esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### State of the Art Comparison and Discussion  
The major advantages of the proposed methodology and the current machine learning methods are shown in Table 8. This work presents a novel method with remarkable benefits, such as a decentralized system, enhanced data privacy, and large-scale deployment simplicity. Exceptional performance measurements enhance these qualities in several aspects. The study suggests that SVM and KNN, when employed under FL, attained an amazing 95% accuracy, surpassing the 94% of Logistic Regression, while Naive Bayes (NB) reached only 89%. Furthermore, precision measurements show that at 99%, NB outperforms SVM and KNN at 94% and LR at 92%. Though recall rates vary somewhat, SVM outperforms LR and KNN at rates of 96% and NB at 79%. All models, though, routinely perform quite well when considering the F1 score. In such sequences, KNN scores 95%, LR scores 94%, and SVM scores 96%. The difference shows how considerably FL enhances the capacity of intrusion detection in IoT systems, signaling a new era of reliable cybersecurity solutions that prioritize data privacy, distributed processing, and flexible deployment. Promising results have been obtained from the suggested model applied to machine learning models. SVM and KNN, particularly, reach high accuracy rates of 95% on both the test and validation datasets, with the global models continually showing excellent performance in classification correctness. Furthermore, an analysis of the state of the art emphasizes FL's advantages over traditional machine-learning techniques regarding data privacy, decentralized processing, and scalability. These results highlight how much FL enhances the security of IoT deployments, particularly by lessening the negative effects of selective forwarding attacks in networks with RPL enabled.  
Hyperparameters, including batch size, learning rate, and dropout rate, impacted the performance of the FL-DSFA. The research utilized a learning rate of 0.001, a batch size of 32, and a dropout rate of 0.25 to avoid overfitting and improve model generality. These hyperparameters have a straight-wedged model's accuracy and convergence pace. The LR of 0.001 ensured steady convergence, though the dropout rate of 0.25 enhanced generalization, despite producing underfitting. The optimization of these hyperparameters was vital for attaining optimal efficacy and performance in the proposed model.  
Figure 13: Global models’ performance on validation data.
## 8 Conclusions and Future Research  
In this paper, we presented MT-SOTA as our scheme for software over-the-air updates for software-based vehicle architectures. We furnished a theoretical explanation and analysis of this approach, considering various parameter values to encompass diverse use cases, ultimately representing various software architectures. The outcomes of our study illustrate the performance enhancement realized through MT-SOTA, coupled with a reasonable storage overhead. The MT-SOTA scheme is scalable, convenient, and flexible for integration into existing automotive software architecture platforms and new software-defined vehicle architectures. The execution time and memory requirements show that the Merkle-tree-based signature is suitable for deployment in backend repositories and most importantly in resource-constrained ECUs. The MT-SOTA is parameterized to fit the need of OEM applications and architecture-use cases. The intended performance and ECU resources, including factors such as the presence of hash engines in hardware, necessary calculation speed, and memory capacity, are carefully considered when determining the MT-SOTA parameters to achieve optimal outcomes. As shown through the measurement results and proposed optimization methods, MT-SOTA accelerates the time required to verify a software component update in the field. The removal of a software component exhibits enhanced efficiency with MT-SOTA, leading to reduced time due to the absence of hash calculations on the eliminated software component. While MT-SOTA offers several advantages for software over-the-air updates, it also comes with certain limitations. Implementing and managing Merkle trees can be complex. OEMs are required to establish the Merkle trees within their repositories and communicate the structure to the ECUs in the vehicle. Constructing the initial Merkle tree contents involves computing hash values for all software blocks, which can be time-consuming and resource intensive. The MT-SOTA's efficiency diminishes when the number of software clusters is restricted. Consequently, one potential proposal is to adopt a per-vehicle Merkle tree approach. Future work is to study the MT-SOTA for high Computing ECUs or zone controller ECUs with the AUTOSAR adaptive platform as well as the new AUTOSAR \"Vehicle API\" variant. Our forthcoming research endeavors encompass the exploration of the MT-SOTA methodology to tackle the complexities of variant management. This involves effectively managing diverse software configurations and adaptations within an ECU. The primary incentive is to establish a single signature, generated utilizing the Merkle tree scheme, for the ECU. This signature will subsequently undergo verification at the ECU level, accommodating distinct vehicle models, options, or features.
###### Abstract  
Acne-prone skin is a common condition in adult women, and skin imperfections could affect quality of life and self-esteem. This study aimed to investigate the efficacy of a cosmetic combination regimen for face care (a cleaner gel and a serum containing niacinamide, retinol, and alpha hydroxy acids). A total of 20 women with acne-prone mixed or oily skin were enrolled in a prospective 42-day trial. Sebum content, skin radiance, skin profilometry, and evaluation of face area occupied by pores were evaluated at baseline and after 14, 28, and 42 days. In addition, a face lipidomic evaluation was performed at baseline and after 42 days. Finally, self-assessment questionnaires at each visit checkpoint were performed to evaluate efficacy and tolerability of the tested products. All the subjects but one concluded the study. Both products were very well tolerated and 84% of the subjects reported a global clinical improvement. Skin sebum content was significantly (\(p<0.05\)) reduced at each of the evaluation time points (\(-9.9\%\) at day 14, \(-19.4\%\) at day 28, and \(-23.7\%\) at day 42). The tested regimen significantly decreased the gloss parameter (mattifying effect) at day 14, 28, and 42, with a maximum reduction of \(7.2\%\) at the end of the study period. The pores area demonstrated a significant reduction at each of the checkpoint evaluations in comparison with baseline. Inflammatory and non-inflammatory lesions were significantly reduced by \(16\%\) at day 28 and day 42 (\(p<0.01\)). Lipidomic analysis demonstrated that this cosmetic face care regimen induced significant and positive effects in face sebum lipids composition, characterized by a significant increase in ceramides and triacylglycerglycerols and a decrease in fatty acids and oxidized fatty acids.  
acne-prone skin; lipidomic; sensitive skin Article
## 2 Methodologies
### Element Failure Probability of Slope
In this paper, two new concepts of EFP based on the external overload coefficient (\(k_{\text{F}}\)-\(u_{\text{c}}\)-EFP) and based on the bulk density overload coefficient (\(k_{\gamma}\)-\(u_{\text{c}}\)-EFP) are proposed for the first time. According to the concept of slope in the limit state, it can be determined that if the slope has been destroyed without applying any external load or applying any opposite direction load, then the external overload coefficient and bulk density overload coefficient are both less than 0. In addition, when plastic flow occurs in the discrete triangular element, the relative velocity of the element will be generated compared with that of the fixed element on the boundary; that is, the velocity at the centroid of the element is greater than 0, which is the failure element. Therefore, the slope failure function based on \(k_{\text{F}}\)-\(u_{\text{c}}\)-EFP is expressed as:  
\[I_{k_{F}}^{e}(t_{m})=\left\{ \begin{array}{rl}0\text{ if }k_{\text{F}}(t_{m}) \geq 0\\ 0\text{ if }u_{\text{c}}^{e}(t_{m})=0\text{ and }k_{\text{F}}(t_{m})<0\\ 1\text{ if }u_{\text{c}}^{e}(t_{m})~{}>~{}0\text{ and }k_{\text{F}}(t_{m})<0\end{array} \right. \tag{16}\]  
The slope failure function based on \(k_{\gamma}\)-\(u_{\text{c}}\)-EFP can be expressed as:  
\[I_{k_{\gamma}}^{e}(t_{m})=\left\{ \begin{array}{rl}0\text{ if }k_{\gamma}(t_{m}) \geq 1.0\\ 0\text{ if }u_{\text{c}}^{e}(t_{m})=0\text{ and }k_{\gamma}(t_{m})<0\\ 1\text{ if }u_{\text{c}}^{e}(t_{m})~{}>~{}0\text{ and }k_{\gamma}(t_{m})<0\end{array} \right. \tag{17}\]  
The EFP of the slope can be unified as:  
\[P_{fe}^{M-u_{\text{c}}\text{-EFP}}=\frac{\sum\limits_{t_{m}=1}^{n_{m}}I_{M}^ {e}(t_{m})}{n_{m}}\times 100\% \tag{18}\]  
where \(P_{fe}^{M-u_{\text{c}}\text{-EFP}}\) = the failure probability of slope element \(e\), of which \(M=k_{m},k_{\text{F}},k_{\gamma}\). The failure risk of the slope element can be expressed as:  
\[R_{fe}^{M-u_{\text{c}}\text{-EFP}}=P_{fe}^{M-u_{\text{c}}\text{-EFP}}C_{e} \tag{19}\]  
where \(R_{fe}^{M-u_{\text{c}}\text{-EFP}}\) = the failure risk of slope element \(e\); \(C_{e}\) = the failure risk coefficient of slope element \(e\), \(e=(1,\dots,N_{e})\). Further the failure risk of a slope is:  
\[R^{M-u_{\text{c}}\text{-EFP}}=\sum\limits_{e=1}^{N_{e}}R_{fe}^{M-u_{\text{c }}\text{-EFP}} \tag{20}\]  
where \(R^{M-u_{\text{c}}\text{-EFP}}\) = slope failure risk by \(M\)-\(u_{\text{c}}\)-EFP. The mean and standard deviation of the external overload coefficient \(k_{\text{F}}(t_{m})\), bulk density overload coefficient \(k_{\gamma}(t_{m})\) and slope stability coefficient \(k_{m}(t_{m})\) can be obtained by statistics. Specifically, they are:  
\[\mu_{M}=\frac{\sum\limits_{t_{m}=1}^{n_{m}}M(t_{m})}{n_{m}} \tag{21}\]\[\sigma_{M}=\sqrt{\frac{\sum\limits_{t_{m}=1}^{n_{m}}(M(t_{m})-\mu_{M})}{n_{m}-1}} \tag{22}\]  
where \(\mu_{M}\), \(\sigma_{M}\) = the mean value and standard deviation of \(M\); \(M=k_{m},k_{F},k_{\gamma}\); \(t_{m}=(1,\ldots,n_{m})\).
## 3 Results
### Augusta  
Here, we present the results of a multi-temporal InSAR analysis performed using SAR images from the ENVISAT sensor and covering the time interval 2003-2010. The study has the objective of identifying active ground movements of tectonic and anthropogenic origin in Augusta Bay in southeastern Sicily [31; 32].  
In this study, 33 and 55 SLC acquisitions were selected in the temporal spans of 27 August 2003--11 November 2009 and 26 February 2003--22 September 2010 for the descending and ascending orbit, respectively. We obtained evident subsidence in the Augusta graben (Figure 7). The latter is a structural basin extended in the northwest (NW) direction and developed in the last two to three million years. The basin is enclosed to the northeast (NE) by the Mt. Tauro Horst, and detached from the River Marcellino graben to the southwest (SW) by the Petraro Horst. The retrieved pattern (Figure 7) shows an elongated shape with the major axis along the NW-southeast (SE) direction, which is parallel to the maximum graben extent. The affected area shows an edged border along the eastern rim, which is characterized by the narrow escarpment of the Mt. Tauro fault, and a more gradual deformation gradient along its western limit. Generally, the subsidence phenomenon is \(\sim\)8.5 km long (NW-SE) and \(\sim\)4 km wide, with a mean velocity of -6 mm/yr. Moreover, large displacements occur along an NW-SE alignment, parallel to the Mt. Tauro fault, with the highest rates (up to -18 mm/year) close to the recent urbanization of Augusta city, built over redeemed saltworks. The displacement time series mainly shows a linear lowering trend reaching a total amount of \(\sim\)70 mm and \(\sim\)40 mm for the ascending and descending cases, respectively [33]. The lowest value for the descending orbit is caused  
Figure 6: Deformation map and time series for the Sibari plain retrieved by C-band ENVISAT InSAR data.  
by the shorter considered period, while the minor linearity (especially in the last period) depends on the smaller number of SAR acquisitions in the InSAR processing.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
###### Abstract  
The orbit maneuver detection is crucial in Global Navigation Satellite System (GNSS) precise orbit determination, which is necessary for adjusting data processing strategies. The frequency of orbit maneuvers for the BeiDou Navigation System is significantly higher than that of other navigation systems, especially for geosynchronous orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. We propose a novel real-time and postprocessing method for detecting orbit maneuvers for BeiDou satellites based on the orbit differences between the epoch-updated orbit estimated using square root information (SRIF) and the predicted orbit according to the precise orbit estimated during non-maneuver period, as well as the orbital state difference during maneuver and non-maneuver periods. This method has significant advantages over using observation residuals and it is not affected by observation outliers, thus improving the accuracy and loneliness of orbit maneuver detection. We demonstrated that 32 orbit maneuver events of BeiDou satellites were successfully detected in 2022, of which 1 was for medium Earth orbit (MEO), 7 were for IGSO with an average detected maneuvering time of 7-8 min, and 24 were for GEO satellites with an average detected time of 4-5 min. Moreover, our method can be easily integrated into current real-time filter-based precise orbit determination (POD) processing without any extra task line, which simplifies the overall data processing. The data used in this method can be accessed easily, including GNSS observation data, broadcast ephemeris, and other open-source information files.  
BeiDou; maneuvering detection; square root information filter; orbit difference +
Footnote †: journal: Remote Sensing  
**2023**, _15_, 2558. [[https://doi.org/10.3390/rs15102558](https://doi.org/10.3390/rs15102558)]([https://doi.org/10.3390/rs15102558](https://doi.org/10.3390/rs15102558))
## 5 Experimental Results and Analysis
### Ablation Experiments
#### 5.4.3 Different Multi-Channel Fusion Modules  
To verify the effect of multi-feature fusion module based on the cross-channel attention mechanism, we compared the module with the traditional natural splicing approach on the NTU RGB+D 60 dataset, and the experimental results are shown in Table 4. The experimental results show that, when a multi-feature fusion module based on a cross-channel attention mechanism is not used and the simple coequal fusion method is used to fuse multi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Hypergraph Construction Method**} & \multicolumn{2}{c}{**Input Data**} & \multicolumn{2}{c}{**NTU RGB+D 60**} \\ \hline
**PCJ** & **N-PCJ** & **N-PCB** & **Joint** & **Bone** & **CS(\%)** & **CV(\%)** \\ \hline \(\surd\) & & & \(\surd\) & & 88.6 & 94.0 \\  & \(\surd\) & & \(\surd\) & & 88.7 & 93.8 \\  & & \(\surd\) & & \(\surd\) & 89.2 & 94.2 \\ \(\surd\) & \(\surd\) & & \(\surd\) & & 90.7 & 95.4 \\  & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 90.3 & 95.2 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 91.7 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of action recognition accuracy of different hypergraph construction methods and different input data information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**NTU RGB+D 60**} \\ \cline{2-3}  & **CS(\%)** & **CV(\%)** \\ \hline Only temporal convolution blocks & 81.2 & 86.3 \\ \hline Only spatial convolution blocks & 82.1 & 87.7 \\ \hline Temporal convolution blocks + spatial convolution blocks & 91.2 & 96.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the action recognition accuracy of different structural neural networks.  
channel features, the accuracy of action recognition on CS (cross-subject) protocols and CV (cross-view) protocols is 88.4% and 93.2%, respectively; when the multi-feature fusion module based on cross-channel attention mechanism is used, the accuracy of action recognition on CS (cross-subject) protocols and CV (cross-view) protocols is 91.2% and 96.7%, respectively, an improvement of 2.8% and 3.5% compared to the former. This shows that although the introduction of higher-order correlations can improve the action recognition performance, the traditional coequal fusion method does not yield the best combination of multi-channel feature performance, suggesting that different higher-order correlations have different effects on representation learning. Therefore, this paper correlates each channel feature with an attention score, thus adaptively identifying the importance of different channel features to the overall action recognition and making better use of the complementary representations of multi-channel features, which helps improve the accuracy of action recognition.
## 3 Brief Overview of EMD-Based Methods
The detailed coefficients of SWT, which contain valuable information, are extracted and used to reconstruct the denoised signal using inverse wavelet transform. As shown in Figure 1e, the previous method [21] is based on a multi-step technique that employs EMD, DFA, and SWT. Certain IMFs may be noise-free after EMD, while others may be dominated by noise; categorization is performed by determining an appropriate threshold using the DFA. Existing techniques employ predetermined DFA slope scores; however, previous work suggests an adaptive estimate based on each mode's greater frequency content. It is based on a plot of the scaling exponent vs. mode to estimate the threshold. The threshold is determined as the maximum fluctuation change or maximum decrease in long-range correlations between two consecutive scaling exponents of two consecutive modes. Further denoising procedures have only been applied to noisy modes because of information loss if signal-dominant IMFs are totally discarded. In short, instead of rejecting the IMFs corresponding to noise (as in the conventional method), the previous method employs SWT on modes outside the threshold before the desired signal is finally reconstructed. Any EMD-based method (and the previous method [21] is not an exception) may not perform optimally due to the property of mode mixing, which occurs when multiple IMFs are manifested within a single IMF, and the sensitivity of the EMD to noise. These issues can cause noise to leak into some of the signal modes, leading to their rejection and suboptimal denoising. These limitations motivate the proposed method as presented in this paper and is detailed next.
###### Abstract  
Background: This research was designed to carry out a meta-analysis in order to evaluate the dissimilarities in the stability of alveolar bone round dental implants and implant failure between platform switching (PS) and platform mating (PM). Methods: The investigation utilized a comprehensive search strategy that incorporated controlled vocabulary (McSH) and free-text terms. This search was performed by two reviewers to identify published systematic reviews. Three major electronic databases, including Medline via PubMed, the Cochrane database, and Embase, were searched up to November 2023. Results: Initially, 466 articles were identified, but only twelve studies met the criteria for inclusion in the meta-analysis. The results showed that the pooled mean difference for reducing marginal bone loss (MBL) was \(-\)0.60 (95% confidence interval (CI), \(-\)0.91 to \(-\)0.28; \(p\) < 0.01). A sensitivity analysis was conducted by excluding a single study, which yielded a result of \(-\)0.46 (95% CI, \(-\)0.66 to \(-\)0.25; \(p\) < 0.01). The test for overall effect was significant (\(p\) < 0.01), and it revealed that there were significant differences between subgroups. However, the meta-analysis on implant failure did not show a significant difference between PS and PM implants. Conclusions: In conclusion, the study found that PS implants are more effective in reducing MBL compared to PM implants. Nevertheless, no significant difference was observed in the long-term effectiveness of reducing MBL and implant failure rate.  
alveolar bone loss; dental implant-abutment design; dental prosthesis; implant-supported
# Corpsia--A Feasible Second Best Solution?
## 2 Institutional and Technical Background
### Challenges for CORSIA's Climate and Regulatory Integrity
#### 2.3.4 Assessment Procedures  
While assessment procedures improve the climate and regulatory integrity of carbon offset projects, they come with their own costs. Safeguard clauses have been developed to identify and reduce adverse effects and verification and monitoring processes have been implemented to ensure additionality, permanency, and avoidance of carbon leakage [16]. However, assessment is partially subjective, as project characteristics are difficult to validate [26] and core indicators can be subject to manipulation [31]. Many certified offset projects are suspected of not being effective in terms of net GHG reduction [5; 19]. A critical challenge is determining the optimal balance between compliance and supply. If quality standards are too stringent, desirable projects may not participate [42]. Satisfying requirements with respect to additionality, permanence, and avoidance of adverse effects increases transaction and investment costs. For example, where there is a risk of non-permanence, verification standards require carbon offset projects to insure against it by making use of buffer accounts. Projects contribute a share of carbon credits to a buffer account and, in case of reversals, an equivalent number of buffer credits are canceled. This creates high opportunity costs. These may be reduced by simplifying validation processes, although at the cost of lower climate and regulatory integrity [7]. As a result, adverse selection challenges offset standards [43; 44]. Large-scale projects have lower transaction costs than small-scale projects, and transaction costs vary between project types. For example, they are on average lower for carbon storage compared to carbon reduction [45].
## 2 Results and Discussion
### System Requirements  
The software was implemented on a Laptop DELL XPS M1210, Centrino Duo Processor, 1 GB RAM Memory and 80 GB Hard drive, using Eclipse Europa version 3.3.0 as Java Project Editor or Manager, with the Java version 1.6 Compiler and Java 3D 1.5.1 plugin.  
The alphanumerical output was checked using HyperChem version 7.0 and 7.5.  
The application was tested into an Intel Pentium 4 Workstation using Microsoft Windows XP, Java J2 SE JRE 1.6.0_07 and Java 3D.  
Any computer that support Windows XP, Java and Java 3D could run this application.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline N & Nanotube & MCAD & HyperChe \\ \(\circ\) & & (sec) & m (sec) \\ \hline
1 & (C6)3 & 7 & 240 \\ \hline
2 & (C6)5 & 7 & 320 \\ \hline
3 & (C6)10 & 8 & 450 \\ \hline
4 & (C4N2)10 & 160 & 550 \\ \hline
5 & (C12)10 & 9 & 1320 \\ \hline
6 & (C6)20 & 9 & 700 \\ \hline
7 & chiral, C12, 15 Å length & 10 & NA* \\ \hline
8 & armchair, C12, 15 Å length & 10 & NA \\ \hline
9 & zigzag, C12, 15 Å length & 10 & NA \\ \hline \end{tabular}
* NA = not achievable due to difficulties to build the nanotube.  
\end{table}
Table 1: Nanotube generation MCAD performance, and its comparison with HyperChem.  
This algorithm can be installed in your own computer. For such a task you can access it through the supplementary material together with instructions for installation.