esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Why Is the Integration of Security and Safety Needed?  
In recent times, computer networks have been widely applied in several applications; any failure in these systems could have critical outcomes. There are various hypotheses about the characteristics such crucial systems must maintain, and the methods employed to protect them. Two such attributes are security and safety. Nevertheless, modern designs are usually needed to meet these two attributes simultaneously. Considering safety and security, common goals are needed to protect peoples or systems; therefore, safety-critical assets are considered.  
[PERSON] et al. [81] stated that the marine industry is a critical sector, and it is essential to combine safety and security concerns on the sea. The integration of two aspects concentrates on analyzing the energy supply vulnerabilities and introduces a methodology to evaluate the system's exposure using the spatial composition of maritime regions. This study contributes a GT-based model for offering safety and security in a maritime territory. Indeed, the developed model utilizes links, such as roads and ports, as nodes. [PERSON] et al. [84] demonstrate the significance of public safety and security (PSS) in mobile networks. In this study, a risk evaluation model, using BN, is proposed for the current PSS telecommunication services.  
How Have Bayesian Network- and Graph Theory-Based Methodologies Been Utilized for Security and Safety Studies in CI?  
This RQ assists in knowing which models are used for safety and security integration, functionalities, and applicability. In this SLR, it is observed that 80%, 15%, and 5% of the included studies use BN and GT, as well as both GT and BN, respectively, as shown in Figure 8.  
From Figure 9, it is observed that the developed models based on BN or GT in included studies were utilized to have two sorts of purposes, including diagnosis and prediction. The term diagnosis represents identifying the nature or cause of the incidents or other risks in the systems. In contrast, the prediction is associated with forecasting potential cyber threats in the respective CIs. This study identifies that 48% of approaches perform a diagnosis of the risks in different applications. However, 36% and 16% of papers ensure performance prediction and both prediction and diagnosis, respectively.  
Figure 8: Characterization of BN and GT models in included studies.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 3 Methodology
### Similarity Resilience  
Unlike static hash-based fingerprinting approaches, similarity mapping introduces a natural resilience to fingerprint subversion, particularly in relation to TLS features. An adversarial action to flip a single TLS extension would generate a completely different JARM or hash signature, but with a Minhash and LSH approach similarity relationships remain proportional to the Jaccard similarity of the underlying feature set.  
For a feature vector with dimensionality d and k hash functions in the MinHash signature, the expected difference in similarity scores follows the statistical properties of MinHash sampling. The probability that two sets with Jaccard similarity \(J(A,B)\) will have the same MinHash value for a random hash function is exactly \(J(A,B)\). This means that small changes to the feature vector result in proportionally small changes to the similarity score. From a practical perspective, the proportional response to changes imposes a degree of natural resilience to subversion. Adjusting some of the core TLS parameters (cipher suites, ALPN, extensions) beyond minor alterations can also lead to compatibility issues,  
Figure 5: Graph displaying TLS features enriched with HTTP header data. The resulting feature matrix \(M\in\{0,1\}^{n\times d}\) has dimensions \(n=16\),\(254\) (fingerprints) and \(d=2124\) (features), representing the complete binary feature space of the TLS and HTTP characteristics. Known good domains are coloured green, known bad domains, red and unknown domains, orange.  
impact handshake stability and security and, importantly, restrict supportable clients for malicious actors.  
Additionally, when a node has a Jaccard similarity of 1 compared to other points in the dataset, it indicates there is zero similarity between other fingerprints. These isolated nodes, or clusters of isolation, could indicate novel infrastructure, either from new, unique TLS configurations or deliberate evasion techniques such as TLS randomization or customized TLS stacks.  
While this research focused on the foundations for using the MinHash and LSH techniques for fingerprinting and similarity mapping, practical evaluation of the techniques' performance against targeted adversarial subversion was not undertaken. The section on future work suggests some approaches for how this could be evaluated, along with some further avenues of research.
## 4 Conclusions  
In this study, preoperative planning for orthognathic surgery was performed in a virtual space. It was shown that VR simulation could provide easy handling of 3D models, for adjusting the maxilla and mandible with intuition. Orthognathic surgery was performedwith an AR navigation system, which provided the operator with the preoperatively planned 3D model superimposed with the actual surgical site. The differences between the preoperative plan and postoperative 3D model were 1.18 mm at ANS, 4.22 mm at PNS, 2.05 mm at Right MxM1, 2.02 mm at Left MxM1, 2.34 mm at Pog, 4.32 mm at Right MnM1, and 4.88 mm at Left MnM1. The Mean value was 3.00 mm and the standard deviation was 1.44 mm. The deviation in our findings was measured as somewhat high. This difference is considered a depth problem; results that are more accurate can be obtained when depth cameras and optical markers are used. In the future, we need to apply haptic-related VR simulation; it will provide us with great advantages for 3D medical simulations, with accurate manipulation and immersiveness. AR navigation has great potential for medical applications; it has many advantages, including displaying a real-time augmented 3D model of the patient, and it is easily applied in the surgical field, without complicated 3D simulations or 3D-printed surgical guides.  
Conceptualization, S.-Y.M. and [PERSON]-[PERSON]; methodology, S.-Y.M., [PERSON]-[PERSON]; software, S.-Y.M. and [PERSON]-[PERSON]; validation, S.-Y.M., [PERSON]-J.K. and [PERSON]-[PERSON]; formal analysis, S.-Y.M. and [PERSON]-[PERSON]; investigation, S.-Y.M., [PERSON]-[PERSON] and [PERSON]-[PERSON]; resources, S.-Y.M.; data curation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--original draft preparation, S.-Y.M. and [PERSON]-[PERSON]; writing--review and editing, S.-Y.M., J.K. and [PERSON]-[PERSON]; visualization, S.-Y.M. and H.-J.K.; supervision, S.-Y.M. and J.K.; project administration, S.-Y.M.; funding acquisition, S.-Y.M. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a research fund from Chosun University Dental Hospital, 2020.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Chosun University Dental Hospital (CUDHIRB 2007003 and 26 November 2020).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
References
# Topology Optimization of an Aerospace Bracket: Numerical and Experimental Investigation
## 2 Materials and Methods
### ANSYS Static Structural Setup for Topology Optimization Analysis  
ANSYS Workbench was used as the main platform in our work, orchestrating model setup, structural analysis, and topology optimization, along with all required pre- and post-processing steps, such as mesh generation and refinement, design optimization, and verification. In this work, we aim to minimize compliance, which implies minimizing the strain energy (when only mechanical loads exist) given that lower strain energy leads to increased stiffness. This can be achieved by adding material in regions with higher stress values and removing it from areas that exhibit no or low stress. However, in the studied case, the problem of compliance minimization is considered (equivalently, stiffness maximization) with a given volume fraction constraint, and the additional limitation that the optimized shape cannot lie outside the bracket's initial surface boundaries. Therefore, the possibility of rearranging material to strengthen weak regions is not considered, but instead, only the reduction of material from areas exhibiting low stresses, with the aim of maintaining the original component's stiffness.  
Within the ANSYS workbench, the Geometry, Static Structural, and Structural Optimization modules were selected and set up. As noted in [28], these modules are needed for performing topology optimization with the methods supported by ANSYS, such as the density-based and the level-set methods. The initial models were modeled in SolidWorks and imported into the workbench through the Geometry Import function, and then transferred to the Static Structural and Structural Optimization modules for meshing, structural analysis, and subsequent topology optimization. After successful mesh generation, analysis, and optimization, the optimized models were transferred back to the Static Structural module for design validation, as described by the protocol used in [29]. These modules are linked in a way that permits information exchange in an iterative manner.
# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
## 5 Conclusions  
This article describes an intelligent smartphone positioning system based on acoustic localization. The encoding scheme we designed is simple, and decoding only requires simple cross-correlation operations. The signal frequency designed in this article is much higher than the frequency of environmental noise. The accuracy and stability of TDOA measurement are greatly affected by multipath effects. Traditional cross-correlation algorithms cannot solve this problem. Based on cross-correlation algorithms, we developed Algorithms 1-3. We not only decoded the acoustic node signals but also obtained stable and high-precision TDOA measurement results. In terms of positioning algorithms, we chose a reliable maximum likelihood algorithm as the basic positioning algorithm. Static positioning experiments were conducted in two scenarios, achieving an average positioning accuracy of decimeter level. In the future, we still need to solve the following problems:  
1. Large scene smartphone positioning: our acoustic nodes are easy to deploy, and we have the ability to achieve positioning in large indoor spaces. However, there is a problem of near-far effect in large indoor spaces. We plan to overcome this problem using the normalization method.
2. Dynamic positioning: acoustic signal is susceptible to Doppler effects. This issue is something we need to address in the future. We plan to choose methods in the field of communication, such as carrier frequency offset compensation.
3. Switching between dynamic positioning and static positioning: When performing the positioning function, the user may be in a stationary state or a moving state. In the moving state, we can use an extended Kalman Filter to improve positioning accuracy. We plan to use TOA information to detect movement distance and determine whether it is stationary.
4. Adaptive extraction of valid acoustic data segments: this article does not study the adaptive extraction method. However, signals from acoustic nodes can be encoded and decoded, which provides the possibility for adaptive extraction.
5. Smartphone outside of the rectangle of four nodes: When the Smartphone is outside the acoustic nodes, fingerprint positioning can be used. In addition, increasing the number of acoustic nodes and using time-division, space-division, and code-division technologies can also achieve localization.  
[PERSON] contributed the analysis of experimental data, the writing of manuscript and the programming design. [PERSON] did reviewing, and experimental assistance. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors gratefully acknowledge the valuable suggestions and the support from the UNIP (Ubiquitous Navigation and Indoor Positioning) Groups directed by [PERSON] of Wuhan University.  
The authors declare no conflict of interest.
###### Abstract  
This paper presents an economic assessment of introducing solar-powered residential battery energy storage in the Madeira Island electric grid, where only micro-production for self-consumption is currently allowed. The evaluation was conducted against six local micro-producers using one year of energy consumption and solar photovoltaic production measurements and two distinct storage control strategies. Several inverter sizes and storage capacities were considered based on the six micro-producers' consumption and production profiles. The results were then analyzed concerning year-long simulations and a projection for the next ten years. To this end, several indicators were assessed, including self-consumption, profit per Euro invested, number of cycles and storage degradation. The results obtained show that, despite the benefits of storage to increase the self-consumption rates, considerable drops in the storage prices are still necessary to achieve profitability during these devices' lifetime. Furthermore, our results also highlight a very interesting trade-off between self-consumption, pre-charge and profitability, in a sense that higher levels of pre-charge increase the chances of reaching profitability even though this will imply considerable drops in the levels of self-consumption.  
residential PV-BESS; self-consumption; greedy control; battery pre-charge; local policies; economic viability +
Footnote †: journal: Applied Sciences
## 3 Mathematical Model of Geolocation and Tracking Scenarios
### Algorithm Performance Evaluation  
To know the performance of the emitter geolocation algorithm, Cramer-Rao lower bound (CRLB) is the best validator. CRLB is the inverse of the Fisher Information Matrix (FIM) [51]. In addition, an unbiased estimator performance for the emitter position and velocity is the Posterior CRLB (PCRLB). The PCRLB was employed to verify the algorithm performance of optimizing geolocation and tracking estimation by determining the increase in RMSE. The deriving of PCRLB and algorithm performance of nonlinear measurements is presented in Appendix A [52]. RMSE of position, velocity, and rotation rate for target-tracking are defined as [53].  
\[\left\{\begin{aligned} \text{RMSE}_{k}^{pos.}&= \sqrt{\frac{1}{N}\sum_{n=1}^{N}\|\mathbf{u}_{k}^{n}-\mathbf{u}_{k}^{\sigma^{n}}\|^{2}} \,\\ \text{RMSE}_{k}^{vel.}&=\sqrt{\frac{1}{N}\sum_{n=1}^ {N}\|\mathbf{u}_{k}^{n}-\mathbf{u}_{k}^{\sigma^{n}}\|^{2}}\,\\ \text{RMSE}_{k}^{rot.}&=\sqrt{\frac{1}{N}\sum_{n=1}^ {N}\left(\omega_{k}^{n}-\omega_{k}^{\sigma^{n}}\right)^{2}}\,\end{aligned}\right. \tag{47}\]  
where \((\mathbf{u}_{k}^{n},\mathbf{u}_{k}^{n},\omega_{k}^{n})\) and \((\mathbf{u}_{k}^{\sigma^{n}},\mathbf{u}_{k}^{\sigma^{n}},\omega_{k}^{\sigma^{n}})\) are the true and estimated target positions, velocities, and rotation rate at the \(N\) runs of the Monte Carlo iterative [54; 55].  
The metrics have been used to compare the performance of all proposed algorithms. For a fair comparison, 100 runs of independent Monte Carlo have been made. The performance is verified by the RMSE of the position, velocity, and rotation rate defined in (47). In the considered scenarios, the initial estimate \(\mathbf{\upkappa}_{0|0}\) was created randomly from the Gaussian distribution \(\mathcal{N}(\mathbf{x}_{0|0},\mathbf{P}_{0|0})\) in each run.
## 3 Results and Discussion
### S-Parameters  
The S-parameters of the MIMO antenna, both simulated and measured, are plotted in Figure 11. The proposed MIMO antenna covers dual bands, ranging from 2.1 to 3.6 GHz and 5.9 to 7.4 GHz and encompassing three bands: 2.5 GHz (2.3-2.7 GHz), n78 (3.3-3.6 GHz), and n96 (5.925-7.1 GHz). It was designed for WiMAX, sub-6 GHz, and sub-7 GHz applications. The proposed antenna provides improved isolation exceeding 20 dB between its ports across the operational frequency bands. The simulation and measurement results exhibit good agreement.  
Figure 10: Fabricated MIMO antenna: (**a**) front; (**b**) back.  
Figure 9: S21 variations with respect to ground plane.
## 4 Experiments
### Comparison with Existing Work  
The most relevant literature presents a two-stage architecture based on CNN. The first stage involves concatenating feature maps of questions and answers along the channel direction to obtain input data, followed by classification using a second-stage CNN with a tanh activation function for result interpretation. The final design uses multi-label outputs, with each result producing six labels represented by 1, 0, and \(-1\), indicating whether the rules are met, not met, or not applicable, respectively. These results are matched with a predefined set of evaluation rules to provide corresponding explanations. When the architecture incorporates residual modules and feature concatenation, the training accuracy for six questions reaches a maximum of 82.26%. However, these designs lead to overly complex input features and numerous possible output vector combinations (a total of 3\({}^{6}\) combinations). When the number of questions increases to twelve, the accuracy significantly drops to 69.7%.  
In this paper, we propose a new architecture. Our contribution lies in optimizing the input features by concatenating question numbers and answers and choosing appropriate channel ratios to reduce the number of features, channels, and overall model complexity that result from directly concatenating questions and answers, effectively avoiding overfitting. Additionally, the new architecture optimizes the output vector. By designing the task as multi-class classification, the complexity of the output vector is reduced, and accuracy is improved. In addition to optimizing input features and output vectors, we introduce improved DenseNet. The experiment selects the pre-trained model DenseNet201 [10] as the backbone architecture, which is suitable for this task, and adds new fully connected layers for feature extraction and classification, using softmax instead of tanh as the final activation function. This design leverages the feature extraction capabilities of the pre-trained model while tailoring feature learning and extraction to specific problem requirements. Furthermore, we implemented an AI scoring system application with a user interface, integrating the classified prediction results into a rule-decoding system that calculates raw scores, performs norm-based standard score conversion, and achieves level conversion. These processes ensure the accuracy of the prediction results. Based on the predictions and decoding rules, this paper can provide detailed evaluations of answer penalties, such as unmet specific conditions. In addition to improving classification efficiency and accuracy, it offers a basis for penalties, providing targeted guidance for children's training and improvement in future clinical applications, thus enhancing children's abilities. Through the proposed improvements, we hope to provide a more efficient and accurate result for automated scoring systems.
# A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
## 2 Related Work
### SISR Based on the Conditional Diffusion Model  
Diffusion models are a type of image generation models that is inspired by nonequilibrium thermodynamics. It defines a Markov chain of diffusion steps (the current state is only related to the previous state) and learns the reverse diffusion process to reconstruct the original data from noise. This model can be used for tasks such as image smoothing, denoising, and image generation. In image generation, the diffusion model can iteratively update pixels to produce diverse and realistic images. In terms of SISR, diffusion models can increase the detail and quality of an image by denoising it from additive Gaussian noise.  
[PERSON] et al. [22] proposed a novel approach to SISR by leveraging diffusion models, which efficiently diffuse the residual between HR and LR images to reduce training costs. By combining joint diffusion results with the LR image's residual, their approach achieves SOTA performance in generating HR images at that period. [PERSON] et al. [23] proposed a more generalized generative SISR method that generates HR images directly using LR image-guided diffusion derived from other conditional generation scenarios. However, this approach requires stitching LR images and incurs a huge computational effort. The diffusion models need to recover both the high- and low-frequency content of the image, which prolongs the convergence time and may inhibit the model's attention to fine-grained information and texture details, leading to generating images that deviate further from the reference image. Similarly, [PERSON] et al. [24] used residual diffusion for SISR but incorporated a CNN in image preprocessing to capture better pixel neighborhood relations, as opposed to [22], which used interpolation.
## 5 Results
### Using Three Channels of Dynamic V2X Traffic Assignment with Different CBR Thresholds
#### 5.3.2 Low Threshold: All Three Available Channels Will Be Used  
In this scenario, all three channels are used. The CBR threshold is now set to be equal for all ITS facilities protocols. With this, the Middleware switches between channels relatively simultaneously. This is an excellent way to understand the dynamic shifting behavior. The first CBR level is set to 0.2, the second to 0.2, and the third to 0.5.  
Figure 11: CCH CBR values—three dynamic channels and one static channel comparison, threshold = 0.8.  
Figure 12: SCH1 CBR values—three dynamic channels, threshold = 0.8.  
Figure 10: CCH CBR values—three dynamic channels (SAM, CPM, CAM, and PAM), threshold = 0.8.  
Figure 14 shows that the CBR value increases nicely up to 0.2, and then when it reaches its height, the saturation settles in the vicinity and fluctuates near 0.2 until the end of the simulation.  
On SCH1, the CBR of the channel is zero for about 7.5 s, so no transmission was sent until that. When the CBR on CCH reaches and exceeds the threshold (Figure 14), the middle layer detects that and immediately switches the next packet in the queue to the next SCH1 channel. From then on, SCH1 will also saturate and then concentrate around 0.2 (Figure 15).  
Figure 14: CCH CBR values—three dynamic channels, thresholds = 0.2, 0.2, 0.5.  
Figure 13: SCH2 CBR values—three dynamic channels, threshold = 0.8.  
On the SCH2 channel, no CBR was measured until the 13 th second. When the SCH1 exceeds its threshold level, the Middleware will transfer the current message to SCH2, so we see that, around the 13 th second, the CBR value increases on this channel (Figure 16). Thus, it puts the instant message on SCH2 if the CBR on SCH1 exceeds the threshold and the CCH channel is also above the threshold.  
We summarize our experiments with low CBR threshold in the three dynamic channel scenarios (Figure 17). It shows that channel load can be measured only on CCH in the first thirds. After it exceeds its threshold value at 7.5 th seconds, the CBR of the second most prioritized SCH1 channel starts to increase. Between 8 and 13 s, the SCH1 and CCH channels keep switching since, when SCH1 exceeds its threshold, the next packet can be transmitted on CCH and vice versa. After 13 s, CCH and SCH1 are both saturated at one moment, so the Middleware puts the given packet onto the SCH2.