esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 2 System Framework Design  
### Overview of the System Framework  
Human action recognition using a point cloud generated by millimeter-wave radar has to overcome the sparsity and inhomogeneity of the millimeter-wave point cloud. Moreover, the acquired millimeter-wave point cloud includes much noise in indoor environments due to multipath effects and other factors. In addition, the data volume of the multiplayer point cloud is enormous, and it will generate a substantial computational overhead if deep learning models are directly used for training and recognition without processing. Thus, we designed targeted system frameworks to solve these problems, as shown in Figure 1. One of the modules we designed is the data preprocessing module, which first filters the point cloud data through traditional pass-through filters and statistical filters [21], and then further processes the data using the millimeter-wave point cloud inter-frame disparity filtering proposed in this paper to obtain point cloud data of a higher quality. Then, we used the DBSCAN clustering algorithm and the Hungarian algorithm for inter-frame target matching and differentiation to differentiate the point clouds with multiple targets. The third part of the system framework is the data optimization and dimensionality reduction module, which mainly solves the problems of sparse millimeter-wave point clouds [22] and large amounts of multi-person point cloud data by stacking a number of frames and rounding the \(Y\)-axis component. The fourth part of the system framework is the CNN + LSTM recognition module, which performs the multi-dimensional feature extraction on the human point clouds through the powerful spatial feature extraction capability of CNN [23] and the temporal feature extraction capability of LSTM [24]. A detailed description of the above four modules is presented as follows.
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 2 Linear DSP MIMO Technique for MDM Systems
The higher convergence speed of RLS leads to higher throughput efficiency. Generally, for a conventional MIMO-FDE, training sequence is required for initial weight matrix updating to ensure the decision-directed (DD) equalization after training symbols for continuous tracking. Therefore, slower convergence speed will result in a longer training sequence and then lower spectral efficiency. While RLS algorithm owns faster convergence speed compared with LMS algorithm, less training sequences are required so that higher spectrum efficiency would be achieved, especially in the long-haul MDM systems. Figure 2: Number of multiplies per symbol vs. filter tap length for TDE and FDE. FDE can significantly reduce the computational complexity (m = 10). What's more, as mentioned above, less training sequences are required for RLS algorithm to perform update processing. The computational complexity per mode per symbol of RLS algorithm is comparable with LMS, which is shown in Table 1. Table 1 is the complexity comparison between the FD-RLS and the conventional FD-LMS algorithms, in which \(m\) is the mode number and \(N_{f}\) is the FIR filter length for each even/odd block equaling \(2 LR_{\rm s}\Delta\tau\). Let us consider a 15 spatial modes transmission system [42] where \(m~{}=~{}30\) and \(N_{f}~{}=~{}1024\), the complexity ratio \(C_{\rm RLS}/C_{\rm LMS}\) could be derived to be around 1.33. The complexity ratio for different channels number \(m\) is shown in Figure 3, which indicates that the complexity of RLS-FDE is comparable with, or rather slightly higher than LMS-FDE. Moreover, considering that RLS-FDE requires less training symbols as compared to that for the LMS algorithm and RLS can be switch into DD-LMS algorithm after initial weight matrix equalization, the computational complexity per training symbol of RLS algorithm is much lower than LMS.
###### Abstract  
Nowadays, there is a high demand in many industrial countries for healthy foods and sustainable products and so to explore innovative food technologies, such as fortification with bioactive compounds such as antioxidants and polyphenols, that may be sourced from herbs or by-products. This study's objective was to explore the fortification of refined olive oils with natural bioactive compounds such as the herbs rosemary (_Rosmarius officinalis_, L.), basil (_Ocimum basililicum_, L.), sage (_Salvia officinalis_, L.), lemon balm (_Melissa officinalis_, L.), St. John's word (_Hypericant perforatum_, L.), pink savory (_Saturaj thumbra_, L.), dittany (_Origanum dictamus_, L.), and by-products such as pomace from olives, olive leaves (_Ola europaea_, L.), orange peel (_Citrus aurantium_, L.), lemon peel (_Citrus litmon_, L.), pomerangate peel (_Punica granatum_, L.), and mandarin peel (_Citrus reticulata_). The fortification of the refined olive oils was performed with the use of different methodologies such as conventional maceration (CM), incubation shaking maceration (ISM), and ultrasound-assisted maceration (UAM). Folin-Ciocalleau (POLIN) and Ferric-Reducing Antioxidant Power (FRAP) assays were used to measure their total phenolic content and antioxidant capacity. All methods demonstrated that the temperature, sample concentration, and time of maceration are considered of high importance in the fortification of refined olive oil. In vitro digestion process showed the bioavailability of the antioxidant and phenolic compounds in the fortified olive oil that ranged from 4.84% to 53.11%. Furthermore, the refined olive oils fortified with pomace, basil, St. John's word, and pomerangate peel presented the highest antioxidant and phenolic predicted bioavailability indices during the in vitro process compared to the control refined olive oil. Finally, fortification with natural herbs or by-products can be considered an innovative method for the enhancement of the nutritional value of refined olive oils.  
refined olive oil fortification; herbs; plant by-products; conventional maceration (CM); incubation shaking maceration (ISM); ultrasound-assisted maceration (UAM); in vitro predicted bioavailability
# A Low-Power Read-Decoupled Radiation-Hardened 16T SRAM for Space Applications
## 3 Simulation Results and Analysis  
All performance metrics were evaluated based on a 90 nm CMOS technology and a 1 V supply voltage. To demonstrate the effectiveness of the proposed design, its cell-level performance is compared against several contemporary radiation-hardened SRAM cells, including RHBD10T, SIS10T, WE-QUATRO, QUCCE12T, EDP12T, SEA14T, and SAW16T. Key metrics--including read and write access time, read static noise margin, word line write trip voltage, and hold power consumption--were analyzed and compared.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 5 Handover Challenges in Drone Networks
### Handover Failures  
The HOF may occur due to the fast movements of drones and the delay in the handover initiation or procedure with connected drones. Several works have been conducted to address this issue in mobile networks [74; 75]. In [74], the authors proposed a distance-based handover algorithm for femto and macro cells to mitigate the HOFs and unwanted handover. In [75], the reactive handover method was used to delay the handover process until the UE loses connection from the previous BS or reaches the most predictable position. This technique minimized the overall handover performance. However, in the case of drones, further investigations that include various mobility speeds and system settings scenarios are still needed.
## 3 Results
### Biochars as Amendment for Greenhouse Media for Seeding Production  
#### 3.2.1 Tomato Seeding Growth  
The total fresh weight (TFW), total dry weight (TDW) and GI of SBB-amended mixes had positive linear correlations with the biochar incorporation rate, while HB-amended mixes showed quadratic correlations (Figure 4a-c). All TFWs, TDWs and GIs in biochar-amended mixes were significantly lower than the control, except for those in 50% HB-amended mixes. Tomato seedlings grown in all SBB-amended mixes had similar SPAD to the control (except 100% SBB, Figure 4d); however, seedlings grown in all HB-amended mixes had significantly lower SPAD (except 10% HB).  
All tomato seedlings grown in biochar-amended mixes had significantly shorter root lengths than the control (except 30% HB, Table 2). Except for seedlings grown in 50% SBB, 30% HB and 50% HB, all tomato seedlings grown in biochar-amended mixes had significantly smaller root surface areas than the control. Seedlings grown in all biochar-amended mixes had similar or wider root diameters compared to the control; however, they all had fewer root tips (except 30% HB and 50% HB).  
Figure 3: The correlation between substrate total porosity **(TP, a)**, containing capacity **(CC, b)**, air space **(AS, c)** and bulk density **(BD, d)** and biochar incorporation rate. SBB = sugarcane bagasse biochar; HB = mixed hardwood biochar. SBB (10%, 30%, 50%, 70% and 100%; by vol.) incorporated with 30% perlite with the rest being peat moss; HB (10%, 30%, 50% and 70%; by vol.) incorporated with peat moss.
# A Second Harmonic Wave Angle Sensor with a Collimated Beam of Femtosecond Laser
## 3 Design of the Second Harmonic Wave Angle Sensor and Experiments
The calculation can be modified by considering [PERSON]'s law as it has been reported in the previous study [16]. Even so, [PERSON]'s law can only explain changes in the measurement sensitivity and angle range based on the measurement trend line. However, it cannot explain the difference in the contours of the SHG spectrum. Table 3 shows the result of the previous study related to the SHG concept for detecting angle displacement. Compared to the results of the previous frequency domain angle sensor [16] by using a focused beam method with a similar type of crystal, the current result has a twice wider measurable angle range in total with three different sensitivities, in which the previous study only reported one measurement sensitivity. Another previous study of SHG angle sensors in the domain of time [15] utilizing beta barium borate (BBO) crystal has reported an even narrower measurement range (1040 arc-second). Taking closer identification in the measurement trend line, the split peak was the main cause of the measurement sensitivity discrepancy, as was shown in the calculation. Figure 12 shows an exemplification of the split peak. The single peak is also plotted in the figure for comparison. A split peak of the second harmonic wave spectrum appeared when the crystal was rotated by 6147.77 arc-second from its initial position. In contrast, only one peak appeared when the crystal was rotated by 6753.64 arc-second. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Crystal Position** & **Peak Wavelength Range** & **Angle Range** & **Sensitivity** & **Resolution** \\
**mm** & **nm** & **Arc-Second** & **nm/Arc-Second** & **Arc-Second** \\ \hline \multirow{4}{*}{30} & 765.44–802.88 & 13,328.38 & 0.00294 & 6.80 \\  & 755.84–760.80 & 3506.75 & 0.00139 & 14.39 \\  & 747.04–751.04 & 2163.18 & 0.00185 & 10.81 \\  & 765.60–802.88 & 13,203.10 & 0.00294 & 6.80 \\
50 & 756.32–761.12 & 3499.81 & 0.00142 & 14.08 \\  & 747.84–752.00 & 3245.53 & 0.00202 & 9.90 \\  & 765.44–803.20 & 13,339.97 & 0.00294 & 6.80 \\
70 & 756.32–761.28 & 3382.52 & 0.00143 & 13.99 \\  & 746.40–751.36 & 2534.10 & 0.00223 & 8.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Angle measurement results in several crystal positions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Domain** & **Type of** & **Crystal** & **Angle Range** & **Sensitivity** & **Resolution** \\  & **Laser Beam** & **Arc-Second** & **Arc-Second** & **Arc-Second** \\ \hline Time [15] & Focused & BBO & 1040 & 1.18 mV/arc-second & 0.36 \\ Frequency [16] & Focused & MgO:LiNbO\({}_{3}\) & 10,752 & 0.00314 nm/arc-second & 3.00 \\ Frequency (in this study) & Collimated & MgO:LiNbO\({}_{3}\) & 13,340 & 0.00294 nm/arc-second & 6.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Benchmarking against previous studies.
## 4 Numerical Simulation Results and Discussions
### Variation of the Environmental Impact Radius with Ambient Wind Velocity
Further, \(r_{\rm{criti}}\) decreased with the rising of \(V_{0}\) under various \(U\) for the vertical outlet, according to Figure 20. When there was ambient wind, \(V_{0}\) was more sensitive to the impact of \(r_{\rm{criti}}\) than without ambient wind. To be clear, when \(V_{0}\) increased from 3 m/s to 6 m/s, in the case of no ambient wind, \(r_{\rm{criti}}\) declined to 3 m from 7 m, with a reduction of 4 m. By  
Figure 19: Attenuation of \(C_{\max}\) along the \(r\) direction for various \(U\): (**a**) vertical discharge pattern; (**b**) horizontal discharge pattern. Figure 20: Variation of \(r_{\rm{criti}}\) with \(U\) under for \(V_{0}\). contrast, \(r_{\rm criti}\) was reduced to 15 m from 31 m when there was ambient wind, showing a reduction up to 15 m.  
Regarding the horizontal outlet, \(r_{\rm criti}\) also decreased with the increase of \(V_{0}\) under various \(U\) for the horizontal outlet. The greater the ambient wind \(U\), the weaker the impact of \(V_{0}\) on \(r_{\rm criti}\). After \(U\), a \(r_{\rm criti}\), however, merely decreased with the increase of \(U\), rather than varying with the changing \(V_{0}\). Further, the \(r_{\rm criti}\) of the vertical outlet was smaller than that of the horizontal outlet within the parameter range when \(V_{0}\) and \(U\) were constant, in this study. However, a smaller variation was found in \(r_{\rm criti}\) of the upper vents for the two discharge patterns when \(V_{0}\) was smaller and \(U\) was larger. When \(V_{0}=3\) m/s and \(U=4\) m/s, for instance, the environmental impact radius \(r_{\rm criti}\) of the vertical and horizontal outlets were approximately equal, being 31 m and 33 m, respectively.
## 3 Numerical Simulations and Results  
We simulate the optical system using the spectrum of plane-waves method [27] for propagation between SPP and MC, while using ray tracing for propagation between the front face of MC and rear face of the lens, for evaluation of wavefront aberrations, and for optimization of aspherical shapes of MC interfaces. The Debye-Wolf diffraction integral [28] was used to compute the focal spot. Two different aspherical lenses with NA 0.21 (low NA asphere) and 0.47 (high NA asphere) were used, to differentiate between effects caused by strong focusing and effects which are not caused by strong focusing. A simple Wolfram Mathematica code was written to perform the simulations, which is placed as a supplementary material. We briefly describe the methods used for simulations in the first subsection, while we give the expression for all aspherical surfaces, including both lenses and MC for both variations, in the second subsection, and we present and discuss the results regarding the focal spot in the third subsection.
###### Abstract  
This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran's I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen ore-forming elements (Cu, Pb, Zn, W, Sn, Mo, F, Au, Fe\({}_{2}\)O\({}_{3}\), etc.) in stream sediment samples in the Bayantala-Mingantu district, North China. The results showed that the OGE analysis performed at lower levels of scale greatly improved the quality of anomaly recognition: more than 80% of the known ore spots, no matter what their scales and mineral species, were predicted in less than 45% of the study area, and most of the ore spots falling outside the delineated anomalous regions occur nearby them. OGE can extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it outperforms other convolutional autoencoder models such as GAUGE in anomaly detection.  
object-based image analysis; graph neural network; geochemical anomalies; fractal dimension +
Footnote †: journal: Appl. Sci. 2022, 12, 10029. [[https://doi.org/10.3390/app121910029](https://doi.org/10.3390/app121910029)]([https://doi.org/10.3390/app121910029](https://doi.org/10.3390/app121910029))
## 3 Results and Analysis on a Meta-Fiber Platform  
### Analysis Using Angular Spectrum Propagation Theory on a Single-Fiber Platform  
After choosing nano-cuboids with the proper size (Figure 3c), the far-field performance of the device is evaluated with the corresponding transmittance and phase according to Figure 3b using the angular spectrum propagation theory. The parameters of a Corning SMF 28e single-mode fiber are used for the simulation. The angular spectrum in the far field is calculated after Fourier transformation. Figure 4a,b shows the far-field angular spectrum profile without collimation. The divergence angle is defined when the intensity drops to \(e^{-2}\), which is about 11.06\({}^{\circ}\) according to Figure 4b. By adding a collimating metalens (\(\phi_{L}\)), the divergence angle can be reduced to 1.97\({}^{\circ}\), as shown in Figure 4c,d. Figure 4e,f shows the metasurface-based 3 \(\times\) 3 collimating splitter, which synthesizes the collimating phase and Dammann grating together (\(\phi\)). The incident large-divergence Gaussian beam is collimated and splits into nine beams with uniform intensity within the FOV of 62\({}^{\circ}\)\(\times\) 62\({}^{\circ}\), matching well with the prediction using Equation (3). The calculated diffraction efficiency and uniformity of the collimating splitter reach 99.1% and 0.21%, respectively. Compared with the optimized results of the Dammann grating, the uniformity gets worse mainly for  
Figure 3: Design of the unit cell. (**a**) Schematic of the unit cell. (**b**) Simulated transmittance (blue line) and normalized phase (red line) of the unit cell. The width \(d\) varies from 100 nm to 450 nm. (**c**) Width profile of the designed metasurface.  
two reasons: first, the phase profile after synthesizing will deviate from the optimized phase for splitting; second, there will be a difference between the practical complex amplitude of the chosen silicon structures (according to Figure 3b) and the profile (\(\phi(x,\,y)\)) with unity transmittance.  
The diffraction efficiencies of the metasurface collimating splitter extracted from different diffraction orders are extracted and shown in Figure 4h. The diffraction efficiencies of the previously optimized Dammann grating are also extracted for comparison in Figure 4g. Both of the results show good uniformity and near-unity efficiency, which confirms the validity of the phase synthesis.
# Chemo-Mechanical Model for the Expansion of Concrete due to Alkali Silica Reaction
## 4 ASR-Induced Volume Expansion  
### Expansion Pressure Caused by ASR  
The swelling ASR gel due to water imbibition will first be accommodated by easily accessible pores around the aggregate without producing pressure [16]. Once the easily accessible pores are filled, further water imbibition will produce pressure. The infiltration of ASR gel will then take in place as a consequence [18]. The volume fraction of the space available to the ASR gel due to the infiltration of the gel can be calculated as  
\[\frac{dv_{ift}}{dt}=D_{g}^{\prime}(1+\beta<\varepsilon_{v}-\varepsilon_{0}^{ \prime}>^{2})\frac{p}{v_{ift}}, \tag{26}\]  
where \(D_{g}^{\prime}=(N_{a}n_{ift}a_{ift})^{2}D_{g}\), is the equivalent permeability for the volume infiltration; \(p\) is the pressure; \(n_{ift}\) the number of infiltration paths in the representative volume that can be assumed nevertheless; \(a_{ift}\) is the average cross section area of one infiltration path, and here the infiltration path is taken as capillary pores with diameter of 10 \(\mu\)m [46]; and \(D_{g}\) is the permeability for the gel movement depending on temperature given in Appendix A. \(\varepsilon_{v}\) is the volumetric strain, \(\varepsilon_{0}^{\prime}\) represents an empirical finite threshold, taken here as 0.01%, and exceeding this threshold will increase the permeability.  
The total space fraction available for the ASR gel in one representative volume of concrete is given by  
\[v_{ts}=v_{bg}+v_{itz}+v_{0}+v_{ift}+v_{def}, \tag{27}\]  
where \(v_{bg}\) is the volume fraction of the basic gel (m\({}^{3}\)/m\({}^{3}\) of concrete), \(v_{bg}=N_{a}V_{bg}\), \(v_{itz}\) is the volume fraction of the interface transition zone (ITZ), and the thickness of ITZ is considered to be constant and independent of the aggregate size [16; 20]. \(v_{0}\) is the pore volume fraction surrounding the aggregate without producing any pressure (m\({}^{3}\)/m\({}^{3}\) of concrete), taken here as 1.0E-5; \(v_{ift}\) is the volume fraction of the space available to the ASR gel by the infiltration (m\({}^{3}\)/m\({}^{3}\) of concrete); and \(v_{def}\) is the volume fraction produced by mechanical deformation. We set \(v_{def}\) equal to the volumetric strain of concrete.  
The volumetric strain of the ASR gel can be measured as  
\[\varepsilon_{V}^{gel}=\frac{<v_{gel}-v_{ts}>^{+}}{v_{ts}}, \tag{28}\]  
where \(v_{gel}\) is the total volume fraction of gel after imbibition (m\({}^{3}\)/m\({}^{3}\) of concrete), \(v_{gel}=v_{bg}+v_{w}\), and \(v_{w}=w_{1}/\rho_{w}\). \(<X>^{+}\) is equal to X if \(X>0\) or equal to 0 if \(X\leq 0\).  
Then, the volumetric stress of the gel is given as  
\[\sigma_{V}^{gel} = Ke_{V}^{gel}, \tag{29}\] \[p = -\sigma_{V}^{gel}, \tag{30}\]  
where \(K\) is the bulk modulus of the ASR gel, which ranges between about 1.64 to 2.87 GPa for different alkali solutions, with an assumed Poisson ratio of 0.2 [47]. \(K\) can be even higher than 10 GPa for a gel containing high concentration of calcium [48]. In the present paper, it is set equal to that of water [18].
## 1 Introduction
In order to maintain the availability and location of their content for consumers and center routers, content producers must be able to move their material across locations as quickly as feasible. This is referred to as producer mobility. As a result, the author of [13] concluded that NDN does not aid producer mobility from a unique perspective on mobility assistance. NDN is impacted by a scaling issue with routing table size. Additionally, when providers relocate to a new location, the name system presents major scalability issues [14]. Significant handoff delay and excessive I_packet losses while broadcasting to a producer's prior location are other problems that need to be rectified [15; 16]. The I_packets continue to follow the prefix trail in the FIB whenever a producer switches locations, thus failing to get to the producer and being dropped. Due to the indirect points utilized to allow producer mobility, the data channel is likewise constrained and difficult. As a result, there is a long handoff interval as a result of conditions.