esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Benford's Law's Powerful Applications in Image Forensics
## 3 Results
### The DCT Coefficients  
Firstly, the DCT coefficients are discussed. A random input image is considered and the DCT coefficients are calculated and compared to the NBL. Since the quantization is not applied yet and there is no QF involved, the original law is used. The comparison is showed in Figure 11 for a random image from the UCID database. The channel that was used is the luminance.  
Figure 10: The “double-compressed” image.  
Figure 9: The “single-compressed” image.  
The results are now presented in the same manner for a \"double-compressed\" image (an image which is originally compressed once and from which the coefficients are extracted by performing an incomplete second compression). In Figure 12, the input image is firstly compressed with \(\text{QF}=50\). Then, the DCT coefficients are calculated and compared with the NBL, using the luminance channel.  
For a better understanding, a batch of 10 images from the UCID database is considered. The DCT coefficients are again compared to the NBL. This time, the average p-value is calculated. Both cases are considered: when the images are initially uncompressed and when they are compressed with different quality factors.  
The experiment is conducted on all channels. Even if, by definition, the first step of JPEG compression consists of converting the image from RGB to \(\text{YC}_{\text{B}}\text{C}_{\text{R}}\), we also select, one by one, the R, G, and B channel and perform the same algorithm steps to extract the coefficients. The results are shown in Table 2.  
As one can observe, the DCT coefficients obey the law, even if an image was compressed before. Therefore, using this method, they do not provide valuable information in detecting fraudulent data. However, we consider that is especially important to examine them carefully since the cases when the NBL applies are determined rather experimentally than mathematically.  
Figure 11: The DCT coefficients compared to the NBL for an uncompressed image.  
Figure 12: The DCT coefficients compared to the NBL for an image originally compressed with \(\text{QF}=50\).
## 2 Related Work and Our Motivation
### Related Work
By equalizing the transmission time of all spatial streams in all MTs according to their modulation and coding (MCS) level, the authors achieved maximum performance of system throughput and minimized the space channel time in the WLAN's downlink MU-MIMO channel. However, the frame-size-determination scheme in this study did not consider the expense of delay. Although all the above proposals contributed several schemes to enhance the performance of WLANs, none of them proposed the cost of delay. To the best of our knowledge, there is little research that explored a realization of the optimal trade-off between maximizing throughput and minimizing delay in WLAN downlink MU-MIMO transmission.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Keyword Analysis of Research Hotspots  
The co-occurrence column in the keyword dataset shows the extent to which terms appear together in a body of research, demonstrating thematic grouping and research hotspots involving in-vehicle communication and mobile communication systems. Figure 6, generated using VOSviewer, shows co-occurrence keywords. The keyword \"5G mobile communication systems\" has the most outstanding co-occurrence value (607), confirming its dominance and centrality in recent studies. \"Accidents\" (489) and \"accident prevention\" (307) also follow closely behind, reinforcing the growing focus of research on vehicular safety and crash mitigation.  
Figure 5: Most cited countries.  
Figure 6: Co-occurrence keywords.  
Moreover, \"Current\" has a high co-occurrence (189), which may indicate foundational or baseline studies about vehicle technologies. The high value for \"Access control\" (178) emphasizes increasing attention to security and authentication issues about vehicular networks. Likewise, \"adaptive control systems\" (194) and \"6G\" (126) indicate the presence of next-generation intelligent network protocol tendencies.  
The emerging themes, such as \"accident detections\" (61) and \"adaptive boosting\" (78), reflect the growing interest in incorporating machine learning and predictive analytics into vehicular environments. The lower co-occurrence term of \"4G mobile communication systems\" (63) suggests a shift in focus towards newer 5G and 6G technologies. These trends suggest that safety, connectivity, and advanced communication protocols are the primary focus areas for ongoing research, as mentioned in [18].  
One emerging theme in bibliometric mapping is the increasing application of artificial intelligence (AI) and machine learning (ML) to in-vehicle communication systems. Core phrases'vehicular networks' and 'automotive cybersecurity' are regularly accompanied by deep learning, reinforcement learning, and AI-based intrusion detection. Such development depicts a dependency on models of AI for advanced predictive algorithms, maintenance, traffic, and real-time threat monitoring within in-vehicle networks. The use of publications in [18; 19], focusing on resource optimization using deep reinforcement learning and mobility-aware edge computing, highlights the influence that AI is having on the system design and efficiency of modern vehicular systems.
## 3 Results
### Geometrical Deviations Evaluation
#### 3.1.4 Flatness Deviations  
Flatness deviation results for the constant \(Z\) strategy and circular pocket strategy are presented in Figure 22, and for the constant stepover-pocket auto border strategy, the spiral strategy is shown in Figure 23 with a scale of 1000:1.  
The flatness values for each production strategy are shown in Table 6. The results show that the best equality value was obtained for the Circular pocket strategy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Section** & **Constant Z** & **Circular–Pocket** & **Constant Stepover–Pocket Auto Border** & **Spiral [mm]** \\  & **[mm]** & **[mm]** & **[mm]** & **Spiral [mm]** \\ \hline +XZ & 0.0041 & 0.0116 & 0.0112 & 0.0103 \\ +YZ & 0.0065 & 0.0089 & 0.0036 & 0.0043 \\ \(-\)XZ & 0.0111 & 0.0063 & 0.0066 & 0.0103 \\ \(-\)YZ & 0.0052 & 0.0096 & 0.0112 & 0.0114 \\ \hline Average & 0.0067 & 0.0091 & 0.0081 & 0.0091 \\ Standard deviation & 0.0031 & 0.0021 & 0.0037 & 0.0032 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured straightness deviations.  
Figure 23: Flatness deviations (**a**) constant step over-pocket auto border; (**b**) spiral strategy.  
Figure 22: Flatness deviations (**a**) constant \(Z\) strategy; (**b**) circular Pocket strategy.
## 1 Introduction
### Research Objectives  
This study aims to show the complete workflow for deploying a pre-trained DNN model from a GPU-based development platform to two popular ARM-based microcontrollers: Raspberry Pi 4 and ARM Cortex-M7. The focus is on DNN conversion and post-training quantization to create a model that is lightweight, faster, and less resource consuming. A key challenge addressed is the optimization of the representative dataset size and content for pre-inference calibration, ensuring minimal information loss during quantization. Performance is evaluated in terms of accuracy, latency, and memory usage, highlighting the feasibility of real-time ECG analysis on embedded systems. The workflow is applied to a high-performing CNN model for detection of ventricular fibrillation during out-of-hospital cardiac arrest [67]. This approach can be extended to numerous published DNN models optimized for rhythm analysis during cardiopulmonary resuscitation. Thus, we make the global link from GPU-based deep learning to AED deployment of critical ECG processing algorithms that can provide enhanced performance and improve resuscitation practices.
## 3 Results and Discussions
### \(O_{2}\)\({}^{+\bullet}\) Reagent Ion Measurements with Propofol  
O\({}_{2}\)\({}^{+\bullet}\) reacts with propofol via charge transfer leading to two product ions. The dominant one, up to about an _E/N_ of 135 Td, is found at _m/z_ 178.141, which is C\({}_{12}\)H\({}_{18}\)O\({}^{+\bullet}\) that results from non-dissociative charge transfer. Given the high recombination energy of O\({}_{2}\)\({}^{+\bullet}\) (12.07 eV) [32] and the low ionisation potential of propofol (which must be less than 9.3 eV (see below)), there will be sufficient energy in the charge transfer process to result in direct dissociation (dissociative charge transfer [33]). This leads to the second observed product ion, C\({}_{11}\)H\({}_{15}\)O\({}^{+}\), at _m/z_ 163.117, resulting from the loss of one of the methyl groups. This ion was also observed in a study of the reactions of Hg\({}^{+}\) (recombination energy 10.44 eV) with propofol [5]. In our SRI-ToF-MS, collisions of C\({}_{12}\)H\({}_{18}\)O\({}^{+}\) with the buffer gas molecules in the drift tube enhance C\({}_{11}\)H\({}_{15}\)O\({}^{+}\) production. This collision-induced fragmentation enhancement results in the fragment ion becoming the dominant ionic species above about 135 Td.  
Charge transfer processes often lead to product ions similar to those that are dominant in electron ionisation (EI) mass spectrometry (MS). This is the case for propofol, with our results being in good agreement with the 70 eV EI-MS spectrum of propofol [34]. Although the ratio of the two major ions at the nominal _m/z_ values of 163 and 178 from the EI mass spectrum differ from our observations, owing to differences in the internal energies imparted, the two mass spectra indicate the facile loss of one of the methyl groups from the molecular radical parent cation.
## 1 Introduction and Background
### Network Performance and Response Time Limits (\"Acceptable\" Waiting Time)  
Until this point we made a case that geographic data is large and despite the impressive technological developments, serving high quality data means some amount of waiting for the user. But how long do the users find it acceptable to wait, and how do we measure the quality of our service? User experience studies are helpful for the former question, and \"network performance\" measures are helpful for the latter. Network performance can be measured computationally, or based on users' _perceived performance_[25]. For both computational and perceptual measures, latency and throughput are important indicators, and often a _grade of service_ and _quality of service (QoS)_ is obtained within a project. Perceived performance is very important for the system usability; because it has implications as to how much a user is willing to wait for a download. In an earlier study [PERSON] (1968) found that users consider a response time \"instant\" if the waiting was under 0.1 seconds [26]. In 2001, Zona Research reported that if the loading time exceeds 8 seconds (plus minus two), a user would seek for faster alternatives [27]. Similarly, [PERSON] (1997) reported a 10-second rule [6], and in a 2010 study confirmed this with another usability study coupled with eye tracking [8]. In this study, [PERSON] (2010) [8] also confirms the three important response time limits that he established earlier ([PERSON] 1993) [28]: _0.1 seconds_ feels \"instant\", under _1 second_ enables \"seamless work\" and up to _10 seconds_ most users will keep their attention on the task [28].
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Screening Models
#### 3.1.6 Hssm  
The HSSM model was specifically crafted to simulate the transport of LNAPL through homogeneous mediums [392]. It is important to interpret the simulation results with a degree of caution, considering the model relies on numerous site-specific assumptions. The model encompasses the 1D vertical flow of LNAPL in the vadose zone, its movement to the water table, dispersion at the water surface, and the 2D vertically averaged flow of LNAPL through the aquifer towards various uptake points, such as in the groundwater. Although the model is available for download, it is no longer updated or supported. Despite its lack of ongoing technical support and aging, this model continues to be utilized and even customized to meet specific user needs. For instance, a modified version of HSSM has been adapted to accommodate the rectangular shape of a leak and simulate the infiltration and redistribution of NAPL from leaking tankers [393]. Present applications of this model include its integration with other models like MT3 DMS [394], predicting pollutant concentration in surface waters [395], and tracking pollutants like benzene leaking from pipelines [396].
## 2 Background
### Imputation Method for Handling Missing Values  
Missing values are one of the most frequent issues in data analysis; they can occur for many reasons such as malfunctioning sensing systems or survey questions left blank. Imputation, the process of replacing missing values, has been extensively studied, and for this study, we compared three methods, mean imputation, nearest neighbor imputation, and multiple imputation.  
Mean imputation is one of the simplest methods; it entails filling in missing values with the corresponding means [26]. Medians can be used instead of means for robustness. For categorical variables, the missing values are usually replaced with the most frequent values. Although this approach is simple and can be powerful, it has a limitation that feature variances are underestimated.  
Nearest neighbor (NN) imputation, or hot-deck imputation, replaces missing values with the corresponding variables in the closest instance [27; 28]. Because imputation based on a single nearest instance may not be robust, there have been several studies to improve NN imputation by using multiple nearest points [29; 30; 31].  
Unlike mean and NN imputation in which a missing value is replaced by a single value, multiple imputation samples a missing value multiple times from the predefined distribution [32]. Then, the multiple data sets are generated by the random sampling of missing values, and the result is obtained by an ensemble of the results of each data set. The parameters in the distribution can be estimated by expectation-maximization algorithm [33; 34] and Markov chain Monte Carlo method [35] when they cannot be found analytically.
## 2 Materials and Methods
### Selecting the Most Effective Color Features  
At the beginning of this step, a large number of 126 color features is available for each object. However, the use of all extracted features in classification is not feasible for working in real time, since the conversion to all color spaces would be a time-consuming step, besides the time required in the classifier. More importantly, the risk of overfitting during the learning phase would be very high, producing bad results in practice.  
For this reason, a preceding study is performed with the purpose of selecting the most effective among the available color features. The approach is based on a hybrid of artificial neural networks (ANNs) and the simulated annealing (SA) algorithm for the selection of effective and discriminative features. The ANN is a classical multilayer perceptron (MLP), whose parameters are defined in Table 3. This classifier is controlled by a metaheuristic SA process that tests different combinations of the features in a controlled way.  
In detail, the process is as follows. First, all extracted scalar features are considered as a 126-vector; each of them is associated with a class label from 1 to 4, according to its ripening stage, by a human expert. In the next step, smaller size vectors of features are obtained, for example, vectors with 7, 12, or 18 features, by means of an automatic SA optimization algorithm. These reduced vectors (input) and the corresponding labels (output) are sent to the MLP neural network for training and testing. Specifically, the dataset is divided into disjoint sets of 70% for training, 15% for validation, and the remaining 15% for testing. As a result, the mean square error (MSE) obtained for the test set for each combination of features is measured, and used as the criterion for selecting the most effective features. The metaheuristic SA method controls the selection of the combination of features, according to the MSE values. Finally, the vector of features which has the lowest value of the MSE is selected as the optimal set of color features for the following step.
###### Abstract  
This research addresses the challenges of visually impaired individuals' independent travel by avoiding obstacles. The study proposes a distance estimation method for uncontrolled three-dimensional environments to aid navigation towards labeled target objects. Utilizing a monocular camera, the method captures cuboid objects (e.g., fences, pillars) for near-front distance estimation. A Field of View (FOV) model calculates the camera's angle and arbitrary pitch relative to the target Point of Interest (POI) within the image. Experimental results demonstrate the method's proficiency in detecting distances between objects and the source camera, employing the FOV and Point of View (POV) principles. The approach achieves a mean absolute percentage error (MAPE) of 6.18% and 6.24% on YOLOv4-tiny and YOLOv4, respectively, within 10 meters. The distance model only contributes a maximum error of 4% due to POV simplification, affected by target object characteristics, height, and selected POV. The proposed distance estimation method shows promise in drone racing navigation, EV autopilot, and aiding visually impaired individuals. It offers valuable insights into dynamic 3D environment distance estimation, advancing computer vision and autonomous systems.  
distance estimation; navigation aid; object detection; field of view; visual impairment; computer vision +
Footnote †: journal:  
Article  
A Lightweight Robust Distance Estimation Method for Navigation Aiding in Unsupervised Environment Using Monocular Camera
# Secure PD-NOMA with Multi-User Cooperation and User Clustering in Both Uplink and Downlink PD-NOMA
## 5 Conclusions and the Future Work  
The most essential design issues for the PD-NOMA to perform well in both uplink and downlink scenarios are secure communication, cooperation, and power allocation through user clustering. This study describes a spread-based technique using short codes for secure PD-NOMA communication against eavesdroppers who are not registered. The proposed study utilizes the D&F Cop-PD-NOMA technique to improve the performance of weak users. In addition, a low-complexity, three-step user clustering technique is presented to reduce the outage probability that occurs in PD-NOMA systems. The proposed study can be expanded in future work to incorporate more cutting-edge methods that can help to provide secure communication in denser NOMA networks. This strategy can be expanded to accommodate the dynamic PD-NOMA environments' frequency and/or time selectivity. Furthermore, substantial adaptive user clustering can be used to find the best relay stations for cooperation. Additionally, the more advanced PLS techniques will help to determine the optimum solution to the challenges that expose PD-NOMA systems to vulnerability. Overall, the proposed study lays the groundwork for reliable and secure communication in PD-NOMA networks.  
Conceptualization, A.M., M.M. and H.M.; formal analysis, M.M. and M.M.N.; data curation, A.M. and M.A.E.; investigation, A.M. and M.M.; methodology, A.M. and H.M.; project administration, M.M.; supervision, M.M., H.M., M.M.N. and M.A.E.; validation, A.M.; writing--original draft, A.M.; writing--review and editing, M.M., M.M.N. and M.A.E. All authors have read and agreed to the published version of the manuscript.  
Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R137), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) for this publication.  
The authors declare no conflict of interest.
# Spatial Data Reconstruction via ADMM and Spatial Spline Regression +
## 4 An ADMM Algorithm for Constrained Spatial Smoothing
For the \(\mathbf{f}\)-update step in each iteration, Equation (15) is equivalent to  
\[\underset{f}{\text{minimize}}\quad\left\|\left(\boldsymbol{\alpha}^{\mathsf{T }}+\rho\mathbf{g}^{\mathsf{T}}\right)/2-\mathbf{f}\right\|_{2}^{2}+\lambda \int_{\Omega}(\
abla^{2}f)^{2}\,\mathrm{d}\mathbf{p}, \tag{19}\]  
which is exactly the form of (8) with \(h_{j}=\left(\alpha_{j}+\rho g(\mathbf{p}_{j})\right)/2\) and \(\mathbf{w}_{j}=0\), thus can be solved efficiently by SSR. It should be noted that \(\lambda\) is the penalty parameter which controls the smoothness of \(f\). If it is small, we put little emphasis on the smoothness, and the estimated surface \(f\) will be over fitted. If it is too big, the surface will be too smooth, which can cause underfitting. For the case with attributes, the algorithm does not require major changes. We just need to replace \(\mathbf{f}\) by \(\mathbf{f}+\mathbf{W}\boldsymbol{\beta}\) in (19), where \(\mathbf{W}:=(\mathbf{w}_{1},...,\mathbf{w}_{n})^{\mathsf{T}}\) represents the attributes and \(\boldsymbol{\beta}\) is the corresponding contributions. Our proposed ADMM training algorithm is able to efficiently reconstruct the spatial field and fit the covariates for our constrained spatial sparse recovery problem. In \(\mathbf{g}\)-update step, it enforces the constraints by solving a constrained QP with no need to worry about smoothing; in a \(\mathbf{f}\)-update step, it approximates the obtained \(\mathbf{g}\) with a smooth \(f\) using the SSR-based smoothing technique. In this way, we decouple the handling of smoothing and constraints which was not possible in pure SSR previously.
## 2 Materials and Methods
### Datasets for Model Evaluation
#### 2.4.1 fAPAR3g  
The Global Inventory Modeling and Mapping Studies third generation GIMMS fAPAR3g product is used to calculate the Absorbed Photosynthetically Active Radiation (APAR). The fAPAR3g GIMMS satellite data is based on Normalized Difference Vegetation Index (GIMMS NDVI3g [40]), derived from the Advanced Very High Resolution Radiometers (AVHRR) aboard a series of US National Oceanic and Atmospheric Administration (NOAA) satellites. The spatial-temporal resolution of the dataset is 8 km and has a 15-day time step. The original 8-km fAPAR3g data are re-aggregated to the ORCHIDEE resolution of 0.5\({}^{\circ}\) using bilinear interpolation.