esa-sceva/satcom-chunk-collection huggingface dataset:
# Measuring the Effect of Mental Health on Type 2 Diabetes
## 5 Results  
### Study Population  
The population of this study is summarized in Table 2. The target variable is diabetes, and it is divided into diabetes and no diabetes. A dummy variable is created for mental health that takes only values of 0 or 1, which indicate low or high values based on the average value (3.185). The dummy variable is used in the causal inference model as the treatment variable. This study uses input variables like physical activity, vegetable, heavy drinker, fruits, age, cholesterol, sex, smoker, health, BMI, and blood pressure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & & **Items** \\ \hline Target variable & Diabetes & 0 = no diabetes, 1 = diabetes \\ Feature variables & Physical activity & Physical activity in the past 30 days (not including a job) \\  & & 0 = no, 1 = yes \\  & Vegetable & Consumption of vegetables more than once per day \\  & & 0 = no, 1 = yes \\  & Heavy drinker & Heavy drinker (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week) \\  & & 0 = no, 1 = yes \\  & Fruits & Consumption of fruit more than once per day \\  & & 0 = no, 1 = yes \\  & & 1 = 18-24, 2 = 25-29, 3 = 30-34, 4 = 35-39, 5 = 40-44, 6 = 45-49, \\  & Age & 7 = 50-54, 8 = 55-59, 9 = 60-64, 10 = 65-69, 11 = 70-74, 12 = 75-79, \\  & & 13 = 80 or older \\  & Cholesterol & 0 = no high cholesterol, 1 = high cholesterol \\  & Sex & 0 = female, 1 = male \\  & & Consumption of at least 100 cigarettes (5 packs = 100 cigarettes) in \\  & Smoker & life \\  & & 0 = no, 1 = yes \\  & Health & General health \\  & BMI & 1 = excellent, 2 = very good, 3 = good, 4 = fair, 5 = poor \\  & Blood pressure & Body Mass Index \\ Treatment variable & Blood pressure & 0 = no high blood pressure, 1 = high blood pressure \\  & & Days during the past 30 days where one’s mental health was not \\  & & & good (including stress, depression, and problems with emotions) \\  & & & Scale 1-30 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of variables.  
\begin{table}
\begin{tabular}{l l l r r} \hline \hline
**Variables** & & & **Frequency** & **Percentage** \\ \hline Target variable & Diabetes & no diabetes & 213,703 & 84.2 \\  & & diabetes & 39,977 & 15.7 \\ Feature variables & Physical activity & No & 61,760 & 24.3 \\  & Yes & 191,920 & 75.7 \\  & & No & 47,839 & 18.9 \\  & Vegetable & Yes & 205,841 & 81.1 \\  & Heavy drinker & No & 239,492 & 94.4 \\  & & Yes & 14,4256 & 5.6 \\  & Fruits & No & 92,782 & 36.6 \\  & & Yes & 160,898 & 63.4 \\  & & 18-24 & 5700 & 2.2 \\  & & 25-29 & 7598 & 3.0 \\  & & 30-34 & 11,123 & 4.4 \\  & & 35-39 & 13,823 & 5.4 \\  & & 40-44 & 16,157 & 6.4 \\  & & 45-49 & 19,819 & 7.8 \\ Age & & 50-54 & 26,314 & 10.4 \\  & & 55-59 & 30,832 & 12.2 \\  & & 60-64 & 33,244 & 13.1 \\  & & 65-69 & 32,194 & 12.7 \\  & & 70-74 & 23,533 & 9.3 \\  & & 75-79 & 15,980 & 6.3 \\  & & 80 or older & 17,363 & 6.8 \\  & & No high cholesterol & 146,089 & 57.6 \\ Cholesterol & & High cholesterol & 107,591 & 42.4 \\  & & & \\ \end{tabular}
\end{table}
Table 2: The population of this study.
## 4 Performance Evaluation
### Simulation Result
Figure 19 demonstrates this situation, after combining the coverage control algorithms, the network can transmit the least amount of data under the premise of guaranteeing the coverage, reducing the burden of sink and also filtering out the repeated and redundant data, Especially in large-scale sensor networks, combining the routing algorithms with the coverage control technique is necessary, and the NCS adopted in this paper further amplifies this advantage. Figure 19: Amount of data that the sink needs to receive and process in each round with different number of nodes. Figure 8.0 End-to-end average delay with different communication radius. Table 5 shows the summary of the critical simulation results in comparison with the baselines. It can be clearly observed that ERG-NCS has an excellent performance in several important performances of WSN routing technology research, which has the best network lifetime, packet delivery rate, throughput, delay and network overhead. ERG-NCS, as a novel routing technology with comprehensive performance, will have great application value in the field of WSN.
## 3 Materials and Methods
### Model Inference  
To produce our final generation estimates, we followed the same data processing steps laid out in the previous sections, but with relaxed filters as described in Section 3.4.  
Predictions for all images were generated during the period 2015-2022, using our sounding-level models described in Section 3.5. For inference, the sounding predictions were averaged from all four instances of a sounding model (one model for each fold). Then, these predictions were converted to generate time series features and rolling 30-day average capacity factor predictions using the generation models as described in Section 3.6. Similarly to the sounding models, the predictions were averaged from all four instances of a generation model to obtain the final generation prediction. The result is a set of predictions, one per day per plant, of the average capacity factor of that plant over the preceding 30 days. These were then summed and weighted by how much the 30-day interval overlaps with the year to produce the estimates of the annual plant-level capacity factor.  
The sounding and generation models were run on NDT and FGD structures separately to estimate the activity of the entire plant. For plants that have only FGD or only NDT, the estimation process was straightforward: simply use the prediction from the single applicable model. For plants with both NDT and FGD, predictions were aggregated from both model types by weighting the NDT model prediction two times more than the FGD model prediction (\(\frac{2*\text{NDT}+\text{FGD}}{3}\) ), reflecting the lower error and increased confidence in the NDT models.
## 3 System Model, Trust Model and Design Goals
### Design Goals  
Vehicular networks are challenging due to network dynamics and high speed. Having these challenges, it is not easy to accurately assess trust among vehicles. To cope with these challenges, the following design goals are desirable.  
* _Accurate trust assessment_: Many malicious attacks can interrupt vehicular networks. During the aforementioned situation, a trust management scheme is urgently needed to rank good and bad vehicles.
* _The relay ratio improvement_: The target vehicle in VIoT may drop the information due to selfishness or other packet dropping attacks. In this situation, if a requesting vehicle randomly selects a target vehicle, then the relay ratio will be degraded. Therefore, we need an efficient trust assessment scheme to improve the relay ratio by selecting a reliable vehicle.
* _Robustness against adversaries_: The central security architecture should be capable of tackling adversaries, i.e., the collusion of vehicles to provide bogus feedback to RSU [35]. The central authority also makes sure that RSUs are not compromised in VIoT.
## 3 Performance Evaluation-Simulation Results
This suppression of the crosstalk terms can be achieved by engineering the WDM MUX and, in particular, the filter cross-point position. Therefore, we performed a study in which we investigated the impact of the idler/crosstalk effect and the filter transfer function interaction on the BER signal performance. The results are shown in Figure 9 for the two worst-case scenarios mentioned above. Figure 9a shows all crosstalk terms present at point C1 as a function of filter cross-point shift (\(\Delta\)\({}_{\text{FCS}}\)). We define the filter cross-point as the point of intersection of the two filter transfer functions of Figure 7. Hence, the filter cross-point shift is the relative shift of the filter cross-point of the new filter from the original filter cross-point of 1337.2 nm used in Figure 8, obtained by measurements of the filter in our lab. The crosstalk terms at point C2 are shown in Figure 9b. It is clear that for 1 nm \(\leq\)\(\Delta\)\({}_{\text{FCS}}\)\(\leq\) 5 nm, all of the crosstalk terms are below -15 dB for both  
Figure 9: Crosstalk on: (**a**) S\({}_{\text{U}}\) = 1344 nm at point C1; (**b**) S\({}_{\text{L}}\) = 1336 nm at point C2 as a function of WDM MUX filter cross point shift (\(\Delta\)\({}_{\text{FCS}}\)); BER at 10 Gbps non-return-to-zero (NRZ) of (**c**) S\({}_{\text{U}}\) = 1344 nm at point C1; and (**d**) S\({}_{\text{L}}\) = 1336 nm at point C2 (note: crosstalk terms \(<\)–90 dB are under the noise floor). cases, and hence, now, the BER degradation due to the crosstalk terms is negligible, as can be seen in Figure 9c,d. Our analysis shows that with the proper design of the filters (and their associated filter transfer function), as well as the proper selection of pump wavelengths and powers, the O- and E-band HROPA can achieve error free performance for the entire band and for all possible transmitter wavelengths within the CWDM channel band, as defined by the ITU standards [1]. The HROPA is based on separating the two amplification processes, e.g., Raman and OPA, so that we can optimize each one separately, because the OPA requires a short fiber length to achieve a wider gain bandwidth, whereas the Raman process requires a long fiber length to achieve high gain. Note that current technology allows us to design and fabricate WDM filters with a filter cross-point within \(\pm\)1 nm from the desired wavelength, as required based on our analysis.
# Exergaming-Based Rehabilitation for Lateral Trunk Flexion in Parkinson's Disease: A Pilot Study
###### Abstract  
(1) Background: Axial postural deformities represent a more common disabling motor complication in [PERSON]'s disease. This study aims to investigate the clinical and neurophysiological effect of a rehabilitation treatment based on exergaming. (2) Methods: A pilot observational study was conducted on nine subjects affected by [PERSON]'s disease and lateral trunk flexion, as well as on nine healthy controls with regard to some clinical and neurophysiological outcomes (3) Results: Statistically significant improvements were observed in all clinical assessment outcomes taken in to consideration: Berg balance scale (\(p\) = 0.0078), timed up and go tests (\(p\) = 0.03), degrees of lateral trunk inclination (\(p\) = 0.0039), and anterior/posterior trunk inclination (\(p\) = 0.0039). Regarding neurophysiological outcomes, the pressure pain threshold was enhanced and statistically significant in all areas assessed. Moreover, tensiomyography highlighted a statistically significant improvement in the maximal radial displacement of the ipsilateral erector spinae muscles. (4) Conclusions: The clinical and neurophysiological outcomes suggest both peripheral and central effects of exergaming. Peripherally, exergaming seems to lead to a postural trunk correction through a reduction in muscle stiffness in the ipsilateral erector spinae. Centrally, exergaming seems to lead to a central pain modulation through an upregulation of cortical connectivity associated with cognitive tasks. Taken together, these results also indicate that exergaming can be a feasible and enjoyable complement to traditional rehabilitation, potentially enhancing patients' motivation and adherence.  
lateral trunk flexion; physiotherapy; virtual reality; tensiomyography; algometer assessment Article
## 2 Results and Discussion
### Influence of data smoothing  
Digital surface models derived from imagery are typically represented using triangular irregular networks (TINs) or regular grids. When producing gridded DSMs, the matched points (scattered) are interpolated on a regular grid and often have a noisy pattern caused by the combined effect of various  
\begin{table}
\begin{tabular}{r r r r r r r} \hline
**Resolution** & \multicolumn{3}{c}{**LE90 (m) vs. slope \({}^{\text{(a)}}\)**} & \multicolumn{3}{c}{**LE90 (m) vs. slope \({}^{\text{(b)}}\)**} \\ \multicolumn{1}{c}{(**m**)} & \(<20\%\) & \(20\%-40\%\) & \(>40\%\) & \(<20\%\) & \(20\%-40\%\) & \(>40\%\) \\ \hline  
\hline \end{tabular}
\end{table}
Table 2: Summary of residuals vs. slope for Cartosat-1 DSMs generated over Salon de Provence test site. (a) for DSMs generated at the desired resolution directly from the Cartosat-1 images. (b) for DSMs generated by downsampling the 5m resolution Cartosat-1 DSM. All values refer to a comparison over the whole test site.  
types of errors and natural surface roughness and need to be further processed [52]. Data smoothing is a technique widely used to remove peaks and smooth down (regularize) the generated surfaces [53,54].  
The use of a moving average low-pass filter with 7 \(\times\) 7 kernel size in post-processing slightly improved the results. When evaluating the multi-resolution Cartosat-1 DSMs in the range of 5 - 20 m, for Mausanne les Alpilles we observed an improvement in the RMSE between 0% for 5 m resolution and +16.0% for 20 m resolution. For Salon de Provence we obtained an improvement in the RMSE between +3.9% for 10 m resolution and +15.9% for 20 m resolution. When analyzing results in the range of 25-85 m resolution, for Mausanne les Alpilles we observed an improvement in the RMSE between +0.5% for 85 m resolution and +26.7% for 45 m resolution. For Salon de Provence we recorded an improvement of the RMSE between -11.1% (a decrease) for 85 m resolution and +19.0% for 25 m resolution with respect to the high-resolution reference DSM, and an improvement between +4.9% for 65 m resolution and +20.5% for 85 m resolution with respect to MNT DBTOPO(r) DTM. Finally, when analyzing results at the SRTM90 resolution, for Mausanne les Alpilles we obtained a worsening of results (-23.1% for RMSE), while for Salon de Provence we obtained again an improvement of results (+10.8% for RMSE when compared to the high-resolution reference DSM and +19.5% for RSME when compared to MNT DBTOPO(r) DTM).
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 4 Proposed GCRN-Based Surround AEC  
To recover the near-end signal from microphone recordings directly without decorrelating the far-end signals, a GCRN model-based algorithm was proposed as shown in Figure 3. Note that we chose the B-format signals together with the microphone signals as the inputs of the neural network. In Figure 3, \(X_{m,R}^{c}\) and \(X_{m,I}^{c}\) represent the real and imaginary part of the compressed complex spectrum of B-format signal \(x_{m}^{B}(n)\), respectively. They can be defined as follows:  
\[X_{m,R}^{c}=|X_{m}^{B}|^{\beta}\cdot\cos(\theta),X_{m,I}^{c}=|X_{m}^{B}|^{ \beta}\cdot\sin(\theta), \tag{3}\]  
Figure 2: Diagram of a surround sound acoustic echo system.  
where \(X_{m}^{B}\) denotes the complex spectra of \(x_{m}^{B}(n)\), \(|X_{m}^{B}|\), and \(\theta\) represents the magnitude and phase information of \(X_{m}^{B}\), respectively. The value of the power compressed coefficient \(\beta\) was set as \(1/2\) in this work [25; 26]. Accordingly, \(Y_{R}^{c}\), \(Y_{I}^{c}\) and \(\hat{S}_{R}^{c}\), \(\hat{S}_{I}^{c}\) denote the real and imaginary part of the spectra of the microphone and estimated near-end speech signal correspondingly.
# Laser Applications in Metal Orthodontic Bracket Debonding: A Systematic Review
## 1 Introduction
They differ in the energy source (flashlight, diode, chemical reaction), the gain medium (solid state lasers such as Er,Cr:YSGG, Er:YAG, Nd:YAG, gas lasers, or diode lasers), the resonator type (mirrors, coated facets), and wavelength [25]. When performing the laser-assisted debonding of orthodontic brackets, it is crucial to avoid damaging the enamel and prevent excessive temperature increases while minimizing the residual adhesive material on the tooth surface, as indicated by the adhesive remnant index (ARI) score [26; 27; 28; 29]. The removal of adhesive residues can cause further enamel damage, particularly when rotary instruments are used for this purpose [30]. The loss of enamel surface and associated exposure of enamel prism tips to the oral environment can reduce the resistance to plaque acids and increase susceptibility to demineralization [31]. An increase to the intracavitary temperature of 5.5 \({}^{\circ}\)C causes pulpitis or pulp necrosis in 15% of irradiated teeth [32; 33; 34], while a 10 \({}^{\circ}\)C increase in temperature on the external root surfaces of teeth causes bone resorption and tooth ankylosis [11; 35]. Therefore, careful consideration of the laser parameters--such as wavelength, power, and duration--is essential to optimize the debonding process while minimizing potential harm to the enamel and underlying structures [36]. This systematic review aimed to assess the effectiveness and safety of different laser wavelengths to debond orthodontic metal brackets in comparison to traditional plier-based methods. Upon reviewing the available literature on laser irradiation for bracket debonding, it became evident that a comprehensive review of this topic was necessary. To date, no systematic review has been published on this subject. Undertaking such a review could stimulate further research, potentially providing valuable insights and benefits for both dental professionals and patients regarding the efficacy and safety of this technique.
# Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing
## 4 Mean-Field Games Equilibrium and Optimal Strategies
### Mean-Field Game Equilibrium Algorithm
(2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] SmartDefense: A distributed deep defense against DDoS attacks with edge computing. _Comput. Netw._**2022**, _209_, 108874. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON] Denial of service attacks in edge computing layers: Taxonomy, vulnerabilities, threats and solutions. _Ad Hoc Netw._**2024**, _152_, 103322. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Collaborative prediction and detection of DDoS attacks in edge computing: A deep learning-based approach with distributed SDN. _Comput. Netw._**2023**, _225_, 109642. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Request pattern change-based cache pollution attack detection and defense in edge computing. _Digit. Commun. Netw._**2023**, \(9\), 1212-1220. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] DefQ: Defensive Quantization Against Inference Slow-Down Attack for Edge Computing. _IEEE Internet Things J._**2023**, _10_, 3243. [CrossRef]
* [PERSON] and [PERSON] (2024) [PERSON]; [PERSON] [PERSON] A review of attacker-defender games: Current state and paths forward. _Eur. J. Oper. Res._**2024**, _313_, 301-417. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Game theory for multi-access edge computing: Survey, use cases, and future trends. _IEEE Commun. Surv. Tutor._**2019**, _21_, 260-288. [CrossRef]
* [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A game-theoretical approach for mitigating edge DDoS attack. _IEEE Trans. Dependable Secur. Comput._**2021**, _19_, 2333-2348. [CrossRef]
* [PERSON] and [PERSON] (2023) [PERSON]; [PERSON] Dynamic stochastic game-based security of edge computing based on blockchain. _J. Supercomput._**2023**, _79_, 15894-15926. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Optimal defense strategy based on the mean field game model for cyber security. _Int. J. Distrib. Sens. Netw._**2019**, _15_, 1550147719831180. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON] Data security defense and algorithm for edge computing based on mean field game. _J. Cyberscur._**2020**, \(2\), 97. [CrossRef]

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
## 5 Conclusions  
Considering longitudinal non-uniform subsurface geometry conditions and different incidence directions and arbitrary angles of the SV-wave, the longitudinal seismic response and damage characteristics of a water-conveyance tunnel were investigated in this paper using the numerical method. The main conclusions can be summarized as follows: (i) an inclined subsurface geometry may focus both left-side and right-side obliquely incident waves due to refraction or total reflection; (ii) a tunnel in a site adjacent to a rock mountain may exhibit higher horizontal and vertical acceleration responses than a tunnel in a homogeneous plain site due to the refraction or reflection induced by the rock mountain; and (iii) a tunnel in a site adjacent to a rock mountain may be subjected to compression in multiple directions, thus damage may appear in multiple circumferential directions, and, as the incidence angle decreases, the mode of tensile damage for right-side incidence may change from multi-directional damage to unidirectional damage, while that of tensile damage for left-side incidence may always be multi-directional.  
The chosen incidence angles in this study were all smaller than the critical incidence angle of the SV-wave, and, thus, the horizontal component of the input wave and the seismic response were higher than those in the vertical direction. The calculation and analysis results of cases in which the vertical component is higher may be needed in the future.  
Conceptualization, A.C.; methodology, E.Y.; software, Y.R.; validation, Y.R.; investigation, M.L.; resources, A.C.; data curation, E.Y.; writing--original draft preparation, E.Y.; writing--review and editing, Z.L.; visualization, M.L.; supervision, E.Y.; project administration, A.C.; funding acquisition, E.Y. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Knowledge Innovation Program of Wuhan-Shuguang (No. 2022010801020242), the National Natural Science Foundation of China (Nos. 51908236, 51978304, 51778260, and 52079009), and the China Postdoctoral Science Foundation (2019M652649).  
Data Availability Statement: Data is contained within the article.  
The authors also thank [PERSON] from the Huazhong University of Science and Technology and [PERSON] from the Changjiang River Scientific Research Institute for their help. The first author is grateful for his wife, [PERSON], for the support and care.  
Author [PERSON] was employed by the company China Communications Construction Company Second Harbor Engineering Company Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.