esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### In Planta Test  
To test the effect of actinobacteria in reducing stem rot disease caused by _S. rolfsii_, a pot experiment was conducted. Twenty mungbean plants were grown in sterile soil in a 35 \(\times\) 52 \(\times\) 4 cm pot with capacity of each well 3 \(\times\) 3 \(\times\) 4 cm. Inoculation of _S. rolfsii_ was conducted by directly placing one agar plug of _S. rolfsii_ onto the basal stem of each mungbean seedling (seedling stage, 5-10 cm height with 2 leaves), whereas the cell-free CF or spore suspension of the actinobacteria was applied via spraying onto the basal stem of the mungbean in a volume of 10 mL for each plant. The cell-free CF of selected actinobacteria was diluted at a 1:1 ratio (DW: cell-free CF), whereas the spore suspension of the actinobacteria was prepared by harvesting spores from 10-day-old colonies and adjusting the concentration to 10\({}^{8}\) spore/mL with sterile distilled water. The experiment was performed using a randomized complete block design (RCBD) with four treatments: (1) treated with 10 mL DW (control); (2) mungbean inoculation with _S. rolfsii_ alone; (3) mungbean inoculation with _S. rolfsii_ and 10 mL cell-free CF; (4) mungbean inoculation with _S. rolfsii_ and 10 mL spore suspension of the actinobacteria. Five mungbean plants were inoculated in accordance with each method and the experiment was conducted three times. The tested mungbean plants were incubated at ambient temperature and the disease progress with stem rot and wilting was observed after 7 days of inoculation. The disease scores were determined by the method previously described by [PERSON] et al. [49], with some modifications, based on assessing the external symptoms of the plant (0 = no symptoms, 1 = lesion development without wilting, 2 = lesion development with up to two wilted leaves, 3 = more than two wilted leaves, 4 = plant damping-off and dead). The disease scores were converted to a disease severity index (DSI) as follows:  
\[\text{DSI}\left(\%\right)=\frac{\sum(\text{Scale}\times\text{Amount of plants})}{\text{Maximum level}\times\text{Total number of plants}}\times 100 \tag{3}\]
## 2 Materials and Methods
### Artificial Intelligence Techniques  
#### 2.4.1 Support Vector Regression (SVR)  
In 1995, Vapnik put into practice a support vector machine (SVM): an observed-based learning approach. The minimization of structural risk as well as statistical learning theory are the most important functions of the SVM. Nevertheless, the properties that distinguish SVM from ANN are complexities, minimization in error, and gain in the network's performance capability. SVM can be categorized into linear support regression and nonlinear support regression (NVVR). SVR can be considered an SVM on the basis of layers that entail the kernel function's weight on the input data as well as the function sum of the weighted kernel function of the data targets. By and large, SVM is codified to two codes, namely, support vector regression (SVR) and support vector classifier (SVC). SVR deals with prediction, whereas SVC treats classification problems. SVR is designated as:  
\[f(x)=w\times\Phi(x)+b \tag{10}\]  
where \(w\) = vector's weight displayed in the feature space, \(\Phi\) = transfer function, and \(b\) = bias. Therefore, problems involving SVR function \(f(x)\) are given as:  
\[Minimize:\ \frac{\parallel w\parallel^{2}}{2}+C\left[\sum_{i=1}^{N}\ \xi+\xi^{*}\right] \tag{11}\]  
subject to the conditions:  
\[y_{i}-f(x)\leq+\varepsilon+\xi_{i} \tag{12}\]  
\[f(x)-y_{i}\leq+\varepsilon+\xi^{*} \tag{13}\]  
\[\xi_{i},\ \xi^{*}_{i}\ \geq 0,\ i=1,2,\ldots N \tag{14}\]  
where \(\parallel w\parallel^{2}\) = weight norm vector, \(C\) = penalty parameter, \(\xi_{i}\) and \(\xi^{*}\) = slack variables. By using Lagrange functions, the solution of the non-linear regression function is presented according to the optimization:  
\[f(x)=\sum_{i=1}^{N}(\alpha_{i}-\alpha_{i}^{*})K(x,x_{i})+b \tag{15}\]  
where \(K(x,x_{i})\) = kernel function and \(\left(\alpha_{i}\ and\ \alpha_{i}^{*}>0\right)\) = binary variables. There exist several kernel functions, including sigmoid, linear, and polynomial, but the most commonly used kernel function is the radial basis function (RBF). Consequently, the RBF kernel was used in this article. The RBF kernel adopted here is provided below.  
\[K(x,x_{i})=exp\Big{(}-\gamma\parallel x_{i}-x\parallel^{2}\Big{)} \tag{16}\]  
where \(\gamma\) = kernel parameter. SVR performance is affected by three parameters, which are C, \(\gamma\), and \(\varepsilon\) (size).
## 6 What's Next  
The experimental situation in the 100 TeV-100 PeV energy region must be clarified to solve the longstanding problem of the origin of the knee and to give solid foundations to CR models up to the highest observed energies. A new experiment, able to measure, at the right altitude and with high statistics, the elemental composition exploiting the techniques used so far in different apparatus, is mandatory to investigate the unknown uncertainties affecting the results so far obtained by shower arrays.  
The only experiment that meets these requirement is LHAASO, a new multi-component array developed starting from the experience of the high altitude experiment ARGO-YBJ. The apparatus is located at high altitude (4410 m asl, 600 g/cm\({}^{2}\)) in the Daochen site, Sichuan province, P.R. China. LHAASO is expected to measure the energy spectrum, the elemental composition and the anisotropy of CRs in the energy range between 10\({}^{12}\) and 10\({}^{17}\) eV [11; 12; 72; 73]. The experiment is constituted by a 1 km\({}^{2}\) dense array of plastic scintillators and muon detectors. At the center of the array a 300 \(\times\) 300 m\({}^{2}\) water Cherenkov facility will allow the detection of TeV showers. An array of 18 wide field of view Cherenkov telescopes will image the longitudinal profile of events. Neutron monitors will study the hadronic component in the core of air showers. LHAASO will study CR physics with different detectors and techniques starting from the TeV range, thus overlapping direct measurements in a wide interval. In Tables 1 and 2 the characteristics of the LHAASO-KM2A array are compared with other experiments. As can be seen, LHAASO will operate with a coverage of \(\sim\)0.5% over a 1 km\({}^{2}\) area. The sensitive area of muon detectors is unprecedented (more than 40,000 m\({}^{2}\)), about 17 times larger than the CASA-MIA experiment, with a coverage of about 5% over 1 km\({}^{2}\). For the first time the \(N_{e}/N_{\mu}\) correlation will be studied at high altitude with high statistics. This suite of independent instruments will also allow a deep investigation of the characteristics of the hadronic interaction models. The capability of hybrid measurements with Cherenkov telescopes operated in combination with a shower array have been demonstrated by the ARGO-YBJ measurement of the light component energy spectrum.  
In addition, LHAASO will act simultaneously as a wide aperture (\(\sim\)2 sr), continuously-operated gamma-ray telescope in the energy range between 10\({}^{11}\) and 10\({}^{15}\) eV. The first results obtained during the first year of data taking with only a portion of the apparatus opened for the first time the PeV sky to observations, showing that the Northern hemisphere contains a lot of galactic PeVatrons.  
Figure 9: The energy spectra of the light component measured by ARGO–YBJ compared to the _light_ and _heavy_ components measured by KASCADE–Grande.  
Other projects under way to investigate, with a much higher energy threshold, the high energy tail of the galactic spectrum and the transition region are HiSCORE [74] and GRAND [75].
## 2 System Model
### Performance of the System with Finite LTMB  
According to [25], the decoding error probability (DEP) at U2 cannot be negligible when \(n\) is finite. Given a DEP, denoted by \(\delta\), according to [25; 26], the data rate from UAV to U2 can be approximated as  
\[R\approx\log_{2}(1+\gamma_{u_{2}})-\sqrt{\frac{\gamma_{u_{2}}(\gamma_{u_{2}}+ 2)}{n(\gamma_{u_{2}}+1)^{2}}\frac{Q^{-1}(\delta)}{\ln(2)}+\frac{\log_{2}(n)}{2 n}}, \tag{7}\]  
where \(Q^{-1}(\cdot)\) is the inverse of the \(Q\) function.  
Similarly, given the data rate \(R\), the DEP at U2 is given by  
\[\delta=Q\left(\frac{\sqrt{n}(1+\gamma_{u_{2}})\left(\ln(1+\gamma_{u_{2}})+\frac{1 }{2}\ln(n)-R\ln 2\right)}{\sqrt{\gamma_{u_{2}}(\gamma_{u_{2}}+2)}}\right). \tag{8}\]
# Monitoring Land Cover Change on a Rapidly Urbanizing Island Using Google Earth Engine
## 1 Introduction
As the fifth-largest island in China, the main change of land cover that has occurred on Pingtan Island over the past 30 years is due to its strategic position on the southeast trade routes [22]. Pingtan Island has fragile ecosystems, which makes it prone to severe soil loss during heavy rainstorms and through wind erosion on the island [23]. With the implementation of China's reforestation strategy 'Grain for Green Project' [24; 25], the first-phase construction of coastal shelter forests was carried out by the Fujian government from 1988 to 2000, followed by the second phase of coastal shelter forests planting between 2000 and 2005. Subsequently, China's policy to boost the economy and social development was initiated through a 'Comprehensive Pilot Zone' program that was started in Pingtan County in 2009, which led to accelerated development of the island. Next, in 2014, Pingtan Island was approved as a free trade zone [26]. It was also planned to be built into an international tourism island as well as an important regional economic hub. As a result, Pingtan Island has undergone substantial change in recent years, with the total gross domestic product (GDP) increased from 46.03 million USD to 4159.56 million USD (1990-2019). Notably, it has achieved substantial change in reforestation [25], which plays a highly critical role in protecting the Pingtan Island environment by blocking wind and stabilizing the soil. Although several studies have documented changes on this island, such as coastline change [9] and tourism [10], few studies have examined the spatio-temporal patterns of land use change in this island environment. This study leveraged GEE to map the LULC dynamics and major land cover conversion process on Haitan Island (the largest island of Pingtan County). Satellite images from 1990, 2000, 2007, and 2019 were chosen to capture the mostly undeveloped state in 1990, when the island had undergone reforestation in 2000, after reforestation and before the comprehensive development establishment in 2007, and 10 years after the initiation of 'Comprehensive Pilot Zone' development in 2019. The objectives of this study were to quantify the impacts of a national reforestation program through land cover conversion and analyze the driving factors for land use change.
## 2 Methodology
### Consumption Characterization
Therefore, we can say that  
\[\sum_{i}x_{i,j}=C_{j},\text{ for each month }\quad j=1,2,\ldots,12 \tag{1}\]  
Another constraint is to ensure that the consumption for certain end uses remains constant over the various months. These categories are household appliances, hot water, and cooking. Given a fixed type of end use, \(x_{ij}\) must remain approximately constant for each month, and this can be expressed as follows:  
\[x_{i,j}\approx\text{constant, for each month }j \tag{2}\]  
where \(i\) = household appliances, hot water, cooking  
Another important consideration is related to lighting. The total consumption for lighting must be realistically distributed throughout the year, considering that it strongly depends on solar radiation and human habits. To achieve this, a binary variable \(L(t)\) can be modeled when lighting is needed. The first step is to analyze the solar radiation values (\(R(t)\)) for each hour of the day throughout the year. Each value \(R(t)\) represents solar radiation at a specific hour \(t\), measured in \(W/m^{2}\). A binary variable \(L(t)\) is introduced to represent whether the lighting is on or off at a given hour \(t\). This variable takes the following values:  
\[L(t)=\left\{ \begin{array}{ll}1,\text{ The lighting is ON at time }t\\ 0,\text{ The lighting is OFF at time }t\end{array} \right. \tag{3}\]  
The lighting activation depends on two main conditions:  
1. Solar Radiation Condition: The lighting is on (\(L(t)\) = 1) if the solar radiation is below a certain threshold, \(R(t)\) < 50 W/m\({}^{2}\). This represents a low natural light condition. 2. Nighttime Condition: Even if \(R(t)\) < 50 W/m\({}^{2}\), the lighting is off (\(L(t)\) = 0) during the nighttime hours (from 1:00 AM to 6:00 AM), assuming that people sleep and, therefore, do not need lighting. This time range has been assumed uniform for the whole annual period. However, it could be adjusted by considering the sunrise and sunset times throughout the year. Nevertheless, changing this time range would not significantly affect the final results. Combining the two conditions, \(L(t)\) can be calculated as  
\[L(t)=\left\{ \begin{array}{ll}1,\text{ if }R(t)<50\text{ W}/m^{2}\;\wedge\;t\
otin\;[1,6]\\ 0,\text{ otherwise }\end{array} \right. \tag{4}\]  
This expression changes to  
\[L(t)=\left\{\begin{array}{ll}1,\text{ if }  
Knowing that the total annual consumption for lighting is \(T_{Lighting}\), this value can be distributed on an hourly basis according to the number of hours the lighting is on during each month. If \(H_{j}\) is the total number of hours when \(L(t)=1\) for month \(j\), the monthly consumption for lighting can then be calculated as follows  
\[X_{Lighting,j}=\left(\frac{T_{Lighting}}{\sum_{j=1}^{12}H_{j}}\right)\cdot H_{ j}\quad\text{ for each month }j=1,2,\ldots,12 \tag{7}\]  
This approach ensures that the total annual lighting consumption is distributed proportionally based on the actual lighting needs each month, considering both solar radiation levels and nighttime hours.
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Parameter Setting  
In this subsection, the re-blur method including blur type and filter parameters are discussed. We also discuss the exponent coefficients of luminance, contrast, structural and blurriness, i.e., \(\alpha\), \(\beta\), \(\gamma\) and \(\lambda\) in Equation (10), to achieve the best performance of our proposed metric.
## 5 Experimental Results and Discussion
### Dual-Robot Collaborative Grinding Experiment  
In order to validate the effectiveness of the proposed method, according to the discussion in Sections 3 and 4, dual-arm collaborative grinding experiments are carried out in two different scenarios:  
Scheme 1: the attitude of the workpiece remains unchanged in the grinding, and the workpiece installed by the manipulator 1 is set to be in a horizontal state.  
Scheme 2: the attitude of the workpiece changes in the grinding, the position of the end of the workpiece clamping manipulator 1 is set to remain unchanged, and only the attitude changes.  
The experimental scene for two different grinding scenarios is shown in Figure 4:  
The manipulators used for the experiment were Franka Emika, whose main parameters are shown in Table 1:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Axes** & **Weight** & **Load** & \begin{tabular}{c} **Repeat Positioning** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Reach Distance** \\ \end{tabular} &
\begin{tabular}{c} **Control** \\ **Frequency** \\ \end{tabular} \\ \hline
7 & 18 kg & 3 kg & 0.1 mm & 855 mm & 1 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Franka Emika’s main parameters.  
Figure 4: Experimental scene.  
After calibration, the relationship between the coordinate systems of components in the two-arm system can be obtained as shown below (unit of length:m):  
\[\begin{split}\begin{smallmatrix}\text{\tiny{B}}_{1}\\ \text{\tiny{B}}_{2}\end{smallmatrix}T&=\left[\begin{array}{cccc}-0.9985 &0.0383&0.0396&0.7970\\ -0.0377&-0.9992&0.0162&0.0059\\ 0.0401&0.0146&0.9991&-0.0012\\ 0&0&0&1\end{array}\right],\\ \begin{smallmatrix}\text{\tiny{E}}_{2}\\ \text{\tiny{T}}\end{smallmatrix}T&=\left[\begin{array}{cccc}1&0&0&-0.07071\\ 0&1&0&-0.07071\\ 0&0&1&0.2\\ 0&0&0&1\end{array}\right],\\ \begin{smallmatrix}\text{\tiny{O}}\end{smallmatrix}T&=\left[\begin{array}{cccc} 0.7071&0&0.7071&-0.0636\\ -0.7071&0&0.7071&0.1485\\ 0&-1&0&0.1\\ 0&0&0&1\end{array}\right]\end{split} \tag{14}\]  
According to the presentation in Section 3, the impedance parameters set for the two arms are given in Table 2. Table 2 shows the impedance control parameters selected for each of the two robotic arms in this experiment.
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 1 Introduction
Researchers have correlated the distribution of these cells with aneurysm rupture site, intra-luminal thrombus development, and overall disease severity [21]. IHC analysis has been used previously to investigate the regional distribution of inflammatory cells in AAA tissue [16]. However, only circumferential heterogeneity has been reported. The invasive assessment of the aortic tissue, although central in improving the understanding of disease progression and aortic rupture, represents the first step towards the improvement of clinical guidelines for an accurate, patient-specific evaluation of rupture risk. Equally important is the need to obtain non-invasive means to access information on the state of individual aortas. To this effect, there have been several efforts to model the biomechanics of AAAs, both fluid and solid, that have led to so-called biomechanics-based indices as a surrogate measure to estimate growth and rupture risk [4; 5; 22; 23]. The heterogeneity along the length and circumference of the aorta should be incorporated in these parameters. In the current study, biaxial testing and histological analysis were performed on human aortic specimens ex vivo. Tissue samples were obtained from a population of open surgical repair patients in order to characterize the heterogeneity of mechanical properties and inflammatory processes of the aneurys tissue along the length and circumference of the aorta. Similarly, a series of non-invasive in vivo image-based parameters were obtained along the length and circumference of each aneurysm, namely computational fluid dynamics-based wall-shear stress and three-dimensional principal strain. Hence,this work provides a comprehensive investigation of the structural and biomechanical heterogeneity of the abdominal aortic aneurysm.
## 3 Data Acquisition and Processing  
### Kinetics and Kinematics  
The motion and EMG data used for this study were part of a larger data set, and the acquisition and processing steps have been described in greater detail elsewhere [39]. Each gait trial began with quiet standing, then each study participant reached their comfortable (self-selected) walking speed after a few strides. Stance and swing times, contact forces, moments, and power (kinetics), limb-segment kinematics, and EMG data acquired simultaneously. The gait walkway was twelve meters in length. Study participants reached a comfortable and consistent walking velocity using the last few meters of the walkway to decelerate. Gait analysis was limited to the middle six meters of the trial [41]. Study participants wore light shirts, shorts, and their own flat-sold shoes. Stance (approximately sixty percent of the gait cycle) and swing times (the remaining forty percent of the gait cycle) were determined using contact-closing footswitches [44] placed in the study participant's shoes. The lower extremity joint centers were estimated by placing round reflective markers over pre-determined anatomic landmarks [40].  
Study participants performed several preliminary walking trails to become acclimated to the test environment. Normal gait is essentially symmetric [45]; thus, only data from the  
Figure 1: The flowchart demonstrates the steps used to determine the power produced by EMG-to-force processing (EFP, left column) and inverse dynamics (kinetics plus kinematics, KIN, center and right columns).  
right leg were analyzed. Data from five (consecutive) noise-free trials were averaged for each study participant.  
Motion data was sampled at 100 Hz with an eight-camera system (Motion Analysis Corporation Model Hawk, Rohnert Park, CA, USA). A fourth-order Butterworth filter, with a cut-off frequency of 6 Hz, was used for bi-directional smoothing of marker coordinates. Angles and orientation of limb segments (and their first (velocity) and second (acceleration) derivatives) were determined as described elsewhere [23]. Ground reaction force (GRF) data, collected at 600 Hz, were acquired with paired force platforms (AMTI type OR6-7). The center of pressure location was determined from smoothed GRF data as described elsewhere [23]. Ground reaction force and motion data were temporally paired, and the inertial properties (body segment parameters) of the limb segments were used to determine joint moments [46]. Inverse dynamics (kinetics plus kinematics, KIN) knee power was determined from the product of the net knee moment and its angular velocity using in-house software.
# A New Fault Diagnosis Method for Unbalanced Data Based on 1 DCNN and L2-SVM
###### Abstract  
In general, the measured health condition data from rolling bearingss usually exhibit imbalanced distribution. However, traditional intelligent fault diagnosis methods usually assume that the data categories are balanced. To improve the diagnosis accuracy of unbalanced datasets, a new fault diagnosis method for unbalanced data based on 1 DCNN and L2-SVM is proposed in this paper. Firstly, to prevent the minority class samples from being heavily suppressed by the rectified linear unit (ReLU) activation function in the traditional convolutional neural network (CNN), ReLU is improved by linear and scaled exponential linear units (SELUs). Secondly, to solve the problem where the cross-entropy loss treats all input samples equally, it is replaced by the L2-support vector machine (L2-SVM) loss. Furthermore, a dynamic adjustment parameter is introduced to assign less misclassification cost to the majority of class samples. Finally, we add a new modulation factor that reduces the weight of more distinguishable samples to generate more focus on training indiscernible samples. The proposed method is carried out on two kinds of bearing datasets. The experimental results illustrate a significant improvement in recognition accuracy and the higher diagnosis performance of the model when dealing with unbalanced data compared with other intelligent methods.  
bearing; convolutional neural network; deep learning; fault diagnosis; unbalanced data +
Footnote †: journal: Applied Sciences