esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Batch Adsorption Method  
In the batch method, conical flasks of the 100 mL volume closed with a silicone stopper were used. Then, 50 mL of dye solution of the initial concentrations 100-500 mg/L (kinetic studies) or 500-9000 mg/L (equilibrium studies) and 0.5 g of the anion exchanger were added. The flasks were placed in an Elphin+ 358S mechanical shaker (Elphin, Poland) with a vibration amplitude of 8 units and rotary of 180 cycles/min at 25 \({}^{\circ}\)C and mixed from 1 to 240 min (kinetic studies) or 24 h (equilibrium studies). The effects of Na\({}_{2}\)CO\({}_{3}\) and Na\({}_{2}\)SO\({}_{4}\), as well as SDS and TX-100 additions on DY50 sorption process on A21, A23 and A24 were studied using the conditions listed in Table 1. After shaking, the resin was separated from the solution, and the concentration of DY50 was measured spectrophotometrically (UV-Vis spectrophotometer Cary 60, Agilent, Santa Clara, CA, USA) at the maximum absorbance wavelength (394-403 nm depending on the auxiliaries, Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Conditions**} & \multicolumn{2}{c}{**System**} \\ \cline{2-3}  & **Na\({}_{2}\)SO\({}_{4}\)/Na\({}_{2}\)CO\({}_{3}\)** & **SDS/TX-100** \\ \hline Salts/surfactants concentration (g/L) & 5, 15, 25 & 0.1, 0.25, 0.5 \\ Dye concentration (mg/L) & 500 & 500 \\ Time (min) & 15 & 15 \\ Volume (mL) & 50 & 50 \\ Anion exchanger mass (g) & 0.5 & 0.5 \\ Temp. (\({}^{\circ}\)C) & 25 & 25 \\ Vibration amplitude, rotary (cycle/min) & 8, 180 & 8, 180 \\ Maximum absorbance wavelength (nm) & 399/403 & 394/397 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Adsorption process conditions in the systems containing salts and surfactants.  
Figure 2: Physicochemical properties of the anion exchangers [52; 53] and dye.  
The effect of initial pH of the solution on the adsorption effectiveness of DY50 on the weakly basic resins A21, A23 and A24 was investigated by shaking 0.5 g of the resin with 50 mL of a solution of different initial pH containing 500 mg/L of dye. The pH was adjusted with 1 M NaOH and 1 M HCl. After the sorption process (\(t=15\) min), the DY50 concentration was measured spectrophotometrically.  
The batch adsorption/desorption experiments were done three times, and the average values are given in the figures and tables. Maximum error was found to be \(\pm 3\%\).
## 3 Numerical Results and Discussions
### Comparison between TSC and DSTSC
The DSTSC did not overheat on the bottom because the air velocity is sufficiently high enough to extract the heat from the wooden back plate, as we can notice in Figure 10.  
Figure 5: Heat exchanger efficiency \(\epsilon_{HX}\). Figure 6: Temperature distribution on the perforated absorber plate for the DSTSC (**left**) and TSC (**right**) at an airflow rate of 500 m\({}^{3}\)/h. Figure 10 and 11 present the numerical results for an airflow of 500 m\({}^{3}\)/h. Compared to the 100 m\({}^{3}\)/h case, the solar collectors no longer overheat, since the air velocity is sufficiently high to extract the heat from both collectors. However, we can still notice slight overheating at the bottom of the TSC. Figure 8: Temperature distribution in a median plane for the DSTSC (**left**) and TSC (**right**) at 100 m\({}^{3}\)/h. Figure 7: Solar Collector efficiency. The heat transfer coefficient was analyzed to characterize the thermal performance of the perforated absorber plate of each solar collector. Figure 12 shows the evolution of the heat transfer coefficient, and it is notable that the DSTSC has slightly better performance. However, if we consider the additional heat flux of the separation plate, the results are significantly different, as shown in Figure 13. Indeed, the thermal performance of the DSTSC now became much higher than the TSC. One limitation of these results, however, is that they do not include the heat flux of the back plate of both solar collectors. Figure 10: Temperature distribution in a median plane for the DSTSC (**left**) and TSC (**right**) at an air flow of 500 m\({}^{3}\)/h. Figure 9: Velocity distribution in a median plane for the DSTSC (**left**) and TSC (**right**) at an air flow of 100 m\({}^{3}\)/h. Figure 11: Velocity distribution in a median plane for the DSTSC (**left**) and TSC (**right**) at air flow of 500 m\({}^{3}\)/h. Figure 10.0 Computed results on the evolution of the heat transfer coefficient for the TSC and DSTSC. Figure 10.1 Computed results on the evolution of the total heat transfer coefficient for the TSC and DSTSC.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 3 Time Scale Algorithm Principle
### KPW Time Scale Algorithm
 \tag{9}\]  
where \(q_{1}\), \(q_{2}\) and \(q_{3}\) represent the process noise parameters of White Frequency Modulation (WFM), Random Walk Frequency Modulation (RWFM) and Random Run Frequency Modulation (RRFM), respectively, which are estimated by least squares fitting of Hadamard total variance [24]:  
\[Total\sigma_{H}^{2}(\tau)=(10/3)q_{0}\tau^{-2}+q_{1}\tau^{-1}+q_{2}\tau/6+11q_{ 3}\tau^{3}/120 \tag{10}\]  
Through Equation (5), we can see that another key factor of the KPW algorithm is the weight. The weight selection equation of the traditional weighted time scale algorithm is generally [8]:  
\[\omega_{i}=[\sum_{i=1}^{N}\frac{1}{\sigma_{H}(\tau)}]^{-1}[\frac{1}{\sigma_{H }(\tau)}] \tag{11}\]  
where \(\sigma_{H}^{2}(\tau)\) represents the Hadamard variance when the sampling interval is \(\tau\), \(\tau\) represents the sampling interval, and N represents the number of atomic clocks in the clock group. Since the Hadamard variance is a three-sample variance, it is not sensitive to linear frequency drift, so it is suitable for analyzing the frequency stability of rubidium atomic clocks. The expression for Hadamard variance [25] is:  
\[\sigma_{H}^{2}(\tau)=\frac{1}{6(N-3)\tau^{2}}\sum_{i=1}^{N-3}\left[x_{i+3}-3x _{i+2}+3x_{i+1}-x_{i}\right]^{2} \tag{12}\]
## 3 Results
### Accuracy Assessment and Validation  
Figure 8 shows validation results for the three models summarised in Table 5, which was carried out by applying the model coefficients to the 20% reserved dataset and comparing the cLDMC based model estimate of ANPP at each plot against the Landsat NDVI based estimate. For the two spring images, the RMSE was less than 20% while the autumn 2014 image had a slightly higher RMSE (21.6%).  
Figure 7: Maps of ANPP (g dry mass m\({}^{-2}\) year\({}^{-1}\)) for Wales (all habitats listed in Table 2) produced using Landsat 8 NDVI imagery captured on: 19 May 2013 (**a**); 13 April 2014 (**b**); and 6 October 2014 (**c**); and (**d**) a combined ANPP map, produced by mosaicking together the first three maps.
## 2 Materials and Methods
### Data Processing Method
#### 2.2.3 Precision Evaluation Index  
In this paper, bias, STDEV, and RMS values are introduced to evaluate the accuracy of PPP inversion ZTD and PWV. The expressions of bias, STDEV, and RMS are as follows:  
\[\text{Bias}=\frac{\sum_{i=1}^{n}(\triangle x)}{n} \tag{6}\]  
\[STDEV=\sqrt{\frac{\sum_{i=1}^{n}\left(\triangle x-\overline{\Delta x}\right)^{ 2}}{n}} \tag{7}\]  
\[RMS=\sqrt{\frac{\sum_{i=1}^{n}(\triangle x)^{2}}{n}} \tag{8}\]  
where \(n\) represents the total number of samples, \(\Delta x\) is the difference between the two sets of data, and \(\overline{\Delta x}\) is the average value of the difference between the two sets of data.
## 2 Materials and Methods  
### Study Design  
A prospective, multicenter study in patients who underwent serial coronary CTA was conducted during the H2020 SMARTool project (Clinicaltrial.gov Identifiers NCT04448691) [11]. White patients were included by 7 centers from 5 European countries. The study protocol was approved by all local ethical committees, all patients gave their written informed consent to participate in the study, and the procedures followed were in accordance with institutional guidelines. Patients who previously underwent coronary CCTA for suspected CAD, as part of the Evaluation of Integrated Cardiac Imaging for the Detection and Characterization of Ischemic Heart Disease (EVINCI); FP7-222915; n = 152) [12] or ARTreat (FP7-224297; n = 18) [13] clinical studies, were prospectively included to undergo follow-up coronary CTA. Additionally, patients who underwent coronary CCTA in the period 2009 to 2012 for clinical indications (n = 32) and were not originally included in the EVINCI and ARTreat studies were also prospectively included. A full list of inclusion and exclusion criteria is provided in the Supplementary Materials. Among the final SMARTool population, consisting of 263 patients, 187 patients were selected for this study based on the image quality of baseline CCTA as well as on the existence of stented arteries as shown in the Figure 1. For each patient, clinical risk factors, biohumoral variables, and lipidomics data had been collected, and CCTA examination was performedat two time points, i.e., at baseline and at follow-up (mean follow-up 6.22 \(\pm\) 1.42 years, range of follow-up period: 1.86\(-\)11.3 years).
# Transient Time Reduction in Time-Varying Digital Filters via Second-Order Section Optimization
## 4 Results  
### Optimization Outcomes  
As an example let us use the elliptic filter mentioned in Section 3.4. Figure 4 presents the step response of #2 SOS of the final design.  
Table 1 presents the coefficients of the stationery #2 SOS.  
As one can notice, this section's step response is characterized by significant ripple behavior.  
By introducing the iterative SQP solver described in Section 3.5, we have acquired a new time-varying SOS with a significantly improved transient time. Tables 3 and 4 present the set of coefficients (\(a_{k}[n]\) and \(b_{k}[n]\)) that change in the span of five samples (horizon).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficients** & **0** & **1** & **2** \\ \hline a & 1 & \(-1.58314580285905\) & \(0.867241363463590\) \\ \hline b & 1 & \(-1.388888837010072\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear time- invariant coefficients of the #2 SOS.  
Figure 4: Step response of #2 SOS of the 6 th-order elliptic filter.  
As mentioned in Section 3.5, after reaching the horizon (five samples) the coefficients settle on the original #2 SOS parameters to preserve the desired frequency characteristic. Figure 5 outlines the graphical comparison of the LTI and LTV #2 SOS instances.  
Table 5 summarizes the improvement in the reduction in the transient time by using the time-varying coefficients.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(\mathbf{b_{0}}[\mathbf{n}]\) & \(\mathbf{b_{1}}[\mathbf{n}]\) & \(\mathbf{b_{2}}[\mathbf{n}]\) \\ \hline \(n=1\) & 0.9727 & 0.5254 & 0.6714 \\ \hline \(n=2\) & 0.1920 & 0.5303 & 0.7413 \\ \hline \(n=3\) & 0.5733 & 0.7991 & 0.7783 \\ \hline \(n=4\) & 0.9613 & 0.991 & 0.9952 \\ \hline \(n=5\) & 0.4464 & 0.4646 & 0.4498 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time-varying set of \(b_{k}[n]\) coefficients.  
Figure 5: Comparison of step responses of the #2 SOS designed with classical methodology and the proposed time-varying approach.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transient Time Threshold** & **Sample Index** & **Time** \\ \hline \(1\%\) & \(54\) & \(0.053\) s \\ \hline \(2\%\) & \(47\) & \(0.046\) s \\ \hline \(5\%\) & \(31\) & \(0.03\) s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transient time data for classical stationary SOS.
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 3 Accurate Registration
### Building Neighborhood Feature Descriptor  
We built the corresponding relation between the two key point sets by finding similar feature points. The point feature descriptor directly affects the iteration speed and registration accuracy. We propose a seven-dimensional neighbourhood feature descriptor. First of all, the curvature has the invariance of rotation, translation, and scaling, so it is considered as a feature element. The curvature \(k\) is calculated as follow [26]:  
\[k=\frac{\lambda_{0}}{\lambda_{0}+\lambda_{1}+\lambda_{2}} \tag{4}\]  
Figure 2: The influence of neighbor points for the calculation of the angle-invariant feature.  
where \(\lambda\) is from formula:  
\[C\cdot\overrightarrow{v_{i}}=\lambda_{i}\cdot\overrightarrow{v_{i}},\quad i\in \{0,1,2\} \tag{5}\]  
where \(C\) is the covariance matrix of point, \(\overrightarrow{v}\) is the eigenvector and \(\lambda\) is the eigenvalue.  
Then taking into account the spatial distribution and feature of the neighbor points, we add two feature elements \(u\) and \(v\):  
\[u_{i}=\frac{1}{m}\sum_{j=1}^{m}d_{ij}\cdot k_{j},\quad v_{i}=\frac{1}{m}\sum_{ j=1}^{m}d_{ij}\cdot f_{j} \tag{6}\]  
where \(d_{ij}\) is the spatial distance from one point to its neighbor point, \(k_{j}\) is the curvature of the neighbor point, \(f_{j}\) is the neighborhood weighted angle-invariant feature, \(m\) is the number of neighbor points.  
It should be noticed that \(x\), \(y\), \(z\), \(f\), \(k\), \(u\), and \(v\) constitute our feature descriptor, in which \(x\), \(y\), \(z\) are the coordinates of point, \(f\) is the neighborhood weighted angle-invariant feature, \(k\) is the curvature, \(u\) and \(v\) are from formula (6). We use the data obtained by calculating the normal vectors of points to build a feature that fully expresses the neighborhood information, thus the speed and accuracy of feature calculation and correspondence building are improved.
# Sustainable Development of Concrete through Aggregates and Innovative Materials: A Review
## 3 Synthetic Fibers
### Glass Fibers  
Glass fiber is a type of fiber reinforced plastic, containing silica or silicate and can be used in architectural elements but also subjected to mechanical forces. Thus, these elements also show deformations and cracks.  
[PERSON] et al. [71] studied the mechanical behavior of concrete specimens added with different volumes of glass fiber and concrete waste aggregate (CWA). For 30%, 50% and 100% content of CWA, the concrete at 0.25-0.5% glass fiber measured its maximum results of compressive strength, flexural strength and elastic modulus. In comparison with control specimen, the compressive strength, flexural strength and elastic modulus increased at 1.5-7%, 0.5-5% and 19-25%, respectively. [PERSON] et al. [72] investigated the impact of steel and glass fiber incorporation on the durability and mechanical performance of concrete samples at high temperatures from 28 \({}^{\circ}\)C to 800 \({}^{\circ}\)C. The steel fibers enhance the compressive, tensile and shear strength of the concrete at 9-27%, 8-198% and 1-22%, respectively. For concrete samples with inclusion of glass fiber, the compressive and tensile strength improved at 1-18% and 19-213%, respectively.  
[PERSON] et al. [73] reported the impact strength and mechanical properties of self-compacting concrete samples reinforced containing recycled glass fiber reinforced polymers (GFRP). These samples were obtained with 0.25%, 0.75% and 1.25% content of fiber. The incorporation of the glass fiber in the concrete samples improved their impact resistance and mechanical properties. In addition, these authors developed equations to correlate this impact resistance of the concrete samples to the mechanical properties considering high magnitudes of coefficient of determination. On the other hand, the use of glass fiber and nylon fibers in concrete to prevent microcracks in bridge decks has been studied by [PERSON] and [PERSON] [74]. They added glass fibers and nylon fibers in concrete specimens. After compressive tests, they registered that the number of cracks, crack width and length are more in plain concrete compared to concrete containing glass fiber and nylon fibers.  
The glass fibers can be used as rods in a wide range of applications in civil construction as reinforcement of concrete support elements. These rods based on glass fiber can have greater bearing capacity with respect to steel rods. However, it is not recommended to replace steel rods with those of glass fiber considering only their diameter. More technical information of the rods is required to ensure similar mechanical behavior [75].
# A Lifetime Maximization Relay Selection Scheme in Wireless Body Area Networks
## 3 System Model
### Network Model  
In this paper, we consider a WBAN with one coordinator and \(l\) sensor nodes on a human body as shown in Figure 1. The coordinator is placed at the front side of abdomen while the sensor nodes are deployed in the different parts of the human body. Both direct transmission and cooperative transmission are allowed in the network. The sensor nodes deployed in the main body part can be selected as relay nodes because of their shorter distance from the coordinator. Relay nodes should not only transmit their own information to the coordinator, but also relay the information from some other nodes when selected as relay. In addition, relay nodes are allowed to use direct transmission only to comply with the IEEE 802.15.6 two-hop tree topology restriction. Only uplink data transmission from sensor nodes to the coordinator is considered due to application scenarios like health monitoring where most of the information transmitted are sensed data. We assume that the coordinator knows the network topology and the distance between each pair of nodes including itself.  
A simple Time Division Multiple Access (TDMA) Media Access Control (MAC) is employed in this model to deal with multi-sensor transmission. Note that this MAC is a common used version of beacon-enabled superframes MAC specified in IEEE 802.15.6 (see [8] for more details). More specific, time is divided into superframes which has fixed length. A superframe has two parts: the active part and the inactive part. The active part consists of fixed-length time slots and each sensor node has one orthogonal time slot to send its sensed data to the coordinator without collision. If a sensor node is selected as a relay, more time slots are given to it. The number of the extra time slots for a certain relay is decided by the number of relayed sensors it has. Each sensor node transmits the sensed data to the receivers (either relay or the coordinator) in its dedicated slot, whereas relay nodes, if necessary, listen to their corresponding sensor nodes for data reception and transmit the relayed data together with their own data to the coordinator in their allocated slots. During the inactive part, nodes go to sleep mode. When a WBAN sets up or re-starts, the time slots allocation will be made by the coordinator depending on the relay selection results. Normal sensor nodes are allocated time slots first before relay nodes. In this paper, it is assumed that all nodes have enough sensed data to send during their allocated slots.
## 3 Experiments
### Ablation Experiments  
The improved performance of MFCT-3 DOFRNet is mainly attributed to the characterization of facial features, the design of the network structure, the use of the multi-feature combinatorial thresholding technique, and the enhancement of face data using the missing face data generation method. By comparing the experiments in Tables 1-3, we can demonstrate the effectiveness of each of the proposed parameters or components.  
Table 6 demonstrates that the multi-feature combined threshold method can improve accuracy by 0.49%, which indicates that the effective removal of occlusions can increase  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Posture and Expression**} & \multicolumn{2}{c}{**Occlusion**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-7}  & **Top-1** & **Top-5** & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline DenseNet-121 [59] & 96.44 & 98.67 & 77.78 & 91.01 & 87.92 & 95.17 \\ MobileNet-V3 [60] & 96.22 & 98.22 & 68.78 & 83.86 & 83.69 & 91.67 \\ EfficientNet-B0 [61] & 96.44 & 98.44 & 79.37 & 93.12 & 88.65 & 96.01 \\ FaceNet [62] & 98.44 & 99.33 & 81.75 & 91.01 & 90.82 & 95.53 \\ MobileFaceNet [63] & 94.89 & 97.56 & 90.48 & 94.97 & 92.87 & 96.38 \\ SphereFace [64] & 96.00 & 97.78 & 93.39 & 97.35 & 94.81 & 97.58 \\ Cosface [65] & 99.11 & 99.78 & 90.21 & 96.56 & 95.05 & 98.31 \\ Arcface [50] & 99.33 & 99.55 & 98.41 & 99.73 & 98.91 & 99.64 \\ MFCT-3 DOFRNet & 100 & 100 & 98.94 & 99.47 & 99.52 & 99.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition accuracy (%) for the Bosphorus dataset using different methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Posture and Expression**} & \multicolumn{2}{c}{**Occlusion**} & \multicolumn{2}{c}{**Average**} \\ \cline{2-7}  & **Top-1** & **Top-5** & **Top-1** & **Top-5** & **Top-1** & **Top-5** \\ \hline DenseNet-121 [59] & 95.87 & 98.62 & 29.15 & 48.09 & 50.07 & 63.82 \\ MobileNet-V3 [60] & 89.91 & 97.71 & 26.17 & 43.62 & 46.17 & 60.49 \\ EfficientNet-B0 [61] & 92.66 & 98.17 & 33.83 & 54.04 & 52.24 & 67.73 \\ FaceNet [62] & 94.50 & 98.62 & 19.79 & 38.09 & 43.27 & 57.02 \\ MobileFaceNet [63] & 85.78 & 92.66 & 31.28 & 52.34 & 48.34 & 64.83 \\ SphereFace [64] & 96.33 & 98.62 & 68.30 & 82.34 & 76.85 & 87.12 \\ Cosface [65] & 98.17 & 99.08 & 82.98 & 92.98 & 87.41 & 94.50 \\ Arcface [50] & 99.08 & 100 & 85.53 & 94.89 & 89.44 & 96.09 \\ MFCT-3 DOFRNet & 100 & 100 & 93.41 & 96.60 & 95.08 & 97.25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Recognition accuracy (%) for the UMB-DB database using different methods.  
the recognition rate to some extent. The generation of missing face data can be improved by 8.06%, since the neural network is data-driven; additionally, by simulating the missing face data situation, the neural network can focus more on face differentiation features. In addition, we also experimentally confirm that using Focal-Arcface as the training loss function can effectively improve the recognition rate by 2.9%.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 3 Methodology
### EEG Signals  
EEG signals are an essential resource in understanding brain activity that they reflect brain dynamics and interactions. Various brain activities can be localized across the cortex [29]. However, signal amplitudes recorded by channels in certain areas are often greater than others. Additionally, activities originating from specific brain regions may not be measurable across the entire cortex. This makes EEG signals a valuable spatial information source, as their multichannel approach allows simultaneous recording from different cortical areas.  
Moreover, EEG signals graph the potential difference over time in complex domains [6], displaying variability in amplitude and frequency. Therefore, they also serve as valuable sources of temporal and spectral information [30]. Their variability is attributed to mental states, cognitive activities, attention levels, and other factors. EEG signal amplitudes typically range between 5 and 200 millivolts (mV), with frequencies typically fluctuating between 0.5 and 100 Hertz (Hz) [31]. EEG signals are non-stationary and yield large volumes of data due to their high dimensionality. Moreover, EEG signals frequently contain artifacts, including eye and muscle movements, as well as electrical interference [30]. Identifying and removing these artifacts is crucial for obtaining accurate records of brain activity. Despite these challenges, EEG studies are often insightful about brain activity due to their spatial, temporal, and spectral focus.