esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design and Test of Harvesting System  
#### 2.4.1 Picking System Design  
The picking operation for tea buds is significantly dependent on a complex picking system, which consists of a picking manipulator and a path-planning algorithm. When the binocular camera and deep-learning technology are closely integrated, they can complete the detection and location of all tea buds in the current field of vision. Once the 3D coordinates of the tea buds are captured and transmitted to the manipulator control system, the manipulator's path-planning algorithm can calculate the optimal picking path based on this information. In this process, the algorithm needs to apply the inverse kinematic solution to each path node, following which the joint state of the manipulator at each node should be defined. Then, with the aid of the interpolation method, the joint state sequences of the manipulator are generated smoothly over the path, where these state sequences constitute the continuous and accurate joint trajectories of the manipulator. The mechanical arm with the shear hand travels along this trajectory to perform the precise tea bud-picking action.  
In this study, an LM3 robotic arm (produced by Liby Company in Shanghai, China)and an HS-K40 pneumatic shear hand (produced by Shuteng Automation Technology Co., Ltd. in Wenzhou, China) were selected as the main actuators of the harvesting robot (Figure 9). The repeatability precision of the manipulator was \(\pm\)0.5 mm, the maximum terminal speed was 2 m/s, the working radius was 638 mm, and the maximum load was 3 kg, which ensured the stability and high efficiency of the robot in the actual picking operation.  
Figure 9: Physical images and workspace map of the picking system: (**a**) Image of cutting hand, (**b**) image of physical manipulator, (**c**) manipulator workspace top view, and (**d**) manipulator workspace master view.
## 1 Introduction
Satellites such as Sentinel-2 significantly advanced precision agriculture, enabling the detection of abiotic and biotic stress of the cultivated plants and enhancing agricultural management practices [24], providing multispectral images (e.g., with NIR, red-edge bands) with high resolution (10 m) with a revisit frequency of two to five days [25]. Multispectral sensors can integrate other bands, such as red-edge and NIR, which are useful for detecting drought and heat stress of plants by monitoring their physiological status [26]. The Sentinel-2 mission, part of the European Space Agency's Copernicus program, operates with two satellites, Sentinel-2A and Sentinel-2B [25]. Furthermore, the integration of advanced regression analyses, such as machine learning algorithms, with Sentinel-2 data proved to be effective in estimating key crop physiological parameters [27]. Machine learning algorithms, such as Random Forest, can identify non-linear and complex relationships and manage non-normal data distributions; their use is growing in agricultural contexts, especially in crop remote sensing [28]. For instance, [PERSON] et al. [29] applied Random Forest to predict cotton water status from Sentinel-2 images. Recent research has been focused on the prediction of chlorophyll fluorescence, using machine learning models trained using ground-truth data and Sentinel-2 imagery on wetlands [30]. However, few studies investigated the integration of satellite images and machine learning methods for estimating plant chlorophyll fluorescence of crops [31; 32; 33; 34]; given the important role of olive growing in the Mediterranean region and the growing challenges posed by climate change, there is an emerging need for innovative tools to monitor olive groves and manage the related cultivation practices efficiently. To the best of the knowledge of the authors, no studies have been conducted on olive trees in the Mediterranean area for this purpose. Thus, we propose a synergy of advanced regression models to estimate olive tree fluorescence by integrating explainable artificial intelligence algorithms with multispectral Sentinel-2 satellite data. Sentinel-2 multispectral data provides a comprehensive view of vegetation health by capturing reflectance in several spectral bands. These bands, particularly in the red-edge and NIR regions, are sensitive to changes in chlorophyll content and photosynthetic activity, which are directly related to plant stress and overall plant health. The red-edge region, for example, is known to reflect changes in chlorophyll and leaf structural properties, making it a valuable indicator of photosynthetic efficiency [24]. The aim was to provide a useful tool to improve the monitoring of the status of olive groves in the Mediterranean environment, where the increase in adverse climatic events, such as drought and heat waves, can affect this crop. Sentinel-2 spectral data were used as predictors and the performance of different machine learning algorithms was compared.
## 3 Experimental Results and Analysis
### Experimental Results and Analysis  
#### 3.3.1 Training Policy's Performance  
The RL training performance through the AV penetration rate was used to evaluate the learning performance. Figure 7 shows the average reward curve over 200 iterations based on the AV penetration rates. The flattening of the curve in all circumstances indicates that the training policy had almost converged. Moreover, the average reward increased as the AV penetration rate at the non-signalized intersection increased, except for the 50% AV penetration rate. Full-autonomy traffic outperformed the other AV penetration rates; it produced the highest average reward and significant flattening of the curve. In particular, full-autonomy traffic yielded an improvement of 6.8 times compared with the 10% AV penetration rate. Therefore, full-autonomy traffic outperformed the other AV penetration rates in all circumstances. The effectiveness of the leading autonomous vehicle experiment at the non-signalized intersection became more obvious as the AV penetration rate increased.
# Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks
## 2 Related Works  
### Convolutional Neural Network Architecture  
The convolutional layer is a crucial component of a CNN, incorporating two essential characteristics: the local receptive field and weight sharing. The network architecture serves the purpose of extracting and analyzing feature information from images and mapping it to a higher-dimensional space. Convolutional operations involve important parameters such as the number, size, stride, and padding method. These parameters determine the behavior of the convolutional layer. Apart from standard convolution operations, there are variations such as deep convolution and one-dimensional convolution. The downsampling layer, also known as the pooling layer, is primarily used after convolution to reduce the number of parameters in the feature map and prevent overfitting. Simultaneously, pooling operations enlarge the model's receptive field, extracting more comprehensive semantic information.  
Conversely, the upsampling layer increases image resolution through the process of deconvolution. Unlike downsampling, which reduces the size of an image, inverse convolution increases the image size by mapping low-resolution features to high resolution. The Fully Connected Layer (FC), typically positioned at the end of the CNN, adjuststhe number of output nodes based on the desired number specified by the model. The weight matrix implements the connection weights for the FC, encapsulating the calculated values. These weights determine the final output of the node by passing through an activation function.  
In the structure of a neural network, convolution, upsampling/downsampling, and FC are linear operations. However, a challenge arises in deep learning models when linear mappings fail to capture nonlinear relationships. To address this, an activation function is incorporated at the end of each layer, transforming the model from linear to nonlinear mapping. This inclusion empowers the network with the ability to fit nonlinear functions.
###### Abstract  
This study investigates the bioaccumulation capacity of the lichen _Cladonia uncialis_ for radioactive isotopes, aiming to establish its potential as a biomonitor. Conducted under controlled laboratory conditions, the experiment utilised gamma-emitting isotopes to simulate fallout. The lichens were exposed to varying concentrations of these isotopes in three aquariums. The results demonstrated that the bioaccumulation of \({}^{54}\)Mn, \({}^{57}\)Co, \({}^{60}\)Co, \({}^{65}\)Zn, \({}^{137}\)Cs, and \({}^{241}\)Am by _C. uncialis_ was proportional to their initial deposition levels, and this occurred in the first days of the experiment. Analysis of isotope activity in washed and unwashed lichens showed that retention primarily occurred intracellularly, indicating effective bioaccumulation. The study derived the Aggregated Transfer Coefficient (Tag) for each isotope, which ranged from 0.34 to 0.64, and the absorption capacity of the elements increased in the following order: \({}^{54}\)Mn \(<\)\({}^{57}\)Co \(<\)\({}^{60}\)Co \(\leq\)\({}^{65}\)Zn \(<\)\({}^{241}\)Am \(<\)\({}^{137}\)Cs. Absorption efficiency, amounting to approximately 50% for \({}^{137}\)Cs and \({}^{241}\)Am, highlights the potential for lichens to serve as reliable biomonitors for environmental monitoring and estimation of deposition when knowing only the activity of the isotopes in lichen.  
radioisotopes bioaccumulation; lichens; Aggregated Transfer Coefficient (Tag); _Cladonia uncialis_; environmental monitoring
# HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing
###### Abstract  
Although nano/microsatellites have great potential as remote sensing platforms, the spatial and spectral resolutions of an optical payload instrument are limited. In this study, a high spatial resolution multispectral sensor, the High-Precision Telescope (HPT), was developed for the RISING-2 microsatellite. The HPT has four image sensors: three in the visible region of the spectrum used for the composition of true color images, and a fourth in the near-infrared region, which employs liquid crystal tunable filter (LCTF) technology for wavelength scanning. Band-to-band image registration methods have also been developed for the HPT and implemented in the image processing procedure. The processed images were compared with other satellite images, and proven to be useful in various remote sensing applications. Thus, LCTF technology can be considered an innovative tool that is suitable for future multi/hyperspectral remote sensing by nano/microsatellites.  
multispectral sensor; tunable filter; microsatellite; Earth observation; image analysis +
Footnote †: journal: Sensors
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 4 Likelihood Derivation
### Minimizing Negative Log Likelihood  
The likelihood function of the Gaussian distribution is represented as the Gaussian probability density function (PDF). In the following PDF, we express the exponential term as \(h\) to simplify the expression as follows,  
\[g(z) =\frac{1}{\sigma\sqrt{2\pi}}\exp{(h)},\text{where}, \tag{11}\] \[h =-\frac{(z-\mu)^{2}}{2\sigma^{2}}.\]  
From the likelihood function, we then reduce the problem to a mean squared minimization. Firstly, we take the natural logarithm of the likelihood function. Since both likelihood functions are Gaussian, the joint likelihood function can be expressed as follows,  
\[-\ln\left(p\Big{(}\mathbf{Z}^{(1:K)}|\mathbf{x}\Big{)}\right)=- \ln\left(\prod_{(k)=1}^{(K)}\left(g_{1}^{(k)}g_{2}^{(k)}\right)\right) \tag{12}\] \[\overset{(\mathrm{a})}{=}-\ln C\prod_{(k)=1}^{(K)}\exp{\left(h_{ 1}^{(k)}\right)}\prod_{(k)=1}^{(K)}\exp{\left(h_{2}^{(k)}\right)}\] \[\overset{(\mathrm{b})}{=}\sum_{(k)=1}^{(K)}h_{1}^{(k)}+\sum_{(k)= 1}^{(K)}h_{2}^{(k)}+C\] \[\overset{(\mathrm{c})}{=}\sum_{(k)=1}^{(K)}\frac{\left(f_{ \mathrm{d}}^{(k)}-\mu_{\mathrm{d}}^{(k)}(\mathbf{x})\right)^{2}}{2\sigma_{ \mathrm{d}}^{2}}+\sum_{(k)=1}^{(K)}\frac{\left(\Phi^{(k)}-\mu_{\Phi}^{(k)}( \mathbf{x})\right)^{2}}{2\sigma_{\Phi}^{2}}+\] \[\sum_{(k)=1}^{(K)}\frac{\left(\Theta^{(k)}-\mu_{\Theta}^{(k)}( \mathbf{x})\right)^{2}}{2\sigma_{\Theta}^{2}}+C,\]  
where \(f_{\mathrm{d}}\) is the Doppler measurement, \(\Phi\) is the azimuth, \(\Theta\) is the off-nadir angles of the AoA measurement, and \(C\) is a constant term. Since we take the natural logarithm of the joint likelihood, the exponential in (a) can be canceled out, and its product can then be expressed as addition, as shown in (b). Note that the constant term of the PDF, \(\frac{1}{\sigma\sqrt{2\pi}^{\prime}}\), is brought out from the expression, which will then be represented as \(C\) in the following steps. Then, we substitute the exponential term from the Gaussian PDF in (11) to (b). Finally, the equation can be simplified as presented in (c). The goal is to maximize the likelihood function, which is equivalent to finding the minimum mean squared error of the negative log likelihood.
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 2 MVDR-HBT Localization Principle
### Principle of Narrowband Sound Source Localization Using MVDR-HBT
In this paper, therefore, the covariance of the received signal \(\mathbf{R}_{x}\) is replaced by the covariance of the received signal, and the direction is assumed to be \(\theta_{s}\), so that the objective optimization equation is  
\[ \begin{cases}\min\mathbf{w}^{H}\mathbf{R}_{x}\mathbf{w}\\ \textit{subject to }\mathbf{w}^{H}\mathbf{a}(\theta_{s})\ =1\end{cases}  \tag{8}\]  
Assume that the desired signal at this time is represented by \(\mathbf{s}(t)\) with direction \(\theta_{s}\). The received signal contains \(L\) interfering signals with direction \(\theta_{i},i=1,2,\ldots,L\). The noise component of the received signal is modeled as Gaussian white noise and is denoted by \(n(t)\). The covariance matrix of the received signal can be represented by Equation (9):  
\[\mathbf{R}_{x}=E\Big{\{}\Big{|}\mathbf{p}\mathbf{p}^{H}\Big{|}\Big{\}}=\sigma_{s}^{2}\mathbf{ a}(\theta_{s})\mathbf{a}^{H}(\theta_{s})+\sum_{i=1}^{L}\sigma_{i}^{2}\mathbf{a}( \theta_{i})\mathbf{a}^{H}(\theta_{i})+\sigma_{n}^{2}\mathbf{I} \tag{9}\]  
If we replace the covariance matrix \(\mathbf{R}_{x}\) with the interference and noise signal covariance matrix \(\mathbf{R}_{in}\), we can obtain the weight vector of the new MVDR beamformer as follows:  
\[\mathbf{w}_{MVDR}(\theta)=\frac{\mathbf{R}_{x}^{-1}\mathbf{a}(\theta)}{\mathbf{a}^{H}(\theta )\mathbf{R}_{x}^{-1}\mathbf{a}(\theta)} \tag{10}\]  
The signal representation after MVDR weighting is as follows:  
\[\mathbf{p}\ \prime(\theta)=\mathbf{w}_{MVDR}(\theta)\odot\mathbf{p} \tag{11}\]  
When arbitrarily selecting two microphones, there is coherence between the weighted signals of the two microphones. When the relative delay between the two beams of sound waves is zero, the coherence between the two signals is maximized, and the value of the correlation function is maximized, which corresponds to the location of the sound source. The maximum correlation function is expressed as Formula (12):  
\[C_{\text{max}}=\frac{\left\langle{p^{\prime}}_{i}(\theta,t)\cdot{p^{\prime}}_{ j}(\theta,t+\Delta T)\right\rangle}{\|{p^{\prime}}_{i}(\theta,t)\|\cdot\left\|{p^{ \prime}}_{j}(\theta,t)\right\|} \tag{12}\]  
where \({p^{\prime}}_{i}\) denotes the weighted signal received by the i-th microphone, \({p^{\prime}}_{j}\) denotes the weighted signal received by the j-th microphone, and \(\Delta T\) represents the time delay. We take \(\Delta T=\frac{T_{1}-T_{2}}{v}\), where \(r_{1}\) and \(r_{2}\) denote the respective distances from the two microphones to the sound source \(S\).
# A High Gain Modified Quadratic Boost DC-DC Converter with Voltage Stress Half of Output Voltage
## 6 Results and Discussion  
This section describes the qualitative performance of the proposed high gain quadratic boost converter. The complete specification of the proposed converter is specified in the following section. A comprehensive list of the parameter's specifications is given in Table 2. The inductor of the proposed converter was designed in our laboratory following Equations (33) and (35). For the design of the inductor, an 'E' type ferrite core was used, and the turn ratios were calculated theoretically. The required air gap was added by electrical tape, and the inductance was measured at the same time. All other components, capacitor, power MOSFET, Driver IC, and STM32-MCU unit were purchased. The corresponding duty ratio for 400 V output voltage was calculated from the non-ideal voltage gain Equation (31).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Specification** \\ \hline Input Voltage & 24–48 V \\ \hline Output Voltage & (400–408) V \\ \hline Duty ratio & 0.2–0.6 \\ \hline \(f_{\text{Sair}}\) & 50 kHz \\ \hline \multirow{2}{*}{Capacitors} & C\({}_{1}\) = 220 \(\mu\)F/100 V/0.1 \(\Omega\), C\({}_{2}\) = 100 \(\mu\)F/ \\  & 250 V/0.1 \(\Omega\), C\({}_{3}\) = C\({}_{4}\) =100 \(\mu\)F/250 V/0.1 \(\Omega\), C0 = 100 \(\mu\)F/450 V/0.1 \(\Omega\) \\ \hline \multirow{2}{*}{Inductor} & L\({}_{1}\) = 300 \(\mu\)H, L\({}_{2}\) = 600 \(\mu\)H, ESR = 0.2 \(\Omega\) \\ \hline Power MOSFET & SPW52N50C3, 560 V, 52 A 0.07 \(\Omega\) \\ \hline \multirow{2}{*}{Diodes} & CMPFCD86, (600 V, V\({}_{\text{F}}\)-1.5 V, I\({}_{\text{F}}\)-8 A) \\  & RF150 TF3S(SIMPLIS) (600 V, V\({}_{\text{F}}\)-1.5 V, I\({}_{\text{F}}\)-8 A) \\ \hline Microcontroller & STM32, Nucleo H743Z12 \\ \hline Gate Driver IC & TLP250H \\ \hline Fuse & 6.3 A \\ \hline Power supply & Chroma programmable DC power supply, 621 OOH-60 OS \\ \hline Input Power & 158.6 W \\ \hline Output Power & 150 W \\ \hline \multirow{2}{*}{Load Resistance} & Chroma programmable electronic load simulator model 63. 1075 \(\Omega\), I\({}_{0}\)-0.374 A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Component specifications of the proposed converter.  
Figure 11: The voltage gain comparison plot of the recently reported proposed study.
# Usability Measures in Mobile-Based Augmented Reality Learning Applications: A Systematic Review
## 4 Results and Discussion
### Usability Metrics (RQ2)
#### 4.3.1 Performance vs. Self-Reported  
[PERSON] and [PERSON] in [2] clearly defined performance metrics as objective methods in comparison to self-reported metrics, which are mostly subjective. Performance metrics include the usability methods that were collected mostly through observation methodologies and which do not consider the factor of participants' opinion, while self-reported metrics only value the reliability of users' opinions. From the 72 collected papers, there are three categories of metric segregation practiced by scholars--some only practice performance metrics, some practice only self-reported, and some combine both metrics in collecting their usability data. In answering RQ2 on most common usability metrics in MAR learning, Table 9 shows a significant majority of the authors (49) collected only self-reported data, 20 studies collected a combination of both performance and self-reported data, and only 2 authors collect pure performance data.
# Responses of Terrestrial Evapotranspiration to Extreme Drought: A Review
## 2 Methods to Estimate and Measure Evapotranspiration
### Estimation and Measurement of Evapotranspiration
Recently, large artificial intelligence (AI) models have been applied in the field of ET estimation, such as the support vector machine (SVM), random forest regression (RF), the artificial neural network (ANN), and the wavelet random forest (WRF) model [47; 48; 49]. Compared with the conventional empirical model, the AI models show a superior performance in the selection and identification of input variables, as well as finding the optimal correlations between variables [50]. In addition, global ET can be estimated by land surface models (LSMs), for instance, as part of the Global Soil Wetness Project 2 dataset (GSWP-2) [51] and the Global Land Data Assimilation System dataset (GLDAS) [52] products. The reprocessed GLDAS Version 2 (GLDAS-2) data products have been released and could offer important references to support the response of ET to extreme drought impacts. The Monin Obukhov similarity theory (MOST) provides the fundamental equations to estimate ET for LSMs, calculated as the sum of the vegetation transpiration, vegetation evaporation, and soil evaporation [29]. Based on the observation-based forcing data and satellite RS data, LSMs can be a useful approach to derive long-term ET data with a relatively higher spatiotemporal resolution at regional and global scales [53; 54]. Nevertheless, intercomparisons of LSMs showedlarge discrepancies because LSM simulation is susceptible to the model parameters, model structure, and the reliability of the land surface forcing data. With the increase in large satellite RS data and ground-based observations, the data assimilation method can be applied to improve the accuracy of LSMs by assimilating multisource data into the model. Data assimilation is a method for integrating a number of groups of data, such as ground-based observations, RS data and products, and surface meteorological forcing data, into model simulations in order to achieve the best model estimations. Over the last two decades, various global, national, and regional land data assimilation systems (LDASs) have been developed and have generated many useful surface flux products that have improved the ability of LSMs to predicted ET [55] (i.e., ERA-Interim [56], MERRA [57], and NCEP-DOE [58]). However, many challenges remain in the quality of input data, spatial mismatches between model grids, and ground-based observations, that may impact the accuracy of the ET simulation results [55].