esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experimental Results Compared with AAMI Standard  
In this subsection, the accuracy of BP estimation of the proposed approach was compared with the AMMI standard, which is the guideline for the evaluation of cuffless BP monitoring devices. The results evaluated by using mean absolute error (MAE) were shown in Table 5. It is clear that using LSTM or AN can improve the performance of CNN. Moreover, the performance of LSTM is better than that of AN. According to the network structure, the LSTM can save previous ECG information and compare it with current ECG information, thus the variance of ECG signals can be effectively improved. Therefore, the blood pressure can be accurately estimated by using the variance of ECG signals. The effects of AN are sometimes modeled in the CNN; thus, the benefit of AN cannot be fully demonstrated. When LSTM and AN are integrated together, AN may reduce the variety of inputs of LSTM. Thus, the proposed approaches can achieve the best performance.  
The mean error and the standard deviation of error for these four approaches are less than 5 and 8 mmHg, respectively. Moreover, the proposed approach can obtain the smallest standard deviation and the most accurate. Thus, these approaches are sufficiently high for clinical use.  
In order to detail the characteristics of proposed approaches, the Bland-Altman plot and the error distribution of the actual BP and the estimated BP are adopted, and the results are shown in Figure 4. The mean difference of SBP and DBP were 0.189 mmHg and 0.082 mmHg, respectively. The standard derivations of SBP and DBP were 3.96 and 3.19, respectively. Moreover, within the error range of 7.76 mmHg and 6.25 mmHg for SBP and DBP (95% confidence level), the cumulative percentages in the predicted values of SBP and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE** & & **MAPE** \\ \cline{2-5}  & **SBP** & **DBP** & **SBP** & **DBP** \\ \hline CNN & 5.54 & 4.32 & 2.85 & 2.24 \\ CNN + LSTM & 4.39 & 3.02 & 2.28 & 1.60 \\ CNN + AN & 4.60 & 3.32 & 2.38 & 1.75 \\ Proposed approach & 3.90 & 2.31 & 2.04 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of different network structures.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SBP** & **DBP** \\ \hline CNN & \(3.88\pm 4.55\) & \(3.18\pm 3.94\) \\ CNN + LSTM & \(3.22\pm 4.69\) & \(2.42\pm 3.48\) \\ CNN + AN & \(3.34\pm 4.91\) & \(2.60\pm 3.64\) \\ Proposed approach & \(2.94\pm 4.65\) & \(2.02\pm 3.81\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results measured by using MAE.  
DBP were 96.57% and 98.57%, respectively. Thus, the performance for predicting DBP is better than that for predicting SBP.
## 2 Methods
### Downwelling Radiometer  
Downwelling radiance is measured using the Ocean Optics USB 4000-VNIR spectrometer, 350-1100 nm at \(\sim\)1.5 nm FWHM (Dunedin, FL, USA). Light energy is transmitted to the spectrometer through an upward looking opaline glass cosine diffuser with an 180\({}^{\circ}\) FOV. A 3 m 100 \(\upmu\)m optical fiber delivers the light energy through a 25 \(\upmu\)m entrance slit and a multi-bandpass order-sorting filter. It then disperses via a fixed grating across a 3648-element Toshiba linear CCD array. Power and communications occur through a USB 2.0 connection to the central PC. The downwelling radiometer is operated continuously during flight with a 33 ms integration time averaged for 30 scans, and\(3\times\) spectral binning yields a 1 Hz data acquisition rate. The unit is radiometrically cross calibrated with the imaging spectrometer to enable atmospheric characterization of down welling irradiance that is used to provide a surface reflectance product.  
The Gobi-384 thermal imaging camera (Xenics, Leuven, Belgium) has a broad 8-14 \(\upmu\)m LWIR spectral band. It is covered by the uncooled a-Si micro-bolometer detector array (resistive amorphous silicon, FPA) with 384 cross track pixels and a 30\({}^{\circ}\) FOV. This compact component measuring 72 \(\times\) 60 \(\times\) 50 mm uses a Gigabit Ethernet TCP/IP interface to deliver 16-bit radiometrically calibrated thermal imaging data at a 25 Hz frame rate to the central computer running the Xenith software and graphical user interface. Each image frame is coded with a PC timestamp synchronized with the common solution GPS/IMU system. Push broom thermal imagery for each flight segment is generated in post processing where several cross track rows are exported in a series co-registered with the imaging spectrometer. The thermal imagery has factory radiometric calibration for surface temperatures between \(-\)20 \({}^{\circ}\)C and 120 \({}^{\circ}\)C with a noise equivalent temperature difference, NETD \(>\) 50 mK to allow airborne operations over a diverse range of surface temperatures. Thermal calibration stability as a function of microbolometer operating temperature is verified against the GSFC Calibration Facilities blackbody for stable operation over the target temperature range of \(-\)5 \({}^{\circ}\)C to 85 \({}^{\circ}\)C.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods
### Traveling Salesman Problem  
Sparsely distributed points of interest are obtained from the coverage analysis, which can be visited in any order. The traveling salesman problem is used to determine the optimal visiting order of each point of interest by minimizing the total distance, and consequently, the energy consumption. A weighted square adjacency matrix is first created, where the number of rows and columns are equal to the number of points of interest. The total Euclidean distance is subsequently defined as the cost metric to determine the optimal visiting sequence of the points of interest.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 3 System Model
### DnCNN Model  
In this subsection, we model the noisy channel matrix obtained by LS estimation as a two-dimensional image, where \(N_{c}\) and \(N_{s}\) represent the number of subcarriers and OFDM symbols, respectively. The image is input to DnCNN network for denoising to obtain a noisy image of the same dimension. DnCNN consists of 5 convolutional layers, and its specific structure is shown in Figure 2. The first Conv layer is the input layer and uses 64 different filters of size \(3\times 3\times 1\). The filter is followed by Rectified Linear Unit (ReLU), where \(ReLU(x)=max(0,x)\) is a nonlinear activation function. The input layer mainly transforms the input image into an actionable feature map. The hidden layer consists of 3 convolutional layers, each of which uses 64 filters of the size \(3\times 3\times 1\). Each filter is followed by a Batch Normalization (BN) layer and ReLU activation function. BN layer is used because BN can force the input values of each layer neuron back to a standard normal distribution with mean of 0 and variance of 1. That is, the increasingly skewed distribution values after the previous layer of transformation are pulled back to the standard distribution so that the activation input values can fall in the region where the nonlinear function is more sensitive to the input. This prevents the gradient from vanishing and speeds up the convergence. The one-sided suppression of the post-connected ReLU activation function can also be demonstrated, since the action of BN makes the data not distributed on the side of all zeros. The last layer is the output layer, and a \(3\times 3\times 1\) filter is used to reconstruct the noisy image.  
\(\hat{H}_{LS}\) is input to the DnCNN to obtain the channel noise image \(\hat{H}_{Noise}\), so residual learning [25] needs to be added to obtain the channel image \(\hat{H}\) after noise reduction. It is expressed as \(\hat{H}=\hat{H}_{LS}-\hat{H}_{Noise}\). The reason for choosing DnCNN with residual learning as the denoising module is that through residual learning, DnCNN can implicitly remove potentially clean images using the BN operation, making each input layer Gaussian and reducing correlation. Therefore, BN can be complemented with residual learning to improve image denoising ability. Therefore, BN and residual learning can complement each other to improve image denoising ability.  
Although DnCNN has a certain denoising performance under the condition of unknown noise levels, it can only solve the denoising problem under specific noise level. Once the SNR, channel model, or moving speed of the receiver does not match the training model, a large error will be generated. Therefore, it is necessary to know the noise level in the current environment in advance, which can reflect various mismatches, so that DnCNN can be applied to a wider range of noise intervals. However, this is often unknown to the receiver. Therefore, a module is required that is dedicated to estimating the noise level interval of the current environment.
## 3 Results
### Influence Weight of Environmental Factors on the IWUE  
The results of the correlation analysis indicated that IWUE was positively correlated with temperature and GPP (\(p<0.05\)) and negatively with elevation and LAI (\(p<0.05\)), without significant changes with precipitation (\(p>0.05\)) (Figure 5). Temperature and Rg explained 57.3% and 25.0% of the variation in IWUE among all forest types, respectively (Figure 6). The weight value of temperature to IWUE was 2.3 times that of the Rg, whereas the weight values of LAI and precipitation were 8.8%, which was significantly lower than the weight values of temperature and Rg. This indicates that temperature and Rg were the main factors affecting the IWUE of forest ecosystems.  
Figure 3: Latitude effect on IWUE of forest ecosystems. DBF, EBF, ENF, and MF are deciduous broadleaf forests, evergreen broadleaf forests, evergreen needle-leaved forests, and mixed forests, respectively. The blue line is the relation curve between WUE and latitude.
## 6 Simulation and Analysis  
In this section, the proposed mathematical model is verified at first, then the performance of the optimal procedure is presented, and the performance of the proposed transport scheme is investigated finally. The simulations were conducted on a discrete event simulator--The ONE. As for coding and routing strategy, RaptorQ coding and time-limited epidemic routing were implemented. The inter-meeting interval between each node pair was generated using exponential distribution with rate parameter \(\lambda\). Several key parameters were set as follows: \(N=120\), \(K=5\), the number of packets that a node could accommodate \(H\) was set to 2, and \(\lambda=0.01\) was obtained by a random movement model developed in MATLAB, which was then imported in The ONE. Note that the unit of \(\lambda\) was not specified, since \(\lambda\) could be 0.01 s\({}^{-1}\), 0.01 min\({}^{-1}\), or 0.01 h\({}^{-1}\). The average over 100 independent experiments was taken as the result.
# An Enhanced Energy Balanced Data Transmission Protocol for Underwater Acoustic Sensor Networks
## 4 EBET and EEBET: Proposed Techniques
### Protocol Operation
#### 4.1.1 Ebet  
The key insight of this technique is to achieve a dual objective; energy-efficiency and balanced energy consumption. Data transmission occurs either by multi-hopping or by selecting suitable high RL relay node to transmit data to the sink. The core design of EBET is the use of high RL node as a relay node when the residual RLs of two successive nodes differ.  
The EBET works in two phases; route establishment phase and data transmission phase. After network configuration, suitable forwarder nodes are selected by route establishment phase. Then the nodes communicate during data transmission phase.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 4 Node Clustering  
### Cluster Initialization and Parameter Enhancement  
Initially, cluster centers are determined based on their coordinates in the random mote connectivity matrix. The central node of each cluster \(C_{i}\) randomly initializes the centers within the data space, with the boundaries defined by the \(k\) values. A random integer generator within a specified range, represented by the coordinates (\(x_{N}\), \(y_{N}\)), determines each cluster center. The algorithm in Figure 1 details the initialization and update procedures for the K-means cluster center. The process starts by randomly selecting the first cluster center from the given range for each coordinate (\(x\), \(y\)). The mechanism then iteratively updates the cluster center for each cluster using the average coordinates of the nodes within that cluster. This process continues until convergence, ensuring that the cluster center stabilizes and does not change significantly with subsequent iterations, achieving a _new_center_ of the coordinate.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 2 Data
### Reanalysis Product  
Meteorological reanalysis product provides comprehensive global multi-decadal records of historical atmosphere states using a single consistent numerical data assimilation scheme with various past observations. The latest reanalysis, ERA5, produced by European Centre for Medium-Range Weather Forecasts (ECMWF) with a temporal resolution of 1 h and a horizontal resolution of 0.25 degrees is used to simulate the SWD and meteorological parameters (i.e., temperature, pressure and relative humidity) at GNSS stations. In addition, the reanalysis product will also be utilized to calculate the wet refractivity by Equation (1) that is taken as the reference for tomographic result evaluations.  
\[N_{w}=Z_{w}^{-1}\Big{(}k_{2}\frac{e}{T}+k_{3}\frac{e}{T^{2}}\Big{)} \tag{1}\]  
Figure 1: Distribution Map of Continuously Operating Reference Stations (CORS) Station in Guangdong Province.  
where \(e\) is the partial pressure of water vapor, \(T\) is the temperature and \(Z_{w}^{-1}\) is the inverse compressibility factor of water vapor (in general, \(Z_{w}^{-1}\) = 1). The constant \(k_{2}\) = 70.4 K hpa\({}^{-1}\) and \(k_{3}\) = 3.739 \(\times\) 10\({}^{5}\) K\({}^{2}\) hpa\({}^{-1}\).
## 4 Case Study
### Simulation Results and Discussion
#### 4.2.2 Scenario II: 5 Sorts of Cables  
In reality, due to the reducing cost of volume production, the commonly used cables may have a lower single price. Hence, even a cable with a thicker sectional area, if commonly used, may be cheaper than one with a thinner sectional area. That is why the cable connection layout optimization work is usually done based on limited types of cables. In this section, it is assumed that 5 sorts of cables with different sectional areas are at hand. The optimized layouts with such a cable database are shown in Figure 10.  
Similar to Scenario I, the layouts obtained by MBA and BA_FREE adopted the thickest cables (T11 cable) as can be seen in Figure 10a,b, while thinner cables illustrated with yellow lines can be found in (c). Similarly, the layout found by the MBA has no crossed cables while the BA_FREE and BA_MIN are opposite. The 10 trials are shown in Figure 11.  
Figure 10: \(Cont\).  
Figure 9: Total cost corresponding to each second of Scenario I.  
In Figure 11, it can be seen that the optimal solution found by the MBA, BA_FREE, and BA_MIN appeared in the fourth, eighth and second trial. Within the three models, the optimal solution is found  
Figure 11: Ten trails for layout optimization by different algorithms (Scenario II).  
Figure 10: Optimized cable connection scheme in Scenario II. (**a**) Optimized layout obtained by MBA; (**b**) optimized layout obtained by BA_FREE; (**c**) optimized layout obtained by BA_MIN.  
by the BA_FREE. The fitness value corresponds to the CPU time of the best trail for each method is drawn in Figure 12.  
As can be seen in Figure 12, the MBA converged earliest in this scenario, which stabilized at 250 s, while BA_FREE and BA_MIN stabilized at 440 s and 400 s, respectively. All the methods can stabilize after 500 s even in the worst case in this scenario.
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
## 3 Results and Discussion
### Characteristics of Vibration
#### 3.1.1 Vibration in Fixed Gate Opening  
Because the acceleration spectrums are similar under different water depth conditions, Figure 3 only depicts the acceleration spectrums under three directions for the upstream water depth of 20 m. As revealed in Figure 3, the acceleration in three directions decreases gradually with the gate opening decrease, which is consistent with the prototype observation by [PERSON] et al. [28]. [PERSON] et al. pointed out that flow pulsation pressure decreased with gate opening decrease and root mean square (RMS) of pulsation pressure can reach 10% of the upstream total head in large gate opening. However, [PERSON] et al. [29], with Particle Image Velocimetry (PIV) technology through the physical model test, stated that, the smaller is the gate opening and the greater is the flow rate, the greater is the gate vibration. It was confused that gate vibration unexpectedly emerged different results at the same gate opening. The gate operation condition might account for the difference. In this physical model test and prototype observation from [28], the flow passing the gate is free outflow and not restricted by the downstream water depth. Nevertheless, gate vibration has been influenced by the downstream water depth in the physical model test in [29]. This means that different outflow conditions could produce different results. Thus, outflow condition is also an effect factor of gate vibration.  
Furthermore, the dominant frequency of vibration in three directions was also obtained from the results in Figure 3. In lateral direction, the dominant frequency is mainly concentrated around 8 Hz. As gate opening decreases, the frequency is not prominent and vibration energy is dispersed  
Figure 3: Acceleration spectrums in three directions for upstream water depth of 20 m: (**a**) lateral direction; (**b**) vertical direction; and (**c**) horizontal direction.  
gradually. Then, in vertical direction, the dominant frequency gradually decreases with gate opening decrease and is distributed up to 10 Hz. Vibration energy is relatively concentrated with a peak value and there is no energy dispersion. In horizontal direction the dominant frequency with multiple peaks under each gate opening has no obvious change. The frequency is mainly distributed below 20 Hz and frequency bandwidth is relatively wide. Besides, comparing the vibration spectrums at different water depths, we found upstream water depth could markedly change the acceleration magnitude and have a little influence on the dominant frequency yet. Meanwhile, flow pulsation frequency near the gate bottom edge is relatively small. The dominant frequency is 0.13-0.2 Hz. Because vibration frequency is far away from the flow pulsation frequency, the plane gate does not resonate with the flow and is self-excited vibration in fixed gate opening.
## 1 Introduction
An algorithm package called First Level Event Selection (FLES) [14] is used to provide a full event reconstruction in real-time that allows selection of events of interest and, therefore, significantly reduces the amount of data that has to be stored on disk. The algorithms used for the experiment are tested and evaluated step by step using precise Monte Carlo simulated data. Thus, the true outcome of each event is known, and reconstructed particles can be matched with the corresponding simulated particles to allow a performance analysis. The reconstructed particle candidates are identified with a hypothesis, which can be tested and evaluated using simulated data. In real experiments, however, the comparison is not possible, which makes a detailed performance analysis a crucial step for a successful experiment. Therefore, the presented approach will use the already-established tools of the KF Particle Finder to measure the performance within the reconstruction chain of CBM. The Kalman Filter (KF) Particle Finder is a package included in FLES, responsible for particle and decay reconstruction, including the reconstruction of mother particles. When mother particles are created, the type of particle and their properties are inferred by the properties of the daughter particles. Due to inaccuracies in the reconstruction of daughter particles, the reconstruction of multiple mother particle candidates is not uncommon. For that reason, the particles pass through a particle competition to find the best-fitting candidate. That way, the background noise produced by mistakenly created mother particles can be reduced. The presented neural network approach is used to replace the existing particle competition of the KF Particle Finder. For each pair of mother particle candidates that would compete against each other, the neural network classifies the best-fitting mother particles based on their properties. The neural network solves the competition by classifying the most probable candidate out of both competitors. Previous work [15] already investigated a multi-layer perceptron-based approach, demonstrating that such models are generally capable of solving the problem with comparable results. The present work shows improvements in the particle competition by using a more complex model, including a different topology and hyper-parameters. The new model provides a raw classification performance with an error of less than 2% on the test set.
## 3 Results
### Annual Changes
The developed countries headed by the US have made remarkable achievements in the research in this field, with the hot terms of their researches mainly focused on detection/monitoring of basic vital signs of human body (such as the indicators of heart rate and blood pressure), analysis of sleep phase, testing of cardiovascular function parameters, diagnosis of cardiovascular diseases, home care for the aged, and the evaluation tools for intelligent health management, etc.