esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Remote Sensing Measurements  
Hyperspectral images were obtained with the Cubert UHD 185 camera (UHD; Cubert GmbH, Ulm, Germany, [[https://cubert-gmbh.com](https://cubert-gmbh.com)]([https://cubert-gmbh.com](https://cubert-gmbh.com))) with spectral bands between 450 and 950 nm (visible and near infrared ranges, VNIR) along 125 channels with mean spectral resolution of about 10 nm in the VIS, 20 nm in the red-edge, and 40 nm in the NIR region. The camera spatial resolution is 50 \(\times\) 50 pixels. The focal length of the lenses is 23 mm and the Field of View (FoV) in both directions is 15\({}^{\circ}\). The Cubert also provides a panchromatic image of 1000 \(\times\) 1000 pixels.  
Thermal images were gathered from a FLIR Tau2 324 (FLIR Systems Inc., Wilsonville, OR, USA), detecting longwave radiation between 7.5 \(\upmu\)m to 13.5 \(\upmu\)m. The spatial resolution is 324 \(\times\) 256 pixels with a focal length of 9 mm, which provides a FoV of 48.5\({}^{\circ}\) in the horizontal side and 39.1\({}^{\circ}\) in the vertical view.  
Figure 1 shows the experiment setup. Both cameras were located approximately 2 meters above the pots and connected to a computer triggered manually after gas exchange measurements, resulting in a pixel size of 10.5 mm/px for the hyperspectral camera and 6.1 mm/px for the thermal sensor. Cameras were positioned in a payload box usually carried by a UAV as shown in [PERSON] et al. (2019) [52]. Due to instrumentation requirements and space management, the separation of cameras was about 18.6 and 3.7 cm in the horizontal axis.  
Figure 1: Camera set-up and crop overview inside the growth chamber. Two crops were cultivated: soybean and maize, in three water holding capacity (WHC) groups: dry (40% WHC), mid (70% WHC), and wet (100% WHC). For each set we grew six replicates. Images of plants were taken by moving each crop under the box containing thermal and hyperspectral cameras. The wooden box was located 2 m above the pots. Dark blue dotted/dashed line shows the field of view (FoV) of the thermal Flir camera while the red dashed line shows the FoV of the hyperspectral Cubert camera. Both cameras were controlled with an external computer located inside the chamber. Light racks were composed by 28 high-pressure mercury lamps (1000 W) and 14 halogen lamps (400 W). Air temperature and RH were automatically recorded using a climate control computer located outside the growth chamber.
# Research on the Influence of Turbulent Flow Induced by Dunes on AUVs
## 2 Materials and Methods
### Grid Generation and Independence Evaluation  
In STAR-CCM+, the trimmed cell mesher is used for generating grids, with the focus on refining the grid in the region of turbulent flow around the leeward side of the dune. The wall treatment uses a full \(y^{+}\) method, with the AUV's surface utilizing a prism layer of five layers and an expansion ratio of 1.1. By setting the thickness of the prism layer, the non-dimensional wall distance \(y^{+}>30\) is achieved to meet computational requirements. During grid independence evaluation, extreme conditions are considered by placing the AUV very close to the leeward side of the dune. The average resistance acting on the AUV is used as a validation parameter for grid independence evaluation. The results of grid independence evaluation are illustrated in Figure 3. From Figure 3, it can be seen that as the number of grid cells in the computational domain increases, the average resistance acting on the AUV gradually converges. The average resistance values for grid cell numbers of 6.78 million and 9.5 million are already very close. However, performing calculations with a higher number of grid cells would entail significantly higher time costs. Therefore, balancing computational accuracy and efficiency, a grid density corresponding to 6.78 million grid cells is selected for the calculations. In the final simulations, the AUV's position within the turbulent flow field is considered, with its bow located at a distance of 3.5 m from the inlet. As shown in Figure 4, the number of grids used in the final calculation is about 7 million. The calculations are performed using a 20-core computer, it takes about 48 h to calculate one case.  
Figure 3: Grid independence evaluation.
## 2 Problem Setting
### Notations  
An open set \(\Omega\subset\mathbb{R}^{3}\) represents the reference state of a continuum body with material points \(\mathbf{X}\in\Omega\), \(\mathbf{x}=\boldsymbol{\varphi}(\mathbf{X},t)\in\mathbb{R}^{3}\) is the deformation field of the body relative to its reference state at time \(t\in[0,+\infty[\) and \(\mathbf{u}(\mathbf{X},t)=\mathbf{x}-\mathbf{X}\) is the displacement field. The boundary of the material domain \(\partial\Omega\) is composed of \(\Gamma_{u}\) where the displacement is imposed, \(\Gamma_{f}\) where the external force is imposed and \(\Gamma_{c}\) the potential contact zone.  
The gradient of a quantity in the reference configuration is noted \(\boldsymbol{\
abla}\). To describe the deformation, several mechanical quantities are introduced. The identity matrix is denoted by \(\mathbf{I}\) and the deformation gradient is represented by \(\mathbf{F}=\mathbf{I}+\boldsymbol{\
abla}\mathbf{u}\). The Jacobian of \(\boldsymbol{\varphi}\) is denoted by \(J=\det(\mathbf{F})\). \(\mathbf{C}=\mathbf{F}^{\intercal}\mathbf{F}\) represents the Cauchy-Green tensor and \(\mathbf{E}=\frac{1}{2}(\mathbf{C}-\mathbf{I})\) is the Green-Lagrange strain tensor. \(\boldsymbol{\sigma}\) is the Cauchy stress tensor, \(\mathbf{P}=J\sigma\mathbf{F}^{-\intercal}\) is the first Piola-Kirchhoff stress tensor and \(\mathbf{S}=J\mathbf{F}^{-1}\sigma\mathbf{F}^{-\intercal}\) is the second Piola-Kirchhoff stress tensor. To treat rubber-like materials, a general hyperelastic constitutive law is considered. This law is derived from the strain energy function \(W\) that depends on the deformation through \(\mathbf{E}\)[16]. The second Piola-Kirchhoff stress tensor is given by:  
\[\mathbf{S}=\frac{\partial W}{\partial\mathbf{E}}(\mathbf{E}) \tag{1}\]with corresponding fourth-order elasticity tensor  
\[\mathcal{C}=\frac{\partial\mathbf{S}}{\partial\mathbf{E}}=\frac{\partial^{2}W}{ \partial\mathbf{E}\partial\mathbf{E}} \tag{2}\]  
The fourth-order material elasticity tensor \(\mathcal{C}\) is necessary within typical finite element procedures and is used in the linearization procedure later on.
## 2 Experimental
### Batch Optimizations and Adsorptions Studies
#### 2.6.1 Selectivity Experiments  
The selectivity of the MIP for ARVs and NSAIDs was determined in batch rebinding experiments at room temperature using optimum conditions, which were deionized water (pH 7.0) previously spiked with 5 mg L\({}^{-1}\) mixtures of EMI, TENO, NAP, DICLO, IBU, and EFV, and acetaminophen (competitor). The spiked solution (10 mL) was put into a flask with 55 mg of MIP. The resultant solution was agitated for 15 min (360 rpm) at room temperature before being placed into a 3 mL SPE tube. Two polypropylene frits with pore sizes of 10 \(\upmu\)m were placed below and above the MIP to prevent sorbent loss, and the liquid portions were discarded. Following that, the concentration of unabsorbed compounds in the solution was assessed using LC-MS. The ARVs and NSAIDs were then competitively absorbed from the mixture in the presence of acetaminophen. Using Equation (7), the effect of imprinting on selectivity was estimated, where \(K_{d}\) (mg g\({}^{-1}\)) is the distribution coefficient, \(C_{0}\) is the initial solution concentration (mg L\({}^{-1}\)), C is the final solution concentration (mg L\({}^{-1}\)), \(V\) (L) is the solution volume, and \(W\) (g) is the polymer weight. Furthermore, in the presence of a competitor, the selectively coefficient for the binding of ARV and NSAID compounds was estimated using Equation (8), where \(K\) is the selectivity coefficient. In addition, the MIPs selectivity coefficient (\(K^{\prime}\)) was calculated as described in Equation (9).  
\[K_{d}=\frac{C_{0}-C}{W}V \tag{7}\]  
\[K=\frac{K_{d}\;(\text{Target})}{K_{d}(\text{Competitor})} \tag{8}\]  
\[K^{\prime}=\frac{K_{\text{MIP}}}{K_{\text{NIP}}} \tag{9}\]
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 4 Experimental Results
### Experimental Results and Analysis  
#### 4.3.1 Comparison with Other Models  
This section presents a comparison between the proposed model and other models of DBT systems. The detector consists of those presented in each paper. Our proposed model showed the best performance on all metrics compared with the other algorithms as shown in Table 1. Furthermore, compared with the configuration of DeepSORT, it has a similar shape and speed while still showing better performance. The addition of the attention module and proven methods such as fine-tuning improved the performance. We also achieved positive results for our novel attempts, such as adopting the average value of the feature vectors and constructing a sampler.
# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 4 Discussion  
### Impact of Different Pre-Training Tasks on Performance  
During feature representation learning in the pre-trained model, three types of pre-training tasks were designed. This section discusses the difficulty of each task and the performance of the resulting feature representations.  
To differentiate the effects of various pre-training tasks, five groups of pre-training tasks were designed:  
CSR: Randomize the coordinates of each point in the sample with 35% probability.  
COC: Offset the coordinates of each point in the sample with 35% probability.  
CMP: Mask the coordinates of each point in the sample with 35% probability.  
CMP + CSR: Mask the coordinates with 15% probability and randomize them with 20% probability.  
CMP + COC + CSR: Mask the coordinates with 15% probability, randomize them with 10% probability, and offset them with 10% probability.  
These five pre-training tasks were used to pre-train the model for 13,000 steps, resulting in the loss function trends shown in Figure 17.  
The figure shows that the loss function stabilizes the earliest and is the smallest for the coordinate randomization task, indicating that it is the simplest task. The difficulties of training with coordinate offsetting and masking were similar, and both were more difficult than randomization. When combining the three methods, the training difficulty is balanced as the probabilities are adjusted during merging, thereby simplifying some challenging tasks. Therefore, the difficulty order of the tasks and their combinations can be inferred to be as follows:  
\[\text{COC}=\text{CMP}>\text{CMP}+\text{COC}+\text{CSR}>\text{CMP}+\text{CSR}> \text{CSR}.\]  
Figure 17: Impact of different pre-training tasks on loss function.  
Generally, more difficult tasks may enable the model to learn more efficient feature representations, although this depends on the learning capacity and training process of the model. Despite the similar difficulty of the correction and prediction tasks, their feature extraction effectiveness is not identical. The five models using these tasks were subsequently trained and tested for single-sample vector shape classification.  
Initially, the PSBT model was trained using the pre-training tasks, followed by 2000 steps of transfer learning fine-tuning for the vector shape classification task. The results, benchmarked against direct end-to-end training without pre-training, are listed in Table 2, and the accuracy changes are shown in Figure 18.  
The experimental results show that although the difficulty of the COC and masking tasks is similar, the offset correction task is better suited for shape classification. Simpler tasks, such as coordinate randomization, result in smaller performance gains. Nonetheless, each task enhanced model accuracy, indicating that combining multiple pre-training tasks yielded more efficient feature representations.
## 3 Results and Discussion
### Sensory Property  
Sensory evaluation was performed on Shine Muscat grapes harvested at 16, 18, 20, and 22 WAFB; the results are shown in Table 4 and Figure 4. The hedonic sensory scores for each sensory attribute are presented in the radar plot in Figure 4a. The sweetness scores showed a similar pattern to the instrumental total soluble solid contents (TSS) and TSS/TA of grape samples, showing an increasing trend of \({}^{\circ}\)Bx and TSS/TA value as ripening progressed (Figure 1). Thus, the grape samples harvested at 20 and 22 WAFB had significantly higher sweetness scores than those harvested at 16 and 18 WAFB (Table 4). The panelists could distinguish the perception of sweetness at a 2 \({}^{\circ}\)Bx difference. Flavor scores showed a trend similar to that of sweetness. As described in the active odorant analysis (Table 3), the odor activities of the key aroma compounds changed during ripening. From the results of sensory flavor attributes and active odorant analysis, it was assumed that the panelists captured fruity, green, and herbaceous flavors from the grapes harvested at 16 and 18 WAFB, owing to the dominant influence of (\(E\))-2-hexenal, hexanal, (\(E\))-hexen-1-ol, and
## 3 Results  
### Empirical Correlations  
Shear wave velocity can be calculated using some existing empirical equations proposed by a number of researchers. In general, those empirical equations were developed for particular rock types. In the current study, the rock type of the reservoir is limestone. In Section 2.3.1, a number of available empirical correlations for limestone formations were recounted. In this research, at first, the values of \(V_{s}\) were calculated using those equations, and then, those calculated values were compared with the real \(V_{s}\) data obtained from the DSI log. Figure 6 shows the plot of the real \(V_{s}\) log versus the graphs of \(V_{s}\) predicted by those five existing empirical correlations.  
Comparing the real \(V_{s}\) data with the \(V_{s}\) predicted by those five empirical correlations shows that the Pickett equation delivers the most accurate predictions of the \(V_{s}\) values. Therefore, after the potential calibration, this empirical correlation can be deployed in the current oilfield.  
Moreover, the values of the statistical indicators (\(R^{2}\), \(RMSE\), and \(MAE\)) were used to compare the accuracy of those empirical correlations. the simultaneous use of \(R^{2}\), \(RMSE\), and \(MAE\) provides a more comprehensive and balanced evaluation of a predictive model, considering different aspects of its performance and helping to make more informed decisions in various contexts [67; 68]. Table 3 depicts the corresponding results. In this table, the values of \(R^{2}\), \(RMSE\), and \(MAE\) related to all five empirical correlations have been tabulated.  
Figure 5: The GEP algorithm flowchart.  
According to Table 3, the Pickett equation has a better correlation coefficient, \(RMSE\), and \(MAE\) value. This matter can be clearly seen in Figure 6. Concerning the Anselmetti and Eberli equation, it can be observed that the predicted \(V_{s}\) graph is a straight line, thereby calculating the \(V_{s}\) as a constant value (also see Figure 6). In fact, the \(V_{s}\) is a function of many other geomechanical parameters such as in situ stress, poroelastic properties, fluid content, etc., which cannot be represented only by rock density.
## 2 Laser-Generated Ultrasound
### Absorptive Materials
#### 2.1.2 Carbon-Based Absorbers  
Carbon-based materials are used to realize efficient and broadband LGUS transmission. They often, but not always, show higher LGUS transmission efficiency than metal-based materials. (i) Carbon black (CB)-PDMS is used to produce high-frequency ultrasound [52]. It has the potential to generate ultrasound with broad bandwidth above 100 MHz. The amplitude of ultrasound produced by CB-PDMS is slightly less than that by AuNPs-PDMS [43]. (ii) CNT nanocomposites are demonstrated as highly-efficient and high-frequency LGUS transmitters [53; 54; 30; 55]. The CNT nanocomposite shows five times enhancement compared with the above-mentioned 2D AuNPs nanocomposite with the same polymer PDMS [43] and a broad bandwidth up to 120 MHz. The fast heat transition due to the nanoscale dimension of the CNT permits efficient generation of high-frequency ultrasound. Such advantage is shared in AuNPs. On the other hand, the exceptional thermal conductivity of the CNT facilitates better heat conduction, leading to the increased efficiency compared with AuNPs. Furthermore, the CNT has a high damage threshold, allowing generation of stronger pressure. (iii) Carbon nanofibers (CNFs)-PDMS thin films are also investigated [56]. Under 532-nm pulsed laser excitation with the laser fluence of 3.7 mJ/cm\({}^{2}\), the acoustic pressure of 12 MPa is achieved. (iv) More recently, candle soot carbon NPs (CSNPs)-PDMS nanocomposites are explored [57]. Figure 2 shows SEM pictures of CNTs, CNFs and CSNPs used in References [55; 56; 57], respectively. The ratio of photoacoustic conversion efficiency for CSNPs-PDMS, CNFs-PDMS, CNTs-PDMS, and CB-PDMS is 13:4.9:4.1:1 [57]. The unprecedented LGUS transmission performance by CSNPs-PDMS nanocomposites shows the promise for ultrasound therapeutic applications. (v) Besides the carbon NPs, graphite powder is explored for fiber-optic LGUS transmitters [58], where efficient and distributive ultrasound can be generated at specific locations along the fiber.
# Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
###### Abstract  
Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted by using different performance evaluation metrics, such as R, RMSE, and MSE. Hence, this study compared the performances of the BNN model with four machine learning regression analyses, and found that the result from the BNN was superior, with a lower error: R = 0.94, RMSE = 0.17, and MSE = 0.03. Finally, after the evaluation of the models, SHAP was performed to describe the importance of the models' features and to avoid the black box issue.  
blasting; ground vibration; ppv; Bayesian neural network; machine learning regression Article Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration 2023
# Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
## 5 Brief Economic Considerations  
The transmission of high-voltage electricity is managed by the National Transmission System Operator (TSO), which is the joint stock company TERNA. It is the TSO's task to adapt production to the needs of the network, and the correct management of power flows makes it possible to improve both the overall efficiency of the electrical system and its economic performance. Among the measures implemented for the management of the electrical system, Terna reduces wind power production through dispatch orders, resulting in Wind Power Curtailment (WPC) [20]. In 2022, the WPC in Sicily was 30 GWh, mainly due to grid congestion [7; 19].  
An important Italian body involved in the management of renewable sources is the \"Energy Services Manager\" (GSE S.p.A). It is an Italian joint stock company wholly owned by the Ministry of Economy and Finance, which is entrusted with the task of promoting and developing renewable energy sources and energy efficiency.  
Users of one or more wind power generation units whose energy production has been reduced due to dispatch orders issued by Terna may submit a request to GSE and, for informational purposes, to Terna for compensation for the wind power curtailment. Therefore, avoiding such WPC would result in significant economic savings for GSE and TSO. Assuming that the network congestions simulated on WinCreso (on 19 April 2024, in an \(n-1\) grid condition) were entirely caused by excessive wind generation, the economic return from the installation of DTR devices was calculated in each of the REZs. The reduction in economic costs ROE consists of the avoided payment that the TSO would have made to producers due to the circulation of the power proposed by the DTR (\(\Delta\)P\({}_{+\text{DTR}}\)) for one hour in each of the analyzed hot spots.  
In detail, for each DTR simulation, the reduction in economic costs (REC) was calculated using the following operation:  
\[\text{REC}=\text{P}\times\Delta\text{P}_{+\text{DTR}}\left[\frac{\boldsymbol{ \xi}}{\text{h}}\right] \tag{14}\]  
where  
* \(\Delta\)P\({}_{+\text{DTR}}\) is the increase in power that can be carried by the line if the current flow corresponds to that proposed by the DTR simulation;
* P is the average electricity price set at 90 EUR/MWh.  
Table 7 and Figure 5 show the REC of the hot spot of each REZ.  
Considering that the current cost of DTR technology is EUR several tens of thousands per device and taking into account the cost of resolving congestion in the Ancillary Services Market, the results obtained also confirm the economic sustainability of this technology.
## 1 Introduction
Furthermore, while the monitoring and evaluation of urban _EEQ_ changes are relatively common, research employing the same index for mining area _EEQ_ monitoring remains limited [20]. Therefore, we selected the Shendong mining area, a major coal production base in western China as our study region, and adopted the Remote Sensing Ecological Index (_RSEI_) as the indicator for _EEQ_ assessment. Landsat TM, Landsat ETM+, and Landsat OLI products with a 30 m resolution, covering the period from 1990 to 2023 (specifically, June to September), were used to extract _RSEI_ values for the area exhibiting the highest mining productivity. Landsat products with a 30 m resolution, covering the period from 1990 to 2023 (specifically, June to September), were used to extract _RSEI_ values for the area exhibiting the highest mining productivity. The Theil-Sen slope estimator and the Mann-Kendall (MK) test were applied to analyze trends in _EEQ_ evolution. A first-order difference method was employed to quantitatively evaluate the relative impacts of human activities and climate change on _EEQ_. The findings of this study aim to provide crucial ecological environment parameters for the Shendong mining area, serve as a decision-making reference for establishing an efficient environmental protection policy framework, and provide a scientific basis for government departments regarding environmental management.