esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications  
### Operation Frequency Bands  
Figure 2 shows different frequency bands allocated for in-body antennas. The choice of an operation frequency for IBBDs involves several trade-offs. Generally, lower frequencies are attractive and commonly used, as they facilitate lower loss of the biological tissue medium, which can lead to higher efficiency and better tissue safety. However, lower frequencies have the drawback of limiting the communication speed and requiring larger antenna size. In contrast, higher frequency bands provide high data rates and miniaturization at the cost of lower tissue safety. In Figure 2, frequencies below 100 kHz are allocated for short-range inductive IBBDs by the Federal Communication Commission (FCC) in the United States (US) for lower power and data transmission [21]. Medical micropower networks (MMNs) are another FCC-approved short-range frequency band with a 24 MHz spectrum from 413 to 457 MHz. Furthermore, Wi-Fi (902-928 MHz), Bluetooth (2.40-2.483 GHz) and Zigbee (5.725-5.850 GHz) are designated for short-range digital modulation communication for IBBDs by the FCC in the US.  
In Europe, the Electronic Communication Committee (ECC) allocated the 430 to 440 MHz band for ultra-low-power wireless medical capsule endoscopy (ULP-WMCE) and the 2.483 to 2.50 GHz band for active medical implants (AMI). The Medical Device Radio Communication Service (MedRadio) band of 401-406 MHz [4], Medical Implant Communication System (MICS) band of 402-405 MHz [24] and Industrial, Scientific and Medical (ISM) band of 13.56 MHz, 433-434 MHz, 902-908 MHz, 2.40-2.48 GHz and 5.717-5.875 GHz [25] are accepted worldwide for IBBD application.
# Numerical Study on Track-Bridge Interaction of Integral Railway Rigid-Frame Bridge
## 5 Parameters Study
### Influence of the Pier Stiffness  
For the integral rigid-frame bridge, the pier stiffness has a significant influence on the track-bridge interaction. If the pier stiffness is too large, the deformation of the beam is restrained. On the contrary, if the stiffness of the pier is too small, the relative displacement between the beam and track under the action of the braking force increases greatly and the total additional force of the rail exceeds the standard limit value.  
As shown in Figures 20 and 21, the calculation results indicate that the additional braking stress and total additional stress of the rail increase with the decrease of pier stiffness. With the increase of the longitudinal resistance between the CWR and the bridge, the limited value of the minimum pier stiffness increases accordingly. To meet the requirement that the total additional stress of the rail should be less than 92 MPa, the minimum threshold value of the pier stiffness should be about 0.52 \(\times\) 10\({}^{5}\) kN/m if the resistance is 12 kN/m/rail, and the minimum threshold value of the pier stiffness should be nearly 1.0 \(\times\) 10\({}^{5}\) kN/m if the longitudinal resistance between rail and beam is 37.2 kN/m/rail.
## 2 Methods
### Spectral Filter Emphasizing Impulsive Components  
For characterizing cyclo-non-stationary processes, different approaches to filter modeling can be used to highlight the presence of impulsive components in envelope spectrum \(\bar{\mathbf{X}}\), including the following moment-based estimators:  
* Spectral kurtosis (SK): provided there is a cyclostationary process, the highlighting filter (noted as \(\mathbf{\xi}_{1}\in\mathbb{R}^{K}\)) is modeled through kurtosis, having elements defined by the following absolute value moment [27]: \[\bar{\mathbf{\xi}}_{1}[k]=\frac{\left\langle|\mathbf{S}_{\mathbf{X}}[l,k]|^{4}\right\rangle _{l}}{\left\langle|\mathbf{S}_{\mathbf{X}}[l,k]|^{2}\right\rangle_{l}^{2}}-2,\,k\in K, \bar{\mathbf{\xi}}_{1}\subseteq\mathbf{\Xi}\] (4) where \(\left\langle|\cdot|^{n}\right\rangle_{l}\) stands for the empirical moment of the \(n\)-th order, averaging representation \(\mathbf{S}_{\mathbf{X}}\) over \(l\) domain.
* Spectral entropy (SE): an entropy-based filter (noted as \(\bar{\mathbf{\xi}}_{2}\in\mathbb{R}^{K}\)) is proposed that is modeled as follows: \[\bar{\mathbf{\xi}}_{2}[k]=\mathbb{H}\{\mathbf{S}_{\mathbf{X}}[l,k]:\forall l\in L\},\bar{ \mathbf{\xi}}_{2}\subseteq\mathbf{\Xi},\] (5) where \(\mathbb{H}\{\cdot\}\) is the VQ entropy proposed in [28].  
To deal with the nonstationarity of vibration signals, the moment-based estimator can be performed within a wider window of spectral matrix \(\mathbf{S}_{\mathbf{X}}\), as below:  
\[\mathbf{\Xi}[l,k]=\bar{\mathbf{\xi}}_{i}\{\mathbf{x}_{M}[n+l]\}[k],\quad i=1,2, \tag{6}\]  
where \(\mathbf{x}_{M}[n]=[x[n],\cdots,x[n+M-1]]\) is a subvector of size \(M{<}N\) extracted from \(\mathbf{x}\). In the following, when sliding filter \(\mathbf{\Xi}[l,k]\), is denoted as sliding spectral kurtosis (SSK) if computed by \(\bar{\mathbf{\xi}}_{1}\{\cdot\}\). Otherwise, the sliding filter is denoted as sliding spectral entropy (SSE) if computed by \(\bar{\mathbf{\xi}}_{2}\{\cdot\}\).
## 3 Results and Discussion
### Effect of F-T Cycles on Microstructure for Soil Samples
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Types**} & \multirow{2}{*}{**F-T Cycles**} & \multirow{2}{*}{\begin{tabular}{c} **Porosity** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Mean** \\ **Diameter** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Small Pore** \\ **Proportion** \\ **d \textless{} 2 \(\upmu\)m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Medium Pore** \\ **Proportion** \\ **\(\mu\)m \textless{} d \textless{} 5 \(\upmu\)m** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Large Pore** \\ **Proportion** \\ **d \textless{} 5 \(\upmu\)m** \\ \end{tabular} } \\ \hline \multirow{3}{*}{Unmodified SC} & 0 & 0.94 & 2.04 & 83.51\% & 16.49\% & 0 \\  & 1 & 4.35 & 2.28 & 73.24\% & 25.91\% & 0.85\% \\ \cline{1-1} \cline{2-6}  & 3 & 5.84 & 2.49 & 72.40\% & 23.04\% & 4.56\% \\ \cline{1-1}  & 5 & 7.11 & 2.71 & 66.59\% & 25.70\% & 7.71\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} The modified SC \\ 5 \\ \end{tabular} } & 0 & 2.67 & 2.76 & 74.30\% & 23.77\% & 1.93\% \\ \cline{1-1}  & 1 & 7.66 & 3.18 & 68.66\% & 28.28\% & 2.46\% \\ \cline{1-1}  & 3 & 7.59 & 3.36 & 67.58\% & 29.62\% & 2.80\% \\ \cline{1-1}  & 5 & 8.39 & 3.42 & 64.29\% & 32.67\% & 3.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of pore distribution analysis for the two materials.
# Communication-Efficient Vertical Federated Learning
## 3 Proposed Method
### First Step
#### 3.1.2 Autoencoders  
Each client builds their own undercomplete autoencoder, which is used to extract important information from local data and convert it into latent data. These latent data are essentially the compressed knowledge representation of the original data. An undercomplete autoencoder is an unsupervised feed-forward neural network with the same inputs and outputs. The network is fully connected, with an encoder and a decoder. The encoder converts the input to a latent representation with lower dimensionality, which is then mapped by the decoder back to the original input. By minimizing the reconstruction loss, the network learns both the encoder and decoder weights during training. An autoencoder in Figure 3 has three major layers: input, hidden (latent representation), and output. Algorithm 2 describes a common method to train an autoencoder.  
```
procedureAE(\(e,b,X,\eta,\theta\)) \(X\leftarrow[X_{1},X_{2}...X_{n}]\in R^{n*m}\) is the input matrix \(e\leftarrow\) number of epochs \(b\leftarrow\) number of batches \(\eta\leftarrow\) learning rate \(\theta\leftarrow\{W_{h},W_{o},b_{h},b_{o}\}\) where \([W_{h},W_{o}]\in R^{n*d},[b_{h},b_{o}]\in R^{d}\) for\(0\) to \(0\)do \(Z\gets W_{h}TX+b_{h}\) \(\hat{X}\leftarrow f(W_{h}^{T}Z+b_{o})\) \(L(X,\hat{X})\leftarrow||\hat{X}-X||^{2}\) \(g\leftarrow\) compute gradients of \(L(X,\hat{X})\) with respect to \(\theta\) for\(\theta_{i},g_{i}\) in \((\theta,g)\)do \(\theta_{i}\leftarrow\theta_{i}-\eta*g_{i}\)
```  
**Algorithm 2** Autoencoder Training  
Autoencoders are able to deal with linear as well as non-linear data depending on the choice of the activation function, unlike PCA. However, autoencoders train through gradient descent and are slower comparatively. Autoencoders with a single layer and linear activation function perform the same as PCA. In the case of autoencoders with multiple layers and non-linear activation functions, they need to be carefully designed and controlled by regularisation to avoid overfitting.
# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 3 Methodology
### Loss Functions  
In order to combine both experimental recovery and overhead, the method in this paper uses a mean square error loss function (mean squared error [45]) to calculate the difference between a clear image and an enhanced image. Utilizing the mean squared error loss function in underwater image enhancement tasks helps to remove blur and correct color bias. Specifically, the loss function is calculated as follows:  
\[\mathcal{L}(E,G)=\frac{1}{vbi}\|E(i,j)-G(i,j)\|^{2}, \tag{11}\]  
where \((i,j)\) denotes the pixel co-ordinates of the image, and \(E\) and \(G\) denote the underwater image and the clear image, respectively. \(E(i,j)\) and \(G(i,j)\) represent the pixel \((i,j)\) in theunderwater image, and the co-ordinates are \((i,j)\). \(w\) and \(h\) denote the width and height of the image, respectively.
## 2 Materials and Methods
### Statistics and Chemometrics  
The analysis of variance (one-way ANOVA; \(p\) < 0.05) with [PERSON]'s post hoc test was performed using PASW Statistics 26.0 (SPSS Inc., Chicago, IL, USA) to investigate the significant differences in semi-quantitative values of different bioactive compounds classes, in vitro antioxidants, and in vitro enzymatic inhibitory properties. The Pearson'scorrelation coefficients (\(p\) < 0.01 and \(p\) < 0.05; two-tailed) were determined to find the significant correlations between phytochemical contents and biological activities (PASW Statistics 26.0, SPSS Inc., Chicago, IL, USA).  
The metabolomics-based dataset was obtained by data filtering and data normalization process using Agilent Mass Profiler Professional software (from Agilent Technologies, Santa Clara, CA, USA; version B.05.00) as previously described [9; 13]. After normalizing the data, the resulting dataset was further filtered based on an annotation score of 70% and finally processed for multivariate statistical analysis. An unsupervised hierarchical cluster analysis was carried out, setting the similarity measure as 'Euclidean' and 'Wards' as the linkage rule. Afterward, the raw dataset was then interpreted through orthogonal projection to latent structures discriminant analysis using SIMCA 16 (Umetrics, Malmo, Sweden). Cross-validated ANOVA (\(p\) < 0.01) and permutation testing (\(N\) = 100) were used for model validation and to exclude overfitting, respectively. Moreover, the OPLS-DA model was investigated for the fitness parameters (goodness-of-fit R\({}^{2}\)Y and goodness-of-prediction Q\({}^{2}\)Y) and outliers, according to [PERSON]'s T2 test (95% and 99% confidence limit for the suspect and strong outliers, respectively). The variable importance in projection (VIP) was adopted to choose the most discriminant compounds among different solvents used for extraction, selecting those variables with the highest discrimination potentials (VIP score > 1.2), provided with fold-change values obtained by pairwise comparison among different extraction solvents and water. A Venn analysis was finally carried out among VIP markers resulting from different OPLS-DA models.
## 2 Status and Literature Review
### Necessity for Automatic Generation of 2D Drawings
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Information** & **Contents** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Geometric information} & Definition of the form of the model for each building object (e.g., IfcDoorLiningProtertie, IfcSweptSurface, IfcExtrudedAreaSolid, IfcAxix2 Placement3D, etc.) \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Building elements} & Examples of building elements are walls, beams, and doors (e.g., Ifcslab, IfcColumn, IfcBeam, IfcWall, IfcBuildingElementProxy, etc.) \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Structure elements} & Elements used for structural analysis, such as nodes, lines, and loads (e.g., IfcReinforcingbar, IfcStructuralLoad, IfcStructuralMember, etc.) \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{Facility elements} & Air-conditioning facility, drainage facilities, fire-extinguishing facilities, electricity (lamps, motors, heat, circuits, etc.), etc. (e.g., IfcBoiler, IfcAirTerminal, IfcChiller, IfcController, IfcLightFicture, etc.) \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Construction management} & Construction resource information related to construction equipment, materials, and workers (e.g., IfcActor, IfcOccupant, IfcMaterialUsageDefinition, etc.) \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Maintenance} & Information on equipment (e.g., IfcUnitaryEquipment, IfcValve, etc.) \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Construction process} & Construction process, procedures, activity, etc. (e.g., IfcProcedure, IfcEvent, IfcTask, etc.) \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Estimate} & Information on the budget of the project (e.g., IfcAsset, IfcCostItem, IfcCostSchedule, IfcCostValue, etc.) \\ \hline \multirow{2}{*}{9} & \multirow{2}{*}{Material properties} & Information related to properties of materials (e.g., IfcMaterial, IfcMaterialLayer, IfcMaterialLayerSet, etc.) \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{Performers} & Information related to workers such as man-hours, Work Breakdown (WBS) etc. (e.g., IfcWorkCalender, IfcWorkSchedule, etc.) \\ \hline \multirow{2}{*}{11} & \multirow{2}{*}{Other} & Time, constraint relationship, spatial objects, etc. (e.g., IfcSpatialElement, IfcZone, IfcConstraint, IfcRelSpaceBoundary, etc.) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Information type of industry foundation classes (IFC). among the negative factors hampering BIM introduction, apart from increasing productivity and reducing the technological gap by reducing the workload of BIM adopters.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 2 Two-Way Ranging (TWR) Overview
### Time-of-Arrival Estimation  
Equations (1) and (2) form a system of two equations with two unknowns: \(T\) and \(\tau\). The transmit timestamps \(t_{Tx}\) are known quantities, and the receive timestamps \(t_{Rx}\) can be estimated using any number of ToA estimation techniques. ToA estimation is itself a rich field with both legacy and emerging solutions, so we briefly summarize the basic concept here and direct the reader to some relevant publications [40; 45; 46] for further reading.  
Consider the correlation between received signal \(z(t)\) and known transmitted signal \(x(t)\):  
\[g(\tau^{\prime})=\bigg{|}\int dt\;\;z(t)x(t-\tau^{\prime})\bigg{|}^{2}, \tag{5}\]  
where \(\tau^{\prime}\) is a time delay relative to some time reference (analogous but not equivalent to the \(\tau\) described above). By inspection, this correlation is maximized when the signals are aligned in time, so by defining an arbitrary but fixed reference time \(t_{0}\), the local maximum likelihood (ML) ToA estimate is simply  
\[\hat{t}_{Rx}=\hat{\tau}^{\prime}-t_{0}\;\;;\;\hat{\tau}^{\prime}=\arg\max_{ \tau^{\prime}}g(\tau^{\prime}). \tag{6}\]  
This formulation may seem pedantic for the limited scope of this discussion, but it is important for building the more advanced estimators outlined in [40; 45; 46]. There are numerous extensions to this simple estimator, including computationally efficient hardware implementations [5], leveraging phase information to adjust the estimate [45], and various iterative refinement methods [40; 46].
# Energy Trading Strategy for Heat and Electricity-Coupled Microgrid Based on Cooperative Game
## 6 Distribution Solution
### Algorithm Implementation
The energy trading between the MGO and users is modeled as a Nash bargaining game, where the MGO could maximize its operation profit by adjusting the energy supply of the CHP system and bargaining with users, and the user can determine the energy consumption of electricity and heat by bargaining with the MGO. Besides, the NBS of the proposed Nash bargaining game can maximize the social welfare of the whole MG system. Considering the bargaining information between the MGO and the user, a distributed algorithm based on ADMM is proposed to obtain the NBS. Finally, by using numerical studies, the numerical study shows that the proposed energy trading strategy can improve the social benefits for all participants by nearly 5.15%, and the profits of users 1-3 increase by 1.73%, 1.76%, and 1.78%, respectively. Conceptualization, C.Z. and C.B.Z. ; methodology, C.Z. ; software, C.Z. ; validation, C.Z. and C.B.Z. ; formal analysis, C.Z. ; investigation, C.Z. ; resources, C.Z. ; data curation, C.Z. ; writing--original draft preparation, C.Z. ; writing--review and editing, C.Z. ; visualization, C.Z. ; supervision, C.B.Z. ; project administration, C.B.Z. ; funding acquisition, C.B.Z. All authors have read and agreed to the published version of the manuscript. Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control, Anhui University (Nos.KFKT201904); Natural Science Research Project of Colleges and Universities of Anhui Provincial Department of Education (Nos.2020X)ZR02); Domestic visiting program for outstanding young backbone teachers in Colleges and universities in Anhui Province (Nos. gxgnfx2021217); Key teaching research project of Anhui International Business Vocational College (Nos. 2020 XM04); Teaching demonstration course of Anhui Provincial Department of Education (Nos. 2020S)JJSSFK0529). Not applicable. The authors declare no conflict of interest. Figure 10: Comparison of increased economic benefits. ## Appendix A

It is defined that

\[U^{h}_{s,i}=p^{h}_{i}e^{h}_{i}+\lambda^{h}_{i}th^{h}_{i} \tag{11}\]

By substituting Formula (A5) into Formula (24), the problem (24) can be transformed into

\[\max\ \ln\left(U_{g}+\frac{n}{i}\sum\limits_{h=1}^{H}U^{h}_{si}-C_{gss}-B_{MGO} \right)+\sum\limits_{i=1}^{n}\ln\left\{\sum\limits_{h=1}^{H}\left[k^{h}_{i}\ln( 1+el^{h}_{i})-U^{h}_{si}-\alpha_{i}\Delta ht^{2}_{i}\right]-B_{i}\right\} \tag{12}\]

The optimization problem in Formula (A2) can be solved in two steps. (1) For a set of specific variables \(\{P^{h}_{M,gas},P^{h}_{B,gas},el^{h}_{i},th^{h}_{i}\}\), the optimal variable \(U^{h}_{si}\) can be solved by setting the first derivative to zero.
# Mapping [PERSON] Using Sentinel-1 SAR Time Series in Camargue, France
## 3 Methodology
### Temporal Behavior of \(\sigma^{\circ}\) SAR Backscattering over Agricultural Plots  
An analysis of the temporal behavior of the Sentinel-1 backscattering coefficient \(\sigma^{\circ}\) at the VV and VH polarizations as well as the ratio of VV/VH was performed over all the crop types of the reference plots to define the metrics that allow the separation of the rice plots. Figures 4 and 5 show the backscattering coefficient \(\sigma^{\circ}\) averaged for all reference plots of the same crop type at each date and plotted with the corresponding standard deviation. By comparing the temporal behavior of the time series signals for rice with that of each crop type, we observe that the rice plots have a different temporal behavior for both the VV/VH ratio and VH backscattering. In fact, the temporal series of the VV/VH signal for rice approximately follows a Gaussian behavior for the period between May and September (Day of Year (DOY) between 120 and 270) (Figure 5). Moreover for the VH signal, the rice shows an increasing linear profile during the same period. In addition, VV/VH shows a high variance for rice. These unique characteristics of rice are used to classify the rice cultivated plots.  
Figure 3: Workflow overview using a decision tree and Random Forest (RF) classifier.  
The calculated metrics, used for classification, includes the Gaussian fitting parameters of the VV/VH time series, the variance of the VV/VH signal, and the slope of the linear fitting of the VH signal (between May-DOY 120 and September-DOY 270).  
Figure 4: Temporal evolution of SAR backscattering coefficient \(\sigma^{\circ}\) of 11 crop types for VV and VH polarizations. Mean values are represented by dots and standard deviations are represented by the filled color domains surrounding the curves.  
Figure 5: Temporal evolution of the ratio VV/VH for SAR backscattering coefficient \(\sigma^{\circ}\) of 11 crop types. Mean values are represented by dots and standard deviations are represented by the filled color domains surrounding the curves.
# Seismic Performance of Corroded Reinforced Concrete Columns Strengthened with Basalt Fiber Sheets
## 2 Specimen Design and Fabrication  
### Specimen Design  
Four reinforced concrete columns were designed and fabricated with the corrosion rates set to be 0 and 8%, respectively, in the test. The corrosion process was applied to the reinforcing steel. The corrosion rate determined by the mass loss. The concrete strength grade was C40 with the slump of 16 \(\pm\) 2 cm. The thickness of the net protective layer was set as 30 mm. Moreover, commercial concrete was applied in present research and the strength grade was C30. The water-to-binder ratio of concrete in all tested specimens was 0.48, and the mixed proportions by weight of cement, sand, coarse aggregates, fly ash, ground granulated basalt furnace slag and superplasticizer were 210, 719, 1112, 70, 70 and 4.18 kg/m\({}^{3}\), respectively. Three 16 mm diameter grade III steel (HRB400) longitudinal bars were arranged on each side with symmetrical reinforcement. The stirrups were made of grade I steel (HPB235) with the diameter and spacing of 8 mm and 80 mm, respectively. The stirrups at both ends of the specimen were properly densified with the spacing of 50 mm. The geometric size of specimens and the reinforcement form are shown in Figure 1.  
The reinforced concrete columns were strengthened with basalt fiber sheets. The specimens were numbered Z1, Z2, Z3 and Z4, respectively. The specific structural parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & **Designed** & **Axial Compression** & \multirow{2}{*}{**Reinforcement Form**} \\  & **Corrosion Rate** & & \\ \hline Z1 & 0 & 0.2 & not reinforced with FRP \\ Z2 & 8\% & 0.2 & not reinforced with FRP \\ Z3 & 8\% & 0.2 & jacketed with FRP (one layer) \\ Z4 & 8\% & 0.2 & jacketed with FRP (two layers) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of hysteretic specimens.  
Figure 1: Geometric size of column and arrangement of bars.