esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Groundwater Sampling  
The groundwater sample (3 L) was collected from a flowing piezometer (GW38) screened at a depth of 37.9 m in March 2019. Physico-chemical parameters were measured in situ using an HQ40d portable Multi-Parameter Meter (Hach Company, USA). The accuracy of physico-chemical determinations was \(\pm\)0.3 \({}^{\circ}\)C for temperature, \(\pm\)0.002 for pH, \(\pm\)0.5% for electrical conductivity (EC), \(\pm\)200 mS/cm for total dissolved solids (TDS), \(\pm\)0.1 mV for Eh, and \(\pm\)0.01 mg/L for dissolved oxygen. Atrazine concentrations were analyzed by gas chromatography coupled to a mass detector in an Agilent 7890B Series Gas Chromatograph linked to an Agilent 5977B Mass Selective Detector equipped with a DB-5 ms capillary column (J&W Scientific, USA). Physico-chemical parameters and atrazine concentration of the groundwater from this piezometer have belonged to a paired monitoring study since April 2018, with monthly measurements. Before groundwater sampling, the well was drained for 45 min to collect a representative sample from the regional groundwater. Afterward, 3 L of groundwater were collected in a sterile and pre-rinsed polyethylene bottle. The recipient was sealed with a screw cap, avoiding headspace, refrigerated in darkness, and transported to the laboratory not exceeding five hours. At the laboratory, groundwater samples were immediately processed.
###### Abstract  
Spaceborne synthetic aperture radar (SAR) has been proven to be a useful technique for observing the sea surface wind and current over the open ocean given its all-weather data-gathering capability and high spatial resolution. In addition to the commonly used radar return magnitude quantified by normalized radar cross section (NRCS), the Doppler centroid anomaly (DCA) has added another dimension of information. In this study, we combine the NRCS and DCA for a joint inversion of wind and surface current information using a Bayesian method. SAR-estimated Doppler is corrected by a series of steps, including the removal of scalloping effect and land correction. The cost function of this inversion scheme is constructed based on NRCS, DCA, and a background model wind. The retrieved wind results show the quality of performance through comparison with the in situ buoy measurements, showing a mean bias and a root-mean-square error (RMSE) of 0.33 m/s and 1.45 m/s for wind speed and 6.94\({}^{\circ}\) and 35.74\({}^{\circ}\) for wind direction, respectively. The correlation coefficients for wind speed and direction reach 0.931 and 0.661, respectively. Based on the obtained wind field, the line-of-sight velocity of the sea surface current is then derived by removing the wind contribution using the empirical model. The results show a consistent spatial pattern relative to the high-frequency radars, with the comparison relative to the drifter-measured current velocity exhibiting a mean bias of 0.02 m/s and RMSE of 0.32 m/s, demonstrating the reliability of the proposed inversion scheme. Such results will serve as a prototype for future spaceborne sensors to combine the radar return and Doppler information for the joint retrieval of wind vector and surface current velocity. This technique could be readily extended to the radar configuration of rotating beams for monitoring winds and current vectors.  
synthetic aperture radar (SAR); Doppler centroid anomaly; Bayesian method; wind and current velocity inversion +
Footnote †: journal: Marine Science and Engineering  
0000-0001-999-2270][PERSON]  
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## 3 Feature Selection Algorithms
### Principal Component Analysis  
Principal component analysis (PCA) is a statistical method mainly applied to reduce data dimensionality, which keeps relevant information from the original data. PCA application in ML models improve training time and model performance because redundant information in training data is removed. Initial data are projected into new variables, named principal components, with the most data variance. PCA use in SHM systems reduces large sensor-gathered data processing times, thereby enhancing the system response for structural changes or events resulting from structural damage.  
Table 2 shows a compilation of recent works incorporating the PCA technique on the data for subsequent use by a machine-learning model. As can be seen, PCA is a popular technique in recent years due to the advantages that it offers. Although PCA is a powerful  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Damage Characterization Level** & **Description** \\ \hline I: Detection & The SHM system can decide if there is any damage to the structure of interest. \\ \hline II: Localization & The SHM system can determine the existence and location of damage in the structure of interest. \\ \hline III: Assessment & The SHM system can estimate the extent of damage in the structure of interest. \\ \hline IV: Prediction & The SHM system can estimate the remaining lifetime of the structure. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Damage characterization levels.  
tool for ML applications, the PCA computation calculates the eigenvalues and eigenvectors of the data covariance matrix, which is an expensive operation for large data dimensions.
## 3 Discussion
This offers the opportunity to make more meaningful comparisons between data and models and to infer, predict, and eliminate hypotheses on the characteristics of the sectoring patterns. In this study we proposed a 2D model of yeast colony growth, where directional forces along the \(Z\)-axis are neglected. In reality, yeast colonies are three-dimensional (3D). Thus, there may be some important factors our model does not consider. For example, in 3D, high agar concentrations have been shown to influence formation of complex structures such as the vertical growth of stalk-like structures in yeast colonies [69]. In this scenario, complete contact with the substrate is not possible for all cells further impacting nutrient availability. Experimental evidence also shows that microbial colonies do not grow outward in a strictly radial direction due to varying agar concentrations [70] and de-activation of the FLO11 gene which reduces adhesion of the cells to the plate surface [71]. Thus, while cell dependency on nutrients is more complicated to model in 3D, these results suggest it will remain an important factor driving variances in colony morphology. Moreover, we hypothesize that 3D configuration may further promote local spatial organization, sectoring patterns and ordered structure of the colony. In our model, we study the formation of sector-like regions in the context of subcolonies. Under nutrient limitation, our model shows that subcolony sectors become more well-defined and begin near the location of the founder cell. However in 3D, different technologies used to capture and analyze sectoring phenomena may be limited to the colony surface. The capability of _in silico_ experiments creates the advantage of allowing for more detailed analyses of sectoring phenomena where existing scientific tools and technologies may not provide sufficient information.
## 2 Formulation of the Proposed BMUA
### The Formulation of the Proposed BMUA
#### 2.4.4 Optimization Framework with \(\Delta k\)  
The MPVs of the updated parameters are obtained using the linear optimization method [53]. To be specific, the derivatives of objective functions in Equations (30) and (31) with respect to \(\lambda,\mathbf{\phi},\mathbf{\beta}\) and \(\mathbf{\theta}\) are calculated, then are considered as equal to zero. When updating mass, the optimal \(\mathbf{\phi_{m}}^{*}\) is obtained by optimizing \(J_{m}(\lambda,\mathbf{\phi},\mathbf{\beta})\) in Equation (30) with respect to \(\mathbf{\phi}\):  
\[\mathbf{\phi_{m}}^{*}=\left[\sigma_{\alpha}^{-2}{\mathbf{G}_{\phi_{m}}}^{T}{\mathbf{G}_{ \phi_{m}}}+{\mathbf{L}}_{0}^{T}\Big{(}\mathbf{\Sigma}_{\varepsilon}^{-1}\Big{)}_{22}{ \mathbf{L}}_{0}\right]^{-1}{\mathbf{L}}_{0}^{T}\Big{[}\Big{(}\mathbf{\Sigma}_{\varepsilon} ^{-1}\Big{)}_{21}\Big{(}\hat{\lambda}-\lambda^{*}\Big{)}+\Big{(}\mathbf{\Sigma}_{ \varepsilon}^{-1}\Big{)}_{22}\mathbf{\psi}\Big{]} \tag{32}\]  
where \(\Big{(}\mathbf{\Sigma}_{\varepsilon}^{-1}\Big{)}_{21}\) is the left bottom sub-matrix of \(\mathbf{\Sigma}_{\varepsilon}^{-1}\); \(\Big{(}\mathbf{\Sigma}_{\varepsilon}^{-1}\Big{)}_{22}\) is the right bottom sub-matrix of \(\mathbf{\Sigma}_{\varepsilon}^{-1}\); \({\mathbf{G}_{\phi_{m}}}\) is defined as  
\[{\mathbf{G}_{\phi_{m}}}=diag\bigg{(}\bigg{[}{\mathbf{\phi}^{\prime}}^{(N_{m})}^{*}{}^{ T}\bigg{(}{\mathbf{M}}^{*}-\Big{(}{\lambda^{\prime}}^{(N_{m})}^{*}-{\lambda^{(N_{m})} }^{*}\Big{)}^{-1}\Delta{\mathbf{k}}\bigg{)}\bigg{]}\bigg{)}_{N_{m}\times N_{d}N_{m}} \tag{33}\]  
where the sign '\(diag\)' indicates a diagonal matrix, \({\mathbf{M}}^{*}={\mathbf{M}}(\mathbf{\beta})\). By implementing the same procedures with respect to \(\lambda\), the optimal \({\mathbf{\lambda}_{m}}^{*}\) is obtained as  
\[\begin{array}{c}\lambda_{m}^{*}=\left[\sigma_{\alpha}^{-2}{\mathbf{G}_{\lambda,m}}\left(\mathbf{\Sigma}_{\varepsilon}^{-1}\right)_{11}\right]^{-1}\left[\sigma_{ \alpha}^{-2}\left(\mathbf{\phi}^{\prime}{}^{(N_{m})}^{*}{}^{T}{\mathbf{M}}^{*}\mathbf{ \phi}^{(N_{m})}{}^{*}{}  
where  
\[\mathbf{G}_{\mathbf{\phi}_{\perp}}=diag\Bigg{(}\left[{\mathbf{\phi}^{\prime}}^{(N_{\mathbf{m}})^{ \ast}}^{T}\Bigg{(}\mathbf{K}^{\ast}-\bigg{(}\lambda^{(N_{\mathbf{m}})^{\ast}-1}-\lambda^{ \prime(N_{\mathbf{m}})^{\ast}}^{-1}\bigg{)}^{-1}\lambda^{\prime(N_{\mathbf{m}})^{\ast}} \Delta\mathbf{k}\bigg{)}\right]\Bigg{)}_{N_{\mathbf{m}}\times N_{\mathbf{m}}N_{\mathbf{m}}} \tag{40}\]  
where \(\mathbf{K}^{\ast}=\mathbf{K}(\mathbf{\theta})\). The optimal \(\mathbf{\lambda}_{k}{}^{\ast}\) is expressed as  
\[ \begin{split}\mathbf{\lambda}_{k}{}^{\ast}=\Big{[}\sigma_{eq}^{-2} \mathbf{G}_{\lambda,k}+\Big{(}\mathbf{\Sigma}_{\varepsilon}^{-1}\Big{)}_{11}\Big{]}^ {-1}\Big{[}\sigma_{eq}^{-2}\Big{(}\ \ {\lambda^{\prime(N_{\mathbf{0}})}^{\ast}}^{T}\mathbf{K}^{\ast}{\mathbf{\phi}^{(N_{\mathbf{0}}) }^{\ast}}^{\ast}\ \Big{)}\\ +\Big{(}\mathbf{\Sigma}_{\varepsilon}^{-1}\Big{)}_{11}\mathbf{\hat{ \lambda}}+\Big{(}\mathbf{\Sigma}_{\varepsilon}^{-1}\Big{)}_{12}\big{(}\hat{\mathbf{ \psi}}-\mathbf{L}_{0}{\mathbf{\phi}}^{\ast}\big{)}\Big{]}\end{split}
# Detection of _Camellia oleifera_ Fruit in Complex Scenes by Using YOLOv7 and Data Augmentation
###### Abstract  
Rapid and accurate detection of _Camellia oleifera_ fruit is beneficial to improve the picking efficiency. However, detection faces new challenges because of the complex field environment. A _Camellia oleifera_ fruit detection method based on YOLOv7 network and multiple data augmentation was proposed to detect _Camellia oleifera_ fruit in complex field scenes. Firstly, the images of _Camellia oleifera_ fruit were collected in the field to establish training and test sets. Detection performance was then compared among YOLOv7, YOLOv5s, YOLOv3-spp and Faster R-CNN networks. The YOLOv7 network with the best performance was selected. A DA-YOLOv7 model was established via the YOLOv7 network combined with various data augmentation methods. The DA-YOLOv7 model had the best detection performance and a strong generalisation ability in complex scenes, with mAP, Precision, Recall, F1 score and average detection time of 96.03%, 94.76%, 95.54%, 95.15% and 0.025 s per image, respectively. Therefore, YOLOv7 combined with data augmentation can be used to detect _Camellia oleifera_ fruit in complex scenes. This study provides a theoretical reference for the detection and harvesting of crops under complex conditions.  
object detection; YOLOv7; data augmentation; convolutional neural network; _Camellia oleifera_ fruit
## 4 Discussion  
Traditionally, the digital sampling of the SPWM technique has consisted of the digital generation of the triangular and sinusoidal signals; the obtained lists occupy a considerable amount of memory and require relatively sophisticated devices such as FPGAs [8; 9; 10; 13; 17]. The proposed algorithm allows direct calculation of the switching angles without the need for digital comparison of the triangular and sinusoidal signals. This allows for reducing the use of memory, and the simplicity of the algorithm allows its implementation in low-cost processors such as generic microcontrollers or microcomputers like Raspberry Pi.  
The evaluation of THD in the presented cases demonstrated the validity of the proposed algorithm to evaluate theoretically and numerically the switching angles originated by the SPWM technique.  
The proposed algorithm is simple and can be easily reconfigurable by changing the initial conditions such as the frequency modulation index \(m_{f}\), which allows the elimination of harmonics up to a value determined by the switching speed of the processor used. For example, when \(m_{f}=100\), the digitally SPWM will eliminate the harmonics until harmonic 100.  
The accuracy in assessing the angles depends on the number of samples \(N_{s}\); the greater the number of samples, the greater the accuracy. For the analyzed cases, 10 samples were sufficient.  
Digitally, SPWM presents more robustness than the natural SPWM; its implementation is more economical and smaller. The reconfiguration capability gives it more flexibility.
## 2 Materials and Methods
### Data Processing
The study excluded hand movements relative to forearms due to the interference of protective gloves with IMU sensor installation and the potential impact on the production cycle. Additionally, the standing posture of employees necessitated the omission of a lower extremity movement analysis. In addition, sensor-based linear acceleration (in one-thousandths of the Earth's gravitational acceleration (mili g), where g = 9.81 m/s\({}^{2}\) is the gravitational constant) was measured in 3 translational degrees of freedom (X,Y,Z). The cartesian components were then used to calculate the resultant linear acceleration and expressed in m/sec\({}^{2}\). A basic formula was used to calculate the magnitude of the resulting acceleration as equal to the square root of the sum of the squares of its components along the axis. The linear velocity was calculated by integrating the appropriate acceleration signal between neighbouring samples using the Integration function in MR3 software, assuming a known initial velocity. Integration is a process that sums up the acceleration over each small time interval to find the change in velocity for that interval. The resulting velocity underwent further processing (smoothing), to ensure that it accurately represents the movement being measured. The following parameters were selected to characterise workers' mobility: the median (central tendency) and \(\pm\) interquartile range (IQR, a measure of dispersion for skewed distributions) for the maximum value (Max), minimum value (Min), and range of motion (ROM, calculated as Max-Min). Movements exhibiting extreme median values (Min or Max), asymmetry (e.g., excessive tilts from the neutral position or significant side differences), or exceptionally low or high variability (indicated by the IQR) were identified as potential indicators of risk for work-related musculoskeletal disorders (WRMDs).
# Data-Driven Policy Learning Methods from Biological Behavior: A Systematic Review
## 4 Explaining Behavior via Reward Function: Inverse Reinforcement Learning
### Bayesian Optimization Method  
Bayesian IRL (BIRL) [51; 52; 110] uses a probability distribution approach to address uncertainty when estimating the reward function for inverse reinforcement learning. BIRL uses a Bayesian framework and prior knowledge to determine the posterior distribution preferred in expert behavior, producing alternative strategies that may outperform the suboptimal strategy demonstrated by the expert. The probability of demonstrating the entire trajectory to an expert is expressed as \(Pr(\tau\mid R)\). Thus, the posterior probability \(Pr(R\mid\tau)\) of the reward function \(R\) can be derived:  
\[Pr(\tau\mid R)=\frac{1}{Z}e^{\alpha\mathbb{E}(\tau,R)}, \tag{14}\]  
\[Pr(R\mid\tau)=\frac{1}{Z\cdot P(\tau)}e^{\alpha\mathbb{E}(\tau,R)}P(R). \tag{15}\]  
Bayesian ideas can also be embodied in Gaussian process IRL (GP-IRL) and maximum likelihood IRL (ML-IRL). The GP-IRL models the reward function as a Gaussian process and treats the distribution of the reward function as a posterior distribution, computed by Bayesian updating [111]. It employs a Gaussian process to learn the nonlinear reward function. The uncertainty is addressed by determining the posterior distribution. Unlike BIRL, the ML-IRL directly uses the likelihood function to perform the estimation. Boltzmann exploration makes the likelihoods differentiable, thus inferring a reward function [53].  
As IRL methods based on the probabilistic approach, BIRL and GP-IRL impose a prior on the relevant parameters to learn the degree of suboptimality in expert demonstrations explicitly and provide a posterior distribution. In contrast, the ML-IRL is based on maximum likelihood estimation, which cannot incorporate prior knowledge and handle uncertainty. IRL with the Bayesian optimization method has been used to learn driving strategies [54], mobile robot navigation [55; 56], and robot demonstrative learning [57] with good performance. Hierarchical BIRL extended on the original basis outperforms MaxEnt-IRL in cab driver route selection based on maps and GPS data [58]. This method is also used to learn the behavior of animals [59; 60], the behavior of pedestrians [61], user behavior modeling [62], and market trading strategies [63]. Nevertheless, IRL based on the Bayesian framework or Gaussian process significantly increases the computational difficulty and remains difficult to extend to applications in large environments before solving complex computational volumes. The ML-IRL, in contrast, could be computationally simpler but lacks robustness to uncertainty.
# Selection of Key Frames for 3D Reconstruction in Real Time
## 4 Results and Discussion
### Classification  
The first step was to determine the hyperparameters with the training set. We found the best hyperparameters by Grid Search [22]. By choosing the k = 3 for the k-fold cross validation, we increase the amount of test data. Although the dataset comprises more than 6000 frames, the dataset is separated in three sections, as one can see in Figures 6 and 7. Each section contains about 2000 frames, and thus it is important to increase the test set to avoid a marginal amount of data with respect to a section. The three-fold cross-validation achieved a score of 87.51%. Furthermore, we trained the Random Forest with the training  
Figure 5: Image frequency magnitude threshold identification: (**a**) image satisfies the mean frequency magnitude threshold and (**b**) image does not satisfy the mean frequency magnitude threshold.  
set and tested the predictions with the same data. The test resulted in a correct prediction rate of 94.52%. Finally, to evaluate the algorithm and to see if the classifier generalizes well, we tested the trained classifier with the test set, which was never seen during training. The Random Forest contains 20 decision trees with a maximum depth of 20, respectively. The test resulted in a correct prediction rate of 86.09%. Consequently, it seemed that the classifier does neither overfit nor underfit and is generalized with more than 85% of the correct prediction rate. In Figure 8, we can see that 92% of the class _bud_ was positively predicted as _bad_, and therefore about 8% was negatively predicted as _good_. On the other hand, the performance of the class _good_ was worse. Only 66% of the class _good_ was positively labeled as _good_, which is slightly better than a random guess. Thus, 34% of the class _good_ was negatively labeled as _bad_. Therefore, we took a closer look at the ratios of those predictions. In total, the number of frames in the test set was 1964. On the other hand, the precision of the class _bad_ was nearly 91% with a recall of nearly 92%. The precision with respect to the class _good_ on the test set was about 70%, providing a recall of 66%. Figure 9 represents a RF based on the training of \(S_{max}(a)\), and \(E_{S}(\omega)\) obtained a test score of 86.1%. The red area denotes the class _bad_, and the blue area denotes the class _good_. The strong overlap was responsible for the relatively low precision of class _good_. Investigating more detailed representations of both classes, we saw that class _bad_ generalized well on unseen data, but class _good_ tended to overfit; neither revealed a good performance on training data or on test data. Table 1 represents the feature importance during training. The features regarding the angular velocity seem much more important, by factor ten, and had greater impact on the splitting of the leaves during training. However, the combination of acceleration and angular velocity was mandatory and achieved the highest performance.
# Device-To-Device Communication in 5G Environment: Issues, Solutions, and Challenges
## 7 Challenges in D2D Communication  
There are various open issues and challenges that need attention in securing a D2D communication in 5G environments. Even though there extensive work has been carried out regarding interference management for D2D communication according to the previous legacy networks (LTE, WiMAX, and LTE-A), interference management for D2D communication in 5G cellular networks however is still considered as a new research topic. The open issues and challenges are highlighted in the following discussion.