esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 5 Redundancy
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Requirement** & **Description** \\ \hline \multirow{8}{*}{Hardware} & Duplication of Critical Components & Essential components like servers, power supplies, networks, and storage units must be duplicated to prevent a single point of failure. \\ \cline{2-3}  & Network Devices & Routers, switches, and cables should be configured redundantly to ensure continuous connectivity. \\ \cline{2-3}  & Load Balancing & Workload distribution among active components to avoid overloading a single component. \\ \cline{2-3}  & Automatic Failover & Automatic mechanisms to switch to a backup component in case of primary component failure. \\ \cline{2-3}  & Physical Isolation & Redundant components should be in different physical locations to protect against local events affecting both systems. \\ \hline \multirow{8}{*}{Software} & Application Redundancy & Critical applications should run on redundant instances, on the same server (virtualization) or different servers. \\ \cline{2-3}  & State and Data Management & Synchronize data and maintain state consistency between active and backup systems. \\ \cline{2-3}  & Failover Mechanisms & Enable seamless and automatic transition between the primary and backup systems in case of failure. \\ \hline \multirow{8}{*}{Operated Procedures} & Testing and Validation & Perform regular failover tests to ensure that the backup system operates correctly. \\ \cline{2-3}  & Seamless Updates & Allow updates and maintenance without interrupting service, using appropriate update strategies. \\ \cline{2-3}  & Patching and Maintenance & Apply patches and conduct maintenance without affecting system availability. \\ \cline{2-3}  & Monitoring and Alerts & Implement active monitoring with real-time alerts for failures and anomalous conditions. \\ \hline \multirow{8}{*}{Management and Procedures} & Documentation and Procedures & Maintain comprehensive documentation of the redundant system architecture, failover procedures, and recovery plans. \\ \cline{2-3}  & Personnel Training & Train personnel in the redundant system’s operation, maintenance, and recovery, and conduct emergency drills. \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Essential requirements for implementing an effective redundant system. Redundancy is essential for ensuring reliable, secure, and safe substation automation and control. Adhering to standards and best practices strengthens infrastructure resilience against failures and cyber threats. Local backups and redundant systems, such as Grandmaster Clock links, maintain network protection during communication failures. Medium-voltage (MV) substations typically do not require redundant merging units, while high-voltage (HV) systems demand them due to their critical nature. Table 21 summarizes redundant systems in CPAC, detailing their advantages, disadvantages, and applications.
## 4 Discussion
### The Impact of Vegetation Type on Vegetation Recovery  
When studying vegetation at regional or global scales, it is essential to consider the differences between vegetation types, as this factor significantly influences the vegetation recovery period after drought [104; 105]. In our study, we observed that croplands had the shortest recovery period, followed by grasslands, while forests exhibited the longest recovery period (Figure 9). The results from [PERSON] et al.'s study, which used SPEI and NDVI to quantitatively assess the impact of meteorological drought on vegetation loss and recovery, are consistent with our findings. Specifically, their study found that grasslands required less time to recover from vegetation loss compared to forests [76]. The shorter recovery period observed in croplands was primarily attributed to human interventions, as croplands have strong economic significance, leading to increased attention given to their management. Soil improvement practices, such as enhancing fertility and water retentioncapacity, have been implemented in croplands [106]. Additionally, the selection of crop varieties with greater drought resistance [107] and the implementation of irrigation during drought periods contribute to reduced drought impact on crops and facilitate rapid recovery of drought-affected crops after drought ends [108; 109]. The shorter recovery period of grasslands after drought was primarily due to the characteristics of herbaceous plants, which generally have shorter lifespans and faster growth rates. Grassland communities exhibit rich species diversity [110], providing them with strong adaptability to environmental changes and the ability to readily access direct rainfall replenishment, thereby leading to a shorter recovery period after drought [111; 112]. In contrast, forests, which have relatively slower growth rates than regions with herbaceous plants, experience severe damage to the root systems of shrubs and trees under drought stress, necessitating a longer period to return to their original state [113; 114]. The soil in forests is deeper but holds less water, making water reabsorption and utilization more challenging [115]. Moreover, forest ecosystems are complex and involve intricate relationships among plants, animals, and microorganisms. The restoration of ecological relationships within forests requires a longer recovery period [116; 117; 118] if they are to rely on their own capacities for repair.
## 2 Mission Overview  
### Mission and Instrument Requirements  
EnMAP relies on a prism-based dual-spectrometer instrument design to cover the 420-2450-nm spectral range. An artistic representation of an EnMAP overpass depicting the field-of-view of the VNIR and SWIR spectrometers and other acquisition details is displayed in Figure 1. The VNIR spectrometer covers the 420-1000-nm spectral range with an SSD between 5.5 and 7.5, whereas the SWIR spectrometer covers 900-2450 with an SSD between 8.5 and 11.5. Spectral resolution is required to be about 1.2 larger than the sampling distance. Threshold requirements for the signal-to-noiseratio (SNR) at the reference radiance level, defined by 30% surface albedo, 30\({}^{\circ}\) Sun zenith angle (SZA), 0.5 km above sea level and 21 km atmospheric visibility, are 400:1 and 180:1 for the VNIR and SWIR ranges, respectively, with a 14-bit radiometric resolution. The required radiometric calibration accuracy is 5%, and the spectral calibration uncertainty is 0.5 nm in the VNIR and 1 nm in the SWIR. Spatial sampling is defined by a GSD of 30 m, a swath width of 30 km and a length in multiples of 30 km up to 1000 km per orbit and 5000 km per day (limitations mostly posed by onboard memory and power consumption constraints). The selected orbit is Sun-synchronous, with a local time of 11:00 for the descending node. An off-nadir pointing capability of up to 30\({}^{\circ}\) enables a revisit time of four days at the Equator and better at higher latitudes. The expected mission lifetime is five years. A summary of selected mission and instrument parameters is presented in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Mission Requirements** \\ \hline
**Spectral range** & 420–2450 nm \\
**Ground sampling distance** & 30 m \\
**Swath width** & 30 km \\
**Swath length** & up to 1000 km/orbit \\
**Coverage** & Global in near-nadir mode (VZA \(\leq\)5\({}^{\circ}\)) \\
**Orbit** & Sun-synchronous,11:00 local time descending node \\
**Daily coverage** & 5000 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected EnMAP mission and instrument specifications. The reference radiance level for the definition of signal-to-noise ratio (SNR) figures is 30% surface albedo, 30\({}^{\circ}\) Sun zenith angle, 0.5 km above sea level and 21 km atmospheric visibility. VZA stands for view zenith angle, VNIR for visible near-infrared and SWIR for shortwave infrared.  
Figure 1: Representation of an EnMAP overpass featuring the dual-spectrometer instrument concept. The field-of-views (FOVs) of the visible near-infrared (VNIR) and shortwave infrared (SWIR) spectrometers are represented in blue and red, respectively.
## 6 Simulations Results and Discussions
### Total Number of Packets Collected over the Rounds  
Figure 14 illustrates the cumulative number of packets collected from the FoI by the EEUCH protocol as the rounds progress and the SNs consume energy. An SN can sense its surroundings and share the sensed information if it has sufficient energy. As discussed earlier, EEUCH protocol consumes energy linearly, resulting in a longer network lifetime, which allows the SNs to remain active for a longer period of time. This prolonged lifetime results in a greater amount of data being collected from the FoI as the SNs have energy to sense and share information. In contrast, other benchmark routing protocols consume energy nonlinearly due to the early death of CH, leading to a shorter network lifetime and less data collection from the FoI. Figure 15 provides further evidence of the EEUCH protocol's ability to collect more data by showing the total number of packets gathered from the FoI as the number of dead SNs increases, i.e., at the first SN death, ten, fifty, and one hundred percent of SNs' death. The total number of packets collected is indicated above each bar.  
Figure 14: Total number of packets collected from the FoI over the number of rounds.  
Figure 13: Reduction in the network average energy versus percentage of node deaths.
# N-Heterocyclic Carbene-Gold(I) Complexes Targeting Actin Polymerization
## 4 Discussion
In other words, the microtubule metabolism can influence the organization, spatial distribution, and function of the nearby actin filaments [46]. With this in mind, we started with in silico studies that allowed us to assume that both complexes may bind the actin, sharing the same binding site of LA, used as the reference molecule. In particular, **AuL4** is positioned inside the nucleotide-binding site of actin, whereas **AuL7** exposes part of its structure to the solvent. Next, we performed immunofluorescence assays in the two cancer cell models above-mentioned. The outcomes suggest that both complexes induced dramatic changes in actin organization; indeed, the filaments appeared thicker and with an abnormal spatial distribution, with respect to the control-treated cells, and in a similar rearrangement produced by LA treatment. In order to fully understand if these changes were due to a direct effect rather than the already studied interference with the tubulin polymerization reaction, we carried out some direct enzymatic assays on the purified rabbit actin, so that any other possible secondary effect could be excluded. Effectively, both complexes inhibited the actin polymerization in a similar extent to the CB, but lesser than LA, used as reference molecules. Conversely to LA, **AuL4** and **AuL7** were not able to promote the F-actin depolymerization. In conclusion, both the complexes inhibited the actin polymerization directly, but not the inverse reaction, and induced dramatic change in the organization and function of the actin system; this is a feature that discloses another--and not foreseen--important anticancer target of our NHC-gold complexes.
# Calculation of View Factors for Building Simulations with an Open-Source Raytracing Tool
## 3 Validation of RADIANCE Generated Results against Analytical Solutions  
The four examples presented in this section are intended to demonstrate the feasibility of calculating view factors between differential surfaces and arbitrary finite planar surfaces with RADIANCE.  
Although, as detailed in Section 2.1, RADIANCE has been extensively validated in the last three decades, the validation studies have exclusively focused on lighting and daylighting simulations. Such studies have involved the comparison of empirically measured values of illuminance or luminance with those generated through RADIANCE simulations. To the best of the authors' knowledge, the results and methodology presented in this paper are the original validation of RADIANCE for the purposes of view factor calculations.  
The first two examples involve finite planar surfaces that are perpendicular or parallel to the differential surface. The third example consists of a sphere that is constructed from planar surfaces. The fourth example involves a box whose constituent surfaces surround the differential surface from all sides. In addition to validating the results generated through RADIANCE, the examples presented in this section also constitute the basic steps that are required for calculation of more complex scenarios.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Switching Frequency  
The switching frequency of the dc/dc converter must be constrained to the limit imposed by the Mosfets; otherwise, the SMC will not be able to properly regulate the charger/discharger.  
The switching frequency \(F_{suc}\) is the result of the Mosfet activation/deactivation at the limits of the hysteresis band, thus the ripple in the switching function \(\delta\mathbf{\Psi}\) must be calculated. The correct operation of the SMC guarantee that \(v_{dc}=v_{r}\) (bus voltage regulation) and \(\frac{1-d}{n}\cdot i_{m}=i_{dc}\) (current balance at the output capacitor); hence, from the switching function definition (19) it is obtained the following ripple equation:  
\[\mathbf{\Psi}_{ripple}=K_{v}\cdot\delta v_{dc}+\frac{1-d}{n}\cdot\delta i_{m} \tag{42}\]  
The charger/discharger must be designed to provide low-harmonic distortion to the DC bus; therefore, \(\delta v_{dc}\) must be small, but \(\delta i_{m}\) depends on the transformer parameters. In addition, Equations (1) and (2) show that the derivatives of \(v_{dc}\) and \(i_{m}\) have opposite signs, as consequence, the most negative value of \(\delta v_{dc}\) occurs when \(\delta i_{m}\) has the most positive value. Therefore, the maximum value of Equation (42) is achieved when \(\delta v_{dc}\approx 0\), which is used to obtain the maximum switching frequency of the charger/discharger:  
\[\lim_{\delta v_{dc}\to 0^{+}}\Psi_{ripple}=\delta\mathbf{\Psi}=\frac{1-d}{n} \cdot\delta i_{m} \tag{43}\]Replacing Equations (15) and (17) into (43) leads to an expression for the maximum switching frequency of the charger/discharger:  
\[F_{sw,max}=\frac{v_{b}\cdot M\cdot L_{q}}{2\cdot\delta\Psi\left(L_{m}\cdot M+n \cdot L_{q}\right)^{2}} \tag{44}\]  
Finally, the parameter \(\delta\Psi\) of the SMC, which corresponds to the peak value of the hysteresis band, must be calculated from (44) to ensure a maximum switching frequency in agreement with Mosfets limitations.
## 5 Results and Discussion
### Results with the Peak Signal-to-Noise Ratio (PSNR)
Otherwise, for an image quality considered excellent, 36.24 is the best PSNR obtained by the adaptive model.
## 2 Materials and Methods
### Statistical Analytic Strategy  
SPSS 22.0 and R software, version 3.6.1, were used to conduct preliminary analysis and machine learning (caret R package). We first shuffled the effective sample's participant rows using a fixed random seed (for subsequent, consistent replication). Then 70% (\(n=1382\)) of the dataset was randomly selected as a training sample and the remaining 30% (\(n=592\)) as a test sample. To simulate validation data for each analysis, we used k-folds repeated cross-validation (RCV), splitting the training sample into ten folds of sub-samples, training nine of the ten sub-samples, and testing the tenth sub-subsample. In these simulations, we used a fixed random seed to ensure consistent replication. We then validated the performance of the aggregated final predictive model for each analysis separately on the external test sample.  
Next, regression-based symptom forecasting methods with supervised machine learning were conducted with problematic smartphone use as the continuous dependent variable. The predictor variables were gender, age, ethnicity, screen time, internalizing problems, self-regulation, and FoMO). Five separate machine learning algorithms were compared (e.g., \"shrinkage\" methods-ridge, lasso, and elastic net regression algorithms, extreme gradient boosting, and random forest). Those algorithms were selected because of their resistance to overfitting and prediction of nonlinear associations [69]. We compared the algorithms using root mean squared error (RMSE), mean absolute error (MAE), R-squared values, and statistical tests.
## 3 Derivation of Average SNR for Dynamic Directional Modulation  
Next, we establish a connection between the signal-to-noise ratio (\(SNR\)) that can be observed in a given transmission angle and the artificial orthogonal noise injected into that direction. The average radiation pattern transmitted with DDM (\(rp_{aw\_DDM}(\phi)\)) is obtained as  
\[rp_{aw\_DDM}(\phi)=\frac{1}{MP}\sum_{p=1}^{P}\sum_{m=1}^{M}|s_{mp\_DDM}(\phi)| ^{2}. \tag{9}\]  
Similarly, the average radiation pattern transmitted in the conventional modulation (\(rp_{aw\_conv}(\phi)\)) is given by  
\[rp_{aw\_conv}(\phi)=\frac{1}{M}\sum_{m=1}^{M}|s_{m\_conv}(\phi)|^{2}. \tag{10}\]  
Then, we can compute the average radiation pattern of the injected artificial orthogonal noise (\(rp_{aw\_W}(\phi)\)) as  
\[rp_{aw\_W}(\phi)=rp_{aw\_DDM}(\phi)-rp_{aw\_conv}(\phi). \tag{11}\]  
Now, we define an average signal-to-noise ratio that depends on the observation angle as  
\[SNR_{aw}(\phi)=\frac{rp_{aw\_conv}(\phi)}{rp_{aw\_W}(\phi)+N_{AWGN}} \tag{12}\]  
where we have also included the contribution of additive white Gaussian noise \(N_{AWGN}\) in the communication, assuming that it is uncorrelated to the artificial noise and uniform for all observation angles. At the secure observation angle, \(SNR_{av}(\phi_{0})\) coincides with \(SNR(\phi_{0})\) that would be obtained in a conventional modulation.
## Appendix B GNSS Range Error Computation  
GNSS pseudorange measurements can be expressed as:  
\[\rho=r+cdt+\varepsilon \tag{4}\]  
where \(\rho\) is the pseudorange, \(r\) is the true range (true distance between the satellite and the receiver position), \(cdt\) is the receiver clock bias and \(\varepsilon\) is the total error which include atmospheric errors, orbital errors and multipath.  
With the availability of an accurate reference solution for the user locations and with the knowledge of satellite positions from the ephemeris data, the true range \(r\) can be calculated. Now the problem lies in the segregation of \(\varepsilon\) from \(cdt\).  
The clock bias can be estimated in a filter by fixing the known receiver position and velocity from the reference solution and by modeling \(\varepsilon\) as a stochastic parameter. However, despite having a high observability obtained by fixing user position and velocity, accurate clock estimation cannot still be obtained. This is because of the fact that, in presence of large biases, \(cdt\) and \(\varepsilon\) get highly correlated due to which some part of \(\varepsilon\) gets consumed in \(cdt\).  
Thus, to mitigate the effect of multipath and other biases and noise in the estimation of \(cdt\), an FIR filter is applied to the estimated clock bias. Finally, the range error is computed as:  
\[\varepsilon=\rho-r-\widehat{cdt} \tag{1}\]  
where \(\widehat{cdt}\) is the filtered clock bias.  
(c 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ([[http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/)]([http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/))).
## 5 Discussion
### Model Generalization Analysis  
To verify the recognition accuracy of CellGAN on different datasets, Table 4 presents the comparative experimental results with other models in the MoNuSeg, GlandCell, and Nuclues datasets. CellGAN outperforms other convolutional or Transformer-based methods on all datasets. In the MoNuSeg dataset, this method achieves an IoU of 63.72% and a Dice of 77.84%, improving by 22.74% and 19.90%, respectively, compared to ResUNet, and by 1.44% and 1.81%, respectively, compared to SwinUNet; in the GandCell dataset, the IoU is 83.72% and the Dice is 91.14%, improving by 3.23% and 2.15%, respectively, compared to ResUNet, and by 4.43% and 3.19%, respectively, compared to SwinUNet; in the Nucleus dataset, the IoU is 92.53% and the Dice is 86.10%, improving by 23.45% and 15.96%, respectively, compared to ResUNet, and by 0.70% and 0.72%, respectively, compared to SwinUNet. The experimental results show that this method can achieve stable and superior performance compared to current mainstream methods on various cell microscopy image semantic segmentation datasets, with only slightly lower metrics than the convolutional-transformer hybrid architecture UTNet in the GlandCell dataset.  
Figure 10 demonstrates the good segmentation performance of the CellGAN model on the Nucleus dataset under low-contrast conditions. For instance, the results within the green bounding box indicate that the CellGAN model outperforms the Unet model.
## 1 Introduction
[PERSON] et al. proved the ability of the Smart Spring to control the dynamic impedance characteristics of the structure through experimental research, and the vibration reduction performance was proved by the wind tunnel test [15]. [PERSON] et al. designed a control scheme to make the stiffness of the Smart Spring device change continuously and compared it with the control effect of the state switching control algorithm [16]. To sum up, the research into Smart Spring has been mainly on the aspect of torsional vibration control of the helicopter blade, and the research into Smart Spring vibration reduction applied to multi-support shafting has been mainly dynamic modeling and characteristic analysis. [PERSON] [17] carried out dynamic analysis on the shaft system with Smart Spring, studied the design method of intelligent spring parameters, and analyzed the vibration control effect of the intelligent spring in multi-support shafting. [PERSON] [18] made a preliminary exploration of the multi-span shafting vibration reduction of the intelligent spring, established the multi-span shafting dynamic model of the intelligent spring, analyzed the influence of intelligent spring control force and its parameters on vibration reduction performance, and optimized the parameters of the intelligent spring with a genetic algorithm. At present, there is a lack of theoretical guidance and related experimental verification for the research on using Smart Spring to reduce the vibration of tail drive shafting. The application of Smart Spring to the vibration reduction of the tail drive shaft system needs further research. In this paper, the control strategy of exerting fixed control force and function control force is put forward. By using the ADAMS and MATLAB joint simulation method, the research on over-critical vibration control of three-support shafting with Smart Spring support is carried out, and the accuracy of the joint simulation method is verified by experiments. Research on Vibration Reduction of the Shaft System with Fixed Control Force Applied by Smart Spring