esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 5 Simulation Results
### Performance of the Msg3 PR Scheme
#### 5.1.1 The Consequences of the Capture Effect and Its Properties  
Before discussing the performance metrics of the Msg3 PR scheme, we describe the consequences of the capture effect and its properties. Table 3 shows the RA success probability for given ramping numbers defined as in Equation (20), when the number of accesses is high, i.e.,\(\mathbb{P}(S_{u}|N=60,R_{u}=r)\). As seen in Table 3, \(\mathbb{P}(S_{u}|N=60,R_{u}=r)\) increases as \(r\) increases. For example, \(\mathbb{P}(S_{u}|N=60,R_{u}=1)=4.9\%\), whereas \(\mathbb{P}(S_{u}|N=60,R_{u}=8)=28.4\%\) when \(PRS=1\) dB. In addition, \(\mathbb{P}(S_{u}|N=60,R_{u}=1)=4.9\%\), whereas \(\mathbb{P}(S_{u}|N=60,R_{u}=8)=99.9\%\) when \(PRS=4\) dB. In other words, the probability of RA success for a device with \(R_{u}=8\) is 5.7 times that of a device with \(R_{u}=1\) when \(PRS=1\) dB. Furthermore, in the case of \(PRS=4\) dB, the difference in the success probability is further increased to approximately 20.4 times. From Table 3, the RA success probability increases as the Msg3 PR scheme uses higher \(PRS\) values.  
However, this is not always true, as seen in Figure 5, which shows the RA success probability according to the location of the device. Note that the heavy-access load (\(N=60\)) case is considered, i.e., \(\mathbb{P}(S_{u}|N=60,X_{u}=x)\), which can be derived from \(\mathbb{P}(S_{u}|N=60,Z_{u}=k)\). We observe that the RA success probability decreases as the device moves away from the eNB. This is because \(K_{max}(x)\) defined in Equation (6), decreases as \(x\) increases, and thus, the capture probability declines. One interesting point in Figure 5 is that the RA success probability is not necessarily high when the \(PRS\) is high; in other words, the trend noted in Table 3 is not always true. Based on the distance, the RA success probability when \(PRS=4\) dB is often less than the RA success probability with \(PRS=3\) dB. Moreover, even when \(x\) is between approximately 600 m and 700 m, the RA success probability for a \(PRS=4\) dB is lower than that with a \(PRS=2\) dB. The reason is that having too large of a \(PRS\) value in certain areas lowers the maximum ramping number, as seen in Equation (6), and, therefore, the RA success probability can be decreased. As a result, as the \(PRS\) increases, the devices close to the eNB have a higher probability of being captured, while the devices further from the eNB cannot perform power ramping well due to their lower \(K_{max}\). Hence, we can make an inference about performance: an increase in \(PRS\) does not necessarily lead to an increase in RA performance.  
Figure 5: RA success probability according to the location of the device with \(N=60\). i.e., \(\mathbb{P}(S_{u}|N=60,X_{u}=x)\).
# In Search of the Densest Subgraph
## 3 Measures of Graph Density and Associated Results
### Polynomial-Time Solvable Cases
#### 3.1.5. Densest Subgraph with Concave Size Function  
A different generalization of the edge density was proposed by [PERSON] and [PERSON] [12]. Let \(f\) be a positive real-valued, non-decreasing function. The \(f\)-density of a graph \(S\) is defined by  
\[\rho_{4}(S,f)=\frac{|E(S)|}{f(|V(S)|)}\,.\]  
They also allow that the edges are assigned non-negative weights, and then the density becomes  
\[\frac{w(S)}{f(|V(S)|)}\,,\]  
where \(w(S)\) denotes the sum of edge weights in \(S\).  
Consider the case where \(f\) is a concave function, e.g., \(f(|V(S)|)=\sqrt{|V(S)|}\). Then, \(f(|V(S)|)\) grows slower than linear. This means the density gives preference to larger vertex sets. For example, if two graphs \(S_{1},S_{2}\) have \(m_{1}=100\) and \(m_{2}=200\) edges, and \(n_{1}=25\) and \(n_{2}=50\) vertices, respectively, then their edge density is the same: \(\rho_{1}(S_{1})=\rho_{1}(S_{2})=100/25=200/50=4\). However, with \(f(|V(S)|)=\sqrt{|V(S)|}\), we get \(\rho_{4}(S_{1},f)=\frac{100}{\sqrt{25}}=20\) and \(\rho_{4}(S_{2},f)=\frac{200}{\sqrt{50}}=\frac{2}{\sqrt{2}}\cdot\frac{100}{ \sqrt{25}}=\sqrt{2}\cdot 20>\rho_{4}(S_{1})\). Thus, indeed, the larger of the equally dense graphs is preferred.  
An important general result of [PERSON] and [PERSON] [12] is that the \(f\)-densest subgraph can be found in polynomial time for any real-valued, non-decreasing, polynomial-time computable concave function. On the other hand, if the function is strictly convex, then the problem becomes **NP**-hard. Nevertheless, approximate optimization is still possible in the convex case, with an approximation factor depending on the function. For example, if \(f\) is the quadratic function \(f(|V(S)|)=\lambda|V(S)|+(1-\lambda)|V(S)|^{2}\), with a constant \(\lambda\in[0,1)\), then a constant approximation ratio of \((2-\lambda)/(1-\lambda)\) can be achieved in polynomial time. All these results carry over to the edge weighted case.
# Entropy Wavelet-Based Method to Increase Efficiency in Highway Bridge Damage Identification
## 3 Damage Identification Methodology and Their Fundamentals  
This section introduces the mathematical concepts underlying the proposed method, named EWM, for identifying damage in bridges. Specifically, it describes the two theoretical fundamentals of the algorithms on which this new method is based (the Shannon entropy and the CWT).
## 1 Introduction
By the importance of component, the importance of node, or the comprehensive evaluation method of multiple indicators, the key components are identified from the perspective of importance measurement, focusing on the characteristics of a single node and its position in the system, but not considering the interaction between nodes or the dynamic influence of each node in the propagation process, which may lead to inaccurate discrimination results. In view of the problems existing in the above key component identification methods, this paper proposes a key component identification method based on the influence of fault propagation. This method not only considers the importance of each component in the system, but also considers the influence of each component on other components as the initial fault propagation source. The fault propagation structure diagram of each component established in this paper can more clearly show the process of fault propagation to other components as the initial fault propagation source, and qualitatively describes the influence of components in the propagation from the structure. Based on this diagram, the dynamic influence value of fault propagation of each component is calculated quantitatively, and the influential components of CNC lathe in the process of fault propagation are identified and sorted using this value as an index, which provides a theoretical basis and practical reference for subsequent fault detection and health maintenance.
###### Abstract  
Photovoltaic greenhouses have been claimed to be a solution to cover the energy demand of the protected crops sector. Thus, there is a need to know what is the maximum percentage of shading produced by roof-top photovoltaic panels that does not affect crop yields. The present study analyzes the effects of increasing percentages of shading in a greenhouse tomato crop located in the southeast of Spain. For this study, photovoltaic panels have been simulated with opaque sheets located in the roof-top of a north-south oriented greenhouse. Three treatments of top roof shading percentage (15%, 30% and 50%) where studied and compared with the control treatment without shading (0%). During the study, parameters registered were radiation, temperature, pH and electric conductivity of the substrate, crop yields and fruit quality. Results of the analysis show that higher percentages of shading in the roof-top of greenhouses reduce so much available radiation for the crop causing a reduction in the yield and fruit quality, even in Mediterranean areas where radiation is not a limiting factor.  
photovoltaic greenhouse; tomato; shading; microclimate; yield; fruit quality +
Footnote †: journal: Applied Sciences
# Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering
## 3 Method
### Semi-Supervised Phase
#### 3.1.2 Semi-Supervised Loss Function and Details  
In the semi-supervised phase, we adopt different augmentation methods for labeled and unlabeled data (as shown in Figure 3).  
Specifically, labeled images are represented as \(x=\{x_{1},\ldots,x_{n}\}\), and their labels are represented as \(y=\{y_{1},\ldots,y_{n}\}\). The unlabeled images are represented as \(u=\big{\{}u_{1},\ldots,u_{\mu n}\big{\}}\), and the network is represented as \(f_{\theta}\). A(\(\cdot\)) represents a strong augmentation function and a(\(\cdot\)) represents a weak augmentation function. The loss of annotation data can be expressed as:  
\[\mathcal{L}_{s}=\frac{1}{n}{\sum\limits_{i=1}^{n}{\rm H}(f_{\theta}({\rm a}(x _{i})),y_{i})} \tag{1}\]  
The Pseudo-labels of unlabeled data can be expressed as:  
\[y_{i}^{\prime}=\text{argmax}(f_{\theta}({\rm A}(u_{i})) \tag{2}\]  
The loss of unlabeled data can be expressed as:  
\[\mathcal{L}_{u}=\frac{1}{\mu n}{\sum\limits_{i=1}^{\mu n}{1(\max(f_{\theta}({ \rm A}(u_{i}))\geq\tau){\rm H}(f_{\theta}({\rm A}(u_{i})),y_{i}^{\prime})}} \tag{3}\]  
where \(\mu\) represents the ratio of unlabeled data to labeled data; \({\rm H}(\cdot,\cdot)\) represents cross entropy loss; \(\tau\) is a scalar hyperparameter denouncing the threshold, and when the confidence exceeds the threshold, the pseudo-label is retained. Therefore, the final loss in the semi-supervised phase can be expressed as:  
\[\mathcal{L}_{\rm ss}=\mathcal{L}_{s}+\lambda_{u}\mathcal{L}_{u} \tag{4}\]  
Among them, the hyperparameter \(\lambda_{u}\) is the relative weight of unsupervised loss, which is used to balance supervised loss and unsupervised loss (use the same hyperparameter settings as FixMatch did in most datasets).
## 2 Problem Description and Modeling
### Nonlinear Velocity Mapping of Hydraulic Motor  
As previously mentioned, acquiring the dynamic model of a hydraulic-driven system and accurately identifying its parameters can be a costly and time-consuming process [29]. Consequently, we have proposed hydraulic motor velocity mapping to describe the dynamic characteristics of the hydraulic motors. In order to render the velocity mapping sufficiently accurate and practical, we have assigned distinct control currents to the left and right actuator and tested the angular velocity of the motor under varying control currents. The process of data collection and development of the current-velocity mapping procedure is detailed in Algorithm 1.  
According to the dynamics of skid-steer mobile robots, the angular velocity of each motor is not only related the its own control current, but also influenced by the control current of the other side motor, since the mobile robot is driven by actuators on both sides. Therefore, we need to obtain the following relationship as the current-speed mapping of each side hydraulic motor:  
\[\begin{array}{l}Mapping_{l}\gets w_{m,l}=f_{l}(u_{l},u_{r})\\ Mapping_{r}\gets w_{m,r}=f_{r}(u_{l},u_{r})\end{array} \tag{7}\]  
where \(w_{m,l}\) and \(w_{m,r}\) are the angular velocity of the left-side motor and right-side motor, \(u_{l}\) and \(u_{r}\) are the control current of each side motor, and \(f_{l}\) and \(f_{r}\) represent nonlinear functions.  
The control current \([u_{l},u_{r}]\) and motor angular velocity of the left and right motors \([w_{m,l},w_{m,r}]\) were collected. To accurately fit the mappings, the spline interpolation method was employed; the result is illustrated in Figure 2.  
```
Input:cmd (control current) \([u_{l},u_{r}]\) Output: current-velocity mapping of the left and right motor \([Map_{l},Map_{r}]\) Params: current range \([u_{\min},u_{\max}]\), sampling step \(\Delta u\)
1: for \(u_{l}=u_{\min}:\Delta u:u_{\max}\) do
2: for \(u_{r}=u_{\min}:\Delta u:u_{\max}\) do
3: \(cmd=[u_{l},u_{r}]\)
4: send \(cmd\) to hydraulic system
5: collect motor velocity \([\omega_{m,l},\omega_{m,r}]\)
6: record \(cmd\) and \([\omega_{m,l},\omega_{m,r}]\) when the motor speed is stable
7: end for
8: end for
9:\(Mapping_{l}\getscmd\), left motor velocity \(\omega_{m,l}\)
10:\(Mapping_{r}\getscmd\), right motor velocity \(\omega_{m,r}\)
```  
**Algorithm 1**Current-Velocity Mapping Building
# Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping
###### Abstract  
In this paper, a fast numerical reconstruction of the integral imaging based on a determined interval mapping is proposed. To reduce the computation time, the proposed method employs the determined interval mapping instead of the use of magnification. In the numerical reconstruction procedure, the acquired elemental image array (EIA) from the 3D object is displayed. The flipped elemental image (EIs) are numerically formed by the virtual pinhole array. Then, the determined interval depending on the reconstruction plane is calculated and applied to each flipped EI. These flipped EIs are shifted to match the determined interval at the reconstruction plane and superimposed together. After this superimposed image is divided by the number of the superposition, the position error between the location of the shifted EI and the pixel position of the reconstruction plane is corrected by interpolation. As a result, the refocused image depending on the reconstruction plane can be reconstructed rapidly. From the experimental result, we confirmed that the proposed method largely decreased the computation time compared with the conventional method. In addition, we verified that the quality of the reconstruction by the proposed method is higher than the conventional method by the use of the structural similarity index method.  
integral imaging; numerical reconstruction; interpolation; visualization +
Footnote †: journal: Applied Physics Letters B
# Research on the Classification of ECG and PCG Signals Based on BiLSTM-GoogLeNet-DS
## 5 Experiments and Results
### Evaluation Metrics  
To test the proposed algorithm, the classifications, accuracy, sensitivity, specificity, and F1 score are used to measure the performance. Since the signals are divided into normal and abnormal signals, the abnormal signals are regarded as positive and the normal signals are regarded as negative. If the positive signal is classified correctly, it is regarded as the true positive signal (TP), otherwise it is regarded as the false negative signal (FN); if the negative signal is classified correctly, it is regarded as the true negative signal (TN), otherwise it is regarded as the false positive signal (FP) [27,28].  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}\times 100\% \tag{19}\]  
\[Sensitivity=Recall=\frac{TP}{TP+FN}\times 100\% \tag{20}\]  
\[Specificity=\frac{TN}{TN+FP}\times 100\% \tag{21}\]  
\[Precision=\frac{TP}{TP+FP}\times 100\% \tag{22}\]  
\[F1\ Score=\frac{2\times Recall\times Precision}{Recall+Precision}\times 100\% \tag{23}\]
# Numerical Analysis of EOD Helmet under Blast Load Events Using Human Head Model
## 4 Conclusions  
This paper analyses the injuries produced in the brain and the level of protection offered by the EOD helmet exposed to different quantities of an explosive charge, by developing numerical simulations of the head exposed to the explosions while wearing and not wearing a helmet.  
The use of the helmet delays the impact of the shockwave on the wearer's head. The helmet absorbs part of its energy both through the effect of the aramid fiber visor and shell and through the compression of the internal foams. In all the cases studied, wearing an EOD helmet reduces the maximum values in the measured damage parameters: 80% in CG acceleration, 95% in HIC and GSI, 70% in ICP, 60% in cerebellum strains.  
It is concluded that the helmet reduces the level of severity of injuries on the user: from a highly probable death due to the non-use of protective equipment to a low probability of injury, of a mild and localized nature. The EOD shell has good effectiveness for TNT front loads of up to 100 g at a distance of 681 mm. Beyond this amount, the EOD helmet may not be effective.  
This study is one of the first to analyze and develop a numerical tool for this type of helmet using a human head model for considering the biomechanical response and possible damage. Therefore, it provides further insight on the future investigations with the EOD helmet for a bomb-disposal officer by analyzing possible brain damage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cases** & **Without Helmet** & **With Helmet** \\ \hline
0.050 kg TNT & & \\
0.075 kg TNT & & \\
0.100 kg TNT & & \\  
**Author Contributions:** Conceptualization, J.A.L., J.A.-M., and M.R.-M.; methodology, B.V.M., M.R.-M., and I.R.; simulations, B.V.-M. and J.A.-M.; investigation, J.A.-M., B.V.-M., and M.R.-M.-; writing--review and editing, J.A.-M., J.A.L, A.C., B.V.M., I.R., and M.R.-M.; supervision, J.A.L., M.R.-M., A.C., and J.A.-M.; funding acquisition, M.R.-M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been carried out within the framework of the research project DPI2017-88166-R of FEDER program financed by the Ministerio de Economia, Industria y Competitividad and the Spanish Ministry of Education, Culture and Sports for the professor's mobility program [PERSON]'s 2018 grant (CAS18/00292).  
**Acknowledgments:** The authors acknowledge the Ministry of Economy and Competitiveness of Spain and FEDER program under the Project DPI2017-88166-R for the financial support of the work, and [PERSON] thanks the Spanish Ministry of Education, Culture and Sports for the professor's mobility program [PERSON]'s 2018 grant (CAS18/00292).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Study Area and Data
### Data  
#### 2.2.1. Precipitation Data  
(a) Gauge Data  
Figure 1 shows the spatial distribution of the 28 rainfall gauge stations. We collected hourly rainfall data during 2002-2011 from Japan Meteorological Agency (JMA) and Ministry of Land, Infrastructure, Transport and Tourism (MLIT). To be consistent with SPPs, the daily gauge rainfall was shifted to Coordinated Universal Time (UTC) while hourly data remain with Japan Standard Time (JST, UTC+9h), as the discharge data are recorded in local time.  
(b) GSMaP NRT Data  
GSMaP-NRT hourly data were obtained from the Japan Aerospace Exploration Agency (JAXA). It has a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and 4 h latency [12]. The GSMaP uses a combined algorithm of microwave (MW) and infrared (IR) data [11]. It adopted the Kalman filter to update the precipitation rate produced by forward propagation of the rainfall which obtained from MW sensors. According to the selected period of gauge rainfall, we employed version 6 of GSMaP-NRT (v6) in this study. The data can be accessed freely after registration on the JAXA GSMaP website [39].  
(c) IMERG Early Data  
The IMERG products of the National Aeronautics and Space Administration (NASA) is global rainfall product with a temporal resolution of 0.5 h and a spatial resolution of 0.1\({}^{\circ}\)[13]. TRMM-era (1997-2015) data have been reprocessed using the IMERG algorithm for 2000-present, creating a long-term continuous IMERG data record. There are three types of products (Early, Late, and Final) from NASA. Among them, the IMERG Early Run uses historical climate data for calibration, and the latency is 4 h. In this study, we employed the IMERG Early Run dataset with the same resolution and latency of GSMaP-NRT, which is available on the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) website [40].  
It should be noted that all hourly precipitation data are prepared in JST, whereas daily precipitation data are prepared in UTC. Furthermore, we extracted 47 grids of SPPs in the Fuji River basin (Figure 1) to compare with gauge rainfall in the watershed scale.
# An Aeromagnetic Compensation Algorithm Based on Radial Basis Function Artificial Neural Network
## 4 Real Measured Data Test  
### UAV Compensation Flight  
In order to establish a suitable UAV aeromagnetic compensation model, this paper used a rotor UAV to carry out a compensation flight in Ma'anshan City, Anhui Province, China. The terrain is flat, there are no interference factors such as high-voltage power poles around, and the surrounding geomagnetic field does not change much, which is very in line with the hypothetical conditions set by the T-L model. This paper developed a UAV magnetic survey system, as shown in Figure 8, which is mainly composed of two parts: UAV platform and aeromagnetic survey equipment. The selected UAV platform is a  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Set** & **Training Set** & **Method/Model** & \(\text{STD}_{p}\) & \(\text{STD}_{f}\) & IR \\ \hline Flight A & Flight B & BP-ANN & 5.5873 & 0.0792 & 70.5465 \\  & & BRF-ANN & 0.0649 & 86.1052 \\ Flight B & Flight A & BP-ANN & 5.5927 & 0.0828 & 67.5160 \\  & & RBF-ANN & 0.0651 & 85.9473 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of compensation results of BP-ANN and BRF-ANN.  
Figure 7: Simulation data compensation results: (**a**) test set flight A; and (**b**) test set flight B.  
fuel-powered unmanned helicopter SU-H2M, which can take off and fly autonomously, and has the characteristics of long endurance, fast speeds, and a large cruising distance. The maximum payload is 45 kg, the battery life is 2 h, and the general cruising speed is 60 km/h. The aeromagnetic measurement equipment is mainly composed of five parts, as shown in Figure 9: (1) a high-precision potassium pump magnetometer (GSMP-35U used to measure the total magnetic field strength of the geomagnetic field; (2) a triaxial fluxgate magnetometer (TFM100-G2) used to record aircraft attitude change information; (3) a laser altimeter from the MDL company with a range of 0.05 m-200 m; (4) an inertial navigation module, using OEM 62 GPS locator, with a static plane positioning accuracy within \(\pm\)2 m; and (5) the data collector and data processing platform, as shown in Figure 10 (detailed parameters are shown in Table 2).  
Figure 8: UAV detection platform.  
Figure 9: Aeromagnetic measurement equipment.  
A total of two flights were carried out in this experiment, and the aeromagnetic interference data measured in the two compensation flight experiments are named Flight C and Flight D. The flight data of the two compensated flights are shown in Figures 10 and 11.  
\begin{table}
\begin{tabular}{c c} \hline
**Measuring Range** & **20,000 nT-120,000 nT** \\ \hline Gradient capacity & 50,000 nT/m \\ Sensitivity & 0.0003 nT@1 Hz \\ Resolution & 0.0001 nT \\ Measurement accuracy & \(\pm\)0.05 nT \\ Sample rate & 1, 2, 5, 10, 20 Hz \\ Operating temperature & \(-\)20 \({}^{\circ}\)C\(\rightarrow\)55 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 2: Main technical parameters of GSOMP-35U high-precision potassium optical pump magnetometer.  
Figure 10: Flight C and Flight D flight paths.