esa-sceva/satcom-chunk-collection huggingface dataset:
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 3 Description of the Simulation System  
A MATLAB Simulink(r) environment was used to create an artificial multi-loop simulation model. The model, which is shown in Figure 1, consists of five control loops, four of which are PID controllers (\(R_{1}\), \(R_{2}\), \(R_{3}\), and \(R_{4}\)) and one of which is a PI controller (\(R_{5}\)). The control errors from the loops of 2000 samples each, denoted as \(\epsilon_{1}\), \(\epsilon_{2}\), \(\epsilon_{3}\), \(\epsilon_{4}\), and \(\epsilon_{5}\), respectively, are analyzed in the subsequent analysis. The model incorporates the possibility of applying noise. Two variants are provided; the first simulates the distortion of simulation data by Gaussian noise (represented by the variable \(\sigma_{i}\)), while the second simulates the distortion of simulation data by both Gaussian and Cauchy noise (represented by the variable \(\gamma_{i}\)). Additionally, a sinusoidal signal can be added to the Cauchy disturbance to simulate known-frequency loop oscillations and their propagation.  
The transfer functions of the simulated process are expressed as linear models in the following form:  
\[G_{1}(s) =\frac{1}{0.15s+1},\quad G_{2}(s)=\frac{0.25(-s+1)}{s(2s+1)},\quad G _{3}(s)=\frac{1}{(s+1)(0.04s^{2}+0.04s+1)},\] \[G_{d}(s) =\frac{-0.25(-s+1)}{s(s+1)(2s+1)},\quad G_{4}(s)=\frac{1}{(s+1)^{4 }},\quad G_{5}(s)=\frac{1}{(0.2s+1)^{2}}e^{-s}. \tag{14}\]  
The feedforward filters are defined as follows:  
\[F_{3}(s) =\frac{1}{4s^{2}+3.2s+1},\quad F_{4}(s)=\frac{1}{s^{2}+1.2s+1}, \quad F_{5}(s)=\frac{1}{4s^{2}+2s+1},\] \[F_{d3}(s) =\frac{1}{2s+1},\quad F_{d4}(s)=\frac{0.1}{s+1},\quad F_{d5}(s)= \frac{4}{2s+1}. \tag{15}\]  
The controller parameters in Table 1 are rough-tuned, and disturbance decoupling is achieved using an industrial design approach [35], as illustrated in Figure 2.  
Figure 3 presents the simulation data generated with Gaussian noise. The noise parameters for each \(\sigma_{i}\) variable were randomly selected.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(i=1\) & \(i=2\) & \(i=3\) & \(i=4\) & \(i=5\) \\ \hline P & 0.2 & 2 & 0.13 & 0.27 & 0.01 \\ I & 0.01 & 0.2 & 0.5 & 0.61 & 0.2 \\ D & 0.02 & – & 0.08 & 0.21 & – \\ N & 10 & – & 10 & 100 & – \\ \hline \end{tabular}
\end{table}
Table 1: PID implementation—tuning parameters of simulated controllers \(R_{i}\).  
Figure 1: Simulated multi-loop PID-based control layout.  
Figure 2: The industrial implementation of feedforward disturbance decoupling.  
For the second scenario, both Gaussian and Cauchy disturbances were randomly chosen and a sinusoidal signal with a frequency of 30 Hz was added. The trends resulting from these assumptions are illustrated in Figure 4.  
Because the relationships between the regulation errors from the control loop are known, Figure 5 shows the expected causality diagram for the simulation model. The solid line indicates a direct relationship and the dashed line indicates an indirect relationship.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 2 M-C Criterion and BKA-MLP Model Principle
### Mlp  
MLP [29] is the artificial neural network input layer, a hidden layer, and the output layer. The layer contains multiple neurons; each neuron converts weighted input into output through an activation function. The basic structure is shown in Figure 1.  
In order to increase the computational efficiency of the model and reduce the risk of overfitting, a single hidden layer MLP network structure was adopted in this study. The number of neurons in the input layer is the number of input features set to m, the number of neurons in the hidden layer is set to n, and the number of neurons in the output layer is 1. In the forward propagation process, the output feature vector \(x\in R^{m}\) is passed to the hidden layer through the input layer, and the calculation is as follows [30]:  
\[z^{(1)}=W^{(1)}x+b^{(1)} \tag{8}\]  
Figure 1: MLP neural network structure.  
where \(z^{(1)}\) represents the linear combination result of input and output to the hidden layer; \(W^{(1)}\in R^{n\times m}\), is the weight matrix from the input layer to the hidden layer; and \(b^{(1)}\) is the bias vector of the hidden layer.  
The calculation process from the hidden layer to the output layer is as follows [31]:  
\[z^{(2)}=W^{(2)}a^{(1)}+b^{(2)} \tag{9}\]  
where \(z^{(2)}\) represents the linear combination result from the hidden layer to the output layer; \(W^{(2)}\in R^{1\times n}\), is the weight matrix from the hidden layer to the output layer; \(a^{(1)}\) is the hidden layer output; and \(b^{(2)}\) is the output layer offset.  
Activation functions introduce nonlinear properties, which enable neural network models to learn to represent complex nonlinear relationships. In the whole process, from the input feature to the hidden layer and then to the output layer, the activation function outputs the output of each process through nonlinear mapping. The main types of activation functions are as follows [32]:  
\[ReLU(x)=max(0,x) \tag{10}\]  
\[sigmoid(x)=\frac{1}{1+e^{-x}} \tag{11}\]  
\[tanh(x)=\frac{1-e^{-2x}}{1+e^{-2x}} \tag{12}\]  
The MLP model constantly adjusts and updates the connection weights and thresholds between neurons during training. The mean square error (_MSE_) is used as a fitness function to continuously optimize and find the minimum value to iteratively update the model during running, as shown [33] in Equation (13):  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})}^{2} \tag{13}\]  
where \(y_{i}\) represents the true value of sample \(i\); \(\hat{y}_{i}\) represents the predicted value of sample \(i\); \(n\) is the number of samples.
## 2 Methodology
### Data  
As detailed in our previous work [26], the AOSR strategy provides a mechanism to handle different classifications of products. It recommends a general structure of different warehouse zones to match the SKU requirements. The racks in each zone can be further divided into different levels. The number of racks and levels are flexible and can be configured initially before launching the setup. As a constraint for experimental purposes in this paper, all the AOSR racks are divided into three levels with each level containing space for five SKUs, yielding a total of 15 SKUs in a rack. This implies that, for a minimal setup, there is storage capacity for around 2000 products (distributed as defined in the dataset used [26]) with the flexibility to support up to 20,000 products to be scheduled in a single day in a larger setup. In order to justify the need and comprehensiveness of data, the industrial dataset utilised to test AOSR 2.0 provides a variety of different classifications of products, e.g., finished goods or raw materials and fast or slow-moving products, in compliance with the test datasets provided by different logistics and warehousing companies e.g., Eurosped [37] and DGI Global [38]. The details of different product categorisations applied to the AOSR 2.0 strategy are discussed in our other work [28], which includes how the products are classified in the datasets. In order to ensure comprehensive experimentation, AOSR 2.0 caters to six different types of SKUs (Each/Box, Box/Case, Case/Pallet, Barrel, Cylinder, Single Pallet) and six different types of product characteristics (hazardous or non-hazardous, fast or slow, and finished good or raw material) with 20 different product categories. Thus, the number of product possibilities that can be catered for can be represented as:  
\[n^{2}\ *\ Categories\ *\ Characteristics\ *\ SKUs. \tag{1}\]  
Then, the possibility of finding a certain product \(x\) in \(n\) advance shipment notices (ASN) or \(n\) advance delivery notices (ADN) that has any particular category out of the 20 possible, with any particular characteristic out of the six possible, and having any particular SKU out of the six possible, can be calculated with the following equation:  
\[P_{\text{(x)}}=\frac{1}{n^{2}*20*6*6} \tag{2}\]  
In a complex warehouse environment, finding product possibilities can sometimes help to adjust space and product allocation within designated areas. A simplified solution to find a product possibility in a certain warehouse region can help improve search ability and efficiency in a warehouse solution [14]. As AOSR WMS maintains a more generic solution, it becomes easier to apply a different set of requirements as per business requirements.
## 4 Data and Methodology  
The 3D geological model construction was performed by applying software packages, which are widely implemented in geosciences. In particular, the presentation of the first and the second models was carried out using the ArcGIS 10.6.1 software package, while the third one was performed by the SURPAC2000 software package. The methodological process of geological models in the ArcGIS 10.6.1 software package includes the georeferencing of the original geological maps [3; 4] and the existing formations' digitalization; moreover, a colored legend was extracted based on the age of each formation. Then, a detailed digital elevation model (DEM) of the study area was implemented, resulting in the three-dimensional configuration of geological formations; for this purpose, the ArcScene application (extension of ArcGIS 10.6.1 software) was applied. Regarding the corresponding process in the SURPAC2000 software package, the principles of digitalization are identical to the ArcGIS 10.6.1 software package ones, while limited technical differentiations are documented.
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 4 Method
### Method Execution Process  
Given the concept of concealing the privacy contained in the image and subsequently restoring it with preserved utility, we drew Figure 2 to illustrate the whole process of our approach.  
In the beginning, we carry out the process of identification of facial regions on the original image in the dataset to crop out the face area. Then, the cropped face is transformed into facial embedding and classified into one cluster. Furthermore, we utilize its corresponding centroid to synthesize surrogate images. The surrogate face and four de-identified cropped faces modified by four face methods (covering the eyes, blurring the face, adding Laplace noise, and transforming into a cartoon face) are fed into the model to reconstruct the image that conceals identity while preserving utility.  
The semantic-related estimator takes advantage of the difference in attributes within the serving scene before and after processing to quantify the service quality loss of images. It adjusts the manner in which the whole model is trained with the intention of the model generating facial images that fulfil the goal of privacy removal and utility preservation, supplying reliable and secure facial images that are suitable for use in the facial dataset that is related to specific machine-learning tasks.
## 3 Overview of the Proposed Method
### Generate Situations at the Logical Level
#### 4.3.2 Generate the Set of Logical Situations Associated with Each Functional Situation  
Logical situations are obtained by combining the value ranges of the parameters describing each functional situation. A systematic combination method is used, i.e., each of the subspaces of variation of each parameter appears in each combination obtained. In addition, special care must be taken when combining value ranges. Constraints between the value ranges may be defined in order to ensure that inconsistencies in the obtained situations are avoided.  
Suppose the following parameters are used for the description of the scenarios: Number_ lanes, Curvature, Speed_Ego (km/h), Rainfall level and Speed_OtherVehicle.1 (km/h). The value ranges for each parameter are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value Ranges** & **Identification** \\ \hline Number\_lanes & 2 & A1\_1 \\  & 3 or more & A1\_2 \\ \hline \multirow{2}{*}{Curvature} & Low & A2\_1 \\  & High & A2\_2 \\ \hline Speed\_Ego & Low (0, 60 km/h) & A3\_1 \\ \hline Rainfall level & Light & A4\_1 \\  & Heavy & A4\_2 \\ \hline Speed\_OtherVehicle.1 (km/h) & Low (0, 60 km/h) & A5\_1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of parameters and value ranges for obtaining logical situations.  
By combining the ranges of these five parameters in a systematic way (2 ranges \(\times\) 2 ranges \(\times\) 1 range \(\times\) 2 ranges \(\times\) 1 range), we obtain the eight logical situations described in Table 4.  
The number of logical situations obtained depends on the number of parameters considered and the ranges of values associated with each of them. A combinatorial explosion can be observed as soon as the parameters are numerous and the space of variation of each one is broken down into two or three ranges. This means that the decision to split or not a space of variation into ranges of values, and the identification of the constraints inherited from the ODD and the function specification, must be done carefully, and in collaboration with experts. The number of ranges for each parameter is determined according to the difference of sensitivity of the AV technologies. This procedure aims to limit the number of combinations and then the number of logical situations generated.  
Obtaining logical situations is the phase which precedes the generation of concrete scenarios. The high number of concrete scenarios that can be obtained led us to propose a sensitivity analysis of the AV to logical situations and events in order to prioritize the exploration of those that are at risk for the AV.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.5 Eps Lai  
The SAF EPS LAI product is generated on a 10-day basis at the spatial resolution of 1.1 km from the Advanced Very High Resolution Radiometer (AVHRR) sensor onboard the Meteorological-Operational (MetOp) satellite constellation. LAI is estimated with a hybrid retrieval approach that relies on the PROSAIL model inversion with a Gaussian Process Regression (GPR) [4]. The PROSAIL radiative transfer model is first applied in direct mode to build a representative database containing three short-wave channels (0.6 \(\upmu\)m, 0.8 \(\upmu\)m,1.6 \(\mu\)m) and LAI for a broad set of canopy parameterizations and observation conditions. The generated simulations are then used to train a GPR. Finally, after calibration, the GPR is applied to estimate effective LAI using as inputs the atmospherically corrected cloud-cleared 10-day nadir normalized spectral reflectance factor in the red, NIR and Mid-infrared (MIR) EPS bands.
###### Abstract  
Studying the spatiotemporal distribution and motion of water vapour (WV), the most variable greenhouse gas in the troposphere, is pivotal, not only for meteorology and climatology, but for geodesy, too. In fact, WV variability degrades, in an unpredictable way, almost all geodetic observation based on the propagation of electromagnetic signal through the atmosphere. We use data collected on a dense GPS network, designed for the purposes of monitoring the active Neapolitan (Italy) volcanoes, to retrieve the tropospheric delay parameters and precipitable water vapour (PWV). This study has two main targets: (a) the analysis of long datasets (11 years) to extract trends of climatological meaning for the region; (b) studying the main features of the time evolution of the PWV during heavy raining events to gain knowledge on the preparatory stages of highly impacting thunderstorms. For the latter target, both differential and precise point positioning (PPP) techniques are used, and the results are compared and critically discussed. An increasing trend, amounting to about 2 mm/decades, has been recognized in the PWV time series, which is in agreement with the results achieved in previous studies for the Mediterranean area. A clear topographic effect is detected for the Vesuvius volcano sector of the network and a linear relationship between PWV and altitude is quantitatively assessed. This signature must be taken into account in any modelling for the atmospheric correction of geodetic and remote-sensing data (e.g., InSAR). Characteristic temporal evolutions were recognized in the PWV in the targeted thunderstorms (which occurred in 2019 and 2020), i.e., a sharp increase a few hours before the main rain event, followed by a rapid decrease when the thunderstorm vanished. Accounting for such a peculiar trend in the PWV could be useful for setting up possible early warning systems for those areas prone to flash flooding, thus potentially providing a tool for disaster risk reduction.  
GNSS; tropospheric delay; precipitable water vapour; thunderstorm; Somma Vesuvius Weather Forecasting and Climate Monitoring
## 3 The Proposed Method
### Framework Validation Methods  
A two-phase validation process is proposed in this research. In the first phase, the performance of the Detector Module is evaluated using two separate validation methods: (1) stratified k-fold cross-validation and (2) train-validate-test split (dividing the images randomly into three sets: training, validation, and test dataset). The metrics for this validation were average precision (AP) over different IoU thresholds (e.g., AP\({}_{50}\), average precision over IoU threshold value equal to 50%) and mean average precision (mAP) [34]. Additionally, as the final classification is based on the average detector score on the sequence of frames, our framework as a whole should be validated on the unseen objects as well. Metrics such as precision, recall, and confusion matrix are calculated for each class to assess the overall framework performance.  
For the second phase, in the case study, the framework was tested in real factory settings for 17 working days in the production phase, where it monitored approximately  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline
**Module** & \multicolumn{2}{c|}{**Role**} & \multicolumn{2}{c|}{**Type**} \\ \hline Video Capture & - & Grab image data from cameras & - & Async capturing \\ \hline Detector & - & Detect objects in a frame & - & YOLOv4 \\ \hline Tracker & - & Track detected objects in the video & \multirow{2}{*}{-} & \multirow{2}{*}{Hybrid (based on IoU \& Median Flow)} \\  & - & Assign ID to each object & & \\ \cline{3-3}  & - & Average Detector score for each object & & \\ \hline Streamer & - & Stream the predictions in video format & - & Async streaming \\ \hline Classifier & - & Filter weak detections & - & Appearance thresholding \\  & - & Classify detected objects based on average score & - & Weighted voting \\ \hline Video Writer & - & Save video of an object that belongs to a specific class & - & Async video writing \\ \hline SQL Inserter and Database & - & Insert classification output into an SQL database & - & Async inserting \\  & - & Store the prediction data & - & \\ \hline UI Software & - & Retrieve prediction data from the database and show & - & Ignition\(\oplus\) \\  & - & them to the user & - & Web-based \\ \hline \end{tabular}
\end{table}
Table 1: Framework modules.  
3000 joists and a total of 111,492 nodes. The results of missed welds detections are compared with the corresponding quality control reports in Section 6.2.4.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 3 Method
### Obtain Grayscale Spatial-Temporal Map  
The process of obtaining spatial-temporal features is shown in Figure 3. In the image \(\textbf{PsI_{t}}\) and \(\textbf{RsI_{r}}\) for the convenience of observation, different grayscale values are chosen for the target.  
First, after inputting the current frame \(\textbf{I_{t}}\) into the yolov5 module, we obtain the bounding box and the segmentation mask of each target. Then, we apply these data into the foreground mask module and target-STF module to obtain the foreground mask and target-STF map, respectively. Finally, we use the foreground mask on the region-STF module to obtain the region-STF map.  
In the target-STF module, first, we create a single-channel grayscale map \(\textbf{PsI_{t}}\) of the same size as frame \(\textbf{I_{t}}\) with a grayscale value of 0. Second, according to the bounding box and the segmentation mask, we copy the mask of each target to the grayscale map with different grayscale values using Equation (1):  
\[\textbf{g}^{\textbf{v}}\textbf{p}_{\textit{Pm}_{t}^{i}}(\textbf{x},\textbf{y} )>0:\textbf{g}\textbf{v}_{\textit{PsI_{t}}}(\textbf{x},y)=i+1 \tag{1}\]  
\(\textbf{g}^{\textbf{v}}\textbf{p}_{\textit{Pm}_{t}^{i}}(x,y)\) is the grayscale value of \((x,y)\) in segmentation mask \(\textbf{Pm}_{t}^{i}\) and \(\textbf{g}\textbf{v}_{\textit{PSI_{t}}}(x,y)\) is the grayscale value of \((x,y)\) in map \(\textbf{PsI_{t}}\). After the operation is completed, we will obtain the target-STF map, and the grayscale value of target \(\textbf{p}_{t}^{i}\) is \(i+1\) in the target-STF map.  
The foreground mask module is similar to the target spatial-temporal features module. First, we create a single-channel grayscale map \(\textbf{FmI_{t}}\) of the same size as \(\textbf{I_{t}}\) with a grayscale value of 0. Then, according to the bounding box and the segmentation mask, the mask of each target is copied to \(\textbf{FmI_{t}}\); with a gray value of 255, we will obtain the foreground mask \(\textbf{FmI_{t}}\).  
In the region spatial-temporal features module, first, dilate the foreground mask, then fill it with different gray values in each white area in the foreground mask using Equation (2):  
\[(\textbf{x},\textbf{y})\in\textbf{cont}_{\textit{FmI_{t}}}^{i},\textbf{ RsI_{t}}(\textbf{x},y)=i+1 \tag{2}\]  
\(\textbf{cont}_{\textit{FmI_{t}}}^{i}\) is the set of contours in \(\textbf{FmI_{t}}\), and \((x,y)\) is the point included in \(\textbf{cont}_{\textit{FmI_{t}}}^{i}\). After the operation is completed, we will obtain the target-STF map.