esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Herbicide Use in Fodder Crops with Low-Cost Remote Sensing and Variable Rate Technology
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Reference Data**} \\ \hline  & & **1** & **2** & **3** & **Total** \\ \hline  & 1 & a & b & c & D \\ Classified & 2 & d & e & f & E \\ data & 3 & g & h & i & F \\  & Total & A & B & C & T \\ \hline \hline \end{tabular}
\end{table} 
Table 1: [PERSON]’s kappa coefficient matrix [17]. sum of the columns of the matrix; the value of k1 is calculated by applying Equation (1); and the value of k2 is obtained by applying Equation (2) [17]. \[\text{k1}=\frac{(a+e+i)}{T}, \tag{1}\]  
\[\text{k2}=(\frac{A}{T}\;\times\;\frac{D}{T})+(\frac{B}{T}\;\times\;\frac{E}{T} )+(\frac{C}{T}\;\times\;\frac{F}{T}), \tag{2}\]  
The value of kappa is determined as shown in Equation (3) [17]. \[\text{kappa}=\frac{k1-k2}{1-k2}, \tag{3}\]  
The precision of each reference value is determined according to Equations (4)-(6) [17]. Precision 1 = \(\frac{a}{A}\), (4)  
Reference value 2:  
Precision 2 = \(\frac{e}{B}\), (5)  
Reference value 3:  
Precision 3 = \(\frac{i}{C}\) (6)  
Thus, it is necessary to find methodologies to know the variation in the number of weeds in the plot in a faster and more expeditious way, ensuring that the herbicide application is performed only in the areas where it is necessary, with the detail that the crop requires, and in a manner compatible with application sprayers. Although the advances in variable-rate control systems allow for individual opening and closing of the sprayer nozzles to be controlled in a very short time [18], the most common sprayers can still only provide metric-level accuracy due to their boom sections, it not being necessary to create prescription maps with centimeter or even sub-centimeter detail. These methodologies of weed control may benefit farmers by increasing grower profits and reducing the amount of herbicide released into the environment. Therefore, the objective of this trial was twofold: (1) to propose a methodology for mapping weeds through digital imagery in a sufficiently rapid, low cost, and autonomous manner for the farmer; and (2) to evaluate the efficiency of variable-rate herbicide application on a fodder crop.
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
## 4 Discussion
In addition, the gross errors have also effect on the ED IFCB estimation if the data quality check processing does not remove all of them. The error in ED IFCB estimate at the epoch will be introduced into the IFCB estimates at all subsequent epochs due to accumulation. Likewise, the latter epochs may also show similar behavior. As a result, the IFCB estimates will exhibit drifting, such as the IFCB estimates of C15 shown in Figure 6. The ED strategy is suitable for GPS as the peak-to-peak amplitudes of single-day IFCB series can be up to 0.269 m [20].
## 3 Kinematics Modeling of Self-Insulating Mechanical Arm Joints
### Forward Kinematics
 \tag{10}\]  
Then, the transformation matrix from the base coordinate system to the end-effector coordinate system of the mechanical arm is  
\[{}^{O_{1}}T_{o_{2}}={}^{O_{1}}T_{o_{2}}{}^{o_{1}}T_{C}{}^{C}T_{O_{2}}{}^{O_{2} }T_{o_{2}} \tag{11}\]  
For the shoulder joint and wrist joint with the same structure, we marked them as part I and part II, as shown in Figure 4. The physical quantities corresponding to Part I and Part II are represented by subscript 1 and subscript 2, respectively. Since the total transformation matrix is composed of the multiplication of these four transformation matrices, it contains a total of seven variables: the three pose angles of the first joint (\(\theta_{m1}\), \(\theta_{r1}\), \(\theta_{b1}\)), the rotation angle \(\alpha\) of the intermediate elbow joint, and the three pose angles of the second joint (\(\theta_{m2}\), \(\theta_{r2}\), \(\theta_{b2}\)). The relationships between these seven variables and the lengths of the 10 driving cables of the mechanical arm are as follows:  
\[ \begin{array}{l}l_{1}=\sqrt{(h-0.5d)\sin^{2}\theta_{b_{1}}+(dh-0.5d^{2}) \sin\theta_{b_{1}}\sin\theta_{m_{1}}+0.25d^{2}+\left((h+0.5d)\cos\theta_{b_{1} }+h\right)^{2}}\\ l_{2}=\sqrt{(h-0.5d)\sin^{2}\theta_{b_{1}}+(-dh+0.5d^{2})\sin\theta_{b_{1}}\sin \theta_{m_{1}}+0.25d^{2}+\left((h+0.5d)\cos\theta_{b_{1}}+h\right)^{2}}\\ l_{3}=\sqrt{(h+0.5d)\sin^{2}\theta_{b_{1}}+(-dh-0.5d^{2})\sin\theta_{b_{1}}\sin \theta_{m_{1}}+0.25d^{2}+\left((h-0.5d)\cos\theta_{b_{1}}+h\right)^{2}}\\ l_{4}=\sqrt{(h+0.5d)\sin^{2}\theta_{b_{1}}+(dh+0.5d^{2})\sin\theta_{b_{1}}\sin \theta_{m_{1}}+0.25d^{2}+\left((h-0.5d)\cos\theta_{b_{1}}+h\right)^{2}}\\ l_{5}=l_{50}+\sqrt{k_{1}^{2}+k_{2}^{2}+2k_{1}k_{2}\sin\alpha}-\sqrt{k_{1}^{2}+ k_{2}^{2}}\\ l_{6}=l_{60}+\sqrt{k_{1}^{2}+k_{2}^{2}-2k_{1}k_{2}\sin\alpha}-\sqrt{k_{1}^{2}+k_{2}^{2}}\\ l_{7}=2h+2l+\sqrt{(h-0.5d)\sin^{2}\theta_{b_{2}}+(dh-0.5d^{2})\sin\theta_{b_{2} }\sin\theta_{m_{2}}+0.25d^{2}+\left((h+0.5d)\cos\theta_{b_{2}}+h\right)^{2}}\\ l_{8}=2h+2l+\sqrt{(h-0.5d)\sin^{2}\theta_{b_{2}}+(-dh+0.5d^{2})\sin\theta_{b_{2} }\sin\theta_{m_{2}}+0.25d^{2}+\left((h+0.5d)\cos\theta_{b_{2}}+h\right)^{2}}\\ l_{9}=2h+2l+\sqrt{(h+0.5d)\sin^{2}\theta_{b_{2}}+(-dh-0.5d^{2})\sin\theta_{b_{2} }\sin\theta_{m_{2}}+0.25d^{2}+\left((h-0.5d)\cos\theta_{b_{2}}+h\right)^{2}}\\ l_{10}=2h+2l+\sqrt{(h+0.5d)\sin^{2}\theta_{b_{2}}+(dh+0.5d^{2})\sin\theta_{b_{2} }\sin\theta_{m_{2}}+0.25d^{2}+\left((h-0.5d)\cos\theta_{b_{2}}+h\right)^{2}} \end{array}  \tag{12}\]  
Here, \(l_{i}\) (\(i=1\), \(2\), \(\ldots\), 10) denotes the lengths of the ten driving cables. \(k_{1}\) and \(k_{2}\) represent the length of the elbow cable bracket and the length of the intermediate link, respectively, as illustrated in Figure 6.  
Among them are \(h=15\) cm, \(d=10\) cm, \(k_{1}=5\) cm, \(k_{2}=10\) cm, and \(l=50\) cm. In practical applications, the dimensional parameters of the manipulator can be appropriately adjusted to adapt to the requirements of the working environment.
## 3 Flow Velocity Measurement Method
### The Implementation Steps of Measuring Particle Velocity  
The implementation steps of the particle velocity measuring process for the ultrasonic image during actual application can be summarized as follows:  
Step 1: begin with a large interrogation sample to obtain the whole dynamic range of the displacement and then perform a standard interrogation process.  
Step 2: obtain an initial displacement complying with the one-quarter rule, which means that the box is divided from one to four.  
Step 3: replace incorrect data of the velocity vector field by using local mean and low-pass filtering methods. A filter kernel with the same window size is enough to smooth spurious emission and then suppress fluctuations by using a moving average filter.  
Step 4: detect the extremums and repeat them by initial interpolation results. The newly detected displacements are used to obtain a higher resolution for the next step, so the criterion of extremum detection is more stringent than before.  
Step 5: deform the recordings according to the filtered velocity vector field.  
Step 6: pass interrogation on the deformed image within the interrogation window using the cross-correlation function and add the corrected result of the cross-correlation function to the filtered velocity profile field.  
Step 7: project the detected displacements, that is, velocity, on the next higher level. Offset the interrogation windows by using the displacements.  
Step 8: inspect the velocity vector field for extremums and replace them with interpolation.  
Step 9: put an interrogation at the targeted sampling and interrogation window size, which is without extremum removing and smoothing.  
Step 10: increase the resolution of the next level and repeat the above step about three times until the resolution of the actual ultrasonic image is reached. The increment of displacement will decrease gradually, along with the iterative number increasing. Lastly, the best displacement can be obtained and can be calculated as the detected feature vector.  
Step 11: incorrect the obtained vector according to Formulas (5) and (6), translate it to flow velocity, and calculate the velocity profile distribution of the flow field.  
Step 12: recalculate the vector with certain pixel distances by the depth-averaged approach and put the vector on the interrogation window. If further resizing the search region of the desired correlation peak, the final obtained velocity's spatial resolution could be further improved.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 4 Result and Discussion  
The match between the source and loads lead to the anti-islanding methods being unable to detect the islanding conditions. The closer the power match between the loads and the sources, the lesser the changes of the various parameters (like voltage change, frequency change, etc.) occur during the islanding event. This means that the anti-islanding methods which depend on monitoring these parameters will tend not to detect the islanding event. To overcome this, monitoring more than one parameter is required.  
For the developed method, the power mismatch between the source and the loads are 38.41% to \(-\)24.39% as shown in Figure 3. That is, the case of any power mismatch between these limits will not be detected by the proposed methods. This NDZ has to be determined by conducting the load flow analysis of a sample of 13-bus system as shown in Figure 4.  
Figure 4: Load flow analysis of the 13-bus system.  
Figure 3: Changes on load with the limit of NDZ.
## 2 Materials and Methods
### Model Selection
#### 2.4.4 Light Gradient Boosting Machine  
LightGBM is a highly efficient distributed gradient boosting framework [46]. It was created to address machine learning tasks featuring large-scale datasets and high-dimensional features. LightGBM has shown outstanding performance in numerous machine learning competitions and real-world applications. Compared with GBoost and XGBoost, LightGBM boasts even swifter training speeds and lower memory usage. These benefits are attributable to the histogram algorithm, gradient-based one-side sampling technique, and leaf-wise growth strategy. First, LightGBM uses the histogram algorithm to identify feature gradients and Hessians, which accelerates gradient boosting decision trees' training. Second, the LightGBM algorithm takes advantage of gradient-based one-side sampling to select data points for each subset. This strategy involves prioritizing data points with higher gradients and rejecting those with lower gradients; it effectively decreases the amount of training data while preserving model accuracy, thus expediting training. Lastly, leaf-wise growth minimizes the loss function and consequently enhances model accuracy: it involves choosing the leaf node with the highest gain from all leaf nodes in each step and then splitting that node until the specified number of leaves is reached.
## 3 Equilibrium and Thermodynamic Aspects of Cr(VI) Sorption
### Sorbent Affinity toward Cr(VI)  
The sorbent affinity toward Cr(VI) can be evaluated from the Langmuir equilibrium constant (\(K_{L}\)): the higher \(K_{L}\) the higher the sorbent affinity for Cr(VI). Among tested sorbents (Table 1), _Sargassum bewamon_ (\(K_{L}=1160\) L mmol\({}^{-1}\)) [19] and _Aspergillus niger_ (\(K_{L}=1140\) L mmol\({}^{-1}\)) [37] exhibit the highest affinity for Cr(VI), followed by L-cysteine functionalized magnetite (\(K_{L}=215\) L mmol\({}^{-1}\)) [10], magnetic ion exchange resin (\(K_{L}=181\) L mmol\({}^{-1}\)) [38] and calcinated Al/Fe oxide-oxyhydroxoxide composite (\(K_{L}=123\) L mmol\({}^{-1}\)) [67]. For practical purposes, it is also relevant to evaluate sorbent affinity at low Cr(VI) initial concentration. This parameter can be estimated from the initial slope of the Langmuir isotherm, which is the product of the maximum sorption capacity, \(q_{m}\), and \(K_{L}\). The highest values for Cr(VI) affinity at low Cr(VI) concentration have been reported for polypyrrole graphene oxide nanocomposite [62] and _Sargassum bewamon_[19] (see Table 1).
## 4 Results and Discussion  
### Creation of the Model of Static Property of PAMs in Antagonistic Connection  
The calculation of the function of the course of the measured static property of the actuator was carried out using the course of one half of the static property quadrant 1 (Figure 6) [33]. A general form of the equation for the solution of the linear regression task (by the method of least squares) in the matrix form for the basic form (without expressing the error) is:  
\[y=a_{0}+a_{1}x_{1}+a_{2}x_{2}+\cdots+a_{k}x_{k}, \tag{15}\]  
and in the matrix form  
\[\mathrm{X}=\mathrm{XA}, \tag{16}\]  
with the dimensions \(\mathrm{Y}=Nx_{1}\), \(\mathrm{X}=Nx(1+k)\), \(\mathrm{A}=(1+k)x_{1}\), where \(N\) is the number of measured values.  
Figure 6: Approximation of the actuator static characteristic by exponential function.  
The selected function form was  
\[y=a_{0}+a_{1}e^{-x}+a_{2}xe^{-x}, \tag{17}\]  
If in the following expression of the relationship between the actuator arm displacement at the input air pressure into the AM, according to Function (17), we introduce for the given physical parameters by the substitution:  
\[y=\beta, \tag{18}\]  
\[x=p_{svd}=p_{1}-p_{2} \tag{19}\]  
whereas the given parameters may have the following values:  
\[\alpha=(\beta_{1},\beta_{2},\dots\ \beta_{3}) \tag{20}\]  
\[p_{svd}=(p_{1},p_{2},\dots\ p_{3}) \tag{21}\]  
The set of equations in the matrix form then has the following form  
\[\left[\begin{array}{c}\beta_{1}\\ \beta_{2}\\ \dots\\ \beta_{3}\end{array}\right]=\left[\begin{array}{ccc}1&e^{-p_{1}}&p_{1}e^{-p _{1}}\\ 1&e^{-p_{2}}&p_{2}e^{-p_{2}}\\ \dots&\dots&\dots\\ 1&e^{-p_{3}}&p_{3}e^{-p_{3}}\end{array}\right].\left[\begin{array}{c}a_{0}\\ a_{1}\\ a_{2}\end{array}\right] \tag{22}\]  
By solving this set of equations, the following values of coefficients were obtained for the used AMs  
\[a_{0}=35.135\]  
\[a_{1}=-34.4443\]  
\[a_{2}=5.470\]  
and therefore  
\[\beta(p_{svd})=35.135-34.4443e^{-p_{svd}}+5.470p_{svd}e^{-p_{svd}} \tag{23}\]  
As the antagonistic actuator with two AM moves, depending on the sign of pressure \(p_{svd}\) in two directions from the initial (reference) point, corresponding to the equality of maximum pressures in both muscles, the static property of such an actuator will be four-quadrant and symmetric. It is assumed that the properties of both AMs are approximately the same.
# Artificial Neural Network Framework for Hybrid Control and Monitoring in Turning Operations
## 2 Materials and Methods
### Structured Data Preprocessing for ANN: Splitting, Stratification, and Preparation  
The ANN system comprises forward and inverse models, trained to predict energy consumption and cutting forces, respectively. The framework evolved through multiple stages of optimization, improving predictive accuracy and adaptability to real-world machining  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Independent Variables**} & \multicolumn{3}{c}{**Dependent Variables**} \\ \hline  & **Speed** & **Force** & **Current** & **Active Power** & **Power Factor** \\  & **\(\mathbf{m}\)** & **\(\mathbf{F}\)** & **\(\mathbf{I}\)** & **\(\mathbf{P}\)** & **\(\mathbf{cos\,\varphi}\)** \\  & **[rpm]** & **[daN]** & **[A]** & **[kW]** & **[kW]** \\ \hline
1 & 16 & 12.262  
conditions. The software models were developed in Python 3.8.10, ensuring compatibility with other solutions using JSON and CSV formats for data exchange and interaction.  
Forward and inverse modeling (bidirectional learning) was applied to predict machining parameters by training two complementary neural network models: the forward model, which estimates electrical parameters such as current I and power P based on input machining conditions like force F and spindle speed n, and the inverse model, which determines the required force F needed to achieve specific electrical responses, enabling the adaptive recalibration and optimization of machining processes.  
The dataset preprocessing starts with reading the raw data from a CSV/JSON file and introducing an auxiliary check (\"F_zero_flag\") to indicate instances where the applied force (F [daN]) is zero. This flag is essential for ensuring stratified sampling, maintaining a proportional distribution of zero-force cases across all subsets. To begin partitioning, 15% of the data is allocated for testing, while the remaining 85% is reserved for training and validation. The stratification ensures that the dataset remains balanced, preventing any subset from being biased towards specific operating conditions.  
The next step was splitting the 85% of remaining data into training (70%) and validation (15%) sets, ensuring a final 70/15/15 split across training, validation, and test sets. This separation allows the model to be trained effectively while using the validation set for hyperparameter tuning and early stopping, preventing overfitting.  
Finally, the processed data were saved into separate CSV/JSON files for training, validation, and testing. The structured partitioning ensures that the model can generalize well across different operational conditions while maintaining data integrity. This method effectively prevents data leakage and allows for robust model evaluation, ensuring that performance metrics reflect real-world applicability.
## 1 Introduction
Although the BP neural network has a good fitting effect, the setting of the network hyperparameters significantly affects the fitting efficiency and accuracy of the network. The learning rate, initial weights, and thresholds are the parameters that most affect the network performance. The learning rate of the traditional BP network is fixed, regardless of the magnitude of error, always with a fixed learning rate to adjust the weights, etc. If the learning rate is too high, it may not be possible to directly cross the global optimum resulting in a failure to converge. If the learning rate is too low, the loss function changes slowly, there is a large increase in the convergence complexity of the network, and the process is easily trapped in local extremes. In addition, the neural network requires constant iterative updating of weights and thresholds during the computation to perform well [20, 21]. The initialization of weights and thresholds of traditional BP networks is randomly generated, and during the training process, phenomena such as gradient disappearance and gradient explosion are often encountered. Therefore, proper initialization of the weights can effectively avoid these two issues and improve the model performance and convergence speed. On the basis of the above analysis, this paper proposes a design method (called SMPO) that uses an improved BP neural network (called GAALBP) to establish a telescope surrogate model and optimizes the model parameters using a GA to optimize the thermal design of a telescope. GAALBP employs a GA to optimize the initial network weights and thresholds, and the learning rate adaptively changes with the error during the training, allowing for the training of a better surrogate model and providing a physical basis for subsequent parameter optimization. The remainder of this paper is organized as follows: Section 2 details the proposed design methodology of SMPO. Section 3 describes the application of SMPO to the parameter optimization of the thermal design parameters for a space telescope and compares the results with those obtained using the traditional method of manual parameter optimization. Section 4 presents the conclusions of the study.
## 3 Semantic Segmentation of 3D Point Clouds
### Local Feature Aggregation Classifiers  
The semantic features \(F_{out}\) extracted by the Feature Extraction network are transformed into specific semantic labels by the classifier. In previous semantic segmentation of 3D point clouds, the classifier generates point-wise semantic labels individually via MLPs implemented by fully connected layers. A fully connected layer consists of a linear transformation and a non-linear activation function. However, the non-linear activation function results in neighbor inconsistency in the prediction. To address this problem, we introduce a feature aggregation (FA) module into the MLP classifier, which enables the classifier to reference neighborhood information during classification. With this module, the contextual awareness ability of classification is enhanced.  
For a given point \(\mathbf{p_{i}}\) in \(P\), we have used the KD-Tree algorithm to construct its neighborhood \(N_{i}=\left\{\mathbf{p_{i}^{j}}\right\}1\leq j\leq k\right\}\).  
As shown in Figure 4, the feature aggregation module searches for the \(k\) nearest neighboring points and represents the feature of the neighboring point \(\mathbf{p_{i}^{j}}\) as \(\mathbf{e_{i}^{j}}\), which comes from the output feature \(F_{out}\) that has been processed by MLP.  
The feature \(\mathbf{e_{i}^{j}}\) is further addressed by the learnable weight \(w_{i}\) and activation function to obtain the neighborhood feature:  
\[\hat{\mathbf{e}_{i}^{j}}=\text{ReLU}\Big{(}w_{i}\mathbf{e_{i}^{j}}\Big{)}. \tag{10}\]  
Finally, we use channel-wise max pooling to aggregate the neighborhood feature \(\hat{\mathbf{e}_{i}^{j}}\) to obtain the final feature of the point \(\mathbf{p_{i}}\):  
\[\mathbf{e_{i}}=\text{maxpooling}(\{\ \hat{\mathbf{e}_{i}^{j}}\Big{|}1\leq j\leq k\}). \tag{11}\]  
The max pooling operation may cause the disappearance of the feature of the origin point. To address this issue, we combine the original feature with the feature after max pooling. Finally, the final output feature \(F_{c}\) are obtained after MLPs and dropout. \(F_{c}\) is represented by a 2D matrix \((n\times c)\), where \(c\) represents the number of categories in the entire dataset.  
Figure 4: Local feature aggregation classifier.
## 2 Modeling of the Poloidal Field Coil System on HL-3
### Tuning of the Model Parameters  
Considering the fact that PF coils are energized at different times in a discharge, Equation (5) is treated as a time-varying state equation with parameter changes. In addition, the accuracy of the model is the foundation for performing zero-field optimization and developing subsequent control algorithms. The parameters of the model calculated by TokSys do not take into account the errors in the actual construction process, and there are some differences with the actual situation. To make sure that the simulation model fits the hardware characteristics of the actual system, the mutual inductance and resistance coefficients are calibrated using single-coil discharge experimental data. The input is the actual voltage supplied during the experiment, while the outputs are the coil current and the eddy current in the vacuum chamber. The voltage/current of the flat top section is used to calculate the resistance of the coil, and then, the self-inductance is adjusted to ensure that the simulation output is consistent with the experimental results. The calibrated model was then validated against a normal plasma discharge shot, and the results are shown in Figure 2. It can be seen that both in the current ramp-up phase and the flat-top phase, the corrected response model's output closely fits the experimental data with an error level less than 5%. This demonstrates the reliability of the time-varying parameter model, which forms a solid basis for the following field-null optimization work.  
Figure 2: Experimental (EXP) measurements and simulated results from TokSys for ICS, IPF6, IPF (**a**), and total eddy current IVV (**b**). Some of the current values in (**a**) has been multiplied by a constant for a clearer view, e.g., “3*IP6” in the legend means the curve referring to IP6 in (**a**) is multiplied by 3.