esa-sceva/satcom-chunk-collection huggingface dataset:
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 7 Conclusions  
In this study, the YOLOv5s and LSTM models were improved and used to train the image data and CSI data; then, physiological information was combined with the target posture estimated by these models to judge whether the target's posture was fall. Information about the target posture was sent to the mobile phone of the guardian, achieving real-time health monitoring of elderly people. The experimental results show that, in a complex environment, the proposed method can accurately, stably, and quickly judge whether the target person is in an emergency situation.  
Although pose estimation technology based on CSI faces great challenges in monitoring the displacement of multiple targets, it is very suitable for the application scenario of a solitary elderly target. Further, the technology is low in cost, and it is expected to become a part of the auxiliary smart home medical system.  
In future work, we will study more lightweight and effective models that can accurately and quickly estimate the 3D posture of the human body and combine more physiological information, allowing us to monitor the health of a target in real time, initially predict some diseases, and provide more abundant and effective health protection mechanisms for people in a home environment.  
Conceptualization, T.B., J.L. and J.C.; methodology, T.B., J.L., J.C. and G.W.; software, T.B., J.L. and J.C.; validation, T.B., J.L., J.C. and G.W.; formal analysis, T.B., J.L., J.C. and G.W.; investigation, T.B., J.L., J.C., G.W. and Q.Z.; resources, T.B., J.L. and J.C.; data curation, T.B., J.L. and J.C.; writing--original draft preparation, T.B., J.L. and J.C.; writing--review and editing, T.B., J.L., J.C., G.W. and Q.Z.; visualization, T.B., J.L. and J.C.; supervision, T.B.; project administration, T.B.; funding acquisition, T.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guangxi Science and Technology Program (grant. no. GuikeAD21238038) and the National Natural Science Foundation of China (grant. no. 62161031).  
Not applicable.  
The data that support the finding of the study are available from Dr. [PERSON] at [EMAIL_ADDRESS] upon reasonable request.  
The authors declare no conflicts of interest.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 3 Results
### Validation of River Monitoring Station Data
#### 3.1.1 Comparison of Hourly Data  
The hourly variations in the river monitoring station data and the HPP data are presented in Figure 13. On 1 May 2023, the hourly average discharge values obtained from HPP were 50.84 m\({}^{3}\)/s, while the observation station recorded 49.03 m\({}^{3}\)/s. The hourly averages were computed by taking the mean of the discharge values recorded during each hour. The maximum discharge observed in HPP was 74.24 m\({}^{3}\)/s, whereas it was 63.54 m\({}^{3}\)/sin the observation data. The minimum discharge measured in HPP was 37.24 m\({}^{3}\)/s, compared to the 38.83 m\({}^{3}\)/s recorded by the observation station. The difference between the two datasets was determined to be 2.4%, with a high correlation value of r\({}^{2}\) = 0.92. HPP data are recorded every 10 min, while the observation station records measurements twice every minute. These differences in measurement frequency contribute to variations in discharge values, especially under rapidly changing hydrological conditions.  
The data from 27 June and 1 September 2023 show that while the alignment between the HPP and the river monitoring station remains consistent, the differences become slightly more pronounced. On 27 June, the hourly average discharge recorded by the HPP was 61.28 m\({}^{3}\)/s, while the river monitoring station recorded 60.16 m\({}^{3}\)/s. The maximum discharge was 79.18 m\({}^{3}\)/s at the HPP and 75.98 m\({}^{3}\)/s at the river monitoring station, while the minimum discharge was 52.48 m\({}^{3}\)/s at the HPP and 54.12 m\({}^{3}\)/s at the river monitoring station. The difference between the two datasets was calculated to be 2.9%, with a correlation value of r\({}^{2}\) = 0.92. On September 1, the differences increased slightly, with the hourly average discharge from the HPP at 32.41 m\({}^{3}\)/s and the river monitoring station at 33.76 m\({}^{3}\)/s. The maximum discharge was 41.56 m\({}^{3}\)/s at the HPP and 39.74 m\({}^{3}\)/s at the river monitoring station, while the minimum discharge was 30.74 m\({}^{3}\)/s at the HPP and 30.56 m\({}^{3}\)/s at the river monitoring station. On this date, the difference between the two datasets was calculated to be 2.7%, with a correlation value of r\({}^{2}\) = 0.91.  
The differences in measurement frequency, particularly during periods of rapidly increasing or decreasing discharge, become more evident. The less frequent measurements from the HPP make it more difficult to capture sudden changes; however, the overall trend between the two datasets remains consistent. The river monitoring station records higher values than the HPP during periods of rapid discharge increases, which can be attributed to the station's more frequent data collection. This allows the river monitoring station to capture short-term discharge fluctuations more accurately.
## 4 Application Example
### Influence of the Cross-Sectional Height of the Spillway Tunnel  
The cross-sectional height is considered in three cases, in which the cross-sectional height is 0.8 times, 1.0 times and 1.2 times the original height. The air velocity and air pressure in the different cases are shown in Figure 9. The corresponding total ventilation rates of the air supply system are 3429, 4537 and 5185 m\({}^{2}\)/s, respectively.  
As observed, with the decrease in the cross-sectional height of the spillway tunnel, the air velocity in the air intake well decreases, while the velocity in the spillway tunnel notably increases.  
Figure 8: Comparison of the pressure in the spillway tunnel and the air intake well.  
The sub-pressure in the air intake well and the spillway tunnel decrease, as well as the total ventilation rate. The reason is that the cross-sectional area leads to a greater driving force and the resistance becomes higher at the same time at a relatively low rate. Consequently, the variation mentioned above appears.  
The analysis indicates that the cross-sectional height of the spillway tunnel has a great effect on the ventilation characteristics of the air supply system at the Jinping-I Dam. It is critical to balance the ventilation rate and air velocity and sub-pressure in the air intake wells and spillway tunnels.
## 5 Experiments
### Experimental Results
#### 5.2.2 Benchmark Models
Figure 13 presents the size of the solution set (bigger is better) generated by the two methods. From the results, it can be seen that for the more complex models (depth 4 and depth 5), BAG-DSM found many more solutions than the baseline. There were some cases where the baseline found larger solution sets, especially for the models with depth 3. However, it should be noted that here we compare only the size of the solution set and not the _quality_ of the solution sets. This means that the baseline approach might have found 100 solutions with a distance of 5, i.e., require 5 attribute changes, compared to BAG-DSM with 10 solutions with a distance of 3. Figure 11 already showed that the quality of the solutions generated by the Bayesian is better than those generated by the baseline in the majority of the cases. Statistical tests have been performed for equally distant solutions found by both methods, and similar findings, as above, are confirmed (Supplementary Materials--Section S2.1.2). Namely, an overall significant stochastic dominance is observed, while such dominance is lacking over models with uniformly distributed weights (\(p\)-value is 0.07) and models with a depth of 3 (\(p\)-value is 0.6).
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 2 Fluid-Structure Interaction Numerical Analysis Method
### Equation of Fluid-Structure Interaction  
The fluid-structure interaction must follow the most basic principle of conservation. Therefore, at the fluid-structure interaction interface, the conservation of fluid stress and solid stress (\(\tau\)), displacement (\(d\)), heat flow (\(q\)), temperature (\(T\)), and other variables should be met, satisfying the following equations:  
\[\left\{\begin{array}{c}\tau_{\rm f}n_{\rm f}=\tau_{s}n_{s}\\ d_{\rm f}=d_{s}\\ q_{\rm f}=q_{s}\\ T_{\rm f}=T_{s}\end{array}\right. \tag{7}\]
## 3 Experimental Results and Analysis
### Deep Learning Modeling Results  
#### 3.4.1 Basic Deep Learning Modeling Results  
Deep learning models, such as CNNs, possess a large number of hyperparameters that directly influence model performance, including the learning rate and batch size. Through systematic search, the optimal combination of these hyperparameters can be identified to enhance the model's performance on the validation or test set. However, hyperparameter optimization typically demands substantial computational resources. By prioritizing the search for commonly used parameters with the greatest impact on model performance, the most effective results can be achieved within limited resource constraints. Therefore, based on fundamental modeling experience and the literature references, the range of hyperparameters utilized in the deep learning model of this study is presented in Table 4.  
After the grid search, the modeling results of six deep learning models are shown in Figure 6. The first column represents \(\mathbb{R}^{2}\), the second column represents RMSE, and the third column represents MAE. The first row is for the straight channel, and the second row is for the hexagonal channel. For the straight channel, LSTM shows the highest prediction accuracy, with an \(\mathbb{R}^{2}\) of 0.89317, RMSE of 1219.79, and MAE of 118.88. Next are the BiGRU and BiLSTM models, both with \(\mathbb{R}^{2}\) values exceeding 0.8923, and their fitting effects are also good. In the prediction results of the hexagonal channel, the fitting effect of LSTM is still relatively good, but the BiGRU and BiLSTM models maintain the highest accuracy results, with \(\mathbb{R}^{2}\) values of 0.99844 and 0.99835, respectively. The Transformer and CNN models perform relatively poorly, which may be due to certain limitations in handling complex spatial relationships and sequence features. To improve the performance of these models, it may be necessary to introduce an attention mechanism to enhance the models' ability to capture key features and thereby improve prediction accuracy.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Hyperparameter Range** \\ \hline \multirow{4}{*}{CNN} & batch\_size = [8, 16, 32, 64, 128, 256] \\  & learning\_rate = [0.01, 0.001, 0.0001] \\  & Dropout\_rate = [0.01, 0.05, 0.1] \\  & batch\_size = [8, 16, 32, 64, 128, 256] \\  & learning\_rate = [0.01, 0.001, 0.0001] \\  & dropout\_rate = [0.01, 0.05, 0.1] \\  & hidden\_dim = [16, 32, 64] \\  & num\_layers = [1, 2, 3] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameter range of each deep learning model.
[11] mapped SOC content and stock, texture, pH, and CEC in the topsoil cropland of Brazil using Random Forest (RF). [PERSON] et al. [12], [PERSON] et al. [13] and [PERSON] et al. [14] mapped soil organic matter, pH, total nitrogen, and other soil properties in the 1980s using ML models fitted by elevation, normalized difference vegetation index, precipitation, and other environmental covariates. At the regional scale, using the Boruta feature selection algorithm and eight methods (e.g., Boosted Regression Tree and Support Vector Machine), [PERSON] et al. [15] mapped soil carbon fractions in southeastern America. There are also several SOC studies using DSM in Northeast China [16; 17; 18; 19]. Though these studies partly reveal regional SOC disturbance patterns, they are mainly focused on topsoil (0-20 cm) with older soil sampling data (1980s, 2000s, and 2013). Meanwhile, they did not only focus on croplands, and the spatial-resolution was relatively low (\(\geq\)1000 m). However, due to the effect of human management, there is more complicated and changeable SOC distribution in croplands [20]. [PERSON] et al. [19] proved that cropland SOC overall increased 0.5 g kg\({}^{-1}\) in North and Northeast China from 1985 to 2004. In order to update the latest spatial and vertical distribution patterns of cropland SOC in the Northeast Plain China, it is important to use the relatively new soil sampling data and the most informative covariates to map SOC in specific layers at a fine resolution, which is essential to assess soil health status after long-term cultivation and to adjust precise agricultural policies. To address the knowledge gaps and limitations, we aim to update the 90 m spatial-resolution cropland SOC content maps in North and Northeast China using Quantile Regression Forest fitted with 40 environmental covariates selected by the feature selection algorithm and 396 SOC samples in 2017. The objectives of this study are twofold: (1) to clarify the influence of mechanized operation on the spatial and vertical distribution patterns of SOC; (2) to build an optimal database of environmental covariates in the area and illustrate the main controlling factors in the modeling process.
## 4 Numerical Results
### Pressure and Force  
Figure 10 gives the comparison of hydrostatic pressure with the dynamic pressure at three particular points, namely \(P_{2}\), \(P_{3}\), and \(P_{4}\), in case 2. It should be noted that the method is adopted to determine the height of the liquid surface of the tank under different ships' rolling angles to calculate the hydrostatic pressure [64]. From this figure, it can be seen that the maximum dynamic pressure is bigger than hydrostatic pressure in all three of these points, even 38% at \(P_{4}\).  
Figure 11 gives the comparison of dynamic pressure at two particular points in two cases, namely case 1 and case 3. From this figure, it can be seen that the curve of the dynamic pressure in case 3 (red curve) is higher than the curve in case 1 (green curve), which demonstrates that a larger rolling angle would induce a bigger dynamic pressure.  
Figure 10: Comparison between hydrostatic and dynamic pressure in case 2 at three probes. (**a**) \(P_{2}\); (**b**) \(P_{3}\); (**c**) \(P_{4}\).  
Figure 9: Snapshots of flow field in different rolling periods. (**a**) case 3; (**b**) case 4.  
When setting the solid particles on a sidewall, the sidewall is actually discretized with a uniform grid. Thus, the sloshing-induced pressure at each cell could be obtained by the pressure of the corresponding particle. After that, the sloshing-induced force on the sidewall can be calculated by integration [65], as follows:  
\[F=\sum_{i=1}^{N}P_{i}(\Delta z_{i}\cdot W_{i}) \tag{15}\]  
where \(P_{i}\) is the sloshing-induced pressure at the \(i\)-th particle on the sidewall; \(\Delta z_{i}\) and \(W_{i}\) are the height and length of the \(i\)-th cell, respectively. In this paper, both \(\Delta z_{i}\) and \(W_{i}\) are equal to the initial space between solid particles.  
Figure 12 gives the comparison of the sloshing-induced forces on the port sidewall of the tank in case 3 with that in case 4. In addition, the hydrostatic force is marked as a red dash line. From this figure, it can be seen that the peaks of sloshing-induced force in both cases are bigger than hydrostatic force, even 20% in case 4. Furthermore, the maximum sloshing-induced force in case 4 is larger than that in case 3. These results can also be seen by the fact that the high pressure area in case 4 is wider than that in case 3, as illustrated in Figure 9. Meanwhile, a bigger sloshing-induced force would be expected when liquid sloshing is more violent since the liquid sloshing in case 4 is more violent than in case 3. Therefore, it could claim that the sloshing-induced force is not positively correlated with the ships' undamped natural rolling frequency.
## 5 Selective Bio-Inspired Algorithms with Hybrid Optimization
### Hybrid ACO-PSO Algorithm  
The main aim of ACO-PSO hybridization is to improve inter-cluster data aggregation in WSNs. This proposed technique also improves the network's lifetime over many optimization techniques. In this approach, ACO results in local updates, and PSO gives a better outcome for global updates. The combination of ACO-PSO enhances the durability and performance by 6% over previously used optimization techniques like an ant colony, cuckoo search, flower pollination, etc.  
The design procedure for the ACO-PSO algorithm is:  
* Step 1. Initialize the number of wireless sensors;
* Step 2. Calculate the energy level for each sensor node;  
If E > 0, there will be a selection of CH otherwise, go to Step 2 again. After CH selection, implement the ACO-PSO algorithm to find a new path.
* Step 3. Calculate the energy dissipation for each sensor node.  
If a dead node is found, evaluate otherwise, go to Step 2. For different parameters, evaluate the performance of the network.  
Hybrid ACO-PSO-based data aggregation is used to increase the inter-cluster data aggregation. Extensive investigation shows that the suggested approach significantly increases network lifespan when compared to previous strategies. It divides the sensor network into several pieces, referred to as clusters, with cluster heads chosen for each  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithms** & **GA** & **PSO** \\ \hline Operators used & Selection, cross-over, mutation. & Inertia, cognitive, social. \\ \hline Ability to search global optima & High & Low \\ \hline Implementation & Hard & Simple \\ \hline Trapped on local optimum & Sometime & Often \\ \hline Computer efficiency & Low efficient & Highly efficient \\ \hline \hline \end{tabular}
\end{table}
Table 2: Combination of GA-PSO techniques in WSNs.  
cluster. Then, using short-distance connections, tree-based data aggregation is used to acquire sensory data directly from cluster heads. The use of compressive sensing decreases the size of the packets that are sent across the sensor network. The ACO-PSO algorithm determines the shortest path between the sink and cluster heads. For simulation, the MATLAB simulation tool is generally utilized by researchers. It helps in comparing the proposed approach's performance to that of existing technology, GSTEB, in terms of stability period, network lifespan, residual energy (average remaining energy), and throughput.
## 2 Related Work
Therefore, in [27; 28], outage probability, symbol error rate and bit error rate of the BPS and PPS with outdated channel estimates were respectively investigated for dual-hop AF relaying systems in Rayleigh fading channels. In [29], the outage probability of the efficient PPS with outdated CSI and channel estimation error was presented for dual-hop DF relaying systems in Rayleigh fading channels. The study on efficient PPS has focused on only dual-hop cooperative systems so far. Hence, in this paper, we extend the dual-hop system model to the multi-hop (i.e., more than two hops) system model and focus on proposing a novel efficient PPS scheme for multi-hop DF relaying systems with limited CSI feedback, where ideal CSI feedback is assumed to show the best outage performance of the proposed efficient PPS and compare with the outage performance of the BPS. In addition, we consider Nakagami-\(m\) fading channels to analyze its outage probability.
## 4 Conclusions  
The feasibility of early detection of _Aspergillus parasitic_ infection in maize kernels was verified by using NIR hyperspectral imaging. PCA was applied on image data to extract ROIs from background, labels and others. With loading line plot, wavelengths at which images contained bad pixels were recognized. Germ region data was more effective in discrimination of contamination levels than whole kernel data, which was confirmed by separability computation.  
The classification model based on germ region data (of all kernels orientated with germ up) showed the best results with the preprocess method of a combination of MAS and SNV. The accuracies were 97.92% and 91.67% for calibration and validation, respectively, which demonstrated the ability to separate sound kernels and kernels of different contamination levels of D1-2 (the group consisted of D1 and D2), D3-4 (D3 and D4), and D5-7 (D5, D6, and D7). Germ orientation was found to affect the performance of classified models. Despite this, accuracies of the model, which were built based on data of the mixture of kernels with germ up and down, were 95.83% and 84.38% for calibration and validation respectively, which still showed the possibility for the application of the presented method.  
Five key wavelengths (1145, 1408, 1935, 2103, and 2383 nm) were selected according to the loading line plot of PCs used in the model, and were associated with amino acid, starch, water, carbohydrate, and aliphatic compounds, respectively. These are the main ingredients in maize germ, which indicated that variation in different contamination levels was mainly caused by changes of ingredient contents in maize germ due to the fungal growth. The evaporation of water from the maize kernels after inoculation adversely affected the experimental results. Therefore, this process should be noticed in future studies. Furthermore, for future studies, a larger data set of kernels needs to be prepared for further analysis on feature extraction of fungal infection, and different varieties of maize kernels need to be tested to verify the practical application of the presented method.  
This work was supported financially by the China National Science and Technology Support Program (2012 BAK08B04).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.