esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation and Modification of Hydrochar  
HTC of sewage sludge was performed in a stainless steel AISI 316 batch reactor of 2 L internal volume. The reactor, built in house, was designed for temperatures and pressures, respectively, up to 300 \({}^{\circ}\)C and 140 bar. The reactor top flange is connected to two pipes that allow nitrogen gas purging in order to ensure not oxidizing conditions. The reactor is equipped with a pressure transmitter, two pressure gauges and four thermocouples positioned at different heights within it. The pressure and temperature transmitters send data to software that allows the control of the reactor temperature and the monitoring of the four temperatures and the pressure. At the end of the HTC run, the reactor is cooled down to room temperature and then depressurized. Further details about the HTC reactor could be found in [PERSON] et al. [21].  
1.7 kg of sewage sludge (0.8 kg of palatable sludge and 0.9 kg of digestate) were introduced into the reactor for each HTC run, in order to have a feedstock with a dry matter content equal to 12%. HTC tests were performed in duplicate at three different temperatures: 190, 220 and 250 \({}^{\circ}\)C, while the reaction time was set at 3 h for all the tests. At the end of each HTC run, the solid-phase material was separated from the liquid phase by vacuum filtration and later dried at 105 \({}^{\circ}\)C for 24 h to remove residual moisture. The obtained dry samples were designated as 190 HC, 220 HC and 250 HC on the basis of their treatment temperature.  
To produce the KOH-modified hydrochar, 5 g of hydrochar powder were mixed with 500 mL of a 2M KOH solution and then stirred for 1 h at room temperature. The modified hydrochars were separated from the liquid phase by vacuum filtration, then washed with deionized water and finally dried for 24 h at 105 \({}^{\circ}\)C. The obtained dry samples were labeled as 190 MHC, 220 MHC and 250 MHC, on the basis of the treatment conditions as for the raw hydrochars, where M refers to the alkali modification process.  
The hydrochar mass yield is here defined, as is the usual case [22], as the percentage ratio between the mass (on a dry basis, d.b.) of the solid (hydrochar) remaining after HTC and the mass (on a d.b.) of the raw sample before thermal treatment. Similarly, a mass yield is defined also for the KOH modification as the mass (on a d.b.) remaining after KOH treatment and the mass (on a d.b.) of the raw hydrochar before such treatment.
## 4 Simulation Evaluation
### Evaluation of SC-Sched
#### 4.2.3 Result of Mobile Sink Scenario  
Using a mobile sink is one of the features of SC-Sched. Here, we evaluate the effect of traffic load distribution on network lifetime in the mobile sink scenario.  
Table 6 shows the simulation results of four metrics and the improvement in network lifetime (compared to the network lifetime when the number of data collection points is fixed to 1). As the number of data collection points increases, the maximum energy consumption is decreased. In addition, the improvement in network lifetime increases with the number of nodes. This indicates that when the node density is high, SC-Sched can distribute the traffic load to more nodes and better balance the traffic by exploiting sink movement.
# Relay Selection Schemes for FSO Communications over Turbulent Channels
## 2 System Model
### Gamma-Gamma (G-G) Channels  
The PDF of G-G channels, \(f_{h}(h)\) is given by [16]  
\[f_{h}(h)=\frac{ab\xi^{2}}{A_{O}\beta\Gamma(a)\Gamma(b)}\times C_{1,3}^{3,0} \bigg{[}\frac{abh}{\beta A_{O}}\bigg{|}\xi^{2}-1,\xi^{2}-1,b-1\bigg{]} \tag{17}\]  
where \(C_{p,q}^{m,n}[.]\) is the Meijers G-function in [14], \(\Gamma(.)\) is the Gamma function, and \(a\) and \(b\) are the effective number of large-scale and small-scale eddies of scattering environment, respectively. Their values for plane wave are given by [11; 17]:  
\[a=\left[\exp\left(\frac{0.49\sigma_{R}^{2}}{\bigg{(}1+1.11\sigma_{R}^{\frac{1 2}{5}}\bigg{)}^{\frac{7}{6}}}\right)-1\right]^{-1} \tag{18}\]  
\[b=\left[\exp\left(\frac{0.51\sigma_{R}^{2}}{\bigg{(}1+0.69\sigma_{R}^{\frac{ 12}{5}}\bigg{)}^{\frac{7}{6}}}\right)-1\right]^{-1} \tag{19}\]  
Hence, a plane wave propagation is assumed and the Rytov variance, \(\sigma_{R}^{2}\), as a function of distance is as in [11; 17].  
\[\sigma_{R}^{2}=1.23\xi^{2}C_{n}^{2}d^{\frac{11}{6}} \tag{20}\]
# Radiometric Identification of Signals by Matched Whitening Transform
## 3 Reversing Phase and Frequency Offsets  
The first challenge to radiometric identification surfaces before the algorithm is implemented. Signals are often made available with uncorrected phase rotations. There are two types of rotations. Fixed rotation is cause by a constant phase offset of the reference carrier. Time-varying rotation is caused by the frequency mismatch of the reference carrier. The mismatch could be hardware-related or caused by Doppler. Either way, it is an unknown quantity. The frequency mismatch, termed _offset frequency_\(f_{d}\) causes a corresponding time-varying phase which results in constellation smearing. This is different than fixed phase offset which causes the entire constellation to rotate. Figure 4 shows the time varying phase offset under two SNR levels. Both fixed and time varying rotations must be reversed prior to radiometric identification.
# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 2 Full-Field Displacement Monitoring of a Structure Based on Natural Texture Feature Monitoring
### Validation of the Structure's FFDV Accuracy  
First, the structural lower-edge deformation values were validated. The extracted deformation values were compared with values measured using the dial gauge, as shown in Table 3 (owing to space limitations, only data for operating condition D01 are shown). Table 3 shows that the maximum error of the edge deformation of the structure was 4.41% and that the deformation of the extracted structure was consistent with the actual deformation.  
Subsequently, we validated the displacement vector in the plane. Under each operating condition, a Leica ScanStation P50 3D laser scanner was used to scan the test beam. During the scanning, a built-in coaxial camera was used to perform synchronous shooting, and the color point cloud of the test beam was obtained, as shown in Figure 21. The Leica Cyclone point-cloud processing software was used to automatically extract the center coordinates of the code marks, as shown in Figure 21.  
Figure 20: Structure surface FFDV under D03 condition.  
The same code-mark position coordinates were used before and after deformation for the displacement calculation. The code marks of the center displacement vector are shown in Figure 22 (based on the D01 400 kN condition).  
The displacement vector near the code marks was compared with that shown in Figure 22, based on the D01 400 kN condition (see Figure 23).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Load Level**} & **Deformation** & **Measured Value of** & **Extracted Deformation** & **S = R2 – R1** & **Error Value** \\  & **Extraction Position** & **Dial Indicator R1/mm** & **Value R2/mm** & **/mm** & **/S 1/R1/\%** \\ \hline \multirow{3}{*}{100 KN} & 2080 & 2.92 & 2.95 & 0.03 & 1.03\% \\  & 3580 & 3.63 & 3.47 & \(-\)0.16 & 4.41\% \\  & 5080 & 2.93 & 2.97 & 0.04 & 1.37\% \\ \hline \multirow{3}{*}{200 KN} & 2080 & 4.76 & 4.71 & \(-\)0.05 & 1.05\% \\  & 3580 & 5.46 & 5.33 & \(-\)0.13 & 2.38\% \\  & 5080 & 5.02 & 4.85 & \(-\)0.17 & 3.39\% \\ \hline \multirow{3}{*}{300 KN} & 2080 & 6.45 & 6.33 & \(-\)0.12 & 1.86\% \\  & 3580 & 7.34 & 7.38 & 0.04 & 0.54\% \\  & 5080 & 6.34 & 6.44 & 0.1 & 1.58\% \\ \hline \multirow{3}{*}{400 KN} & 2080 & 8.18 & 8.04 & \(-\)0.14 & 1.71\% \\  & 3580 & 9.41 & 9.33 & \(-\)0.08 & 0.85\% \\ \cline{1-1}  & 5080 & 8.32 & 8.06 & \(-\)0.26 & 3.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy validation of deformation measurement.  
Figure 21: Location coordinate extraction of code marks.  
Figure 22: Code-mark displacement calculation results.  
As shown in Figure 23, the vector length error of the code-mark position was within 0.5 mm, and the angle error was within \(1^{\circ}\), indicating that the extracted FFDV can accurately reflect the full-field displacement characteristics of the structure.
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 2 System Operation Principle
### GPS Passive Radar Geometry  
The PR configuration considered in this work is depicted in Figure 1. A local Cartesian coordinate system centred in the PR location is selected to define the system geometry and to generate the position of the targets. A single hemispherical antenna with right hand circular polarization was considered to acquire the direct signal from the line of sight GPS satellites and a linear array composed of 5 single vertical polarized elements was selected for the surveillance channel. This configuration provides the following target information in a 3D vector: bistatic range and Doppler shift from coherent signal processing, and azimuthal DoA after applying digital beamforming techniques.  
The bistatic range \(m\) is determined by the difference of the path lengths travelled by the wave received directly from the illuminator and the wave scattered by the target, as expressed in (2).  
\[m=R_{T}+R_{R}-L \tag{2}\]  
where \(R_{T}\), \(R_{R}\), and \(L\) are the distances from the satellite to the target, from the PR to the target, and from the satellite to the radar, respectively. Due to satellite movement, the whole parameter set has a time dependence and could be different in each processing interval.  
The second PR measurement is the Doppler shift, \(p\), which is the frequency difference between the reference signal and target echo. In a moving transmitter environment, a Doppler frequency appears in both acquired signals: in the reference one due to satellite movement; and in the radar echo due to both transmitter and target movements. To calculate the Doppler shift, the projections of the current velocity vector on all signal paths are considered:  
\[p=-\frac{(\vec{v}_{t}-\vec{v}_{sat})\cdot\vec{R}_{T}+\vec{v}_{t}\cdot\vec{R}_ {R}+\vec{v}_{sat}\cdot\vec{L}}{\lambda} \tag{3}\]  
where \(\lambda\) is the carrier wavelength, \(\vec{x}\cdot\vec{y}\) represents the scalar product between vectors \(\vec{x}\) and \(\vec{y}\), and \(\vec{R}_{R}\), \(\vec{L}\) and \(\hat{R}_{T}\) are the director vectors from receiver to target, from transmitter to receiver, and from transmitter to target, respectively.  
Figure 1: PR scenario based on satellite IoO, consisting of a hemispherical reference antenna to allow acquisition of all GPS signals in line of sight, and a surveillance array to acquire targets echoes.  
Finally, the presence of a surveillance array allows us to estimate the target's azimuth \(\phi\), which is related to the target location as \(tan(\phi)=y_{t}/x_{t}\).
# Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz
## 4 Discussion  
Compared with the mostly traditional model, the channel model based on the antenna stationary subinterval has the following advantages:  
1. The channel satisfies the linear characteristic. The clusters have a linear superposition characteristic just the same as the multipath components.  
Figure 8: Cluster distribution over the antenna array axis. (**a**) The PDP over the antenna array; (**b**) multipath cluster distribution over the antenna array.  
Figure 9: Channel correlation matrix modelling.  
2. The cluster parameters are independent. Time delay, power and angle of different clusters can be described by random distribution.
3. The intra-cluster multipath components can be simplified as a single synthetic signal. Then, what are the basic conditions? Firstly, the channel is non-stationary. This lies in the non-point source radiation, and so sub-channel parameters of the antenna elements are random. The extraction of channel parameters discussed earlier has fully proven it.  
Secondly, theories prove that similar elements of the multi-antenna system are correlative and shift invariantly in far-field propagation [27]. The condition of far-field is quite easy to satisfy, due to the similar fading, and then the stationary interval in the antenna array domain is supposed to be held.
# Autonomous Orbit Determination of LLO Satellite Using DRO-LLO Links and Lunar Laser Ranging
## 3 Autonomous Orbit Determination Algorithm
### Extended Kalman Filter Algorithm
 \tag{14}\]  
where \(\mathbf{K}_{i+1}\) is [PERSON] gain; \(\mathbf{R}_{i+1}\) is the covariance matrix of the measurement noise; \(h\big{(}\mathbf{X}_{i+1|i}\big{)}\) is the prior measurement prediction value calculated using prior state parameters; \(z_{i+1}\) is the observation data of the current epoch (inter-satellite ranging data and laser ranging data are obtained simultaneously); and \(\mathbf{H}_{i+1}\) is the design matrix of the state parameters estimated based on the observed data, and the calculation formula is as follows:  
\[\mathbf{H}_{i+1}= \begin{bmatrix}\frac{\partial z_{\mathrm{com}}(t_{i+1})}{ \partial\mathbf{X}_{i+1|i}}\\ \frac{\partial\rho(t_{i+1})}{\partial\mathbf{X}_{i+1|i}}\end{bmatrix} = \begin{bmatrix} \frac{\partial z_{\mathrm{com}}(t_{i+1})}{\partial t_{i+1|i}}&\frac{ \partial z_{\mathrm{com}}(t_{i+1})}{\partial t_{i+1|i}}&0\\ \frac{\partial\rho(t_{i+1})}{\partial t_{i+1|i}}&\frac{\partial\rho(t_{i+1})}{ \partial t_{i+1|i}}&0\end{bmatrix}  \tag{15}\]
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 3 Proposed Superframe Structures
### Proposed Scheme
#### 3.1.2 Data Transmission Phase  
After the successful completion of the handshake period, the data transmission phase immediately starts. A data transmission phase comprises of control period, announcement period, and data transmission period.  
Each patient's vehicular node is allocated a separate control slot to send their data slot requests. A data slot request comprises a number of data slots required by the patient's data block along with its nature of sensitivity. In this work, the data sensitivity is divided into eight different levels by assigning three bits in its frame structure. The sensitivity level increases from 0 to 7, where 0 means least sensitive and 7 means the most sensitive data. Each datum requesting patient's vehicles after computing its number of required slots along with its sensitivity level sends the control message to their attached \(RSU\) during the allocated control slot.  
After the completion of the control period, the \(MN\) of the attached \(RSU\) has complete knowledge about the number of vehicles that want to send data and how many data slots are required by them. During the announcement period, \(MN\) disseminate information about slots allocation to all successful vehicles with their starting slot number and the number of slots assigned to each of them. In case there is no data slot request received from the vehicles, then there will be no data slot in that session. In addition, \(MN\) broadcasts about the start of the next session in its announcement period, so vehicles must be aware of the start of the upcoming session.
## 3 Results and Discussion
### AFM Analysis
#### 3.2.4 Fractal Analysis of Leaf Surface
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Furrow Parameter** & **Unit** & **S50** & **S20** & **S10** \\ \hline Max. depth & \(\mu\)m & 0.8207 & 0.6314 & 0.1169 \\ \hline Mean depth & \(\mu\)m & 0.3016 & 0.1771 & 0.03247 \\ \hline Mean density & cm/cm\({}^{2}\) & 0.2105 & 16,011 & 44,170 \\ \hline
**Texture Parameter** & **Unit** & **S50** & **S20** & **S10** \\ \hline Texture Isotropy & \% & 60.72 & 49.54 & 55.38 \\ \hline First Dir. & \({}^{\circ}\) & 0.02569 & 180 & 0.01696 \\ \hline Second Dir. & \({}^{\circ}\) & 90.01 & 44.96 & 161.5 \\ \hline Third Dir. & \({}^{\circ}\) & 33.78 & 135 & 168 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Furrows and texture parameters of Andiroba leaf abaxial side. comparatively, S10 has its surface microtexture more heterogeneous than S50 and S20 that exposed similar \(\beta\) coefficients values. Furthermore, fractal succolarity, which relates topographical lower and upper bands, confirmed that stomata are regions of greater fluid penetration, which reveals yet another unique characteristic of the different patterns of the leaf microtexture as a function of the scale size. The lowest succolarity was registered for S20 and the highest for S50 (Table 4), exhibiting a significant global difference (\(p<0.05\)), where the Tukey test reveals that S20 and S10 are statistically different from S50. When succolarity (surface percolation) is equal to 0.5, the surface has ideal percolation, but the surface percolation is lower if the value is less than 0.5. In this way, we can conclude that S20 absorbs and retains less water than larger and smaller areas outside the stomata due to its lower fractal succolarity. Notably, it is clear that leaf surface spatial patterns change according to scale size, suggesting that these physical and mathematical tools can be applied to investigate topographical differences in plant species whose anatomies are similar. Finally, we employ a toolkit from statistics and information theory to characterize the degree of uniformity of the asperities over the spatial domains of the leaf surface, using surface entropy and outlier fraction quantitative parameters. Table 4 unveils that all images presented very high values for the topographic entropy (E \(\sim\) 1) and very low values for the outlier fraction (\(\text{f}_{\text{out}}<<1\)) in the local distribution LRMS (x,y), where both do not present significant differences between sizes (\(p>0.05\)). Both results provide evidence that the surface roughness displays a high uniformity at the global and neighborhood-based scale, despite the previously detected differences in the fractal properties. This means that the leaf surfaces have asperities that are hierarchically arranged to promote robustness in uniformity and variations in spatial complexity. Moreover, when scanning the topography with moving windows, it is unlikely to find sudden spatial variations in the roughness.
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
## Materials and Methods
### Optical Measurement for Bio-Sensing Applications  
We tested the optical behavior of Ru(bpy)\({}_{3}\)\({}^{2+}\) based labeling with a non-conventional system for bio-analysis, reported in Figure 2A. We performed a fluorescence emission analysis of HDFs treated with 0.4, 0.7, and 1 \(\upmu\)M of fluorophore for 24, 48 and 96 h (see experimental section for details). The system used for analysis included: A laser source (Coherent) operating at 405 nm to a power of 50 mW. This laser wavelength fell within the Ru(bpy)\({}_{3}\)\({}^{2+}\) excitation curve. A chopper; a monochromator; a PMT Hamamatsu R-908; a lock-in, and a series of lenses to collect the signal at the monochromator entrance slits, completing the detection system. Measurements were collected from 550 nm to 730 nm by using the monochromator to select the wavelength with an error of 0.1 nm. Cells were fixed and dried on a glass coverslip, blocked in a sample holder (Figure 2B). A computerized system for instruments management and data acquisition completed the system. A laser spot of about 3 mm in diameter was used, to a surface covered of \(\sim\)7 mm\({}^{2}\). The average number of cells measured was \(\sim\)3 \(\times\) 10\({}^{5}\). Each spectrum was collected and, using an off-line analysis, the peak maximum recorded.
## 4 Discussions
In this study, the shear strength of the large beams--HB60, MB60, and LB60--were found to be 10%, 17.5%, and 6.6% higher than the strength of beam NB60, respectively. This could be explained by a higher compressive strength of concrete with a recycled aggregate. However, the shear strength of small beams, HS60, MS60, and LS60 was 14.8%, 30.5%, and 27.5% lower than the strength of beam NS60, respectively. It could be concluded that the recycled aggregate qualities have a large effect on the shear behavior of small size RC beams, due to an aggregate interlock mechanism and this mechanism was negligible for large-size RC beams. However, further research is needed to investigate the effect of aggregate interlock for recycled concrete on the shear strength of the RC beam. In addition, [PERSON] et al. [26] found that the shear strength of RC beams with 25, 50, and 100% recycled coarse replacements was 3.7% higher, 15.4% lower, and 16.0% lower, respectively, than that of the RC beam made of natural aggregate concrete. Therefore, the recycled aggregate quality (medium- and low-quality) had an approximately two-fold larger effect than the 50% recycled aggregate replacement. In contrast, the effect of the high-quality recycled aggregate was almost equal to that of the 50% recycled aggregate replacement.
# Research of Personalized Recommendation Technology Based on Knowledge Graphs
## 4 Movie Recommendation Model Based on Knowledge Graph Representation Learning
### TransE Algorithm Improvement
#### 4.1.2 Improvement of Negative Triples Construction Method  
In the training process of TransE algorithm, correct triples and wrong triples are needed. These wrong triples are also called negative triples. We need to construct them ourselves. We propose corresponding improvements to the deficiencies of the original negative sampling algorithm in the TransE model.  
The original negative triple construction process of the TransE algorithm is as follows: Firstly, randomly select a triple existing in the knowledge graphs, that is, the correct triple (h, r, t). Then randomly select one or two entities from the knowledge graphs. In our experiment, we randomly select a movie entity or a contributor entity, and use them as the head entity h' or the tail entity t' to form a negative triple (h', r, t'). However, this construction method has an obvious shortcoming. That is, it is very likely that the constructed negative triples are still correct triples. This situation is more common in many-to-one, one-to-many, and many-to-many relational mapping types. For example, the correct triple (Marvel's The Avengers I, hasActor, [PERSON], [PERSON]) may generate negative triple (Marvel's The Avengers II, hasActor, [PERSON], [PERSON]) after sampling in this way. However, the negative triple is correct, which affects the training effect of the model.  
In order to solve the above problem, we use the Bernoulli sampling algorithm to randomly select entities. The improved negative triple construction process is as follows: First, for each triple connected by the relationship \(r\), we count the average number of tail entities corresponding to the head entities and the average number of head entities corresponding to the tail entities, denoted as \(M1\) and \(M2\), respectively. Then, we calculate the probability \(P\) according to Formula (3) and use it as a parameter of the Bernoulli distribution to extract entities. In this way, different methods of extracting entities will be obtained in different relational mapping types.  
\[P=\frac{M1}{M1+M2} \tag{3}\]  
The extraction method obtained according to the above method is shown in Table 1.