esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of a New-Concept Secure File Server Solution
## 4 Results
### Results on 1 Gbit/s Connection
#### 4.1.2 Results of Writing Performance Tests on 1 Gbit/s Connection  
In the case of file writing, the average transfer speeds calculated for the 1 Gbit/s bandwidth network are illustrated in Table 3 and Figure 3.  
The results show that for all file sizes, the Windows 10--ARDS TestServer client-server configuration showed a spectacular drop in performance, with speeds for smaller files (4 MiB, 500 KiB) approaching zero. It was also observed that during the data transfer of smaller files, the transfer speed was lower for all other client-server pairings, but the two servers provided almost identical performances. The drastic drop in transfer time for Windows 10 in a non-Microsoft Windows 2019 Server environment is a widely known and well-documented phenomenon [21; 22; 23; 24], which can be solved by fine-tuning server-side  
\begin{table}
\begin{tabular}{l r r r r} \hline \hline  & \multicolumn{4}{c}{**Files**} \\  & **1 \(\times\) 1 GiB** & **4 \(\times\) 256 MiB** & **256 \(\times\) 4 MiB** & **2097 \(\times\) 500 kiB** \\ \hline
**Test ID** & \multicolumn{4}{c}{**Average Transfer Speed in MiB/s**} \\ \hline
**Windows 7—ARDS TestServer** & 101.01 & 110.01 & 50.47 & 19.31 \\
**Windows 7—Windows 2019 Server** & 107.99 & 108.58 & 48.95 & 17.90 \\
**Windows 10—ARDS TestServer** & 108.43 & 111.21 & 59.81 & 20.87 \\
**Windows 10—Windows 2019 Server** & 107.90 & 110.74 & 59.08 & 19.48 \\
**Ubuntu 22.04—ARDS TestServer** & 109.82 & 109.10 & 90.95 & 59.51 \\
**Ubuntu 22.04—Windows 2019 Server** & 110.38 & 109.90 & 88.68 & 69.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reading test results on 1 Gbit/s bandwidth.  
Figure 2: Measurement results of reading on 1 Gbit/s bandwidth.  
configurations, client-side system parameter settings, and registry changes, which can be implemented centrally using group policy objects in a domain environment.
As the two parts that resist lateral force, the frame and the core tube, cooperatively operate with different stiffness, an unequal distribution of shear in the two components is caused [16; 17]. To effectively prevent earthquake-induced structural collapse, the damage stage and the residual bearing capacity of structures should be properly predicted in this structural system. A seismic damage model actually reflecting strength and stiffness degradation under cyclic loading is the basis of structural safety margin evaluation and post-earthquake restoration. Based on the initial strength criterion and deformation, seismic damage assessment models based on energy criteria were proposed [18; 19]. Afterwards, the losses of motion stability and the loss of vertical bearing capacity were taken as criterions to judge the collapse of structures [20]. A stress wave-based active sensing method was also used to detect damage in concrete structures [21; 22; 23; 24; 25; 26; 27]. The piezoelectric smart aggregates were adopted for structural health monitoring [28; 29; 30]. However, the existing criterions cannot reflect the influence of loading amplitude and loading path on the cumulative damage of the structures. Since the damage and collapse of a structure is a complex dynamic instability problem, collapse criterion and damage evaluation index on the level of the overall structure should be developed and be applied to concrete-encased steel frame-reinforced concrete core tube structures. In this paper, a 1/5 scale and 10-storey concrete encased steel frame-reinforced concrete core tube building model was constructed according to the design code of China (GB50023-2009). The prototype structure was designed with 8 degrees of seismic fortification. The specimen was measured for a proposed criterion of seismic damage assessment. Dynamic characterization experiments were conducted using velocity sensors on each floor. The natural frequencies were obtained based on the experimental data. Modal analysis was also carried out using a nonlinear finite element program based on a fiber model in OpenSees. The modal analysis considered geometric nonlinearity and material nonlinearity. Then, the damage process under different seismic wave input was revealed based on finite element analysis, and the max story drift angle was chosen to reflect the damage state and to quantify the damage degree. The structural dynamic characteristics indexes such as vibration modes and frequencies can be well detected according to non-destructive structure. A criterion of seismic damage assessment was proposed based on the relationship between the quantitative damage value and dynamic characteristics. The author hopes to provide references for damage evaluation, structural health monitoring and post-earthquake restoration of concrete-encased steel frame-reinforced concrete core tube buildings.
## 3 Results
### Objective 1: Data Procurement
#### 3.1.1 Optimized Data Collection Parameters  
Quality image datasets were collected at PWA both _before_ and repeatedly _after_ the prescribed prairie burn. Following the preliminary data collection tests at the Doak and Hermann prescribed prairie burn sites, the atmospheric, sensor, and mission planning parameters were optimized and implemented for the PWA burn surveys (Table 12).  
The optimal data collection parameters for harvest sites differed slightly from the prairie burn sites due to the height of vegetation and sometimes minimal ground visibility. Quality imagery was collected both _before_ and repeatedly _after_ the selective timber harvest at Volz with the parameters shown in Table 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Consideration** & **Subject** & **Recommendation** & **Justification** \\ \hline \multirow{4}{*}{Atmospheric Conditions} & Sun Angle/ & & Little to no tall objects that would cause long shadows. Wider range of collection times than forested sites. \\  & Time of Collection & & \\  & & & \\  & Brightness/ & Bright to Diffuse/ & Low vegetation height was less sensitive to overexposure from bright light. \\  & Cloud Cover & Clear to Overcast & Motion blur less common with low vegetation height. \\  & Wind & 0–10 mph (med) & Properly exposed image with sharp detail for given lighting conditions. \\  & & & \\  & Shutter Speed & 1/2500–1/3200 & \\  & & & \\  & Aperture and & & \\  & Focal Length & f/3.5 and 21 mm & Wide field of view for high image overlap. \\  & ISO and Zoom & Auto and \(\infty\) & Auto-balanced gain and focused images. \\  & Flight Altitude & 121 m AGL (400 ft) & Sufficient depth of field for sharp detail. \\  & Overlap/Sidelap & 80\% \(\times\) 80\% & Ensured thorough coverage of study area. \\  & Number of Boundary & & Inherently more visible ground cover than forested sites allowed for smaller outside image buffer. \\  & Images & & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Optimal data collection parameters for surveying prescribed prairie burn sites.
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 4 Gain Localized in Both Space and Time
### Linear Gain  
Consider the following linear gain function  
\[\gamma(x,t)=\gamma_{2}u_{0}(x,t) \tag{7}\]  
where \(\gamma_{2}\) is a scaling constant. The net background gain as \(u_{0}\) decays to unity is given by \(\gamma_{2}+\beta\) and the maximum gain \(3\gamma_{2}+\beta\) is attained at the origin. The value of the parameter \(\sigma_{0}\) determines the rate of change in the gain function only. For a smaller \(\sigma_{0}\), the gain function increases to the maximum value more rapidly.  
Figure 4: Effect of cubic gain localized in space: simulated result of a rogue wave with multiple peaks at \(\sigma=1\), \(\beta=-0.1\), \(\gamma\equiv 0\), \(\varepsilon=0.1\) and \(\alpha_{1}=0.042\).  
Recovery of a Rogue Wave from a Pinned Mode  
Remarkably, by restricting the net linear gain to a short period of time only, the rogue wave can be recovered from the otherwise pinned mode discussed in the previous section. The idea is illustrated here with the numerical example discussed in Figure 3c,d. By choosing a net gain profile (Figure 5b) with the same maximum gain and background loss as that of Figure 3d, the pinned mode formed by the spatially localized gain of Figure 3c is destroyed. A rogue wave can thus be recovered by a 'rogue' gain function (Figure 5a).  
Formation of Breather from Localized Gain with Negligible Background Loss  
In the special case where the net gain exists only in a spatially localized region (Figure 5d), the rogue wave evolves into a Kuznetsov-Ma soliton pulsating in the time domain (Figure 5c). In other words, when the background loss goes to zero asymptotically (\(\gamma_{2}=-\beta\)), the localized gain near the origin will trigger the rogue wave to evolve into a breather propagating in the temporal domain instead of decaying back to the background. A pulsating mode with growing peaks can also be observed in a system with constant background gain as discussed in Section 2. The result here shows that a gain function restricted to a finite region can have a similar effect but the growth in amplitude ceases once the gain vanishes.
# High Dielectric Design of Polymer Composites by Using Artificial Neural Network
## 3 Results & Discussions  
### Training of the ANN Model  
Figure 4 shows the MSE drops of the proposed ANN model until 200 epochs when the network learns as expected for a well-trained ANN. This is a good indication of the network's learning process. The blue line and red line represent the decreasing error of the training and validation data, respectively. It measures the network generalization ability and stops training as soon as the generalization does not improve. As shown in Figure 4, the training error was smaller than the validation error as expected, and the error lines were continuously decreased without overfitting.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ranking** & **1** & **2** & **3** & **4** & **5** \\ \hline No. of hidden layers & 4 & 3 & 2 & 4 & 3 \\ Layer 1 & 48 & 64 & 32 & 48 & 48 \\ No. of & Layer 2 & 32 & 64 & 32 & 48 & 32 \\ nodes & Layer 3 & 32 & 48 & & 32 & 32 \\ Layer 4 & 16 & & & 16 & \\ Learning rate & 0.001 & 0.001 & 0.001 & 0.01 & 0.001 \\ Mean squared error & 0.1372 & 0.1384 & 0.1416 & 0.1429 & 0.1431 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top 5 model structures in the hyperband optimization.  
Figure 3: Schematic of the ANN model structure with input, hidden, and output layers.  
The results of predicted \(\varepsilon_{r}\) and experimental \(\varepsilon_{r}\) of composites are shown in Figure 5. The ANN model had high accuracy in the prediction of composite's dielectric constant. Figure 5a presents the prediction results were trained by all 744 databases with a high coefficient of determination (R\({}^{2}\)) of 0.95. The three red circles indicate relatively larger errors between the predicted \(\varepsilon_{r}\) and experimental \(\varepsilon_{r}\) than the other predicted values. Although the prediction error of some data points are relatively large, the ANN model shows a pretty high prediction accuracy. Data points in red circles were collected from the reference [18] using P(VDF-TrFE-CFE) as the matrix and a relatively high frequency of 10\({}^{6}\) Hz. Ferroelectric matrices with high dielectric constant can abruptly decrease dielectric constant at high frequencies, and under these conditions, there were only a limited number of data points in this study. Figure 5b shows the accuracy of an ANN model trained without the data in red circles. As expected, the prediction accuracy is higher than Figure 5a with the R\({}^{2}\) of 0.97.
## 3 Results and Discussion
### pH, Temperature, and REDOX Monitoring
The bioreactor's heating system was intentionally deactivated to induce a temperature drop below the setpoint of 35.0 \({}^{\circ}\)C. The IoT system was preconfigured so that normal operation is within a temperature range of 35.0 \({}^{\circ}\)C to 37 \({}^{\circ}\)C. Upon any deviation from this range, a predefined email notification from a designated email account was automatically sent to the operator's email account. As expected, when the bioreactor temperature fell below 35.0 \({}^{\circ}\)C, the system accurately triggered the alert, prompting the operator to take corrective actions to restore normal operating conditions. The heating system was subsequently reactivated, returning the temperature to the desired setpoint. This notification system could be invaluable, as it can alert the operator to prevent significant malfunctions of the bioreactor and associated equipment, thereby minimizing disruptions of the anaerobic process. Monitoring the reactor's temperature range using the proposed system yielded excellent results and the values from the analog instrument and the sensor were significantly close to one another, thus making it a reliable tool for real-time temperature control. As for REDOX potential, which is an activity indicator of electrons in a system, it plays a crucial role in AD as it affects the activity of anaerobic microorganisms. Since anaerbes are highly sensitive to alterations in REDOX potential [46], maintaining the optimal REDOX levels is of great importance to ensure the smooth operation of an AD reactor. The REDOX values recorded by the IoT REDOX sensor during the experiment are shown in Figure 7. The recorded data showed an average value of \(-\)393.75 \(\pm\) 15.90 mV, which falls within the optimal range of \(-\)200 to \(-\)400 mV [47].
# A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data
## 4 Experiment and Result Analysis
### Traffic Information Detection
After the road target travel mode is distinguished, the road target is visualized. Finally, the space mean speed and time series of vehicle steering ratio of each road entrance section and exit section of the intersection are obtained by using the sliding time window method. The statistical period is 15 min. Figures 11-13 show the space mean speed and vehicle steering ratio information obtained from the collected Wi-Fi data during 9:00-21:00 on a working day at the intersection. In order to show more details of the traffic information changes, the step length of the time sliding window is set to 1 min. Figure 10: Cluster analysis diagram. (**a**) SSE value. (**b**) Average silhouette coefficient. From the above traffic information, it can be clearly seen that there is an obvious evening peak at the intersection between 18:00 and 19:00. The space mean speed of the exit and entrance sections in each direction decreased significantly. There is no obvious rule in the proportion of left-turning vehicles in the entrance sections in each direction, and the fluctuation is more drastic.
## 1 Introduction
For a relatively new technique, MTS is extremely effective, and it has been applied in a variety of fields [17; 19; 21]. For example, MTS has been incorporated in a variety of apps for healthcare diagnoses, fire detection, earthquake and weather forecasting, and loan rating prediction [11; 13; 22; 23; 24; 25; 26; 27]. Furthermore, MTS is capable of eliminating any unnecessary items in the information system and providing useful data to assist healthcare professionals in decision making during diagnoses of diseases [28; 29; 30]. In addition, MTS is able to perform classification duties and recognize the significant multivariate system variables. To significantly improve the process of discovering the most helpful parameters, the Kanri Distance Calculator (KDC) is incorporated into the MTS [31; 32]. KDC-MTS is an extremely efficient approach to improve the accuracy of the predictive model. Both MTS and KDC are statistical methods that incorporate several mathematical ideas [6]. These techniques are used for diagnosing and predicting multivariate data [17; 18; 19; 20]. Although many tools have been created to evaluate the patterns and dynamics of large data sets, only KDC allows users to understand exactly how far they are from the desired target state, and how each variable contributes to the entire distance from the target state. Apart from calculating connections among the factors within a general information set, KDC is also able to calculate the correlation between each of the factors mathematically [7]. With regard to MTS, this research implemented KDC to define the specific factors affecting the performance of post-ACLR patients. Thus, data mining applications can be created by implementing a mathematical, index-based health management system to better recognize and monitor the states of gait abnormality among high-risk patients, to design suitable procedures, and to decrease the number of hospital admissions and claims. For this study, ACLR patients from the Rehab and Physiotherapy Unit of Hospital Tuanku Fauziah at Kangar, Perlis were recruited. Ten patients, all male, with unilateral primary ACLR were chosen for the ACLR group (PG). All the patients underwent similar rehabilitation procedures, starting with passive and then active mobilization after surgery. Our goal is to apply gait analyses and mathematical, index-based health management (MTS and KDC) to diagnose the level of gait abnormality and to identify its contributing factors following ACLR.
###### Abstract  
Human gingival fibroblasts (HGnFs) play crucial roles in periodontal wound healing. This in vitro study examined the impact of varying concentrations of topical oxygen fluid (blue(r)m) on HGnF morphology, viability, proliferation, oxidative stress and proinflammatory cytokine production. The attempt was to underscore the potential of blue(r)m as a less cytotoxic alternative to chlorhexidine in the context of tissue-regeneration improvement. Primary HGnF cell cultures were subjected to oxygen fluid (blue(r)m) at concentrations of 0.6, 1.2 and 2.4% for a duration of 1 min. The positive control was 0.12% chlorhexidine. Cell morphology as well as actin cytoskeleton were assessed using microscopy and immunofluorescence staining. Cell viability and proliferation were assessed through AlamarBlue and trypan blue assays at 1, 2, 7, 10 and 14 days. Levels of reactive oxygen species (ROS) were quantified using DCFH-DA assay. Pro-inflammatory cytokines (IL-18, IL-6, TNF-\(\alpha\), MMP-8 and TIMP-1) were assessed through ELISA. HGnF morphology and actin structure were preserved at all oxygen fluid concentrations. Cell viability and proliferation were significantly higher in the 0.6% and 1.2% groups than in the control and chlorhexidine groups (\(p\leq 0.05\)). ROS levels were low at 0.6% and 1.2%, but increased at 2.4% and with chlorhexidine (\(p\leq 0.05\)). Oxygen treatment reduced IL-18, IL-6, TNF-\(\alpha\) and TIMP-1 expression, while MMP-8 levels increased. Chlorhexidine significantly upregulated the expression of all proinflammatory cytokines (\(p\leq 0.01\)). Oxygen fluid (blue(r)m) therapy improves the viability and proliferation of gingival fibroblasts and offers anti-inflammatory and preliminary antioxidative effects at the cellular level, especially at lower concentrations (0.6% and 1.2%), indicating potential application in periodontal wound management, subject to clinical validation.  
human gingival fibroblasts; topical oxygen therapy; oxygen fluid; cell viability; cell proliferation; reactive oxygen species; cytokines; wound healing
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 2 Materials and Methods
### Overview of Methodology
#### 2.2.3 Uncertainty-Normalised Differences  
The method used to validate these derived uncertainties is based on an analysis of the distribution of SLSTR-A/B differences normalised by the propagated difference uncertainties, i.e.,  
\[\epsilon_{i}=\frac{\Delta\langle L\rangle_{\text{G},i}}{u(\Delta\langle L\rangle _{\text{G},i})}. \tag{8}\]  
In the case where \(u(\Delta\langle L\rangle_{\text{G},i})\) represents an uncertainty due to fully uncorrelated error effects within \(\{\Delta\langle L\rangle_{\text{G},i}\}\), if the variance of differences is well described by its uncertainties, the resulting distribution should be standard normal, i.e., a Gaussian centred on 0 with a standard deviation of 1. Underestimation of uncertainties will lead to an uncertainty-normalised distribution with a standard deviation greater than 1, whereas overestimation of the uncertainties will lead to an uncertainty-normalised distribution with a standard deviation less than 1. As mentioned, particularly at L2, a potentially significant, unquantified covariance between SLSTR-A and B is not included in the estimated difference uncertainty. In this analysis, the effect of this omission would be to cause the uncertainty-normalised difference distribution to narrow, with a standard deviation of less than 1, since the difference uncertainties will be overestimated.  
In the case where \(u(\Delta\langle L\rangle_{\text{G},i})\) represents an uncertainty due to fully correlated error effects within \(\{\Delta\langle L\rangle_{\text{G},i}\}\), the resulting distribution mean informs if the bias is well described by its uncertainties. If the uncertainty well describes the bias, there is a 68% probability that the uncertainty-normalised distribution mean should be less than 1 and, at a 95% probability, it is less than 2.
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 5 Experiment, Results, and Analysis
### Datasets and Training Parameters  
The typical eight types of signals that include 2 FSK, BPSK, 4 FSK, FRANK, NS, EQFM, LFM, and SFM are adopted in this paper. Table 2 lists the detailed radar signal parameters. Note that the training, validation, and testing datasets contain randomly overlapping dual-component radar signals under different SNRs, which are preprocessed by using SPWVD transformation. To conduct the comparative analyses, the SNR ranges from \(-12\) dB to \(10\) dB, and every \(2\) dB is selected. The number of each class of dual-component radar signals under different SNRs is set as 300, and a total of 1,419,600 samples are obtained in the training dataset. For the validation dataset, the number of each class of dual-component radar signals under different SNRs is set as 100, and a total of 473,200 samples were obtained. For the testing dataset, the number of each class of dual-component radar signals under different SNRs is set as 30, and a total of 5160 samples is obtained. The types and parameters of radar signals are the same as those in Reference [49].  
The deep learning framework used in the analyses is Pytorch 1.11 and Python 3.9. Based on the computational complexity and recognition performance, the training parame  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Types** & **Parameters** & **Ranges** \\ \hline
2 FSK & Carrier frequency \(f_{1}\), \(f_{2}\) & 0.01 to 0.46 \\  & Bandwidth \(\Delta f\) & N/32 to N/8 \\ \hline  & Barker codes & [5, 7, 9, 13] \\ BPSK & Carrier frequency \(f_{0}\) & 0.1 to 0.4 \\  & \(T_{s}\) & N/32 to N/16 \\ \hline
4 FSK & Carrier frequency \(f_{1}\) to \(f_{4}\) & 0.1 to 0.4 \\  & \(T_{s}\) & N/32 to N/8 \\ \hline EQFM & Carrier frequency \(f_{1}\), \(f_{2}\) & 0.05 to 0.4 \\  & Bandwidth \(\Delta f\) & 0.05 to 0.3 \\ \hline  & Carrier frequency \(f_{0}\) & 0.1 to 0.4 \\ FRANK & \(T_{s}\) & N/100 to N/50 \\  & Phase number \(M\) & [4, 5, 6, 7] \\ \hline  & Initial frequency \(f_{c}\) & 0.01 to 0.45 \\  & Bandwidth \(\Delta f\) & 0.05 to 0.4 \\ \hline NS & Carrier frequency \(f_{0}\) & 0.1 to 0.4 \\ \hline SFM & Carrier frequency \(f_{0}\) & 0.05 to 0.15 \\  & Bandwidth \(\Delta f\) & 0.05 to 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of various radar signals.  
ters are set as follows: the batch size is 128; epoch is 100; epsilon is 0.001; momentum is 0.01; initial learning rate is set as 0.01; learning decay rate is 0.1 every 10 epochs; dropout is 0.8; weight decay is set as \(1\times 10^{-5}\); and batch normalization with average decay is set as 0.999.