esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling and Analysis
### Mesh Generation  
Considering the structural characteristics of the motor and the complexity of the calculation, the structure is simplified. Meanwhile, in order to improve the accuracy of the calculation, the second order hexahedron element is mainly used in the model finite element mesh. Figure 5 shows the FE mesh of the above four motor models.
## 3 Additional Damping Controller Design  
A traditional sub-synchronous damping controller (SDC) is generally a single-input, single-output system that includes gain, phase shifting, and clipping. The controller often uses the speed deviation signal \(\Delta\omega\), active output \(P_{meas}\) of the fan, and voltage as feedback signals [21].  
In this study, an additional damping controller installed in the DFIG control system GSC and RSC is used, and the electrical characteristics of the access point can be equivalent to a controllable impedance. The bus voltage \(u_{abc}\) of the doubly fed wind turbine is selected as the SDC feedback signal, which not only circumvents the problem of communication delays, but also behaves as an open circuit in the industrial frequency range and does not contain components complementary to the sub-synchronous mode in frequency.
# Smart Lighting Systems: State-of-the-Art in the Adoption of the EdgeML Computing Paradigm
## 5 Discussion
### Protection of Personal Data  
It has been remarked that because future indoor/outdoor LSs become smarter, they need to collect more data about people and their daily activities [58]. This point increases the data privacy concern which at present is a big issue for all IoT systems that handle personal information. In May 2018, the EU put into effect the General Data Protection Regulation. GDPR is a privacy and security law that imposes obligations and fixes penalties for organizations that collect data about people in the EU (a guide to the GDPR may be found here: [[https://gdpr.eu/](https://gdpr.eu/)]([https://gdpr.eu/](https://gdpr.eu/)) (accessed on 22 November 2024)). Because of the advent of the GDPR and subsequent privacy regulations in other countries all over the world, the adherence to the following principles are considered a precondition for actual protection of personal data: data minimization; storage limitation; purpose limitation; accuracy; lawfulness, fairness, and transparency; integrity and confidentiality; and accountability ([[https://gdpr.eu/what-is-gdpr/](https://gdpr.eu/what-is-gdpr/)]([https://gdpr.eu/what-is-gdpr/](https://gdpr.eu/what-is-gdpr/)) (accessed on 22 November 2024)).  
The present SLR on AIoT-based smart LSs points out that, in the set of the selected 26 primary studies, there is just one conference paper (namely, [95]) about the privacy preservation of personal data (Table 34). In the study, the authors propose a solution that addresses the data-minimization principle mentioned above. According to the GDPR, this principle states that only as much data as is really necessary for the purposes specified are involved in the processing.
# Laser Applications in Metal Orthodontic Bracket Debonding: A Systematic Review
## 5 Conclusions  
In conclusion, laser-assisted debonding represents a promising innovation in orthodontics, offering potential benefits for metal bracket removal by enhancing patient comfort and minimizing enamel damage. Erbium lasers, such as Er:YAG and Er,Cr:YSGG, demonstrate particular efficacy when used with water cooling, while diode lasers are safe alternatives when operated at appropriate power settings.  
This systematic review highlights that laser-assisted debonding is comparable to traditional plier-based methods in terms of the shear bond strength (SBS), with most studies reporting no significant differences. The adhesive remnant index (ARI) outcomes varied, but lasers often left less adhesive residue on the enamel, streamlining the debonding process. However, caution is necessary to prevent excessive temperature increases, especially with Nd:YAG lasers, which can pose risks if improperly set.  
Although laser-assisted methods are more frequently studied for ceramic brackets, the findings suggest that they are also effective for metal brackets. Future research should focus on refining laser parameters, evaluating the long-term outcomes, and establishing standardized, evidence-based guidelines to ensure safe and effective clinical applications.  
Conceptualization, P.W., J.M., M.M. and M.D.; Methodology, M.M. and S.K.; Software, S.K. and J.K.; Validation, J.M.; Investigation, P.W., S.K., T.D., J.K., T.H., M.S. and W.D.; Data curation, P.W., S.K., T.D., J.K., T.H., M.S., W.D. and M.M.; Writing--original draft preparation, S.K., T.D., J.K., T.H., M.S. and W.D.; Writing--review and editing, M.A.D., J.M. and P.W.; Supervision, J.M.; Project administration, J.M. and M.D. All authors contributed equally to the manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
The APC was financed by a subsidy from Wroclaw Medical University.  
The authors declare no conflict of interest.
## 3 Simulation Results and Discussion
### Parametric Study of the Sound Absorption Coefficient of HPSET with a Melamine Foam  
Since the sound absorption of HPSET-Melamine foam absorber is influenced by many parameters, we take the same analytical process and investigate the main parameters in this section. The resistivity of the melamine foam is a key factor which dominates the sound absorption. It is demonstrated in Figure 3a that the resistivity of the melamine foam will greatly influence sound absorption both at the anti-resonance frequency and the quarter wavelength resonance frequency. When increasing the resistivity of the melamine foam, the sound absorption at the anti-resonance frequency is enhanced, while the sound absorption at the quarter wavelength resonance frequency is firstly increased and then decreased. The absorption peak at Helmholtz resonance frequency decreases slightly with the increase in resistivity. Hence, a reasonable resistivity is required to match the specific resistance of the incident sound waves for the composite absorber.  
Figure 2(b) demonstrates the variation of the diameter of extended tubes on the overall sound absorption of the present absorber. The tube diameter is a critical factor which controls the sound absorption performance of the sound absorption of HPSET. When the tube diameter is too large, the relative resistance is less than 1, and the sound absorption is decreased. On the opposite, a small-diameter will induce overlarge acoustic resistance which will also decrease the sound absorption.  
As illustrated in Figure 2(c),d, the low frequency sound absorption peak of the combined absorber is greatly shifted to lower frequencies by decreasing the tube number or increasing the tube length, while the high sound absorption peak remains the same. It is noted that the low frequency sound absorption of the HPSET is due to the increase in mass reactance, and the resonance frequency could be tuned via optimizing the tube parameters.  
Figure 2: Analytical results of normal incidence sound absorption of HPSET with melamine foam. (**a**) Normal incidence sound absorption coefficient; (**b**) Characteristic acoustic resistance and reactance.
# Utilization of ICT as a Digital Infrastructure Concerning Disaster Countermeasures in Japan
## 3 Change in the Information Communication Environment in Japan from the 1990s Onward  
### Development of the Advanced Information Communication Network Society  
In Japan, the Basic Act on the Formation of an Advanced Information and Telecommunications Network Society (Basic Act on IT) became effective in 2000, and the advanced informatization was initiated (Ministry of Internal Affairs and Communications, 2002) [25]. Additionally, Japanese society was transformed from a ubiquitous society to a cloud computing society which was conducted by means of a sequence of advanced information strategies from the year 2000 onwards including \"e-Japan\" in 2000, \"u-Japan\" in 2006 and \"i-Japan 2015\" in 2010. These advanced information strategies respectively aimed at the realization of the world's most advanced information technology (IT) nation, ubiquitous network society, and digital society for security and vitality. Therefore, anyone in an internet-accessible environment as well as anyone with an information terminal can access the internet regardless of the time or place, realizing a society where information systems can be easily utilized.  
Further, as mentioned in the previous section, in recent years, Japan has come to an age of IoT and IoE where various things can be connected to the internet. As a result, Japan specifically set up the \"Development of future industry and social transformation (realizing a world-leading'super-smart society\")\" as a first pillar in the latest 5 th Science and Technology Basic Plan (Cabinet decision, January 2016) [3]. This new society is positioned after the \"hunter-gatherer society\", the \"agrarian society\", the \"industrial society\" and the \"information society\", and a series of initiatives towards the realization of this society is called \"Society 5.0\". In Part 1 of the White Paper on Science and Technology 2016 published based on the 5 th Science and Technology Basic Plan [26], the \"super-smart society\" is straightforwardly defined as a society that is \"achieved by integrating the reality and virtual spaces through the advancement of networks, and the development of technology for big data analysis and Artificial Intelligence (AI)\". The technology needed to realize this new society is mostly related to the information field. Therefore, a digital infrastructure is an important fundamental infrastructure that supports various other infrastructures, and its importance will increasingly grow in the new society.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 3 Methodology
### Preliminary  
**Notations.** For clarity and simplicity, calligraphy uppercase letters, such as \(\mathcal{O}\), denote sets. Bold uppercase letters, such as \(\mathbf{W}\), represent matrices. Bold lowercase letters, such as \(\mathbf{w}\), indicate vectors. In addition, the \(ij\)-th element of \(\mathbf{W}\) is represented as \(\mathbf{W}_{ij}\), the \(i\)-th row of \(\mathbf{W}\) is represented as \(\mathbf{W}_{i*}\), the \(j\)-th column of \(\mathbf{W}\) is represented as \(\mathbf{W}_{*j}\), \(\mathbf{W}^{T}\) is the transpose of \(\mathbf{W}\), \(\mathbf{I}\) represents identity matrix. \(\|.\|\) represents the Frobenius norm of a matrix. \(sgn(.)\) represents a sign function, shown as below:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Meaning** \\ \hline MGSPU & Multi-Grained Similarity Preserving and Updating \\ CNN & Convolutional Neural Network \\ BoW & Bag of words \\ GCNs & Graph Convolutional Networks \\ ResNet & Residual Networks \\ LSTM & Long Short-Term Memory Network \\ MGSP & Multi-Grained Similarity Preserving \\ GSA & Global Similarity Aggregation \\ LSI & Local Similarity Interaction \\ SCR & Similarity Consistency Reconstruction \\ \hline \hline \end{tabular}
\end{table}
Table 2: Abbreviation summary.  
\[sgn(x)=\begin{cases}1,&x\geq 0\\ -1,&x<0\end{cases}. \tag{1}\]  
For ease of comprehension, commonly utilized mathematical notations have been compiled in Table 3 to assist in reference.  
**Problem Definition.** This paper concentrates on cross-modal retrieval between two prevalent modalities, i.e., image \(v\) and text \(t\). Let \(\mathcal{O}=\left\{(\mathbf{v}_{i},\mathbf{t}_{i})\right\}_{i=1}^{n}\) be a cross-modal dataset, \(\mathbf{v}_{i}\in\mathbb{R}^{d_{v}}\) and \(\mathbf{t}_{i}\in\mathbb{R}^{d_{t}}\) refer to the image and text in the pairwise instance respectively, \(d_{v}\) and \(d_{t}\) represent the dimension of the corresponding features, \(n\) denotes the number of instances. Our study aims to learn two hash functions \(f^{v}=(\cdot;\mathbf{\theta}_{v})\) and \(f^{t}=(\cdot;\mathbf{\theta}_{t})\) to map images and texts to a common Hamming space: \(\mathbf{B}^{v}=sgn(f^{v}=(\{\mathbf{v}_{i}\}_{i=1}^{n};\mathbf{\theta}_{v}))\), \(\mathbf{B}^{t}=sgn(f^{t}=(\{\mathbf{t}_{i}\}_{i=1}^{n};\mathbf{\theta}_{t}))\), where \(\mathbf{B}^{v},\mathbf{B}^{t}\in\{-1,1\}^{n\times C}\), \(C\) represents the length of the binary code, \(\mathbf{\theta}_{v}\) and \(\mathbf{\theta}_{t}\) are the learnable parameters. In addition, the similarity between two binary codes \(\mathbf{B}_{i}^{v}\) and \(\mathbf{B}_{j}^{t}\) is measured by Hamming distance \(D\left(\mathbf{B}_{i}^{v},\mathbf{B}_{j}^{t}\right)=\frac{1}{2}\left(C-<\mathbf{B}_{i}^{v},\mathbf{B}_{j}^{t}>\right)\), which is the base for implementing cross-modal hashing retrieval.
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 4 Results
On the other hand, in cases where no initial seed was given, the performance of the no-coverage fuzzer was significantly degraded (e.g., highlighted with yellow color in the table). The stochastic tendency in our evaluation table indicates that it would be better to investigate the efforts (engineering, manpower) for providing proper initial seeds for test cases rather than enhancing/adopting better coverage feedback features in some extreme cases. Other factors, such as the type of bug and data parser category, did not affect the evaluation data in a significant way. Nevertheless, we have investigated such issues as well and provide related information in Appendix A. Interestingly, in some cases, coarse granularity coverage feedback fuzzing showed better efficiency (finding the same bug more quickly) compared with the original. The reason for such a phenomenon might be explained with some example cases that we analyze in Section 5. Overall, it is evident that coverage feedback enhances the performance of fuzzing and thus increases the likelihood of discovering unknown bugs in a more efficient way. However, our study suggests that if the target system (such as a closed-source proprietary application) requires significant efforts and costs for applying the coverage feedback feature, it would be better to invest the limited resources in other perspectives such as providing high-quality initial test cases that include various structures and logical semantics of the application with static analysis.
## 2 Materials and Methods
### Determination of Antioxidant Activity  
DPPH and ABTS assays were performed to evaluate the antioxidant activity using a spectrophotometer. The 0.1 mg/mL (75:25 \(v/v\) acetone and DI water solution) of extracted solution was used to determine antioxidant activity. Ascorbic acid was used as positive control. The antioxidant activity was expressed in terms of radical scavenging activity by the following equation:  
\[\text{Radical scavenging activity (\%)}=[(\text{A}_{0}-\text{A}_{\text{s}})/\text{A}_{ \text{s}}]\times 100\]  
where, A\({}_{0}\) is absorbance of blank, and A\({}_{\text{s}}\) is absorbance of sample extract.  
In DPPH assay, as described by [PERSON] et al. (1995) [23] with modifications, 200 \(\upmu\)L of the sample extract was reacted with 2.8 mL of DPPH (0.1 mM in methanol)solution. After incubation for 30 min in the dark, absorbance was measured at 517 nm wavelength against a blank (200 \(\upmu\)L of methanol in 2.8 mL DPPH solution).  
In the ABTS assay, as described by [PERSON] et al. (1999) [24] with modifications, ABTS-+ solution was prepared by mixing ABTS solution (7 mmol/L) with K\({}_{2}\)S\({}_{2}\)O\({}_{8}\) solution (2.45 mmol/L) in a 1:1 volume ratio and stored for 12-16 h in the dark. ABTS-+ solution was diluted with ethanol in a 1:50 volume ratio to an absorbance of -0.7 \(\pm\) 0.02 at 734 nm wavelength. Then, 1 mL of ABTS+ solution was added to 10 \(\upmu\)L of the sample extract and reacted for 15 min in the dark. Absorbance was measured at 734 nm wavelength against a blank (10 \(\upmu\)L of DI water in 1 mL ABTS-+ solution).
## 3 Materials and Methods
### Experimental Setup  
Five supervised regression algorithms were trained using a two-level stratified 10-fold cross-validation approach. In this setup, the outer 10-fold cross-validation (outer-CV) was employed to evaluate the models, ensuring an unbiased estimation of their performance. For hyperparameter tuning, we implemented a nested, stratified 10-fold cross-validation (inner-CV) within each training fold of the outer-CV. This inner-CV was exclusively used for tuning the hyperparameters, ensuring that the tuning process was isolated from the test data (see Appendix B for more information).  
The algorithms used were a linear regression (LR), decision tree (DT), Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator with Least Angle Regression (LASOLARS), and Random Forest (RF). These algorithms were selected to cover a diverse range of modeling techniques, from linear models (LR) to more complex, non-linear models (DT, SVM, RF) and regularized regression methods (LASSOLARS). All these algorithms were implemented using the Python 3.8.10. programming language and the versatile scikit-learn library. A random seed of 0 was used for consistency across the algorithms. To validate the algorithms, as in the work by [PERSON] et al. (2019) [11], we calculated the mean absolute error (MAE) metric between the predicted and actual values.  
It is important to include a feature importance analysis to identify which features are most predictive of cognitive deterioration. For this purpose, we used the SHapley Additive exPlanations (SHAP) algorithm (see Appendix C). The advantage of this algorithm is that it allows for the analysis of the importance of groups of variables, categorized according to their domain (as defined in Table 1).
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 1 Introduction  
Clear visibility is crucial to the success of outdoor computer vision tasks [1]. However, the quality of outdoor images often deteriorates due to haze, i.e., the existence of smoke, dust, fumes, mist, and other floating particles in the atmosphere. Such haze reduces the performance of a subsequent visual analysis. Moreover, haze distribution often has a non-homogeneous character in many real scenes, posing additional challenges [2]. Single-image dehazing as a fundamental low-level vision task aims to recover the latent haze-free image and has attracted increasing attention in the computer vision community over the past few decades [3; 4; 5; 6; 7; 8].  
In the early dehazing methods [9; 10; 11], image priors are usually used to estimate important parameters of the imaging model for hazy scenes. Then, image dehazing is fulfilled by solving the inverse problem of such an imaging model. However, these image priors are not always valid in complex, real hazy scenes. Additionally, the nonhomogeneous distribution of haze is certainly one factor that adds to the difficulties.  
Recently, deep-learning-based methods [12; 13; 14; 15; 16; 17; 18; 19; 20] have directly learned the latent clean image from a single hazy image in an end-to-end manner and have shown promising performance in haze removal. However, the current deep-learning-based dehazing modelsoften have a large number of parameters (e.g., the numbers of parameters of the recent two-branch neural network (TBNN) [19] and the Dehamer [20] network are around 50.40M and 132.45M, respectively) or suffer from a low runtime speed. In addition, to overcome the complex distributions of non-homogeneous images, a lightweight network to deal with nonhomogeneous haze effectively and efficiently is of great value. To this end, we design an efficient re-parameterization residual attention network featuring a training-time multi-branch esidual attention block, an end-to-end lightweight network structure and two new loss functions to achieve a good balance between the dehazing performance and model complexity. We conduct comparative experiments on both non-homogeneous and homogeneous haze scenes to demonstrate the superiority of the proposed RRA-Net. An ablation study is also conducted to validate the main modules of the proposed network.  
Our main contributions are summarized as follows:  
(1) A training-time multi-branch residual attention block is designed for coping with the non-uniformity of haze.  
(2) An end-to-end lightweight network structure (with only about 0.3M parameters) is proposed, which cascades six MRABs followed by a long skip connection with attention and a fusion tail.  
(3) Two novel loss functions, namely the Laplace pyramid loss and the color attenuation loss, are employed to train our RRA-Net.
# A Comprehensive Survey on Knowledge-Defined Networking
### Research Methodology  
This survey is a qualitative research study that critically analyzes and interprets existing research work on knowledge-defined networking. The population of the survey is comprised of all original research works and web articles published related to KDN and artificial intelligence/machine learning/knowledge-based SDN. Out of that population, we sampled 613 of the most relevant research works published from 1980 to 2023 related to architecture, operation, applications, interfaces, functions, languages, and protocols of each plane of the KDN paradigm by searching scientific databases. We used IEEE Xplore, the ACM Digital Library, ScienceDirect, MDPI, and Google Scholar as searching databases. However, after careful analysis, we identified that 43 works presented very similar content to one or more works already in the sample. Therefore, we removed these 43 redundant references, which provided redundant concepts, to finally reach 570 original research references in the cleaned sample. Later, we added five survey articles published on knowledge-/machine learning-based SDN in order to compare our review with existing reviews, thus increasing the total references to 575. Thus, the approach of the survey was longitudinal. The research works were selected based on their relevance to the content discussed in the survey. We used tabular data structures to analyze research qualitatively by grouping them into categories and analyzing the characteristics of the frameworks. We sampled research works based on relevancy without being biased by any publisher or time of publication. However, priority was given to journal publications over conference publications to improve the validity and reliability of the survey. Ethical considerations are not applicable, as this is a survey in the computer networking domain.
## 5 Simulation Experiments and Analysis
### Path Planning Scenarios  
The complexity of the experimental scenario is mainly defined by the shape, proportion, and density of obstacles in the scenario, and the scenario is divided into simple scenarios and complex scenarios. The number of obstacles in simple scenarios is small and the shape of obstacles is regular. The number of obstacles in complex scenarios is large or the shape of obstacles is irregular. In order to verify the completeness and robustness of the proposed algorithm, this paper sets up three different experimental scenarios according to the division of the complexity of the experimental scenario, and the scenarios are complicated in turn. In order to verify the collision-free nature of the path planned by the proposed algorithm for multiple UGVs, the settings of the start point and target point of UGV1 and UGV3 do not show the relationship of upper and lower correspondence but set the relationship of cross-correspondence.