esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calculation Formula of Shear Behavior Capacity
For beams exhibiting the concrete crushing failure mode, due to the significant difference in their stress mechanism from shear failure, when applying this formula, it is recommended to carefully evaluate the adaptability between their failure characteristics and the applicable conditions of the formula in combination with specific engineering conditions. To verify the rationality of the shear behavior capacity formula established in this paper, this study collected multiple groups of experimental data of RC beams strengthened with FRP grids from domestic and international academic circles, and compared the experimental values with the calculated values from the formula. Standards for selecting test beams for verification: To ensure consistency with the research objects of this paper, all test beams were reinforced concrete beams strengthened with FRP grids. Secondly, specimens with complete relevant test data were preferentially selected to ensure accurate calculations. Figure 14: Regression analysis of the effective strain of the FRP grid. In addition, the test results of scholars at home and abroad were taken into account to ensure the breadth and diversity of data sources, thus enabling a more comprehensive verification of the rationality of the formula. Table 10 presents a comparison between the calculated values from the effective strain formula and shear capacity formula proposed in this study against the experimental values of specimens reported in the relevant literature. Three statistical measures, namely the mean value of the ratio, the standard deviation, and the coefficient of variation, were selected to evaluate the fitting results. It can be seen that they fit well. In conclusion, the shear behavior capacity calculation formula for RC beams strengthened with FRP grid-PCM composites considering multiple-parameter influences proposed in this paper can effectively predict the shear behavior capacity of strengthened beams.
## 5 Conclusions  
In this paper, a novel S-/C-band RHCP/LHCP shared-aperture phased array with large-angle beam scanning is proposed. The proposed array, based on high-dielectric ceramic technology, is arranged in one single layer. The optimum interelement spacing is ensured by the miniaturization of the elements to provide wide-angle scanning of larger than \(\pm 50^{\circ}\) for both high- and low-frequency bands without a serious grating lobe. The CP characteristic of the phased array is improved by the sequential rotation technique. The simulated and measured results show that the shared-aperture phased array can effectively operate at the S-band (from 1.93 to 2.03 GHz) and C-band (from 3.32 to 3.71 GHz) simultaneously with a large-angle beam scanning up to \(\pm 50^{\circ}\) and a good CP performance with AR less than 3 dB. Furthermore, the as-proposed array is convenient for expanding to a larger aperture phased array for satellite payloads with direct-to-cellular communications capability.  
Conceptualization, Y.X. and L.H.; methodology, Y.X.; software, Y.X.; validation, Y.X., L.H. and X.W.; formal analysis, Y.X.; investigation, Y.X.; resources, Y.X.; data curation, Y.X.; writing--original draft preparation, Y.X., L.H. and X.W.; writing--review and editing, Y.X., L.H. and X.W.; visualization, Y.X., L.H. and X.W.; supervision, Y.X.; project administration, Y.X. and L.H.; funding acquisition, Y.X., L.H. and X.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 U21A20443).  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 3 Materials and Methods
### Transient Electromagnetic Interference (EMI) and Unintentional Interference  
The interaction between the catenary and pantograph generates transient broadband electromagnetic waves. Numerous studies have been conducted on the characterization of these fast-radiated transient disturbances resulting from the sliding contact of the pantograph on the catenary. These studies utilize various approaches, including a spectral approach, as discussed in article [13], a temporal approach, as explored in article [14], and a combination of both approaches, as examined in articles [15; 16]. The setup utilizes a double-sided transient waveform to investigate the effects on the LTE communication link. In the RailCom project [14; 17; 18], measurement campaigns were conducted on high-speed trains to assess the emissions received by the GSM-R antenna, which were caused by catenary-pantograph contact losses. To examine the influence of fast transient signals, the signal was modulated at the studied RF frequency, effectively confining the transient interference effect to the targeted bandwidth. The statistical analysis of fast transient interferences [13] yielded rise times and hold times within specific ranges.  
Mathematically, a wave can be written as a double-sided transient waveform and is given by  
\[s_{\mathrm{trans}}(t)=A_{0}+e^{-\frac{t}{\tau_{\mathrm{size}}}}+e^{-\frac{t}{ \hbar_{\mathrm{hold}}}}+\sin(2\pi f_{c}t)+u(t) \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Base RMC Configuration & R.7 (10 MHz) \\ Jammer Sampling Rate & 15.36 MHz \\ Power Scale Factor & 0.8 \\ Jammer Radio Gain & 2 dB \\ eNodeB (TX) Radio Gain & 25 dB \\ UE (RX) Radio Gain & 20 dB \\ Center Frequency & 2.2 GHz \\ EVM Peak & 17.771\% \\ EVM RMS & 5.049\% \\ BER & 0 \\ Frequency Offset & 1372.998 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: LTE-A PHY communication link parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency Range & 410 MHz–7.125 GHz \\ Modulation Coding Scheme & 64-QAM, R = 3/4 \\ Channel Bandwidth & 10 MHz \\ Sampling Rate & 15.36 MHz \\ TX Radio Gain & 20 dB \\ RX Radio Gain & 20 dB \\ Jammer Radio Gain & 2 dB \\ Center Frequency & 2.2 GHz \\ Subcarrier Spacing & 30 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: 5G-NR communication link parameters.  
where \(A_{0}\) is the amplitude; \(f_{c}\) is the carrier frequency; \(u(t)\) is the unit step function; \(\tau_{\rm rise}\) is the rise time constant, which can be in the range of 0.1 and 3 ns but was taken as 2 ns for this experiment; and \(\tau_{\rm hold}\) is the hold time constant, which can be in the range of 1 ns and 50 ns but was taken as 30 ns for this experiment. Figure 4 illustrates the double-sided exponential waveform generated using the aforementioned equation, which serves as a means of interference.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 3 Notations and Preliminaries
### Federated Learning  
Consider an FL system with \(m\) clients, each having distinct distributions of data. The clients used for this study are represented as client 1,..., client m. Each client is associated with distinct datasets represented as \(D\) with \(n\) samples distributed across \(K\) classes which are divided into training and testing data. _Let_  
\[D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{n},y_{n})\}\]  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Reference** & **Algorithm/Approach** & \begin{tabular}{c} **Client** \\ **Side** \\ \end{tabular} & \begin{tabular}{c} **Server** \\ **Side** \\ \end{tabular} & \begin{tabular}{c} **Privacy** \\ **Issues** \\ \end{tabular} & \begin{tabular}{c} **Communication** \\ **Overhead** \\ \end{tabular} & \
\begin{tabular}{c} **Data** \\ **Sharing** \\ \end{tabular} &
\begin{tabular}{c} **Importance-** \\ **Based** \\ **Aggregation** \\ \end{tabular} \\ \hline
[18] & Fed-ZDA & ✓ & ✓ & & & & & \\ \hline
[19] & FedFed & & ✓ & ✓ & ✓ & ✓ & \\ \hline
[20] & FedAF & ✓ & ✓ & & & ✓ & \\ \hline
[21] & FedICON & ✓ & & & & ✓ & \\ \hline
[5] & FedLAW & ✓ & & & & & ✓ \\ \hline
[6] & FLAMA & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline
[22] &
\begin{tabular}{c} One Pass \\ Distribution Sktech \\ \end{tabular} & ✓ & & & & & & ✓ \\ \hline
[23] &
\begin{tabular}{c} Federated Daisy \\ Chaining \\ \end{tabular} & ✓ & ✓ & & ✓ & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing works.  
be the data where \(x_{i}\) denotes the _features_ of the \(i\)-th sample, and _class_ is denoted as \(y_{i}\in\{0,1,\ldots,K-1\}\). A local model trained by each client \(m\) using the dataset \(D\). Once the training is complete, the parameters of the local model are sent to the server for aggregation. These parameters are combined by server to update the global model. The objective of federated learning can be represented as minimizing the global loss function with model parameters \(w\) and loss function for client \(i\)\(\mathcal{L}(w;D_{i})\):  
\[\min_{w}\sum_{i=1}^{m}\frac{n_{i}}{n}\mathcal{L}(w;D_{i}) \tag{1}\]  
where \(n=\sum_{i=1}^{m}n_{i}\) is the total number of samples in all clients.
## 4 Numerical Simulation  
### Experimental Design  
To evaluate the effectiveness and practicability of the proposed optimization controller, we conduct the simulation experiment using MATLAB for analysis and modeling. Due to the randomness of the simulation experiment, the experiment is repeated 10 times, and the results are averaged.  
A hypothetical single-lane freeway stretch without on-ramps and off-ramps is considered. There are 10 vehicles (20 state vectors) on the road, including the guidance vehicle (CAV, \(m\) = 1) and the human-driving vehicle (\(n\) = 9). To generate a speed-reduction traffic oscillation, the preceding vehicle decelerates at the maximum comfortable deceleration. After remaining at a low speed for a few seconds, the vehicle accelerates to its maximum speed. According to the previous literature [56], the parameters of the Helly model are set to \(\lambda_{x}\) = 0.7 s\({}^{-2}\), \(\lambda_{y}\) = 0.5 s\({}^{-1}\), \(h\) = 1.2 s. Moreover, the initial minimum distance for vehicles is assumed to be 5 m. The value of the maximum speed \(v_{max}\) is set to 30 m/s. The temporal horizon is 600 s, and the simulation time step is 0.1 s. Additionally, the upper and lower bounds for the acceleration constraint are set to \(a_{min}\) = \(-\)3 m/s\({}^{2}\) and \(a_{max}\) = 3 m/s\({}^{2}\).
# Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children
## 2 Related Work
### Computer-Aided Detection  
Computer-aided detection (CADe), and its related term computer-aided diagnosis (CADx), focuses on the detection or classification of different pathologies (e.g., cancer) based on the digital processing of images [13; 25]. In the body of literature, a significant number of studies discuss the precision to detect or classify different kinds of pathologies or lesions [26; 27; 28; 29; 30; 31; 32; 33]. For example, the authors in Reference [31] review several studies that use CAD to detect breast cancer. Recently, some of these studies have used ML in CAD to process digital images [25; 30]; however, to the best of our knowledge, there are few studies in the use of CAD to detect FASD. We next discuss a study about the application of CAD in FASD.  
The authors in Reference [34] use the Face2 Gene software, which is based on the the facial dysmorphology novel analysis (FDNA) technology, to detect FASD through the analysis of digital images of children's faces (5-9 years old). The authors conclude that Face2 Gene achieves 88% of precision compared to 86% by the manual method (i.e., processed by a medical doctor). The approach followed by the study differs from our study because we do not use images; we process data obtained from several tasks applied to children (e.g., Psychometric task).
## 4 Results  
Initially, we calculated the first derivative of the QoE (PLR) function and QoE (jitter) function presented in Section 3.5 as Equations (3) and (4), respectively. At targeted operating points for PLR and jitter, we calculated the first derivative of the functions. Our preliminary analysis reveals that uncertainty in QoE is highly dependent upon PLR and is almost completely unaffected by the shape of the jitter function, as it decays slowly. Consequently, we used 1000 operating point ranging from 0 to 0.05 for PLR and used only 4 operating points for jitter. We presented the first derivative w.r.t PLR at five operating points of PLR in Table 2 and the first derivative w.r.t jitter at operating points of jitter in Table 3.  
Figure 1: Propagation of uncertainty in Quality of Experience (QoE) due to fluctuation in Quality of System (QoS).  
The results presented in Tables 2 and 3 supports the observation made in the preliminary analysis that the QoE of VoD applications is highly dependent upon the QoE's response to change in PLR in comparison to the QoE's response to change in jitter. Another interesting finding from Tables 2 and 3 is that, for both PLR and jitter, when the magnitude of the operating points increases, the first derivative decreases. This supports the general shape of the QoE vs. QoS function as being close in nature to the one displayed in Figure 1 in Section 3.6.  
Another important factor that affects the propagated uncertainty in QoE due to variations in PLR and jitter measurements is the sample size. We investigate the effect of different sample sizes on the magnitude of the resulting uncertainty in QoE. In this paper, we use 400, 800 and 1200 samples because the UK Government [34] recommends 800 samples per busy hour, and we consider it prudent to also consider a range above and below that recommended sample size.
# An Integration Model for Flux Density Distribution Formed by a Heliostat
###### Abstract
Moreover, the distance between the heliostat and receiver plane is also required to be large enough. Furthermore, the projection method must be applied to bring many more errors. The majority of the heliostats in the current tower solar system apply the mirror with a reflecting surface on the back of the glass. Experiments show that the reflected rays are mainly divided into three parts: the rays directly reflected from the glass surface, the main rays reflected from the metal reflecting surface [11], and part of the main rays reflected off the upper glass surface and then reflected back off the metal reflecting surface. In current studies of solar tower systems, they are all treated as the same reflected beam, which is different from the actual situation. In this paper, under the only assumption of an approximate description of the sun shape, a direct integration method was applied to deduce an integration equation to compute the solar flux reflected from the heliostat at any point on the receiver plane, considering the influence of the optical error of the heliostat and multiple reflections by the two planes. An elliptic Gaussian model was used to describe the optical error of the heliostat. A numerical code was developed to calculate a rectangular spherical heliostat's flux density distribution and intercept factor based on this model. Compared with the experimental data of ten heliostats, the applicability and accuracy of the model were verified.
# Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters
## 3 Results and Discussions  
### Relationships between NDVI and Vegetation Parameters  
The values of NDVI corresponding to each pixel were obtained from optical Landsat images, and then averaged for each reference field. Figure 5 provides a plot of the NDVI as a function of the LAI ground measurements, showing that the NDVI saturates at a value of 0.78 when the LAI reaches a value of around 3 m\({}^{2}\)/m\({}^{2}\).  
An exponential function has been proposed to describe this relationship [30; 44; 47]:  
\[NDVI=NDVI_{\text{max}}+\left(NDVI_{soil}-NDVI_{\text{max}}\right)\,\times e^{-k \ LAI} \tag{1}\]  
where NDVI\({}_{\text{max}}\) (0.78) is the NDVI corresponding to the maximum value of LAI, NDVI\({}_{\text{min}}\) is the bare soil NDVI (=0.15), and \(k\) is the extinction coefficient (=0.96). The coefficient of determination R\({}^{2}\) for this data is 0.79, with a RMSE (relative mean square error) of 0.96 m\({}^{2}\)/m\({}^{2}\). [PERSON] et al. [47] reported a value of \(k\) = 0.71, derived from Formosat-2 images acquired over a larger area in Southeast France including wheat and rice fields, as well as irrigated grasslands. In the case of Landsat images of grassland plots in France, [PERSON] et al. [44] reported a value of \(k\) = 0.69.  
Figure 4: Land use map.
## 3 Results
### Dsc  
The DSC curves for each NiTi rotary instrument are presented in Figure 5. Table 5 summarizes the phase transformation temperatures and associated energies. The DSC curves of TruNatomy Prime and XP-endo Shaper files showed a peak on the heating curve and two separate peaks on the cooling curve (Figure 5A,B). The DSC curves of Spring Endo and Spring H files exhibited single peaks on the heating and cooling curves, respectively (Figure 5C,D). While the peaks for Spring Endo files were present at below room temperature, those for Spring H were observed above room temperature (Figure 5C,D).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **TruNatomy Prime** & **XP-Endo Shaper** & **Spring Endo** & **Spring H** \\ \hline Maximum clockwise & 0.171 \(\pm\) 0.044 \({}^{\text{a}}\) & 1.543 \(\pm\) 0.129 \({}^{\text{c}}\) & 0.978 \(\pm\) 0.295 \({}^{\text{b}}\) & 1.773 \(\pm\) 0.728 \({}^{\text{c}}\) \\ \hline Maximum screw-in force (gf) & 66.42 \(\pm\) 30.10 \({}^{\text{a}}\) & 157.30 \(\pm\) 12.90 \({}^{\text{b}}\) & 499.65 \(\pm\) 185.48 \({}^{\text{c}}\) & 661.32 \(\pm\) 199.82 \({}^{\text{c}}\) \\ \hline Mass of resin blocks after & 3.4188 \(\pm\) 0.0050 \({}^{\text{b}}\) & 3.4118 \(\pm\) 0.0052 \({}^{\text{a}}\) & 3.4201 \(\pm\) 0.0036 \({}^{\text{b}}\) & 3.4202 \(\pm\) 0.0052 \({}^{\text{b}}\) \\ \hline \end{tabular} Different superscript letters in the same row indicate significant differences between files (\(p<0.05\)).  
\end{table}
Table 4: Maximum clockwise torque and screw-in force generated during simulated canal shaping and mass of resin blocks after shaping (mean \(\pm\) standard deviation).  
\begin{table}
\begin{tabular}{c c c c} \hline  & & \multicolumn{3}{c}{**Pearson Correlation Analysis**} \\ \cline{3-4}  & & **Coefficient** & \(p\)**-Value** \\ \hline  & NCF & -0.723 \({}^{\text{a}}\) & \textless{}0.001 \\ \cline{2-4}  & UTS & 0.907 & \textless{}0.001 \\ \cline{2-4}  & ARF & -0.774 \({}^{\text{a}}\) & 0.001 \\ \cline{2-4}  & EM & 0.897 \({}^{\text{a}}\) & \textless{}0.001 \\ \hline  & UTS & -0.616 \({}^{\text{a}}\) & \textless{}0.001 \\ \cline{2-4} NCF & ARF & 0.961 & \textless{}0.001 \\ \cline{2-4}  & EM & -0.642 \({}^{\text{a}}\) & \textless{}0.001 \\ \hline ARF & UTS & -0.656 \({}^{\text{a}}\) & \textless{}0.001 \\ \hline \end{tabular} The asterisk means significant correlation between two variables (\(p<0.05\)).  
\end{table}
Table 3: _Cont._Figure 5: _Cont_.  
Figure 5: Differential scanning calorimetry curves for TruNatomy Prime (**A**), XP-endo Shaper (**B**), Spring Endo (**C**), and Spring H files (**D**). The upper and lower curves represent the cooling and heating curves, respectively.
## 3 Results and Discussion
### Binding Constant and Number of Binding Sites  
The Hill equation was applied to calculate the binding constant and cooperativity of ligand binding to BSA for both the binary and ternary systems.  
\[\log\frac{(F_{0}-F)}{F}=\log K_{b}+n\ \log[\mathrm{Q}]\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & \(\mathbf{k_{q}}\) **(M\({}^{-1}\) S\({}^{-1}\))** & \(\mathbf{K_{b}}\) \\ \hline BSA-ETB & \(3.14\times 10^{12}\) & \(2.02\times 10^{4}\) \\ \hline (BSA-RUT)-ETB & \(4.60\times 10^{12}\) & \(1.12\times 10^{2}\) \\ \hline (BSA-NAR)-ETB & \(6.21\times 10^{12}\) & \(1.61\times 10^{3}\) \\ \hline (BSA-SIN)-ETB & \(4.35\times 10^{12}\) & \(1.02\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quenching constants of the binary and ternary systems at room temperature.  
Figure 3: **(A)**: Stern Volmer Plot (**B**): binding constant from double reciprocal plot (**C**): van’t Hoff plots for thermodynamic parameters for BSA (\(1.5\)\(\upmu\)M)-ETB (\(0.00\)–\(27.5\)\(\upmu\)M) system at \(298/303/310\) K.  
Here in this equation, \(K_{b}\) represents the binding constant, and \(n\) is the cooperativity factor. There is no cooperativity when \(n\) equals 1; thus, \(n<1\) shows negative cooperativity, \(n=1\), \(n>1\) shows positive cooperativity. The binding constants of the BSA-ETB system at the respective temperatures are shown in Table 3 and Figure 3B. The binding constants for the BSA-ETB decreased with the temperature rise. The binding cooperativity \(n=1\) was found for the BSA-ETB system at 273 K, which suggests no influence on the binding cooperativity for the binary system at 298 K. Further, it was also observed for the BSA-ETB system that as the temperature increased, the binding cooperativity decreased (\(n<1\)) and, therefore, negative cooperativity. A medium binding affinity between ETB and BSA is suggested based on the obtained values of \(10^{4}\) M\({}^{-1}\)[33]. The binding constant values of the ternary system, BSA-ETB, in the presence of the three flavonoids RUT, NAR, and SIN, showed a decline in the values compared to the binary system. The BSA-ETB interaction was further studied at different temperatures to determine the thermodynamics of the reactions by the following:  
\[\text{ln}\text{K}_{b}=-\frac{\Delta H^{\circ}}{RT}+\frac{\Delta S^{\circ}}{R}\]  
\[\Delta G^{\circ}=\Delta H^{\circ}-T\Delta S^{\circ}\]  
where \(\Delta H^{\circ}\) is enthalpy change, \(\Delta S^{\circ}\) is entropy change, \(\Delta G^{\circ}\) is Gibbs free energy, \(R\) is the universal gas constant, and \(T\) is the temperature in kelvins (K). A van't Hoff plot between \(\text{ln}(K_{b})\) vs. \(1/T\) (Figure 3C) predicted \(\Delta H^{\circ}\), \(\Delta S^{\circ}\), and \(\Delta G^{\circ}\) values (Table 3). The negative \(\Delta G^{\circ}\) suggested a spontaneous complex formation between ETB and BSA. Furthermore, the negative values of enthalpy, as well as entropy, indicated van der Waals interaction and hydrogen bonds playing a role in complex formation between BSA and ETB.