esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup and Procedure  
### Experimental Instruments  
For the experimental test, a stationary diesel engine, single-cylinder, four strokes, and natural aspiration was used. Detailed engine features are shown in Table 1.  
Figure 1: General scheme of the diagnostic model.  
Figure 2: Flow chart of the diagnostic model.  
The engine is coupled to a dynamometer to apply different loading conditions. A crankshaft angle sensor, Beck Arnley 180-0420, was used in the engine to measure its rotation speed. To measure the pressure inside the cylinder, a KISTLER type 7063-A piezoelectric sensor is installed in the cylinder head. The fuel consumption rate was measured using a gravimetric meter OHAUS PA313, and a stopwatch, the measurement of the gravimetric meter was taken at the beginning and end of the experiment, and with the use of the stopwatch, the duration of the test was measured. The measurement of the intake air flow is performed using a BOSCH 22680 7J600 hot wire meter. To measure the high temperatures of the flue gases, K-type thermocouples were used. For the measurement of emissions, the BrainBee AGS-688 and PCA(r) 400 gas analyzers were used to measure the levels of CO, CO\({}_{2}\), NOx, and HC. Additionally, the opacity levels of the exhaust gases were measured with the BrainBee OPA-100 opacimeter. All measuring instruments are connected to a computer through a data acquisition system.  
The diagram of the experimental test bench is shown in Figure 3. The measuring range and accuracy of the measuring instruments are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **SK-MDF300** \\ \hline Engine type & 1 cylinder \\ Manufacturer & SOKAN \\ Cycle & 4 Strokes \\ Bore \(\times\) stroke & 78 mm \(\times\) 62.57 mm \\ Displaced volume & 299 CC \\ Compression ratio & 20:1 \\ Maximum power & 4.6 hp at 3600 rpm \\ Intake system & Naturally Aspirated \\ Injection system & Direct injection \\ Injection Angle & 20\({}^{\circ}\) BTDC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the diesel engine.  
Figure 3: Engine test bench, (**a**) setup, and (**b**) devices.
# A Fast Point Clouds Registration Algorithm Based on ISS-USC Feature for the 3D Laser Scanner
## 3 Algorithm Design  
### Improved Voxel Filter for Down-Sampling  
Voxel filter [20] is to create a 3D voxel grid for the input point clouds data. It uses the center of gravity of all points in each voxel to approximate display other points in the voxel, that is, uses the center of gravity point to represent all points within this voxel. Using voxel filter can not only reduce the number of points, but also maintain the shape characteristics of the point clouds. However, when traditional voxel filter is used, the center of gravity point is not necessarily a point in the original point clouds, which will lose the fine features of the original point clouds. Therefore, it is necessary to improve the voxel filter. K-nearest neighbor search [21] is performed on the center of gravity points with voxel filter of the point clouds. For a given query point, that is, the center of gravity point, search its nearest neighbor, and K = 1 at this time. The point closest to the voxel center of gravity in the original point clouds data is used to replace the voxel center of gravity, and then perform the same processing on all voxels to acquire the filtered point clouds. The improved voxel filter not only reduces the execution time of the algorithm, but also effectively improves the accuracy of the data.  
Figure 2a is the Bunny model of the Stanford University point clouds library, which has more than 30,000 points. In order to reduce the execution time of the algorithm, the improved voxel filter is used to simplify the point clouds. Figure 2b shows the results of the down-sampling using the improved voxel filter. After down-sampling, the number of point clouds is significantly reduced, only more than 3000 points, and the shape characteristics of the point clouds remain unchanged.  
Figure 3 shows the original point clouds, the point clouds after voxel filter and the point clouds after improved voxel filter of the Bunny model. The original point clouds are set to green, the point clouds after voxel filter are set to blue, and the point clouds after improved voxel filter are set to red. Three point clouds are put together for comparison. The red point clouds and the green point clouds actually coincide. The blue point clouds are the center of gravity of each voxel, which are different from the original point clouds. Therefore, the improved voxel filter will not change the small features of the original point clouds even after sampling, which can improve the point cloud data expression accuracy.  
Figure 2: Bunny model and down-sampling results. (**a**) Bunny model, (**b**) down-sampling results.
## 6 Clustering with Overlap
### Results with Overlap Detection  
#### 6.1.1 Girvan-Newman Graphs with Overlap  
This paper's overlapping community detection algorithm was tested with NBR-Clust using VAT and integrity on a series of Girvan-Newman style graphs. These graphs were generated in the same fashion as described in Section 5.2.1. Each graph has 256 nodes, with 32 overlap nodes, meaning that a high percentage of the nodes (one-eighth) belong to more than one cluster. Each overlap node is a member of two, four or six communities. The number of communities that a node overlaps is consistent within a graph. For each combination of two, four, and six overlapped communities, mixing factor was varied from \(\frac{1}{32}\), which should be easiest to cluster, to \(\frac{1}{8}\), which should be the most difficult. The results for these experiments are shown in Table 9, where \(\mu\) denotes the mixing factor, # the number of memberships of an overlap node, and NMI values represent the average over 10 randomly generated graphs.  
Both integrity and VAT were able to cluster the two-community overlap low-mixing factor graphs perfectly, finding every overlap node and assigning each to the correct clusters. Clustering where nodes overlap six communities is a much more difficult problem, and VAT still managed to achieve an NMI of 0.94 on the low-mixing factor graphs and 0.83 on the graphs with a higher mixing factor. Integrity did about the same, scoring 0.95 on the low-mixing factor graphs and 0.81 on the high-mixing factor graphs. Since misidentified nodes are assigned to multiple incorrect communities, these NMI numbers imply that less than 2% of the nodes were incorrectly identified, even in the worst cases.  
Most clustering took more than one iteration. Only the \(\mu=\frac{1}{32}\) graphs were clustered by integrity in one step. VAT generally took four to seven steps to cluster, with a maximum of 10 steps. Integrity took between one and six steps. The number of steps increased with the number of communities per overlap node, regardless of the mixing factor.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**VAT**} & \multicolumn{4}{c}{**Integrity**} \\ \hline \# & \(\frac{1}{32}\) & \(\frac{1}{16}\) & \(\frac{3}{32}\) & \(\frac{1}{8}\) & \(\frac{1}{32}\) & \(\frac{1}{16}\) & \(\frac{3}{32}\) & \(\frac{1}{8}\) \\ \hline
2 & 1.00 & 0.92 & 0.99 & 0.84 & **1.00** & 0.81 & 0.94 & 0.86 \\
4 & 0.98 & 0.94 & 0.94 & 0.82 & **0.99** & 0.88 & 0.92 & 0.81 \\
6 & 0.94 & 0.87 & 0.87 & 0.83 & **0.95** & 0.85 & 0.86 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Normalized mutual information for Girvan–Newman Graphs with overlap. Bold text indicates one-step clustering.
# On Parameter Estimation for Bandlimited Optical Intensity Channels
## 4 Estimator Algorithms
### Maximum Likelihood Approach
\begin{array}{ll}\hat{A}&=\frac{\sum\limits_{k=0}^{L-1}\omega_{k}\,\hat{z} _{k}}{\sum\limits_{k=0}^{L-1}\sum\limits_{n=0}^{L-1}\omega_{k}\omega_{n}g[(k- n)T]}\\ &=\frac{\Lambda_{\hat{\mu}}}{g(0)\sum\limits_{k=0}^{L-1}\omega_{k}^{2}+\sum \limits_{k=0}^{L-1}\sum\limits_{n=0}^{L-1}\omega_{k}\omega_{n}\,g[(k-n)T]}\,. \end{array}  \tag{25}\]In order to reduce the computational complexity of (25), the even-symmetric property of \(g(t)\) has been exploited in the denominator, whereas the numerator is just the correlation result in (21) evaluated at \(\mu=\hat{\mu}\). Finally, it is to be mentioned that the framework is still the same, no matter whether the optical link is operated with SRC or SDJ pulses.
## 5 Results and Discussion of Findings
### Performance Evaluation of State-of-the-Art Classifiers on Methods
SVM, KNN, DT and MLP obtained their best accuracies, i.e., 0.82, 0.81, 0.79 and 0.88, with a population size of 75. For BDMO and BSNDO, KNN, RF, MLP, DT, SVM and GNB achieved their best performance as follows: 0.71 with a population size of 75; 0.82 with a population size of 25; 0.8 with a population size of 50; 0.86 with a population size of 25; and 0.53 with a population size of 75. The other algorithms yielded 0.83 with a population size of 75, 0.85 with a population size of 25 and 0.80 with a population size of 50 for KNN, RF, MLP, DT, SVM and GNB. We compared BEOSA and BIECSA and found a large degree of variance. For instance, whereas KNN obtained its best value, i.e., 0.83, with a population size of 75 with BEOSA, for BIECSA, the same classifier yielded a value of 0.98 with a population size of 25. Additionally, RF peaked at 0.85 with a population size of 100 and 0.89 with a population size of 25 in BEOSA and BIECSA. MLP obtained its best values, i.e., 0.85 and 0.89, with a population size of 25 on BEOSA and BIECSA, respectively. DT dipped in BIECSA at an accuracy value of 0.78 with a population size of 100, but peaked in BEOSA with an accuracy of 0.85 with a population size of 25. SVM showed a good performance with BIECSA, achieving an accuracy of 0.89 with a population size of 25, whereas with BEOSA, it achieved its best value, i.e., 0.80, with all population sizes. GNB performed better on BEOSA, with an accuracy of 0.85 with a population size of 25, but obtained 0.79 on BIECSA with a population size of 100. A comparative analysis of the plots of the CongressEW and SpectEW datasets showed that the performance of BEOSA on all of the classifiers was outstanding, standing shoulder-to-shoulder with BPSO and significantly outperforming BWOA, BSFO, BGWO, BSNDO, and BDMO. We note that the proposed method proved itself to be well-rounded and robust. Moreover, the good classification performance, derived from the number of features selected by the BEOSA, further confirms the applicability of the method to find the best number of required features, even in real-life problems. Additionally, the fitness function and cost function values were impressive for BEOSA and its variant BIECSA. The experiment using different classifiers in this study has shown that the choice of a classifier with a binary optimizer must be made carefully based on empirical investigation when such hybrid models are being deployed to address real-life problems. Having compared the performance of BEOSA with other related methods using the values obtained for fitness and cost functions, the average number of selected features and classification accuracy, in the following subsection, we compare the computational runtime required for each of the algorithms.
# P-Distill: Efficient and Effective Prompt Tuning Using Knowledge Distillation
## 3 Methodology
### Prompt-Enhanced Distillation (P-Distill)
#### 3.2.2 Prompt Distillation  
In this section, we focus on prompt distillation, a key aspect of the proposed approach. Recognizing the influence of continuous prompts on both the hidden-state and the prediction-layer outputs within the model, we employed the following two distillation techniques: prediction-layer and hidden-state distillations. These techniques focused on different aspects of the teacher model's output to ensure comprehensive knowledge transfer.  
**Prediction-layer distillation** In this method, a student model learns to emulate the predictions of a teacher model. This process involves the student model utilizing soft labels from the teacher model's output, which encapsulate the teacher model's understanding of the data. Particularly, a loss function was used to minimize the difference between the logits \(y^{S}\) and \(y^{T}\) produced by the student and teacher models, respectively. The distillation loss \(L_{pred}\) is formulated as follows:  
\[L_{pred}=KL(softmax(y^{S}_{i}/\theta),softmax(y^{T}_{i}/\theta)), \tag{7}\]  
where \(y^{S}_{i}\) and \(y^{T}_{i}\) are the logits vectors predicted by the student and teacher, respectively, and \(KL\) denotes the Kullback-Leibler divergence, which measures the difference between the probability distributions of the two models. \(\theta\) is a temperature hyperparameter that adjusts the smoothness of these distributions, enabling a more nuanced transfer of knowledge from the teacher to student model. The distillation loss \(L_{pred}\) was then used in the optimization process to update the parameters of the student model, thereby aligning its predictive behavior more closely with that of the teacher model.  
**Hidden-state distillation** Additionally, we also distilled knowledge from the intermediate representations of the teacher model. The concept of distilling knowledge through intermediate representations was initially introduced by [PERSON] [29], with the aim of enhancing the training process of the student model. Based on the provided prompts and inputs, we extracted knowledge from the transformer layers of the teacher model and distilled it into the student model. This process was formalized using the loss function \(L_{hidden}\), which is calculated as the MSE between the hidden states \(H_{S}\) and \(H_{T}\) of the student and teacher models, respectively, as follows:  
\[L_{hidden}=MSE(H_{S},H_{T}), \tag{8}\]  
where the matrices \(H_{S},H_{T}\in R^{n\times d}\) represent the hidden states, \(n\) is the input sequence length, and \(d\) is the hidden-state dimensionality of the two models.  
Figure 3: Illustration of various prompt initialization methods.
## 4 Experiment
### Experimental Results and Analysis
#### 4.5.3 Discussion  
The proposed model's unique architecture and robustness, developed through training on high-resolution industrial images, sets it apart from baseline models. Unlike these models, which may produce artifacts or overlook minor details, our approach consistently delivers high-quality outputs across a wide range of object categories, piquing the interest of researchers and professionals in the field.  
A comparative analysis with baseline models highlights the advantages of our method. As shown in Table 1, our model achieved a significantly lower EMD score (0.0121665) compared to 3D-R2N2 (0.0482132), AtlasNet (0.0759724), and Occupancy Networks (0.0618715). This indicates a more precise 3D reconstruction from 2D images. Our model's superior performance in RGB 2D (MSE) and Depth 2D (MSE) further underscores its effectiveness.  
Visual inspections further validate these findings. The reconstructed 3D models from our approach exhibited fewer artifacts and more accurately preserved intricate details than those from baseline models. This qualitative assessment confirms the quantitative results, showcasing the robustness and consistency of our method across different industrial objects.  
Importantly, our methodology goes beyond generating Digital Shadows by creating true Digital Twin models. These comprehensive virtual representations of physical assets encompass real-time data integration and dynamic updating capabilities. Our model reconstructs accurate 3D geometries and embeds detailed color and texture information, providing a holistic and interactive digital counterpart of physical objects. This comprehensive approach instills confidence in the accuracy and utility of the Digital Twin models.  
Our model's ability to significantly reduce the cost and effort of generating Digital Twins makes advanced manufacturing and maintenance solutions more accessible and practical. The rapid generation of accurate and high-resolution Digital Twin models will enhance operational efficiencies and support decision-making processes in real-world industrial applications. By comparing our results with other studies, the contributions and improvements offered by our model become even more evident, instilling optimism about its potential impact in the field of 3D reconstruction and digital twin technology.
###### Abstract
compared three linear classifiers and five non-linear classifiers to identify the optimal model for stress classification [14], while another study employed a Convolutional Neural Network to detect drought stress, achieving an accuracy of 96.9% [15]. Considering the overall state of research on sweet basil, there is very little attention dedicated to field-based studies, with most research focused on stress detection and growth monitoring under controlled environments. To date, no scientific investigations have explored the application of remote sensing combined with machine learning algorithms to predict basil biomass under open-field conditions. This represents a critical knowledge gap, especially given the growing relevance of precision agriculture technologies in optimizing crop management and sustainability for specialty crops. Addressing this gap, the current work aims to evaluate and compare the performance of eight regression models trained to predict the fresh biomass of sweet basil. Images are acquired through two different remote sensing techniques: a drone equipped with a multispectral-thermal sensor and the PlanetScope satellite. Vegetation indices were extracted and analyzed to identify the most predictive features. Lastly, the predictive models were then trained and assessed to determine their effectiveness in estimating biomass. The central research question is whether the integration of drone and satellite imagery, combined with machine learning algorithms, can enable the accurate and generalizable prediction of sweet basil biomass under open-field conditions. By combining advanced remote sensing technologies with machine learning approaches, this study demonstrates a novel methodology for the real-time, non-destructive biomass estimation of sweet basil in open-field cultivation. The outcomes not only provide valuable insights into the potential of precision agriculture tools for minor crops but also lay the groundwork for future large scale applications aimed at enhancing yield prediction, resource management, and overall farm productivity.
## 2 Materials and Methods
### Calculation Process
#### 2.3.2 Expression and Empirical Formula of Water Diffusion Coefficient  
When saturation is used as the descriptive variable, the total transmission effect of the isotropic porous material with gaseous water and liquid water can be expressed by the Richards equation [46]:  
\[\frac{\partial\theta}{\partial t}=\
abla(D(\theta)\
abla\theta) \tag{2}\]  
where \(\theta\) is the saturation of the material and \(D(\theta)\) is the moisture diffusion coefficient that is mathematically related to \(\theta\). Saturation can be obtained from the volumetric water content \(\Theta\):  
\[\theta=\frac{\Theta-\Theta_{i}}{\Theta_{s}-\Theta_{i}} \tag{3}\]  
where the subscripts \(i\) and \(s\) denote the initial unsaturated state and fully saturated state, respectively.  
Generally, the one-dimensional diffusion equation is used to describe the total transmission effect. Considering the initial and boundary conditions:  
\[\left\{\begin{array}{ll}x=0,&t>0,\quad\Theta=\Theta_{s}\\ x\geq 0,&t=0,\quad\Theta=\Theta_{i}\\ x\rightarrow\infty,&t>0,\quad\Theta=\Theta_{i}\end{array}\right. \tag{4}\]  
Equation (2) can be simplified to [5; 19]:  
\[\frac{\partial\theta}{\partial t}=\frac{\partial}{\partial x}(D(\theta)\frac{ \partial\theta}{\partial x}) \tag{5}\]  
where \(x\) denotes the distance from the inflow surface at which water is applied.  
Considering one-dimensional diffusion with constant boundary saturation and an infinite half-plane, the Boltzmann transformation can be used to obtain the analytical expression of the water diffusion coefficient. Substituting the Boltzmann variable \(\varphi=\frac{x}{t^{1/2}}\) into Equation (4), we obtain:  
\[-\frac{1}{2}\varphi(\frac{d\theta}{d\varphi})=\frac{d}{d\varphi}(D(\theta)\frac {d\theta}{d\varphi}) \tag{6}\]  
where \(\varphi(\Theta=\Theta_{i})=\infty,\varphi(\Theta=\Theta_{s})=0\).  
The expression for \(D(\theta)\) can be written as:  
\[D(\theta)=-\frac{1}{2}\frac{d\varphi}{d\theta}\int_{0}^{\theta}\varphi d \overline{\theta} \tag{7}\]  
Considering the form of \(D(\theta)\), calculating the above equation is difficult. Therefore, a relatively simple form of the water diffusion coefficient is required. Currently, the most common formulae are the exponential function and the power function, which are as follows [47; 48]:  
\[\text{Exponential function}\;D(\theta)=D_{0}e^{\mu\theta} \tag{8}\]  
\[\text{Power function}\;D(\theta)=D_{0}\theta^{n} \tag{9}\]  
where \(D_{0}\) is the diffusion coefficient of completely dry concrete and \(n\) is the empirical constant. According to [49], for concrete materials, \(n\) is generally equal to 6.
## 6 Simulation Results  
In this section, simulations are used to evaluate the performance of the proposed resource allocation framework of MDRL-UL in time-varying dynamic environments by making comparisons with the DRLA [13], the MDRLA [13], the DDPG-UL [32], and the DQN-DDPG [33]. Additionally, we conduct the hyper-parameter analysis to show the impact of hyper-parameter on the proposed resource allocation framework during the testing process. Finally, we analyze the impact of the random search strategy on system performance.