esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies
## 2 Material and Methods
### Development of ANN Models  
In this study, the ANNs were implemented by using the MATLAB software (R2013a) and the best ANN for predicting the number of almond nuts was attained by following the subsequent steps [35]:  
* Based on the review of published papers and the reported results in the case of ANN applications for various purposes, the multilayer perceptron (MLP) artificial neural network model was selected. Detailed information about the MLPs can be found in [PERSON] et al. [35].
* The above-mentioned parameters in Section 2.2 were employed as the network inputs and the almond nut yield was considered as the output;
* After shuffling, the collected dataset was divided into three subsets including training (70%), validation (15%), and testing (15%) of the modeling networks;
* Numerous neural network topologies were trained by employing different approaches including Levenberg-Marquardt, gradient descent (gd), gradient descent with momentum (gdm), and gradient descent with momentum adaptive learning rate back-propagation (gdx);
* The logistic sigmoid (_logsig_) and tangent sigmoid (_tansig_) transfer functions were employed in the hidden layers. The linear transfer function (_purelin_) was examined for output layer [29];
* Trial-and-error method was performed in order to realize the most accurate ANN model;
* To test each network, different arrangements of the training set were used, some modifications were made to make the network more reliable, and finally, performance of the system assessed by subjecting the ANN to the new input configurations.
# Automated Measurement of Grid Cell Firing Characteristics
## 4 Step-by-Step Usage Protocol
### Software Execution  
1. Configuration options must be saved to a script before running the metric measurement software. Specifically, a user should run 'run_config_convert_win.bat' or 'run_config_convert_linux.sh' in the config_convert folder to generate the run_gridcell_metrics.bat or.sh script in the base software directory. This run_config_convert script converts values in the config.txt file into a script that launches the gridcell_metrics software with those configuration values.  
2. For Windows, double-click 'run_gridcell_metrics_with_config.bat' to launch the runtime version. For Linux, run 'run_gridcell_metrics_with_config.sh' to use the runtime version. Alternatively, a user can run the file run_gridcell_metrics.m within MATLAB to run the software from its source code. Result generation may potentially take a few minutes depending on the processing power of the computer.  
Note: For the Linux version, shared object files require an addition to a user's.bashrc (or equivalent based on Linux distribution) file. Specifically, in.bashrc, LD_LIBRARY_PATH should have MATLAB's runtime \"runtime/glnxa64\" and \"bin/glnxa64\" directories added to it. This can be accomplished by, for example, adding the following line to the.bashrc file (replace the MATLAB install directory as needed): \"Export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/  
MATLAB/MATLAB_Runtime/v912/runtime/glnxa64/usr/local/MATLAB/MATLAB_Runtime/v912/bin/glnxa64\".  
3. Decide if any fields should be manually excluded from analyses after creating the detected fields plot (Figure 8). This can be useful if, for instance, the fields colliding with plot borders are unwanted. Specify the fields to be excluded in config.txt with the variables spac_exclud, size_exclud, and ang_exclud using field numbers separated by \" \(\mid\)\" (e.g., \"1 \(\mid\)4 \(\mid\)5\"). The config.txt option report_centroid_positions reports centroid locations for each numbered field. These locations can be used, along with a plot of detected fields, to select certain fields for exclusion. Rerun the program to compute statistics excluding the indicated fields.  
Figure 8: Identifying grid fields to be excluded from analysis. (**A**) Autocorrelogram from a grid cell. (**B**) Red-striped lines indicate two grid fields in the detected grid fields that a user could exclude because they are partially hidden by the plot borders.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 5 LiDAR in Space Missions
### Atmospheric Laser Doppler Instrument (ALADIN)  
ALADIN, aboard ESA's ADM-Aeolus satellite launched in 2018, is the first Doppler wind LiDAR designed for space-based direct detection of global wind profiles [26; 147]. ALADIN provides vertical wind profile observations meeting World Meteorological Organization (WMO) accuracy requirements, crucial for weather forecasting and climate studies [95; 148]. The satellite orbits at a Sun-synchronous altitude of 320 km, with a 7-day repeat cycle [148]. The receiver uses a 1.5 m diameter telescope, combined with two spectrometers: the Mie spectrometer for aerosol and cloud particle backscatter, and the Rayleigh spectrometer for molecular backscatter analysis. Wind profiles are resolved vertically at 250 m intervals from altitudes of approximately 2 km to 30 km [135; 149].  
ATLID--Atmospheric LiDAR (ATLID)  
Atmospheric LiDAR (ATLID) is a high-spectral-resolution backscatter Light Detection and Ranging (LIDAR) instrument aboard the EarthCARE satellite. ATLID is designed to detect cloud boundaries and to profile optically thin clouds and aerosols with high precision [141]. EarthCARE itself operates in a sun-synchronous orbit at an altitude of 393.14 km with an inclination of 97.05\({}^{\circ}\). The satellite completes an orbit every 92.5 min and repeats its ground track every 25 days. This orbit ensures consistent lighting conditions for coordinated atmospheric measurements [139]. ALTID provides vertical resolutions of 103 m (ground to 20.2 km) and 500 m (20.2-40 km). Achieving an initial 140 m horizontal sampling resolution along the satellite track. To improve the signal-to-noise ratio and provide more robust data for atmospheric studies, these measurements are averaged onboard to produce a final horizontal resolution of approximately 10 km. A 60 cm telescope collects the backscattered photons, which are processed to distinguish Mie scattering (narrow-band scattering by aerosols and particles) and Rayleigh scattering (broad-band scattering by atmospheric molecules). Co-polarised and cross-polarised signals are separately detected to classify aerosol types [140; 141].
# Beyond Human Vision: Unlocking the Potential of Augmented Reality for Spectral Imaging
## 3 Results  
As previously said, for the purpose of this work, we focused on the LIBS technique for obtaining the spectral imaging datasets. In short, LIBS imaging allows for the creation of two-dimensional chemical maps of samples by identifying elemental and molecular emission lines (Figure 2) through the analysis of spectral signatures generated during the decay of laser-induced plasma on the sample surface. This method was applied to two distinct samples for illustrative purposes as follows (Figure 3): (i) an oxidized double open-end wrench composed of chrome-vanadium steel, and (ii) a traditional Portuguese tile fragment.  
For the case of the wrench sample, the presence of a spatial distribution of iron (Fe) (I) and (II) emission lines was detected during the analytical phase in step 2, as well as vanadium (V), chromium (Cr), aluminum (Al), and oxygen (O). All of these are consistent with the surface oxidation observed on the sample. On one hand, the presence of iron and vanadium is consistent with the chrome-vanadium steel composition. On the other hand, Cr and oxygen lines were detected with a higher intensity as they are part of the protective coating, revealing the less damaged part.  
For the tile fragment, the distribution of cobalt (Co) was particularly noteworthy as a part of the paint used in the tile. The expected spatial match underscores the capability of LIBS to identify and analyze heritage artifacts, offering significant potential for the study and preservation of cultural heritage artifacts.  
Figure 4 presents the user perspective of the developed interactive tool, illustrating the user-friendly and informative interface that integrates spectral imaging with augmented reality (AR). This interface allows users to observe all relevant characteristics of the spectral map projected on a digital model of the sample, offering a degree of contextualized interpretation and gesture-based interaction of the acquired data not achievable with traditional visualization methods. In particular, from user experience (which can be seen in the video in the Supplementary Materials), we highlight the following interaction capabilities:  
* Moving and rotating the sample: It allows us to position and move the sample freely, analyzing the composition in detail from multiple perspectives;
* Scaling: Allowing us to scale up small samples or reducing larger ones, the solution offers unique perspectives of the samples in integration with additional spectral features;* Texture-based interpretation: Including the texture of the sample surface and possible roughness, it is possible to relate common signal variations (e.g., plasma signal in LIBS) due to the surface properties and better contextualize them.
* Multiple sample comparison: Adding multiple samples at the same time to the environment eases visual comparisons, enabling the deduction of clear connections or distinctions.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description
### Voice Separation for Multiple Speakers  
Real-world voice communication frequently occurs in congested, multi-talker settings. A speech processing system intended to work under such settings must be able to differentiate the speech of distinct talkers. Given a dataset of the main speaker voice and mixed versions of this main voice with other speakers, the system is trained to recognise the speaker voice features using deep learning algorithm. So, at this stage, the system allows the main speaker voice to be separated and identified from other mixed voices.
## 4 Deployment of CATIM in an Industrial Case Study
### Context Ontology: Activity, Operator, Environment  
For the context, we consider the activity as fixed (i.e., the chosen single instruction). For the operator, we consider the single attribute of user experience (values: novice and expert user). For the environment, we consider three attributes: (i) object trackability for situated visualization (values: trackable or not trackable), (ii) noise (values: noisy and acceptable), and (iii) AR device (values: HoloLens and smartphone). In this evaluation, we simulated that these values were automatically recognized by CATIM through sensors (noise), user profiling (experience, device), retrieval of object information from the database (trackability). Various combinations of these simulated values were manually forced in the context vector.  
The concept covering rules, presented in Table 1, are defined once for the scope of the application to formalize the design choices. The following design choices were made together with the operators that constantly collaborate with us. For operator experience, we decided not to show to an expert operator detailed technical information assets such as long text/audio, world-fixed assets, and the video tutorial. We assume that an expert operator knows how to accomplish the task, whereas a novice operator could need more detailed instructions. As to device, with the HoloLens we decided not to show long text because of its limited field of view (FOV), while with the smartphone such screen-fixed content could occlude the real world. As to trackability, when the scene\ objects are not trackable, we decided not to show world-fixed contents. This could be due either to the inadequacy of the target objects for the tracking method chosen or to external factors such as the forbiddance to put the optical marker on the scene. Finally, as to noise, we decided not to use audio in noisy environments.
# One-Photon-Interference Quantum Secure Direct Communication
## Appendix A Details of Encoding and Decoding
Multi-intensity mode: Three different intensity and phase-randomized weak coherent states \(\beta_{\beta_{a}}=\ket{\beta_{a}e^{i\phi_{a}}}\) and \(\hat{\rho}_{\beta_{b}}=\ket{\beta_{b}e^{i\phi_{b}}}\), where \(\beta_{a}\) and \(\hat{\rho}_{b}\) are randomly chosen from the set of \(\{\sqrt{\
u_{1}},\sqrt{\
u_{2}},0\}\). **Step 3: measurement. ** [PERSON] measures the pulses sent by [PERSON] and [PERSON] using single-photon interferometer, and publishes measurement results. Let \(m_{C}\) and \(m_{D}\) denote the measurement outcome of D\({}_{0}\) and D\({}_{1}\), where value \"0\" indicates a no-click event and value \"1\" indicates a click event. [PERSON] and [PERSON] discard the no-click events and two-click events, and retain the one-click events, namely \(m_{C}\oplus m_{D}=1\). The **step 2** and **step 3** are repeated for several rounds until the transmission of \(C_{i}\) is complete. **Step 4: mode matching. ** After all measurements are completed, [PERSON] and [PERSON] publish the modes information. They retain the measurement results of the same modes, and discard the measurement results of different modes. If they both send the multi-intensity mode, they publish the intensity and phase of the weak coherent state. Then they retain pulses with \(\beta_{a}=\beta_{b}\) and \(\ket{\phi_{a}-\phi_{b}}=0\) or \(\pi\). **Step 5: parameter estimation. ** After completing the above steps, [PERSON] and [PERSON] can obtain the gain \(Q_{u}^{i}\) for the current round of communication. They then randomly publish some of the bit values from the coding mode to estimate the quantum bit error rate \(E_{u}^{X,i}\) and use different intensity values in multi-intensity modes to estimate the phase-error rate \(E_{u}^{Z,i}\). Based on this information, they can calculate the mutual information between [PERSON] and [PERSON] \(I(A:B)_{i}\), [PERSON] and [PERSON] \(I(A:E)_{i}\), and the secrecy rate \(R_{i}\). If \(I(A:B)_{i}\) and \(I(A:E)_{i}\) satisfy Equation (A1), they can extract the same SSTS from \(C_{i}\) and add it to the SSTS pool for the next round of communication. The **Step 2** to **Step 5** are repeated for several rounds until the transmission of X is complete. **Step 6: decoding. ** [PERSON] publishes the positions of his measurements which have valid results and [PERSON] publishes the value of the local random bit \(L_{i}\) at these positions to help [PERSON] unmask the codeword \(Z_{i}\). Then [PERSON] uses the same \(S\) to decrypt \(Y\) and obtain the plaintext message \(M\), where \(M=Y\oplus S\). If the SSTS pool is depleted during the transmission process, [PERSON] and [PERSON] will need to transmit \(X_{i}\), which is composed of random numbers and satisfies Equation (A1), to extract the same \(S\). This ensures that they have sufficient SSTS to continue the communication securely.
## 3 Theoretical Analysis of Ultrasonic Trepenning Quality
### Dimensional Error in Trepenning
#### 3.1.2 The Influence of Process Parameters on Dimensional Error  
In order to further clarify the influence of process parameters on the trepenning dimensional error, the residual heights of three kinds of vertical surfaces under different trepenning process parameters were calculated.  
In straight vertical surface trepenning, the relationship between the trepenning step length and the residual height is demonstrated in Figure 11. It can be seen that the residual height increases with the increase in the trepenning step length, when using different sizes of trepenning tools. The use of a larger radius trepenning tool at the same step length can effectively reduce the residual height. However, when the radius of the trepenning tool increases to more than 30 mm, the residual height under the same step length no longer significantly reduces.  
In internal arc vertical surface trepenning, the relationship between the step length and the residual height \(h_{I}\) are demonstrated in Figure 12, where the radius of the internal arc vertical surface to be machined is 40, 60, 80 and 100 mm, respectively. It can be seen that the residual height increases with the increase in the trepenning step length, when using different sizes of trepenning tools. The use of a larger radius trepenning tool at the same step length can also effectively reduce the residual height. When the radius of the internal arc vertical surface is small, trepenning tools of different sizes have a significant impact on the residual height under the same step length. However, as the size of the internal arc vertical surface increases to more than 80 mm, the effectiveness of the increase in the tool radius on the decrease in the trepenning step length is no longer significant.  
The relationship between the step length and the residual height \(h_{E}\) is demonstrated in Figure 13, where the radius of the external arc vertical surface to be machined is 40, 60, 80 and 100 mm, respectively. The relationship between the residual height of the external arc vertical surface and the process parameters is similar to that of the internal arc vertical surface. However, in the trepenning of the external arc vertical surface, when the radius of the trepenning tool increases to more than 20 mm, the residual height does not decrease significantly as the radius of the tool continues to increase when the step length is the same.  
Figure 11: Curve chart of the \(h_{S}\) change with step length.  
Figure 12: Curve chart of \(h_{I}\) change with the step length: (**a**) \(R\) = 40 mm; (**b**) \(R\) =60 mm; (**c**) \(R\) = 80 mm; and (**d**) \(R\) = 100 mm.  
Figure 13: Curve chart of the \(h_{E}\) change with step length: (**a**) \(R\)= 40 mm; (**b**) \(R\) = 60 mm; (**c**) \(R\) = 80 mm; and (**d**) \(R\) = 100 mm.
## 3 Materials and Methods
### Clustering Setting
#### 3.2.4 Post-Processing
In this case, the parameter to maximize ratioOther is defined according to Equation (19):  
\[\text{ratioOther}_{i}=\frac{\mu_{i}s_{i}}{d_{i}^{2}}, \tag{19}\]  
where \(d_{i}^{2}\) represents the square of the distance between the centroid of the lesion region in the central slice and the centroids of all the connected-components in the current slice. In order to reduce false positives identification, ratioOther maximization searches for the connected-component with the centroid that is the closest to the one of the central slice mask. Recalling that breast lesions usually preserve a pseudo-spherical appearance, as the slices move away from the central one, the cross section identifying the lesion is reduced. In Equation (19) the replacement of \(\epsilon_{i}\) with \(d_{i}^{2}\) aims to avoid misclassification of spurious regions in slices different from the central one. Figure 6 shows the results of each post-processing step applied to a segmentation mask obtained by means of sFCM clustering. Unfortunately, breast lesions might be characterized by different scenario--in terms of uniformity, contrast and well-defined boundaries--which a simple thresholding cannot properly manage. Figure 7 shows two segmentation results that allow us to appreciate the lesion non-homogeneity that only clustering approaches can properly manage, thus maximizing the result accuracy. Figure 6: Examples of post-processing steps on two breast lesions (one per row): **(1 st column)** binary mask obtained after sFCM clustering; **(2 nd column)** mask after the small-area removal; **(3 rd column)** mask after both the small-area removal and the eccentricity-based selection; and **(4 th column)** mask after ratio criteria, defined in Equations (18) and (19). With respect to the position in the DCE-MRI sequence, all images have been rotated 90\({}^{\circ}\) clockwise, in order to improve the graphical representation and allow all the images, related to specific lesions, to be displayed in a single line. Figure 7: Two examples of segmentation obtained by sFCM: **(1 st row)** the original DCE-MRI images; **(2 nd row)** the corresponding segmentation. Compared to the images shown in Figures 4 and 6, a 2\(\times\) zoom factor was used, that allows us to better appreciate the lesion non-homogeneity that only clustering approaches can properly manage, thus maximizing the final result accuracy.
# Hybrid Machine Learning Model for Predicting Shear Strength of Rock Joints
## 3 Dataset Description and Processing
### Performance Evaluation Indicators  
To provide a robust and comprehensive evaluation of the machine learning model's predictive capability, four commonly adopted performance evaluation indicators are utilized, including the root mean squared error (\(RMSE\)), the mean absolute error (\(MAE\)), the coefficient of determination (\(R^{2}\)), and the variance accounted for (\(VAF\)) [60]. \(R^{2}\), also known as the coefficient of determination, quantifies the strength of the linear relationship and assesses the goodness of fit between the model's predictions and actual observations [61,62]. \(VAF\) indicates the proportion of the variance in the actual values explained by the model's predictions, measuring how well the model captures the overall trend of the data [63]. \(RMSE\) is the square root of the mean squared difference between the predicted and actual values, and it quantifies the magnitude of the model's prediction error [64,65]. \(MAE\) is the average of the absolute differences between the predicted and actual values, providing a measure of the model's average prediction error [66,67]. The mathematical expressions for these performance evaluation indicators are provided in Equations (9)-(12).  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{K}\left(y_{i}^{obs}-y_{i}^{pre}\right)^{2}}{ \sum\limits_{i=1}^{K}\left(y_{i}^{obs}-E[y^{mean}]\right)^{2}} \tag{9}\]  
\[VAF=\left[1-\frac{\text{var}\left(y_{i}^{obs}-y_{i}^{pre}\right)}{\text{var} \left(y_{i}^{obs}\right)}\right]\times 100\% \tag{10}\]  
\[RMSE=\sqrt{\frac{1}{K}\sum\limits_{i=1}^{K}\left(y_{i}^{obs}-y_{i}^{pre} \right)^{2}} \tag{11}\]  
\[MAE=\frac{1}{K}\sum\limits_{i=1}^{K}\left|y_{i}^{obs}-y_{i}^{pre}\right| \tag{12}\]  
where \(y_{i}^{obs}\) denotes the observed values, \(y_{i}^{pre}\) refers to the predicted values, and \(K\) is the total number of samples.