks on the effectiveness of our model.  
The findings of ablation research examining the influence of convolutional blocks on the identification performance of the CNN-ResBiGRU model are shown in Table 7. This investigation used several sensor modalities from the Myogym dataset. The primary outcomes are outlined as follows: The CNN-ResBiGRU model, when using convolutional blocks, demonstrates superior performance compared to the baseline model that does not use convolutional blocks across all sensor modalities. The CNN-ResBiGRU model has an  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Recognition Performance**} \\ \cline{2-4}  & **Accuracy** & **Loss** & **F1-score** \\ \hline CNN & 86.43\% (\(\pm\)0.54\%) & 0.61 (\(\pm\)0.07) & 54.22\% (\(\pm\)2.97\%) \\ LSTM & 91.79\% (\(\pm\)0.42\%) & 0.33 (\(\pm\)0.01) & 73.00\% (\(\pm\)1.55\%) \\ BiLSTM & 91.20\% (\(\pm\)0.31\%) & 0.50 (\(\pm\)0.04) & 70.62\% (\(\pm\)0.92\%) \\ GRU & 89.71\% (\(\pm\)0.28\%) & 0.46 (\(\pm\)0.02) & 64.66\% (\(\pm\)0.73\%) \\ BiGRU & 89.93\% (\(\pm\)0.41\%) & 0.6 5(\(\pm\)0.03) & 65.91\% (\(\pm\)1.88\%) \\ \hline CNN-ResBiGRU & 97.29\% (\(\pm\)0.20\%) & 0.12 (\(\pm\)0.01) & 92.68\% (\(\pm\)0.59\%) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative evaluation of deep learning models for gym exercise recognition using IMU and EMG data (Scenario V).  
F1-score of 88.08% when only using accelerometer data, in contrast to the model without convolutional blocks, which produces an F1-score of 68.90%. The CNN-ResBiGRU model produces an F1-score of 87.02% for gyroscope data, while the baseline model without convolutional blocks derives an F1-score of 69.29%. The CNN-ResBiGRU model achieves an F1-score of 91.78% when including accelerometer and gyroscope data (IMU), in contrast to the model without convolutional blocks, which achieves an F1-score of 80.93%. By only using EMG data, the CNN-ResBiGRU model demonstrates a substantial improvement in the F1-score, reaching 74.49%. In contrast, the baseline model, which lacks convolutional blocks, only achieves a score of 29.92%. The CNN-ResBiGRU model achieves an F1-score of 92.68% when combining IMU and EMG data (IMU+EMG), whereas the model without convolutional blocks has a lower F1-score of 75.99%.  
This study's discoveries provide evidence for the efficacy of integrating convolutional blocks into the CNN-ResBiGRU model in collecting spatial data from diverse sensor modalities. Convolutional blocks facilitate the acquisition of distinctive patterns within the sensor data, enhancing the model's identification capabilities in various settings. The influence of convolutional blocks is notably significant in the context of EMG data analysis, as shown by the CNN-ResBiGRU model's notable enhancement in F1-score compared to the benchmark model with no convolutional blocks.
# A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks
## 3 Model Framework
### Non-Linear Low-Dimensional Graph Embedding
#### 3.2.1 Spectral Analysis on Single-Layer Graph  
Using the method of graph dimension reduction based on non-linear projection, the projection of sample points in the low-dimensional coordinate system is as follows:  
\[R_{ij}=f(W_{j}^{T}S_{i}), \tag{12}\]  
where \(S_{i}\) is the i-th row vector of the similarity matrix \(S\), \(W_{j}\) is the j-th row vector of parameter \(W\), and \(R_{i}=(R_{i1},\dots,R_{id})\) is the low-dimensional embedding landmark of \(S_{i}\). \(R_{i}\) approximates the reconstruction of \(X_{i}\), which can be represented as  
\[\hat{S}_{i}=\sum_{j=1}^{d}g(R_{ij}W_{j}). \tag{13}\]  
The objective formula for the difference in distance between the original sample \(S_{i}\) and the projected reconstructed sample \(\hat{S}_{i}\) is as follows:  
\[\sum_{i=1}^{n}\Big{|}\Big{|}\sum_{j=1}^{d}g(R_{ij}W_{j})-S_{i} \Big{|}\Big{|}_{2}^{2}\] \[=\sum_{i=1}^{n}g(R_{i}^{T}W)g(W^{T}R_{i})-2\sum_{i=1}^{n}g(R_{i}^ {T}W)S_{i}+const\] \[\propto-tr(\sum_{i=1}^{n}g(WR_{i}^{T})g(R_{i}W^{T})), \tag{14}\]  
where \(W=(w_{1},w_{2},\dots,w_{d})\); hence, the objective formula solving process can be approximated as  
\[\underset{W,R_{i}}{arg\ max}\ tr(\sum_{i=1}^{n}g(WR_{i}^{T})g(R_{i}W^{T}))\ s.t.\ W^{T}W=I. \tag{15}\]  
Therefore, the model ensures that the distance to the sample points on the hyperplane is sufficiently close, and the projections of the sample points on the hyperplane are separated as far as possible.  
Notably, when the function g(0) is a linear function, this objective model transforms into the objective function of regular spectral clustering, as shown in Formula (16):  
\[\underset{S}{max}\ tr(WSW^{T}),s.t.\ W^{T}W=I. \tag{16}\]  
Therefore, Formula (15) is defined as the non-linear objective model of spectral clustering.
# An Incentive Factor-Based Dynamic Comprehensive Evaluation on a High-Speed Railway Track
## 4 Case Study
### Data Processing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Correlation Coefficient** & **P\({}_{prf,L}\)** & **P\({}_{prf,R}\)** & **P\({}_{abs,L}\)** & **P\({}_{abs,R}\)** & **P\({}_{gge}\)** & **P\({}_{slvl}\)** & **P\({}_{twist}\)** & **TQI** \\ \hline P\({}_{prf,L}\) & 1.000 & 0.934 *** & 0.195 & 0.185 & 0.183 & 0.619* & 0.531 & 0.841 ** \\ P\({}_{prf,R}\) & 0.934 *** & 1.000 & 0.189 & 0.181 & 0.236 & 0.6483* & 0.561 & 0.86 ** \\ P\({}_{alvl,L}\) & 0.195 & 0.189 & 1.000 & 0.775 * & 0.353 & 0.223 & 0.225 & 0.420 \\ P\({}_{alu,R}\) & 0.185 & 0.181 & 0.775 * & 1.000 & 0.378 & 0.191 & 0.194 & 0.425 \\ P\({}_{gge}\) & 0.183 & 0.236 & 0.353 & 0.378 & 1.000 & 0.247 & 0.194 & 0.464 \\ P\({}_{std}\) & 0.619 * & 0.6483 * & 0.223 & 0.191 & 0.247 & 1.000 & 0.924 *** & 0.797 ** \\ P\({}_{max}\) & 0.531 & 0.561 & 0.225 & 0.194 & 0.194 & 0.924 *** & 1.000 & 0.729 ** \\ _TQI_ & 0.841 ** & 0.86 ** & 0.420 & 0.425 & 0.464 & 0.797 ** & 0.729 ** & 1.000 \\ \hline \hline \end{tabular} Note: \(\alpha^{\prime}\) represents the strength of the correlation between different indicators. *** means the strongest; *** means stronger; ** strong.  
\end{table} 
Table 5: The correlation coefficient between geometric parameters (unit: mm). different indicators. According to Table 6, these parameters were ranked in order of weight as follows: \(\mathrm{P}_{prf,R}>\mathrm{P}_{prf,L}>\mathrm{P}_{xtlvl}>\mathrm{P}_{t twist}>\mathrm{P}_{altn,R}>\mathrm{P}_{gage}>\mathrm{P}_{altn,L}\). The rankings suggest that surface irregularity had the strongest influence on TQI. Furthermore, the cross-level irregularity and twist irregularity had the second strongest influence. The results are also consistent with Figure 1, which shows that \(\mathrm{P}_{prf,L}\) and \(\mathrm{P}_{prf,R}\) had a strong influence on TQI. The results above can lead to a conclusion that the weights given to the seven parameters are reasonable.
## 4 Experiments and Results
### Validation Dataset  
The dataset used for our experiments consists of five anonymized well data recordings (S055, S075, S085, S140, and S240) containing about 1.14 million valid data sets in total (297,033, 277,146, 153,145, 207,217, and 208,696). Besides the 10 base channels directly recorded from the rig and 6 calculated basic features (cp. Table 4 for details), 1600 extended features (see Section 4.2 for details) have been calculated and taken into account. An expert  
Figure 10: Some of the features based on the sensor data. (**a**) Bit Velocity, (**b**) Bit Accel., (**c**) Pump Power.  
team of drilling engineers has labelled each of the data sets according to one of the ten corresponding borehole operational states (see Table 6), thus providing a reliable ground truth for evaluation.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Irrigation Mapping  
Using the SAR based classification, three different maps were produced which are the WT-RF based map (Figure 9(a)), the PC-RF based map (Figure 9(b)) and the CNN based map (Figure 9(c)). In the three maps, the blue and the red colors represent the irrigated and non-irrigated plots, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Class** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{4}{*}{**CNN on SAR Data**} & **Irigated** & 0.93 & 0.89 & 0.91 \\  & **Non-Irrigated** & 0.95 & 0.96 & 0.96 \\  & **OA** & & 94.1\% \(\pm\) 0.06 & \\  & **Kappa** & & 0.87 \(\pm\) 0.0014 & \\  & **F-score** & & 0.94 \(\pm\) 0.0006 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The values of the overall accuracy, Kappa, F-score, precision, and recall obtained from the CNN method on SAR data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Class** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{4}{*}{**CNN on Optical Data**} & **Irigated** & 0.93 & 0.81 & 0.87 \\  & **Non-Irrigated** & 0.91 & 0.97 & 0.94 \\ \cline{1-1}  & **OA** & & 91.6\% \(\pm\) 0.06 & \\ \cline{1-1}  & **Kappa** & & 0.81 \(\pm\) 0.0016 & \\ \cline{1-1}  & **F-score** & & 0.91 \(\pm\) 0.0006 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: The values of the overall accuracy, Kappa, F-score, precision, and recall obtained from the CNN method on NDVI data.  
The irrigated areas in the classified maps are centered mainly in the western part of the study site. The irrigated part present in the south is the Ebro Delta typically used for rice cultivation which is inundated from April to November.
# Multi-Feature Fusion Method for Chinese Pesticide Named Entity Recognition
## 5 Experimental Results and Analysis
### Analysis of Experimental Results
Thus, verifying that the local feature capture capability of IDCNN helps the model to make labeling decisions. From the experiment results in Table 3, compared with the other six models, BiLSTM-IDCNN-CRF obtains the highest performance, so the precision rate, recall rate, precision rate, and F1 score are improved and reach 78.59%, 68.71%, 91.68%, and 73.32%, respectively, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **P/\%** & **R/\%** & **ACC/\%** & **F1/\%** \\ \hline BERT-CRF & 75.56 & 41.58 & 79.98 & 53.64 \\ BiLSTM-CRF & 67.77 & 52.86 & 88.68 & 59.39 \\ BiLSTM-CNN-CRF & 72.56 & 56.72 & 90.12 & 63.66 \\ GlobalPointer & 77.19 & 57.64 & 85.85 & 66.00 \\ RoBERTa-BiLSTM-IDCNN-CRF & 77.20 & 67.5 & 90.9 & 71.7 \\ BERT-BiLSTM-IDCNN-CRF & 77.9 & 68.70 & 91.60 & 72.9 \\ BiLSTM-IDCNN-CRF & 78.59 & 68.71 & 91.68 & 73.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment results of named-entity recognition based on different models. which means it can obtain richer feature. When predicting whether a word is part of an entity, the BiLSTM-IDCNN-CRF model not only uses the semantic features of the whole sentence but also focuses on the semantic features of the characters around the word, so it obtains better results in the overall performance.
## 3 Results
### Non-Excited Jets  
Figure 3 shows the contours of instantaneous values of the vorticity modulus in the central cross-section and isosurfaces of the so-called Q-parameter (\(Q=1(U_{0}/D)^{2}\)) obtained for \(Ti=0.01\%\), \(0.1\%\) and \(1.0\%\) in the Cartesian and cylindrical coordinate systems. The Q-parameter is defined as \(Q=0.5(\Omega_{ij}\Omega_{ij}-S_{ij}S_{ij})\), where \(\Omega_{ij}\) and \(S_{ij}\) are antisymmetric and symmetric parts of the velocity gradient tensor. It is often used as an indicator of vortical structures and coherent motion of vortices [24]. Here, it nicely reveals the presence of toroidal structures stemming from the Kelvin-Helmholtz instability. They are formed downstream from the nozzle and pass through the centres of rolled-up vortices in the shear layer, where the vorticity level is the largest. Note that the vorticity contours are shown only if their values are larger than \(|\Omega|=1(U_{0}/D)\). Comparing the presented solutions, one can see that qualitatively, they are all similar.  
Figure 3: _Cont._  
As the toroidal structures flow downstream, the axial distance between subsequent vortices becomes smaller, and they combine and eventually break up into tiny turbulent structures. Important questions are where the vortices are formed and where their destruction happens. It can be seen that lowering \(Ti\) moves both these processes further downstream. Comparing the solutions obtained in the Cartesian and cylindrical coordinate systems, the first observation is that when the turbulence level is \(Ti=1.0\%\) or \(Ti=0.1\%\), the results are qualitatively very similar; please compare the cases \(NE-Car-Ti_{1,0}\) vs. \(NE-Cyl-Ti_{1,0}\) and \(NE-Car-Ti_{0,1}\) vs. \(NE-Cyl-Ti_{0,1}\). On the contrary, when \(Ti=0.01\%\), significant differences are readily seen. In the Cartesian system, the toroidal vortices break up very close to the place where they appear (\(x/D\approx 4.0\)). In the cylindrical system they are better formed and seem to be stronger as they \"survive\" over a definitively larger distance downstream. A wavy shape of the vorticity contours on the external edge of the jet core in the region \(x/D=4.0-7.0\) (see Figure 3f) shows the occurrence of the almost undisturbed motion of vortices flowing one after the other.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 6 Future Directions  
Once addressing a DT as a tight combination of an object model, in conjunction with expansive sets of data that directly follow the object that may evolve and continually modify and adapt the model based on the available data, it is made clear that future research should be realized to overcome the current profound lack of reference models and case studies accordingly. That could serve as a direct guidance for DT research and development.  
To broaden the aspect of Agriculture 4.0 that needs DTs and overcome the limitations of mere telemetry narrowed down by IoT schemes implemented till now, further research in implementations using DT modelling, 3D visualization, and simulation schemes targeting virtual nature applications aided by AR, VR, and GIS information systems should be realized.  
Further research into the holistic model perception of plant DTs to overcome the focus merely on the phenotype of plants could vastly support DT research. We can pursue enhancing the accuracy of depicting real-world farms, plants, and crops in the digital realm by combining non-georeferenced visualizations, like gaming engines, with geospatially referenced 3D geo visualizations.  
Implementing additional case studies of digital twins that adhere to the mentioned principles can address challenges related to reliable aggregating and handling of data from diverse sources, integrating multiple streams of information, and employing ad vanced computational methods for data processing and analysis, which could enhance the understanding of the subject and aid further studies.  
Further decommissioning of data in the future resulting from search--Tables 1-3 might be promising. The diverse and abundant potential advantages of DTs in agriculture create anticipation for the future evolution of this technology and its profound impact on the farming sector.  
We aspire for our work to inspire fellow researchers to explore the application of DTs in agriculture even further.  
Conceptualization, N.P., D.P. and P.P.; methodology, N.P., D.P. and P.P.; validation, N.P., D.P., V.C., E.T., R.A.M. and P.P.; formal analysis, N.P., D.P., R.A.M. and P.P.; investigation, N.P., D.P., V.C., E.T., R.A.M. and P.P.; resources, N.P., V.C., E.T., D.P. and P.P.; data curation, N.P., D.P., V.C., E.T., R.A.M. and P.P.; writing--original draft preparation, N.P., V.C. and E.T.; writing--review and editing N.P., D.P., V.C., E.T., R.A.M. and P.P.; visualization, N.P., D.P., V.C. and E.T.; supervision, D.P. and P.P.; project administration, N.P., D.P., R.A.M. and P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Any data presented in this study are available within the article.  
The authors declare no conflict of interest.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Cover Timing Patterns
#### 4.3.2 Message (PDU) Ordering (PT11)  
Pattern  
The hidden data are encoded via a predefined order of transmitted PDUs. Similar to the procedure for the _artificial loss_ pattern, the covert sender influences the sequence of PDUs that are to be transmitted next. In this case, however, the covert receiver must not only compare the received sequence numbers with the expected ones but also save the specific order of the sequence numbers from the received PDUs. With the help of the predefined template, the covert receiver can then extract the covertly transmitted information.
# Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm
## 1 Introduction
Reconstruction discrimination methods, such as principal components analysis, generative adversarial networks, and autoencoder networks, project the data set into a subspace and calculate the reconstruction error, judging samples that do not match the distribution of training samples as anomalies [18; 19; 20; 21]. In addition, Adaptive Markov Inference Game Optimization, long short-term memory networks and time-delay neural networks can achieve good results [22; 23; 24]. However, few studies have investigated multivariate analysis and the models' robustness. Since the existing data cleaning methods can only target a single element to remove the obvious anomalies of the TLE, the improper processing of a large amount of noise causes the model to deviate from the real data structure of the TLE data, which in turn affects the accuracy of TLE outlier and space event detection. Therefore, this paper proposes a probabilistic fitting method combining the Multivariate Gaussian Mixture Model (MGMM) and Multi-Strategy Genetic Algorithm (MGA) to address the limitations of univariate anomaly detection and the poor robustness and tendency to fall into local optima associated with traditional Gaussian mixture models. This approach, referred to as MGMMMGA, aims to improve the accuracy of TLE outlier and space event detection. The remainder of this paper is structured as follows. Section 2 describes the TLE outlier and space event detection method, including sample data generation, the MGMM, and the MGA. Section 3 presents the experimental results and compares the performances of the proposed and GMM methods. Section 4 provides the discussion and the conclusion.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis
#### 5.3.6 Average Delay  
This represents the total time taken for the data transmission from the source node to the border router. With respect to the hop count of the nodes from the root, the delay is calculated in milliseconds. For the data rate of 1 packet/min, the end-to-end delay values are 250 and 270 ms for CAA-ERPL and E-RPL, respectively, for the maximum hop count of 5. Generally, LLN consumes more time for data transmission because of its constrained resources. Average end-to-end delay acts as one of the major metrics that represent the timely delivery of data. In CAA-ERPL, the aggregator node routes the aggregated and compressed data to the border router by forming DODAG, which reduces the number of transmissions to the border router. In E-RPL, all nodes participate in forming the DODAG with the border router for sending. This ultimately results in an increased delay for E-RPL in sending the data to the border router as compared to CAA-ERPL, where aggregator forms DODAG with a border router in routing the aggregated and compressed data, as shown in Figure 9.  
Figure 7: PDR vs. scalability.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm
### Derivation of Fitness Function  
The proposed PSO-based clustering algorithm is clustered according to node distribution. The fitness function is used to determine the distribution of the circular areas where the candidate CH nodes are located. The derivation of the fitness function depends on the two parameters: coverage rate and Intersection-over-Universal.
# Practical Performance Analysis of Interference in DSS System
## 5 Field Test and Performance Analysis
### Neighbor Cell Interference Test
#### 5.2.2 LTE Downlink Interference from NR of Adjacent Cells to DSS Test Cells  
The test results are shown in Figure 15.  
As can be seen in Figure 15, compared to LTE neighbor cells, NR neighbor cells not only do not affect LTE cell downlink performance but also improve LTE cell downlink performance. Regardless of whether it is no-load or 50% load, the NR of neighbor cells will increase the LTE downlink performance of the DSS test cell by about 11% on average, and the throughput can be recovered by 45% at the furthest point with the strongest interference. This is mainly because NR does not need to continuously send full bandwidth CRS, so NR in DSS neighbor cells does not cause downlink interference to LTE in DSS test cells.  
During the uplink test, all test base stations turn on, and the test cell base stations are Xinbin Life Cell, Xinbin Procuratorate, and Xinbin Urban Development Bureau in Table 1, configured as NR Only or LTE Only. We perform uplink packet filling tests at six test points to observe the uplink throughput.
## 3 Application and Dataset
### Dataset for Object Detection Training  
The object detection model needs to be trained in advance on a dataset. The main purpose of this research is to detect different kinds of dishes. There are 16 kinds of dishes, including Fish-dish, Towel-dish, Rice-bowl, Chopsticks-two, Towel, Cup, Spoon, Sopbowl, Water-cup, Tea-cup, Waster-paper, Square-bowl, Chopsticks-one, Paper, Chopsticks-cover, Wine-cup. As Figure 3 shows, the two images contain all 16 kinds of dishes.  
The training set refers to the dataset used to train the model, and the testing set is the dataset used to test the accuracy of the trained model. It should be noted that the data in the two sets must not overlap. For the research, we prepare 343 dish images in total, of which 275 images are used as the training set and 68 images are used as the testing set. All images have the same size, which is 640 \(\times\) 480 pixels. To improve the randomness, each image contains many different kinds of dishes, and the type, quantity, and locations of the dishes are not fixed.  
In image classification projects, each image corresponds to a single label, but in object detection projects, each image typically contains labels of multiple different objects, and the position of each object needs to be recorded. Since this research is an object detection project, the dataset is created by manually drawing boxes for all the dishes in all images and recording the coordinates of the four vertices of each box, which mean the location  
Figure 3: Examples of 16 kinds of dishes in the training dataset. (**a**) first example, (**b**) second example.  
Figure 2: Working process of the proposed empty-dish recycling robot. (**a**) preparation stage, (**b**) grasping process, (**c**) recycling process.  
of the dish. Finally, each image has a label file, which contains the type of each dish and its coordinates in the image. These label files are essential in the subsequent training and testing process. It is important to note that, in order to comply with the CNN model framework, these coordinates are relative coordinates in the range of 0 to 1, and need to be multiplied by the width or height of the image to be the actual absolute coordinates.