esa-sceva/satcom-chunk-collection huggingface dataset:
# Head-Related Transfer Functions for Dynamic Listeners in Virtual Reality
###### Abstract
A many-to-one mapping mechanism in sound localization has been suggested, where the plasticity in the auditory cortex allows us to learn multiple HRTF sets that lead to the same percept [27]. The emerging picture from prior literature indicates mixed results for the need of individual HRTFs in 6-DoF VR. On the one hand, static localization is more accurate with individual HRTFs than with generic HRTFs. On the other hand, VR experiences are head-tracked and multimodal, allowing the listener to use different modalities and motor actions to support auditory localization and ultimately adapt the auditory perception. From an objective perspective, the use of individual HRTFs provides more accurate localization cues tailored for each listener, but the perceptual evidence gathered so far in VR environments does not show uniform support for the need of individual audio delivery. This study compares two individual HRTF sets, attained using measurements or computational modeling, and a generic HRTF set, first objectively using auditory models, then followed by two subjective experiments addressing: (I) static localization; and (II) dynamic audio-visual localization, timbre, and overall quality in a 6-DoF VR. Additionally, another generic HRTF set, containing only horizontal plane measurement points, is included in the dynamic audio-visual localization experiment. The two sets of individual HRTFs are objectively different from the generic HRTF on average. The individual HRTF sets are objectively similar to each other on average. Static localization accuracy is higher with individual HRTFs compared to generic HRTFs. The HRTF sets are equally preferred in localization, timbre, and overall quality in 6-DoF VR.
## 3 Problem Statement  
Early research [24] shows that C-V2X faces challenges when applied in V2V communications due to its centralized architecture, which lacks support for V2V communication. The heavy load generated by periodic messages strongly challenges the LTE capacity and potentially penalizes the delivery of safety applications. On the other hand, DSRC also suffers from high-latency and low-reliability issues in the congested traffic situation [12].  
NR V2X provides higher bandwidth, higher reliability, and lower latency for many advanced V2X applications that have stringent real-time performance requirements. However, not much research work has been carried out on NR V2X [1,14]. A more comprehensive study should be conducted on its implementation in open-source software like ns-3, and then the performance should be realized and compared with the real applications in a microscopic traffic simulator. Lastly, the result should be validated with the V2X radios. In this article, we study the performance of NR V2X in ns-3 for different key performance indicators (KPIs) recommended by 3 GPP under various network settings. The result from the integrated ns-3 with a microscopic traffic simulator and V2X radios are ongoing and left for our next article.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 3 Experiments
### Results  
#### 3.3.1 Spatial Query Results  
For withinDistance queries, buffer queries, kNN queries, and intersect queries, 20 geographic nodes were randomly selected as query inputs. The average number of computational nodes required to execute these queries (rounded up) was calculated for each partitioning algorithm. As shown in Table 2 and Figure 10, GKGP-DC required fewer computational nodes for all types of queries compared to other methods. Specifically, for withinDistance queries and buffer queries, the proposed method only needed three and two computational nodes, respectively. In contrast, algorithms such as Hash, LDG, FENNEL, WStream, and HeiStream required seven to eight computational nodes for most queries. GKGP-DC requires significantly fewer nodes than other algorithms, indicating a lower resource demand. Offline partitioning algorithms like METIS, ParMetis, KAFPPA, and XtraPuLP showed relatively lower computational node requirements for certain queries. For example, ParMetis needed only five computational nodes for kNN queries, which can be attributed to the fact that offline partitioning algorithms focus on minimizing edge cuts. As a result, proposed method can group nodes that are spatially close and tightly connected by edges into the same partition, reducing the number of computational nodes required for some queries.  
The above experimental results can be explained as follows. Traditional graph partitioning methods do not consider spatial correlation; therefore, the spatial neighboring nodes are randomly distributed in different partitions. As a result, traditional graph partitioning methods require more computational nodes when spatial queries are executed. In contrast, the proposed method in this study takes spatial correlation into account, prioritizing the partitioning of geographically close nodes into the same partition. As a result, the number of computational nodes required for spatial queries is significantly reduced, which decreases dependency on computational resources, improves query response times, and enhances overall system performance.
# PFC Inductor Design Considering Suppression of the Negative Effects of Fringing Flux
## 4 Practical Optimization of PFC Converter Using Analyzed Magnetic Components
### Performance Evaluation with Optimized Ferrite Inductors with Delimitated Space around Air Gap  
To improve efficiency, within the next step we proceeded to replace the original alloy core coils with ferrite core coils. The first alternative reflects PFC inductor v1 optimization. It was made on the PQ35/35 core (Figure 8) and its parameters are described in Table 1. Inductor was made with delimitated space around the air gap, thus reduction of the fringing flux and consequently reduction of power losses were expected. From the study of the effect of fringing flux, it was observed that one of the disadvantages of multilayer winding was the generation of proximity effect. Therefore, an additional analysis of the effect of the inductive components was performed by using an inductor made on a ferrite core with one winding layer. The second alternative of PFC inductor was reflecting optimized design on toroidal core, i.e., v2 PFC inductor with unevenly distributed air gap using ferrite material (Figure 10).  
The measured efficiencies for both cases were compared to the efficiency of the original PFC converter solution, and the results are interpreted in Figure 21. It is clear from the  
Figure 20: Efficiency waveform in dependency on the output power of converter (alloy core inductor).  
graphs that the efficiency was 0.3% better than the original configuration, which represents a difference of approximately 13 W.  
When analyzing the efficiency results of an optimized coil converter made with an unevenly distributed shortened air gap, it is clear that the efficiency of the PFC converter with these coils was lower than expected, which probably resulted in increased losses--increase in core volume but also due to increased current ripple [3; 20]. This could be due to the effect of the aluminum heatsink, which was close to the coils (Figure 16), reducing their inductance. The further decrease in efficiency was probably due to the eddy currents that arose in the aluminum heatsink around the relatively large air gap of the coil and thus increased the current consumption of the entire converter.
# A Hybrid Watermarking Approach for DICOM Images Security
## 3 Proposed Method
### Watermark Embedding Process  
#### 3.4.1 Black Background Region Verification  
Before embedding the watermark in the black background region, the watermark is divided into blocks of size n x m. Its insertion depends on the availability of the blocks in the black background region. Therefore, the block size n \(\times\) m should be chosen so as to ensure the availability of enough regions in the black background for the insertion of the watermark. In order to increase the robustness of the system, the targeted block size is that making it possible to ensure the insertion of two copies of the watermark.  
Figure 4: Example of zero-watermark and corresponding encoded Watermark.  
Figure 3: Process of watermark generation.  
In our model, an algorithm has been proposed for the search of the regions. The algorithm searches in the black background region the number of equal-sized regions R1, R2, R3, and Rn of size n \(\times\) m pixels.  
Figure 5 shows an example of the selection of the regions in the black background image to embed the watermark with n \(\times\) m = 8 \(\times\) 8. In this case, the block size chosen is 8 \(\times\) 8. 8 regions have been selected in the RONI (black background) for the insertion of the copies of the watermark. The watermark will be divided into four blocks of 8 \(\times\) 8 pixels that will be inserted in the RONI.
# Evaluating the Effectiveness of COVID-19 Bluetooth-Based Smartphone Contact Tracing Applications
## 5 Evaluation
### Impact of Utilization
The idea is to study the impact of contact tracing in two main aspects: infection rate, and people quarantined. For this evaluation, we only show the final results, that is when the infection is over. Figure 4a displays the proportion of infected individuals depending on the application utilization \(U\) for several values of \(RC\). Note that the infected and quarantine figures are represented as a percentage of the population. We can see that, even with low percentages of utilization, there is a significant reduction on the people infected. Nevertheless, the cost can be very high, requiring a lot of people to be quarantine as shown in Figure 4b. We again recall that percentages greater than 100 mean that some people have been quarantined more than once, or for periods longer than 14 days, which was the quarantine time used in the experiments. Figure 2: Epidemic dynamics with different mobile contact tracing utilization for COVID-19 infection. (**a**) No measures (\(\delta=q=0\)); (**b**) detection and isolation of individuals, no contact tracing (\(\delta=0.05\), \(U=0\)); (**c**) with mobile contact tracing (optimistic utilization) (\(\delta=0.05\), \(U=0.7\), \(RC=0.8\)); (**d**) with mobile contact tracing (pessimistic utilization) (\(\delta=0.05\), \(U=0.5\), \(RC=0.5\)). Figure 4: Efficiency of the contact tracing application depending on utilization: (**a**,**b**) percentage of population infected and quarantined for \(R_{0}=3\); (**c**,**d**) percentage of population infected and quarantined considering other measures (for \(R_{0}=2\)); (**d**) proportion of population quarantined considering also other measures (\(R(0)=2\)). Figure 3: Contact tracing application utilization thresholds for controlling an infection, considering different values of reproduction ratio (\(R_{0}\)) and percentage of susceptible people (\(S/N\)). The pair of values above the lines results in a disease-free equilibrium. Finally, if we consider that some additional measures are taken so that the effective reproduction ratio is reduced to \(R_{0}=2\), we can see in Figure 4c that we can obtain a significant reduction of the infected population even for utilization percentages, and that is more important with a relative cost, since the percentage of quarantined people is hugely reduced (see Figure 4d). The results considering a ratio of already immunized people also evidenced a significant reduction of the quarantines required. These results confirm the utility of contact tracing applications for future outbreaks, even for utilization levels between 40% and 60%.
# Soil Depth Prediction Model Using Terrain Attributes in Gangwon-do, South Korea
## 4 Conclusions  
This paper proposes a statistical model that can predict soil depth, an essential factor in landslide disasters. Since erosion and weathering characteristics depend on the rock type, a soil depth prediction model for each rock type was developed: igneous rock, metamorphic rock, and sedimentary rock.  
* The regression model uses open data provided by the Geotechnical Information DB System; a total of 297 sites were obtained. They were classified into 101 sites for igneous rock, 101 for metamorphic rock, and 95 for sedimentary rock.  
Figure 8: The proposed statistical prediction models result in the verification sites, The soil depth map for **(a)** Igneous rock, **(c)** Metamorphic rock **(e)** Sedimentary rock, and Scatterplots showing the relationship between predicted and measured soil depth for **(b)** Igneous rock (\(R^{2}\) = 0.867), **(d)** Metamorphic rock (\(R^{2}\) = 0.801), **(f)** Sedimentary rock (\(R^{2}\) = 0.814).  
As a result of analyzing the correlation between the six TAs obtained from the numerical map and soil depth, the variables with the highest correlation are SLOPE, and curvature and SCA are found to have relatively low correlation. In addition, a model using three variables (SLOPE, STI, TWI) is determined from \(R_{adj}^{2}\), and RMSE values for multi-collinearity analysis and the combination of six cases for variables. For the models of igneous rock, metamorphic rock, and sedimentary rock, the \(R_{adj}^{2}\) values are 0.698, 0.676, and 0.683, respectively, and the RMSE values are 0.870, 0.876, and 0.981. Additionally, Verification sites used data from 18 igneous rock sites, 37 metamorphic rock sites and 30 sedimentary rock sites. The \(R_{adj}^{2}\) values are 0.859, 0.794, 0.807, and the RMSEs are 0.724, 1.104, 0.288 for igneous, metamorphic, and sedimentary rocks, respectively.  
Therefore, it is expected that the three types of soil depth prediction models proposed will have better applicability and utilization for soil in Korea. However, the data used in this paper are open data provided by the Geotechnical Information DB System and are rather less in number to represent the wide study area. In addition, it should be noted that the relationship between TAs and soil depth used for model development was developed, assuming a linear correlation.  
Formal analysis, investigation, validation, writing--original draft preparation, J.K.; Conceptualization, methodology, writing--review and editing, H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Fund of University of Ulsan grant number 2020-0619.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Discussion
### Limitations  
To our knowledge, this is the first study exploring the utility of pretreatment cortical features in predicting CBT outcomes in OCD patients using the SVR model. Although comprehensive cortical features display a strong predictive power for CBT, several limitations were identified. First, to ensure the predictive ability for CBT is OCD\(-\)specific, patientswith comorbid conditions were excluded. However, at least 50% of OCD patients in the clinical setting have at least one underlying psychological illness [2]. As such, the predictive accuracy of our model may vary according to other patient comorbidities. Therefore, these results cannot be directly applied to common OCD patients. Second, the research was a non-controlled and open-label study. The placebo response of the participants was not resolved. Although the evaluator did not participate in the study, both the therapist and the patient knew the designated treatment. Accordingly, the clinical response may have been exaggerated due to expected bias. Thus, our findings need further validation using sham-controlled studies. Third, we used AAL3 and DK40 templates to extract cortical structural features, respectively, which might lead to biased results. Lastly, due to the relatively small sample size, the current results should be interpreted with caution. Future studies need to improve the research design, expand the sample size, combining more factors, and use more rigorous analysis methods to further verify the predictive effect of cortical structural features on the efficacy of CBT. For example, researchers can try the \"multi-site canonical correlation analysis with reference + joint-independent component analysis\" (MCCAR + jICA), which can precisely identify co-varying multimodal imaging patterns closely related to the reference, such as cognitive scores [39].
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 7 Discussion
### Strengths of the Approach  
One of the core strengths of this approach lies in its modularity, achieved through Bicep's support for reusable modules. By segmenting the infrastructure definitions into discrete, purpose-specific modules (networking, container app, database), the project enables teams to maintain, test, and evolve individual components in isolation. This modular design simplifies onboarding for new team members and reduces the cognitive load required to understand the full infrastructure stack. Instead of wrestling with thousands of lines of monolithic ARM JSON templates, teams can reason about small, well-defined Bicep modules with clear interfaces and dependencies.  
Another notable strength is the level of automation embedded throughout the deployment process. By leveraging GitHub Actions as the CI/CD platform, infrastructure changes are deployed consistently, auditably, and repeatably. Developers can triggerdeployments through familiar Git workflows (merges to a main branch or protected environment branches), eliminating the need for manual portal-driven provisioning that is error-prone and difficult to audit. This automation aligns closely with DevOps principles, reducing lead time for changes and ensuring infrastructure is always defined in code, version-controlled, and reproducible.  
Reproducibility itself is a critical benefit of infrastructure as code. The use of parameter files and environment-scoped configurations enables the reliable instantiation of development, QA, and production environments with environment-specific settings, while sharing a common code base. This reduces configuration drift and simplifies debugging, as developers can recreate an environment from scratch and know it will match its definition. Reproducibility also improves disaster recovery preparedness, as teams can redeploy infrastructure quickly if needed [33].  
Furthermore, the choice of Bicep specifically brings significant benefits over traditional ARM Templates. Bicep's concise, readable syntax improves maintainability and reduces the likelihood of introducing errors due to excessive complexity or nesting. Its native support in Azure and automatic transformation to ARM templates ensure first-class integration without introducing external dependencies or runtime tools.  
The integration of GitHub Actions offers additional advantages. As a cloud-native, widely adopted CI/CD platform, GitHub Actions provides flexibility in designing sophisticated workflows, integrating with secrets management, and supporting manual approvals and branch protections. Teams benefit from tight integration with the GitHub ecosystem, including pull request reviews, issues, and auditing features, creating a seamless DevSecOps workflow.  
In combination, these strengths deliver an infrastructure deployment process that is traceable, scalable, secure, and maintainable, key requirements for modern cloud-native applications.
The regularized MoLE methods reported in the literature use the \(l_{1}\) penalty, also known as least absolute shrinkage and selection operator (Lasso) [27]; the \(l_{2}\) penalty, also known as ridge regression (RR) or Tikonohov regularization [28]; a compromise between \(l_{1}\) and \(l_{2}\) norm [29], also known as elastic net (EN); and the smooth clipped absolute deviation (Scad) [30]. In [23], the authors have used a RR penalty for the gates and a Lasso and Scad penalties to experts in MoLE. They reported successful results, in both, performance and in finding parsimonious models. They compared the results with a RR regularized linear model. Similarly, Ref. [24] applied Lasso penalty on gates and experts for classification problems. Their regularized MoLE performed better than ordinary MoLE, and state of art classifiers. In [26], the authors employed Lasso penalty for experts, and EN penalty for the gates, and reported better results than ordinary MoLE. In [31] the authors have employed a proximal-Newton expectation maximization (EM) to avoid instability while learning the gates. In [32], the authors discuss the theoretical aspects of the use of Lasso in MoLE. All results report regularization as a viable approach to improve the performance of MoLE when dealing with irrelevant variables and small number of samples. The goal of this paper is to check the performance of regularized MoLE models for quality prediction in multimode-multiphase processes. For this purpose, a regularized version of MoLE based on EN penalty has been derived, which has a flexible regularization form. Thereafter, this paper derives three regularized MoLE models, defined as MoLE-Lasso, MoLE-RR, and MoLE-EN. Besides, a set of experiments was run and analyzed, with all regularized MoLEs, and the Mix-PLS [9]. The experiments were run on real multiphase polymerization data, for predicting two quality variables, with a total of 31 batches. The performances of MoLE models with respect to the number of batches for training were checked. The results show that MoLE-Lasso gives the most stable results, even in learning with only a few batches. On the other hand, the Mix-PLS has a tendency to perform better when the number of batches increases. Finally, all regularized MoLE's were also compared to different state-of-the-art models. The results show that the regularized MoLE is a valid choice for modeling multimode processes data. The paper is divided up as follows. Section 2 presents the proposed regularized MoLE. Section 3 presents the experimental results. Finally, Section 4 gives concluding remarks.
###### Abstract  
The underwater motion of the ROV is affected by various environmental factors, such as wind, waves, and currents. The complex relationship between these disturbance variables results in non-Gaussian noise distribution, which cannot be handled by the classical Kalman filter. For the accurate and real-time observation of ROV climbing, and, at the same time, to reduce the influence of the uncertainty of the noise distribution, the ROV state filter is designed based on the mixture of Gaussian model theory with the expectation-maximization cubature particle filter (EM-MOGCPF). The EM-MOGCPF considers different sensor measurement noises, and the addition of mixture of Gaussian (MOG) improves the fineness and real-time properties, while the expectation-maximization (EM) reduces the complexity of the algorithm. To estimate the ROV _xyz_-axis and yaw angular states, we establish a four-degree-of-freedom (4-DOF) ROV kinetics model, which uses a simulation platform for multiple sea state degrees. The results show that the EM-MOGCPF effectively improves the estimation accuracy and exhibits strong adaptability to nonlinear and non-Gaussian environments. We believe that this algorithm holds promise in solving the state estimation challenge in these difficult environments.  
underwater vehicle; state estimation; cubature particle filter; mixture of Gaussian +
Footnote †: journal: Applied  
doi: 10.3390/001-3885-3885-3/201306
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 7 Simulations and Results  
In this section, we will check the performance of the methodologies used in this paper. In order to ensure the reliability of users according to their distances, we have assigned three different discount factors in the Q-learning based power control: very critical (v.c = 0.8), critical (c = 0.5), and normal (n = 0.3). We will check the performance in terms of vehicular speed, number of vehicular users, and the threshold SINR mentioned above. Table 4 shows the simulation parameters used in our setup. The simulation was carried out in MATLAB and Python.