esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Metrics for Adaptive Samples
## 3 Results
### Interleaving
#### 3.2.2 Approximating the Induced Metric  
It is much easier to use a union of Euclidean balls to model the sublevel sets of the distance function \(f_{X}^{L}\). Below, we show that this is a reasonable approximation. The following results may also be viewed as a strengthening of the adaptive sampling result of the previous section (Theorem 1).  
**Lemma 13**.: _The pair \((A_{\widehat{X}}^{L},B_{\widehat{X}}^{L})\) is \((h_{2},h_{2})\)-interleaved in \((0,1)\), where \(h_{2}(r)=\frac{r}{1-r}\)._  
Proof.: It will suffice to show that for \(r\in[0,1)\), \(A_{\widehat{X}}^{L}(r)\subseteq B_{\widehat{X}}^{L}(\frac{r}{1-r})\), and for \(r\in[0,\frac{1}{2})\), \(B_{\widehat{X}}^{L}(r)\subseteq A_{\widehat{X}}^{L}(\frac{r}{1-r})\). Take \(y\in A_{\widehat{X}}^{L}(r)\) so that \(f_{\widehat{X}}^{L}(y)\leq r\). Thus, there exists \(x\in X\) such that \(\mathrm{d}^{L}(x,y)\leq r\). By Lemma 10, this implies that \(\|x-y\|\leq\frac{r}{1-r}f_{L}(x)\), which implies that \(y\in B_{\widehat{X}}^{L}(\frac{r}{1-r})\).  
Consider any point \(y\in B_{\widehat{X}}^{L}(r)\). For some \(x\in X\), we have \(y\in\mathrm{ball}(x,rf_{L}(x))\), so \(\|x-y\|\leq rf_{L}(x)\). Applying Lemma 10, we have that \(\mathrm{d}^{L}(x,y)\leq\frac{r}{1-r}\). Finally, \(y\in A_{\widehat{X}}^{L}(\frac{r}{1-r})\), because \(f_{\widehat{X}}^{L}(x)\leq\mathrm{d}^{L}(x,y)\).
## Appendix A Numerical Parameters of the Particles and of the Selectivity Filter
### Additional Aspects about MD Simulations  
In the present MD model, the SF was aligned with the inner cavity of the channel protein according to its standard crystal structure. Entrance to the filter with ions located within proximity of the carbonyl ring Thr74 of 0.5 nm and below this distance occurred by thermal pushes at the order of 50 events per picosecond. Following thermal agitations, a particle acquires a velocity in the longitudinal \(z\) direction derived from a Boltzmann distribution at T =310 K. By changing this kick rate, the effective pressure exerted from the inner cavity atoms can be changed. New particles can enter into the SF from below at any time step. On the extracellular side of the membrane, the effective pressure of H\({}_{2}\)O molecules and K\({}^{+}\) ions was represented by a potential barrier of 20 k\({}_{\text{B}}\)T at 0.54 nm above the carbonyl ring of  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Particle A** & **Particle B** & **d\({}_{\text{AB}}\) [pm]** & **f\({}_{\text{rep,AB}}\) (Units of k\({}_{\text{B}}\)T/d\({}_{\text{AB}}\))** & **Comment** \\ \hline K\({}^{+}\) & K\({}^{+}\) & - & - & Very strong Coulomb repulsion, no additional repulsion needed \\ \hline K\({}^{+}\) & O (central charge) & 131 & 500 & This repulsion hardly comes to play, because the K\({}^{+}\) remain on the central axis of the SF. \\ \hline K\({}^{+}\) & O (lone pairs charge) & 248 & 100 & Plays an important role for keeping a K\({}^{+}\) coordinated at a site. \\ \hline K\({}^{+}\) & H\({}_{2}\)O & 192.5 & 1000 & Determines how close an H\({}_{2}\)O can get attached to a K\({}^{+}\) while following it through the SF. \\ \hline O (central charge) & H\({}_{2}\)O & 137.5 & 500 & This repulsion hardly comes to play, because the H\({}_{2}\)O molecules remain on the central axis of the SF. \\ \hline O (lone pairs charge) & H\({}_{2}\)O & 137.48 & 350 & Represents an occasional barrier for an H\({}_{2}\)O to pass on, depending on how it is oriented. \\ \hline H\({}_{2}\)O & H\({}_{2}\)O & 137.5 & 500 & The Coulomb force acts between the three charges of each H\({}_{2}\)O. The distance for repulsion is given by the distance between the charge centers. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distances d\({}_{\text{AB}}\) between particles where constant repulsive forces set in, and magnitudes f\({}_{\text{rep,AB}}\) of these forces (k\({}_{\text{B}}\) = 1.38064852 \(\times\) 10\({}^{-23}\) J/K Boltzmann constant, T = 310 K). Particle combinations in which one partner is a C-atom are not listed, as their electron orbitals cannot get close enough, and hence only the Coulomb force applies.  
Gly79. A particle which passed this barrier was considered as emitted from the SF and was not tracked any further.
## 5 Conclusions  
In this paper, we studied the impact of land use changes on ESVs in the rapidly-developing Dongting Lake region in 2000, 2005, 2010 and 2018, and quantified the impacts of individual driving factors and their synergies on the ESVs using XGBoost models. The conclusions were as follows:  
1. The largest changes in land use areas of the Dongting Lake from 2000 to 2018 were in cropland (2.54% decrease) and construction land (plus 1.46%) and the largest values of dynamic degree were in unused land use types, followed by construction land and wetlands.
2. The ESVs of the Dongting Lake showed an increasing trend, and the forestland ESVs were the highest, accounting for approximately 44.65% of the total value. Among the ESVs functions, water containment, waste treatment, soil formation and protection, biodiversity conservation and climate regulation contributed the most to the ESVs, with a combined contribution of 76.64% to 76.99%.
3. The integrated intensity of anthropogenic disturbance showed a U-shaped spatial distribution, decreasing from U1 to U3. The driving factors in descending order of importance were HAI, GPP, Slope, DEM, POP, Temp, GDP, Pre and PM.
4. When the GPP was low (GPP < 900), the SHAP value of the high HAI was greater than zero, indicating that an increase in the GPP of the Dongting Lake increases the EVs. When the SHAP value of the DEM was greater than zero, an increase in the DEM led to an increase in the EVs. Moreover, the high DEM values and low HAI values indicated that the EVs tended to be lower.  
**Author Contributions:** Conceptualization, W.C.; Data curation, G.L.; Formal analysis, Z.Y. and P.B.; Investigation, W.C., Z.Y. and P.B.; Methodology, W.C.; Software, G.L. and W.C.; Supervision, X.Z.; Validation, G.L. and Z.Y.; Visualization, G.L., Z.Y. and P.B.; Writing--original draft, G.L. and W.C.; Writing--review and editing, X.Z. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41701391.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Results  
### Force Control Performance and Low-Frequency Oscillations in Force  
From 10% to 40% of MVC, paired \(t\)-tests revealed impairments in force control performances: (a) increased values in RMSE (\(t_{13}=-10.215\); \(p<0.001\); Figure 2A), (b) greater values in SD (\(t_{13}=-8.239\) and \(p<0.001\); Figure 2B), and (c) less values in ApEn (\(t_{13}=12.247\) and \(p<0.001\); Figure 2C). Moreover, a two-way repeated measures ANOVA on the absolute power showed a significant force level \(\times\) frequency band interaction [\(F\)(1.882, 24.468) = 25.675; \(p<0.001\); partial \(\eta^{2}=0.664\); Figure 2D]. Post hoc analyses showed that the absolute power for four frequency bands significantly increased from 10% to 40% of MVC. At 10% of MVC, the absolute power in 0.5-1.0 Hz was significantly greater than those in 1.5-2.0 Hz and the absolute power in 1.0-1.5 Hz was significantly greater than those in 1.5-2.0 Hz. At 40% of MVC, the absolute powers were significantly different among all pairs of frequency bands.  
Relationship between Changes in Low-Frequency Oscillations in Force and Force Control Performances across Different Targeted Force Levels  
A multiple linear regression analysis using a stepwise method identified a significant relationship between increased values of absolute power in 0-0.5 Hz and higher RMSE values (Y = 0.920 + 0.047 X; \(R^{2}=0.533\); \(p=0.003\); Figure 3A). For SD values, the analysis found a significant relationship between increased values of absolute power in 0-0.5 Hz and 0.5-1.0 Hz and higher SD values (Y = 0.307 + 0.037 X\({}_{0-0.5\,\mathrm{Hz}}\) + 0.018 X\({}_{0.5-1.0\,\mathrm{Hz}}\); \(R^{2}=0.978\); \(p<0.001\); Figure 3B). Finally, a multiple linear regression analysis using a stepwise method reported a significant relationship between increased values of absolute power in 0-0.5 Hz and less ApEn values (Y = \(-0.141\)-0.008 X; \(R^{2}=0.890\); \(p<0.001\); Figure 3C).  
Figure 2: Force control performances and low-frequency oscillations in force across two targeted force levels. (**A**) Force accuracy (RMSE), (**B**) force variability (SD), (**C**) force regularity (ApEn), and (**D**) low-frequency oscillations in force (absolute power) for four frequency bands as a function of different targeted force level. Data are mean \(\pm\) standard error. _Asterisk_ (*) denotes a significant difference between two targeted force levels. _Number sign_ (#) indicates a significant difference in comparison to 0–0.5 Hz. _Percent sign_ (%) indicates a significant difference in comparison to 0.5–1.0 Hz. _Ampersand_ (&) means a significant difference in comparison to 1.0–1.5 Hz.
## 1 Introduction  
Sleep shows dynamic physiological conditions that differ from those during waking. The cardiac rhythm during sleep is modulated by sleep stages, mainly by the periodic appearance of non-REM (NREM) and REM stages that involve switching between two different autonomic states [1; 2; 3; 4]. The cardiac rhythm during sleep could be also affected by pathological phenomena peculiar to sleep, such as sleep disordered breathing (SDB), that causes characteristic heart rate pattern known as cyclic variation of heart rate [5; 6]. To interpret arrhythmias or other electrocardiographic (ECG) phenomena observed during ambulatory ECG monitoring with wearable sensors, it is often necessary to estimate the period of sleep and preferably, the distributions of sleep stages. For these purposes, many methods using the analyses of heart rate variability (HRV) [7; 8; 9; 10; 11] and/or actigraphic body movement (ABM) [12; 13; 14; 15] have been reported. However, their univariate accuracies for estimating the period of sleep and sleep stages are modest and they have no definitive physiological basis by which they can distinguish sleep from awake rest.  
To resolve this problem, we developed a new HRV sleep index named Hsi that detects NREM sleep periods based on the physiological features of cardiorespiratory regulations during sleep. It is well known that the regularity of respiration increases during NREM sleep. Respiration modulates heart rate and generates high frequency (HF, 0.15-0.45 Hz) components of HRV at the frequency of respiration. Consequently, as the regularity of the respiratory cycle increases, the power of the HF component concentrates on a narrower frequency band. We therefore developed Hsi to measure the degree of HF power concentration. Then, we examined the performance of Hsi for discriminating NREM sleep from wake and REM stages and compared it with the performances of conventional indices of HRV and ABM.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 2 Joint Timekeeping Framework
### Traceability by Prediction  
The ISLT unites the satellite clocks and is established through inter-satellite time transfer and communication. All the navigation satellite clocks are traced to the ISLT, especially in the mode of autonomous operation (i.e., operation without support of ground stations). As the ISLT is broadcast, it should confirm synchronization to the GNSS system time, which is steered to the local realization of the Coordinated Universal Time (UTC), modulo 1 s, according to the recommendations of the International Telecommunication Union (ITU) and the General Conference on Weights and Measures (CGPM) [18]. When there is no real-time satellite-ground time transfer link, the synchronization between the ISLTand GNSS system time should be realized by a prediction of the timescale deviation. The satellite clocks, of course, can be predicted and synchronized to the GNSS system time. However, navigation performance will degrade as the divergence of prediction errors increases due to the unstable frequency of spaceborne clocks. The objective of this work is to form a more stable and predictable synthetic clock as the accurate time reference of the navigation constellation, i.e., ISLT, to restrain the degradation of navigation performances.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 2 Overall Design
### Control System Design  
In recent years, there has been significant research on developing intelligent control algorithms for movable sunshade blinds. This section presents a combined control strategy of natural and artificial lighting to improve the office lighting environment. The system automatically adjusts the angle of slats in response to the changes of the Sun altitude angle (\(\alpha\)) and of the Sun azimuth angle (\(\gamma\)), maximizing the use of natural light to improve energy efficiency while enhancing indoor illumination uniformity, and ensuring occupant comfort. To further optimize the adaptive adjustment of the slats, a GA - BP neural network is employed to predict slat angles in real time.  
The control system is structured into three layers: data acquisition, data transmission, and data processing and display. The system architecture is depicted in Figure 3.  
(1) Data acquisition: This layer collects relevant data, including the sensor-measured illuminance, office occupancy status, and current solar angles (\(\alpha\) and \(\gamma\)) to control the LED luminaires.  
(2) Data transmission: The collected data--illuminance, occupancy status, user-defined expected illuminance, and solar angles (\(\alpha\) and \(\gamma\))--are transmitted to the STM32 controller for further processing.  
Figure 3: Architecture of control system.  
Figure 2: Distribution curve of luminaires.  
(3) Data processing and display: This layer involves predicting the slat angles and the illuminance of indoor luminaires. It also calculates the output dimming coefficients using the least squares method.  
In the dimming control system, installing the illumination sensor on the work plane may result in obstruction by office materials, while positioning it at a lower point on the wall could lead to interference from personnel movement, both of which could compromise the quality of lighting control [21]. To address this, the illumination sensor is installed near the luminaire, as shown in Figure 4a.  
Since the objective of this study is to calculate the work plane illuminance, and the sensor measures the ceiling illuminance, it is necessary to establish a mapping relationship between the two planes in order to accurately determine the work plane illuminance.
## 4 Materials and Method
### Validation and Verification of Results  
Because of the lack of traffic data during the flood situation, the model could be validated with the average travel time of the typical traffic for a day that was obtained from IP. The calibration of the traffic model was achieved by comparing the estimated average travel time matrices between TAZs for all of the departed vehicles from one TAZ to another. To this end, this matrix was compared to the IP results matrix. Then, one of the most common metrics to measure the forecasting accuracy of a model, which is mean absolute percentage error (_MAPE_), was calculated. The formula to calculate the _MAPE_ is as Equation (1):  
\[MAPE=\frac{1}{n}\sum_{t=1}^{n}\Bigl{|}\frac{E_{t}-S_{t}}{E_{t}}\Bigr{|}\times 100 \tag{1}\]  
where \(n\) is the number of fitted data, \(E_{t}\) is the travel time reported by IP, \(S_{t}\) is the predicted travel time. This measure is estimated to be 12%. Overall, this measure indicates reasonable prediction fits. Therefore, it can be said that the potential uncertainties of the model and concerns about its accuracy in the next stages of the model integration with flood spots decreases.  
Table 1 shows the estimated average travel times received from IP and predicted values in the current research for the first 10 TAZs in the network. The average travel times in Table 1 are rounded to the closest minute.  
It is worth mentioning that the applied method in the current research for validation of results is similar to that published in [PERSON] [39]. In addition, the model results can be verified in case of disruption, but at the moment those are credible predictions but not validated. Although a higher uncertainty is found for the situation where the disruption took place, the framework presented in this paper demonstrates the potential of combining flood maps and traffic models to estimate the probable consequences of changes. It also indicates that impact of flooding includes the time it takes for the network to be recovered in addition to the time when there is standing water on the road surface.
# Investigating the Reliability of Nonlinear Static Procedures
## 1 Introduction
Regarding the first issue, different criteria are proposed in the literature, as well as certain codes, as discussed in detail in [32]. They are based on conventional criteria that refer to given percentages of the overall base shear attained or that also account for the damage incurred by single elements or a set of walls. Regarding the second issue, usually, the criteria originally proposed in [33] are adopted; this step eventually also requires the conversion of the original curve into an equivalent bilinear curve. Regarding issue (iii), the literature proposes two alternative methods: (i) the N2 method [33] and (ii) the capacity spectrum method (CSM, as introduced for the first time in [34]). They have both been widely used in the last 20 years as they have been introduced into certain standards, together with some coefficients/controls to limit their scope of application. Starting from the original proposals of these approaches, various developments have since been described in the literature. This paper focuses in particular on issues (ii) and (iii), with the aim of comparing the different alternatives proposed in the literature and certain standards (including some proposals that are currently under review for the update of Eurocode 8) and outlining the most reliable ones. An overview of the NSPs outlined in different codes and research studies is given in Section 2. Of course, other issues are still open in the literature, such as the application of NSPs when applied to irregular buildings characterized by flexible or stiff floors [35; 36; 37; 38; 39; 40], but these aspects are beyond the scope of this paper. As described in Section 3, the concept of the fragility curve [41; 42; 43; 44; 45] is adopted to assess the reliability of the alternative NSPs considered. In fact, despite the broad use of NSPs in engineering practice for existing URM buildings, the validation of the available approaches is still limited in the literature. There are different methods used to develop fragility curves [41; 46; 47], i.e., empirical; analytical, based on simplified approaches; or numerical, based on more accurate models. In this paper, the numerical one is selected. More specifically, both NLDA and NLSA are executed on a 3D model set according to the equivalent frame modeling approach and a representative of an existing URM building, which is described in Section 4. The fragility curve obtained by NLDA is adopted as a reference solution to estimate the reliability of the various NSPs adopted. Finally, the main outcomes achieved are illustrated in Section 5, which also outlines the possible advantages and disadvantages of alternative NPSs. As summarized in the Conclusions (Section 6), the repercussions of the achieved outcomes may be significant also in guiding possible recommendations to be included in the future generations of the European Standards, as the topic is quite relevant and the extensive revision of such documents is ongoing.
## 2 Literature Review
### MFA Applied in Different Mechanisms  
MFA has become critical in validation of user identity and electronic devices (or systems) [18; 19], validation of infrastructure connection [20], and validation of interconnected IoT devices, such as a smartphone, tablet, wearable device, or any other digital token [21]. [PERSON] [22] proposed a distributed protocol that allows user authentication using three authentication factors, possession, knowledge, and inference, with the possibility of carrying out the process in the mobile environment of the Android platform with guaranteed authentication support. It turned out to be a robust solution, since it combined three different factors, nevertheless it was limited to the Android platform and the protocol's own complexity increased the risk and vulnerability to common attacks. [PERSON] [23] proposed an MFA mechanism, based on facial recognition, that uses visual cryptography (VC) to secure biometric data, which, for the second factor of authentication uses the shared resources generated by the VC as authentication tokens and verifies them by the same algorithm of facial recognition used to recognize a live facial image of the user. [PERSON] [24] proposed an MFA framework using both the motion signal of handwriting in the air and the geometry of the hand skeleton captured by a depth camera. The downside of the MFA approach presented in the two last mentioned papers is that they require hardware, and biometric techniques, which penalize usability and make the implementation complex and expensive and limits its application to the requirements of the hardware. [PERSON] [25] proposed a master PIN authentication scheme and multi-factor authentication to protect credit card transactions, using elements, such as location and preferred stores, to determine the authentication method; nevertheless, the use of PINs in both authentication methods made it vulnerable to common attacks, in addition to requiring constant user information. [PERSON] [26] proposed an authentication scheme, implemented as challenge-response authentication, where three factors (username, device number, and fingerprint) are used as a secret key between the client and the server. The method of encryption of the secret key is rather robust and interesting; however, the scheme requires biometric information and techniques which need specific hardware, whilst penalizing usability and authentication time.
## 3 Results and Discussion  
### Parameter Setting  
The parameters for the comparison experiments were set as follows: the original size of the image was 640 \(\times\) 640 pixels, so the input to the model was also adjusted to \(640\times 640\times 3\). The ratio of the training set to the test set was set to 8:2. The batch size was set to 4, the epoch was set to 100, the initial learning rate was 0.01, the cyclic learning rate was 0.2, and the optimizer used was SGD (stochastic gradient descent), with a weight decay coefficient of 0.0005, and the iou loss coefficient was set to 0.05.