esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Pairwise Clock Synchronization under Unknown Random Delays  
Although the assumptions of Gaussian and exponential distributions for the random delay component of WSNs are plausible, it might happen that the underlying PDF of the network random delays is unknown in advance. In fact, it is well known that the distribution of the network random delays is difficult to characterize succinctly [30]. Therefore, the performance of estimators particularly designed for a certain distribution may degenerate greatly under another type of distribution, and there is a need for developing efficient estimation methods that are robust to the unknown random delays of WSNs. Based on the classic two-way message exchange mechanism, we describe several robust estimation methods in this section, namely the bootstrap bias correction [30], the composite particle filtering [38] and the least squares estimator [39].
### Motivation and Contributions
Compared with the existing work, these simulation and analysis works are innovative as such a systematic and comprehensive performance analysis has not been reported in the existing hybrid FSO/RF literature. The paper is structured as follows. In Section 2, we present the system model of the hybrid system with atmospheric turbulence and pointing errors. In Section 3, we analyze the communication performances of the hybrid system, and the only FSO link or the only RF link. In Section 4, we analyze the security performances of the hybrid system with FSO link eavesdropping and RF link eavesdropping. In Section 5, we show the simulation results around the performances of the hybrid system. In Section 6, we finally summarize the whole paper.
# A Novel Fragmented Approach for Securing Medical Health Records in Multimodal Medical Images
## 3 Materials and Methods
### Dataset Description  
This research introduces an efficient method to securely send medical images with patient personal information and physician prescriptions embedded inside images without degrading the image quality through the use of a novel steganography technique. The proposed method is applied by hiding texts inside Brain MRI scans. Therefore, in this  
Figure 1: System model of the proposed technique for securely sharing medical image data along with patients’ prescriptions for research purposes.  
research, we utilized two datasets: one containing digital images and another containing text files. The first dataset consists of 3255 MRI scans of 21 patients, each having 155 slices of Brain MRI Flair scans. The dimensions of each of these images are 240 \(\times\) 240 grayscale, and they are saved in PNG format. The Brain MRI dataset was acquired from the MICCAI BraTS, which was introduced as a competition in the MICCAI (Medical Image Computing and Computer-Assisted Intervention) conference in 2012 [32; 33; 34; 35]. The data have been updated and revised multiple times, including in 2015 and 2020. The MRI scans in this dataset include multiple imaging modalities: T1-weighted, T1-weighted with contrast enhancement (T1c), T2-weighted, and Fluid Attenuated Inversion Recovery (FLAIR). These images are preprocessed to a uniform resolution. The second dataset contains plain text files of sample patient personal information and physician prescriptions. The size of those text files ranges from 300 to 5000 characters. Figure 2 shows MRI sample slice sets for different patients acquired from the first dataset, while Tables 1 and 2 show the sample patient information and a prescription that will be hidden inside MRI scans.
# Physical and Mathematical Modeling of the Interaction of Water Droplets and High-Speed Gas Flow
## 4 Conclusions  
As a result of physical and mathematical modeling of the interaction of water droplets with a high-speed gas flow, the following was established.  
1. The piecewise-specified function of the gas velocity and density distribution along the trajectory of liquid droplets with a discontinuity at the intersection of the droplet trajectories with an oblique shock wave can be used for one-dimensional calculations. The velocity and gas density changes at the point where the trajectory of a drop with an oblique shock wave is intercepted can be about 100 m/s and 0.1 kg/m\({}^{3}\), respectively. The coordinate of the intersection of the droplet trajectories and shock wave for the considered experimental setup is in the range from 0.01 to 0.04 m depending on the flow Mach number.
2. The presence of an oblique shock wave has an uncertain effect on the rate of liquid droplets gas-dynamic breakup. An oblique shock leads to a decrease of the gas velocity and corresponding decrease of the gas-dynamic breakup rate for almost all pressures of liquid supply in front of the nozzle at Mach numbers less than 2.5. The increase of flow velocity to M = 3 leads to the process of destruction of drops behind the shock wave intensification.
3. On the basis of a comprehensive analysis of the calculated and experimental data the mathematical model that describes the intensity of gas-dynamic destruction of droplets with a diameter of 5 to 85 microns in a flow with Mach numbers M = 2-3 is proposed. The mathematical model takes into account the influence of the Mach, Weber, Laplace and Reynolds numbers on the rate of gas-dynamic fragmentation of droplets in a high-speed flow. The proposed mathematical model gives a discrepancy with experiment of no more than 3.6%.  
The obtained computational and experimental data and the proposed mathematical model make it possible to estimate the intensity of liquid droplets gas-dynamic breakup in high-speed flows.  
Conceptualization, K.A.; methodology, K.A.; validation, A.M. and A.S.; formal analysis, A.M., O.G. and K.A.; investigation, A.M. and O.G.; resources, A.S.; data curation, A.S.; writing--original draft preparation, A.M., O.G. and K.A.; writing--review and editing, A.M. and O.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation Grant No. 19-49-02031 And the APC was funded by Grant No. 19-49-02031.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PIV & Particle Image Velocimetry \\ Nd:YAG & Neodymium-doped Yttrium Aluminum Garnet; Nd:Y\({}_{3}\)Al\({}_{5}\)O\({}_{12}\) \\ RANS & Reynolds-Averaged Navier-Stokes \\ AUSM & Advection Upstream Splitting Method \\ SW & Shock Wave \\ \end{tabular}
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### gMIMO
#### 3.1.6 Example Designs  
The new and improved capabilities of gMIMO offer a large number of innovative designs. Although they come with certain disadvantages, they show great potential in dealing with challenges related to the future of wireless communications.  
One promising design is the quasi-fractal uniform circular array antenna (QF-UCA MIMO), in which the array consists of inner UCA arrays and an inter UCA, which, in turn, consists of inner UCAs. Its circular symmetry is essential for spatial multiplexing and accurate beamforming. This type of MIMO and its configurations have shown potential for high-capacity transmission, low complexity demodulation, and easy and accurate CSI estimation. However, it requires precise geometric alignment and calibration (ref. [36]).  
In lower frequencies (sub-1 GHz), we can use the mmMIMO, as mentioned before. The mmMIMO is a type of antenna design where different antenna modules, like building blocks, are used in different configurations. This type of design allows us to isolate any potential defective part of the configuration and replace it without reconstructing the whole antenna design from scratch. However, the advantages of mmMIMO are limited to the lower frequencies (ref. [35]).  
gMIMO also gives better opportunities in localizing and detecting the UE. One new way to accurately localize a user is to divide the antenna array into smaller subarrays and position them at certain distances. All subarrays focus their beams on the location of the user, thereby achieving a satisfying localization and beam focusing level. This divided type of gMIMO can be used in THz and mmWave bands. Their usage, however, is limited in the near-field area, which, in turn, is limited to just tens of meters away from the gMIMO subarrays (ref. [27; 31]).  
One innovative approach focusing on the user experience is the end user collaborative MIMO(UE-CoMIMO). This design focuses on combining different antenna elements from multiple devices (smartphones, XR glasses, and other wearables) to create a virtual large antenna array. Thus, personal devices do not necessarily need to be equipped with largerantenna arrays. The UE-CoMIMO supports various use cases, including diversity, rank, and localization augmentations, across indoor and outdoor scenarios; significant improvements in diversity, channel rank, and localization accuracy, even with limited-capability devices; and reduces latency and energy requirements. However, it requires precise synchronization and advanced processing for inter-device collaboration, and increasing implementation complexity along with collaboration with outdoor public devices may induce suffering from frequent handover issues, impacting performance during mobility (refs. [37; 38]).  
The stated example designs are presented in compact form in Table 2.
## 4 Remote Sensing Image Scene Retrieval  
Remote sensing image scene retrieval (RSISR) is also an important application of content-based image scene retrieval (CBISR). Remote sensing (RS) image retrieval, by analyzing the visual content, is crucial for mining geological information. Finding the specific region from a large number of RS images needs to be done before producing applications of RS. A fundamental flowchart of the RSISR method includes two indispensable blocks: feature learning or extraction, which is aimed to map the image which is queried or in the dataset into the feature space, and similarity matching (i.e., feature searching, using an appropriate measure to judge the similarities between the query image and images in the dataset). A block named \"relevance ranking\" is a popular post-processing method for RSISR, which focuses on enhancing the original performance of retrieval. However, it is essentially an interactive block of similarity-matching using the feedback information of users. As this block is also used in many subsequent articles, we will not repeat it. The overall architecture of scene retrieval is shown in Figure 3.  
In the block of feature learning, the low-level (e.g., GLCM) and mid-level features (e.g., Bag-of-Words) are always traditional and handcrafted [53]. Meanwhile, since the same type of scenes might emerge at different scales, orientations, and illuminations, relevant images of diverse appearance are difficult to be retrieved. An increasing amount of triumphant applications prove that the features learned by the rapidly-growing deep learning (e.g., CNN) are more effective for narrowing the semantic gap and describing the RS images. Deep learning can excavate intricate structure in huge data sets, and automatically learn feature representations optimally and powerfully. These kinds of features are, namely, deep features [54; 55]. The feature fusion and the dimension reduction can be done in this block, and the following RSISR methods are merely about deep features in the block of feature representation. As Figure 3 reveals, we transfer the task of RS image retrieval to the task of feature retrieval.  
In the block of similarity matching, features excavated from the images in the dataset are compared to features excavated from the query image [56]. The images in the dataset are ranked according to decreasing similarity. In the methodology section, we will talk about distance measures, the graph models, and hash learning, respectively. The block of similarity matching is what we will focus on.
## 4 Discussion  
Autocrine regulation of insulin secretion by GABA may not be a primary regulatory mechanism for insulin secretion. [PERSON] et al. (2013) previously described the co-secretion of GABA and zinc from \(\beta\)-cells. They reported that during insulin secretion, GABA receptors in human \(\beta\)-cells enhanced membrane depolarization, increasing [Ca\({}^{2+}\)]i when GABA was coupled with GABA\({}_{\text{A}}\) receptors, as if the coupling may be a \"redundant\" secretion pathway. In the current study, we have shown that both extracellular GABA and zinc influence insulin secretion through membrane depolarization when ZIP\(3\) levels are \"optimal\" in SHED \(\beta\)-cells. These results are consistent with a recent report describing GABAergic regulation of the antidiabetic effects of exogenous supplementation with GABA or GABAergic drugs ([PERSON] et al., 2014).  
A known apparatus of depolarization-induced Ca\({}^{2+}\) influx in pancreatic \(\beta\)-cells is VDCC. Given that application of a high concentration of extracellular KCl to insulin-secreting cells is well known to induce Ca\({}^{2+}\) influx, ([PERSON], 1955; [PERSON], 1955) we used KCl-induced increases in [Ca\({}^{2+}\)]i as a functional marker for excitable SHED \(\beta\)-cells. GABA is co-secreted with insulin molecules from \(\beta\)-cells ([PERSON] et al., 2014; [PERSON], 1955). Unlike the hyperpolarizing effects of GABA in the central nervous system, GABA initiates membrane depolarization followed by [Ca\({}^{2+}\)]i elevation in pancreatic \(\beta\)-cells. Therefore, we used the GABA-induced Ca\({}^{2+}\) increase as a unique marker of excitable SHED \(\beta\)-cells. Our experiments showed that Ca-influx in response to GABA was only observed in SHED \(\beta\)-cells in the presence of added zinc and was not seen in undifferentiated SHED cells in response to GABA irrespective of added zinc.  
Our previous study demonstrated that insulin production is proportional to cytosolic zinc levels ([PERSON] et al., 2014). As the cytosolic zinc concentration decreased, a corresponding amount of insulin production by the cells decreased as well. A previous study using patch clamp experiments demonstrated that extracellular concentrations of zinc ions could control insulin secretion via controlling ATP-sensitive potassium channels ([PERSON], 1955). Yet, no zinc transporters were measured in their report. Our older study([PERSON], 1955) using primary rodent islets, as well as the present study, noted that cytosolic zinc levels regulated by ZIP transporters interact with other chloride ion exchangers such as GABA\({}_{\text{A}}\) receptors in insulin homeostasis. If effects of zinc ions on insulin secretion functionally interact with other molecular actions such as GABA-GABA\({}_{\text{A}}\) receptor coupling, it could provide a reason for the limited success of therapeutic zinc supplementation seen in human clinical studies ([PERSON], 1955; [PERSON], 1955). To maintain effective cytosolic zinc levels, our findings indicate that \(\beta\)-cells require maintenance of functional zinc uptake transporters.
## 4 Results and Discussion
### Quick Changes in Vehicles Acceleration Scenario  
The results presented in Table 11 illustrate the performance of the NN-based approached over the INS in the quick changes in acceleration scenario. From observation, it can be seen that the neural network significantly outperforms the INS across all metrics employed, with a maximum CRSE of 8.62 m, 0.33 rad/s for the INS against 79.05 m and 0.67 rad/s for the INS over a maximum distance of approximately 220 m covered. This shows, as expected, that the INS and NN found it more challenging to estimate the displacement of the vehicle compared to the orientation rate. On other metrics, the NN obtains an average CAE, CRSE and AEPS of 5.30 m, 0.93 m and 0.05 m/s compared to that of the INS recorded as 38.92 m, 26.23 m and 0.43 m/s2 across all 13 test sequences evaluated offering up to a 92% improvement on the INS orientation rate estimation. Figure 10 graphically illustrates  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{1}{c}{**IDNN (m)**} & \multicolumn{1}{c}{**INS DR (m)**} & \multicolumn{1}{c}{**IDNN (Rad/s)**} & \multicolumn{1}{c}{**INS DR (Rad/s)**} \\ \hline \multirow{2}{*}{Total Distance Covered by vehicle} & max & 196.71 & & \\ \cline{2-4}  & min & 19.89 & & \\ \cline{2-4}  & \(\mu\) & 104.93 & & \\ \hline \multicolumn{4}{c}{Number of Sequences evaluated} & 11 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
Figure 9: Showing the evolution of the estimation error over time in the roundabout scenario based on the (**a**) Displacement CRSE, (**b**) Displacement CAE, (**c**) Orientation rate CRSE and (**d**) Orientation rate CAE.
# Investigating IPTV Malware in the Wild
## 6 Conclusions  
This paper presented the IITMAF, an effective tool for automatically detecting, collecting, and analysing malware from illegal IPTV technologies. We evaluated the framework through a series of use cases and collections where we demonstrated the effectiveness of the IITMAF and showcased the results that can be achieved using the IITMAF when compared to existing solutions. The evaluation results show that the IITMAF is more effective at detecting malicious capabilities and behaviour from illicit IPTV technologies overall, while VirusTotal is more effective at detecting malware from illicit IPTV app stores and software files.  
Although VirusTotal identified a greater volume of app store and software URLs as malicious, the IITMAF was intended primarily for analysing illicit IPTV websites, with its iterative URL collection results capable of identifying possible external malvertising domains for an illicit website.  
Moreover, the IITMAF produces a more comprehensive analysis report than other solutions, with matched YARA (the pattern-matching tool for malware) rules and dynamic analysis results classifying malware and supplying related TTPs. From the findings (Section 4), the YARA rules detected malicious capabilities in an executable file relating to privilege escalation and a malicious document format. Likewise, the behavioural analysis results of an HTML webpage classified the detected malicious behaviour as an indicator of  
Figure 10: Pie chart for the most common ATT and CK techniques observed.  
ransomware. This demonstrates that the IITMAF can supply actionable threat intelligence for malware detected in the illicit IPTV ecosystem.
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 1 Introduction
This innovation has followed the idea of opening the GNSS signal processing chain before the final on-chip Position-Velocity and Time (PVT) solution, to allow third-party processing capabilities based on non-standard algorithms to improve GNSS performance: for example, aided positioning, differential positioning, precise point positioning. The availability of such measurements, together with the intrinsic network connectivity, can be also exploited to implement forms of distributed interference monitoring, as investigated in [16; 17]. The exercise demonstrated with NGeneApp goes further in this direction, exploiting lower-level measurements still not accessible in the commercial chipsets, with the purpose of precisely and flexibly monitor the presence and the impact of interference sources, using an approach that directly enables distributed interference mapping via crowdsourcing of data. We organize this work in the following sections: Section 2 discusses the theoretical background; Section 3 is a description of the instrumentation, with particular emphasis to the software receiver ported to the microprocessor of the smartphone; Section 4 describes the RFI detection algorithms implemented on the smartphone; Section 5 describes the validation tests and calibrations performed with lab instruments on the RFI detection module; finally, Section 6 reports the observations from some live measurements around the city. Finally, Section 7 draws the conclusions and discuss the expected developments of the work.
# Radio Resource Dimensioning for Low Delay Access in Licensed OFDMA IoT Networks +
## 5 Numerical Results  
In this section, we consider the uplink of a sensor network corresponding to the licensed IoT with parameters summarized in Table 1.  
We assume that the density of nodes \(\rho=500\) sensors per km\({}^{2}\) transmitting on average once each half-an-hour, \(n_{a}=48\) during 20 seconds. The active node density is then \(\lambda_{a}=5.5\) sensors per km\({}^{2}\). We consider a cellular network with ranges between 500 m to 1.5 km corresponding to a collector density of \(\lambda_{b}=0.9\) nodes per km\({}^{2}\) down to 0.1 nodes per km\({}^{2}\) (the ranges are obtained with a confidence margin of 95%). The maximal power is limited to 14 dBm and is uniformly distributed among the allocated RRs. Table 2 gives the matching between the SINR range with the required number of RRs to achieve a target rate of \(C_{0}=500\) bps. This data are derived from the Link Layer Simulation (LLS) provided in [26] on the Physical Uplink Shared Channel (PUSCH) of LTE-Cat M. To evaluate network performances, we consider a path-loss in an urban/suburban environment with  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Intensity of active nodes & \(\lambda_{a}=5.5\) nodes per km\({}^{2}\) \\ Intensity of collectors & \(\lambda_{b}=0.1\) to 0.9 nodes per km\({}^{2}\) \\ Transmission power & \(P_{\text{RR}}=6.3\) dBm \\ RR per node & 1 to 6 RRs \\ Configuration & SISO, single-user \(1\times 8\) SIMO, \\  & \(1\times 8\) MU-MIMO with \(n_{u}=2\) or 4 \\ Okumura-Hata model & \(\alpha=10^{-14.1}\), \(\beta=3.5\) \\ Target data rate & \(\geq\)500 bps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network Parameters.  
\(\alpha=10^{-14.1}\) and \(\beta=3.5\). We consider the following antenna configurations: SISO, single SIMO with \(n_{r}=8\), multiuser SIMO with \(n_{r}=8\) and \(n_{u}=2\) or \(n_{u}=4\).
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 3 Research Methodology
### Selection of DMS Architecture  
The selection of a concrete DMS architecture was essential to support a contextualized and realistic threat assessment. A comparative analysis was performed on architectures  
Figure 2: Workflow of the study selection and data extraction process.  
documented in the recent literature, prioritizing solutions that integrated core safety-critical capabilities such as driver behavior tracking, alert mechanisms, and fail-safe triggers.  
Following the formulation of the research questions, the study proceeded with a comparative evaluation of several DMS architectures to identify the most suitable candidate for analysis. This selection was informed by three criteria: Component integration: The degree to which the system incorporated essential functionalities such as biometric sensing, distraction recognition, and driver intent analysis. Security posture: Evaluation of the system's exposure to typical threat classes using the STRIDE methodology (Figure 3). Standards compliance: Adherence to ISO/SAE 21434 guidelines for cybersecurity in automotive systems. The threat modeling process employed in this phase relied on STRIDE to detect vulnerabilities such as Spoofing, Tampering, Repudiation, and data leakage. Tools such as Ansys Medini Analyze version 2024 R1 were used to construct threat models, enabling a precise mapping of security concerns across the system architecture. The selected architecture demonstrated strong integration of safety-critical components and sufficient modularity to support mitigation strategies, thereby qualifying it as the reference design for the remainder of the study.