esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 5 Experimental Results
### Exploring an Optimal Architecture--CarNet, by Making Experiments on PKLot Dataset
However, we found that an \(11\times 11\) kernel size is the most appropriate option for the parking space occupancy detection problem. \begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Name of Method**} & **Training Accuracy** & **Validation Accuracy** \\ \hline CarNet (window size = 3) & 95.63\% & 88.78\% \\ CarNet (window size = 5) & 95.89\% & 88.35\% \\ CarNet (window size = 7) & 96.13\% & 89.21\% \\ CarNet (window size = 9) & 96.63\% & 90.55\% \\ CarNet (window size = 11) & **97.23\%** & **90.70\%** \\ CarNet (window size = 13) & 97.06\% & 90.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of training and validation accuracies for experiments with different window sizes. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Name of Method**} & **Name of Training Subset** & \multicolumn{3}{c}{**Testing Accuracy**} \\ \cline{3-5}  & & **PUCPR** & **UFPR04** & **UFPR05** \\ \hline \multirow{3}{*}{CarNet (with window size 3)} & PUCPR & 92.60\% & 92.80\% & 95.70\% \\  & UFPR04 & 92.90\% & 94.10\% & 94.50\% \\  & UFPR05 & 92.10\% & 94.50\% & 95.10\% \\ \hline \multirow{3}{*}{CarNet (with window size 5)} & PUCPR & 94.80\% & 93.40\% & 92.40\% \\  & UFPR04 & 94.20\% & 93.60\% & 92.90\% \\  & UFPR05 & 95.30\% & 94.10\% & 95.10\% \\ \hline \multirow{3}{*}{CarNet (with window size 7)} & PUCPR & 96.20\% & 94.20\% & 96.30\% \\  & UFPR04 & 96.30\% & 94.80\% & 95.10\% \\ \cline{1-1}  & UFPR05 & 96.30\% & 95.20\% & 95.90\% \\ \hline \multirow{3}{*}{CarNet (with window size 9)} & PUCPR & 92.70\% & 92.20\% & 93.90\% \\  & UFPR04 & 94.90\% & 95.30\% & 93.70\% \\ \cline{1-1}  & UFPR05 & 93.10\% & 94.30\% & 94.20\% \\ \hline \multirow{3}{*}{CarNet (with window size 11)} & PUCPR & **98.80\%** & **94.40\%** & **97.70\%** \\ \cline{1-1}  & UFPR04 & **98.30\%** & **95.60\%** & **97.60\%** \\ \cline{1-1}  & UFPR05 & **98.40\%** & **95.20\%** & **97.50\%** \\ \hline \multirow{3}{*}{CarNet (with window size 13)} & PUCPR & 97.40\% & 93.30\% & 95.80\% \\ \cline{1-1}  & UFPR04 & 97\% & 95.40\% & 96.20\% \\ \cline{1-1}  & UFPR05 & 97.10\% & 95.10\% & 96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of testing accuracies for experiments with different window sizes. Comparison of CarNet with Respect to Well-Known Architectures by Making Experiments on PKLot and CNRPark + EXT Datasets  
Last but the most important type of experiments we made are to compare the results of our network with those of other well-known architectures, including AlexNet, which showed a noteworthy performance on the current task. To assess proposed architecture, we evaluated our model with two datasets, PKLot and CNRPark + EXT. However, we only used PKLot dataset to build most reliable model. In this section, experiments on CNRPark + EXT are also provided to consider how well generalized our model is.
# Using Graph Neural Networks for Social Recommendations
## 3 Methodology
### RelationalNet Algorithm Architecture
#### 3.2.1. Constructed Graphs  
We now review in more detail the four distinct graph structures that our model uses to facilitate the training process for predicting unknown ratings as illustrated in Figure 2. These graph structures encompass the following:  
* User-user graph \(G_{\mathrm{S}}\) : The user-user graph captures the social interactions between users by establishing links between them. This graph emphasizes the significance of user preferences influenced by their social connections. For instance, within the Yelp dataset, if user A follows users B and C, we create directed graph edges (links) from user A to user B and user A to user C.
* Item-item graph \(G_{\mathrm{T}}\): The item-item graph establishes links between items that exhibit similarities to each other. This graph captures the relationship between items based on shared characteristics. For example, within the Yelp dataset, if two restaurants serve similar food categories, a link is established between these restaurants.
* User-consumed-items graph \(G_{R}\): The user-consumed-items graph is formed based on the interactions between users and items derived from each user's past behavior as given from the utility matrix \(R\). It represents the items that a user has interacted with. For example, if user A watches the movies _Avengers_ and _Avatar_, connections (links) are established between user A and the movie _Avengers_ and between user A and the movie _Avatar_.
* Item-consumed-users graph \(G^{\prime}_{R}\): The item-consumed-users graph connects items with the users who have interacted with them as given from the utility matrix \(R\). It signifies the users who have engaged with a particular item. For instance, if the movie _Avengers_ is watched by users A and B, links are created between the _Avengers_ movie and user A, as well as between the _Avengers_ movie and user B. Note that graphs \(G_{R}\) and \(G^{\prime}_{R}\) are the same bipartite graph with the emphasis on user nodes and item nodes, respectively.  
Compared to existing models, the RelationalNet model introduces the incorporation of item relations through the item-item graph \(G_{\mathrm{T}}\) and item-consumed-users graph \(G^{\prime}_{R}\). Similar to how users learn from their neighboring users in the user-user graph \(G_{\mathrm{S}}\), items may also learn from their neighboring items. This inclusion of item relations enhances the model's capacity to capture complex item-item dynamics, thereby improving the quality of recommendations generated by the RelationalNet model.
# Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks
## 3 Background
### Pearson's Correlation Method  
Although feature selection is an important aspect in the ML field, the abundancy of features in a dataset can increase model complexity and extend training durations. To enhance efficiency and ensure effective attack identification, it has become imperative to transform collected data into a more streamlined dimension [57]. Various feature selection strategies exist in ML, including but not limited to backward feature selection, chi-square, and recursive feature removal, and each considers specific dataset characteristics, dimensionality, and correlation [58]. Our chosen approach for feature extraction revolves around Pearson correlation coefficient method. This method evaluates variable correlation by illustrating the linear relationship between dataset features [39]. Ranging between \(-1\) and \(+1\), the correlation coefficient denotes the strength and nature of the relationship between variables. A value of '0' means that there is no relationship, while '1' refers to a strong positive correlation, and '\(-1\)' indicates a strong negative correlation. Higher absolute values that are closer to '\(+1\)' imply strong relationship between variables [59]. This method is used to identify feature relationships that are vital to get a robust model with high accurate intrusion detection rate.
## 3 Laboratory Tests
### Soils Sampled in Ny-Alesund  
The engineering properties of soils sampled at P3 were investigated. The index properties obtained at three different depths including those reported by [PERSON] et al. (2016) are summarized in Table 1. The measured specific gravities of the soils sampled at depths of 40 and 80 cm were greater than those of soils sampled at the surface, as the soils sampled at the surface included the organic materials. For the grain-size distribution, the soils were divided into coarse particles and fine particles with a No. 40 sieve. A sieve analysis and a laser diffraction particle size analyzer were used to obtain the grain-size distribution curves of the coarse particles and fine particles, respectively. Figure 4 shows the grain-size distribution curves of the soils obtained at three different depths. The median grain size of the soil sampled at a depth of 0 cm was slightly smaller than those sampled at depths of 40 and 80 cm. Note that most of the glacial deposits have high variability even within short vertical or horizontal distances (Svalbard, 2016). The large uniformity coefficient from 46.4 to 98.7 represents a wide grain-size distribution of glacier-deposited soils. The natural water content in fine-grained soils affecting the soil engineering behavior should be evaluated in comparison with Afterberg limits. Liquid limit and plastic limit were determined from BS 1377 (Svalbard, 2016) and ASTM D 4318 (Svalbard, 2016), respectively. Considering the Unified Soil Classification System (USCS), the sampled soils are classified as silty sand (SM).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Depth** & **Specific** & \begin{tabular}{c} **Median** \\ **Diameter** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Gradation** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **Uniformity** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **Gravimetric** \\ **Water Content** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Liquid** \\ **Limit** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Plastic** \\ **Limit** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Plastic** \\ **Index** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **USCS** \\ **(\%)** \\ \end{tabular} \\ \hline
0 & 2.56 & 1.04 & 3.2 & 46.4 & 16.9 & 30.0 & 25.4 & 4.6 & SM \\
40 & 2.68 & 2.29 & 1.4 & 98.7 & 5.5 & 31.6 & 24.5 & 7.1 & SM \\
80 & 2.66 & 2.18 & 1.0 & 59.1 & 5.8 & 33.4 & 23.8 & 9.6 & SM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index properties of the soil samples with depth.  
Figure 3: Maps of the study area: (**a**) for Ny-Alesund research district; (**b**) enlarged for the location of the field test. P1, P2, and P3 denote the three different positions. PTDR and ERT indicate the tested positions of PTDR application and electrical resistivity testing.
# An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
###### Abstract  
The single-frequency precise point positioning (PPP) technique has attracted increasing attention due to its high accuracy and low cost. However, a very long convergence time, normally a few hours, is required in order to achieve a positioning accuracy level of a few centimeters. In this study, an approach is proposed to accelerate the single-frequency PPP convergence by combining quad-constellation global navigation satellite system (GNSS) and global ionospheric map (GIM) data. In this proposed approach, the GPS, GLONASS, BeiDou, and Galileo observations are directly used in an uncombined observation model and as a result the ionospheric and hardware delay (IHD) can be estimated together as a single unknown parameter. The IHD values acquired from the GIM product and the multi-GNSS differential code bias (DCB) product are then utilized as pseudo-observables of the IHD parameter in the observation model. A time varying weight scheme has also been proposed for the pseudo-observables to gradually decrease its contribution to the position solutions during the convergence period. To evaluate the proposed approach, datasets from twelve Multi-GNSS Experiment (MGEX) stations on seven consecutive days are processed and analyzed. The numerical results indicate that the single-frequency PPP with quad-constellation GNSS and GIM data are able to reduce the convergence time by 56%, 47%, 41% in the east, north, and up directions compared to the GPS-only single-frequency PPP.  
precise point positioning; single-frequency; convergence; GNSS +
Footnote †: journal: Sensors
# Geovisualization of Buildings: AI vs. Procedural Modeling
## 3 Comparative Analysis, Results, and Discussion
### Implications
Parametric modeling software such as Grasshopper for Rhino or Autodesk Dynamo provides intuitive visual interfaces for creating parametric designs and automating repetitive tasks. * Rule-Based Systems: Rule-based systems enforce design rules and constraints to ensure compliance with architectural guidelines and regulatory requirements. These rules may encompass zoning regulations, building codes, accessibility standards, and environmental considerations. By integrating rule-based validation checks into the modeling workflow, errors and violations can be detected early in the design process, reducing costly revisions and ensuring compliance with legal mandates. * Real-time Feedback and Iteration: Real-time feedback mechanisms enable designers to interactively manipulate building models and receive immediate visual feedback on design changes. Graphical user interfaces (GUIs) or virtual reality (VR) environments provide intuitive interfaces for exploring and refining designs in real time. User inputs such as gestures, voice commands, or haptic feedback can be captured and interpreted to dynamically adjust model parameters and geometry. * Simulation and Visualization: Simulation and visualization tools facilitate the evaluation and validation of building models through realistic rendering, lighting analysis, and virtual walkthroughs. Building information modeling (BIM) software platforms such as Autodesk Revit or Trimble SketchUp integrate simulation engines for simulating structural performance, daylighting conditions, thermal comfort, and other architectural metrics. By visualizing design outcomes in immersive 3D environments, stakeholders can gain insights into the spatial qualities and functional aspects of proposed buildings. * Scalability and Adaptability: The scalability and adaptability of automated procedural building modeling systems are essential for accommodating diverse project requirements and scales. Cloud-based infrastructure and distributed computing technologies enable parallel processing and resource scalability to handle large-scale modeling tasks efficiently. Furthermore, modular software architectures and open-source frameworks foster interoperability and extensibility, allowing developers to integrate custom modules and adapt the system to specific domain contexts. Automated procedural building modeling harnesses the synergistic capabilities of AI, machine learning, online databases, and advanced computational techniques to streamline the design process, empower designers with creative tools, and accelerate the realization of architectural visions. By leveraging data-driven insights and computational intelligence, architects and urban planners can address complex design challenges, optimize resource utilization, and create sustainable built environments that enrich the lives of inhabitants and communities.
# A Multi-Machine and Multi-Modal Drift Detection (M2D2) Framework for Semiconductor Manufacturing
## 3 Methodology
### Phase 0: Data Preprocessing  
Before any modeling can begin, we must consolidate numeric sensor readings, textual maintenance records, and categorical batch/shift labels into a coherent multi-modal feature set, as shown in Table 1. At the same time, we must filter out noise, address missing values, and standardize the representation [16].  
1. Multi-modal Integration: Text (maintenance logs) is converted into embeddings \(e^{(m)}_{text}(t)\in\mathbb{R}^{d_{text}}\) (using Word2 Vec, Doc2 Vec, or BERT [17]); categorical data (e.g., batch ID) is transformed into one-hot or learned embeddings \(e^{(m)}_{text}(t)\in\mathbb{R}^{d_{text}}\)[18]; numeric sensor readings \(e^{(m)}_{num}(t)\) are then concatenated with these embeddings (as shown in Table 2): \[\widetilde{\mathbf{x}}^{(m)}(t)=\left[\mathbf{x}^{(m)}_{num}(t)\middle\|e^{(m) }_{text}(t)\middle\|e^{(m)}_{cat}(t)\right]\] (2)
2. Denoising and Imputation: Missing sensor entries or misaligned timestamps are handled via linear interpolation or autoencoder-based inpainting [19].
3. Normalization: Each dimension (including text/categorical embeddings) is standardized, for instance via Z-score: \[z^{(m)}_{i}(t)=\frac{\widetilde{\mathbf{x}}^{(m)}(t)-\mu_{i}}{\sigma_{i}}.\] (3)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **Machine ID** & **Recipe ID** & **Wafer ID** & \begin{tabular}{c} **Product** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Temperature** \\ **Parameter** \\ \end{tabular} & \
\begin{tabular}{c} **Pressure** \\ **Parameter** \\ \end{tabular} &
\begin{tabular}{c} **Current** \\ **Parameter** \\ \end{tabular} & **Maintenance Log** \\ \hline
1 January 2025 & Machine 1 & Recipe \(A\) & 001 & Type \(X\) & 850 & 1.5 & 2.1 & \({}^{*}\)_Normal operation\({}^{*}\)_ \\ \hline
1 January 2025 & Machine 2 & Recipe \(A\) & 002 & Type \(Y\) & 860 & 1.6 & 2.2 & \({}^{*}\)_Pressure anomaly\({}^{*}\)_ \\ \hline
1 January 2025 & Machine 1 & Recipe \(B\) & 003 & Type \(X\) & 900 & 1.8 & 2.5 & \({}^{*}\)_Current fluctuations\({}^{*}\)_ \\ \hline
1 January 2025 & Machine 2 & Recipe \(B\) & 003 & Type \(Z\) & 920 & 1.9 & 2.6 & \({}^{*}\)_Normal operation\({}^{*}\)_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example data table for drift detection.  
This produces a cleaned and merged multi-modal feature vector, \(z_{i}^{(m)}(t)\in\mathbb{R}^{D}\), ready for baseline modeling in the next phase.
## 3 Results
### Dimensionality of the Spectral Data
#### Linear Methods:  
First, the complete spectral information with a number of 2,151 bands was used in a correlation analysis. Correlation coefficients of each band combination were calculated and the frequency distribution of their absolute values is presented in Figure 4 (left). It can be seen that the majority of values (\(\sim\)80%) are correlated to each other with absolute values greater than 0.95, indicating the redundancy in the information content. This is also highlighted by the results of a principal component analysis (PCA) over the whole set of bands, giving a first principal component (PC) with a loading of 99.4% and a second and third PC explaining no more than 0.38% and 0.21% of total variance, respectively (without figure).  
Analyzing the correlation between selected spectral indices (see Table 1), it can be seen, that the proportion of pairs with correlation showing absolute values close to 1 is reduced, however the PCA analysis (without figure) shows an even higher loading of the first PC (99.99%). The correlation and  
Figure 3: Temporal development of leaf area index **(a)** and of vegetation height **(b)** for Spring Barley (o) and Sugar Beet (+), plotted with regards to applied WC and N treatments (indicated in an upper right corner of all plots), BL 2008 Experiment.  
the PCA-analysis for both, the individual spectral bands and the pre-selected spectral indices, suggest only a very low number of variables (here 1-3, dependent on the threshold value) needed to represent the spectral database information. This, however, only holds as long as there is no significant non-linearity present in the data that is not implicitly included by the use of complex spectral indices and that cannot be recovered by linear methods.
## 1 Introduction
Related studies that have developed alternative methods for luminescence imaging in PV power plants are summarized in Table 1. When measurements are performed during daytime, extracting the EL or PL signal from the background becomes challenging, as sunlight emission intensity is significantly higher than luminescence signal intensity [12; 13]. Therefore, InGaAs cameras are used for high-irradiance measurements to maximize the luminescence-to-background signal ratio. Additionally, the use of a suitable bandpass filter to block unwanted wavelengths improves this ratio and enhances measurement quality [17]. Daylight measurements typically involve modulating the PL or EL signal and acquiring a sequence of images in which luminescence emission alternates between high and low signal intensity states. The image sequence is digitally processed for filtering and background removal. Modulation is primarily achieved by adjusting the operating point of the PV device using different methods [12]. Between short-circuit (SC) and maximum power point (MPP) conditions, luminescence emission is nearly absent. Maximum PL emission occurs near open-circuit (OC) conditions, while EL emission is achieved when PV devices operate at voltages higher than OC voltage, inducing a reverse current and causing the module to behave as a load. Therefore, by appropriately adjusting the operating point of the PV devices under test, it is possible to modulate both EL and PL signals. Table 1 also presents the main features of each technique, including their capability to perform measurements during night, day or both, as well as whether they are based on EL or PL imaging. It also highlights whether signal modulation, and therefore a sequence of images, is required or not. Additionally, the table shows the hardware required for each approach and whether measurements can be performed during the normal operation of the power plant without disconnections. Finally, the type of camera and optical filter used in each case is also included. This article presents a significant improvement over a previously developed technique [34]. The novelty of the presented approach relies on the generation of an arbitrary current signal in a solar module by using the string current as an energy source for self-powered signal modulation and image acquisition. This method is based on an electronic board connected within the PV string, enabling EL measurement without disconnections and during the normal operation of the power plant. Additionally, an FFT approach is introduced to remove the background signal while the EL signal is modulated according to any periodic signal, which means another improvement in comparison with the previous
# A High-Resolution Texture Mapping Technique for 3D Textured Model
###### Abstract  
We proposed a texture mapping technique that comprises mesh partitioning, mesh parameterization and packing, texture transferring, and texture correction and optimization for generating a high-quality texture map of a three-dimensional (3D) model for applications in e-commerce presentations. The main problems in texture mapping are that the texture resolution is generally worse than in the original images and considerable photo inconsistency exists at the transition of different image sources. To improve the texture resolution, we employed an oriented boundary box method for placing mesh islands on the parametric (UV) map. We also provided a texture size that can keep the texture resolution of the 3D textured model similar to that of the object images. To improve the photo inconsistency problem, we employed a method to detect and overcome the missing color that might exist on a texture map. We also proposed a blending process to minimize the transition error caused by different image sources. Thus, a high-quality 3D textured model can be obtained by applying this series of processes for presentations in e-commerce.  
Keywords:conformal mapping; mesh parameterization; mesh partitioning; pixel extraction; texture mapping +
Footnote †: journal: Applied Sciences
###### Abstract  
This paper presents a method for estimating the parameters of the dynamic error signal in a measurement chain. The method is based on knowledge of the transmittance of subsequent elements in the chain and analysis of the spectrum of the processed signal. The proposed approach allows for determining the parts of uncertainty budget of the measurement chain related with dynamic errors, considering the currently processed signal and the calculation of the expanded uncertainty associated with the dynamic error signal. The paper describes the proposed error model, the algorithm for estimating the error signal parameters, and detail measurement and simulation experiments conducted to verify the effectiveness of the proposed method.  
uncertainty estimation; expanded uncertainty; error model; dynamic error; numerical algorithms; signal processing error parameter estimation; digital signal processing Article