esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The measurement of gilding thickness has become a very common practice, and in this paper, the possible advantages of using a MA-XRF scanner for thickness testing have been analysed in more detail. Apart from allowing the detection of inhomogeneities in the sample surface to a limited extent, avoiding areas that can introduce errors, the MA-XRF dataset provided a larger sample space to enhance the statistical power. The statistical error calculated with the present dataset for estimating a mean gilding thickness is about 0.07 \(\upmu\)m (Equation (3)). As shown in Table 1, the observed standard deviation of AoI B is 0.13 \(\upmu\)m, which, being the same as the calculated statistical error of thickness determination (Equation (2)), leads to a spread of data due to possible changes in \(\sigma^{2}_{\text{samp}}\) of 0.11 \(\upmu\)m (Equation (4)). Therefore, it is reasonable that the result is coherent with the possible thicknesses variation given by the gold leaf manufacturer.  
The present results show how the thickness measurement from a large dataset with a spatial average over a gilded area is possible and has advantages, such as the possibility of detecting the presence of an adhesive nonabsorbing layer used for gilding, with certain limitations. Nonetheless, further studies with different types of mordants are still needed. A careful examination of the histograms (Figure 7) shows that the presence of the resin layer can be inferred from the shift of the average value downwards, but also from the evident modification of the structure of the histogram. The latter fact is supported by the extreme sensitivity that was observed in the MC simulation with the presence of an adhesive resin (shifting the thickness average upwards). Overall, a noninvasive and indirect estimation of gold leaf thickness applied to a homogeneous or inhomogeneous substrate is demonstrated to be possible with the proposed methodology, with some restrictions. In this case study, a titanium dioxide substrate was used and tested. Further studies are required to test the proposed methodology with different substrates, e.g., lead white, extending the method's applicability to older artworks prior to the mass production of titanium dioxide (ca. 1916).  
Conceptualization, S.A.B.L. and G.E.G.; methodology, R.C., S.R. and G.E.G.; MA-XRF data acquisition S.R.; software, S.A.B.L. and A.B.; validation, A.B. and G.E.G.; writing--original draft preparation, S.A.B.L.; writing--review and editing, G.E.G., A.B. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 766311.  
Author [PERSON] was employed by the company Ars Mensurae srl. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Ore Rock Fragmentation Calculation Based on Multi-Modal Fusion of Point Clouds and Images
## 3 Ore Rock Fragmentation Calculation Method
### SDM Calculation  
First, the gradient map is computed, and subsequently, an image SDM is constructed by leveraging the statistical properties of local gradient differences. This process effectively enhances the visibility of edge pixels while suppressing noise, leading to a more distinct delineation of edges.
## 4 Numerical Analysis and Results
### Case Study I
#### 4.1.2 Service 2  
The next service is more for the urgent delivery of data. In this case, the delay-tolerant indicator is about 3 hours, and the data volume is 64 TB. As before, Service 2 (\(S_{2}\)) has similar attributes but different tendencies. This service includes real-time applications, such as Video-on-Demand. These services are delay-sensitive and, therefore, cannot be delayed for more than 3 hours. However, due to the large volume of data, we still grant more importance to energy efficiency and delay attributes than other attributes. It is the service class with the highest QoS requirements, and it switches from one network to another quickly as per users' profiles, such as telephony speech, VoIP, video conferencing, and other real-time activities. If a user is connected to WLAN and loses connection, they can then switch to UMTS for QoS. The same procedure will be followed for all attributes for Service 2, as defined in Tables 6-8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & **Energy** & **Bandwidth** & **Delay Tolerance** & **Delivery Probability** \\ \hline
**Energy Efficiency** & 1 & 7 & 1 & 5 \\ \hline
**Bandwidth** & 1/7 & 1 & 1/7 & 2 \\ \hline
**Delay Tolerance** & 1 & 7 & 1 & 7 \\ \hline
**Delivery probability** & 1/5 & 1/2 & 1/7 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pairwise Comparison Utility matrix as per importance scale.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & **Energy Efficiency** & **Bandwidth** & **Delay Tolerance** & **Delivery Probability** & **Critera Weight** \\ \hline
**Energy Efficiency** & 1 & 7 & 9 & 3 & 0.530345069 \\ \hline
**Bandwidth** & 1/3 & 1 & 7 & 2 & 0.164911216 \\ \hline
**Delay Tolerance** & 1/9 & 1/7 & 1 & 1/5 & 0.041457905 \\ \hline
**Delivery probability** & 1/7 & 1/2 & 5 & 1 & 0.280751063 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized score table for all the attributes with the weight factor.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & **Energy Efficiency** & **Bandwidth** & **Delay Tolerance** & **Delivery Probability** & **Critera Weight** \\ \hline
**Energy Efficiency** & 1 & 7 & 9 & 3 & 0.530345069 \\ \hline
**Bandwidth** & 1/3 & 1 & 7 & 2 & 0.164911216 \\ \hline
**Delay Tolerance** & 1/9 & 1/7 & 1 & 1/5 & 0.041457905 \\ \hline
**Delivery probability** & 1/7 & 1/2 & 5 & 1 & 0.280751063 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Normalized score table with priority vector.  
\(\lambda_{max}=4.156390957\); CI = 0.052130319; CR = 0.057922576 \(<\) 0.1.  
This pairwise matrix also passes a consistency check, which means that priority is selected correctly. We have calculated the weight for all three types of services by users' preferences for different attributes.
## 5 Field Experiment  
### Purpose and Scheme Design of the Experiment  
In order to check the working performance of the whole machine in the actual operation process, a field working performance test of the prototype was carried out in the test field of Weiyuan County, Dingxi City in March 2023 (Figure 20).  
Figure 19: End of simulation.  
Before the field test, a rototiller crushed soil in the field to ensure that the terrain of the test area was flat. The soil in the field was yellow loam soil, the moisture content was 12.2%, the surface temperature was 6.7 \({}^{\circ}\)C, and the wind speed of the air near the surface was 1.29 m/s. The tractor used to drive the machine was a Dongfeng 554 tractor with climbing gear, 6-month-old Codonopsis seedlings were selected as the test seedlings, and the average diameter of the seedling head was 3 mm and the average length was 110 mm. The seedlings were tied with a slip-knot before the test, and the average number in each bundle was 175.  
Performance tests were carried out with the forward speed of the tractor at 0.1 m/s, 0.15 m/s, and 0.2 m/s, respectively, and the output speed of the rotary tillage knife was 400 r/min. At each forward speed, the depth of the rotary tillage knife was fixed at 220 mm and the target seedling spacing was set at 4.4 cm. Three groups of tests were carried out at each operating speed. Referring to JB/T 10291-2013 [29] \"Standard for dryland planting machinery\", the standard deviation of seedling spacing, coefficient of variation of seedling spacing, qualified rate of film side outcrop, qualified rate of planting depth, and planting posture were selected to comprehensively evaluate the accuracy of seedling spacing and planting quality of the prototype.
## 2 Surface Shape Measurement Principle
### Error Compensation and Calibration  
#### 2.3.1 Radial Installation Error  
There will be radial installation errors when the sensor is installed, so the device is calibrated with a standard circular axis. As shown in Figure 7, the radius of the standard circle axis is \(r\), and the detection value is 0 when the radius of the grating probe is \(r_{0}\). We put the standard circular axis at the center of the circle measured by the grating sensor. Theoretically, the measured value of the grating probe should be \((r-r_{0})\), and the actual detection value is
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### Performance Metric  
To evaluate the performance of the models in our study, we will use several metrics, including accuracy, precision, mean square error, recall, F1 score, Matthew's correlation coefficient (MCC), and the ROC curve. These metrics are calculated using specific formulas. For example, accuracy evaluates the system's ability to classify data into the appropriate classes correctly.  
* Accuracy corresponds to the ratio between the number of images correctly classified as positive and the total number of genuinely positive images.
* Recall measures the ability of a classifier to identify accurate positive results.
* The F1 score is the weighted harmonic average of precision and recall.
* The Matthews correlation coefficient (MCC) makes assessing the quality of classifications possible.
* The Mean Square Error (MSE) of an estimator represents the mean of the squares of the deviations, that is, the average of the squared differences between the estimated and actual values.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F1 score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+ \text{FN})}} \tag{5}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}{(\text{Yi}-\hat{\text{Yi}})^{2}} \tag{6}\]  
The variables mentioned previously are:  
* True Positives (TPs): Items correctly classified as positive with a true label.
* False Positives (FPs): Items incorrectly classified as positive with a false label.
* True Negatives (TNs): Items correctly classified as negative with a false label.
* False Negatives (FNs): Items incorrectly classified as negative with a true label.
* _Yi_: The actual data in the dataset.
* _Yi_: The predicted data of the dataset.  
We used the ROC curve, which allows us to evaluate a model's performance using two indicators: sensitivity and specificity. It also offers a visual performance assessment, making it one of the most crucial evaluation metrics for testing a model's effectiveness. The confusion matrix was also employed to provide a detailed representation of the model predictions regarding true positives, false positives, true negatives, and false negatives.
## 4 Experiments
### Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{2}{c}{**3 DUNet [60]**} & \multicolumn{1}{c}{**V-Net [61]**} & \multicolumn{1}{c}{**nnU-Net [62]**} & \multicolumn{1}{c}{**UNetR [43]**} & \multicolumn{1}{c}{**SwinUNetR [46]**} & \multicolumn{1}{c}{**Ours**} \\ \hline DSC (\%) & Liver & \(94.43\pm 4.72\) & \(95.84\pm 3.11\) & \(93.53\pm 4.16\) & \(95.28\pm 4.09\) & \(95.51\pm 3.67\) & \(96.62\pm 2.19\) \\  & Tumor & \(73.84\pm 7.65\) & \(77.66\pm 6.54\) & \(82.91\pm 3.83\) & \(81.07\pm 4.03\) & \(83.24\pm 3.69\) & \(86.47\pm 2.56\) \\ \hline \multirow{2}{*}{95 HD} & Liver & \(8.73\pm 7.63\) & \(4.96\pm 3.62\) & \(4.73\pm 2.82\) & \(7.73\pm 6.82\) & \(4.13\pm 3.18\) & \(3.62\pm 1.91\) \\  & Tumor & \(8.32\pm 3.82\) & \(6.89\pm 2.54\) & \(5.06\pm 1.94\) & \(6.01\pm 1.86\) & \(4.46\pm 1.61\) & \(4.12\pm 1.55\) \\ \hline \multirow{2}{*}{ASD} & Liver & \(1.68\pm 0.55\) & \(1.38\pm 0.48\) & \(1.09\pm 0.42\) & \(1.32\pm 0.46\) & \(1.09\pm 0.39\) & \(0.92\pm 0.28\) \\  & Tumor & \(1.99\pm 0.63\) & \(1.50\pm 0.50\) & \(1.21\pm 0.36\) & \(1.61\pm 0.58\) & \(1.25\pm 0.39\) & \(1.02\pm 0.32\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Parameters (M)** & **GFLOPs** \\ \hline
3 DUNet [60] & 16.9 & 89.4 \\ V-Net [61] & 10.8 & 65.2 \\ nnU-Net [62] & 91.2 & 213.8 \\ UNetR [43] & 58.5 & 155.1 \\ SwinUNetR [46] & 62.1 & 237.9 \\ Ours & 63.4 & 188.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of computational cost and model parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**DSC (\%)**} \\ \cline{2-5}  & **Liver** & \(p\)**-Value** & **Tumor** & \(p\)**-Value** \\ \hline
3 DUNet [60] & \(94.43\pm 4.72\) & \(5.19\times 10^{-3}\) & \(73.84\pm 7.65\) & \(7.64\times 10^{-10}\) \\ V-Net [61] & \(95.84\pm 3.11\) & \(7.37\times 10^{-3}\) & \(77.66\pm 6.54\) & \(4.25\times 10^{-8}\) \\ nnU-Net [62] & \(93.53\pm 4.16\) & \(1.15\times 10^{-2}\) & \(82.91\pm 3.83\) & \(3.19\times 10^{-3}\) \\ UNetR [43] & \(95.51\pm 3.67\) & \(9.64\times 10^{-3}\) & \(81.07\pm 4.03\) & \(1.65\times 10^{-5}\) \\ SwinUNetR [46] & \(95.51\pm 3.67\) & \(5.13\times 10^{-3}\) & \(83.24\pm 3.69\) & \(2.49\times 10^{-4}\) \\ Ours & \(96.62\pm 2.19\) & - & \(86.47\pm 2.56\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different methods on DSC metrics with \(p\)-Values. contours for both the liver and tumor. However, our model and nnU-Net [62] typically predict slightly larger contours for both the liver and tumor, showing better performance in detecting smaller tumors. In particular, for the liver, the average difference between our model's predictions and the annotated contours in the test set is 4.32% smaller, with a standard deviation of 9.53%. For the tumor, the model's predictions are, on average, 4.67% smaller, with a standard deviation of 8.67%.
## 4 Analysis and Discussion
### Model Results Presentation  
Figure 5 illustrates the outcomes of the training of six sets of product data with distinct models utilizing BP neural networks. The mean square error (MSE) of the final model for the training set is recorded as Train ERROR = 2.8%, and that for the test set is recorded as Test ERROR = 4.3%. These values indicate that the prediction model has an accuracy of more than 95%. The training models depicted in Figure 5 employed data normalization to eliminate the differences in dimension between the individual design parameters, as well as the introduction of the activation function Relu to enhance the nonlinear fitting ability of the model. Observing the images reveals that the predicted values for product satisfaction derived from the MLP model exhibit a similar trend to the actual values. This indicates that the model has successfully derived the relationship between the influence of each design parameter on product form. Furthermore, it demonstrates that the problem of the change in user satisfaction between the various indicators of the product caused by the change in the different design parameters has been solved. This provides evidence of the model's feasibility to optimize the design parameters based on the combination of a BP neural network and sustainable design. The feasibility of a design parameter optimization model based on a BP neural network combined with sustainable design was evaluated.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{**Emotional Design**} & \multicolumn{6}{c}{**Sustainable Product Design**} \\ \hline \multirow{2}{*}{**Model**} & **Fuselage** & **Fuselage** & **Screen** & **Product** & **Carrying** & **Product** & **Material** & **Tensile** & **Yield** & **Material** \\  & **Width** & **Thickness** & **Size** & **Weight** & **or Not** & **Categories** & **Density** & **Strength** & **Strength** & **Hardness** \\ \hline
3 gs & 0.18 & 1.00 & 0.00 & 0.20 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.02 \\
4 & 0.00 & 0.44 & 0.00 & 0.22 & 0.00 & 0.00 &  
### Model Results Presentation  
The results of the user satisfaction prediction model that combines emotional design and sustainable design indicate that the prediction model can accurately predict user satisfaction using various product indicators under the design scheme. Furthermore, the mean square error (MSE) of the test set of the prediction model was recorded as Test ERROR = 4.3%, which indicates that the accuracy of the prediction model is more than 95%. This suggests that the prediction model can effectively guide designers in optimizing product design.
# Geospatial Simulation System of Mountain Area Black Ice Accidents
## 3 Proposed Method
### Black Ice Risk Estimation with Meteorological Information  
This study applied the black ice risk estimation model, which is based on the algorithm proposed by [PERSON] et al. [19]. Among the traffic accidents that occurred in 2015, investigations of the weather conditions at the accident sites (related to freezing rain or black ice) revealed that they mostly occurred on a warm front or a low-pressure front with the presence of a temperature inversion layer. This is consistent with the usual meteorological pattern that causes freezing rain. Subsequently, an estimation model that considers variables such as the freezing level, temperature, wind speed, and humidity was proposed (Figure 8). Although it is necessary to apply a more sophisticated model to predict black ice, this study applied a relatively simple estimation model as the primary objective was to link meteorological information with spatial information.  
Generally, meteorological variables are used to explain and predict road surface conditions. Currently, meteorological information in South Korea is collected using AWS, ASOS, and RWIS (Table 1). However, AWS, which is a device for detecting hazardous local weather patterns, lacks data on snowfall amounts, whereas the diverse meteorological information collected by ASOS is irrelevant for the roads. On some of the highways, RWIS  
Figure 8: Black ice risk estimation model used in the study.  
Figure 7: Ortho-rectified image (**left**), digital surface model (**middle**), and 3D model (**right**) obtained by UAV.  
installed a system for collecting, processing, and utilizing meteorological information in real-time, i.e., specific for road management. Currently, such meteorological information can be collected using Open-API functions of the web services operated by the Korea Meteorological Agency (KMA). Our system collected comprehensive weather data from KMA in real-time.
## 5 Experiments and Results  
### Experimental Environment Settings and State Information Acquisition  
Figure 18 depicts an experimental environment containing a water pool, a global camera system, and a computer. The dimensions of the water pool are 3.5 m \(\times\) 2.5 m \(\times\) 1 m (length \(\times\) width \(\times\) depth). The global camera USB8 MP02G that is used to obtain the position of the BSR is mounted on a shelf, which is 2.6 m above the ground, as shown in Figure 18a. Table 2 shows the parameters of the global camera.  
The state information of the robot, which includes the position, yaw angle, surge velocity, and yaw velocity, is crucial for developing the MPC algorithm. The yaw angle is measured while using a high-precision inertial measurement unit (IMU) mounted on the robot. A global camera-based real-time localization system is utilized in order to obtain the position of the robot. The real-time localization system is achieved using the Kalman consensus filter (KCF) algorithm, which is described concretely in reference [33]. The position of the BSR was computed in the host computer. The BSR, as the slave computer, obtains the position information while using the optical fiber communication system. Figure 19 shows the information transmitted between the host computer and the BSR. Based on the position information and the yaw information at each sampling time, the surge velocity and yaw velocity are computed by the difference method.  
Figure 17: The comparison of the control input with the backstepping method in square tracking.  
Figure 18: Experiment set up for biomimetic sphere robot (BSR) trajectory tracking. (**a**) Experiment setup. (**b**) Image of water pool in the view of global camera perspective.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods
#### 5.5.1 Basics of ANNs  
The fundamental element (called a _node_) of an ANN performs a function similar to the action of biological neurons. The simplest ANN is a _multi-layer perceptron_ (MLP), consisting of _layers_ (sets) of nodes, each sending their outputs to the nodes of the next successive layer, without feedback. For a classification task such as SD, training a basic ANN may be viewed as optimizing the combined locations of hyperplane boundaries of complex regions in a data representation space. The initial ANN layer receives a vector of speech samples from an utterance. Intermediate layers may provide various features for classification; their numbers of nodes vary greatly and are chosen empirically, and the resulting features are often difficult to evaluate. Reducing their numbers can result in so-called _bottleneck_ features [42], which are sometimes interpretable.  
In natural neurons, dendrites provide potentials to an axon, which yields a binary output: a brief pulse if the weighted sum of its inputs exceeds a _bias_ threshold. For an algorithmic ANN node, output is specified by an _activation function_\(y=\varphi(w\cdot x+b)\), where \(w\) is a \(N\)-dimensional weighting vector, \(b\) is a scalar bias value, \(x\) is a set of \(N\) input values, and \(\varphi(.)\) is a scalar nonlinear function. The parameters of each model node (i.e., \(w\) and \(b\)) specify the location of a hyperplane in a data representation space; the node's binary output corresponds to either side of this hyperplane. To provide accurate solutions to practical data problems, ANNs generally require many millions of parameters to achieve high performance.  
The basic _fully connected feedforward_ MLP structure is general and costly in size and computation, as all nodes in each layer connect to all nodes in the next layer. Useful information in most data (for speech as well as other signals) is generally spread non-uniformly; thus, a fully connected network is inefficient. ANNs with more than three layers are called deep (DNNs); such detailed architecture assists with handling complex tasks. Most applications use various combinations of the more specific network components described in Sections 5.5.3-5.5.5