esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis
### Effect of Ground Cavity Sidewall Height
As observed from the figure, the aperture field extends well beyond the physical aperture of the antenna, which causes the gain enhancement and aperture efficiency to be greater than 100%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{H_{sw}}\) & **Impedance BW (\(\mathbf{S_{dd11}}\leq-10\) dB)** & **X-pol BW (\(\mathbf{Peak}\leq-25\) dB)** & **Combined BW (\%)** & **Av. Gain (Across Combined BW)** \\ (mm) & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
70 & 55.2 & 25.8 & 25.8 & 8.70 \\ \hline
80 & 54.3 & 34.9 & 34.9 & 9.05 \\ \hline
90 & 53.6 & 42.0 & 42.0 & 9.34 \\ \hline
100 & 52.6 & 45.3 & 42.6 & 9.23 \\ \hline
110 & 51.8 & 46.7 & 42.4 & 9.22 \\ \hline
120 & 51.1 & 47.7 & 42.4 & 9.18 \\ \hline
130 & 50.4 & 48.4 & 43.0 & 8.99 \\ \hline
140 & 50.0 & 49.2 & 43.9 & 8.97 \\ \hline \end{tabular}
\end{table} 
Table 1: OCROP antenna bandwidths (BWs) and gain as a function of sidewall height. Figure 6: OCROP with different sidewall heights: (**a**) Differential reflection coefficient; (**b**) Realized gain (solid, left y-axis) and peak cross-polarization ratio (dotted, right y-axis).
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.2 MODIS Data Preprocessing  
The coordinate system of MODIS data is different from the conventional ellipsoid system, which uses a regular grid as the imaging unit and has deformation in the longitude and latitude diagonal direction. Therefore, the projection of MODIS data should be transformed into the HJ-1 projection coordinate system of UTM_zone_50N, and then the second geometric correction adjustment on the MODIS data is conducted based on Landsat 8 data, causing the MODIS data and HJ-1 data to overlap accurately.
# A Light-Weight Autoregressive CNN-Based Frame Level Transducer Decoder for End-to-End ASR
## 3 Experiments and Results
Table 8 shows the comparison of the three models and presents that the change from the LSTM to CNN (\"RNN-T(LSTM \(\rightarrow\) CNN)\" in Table 8) significantly reduces the number of trainable parameters, from 1 M to 270 k with the comparable WERs and the decoding time. The additional change of the decoding steps from [\(T_{enc}+U\)] to [\(T_{enc}\)] reduces the decoding time by 1.65 times with the comparable WERs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CNN Spec**} & \multicolumn{2}{c}{**\# Trainable**} & \multicolumn{3}{c}{**WER (\%)**} \\ \hline
**Kernel** & **Layers** & **Channels** & **Params** & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline
6 & 1 & 96 & 115 k & 6.6 & 18.4 & 7.0 & 18.7 \\
**6** & **1** & **128** & **177 k** & **6.4** & **18.3** & **6.8** & **18.4** \\
6 & 1 & 192 & 340 k & 6.4 & 18.3 & 6.9 & 18.8 \\ \hline
6 & 2 & 128 & 276 k & 6.5 & 18.6 & 6.9 & 18.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of WER with different layers and channels of CNN in this work. Figure 7: WERs of this work versus the kernel size of CNN ( red +: dev clean, blue O: test clean, purple \(\Diamond\): dev other, black *: test other).
# Experimental Study of 3D Concrete Printing Configurations Based on the Buildability Evaluation
## 4 Results and Discussion
### Height Reduction Test
#### 4.4.2 \(Hrr\) for \(B1>1\)  
The discussion above is theoretically based on \(BI\leq 1\). In a real printing operation, the material would undergo a further slump after the following layers are stacked on and show much more height reduction under the weight from the subsequent layers if it cannot gain strength early enough. One layer would have many slump steps/times (\(BI=n>1\)) after being printed, and its height shrinkage ratio for each step/time could be marked as \(x_{i}\ (i=1,2,\cdots,n)\). Taking a two-step slump (\(BI=2\)) as an example, the printed filament has a height of \(H\) after being printed, a height of \((H\times x_{1})=Hx_{1}\) after the first step/slump at time \(T1\), and \((Hx_{1})\times x_{2}=Hx_{1}x_{2}\) after the second step at \(T2\), and keeps  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer No.**} & **1. Nozzle Positioning** & **2. Printing** & **3. Material Slump** & **Accumulated Height** \\ \cline{2-5}  & **Nozzle Position * & **H, Gap Height ** & **Layer Height after Slump *** & \\ \hline \(0\) & \(h\) & \(h-0\) & \(h\ (1-r)\) & \(h\ (1-r)\) \\ \(1\) & \(2\ h\) & \(2\ h-h\ (1-r)\) & \((2\ h-h\ (1-r))\ (1-r)\) & \(h\ (1-r)+(2\ h-h\ (1-r))\ (1-r)\) \\ \(N\) & \((N+1)\ h\) & \((1+r^{1}+r^{2}+\cdots+r^{N})\ h\) & \((1-r^{N+1})\ h\) & \((N-r^{1}-r^{2}-\cdots-r^{N+1})\ h\) \\ \hline \hline \end{tabular}  
* Nozzle height, the position of nozzle in the Z direction for each layer, achieved by the motion system, i.e., same-layer-height printing. ** Gap height reserved for this layer equals the nozzle position subtracted by the accumulated height in the previous layer. *** Real height of this layer after being printed and undergoing the material slump.  
\end{table}
Table 6: Height data during the printing process.  
Figure 14: Results of the height reduction tests with different numbers of stacked layers.  
its height \(Hx_{1}x_{2}\) from \(T3\) and on. The \(x_{n}\) indicate the height slump ratio, \(x_{n}\in\left(0,\,1\right).\) The height data for each layer are graphed in Figure 15, as the \(Tn\) indicates the printing time on each layer. Then, the HRT of this two-step slump material would be \(HRT_{BI=2}=1-x_{1}x_{2}\). Furthermore, when \(BI=n\), the height reduction ratio would be:  
\[HRR_{BI=n}=r_{BI=n}=1-x_{1}x_{2}\cdots x_{n}=1-\prod_{i=1}^{n}x_{i}\ \ x_{i}\in\left(0,\,1\right) \tag{7}\]  
At the time \(T12\), the previous \(11\) layers all finish their two-step slump and keep the stable height \(H_{n}x_{1}x_{2}\). Thus, when \(n=1\), Equation (7) would be the same as Equation (5). When \(n\geq 2\), it only needs to solve the product of \(x_{n}\) (\(\prod_{i=1}^{n}x_{i}\)), without considering any single value of \(x_{n}\). Therefore, the two-step slump printing can be considered one-slump printing for enough layers printed and a multi-layer slump in the same pattern. Regarding Equation (6), the \(HRR_{BI=n}\) can be derived as:  
\[HRR_{BI=n}=r_{BI=n}=\frac{\delta}{\delta+1}(n>1) \tag{8}\]
# Recent Progress in Hybrid Additive Manufacturing of Metallic Materials
## 5 Effect of Heat Treatments in AM Processed Maraging Steels
### Strengthening Mechanisms' Models
#### 5.1.1 Solid-Solution-Strengthening Model  
The Fleisher equation is used to determine the critical resolved shear stress due to the substitution of solute elements by other alloying elements involved:  
\[\sigma_{ss}=\sum\biggl{(}\left(\beta_{i}^{2}x_{i,\mu}\right)^{\frac{1}{2}} \biggr{)} \tag{2}\]  
where \(x_{i,\mu}\) is the atomic fraction of the element in consideration (\(x_{i}\)) in the matrix and \(\beta_{i}\) is the constant strengthening value that describes the lattice and modulus misfit of the element concerning the major element [95], which in this case is iron. Table 4 shows the iron values of several solution-strengthening constants for alloys studied by [PERSON] et al. [92]. These values are based on the assumption of infinite solid solubility in iron. According to the values in Table 4, Mo and Ti give the strongest solid solution strengthening. With increasing particle volume fraction, the effect of the precipitate forming elements (Mn, Ni, Ti, Mo) decreases. The changes in \(x_{i,\mu}\) are determined by Equation (3).  
\[x_{i,\mu}=\frac{x_{i}-\Sigma x_{ij}f_{j}}{1-\sum\,f_{j}} \tag{3}\]  
where \(f_{j}\) represents the volume fraction of particle \(j\) and \(x_{ij}\) is the volume fraction of element \(i\) in \(j\)[92].
## 4 Results and Discussion  
To verify the feasibility and scalability of the hardware and software solutions proposed in this system, as shown in Figure 11, an experiment was conducted on a transport vehicle measuring 6 m in length, 2.2 m in width, and 2.3 m in height. The IP cameras and camera layout utilized in the experiment are detailed in Tables 2 and 3 of Section 2.1. The cameras, switches, and edge computing device are interconnected via network cables. The switches and edge computing device are powered by vehicle power supply. The configuration of the edge computing device [27] and the algorithmic environment of the system during the test are presented in Table 5.  
To ensure the camera's safety during the experiment and establish a fixed distance between the binocular cameras, an adjustable bracket, specifically designed for the binocular camera and fabricated using 3D printing technology with photosensitive resin material, was created. In this test, we bolted the bracket 2 m above the ground surface to the vehicle's body. However, in actual transportation scenarios, the bracket's fixed position should be carefully planned based on the shape and structure of the transport vehicle to further ensure the camera's safety during the transportation of explosives.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Edge Computing Device: TW-T506S**} & \multicolumn{2}{c}{**System’s Algorithmic Environment**} \\ \hline Processor & NVIDIA Jetson NX & Package & Version \\  & & colored logs & 15.0.1 \\ \hline AI & 21 TOPS & flat buffers & 24.3.25 \\  & & humanfriendly & 10.0 \\ \hline CPU & 6-core NVIDIA Carmel ARM® v8.2 64-bit CPU & mpmath & 1.3.0 \\  & 6 MB L2 + 4 MB L3 & numpy & 1.24.4 \\ \hline GPU & 384-core NVIDIA VoltaTM GPU & onnxruntime & 1.17.3 \\  & with 48 Tensor Cores & opencv-python & 4.9.0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hardware and software configuration.  
Figure 10: Local matching only for the target box area.
# The Meta-Metaverse: Ideation and Future Directions
## 4 Possible Connections and Implementation
### Biology, Genetics, and the Meta-Metaverse  
The conventional Metaverse has the potential to be utilized in the field of biology in various ways [19; 60; 61; 62]. One such application is the utilization of VR environments to simulate and model biological systems and processes [63; 64]. For instance, VR can aid in visualizing and manipulating intricate biological data, such as genetic sequences or protein structures, in a more interactive and intuitive manner. It can also be used to develop simulations of biological systems, such as ecosystems and cell cultures, that enable researchers to test hypotheses and assess the impact of different variables on these systems [65]. Another prospective application is the use of the Metaverse as an online platform for education and outreach, enabling individuals to learn and explore biological concepts in a more immersive and interactive way. However, the implementation of the Metaverse in biology raises a number of ethical and legal concerns, including privacy and security, which should be thoughtfully analyzed and addressed.  
The Meta-Metaverse technology can be applied in biology to gain insights into the structures and patterns underlying genetic sequences, various biological systems, and processes. Researchers can utilize this technology to investigate the organization and structure of biological systems, such as the branching patterns of blood vessels and the distribution of neurons in the brain and assess blood flow efficiency and the way plants optimize photosynthesis. Furthermore, this technology can be used to examine the patterns of biological processes, including cell growth and development, cell division, and cell migration. Moreover, the Metaverse can be employed to understand the patterns of ecological systems, such as the distribution of species in ecosystems and the way ecosystemschange over time, and to study the patterns of biodiversity and ecosystem response to disturbances. Finally, the Meta-Metaverse platforms can be utilized to analyze the patterns of genetic sequences, including the distribution of nucleotide bases in DNA, gene regulation, and gene expression and interactions. Thus, the Meta-Metaverse technology has the potential to enhance biology research by enabling scientists to understand various biological systems, processes, and genetic sequences in a more effective and efficient manner.
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Safety and Interactions
#### 2.2.2 Bioavailability Enhancement with Ginger  
The most comprehensive list of bioenhancing interactions of ginger with other compounds is available in a patent [31]. Ginger aqueous and methanolic extracts are claimed to increase the bioavailability of various APIs by up to 1.5 folds, as listed in Table 2.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Compound** & **Enhancement in Bioavailability (\%)** \\ \hline _Fluoroquinolones_ & \\ Ciprofloxacinacin & 68 \\ P-floxacin & 53 \\ O-floxacin & 49 \\ \hline _Macrolides_ & \\ Erythromycin & 68 \\ Roxythromycin & 72 \\ Azythromycin & 78 \\ \hline _Cephalosporins_ & \\ Cefalexin & 75 \\ Cefadroxil & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximal enhancement of the bioavailability of APIs, vitamins and amino acids claimed to occur when ginger extracts are taken concomitantly [31].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Compound** & **Enhancement in Bioavailability (\%)** \\ \hline _Penicillins_ & 80 \\ Amoxycillin & 76 \\ Cloxacillin & 76 \\ \hline _Aminoglycosoides_ & \\ Kanamycin & 65 \\ \hline _Antimymobacterium agents_ & \\ Rifampicin & 65 \\ Dapsone & 46 \\ Etionamide & 56 \\ Cycloserine & 71 \\ \hline _Antifungals_ & \\ Fluconazole & 120 \\ Ketoconazole & 125 \\ \hline _Antifirals_ & \\ Acyclovir & 82 \\ Zidovudine & 105 \\ \hline _Anticularatives_ & \\ Ranitidine & 147 \\ Cimetidine & 98 \\ \hline _Benzodiazepepines_ & \\ Alprazolam & 76 \\ \hline _Anti-hypertension agents_ & \\ Propanolol & 76 \\ Lisoprini & 76 \\ Amlodipine & 68 \\ \hline _Stereoid anti-inflammatory_ & \\ Dexamethasone & 76 \\ Betamethasone & 75 \\ \hline _Non-steroid anti-inflammatory_ & \\ Diclofenac & 90 \\ Pirooxicam & 86 \\ Nimeesulide & 144 \\ \hline _Immunosupressants_ & \\ Cyclosporin A & 116 \\ Tacrolimus & 75 \\ \hline _Antimetabolites and antitumorals_ & \\ Methotrexate & 87 \\ Doxorubicin & 72 \\
5-Fluorouracil & 110 \\ Cisplatin & 56 \\ \hline _Vitamins and antioxidants_ & \\ Vitamin A & 30 \\ Beta-carotene & 36 \\ Vitamin E & 27 \\ Vitamin C & 25 \\ \hline _Essential amino acids_ & \\ Leucine & 17 \\ Isoleucine & 31 \\ Methionine & 25 \\ Lysine & 15 \\ Valine & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._A bioenhancing effect is also reported for ginger oleoresin. When given to mice at a dose of 30 mg/kg of body weight, ginger oleoresin enhanced the bioavailability of the antiviral drug atazanvir and accelerated its absorption [32].
## 3 Challenges in FSO Communication
### FSO System and Channel Models
#### 3.2.2 FSO Channel Models
The \(\mathcal{M}\)-distributed fading model incorporates components like \(U_{L}\), \(U_{S}^{C}\), and \(U_{S}^{G}\). These components denote the LoS contribution; the scattered component by eddies along the propagation axis coupled to the LoS; and the scattered component by off-axis eddies received by the Rx, respectively. The average power of the LoS component and the total scatter components are given by \(\Omega=E[|U_{L}|^{2}]\) and \(2b_{0}=E[|U_{S}^{C}|^{2}+|U_{S}^{G}|^{2}]\), respectively. Furthermore, the average power of the scattering component coupled to the LoS and the scattering component received from off-axis eddies are \(E[|U_{S}^{C}|^{2}]=2\rho b_{0}\) and \(E[|U_{S}^{G}|^{2}]=(1-\rho)2b_{0}\), respectively. The parameter \(0\leq\rho\leq 1\) denotes the proportion of scattering power coupled to the LoS component. The PDF of the optical channel gain \(h_{\text{a}}\) for the \(\mathcal{M}\)-distribution is expressed as [99; 100]  
\[V(h_{n})=\omega_{s}h_{n}+V_{g}+30\exp\left[-\left(\frac{h_{n}-9400}{4800} \right)^{2}\right], \tag{21}\]1  
where \(\mu=E[|U_{S}^{C}|]^{2}=(1-\rho)2b_{0}\), \(\Omega^{\prime}=\Omega+2\rho b_{0}+2\sqrt{2\rho b_{0}\Omega}\cos(\varphi_{A} -\varphi_{B})\), and \(\varphi_{A}\) and \(\varphi_{B}\) denote the deterministic phases of the LoS component and the coupled-to-LoS component, respectively. The parameter \(\alpha\) is a positive value dependent on the effective number of large-scale cells in the scattering process, while \(\beta\) is a natural number representing the fading parameter. The terms \(A\) and \(a_{k}\) can be defined respectively as [99; 100]  
\[V(h_{n})=\omega_{s}h_{n}+V_{g}+30\exp\left[-\left(\frac{h_{n}-9400}{4800} \right)^{2}\right], \tag{21}\]2 \[a_{k}\triangleq \begin{pmatrix}\beta&-1\\ k&-1\end{pmatrix} \frac{(\mu\beta+\Omega^{\prime})^{1-\frac{\beta}{2}}}{(k-1)!} \left(\frac{\Omega^{\prime}}{\mu}\right)^{k-1}\left(\frac{\alpha}{\beta} \right)^{\frac{k}{2}} \tag{28b}\]Aggregate Attenuation Data: The PDF of \(h=h_{\ell}h_{a}h_{\mathrm{P}}\), representing the characteristics of the propagation channel, can be represented as [89; 101]  
\[f_{h}(h;w_{z})=\int f_{h|h_{a}}(h|h_{a})f_{h_{a}}(h_{a})dt_{a} \tag{29}\]  
where \(f_{h|h_{a}}(h|h_{a})\) represents the conditional probability given a turbulence state \(h_{a}\), with its distribution expressed as [89; 101]  
\[f_{h|h_{a}}(h|h_{a})=\frac{1}{h_{a}h_{\ell}}f_{h_{\mathrm{P}}}\left(\frac{h}{h _{a}h_{\ell}}\right)=\frac{\gamma_{\mathrm{P}}^{2}}{A_{0}^{\gamma_{\mathrm{P} }^{2}}h_{a}h_{\ell}}\bigg{(}\frac{h}{h_{a}h_{\ell}}\bigg{)}^{\gamma_{\mathrm{P }}^{2}-1},\qquad 0\leq h\leq A_{0}h_{a}h_{\ell}. \tag{30}\]  
Hence, \(f_{h}(h;w_{z})\) can be expressed as [89]  
\[f_{h}(h;w_{z})=\frac{\gamma_{\mathrm{P}}^{2}}{(A_{0}h_{\ell})^{\gamma_{\mathrm{ P}}^{2}}}h^{\gamma_{\mathrm{P}}^{2}-1}\int\limits_{h/A_{0}h_{\ell}}^{\infty}h_{ \mathrm{a}}^{-\gamma_{\mathrm{P}}^{2}}f_{h_{a}}(h_{a})dt_{a}. \tag{31}\]
# Thermoeconomic Analysis of Concentrated Solar Power Plants Based on Supercritical Power Cycles
## 2 Materials and Methods  
### The sCO\({}_{2}\) Partial-Cooling Cycle  
The sCO\({}_{2}\) cycle model includes the thermodynamic analysis of the cycle components, such as the supercritical compressor, the heat exchangers, and the turbine. Various equations and parameters are considered to calculate the efficiency and performance of the cycle, which are implemented in the software Engineering Equation Solver (EES) [18]. The partial cooling layout is depicted in Figure 4.  
As shown in Figure 4, the sCO\({}_{2}\) partial-cooling cycle is based on the recompression configuration, but it differs from this layout because the Main Compressor (MC) is split in two, introducing an intermediate cooling between the two compressors. Only a portion of the sCO\({}_{2}\) is cooled, while another percentage is introduced into the Auxiliary Compressor (AC). The main compressor provides the more pressurised sCO\({}_{2}\) stream to the Low Temperature Recuperator (LTR), while the auxiliary compressor connects directly to the High Temperature Recuperator (HTR). Heat is supplied through the Primary Heat Exchanger (PHE), located upstream the Turbine (T). The cycle is refrigerated by an air-cooled Pre-Cooler (PC). The equations governing the behaviour of all the elements of the cycle can  
Figure 3: Schemes in use of the microchannel central solar receiver: (**a**) top view of one of the prism-shaped cavities formed between two consecutive and converging absorber panels; (**b**) projected view of one of the prism-shaped cavities, distinguishing in the same absorber panel two fluid flow passes with increasing compactness.  
be found in the technical literature [10], so they are not summarised in this article. The thermodynamic properties at each of the points marked in Figure 4 are listed in Table 1.  
As seen in Table 1, to generate 50 MW\({}_{e}\), the power cycle requires 103.42 \(MW_{th}\) from the solar subsystem at the design point. For this analysis, thermal storage has not been considered, although it would be necessary to ensure the dispatchability of the plant. Since the study conducted is under design conditions, considering or not considering thermal storage would involve considering or not considering the additional cost of such storage, which would be the same for all the STPP configurations analysed.
###### Abstract  
Background: Knee-related injuries often result from poor movement patterns that destabilize the joint and increase stress on knee structures. Understanding the influence of foot positioning on knee biomechanics is critical for identifying high-risk movement patterns and preventing injuries. Methods: Twenty healthy male participants performed side-cutting movements at three different foot progression angles. One participant's data were used to develop and validate a knee finite element model with high-speed dual fluoroscopic imaging (DFIS). Combined with a musculoskeletal analysis, the model simulated internal knee loads under various foot-positioning conditions. Results: The analysis revealed that, as the external foot progression angle increased, the ankle plantar flexion decreased, while the ankle internal rotation and knee valgus moments increased. Higher stress concentrations were observed on the ACL, lateral meniscus, lateral tibial cartilage, and medial collateral ligament, particularly at the femoral-tibial ACL attachments. Conclusion: The findings suggest that a toe-out foot position elevates the risk of knee injuries by increasing stress on key structures, whereas a toe-in position may enhance joint stability, reduce the ACL injury risk, and promote favorable muscle activation patterns.  
foot progression angles; finite element analysis; non-contact ACL injuries; mechanical stress distribution; joint kinematics; cutting maneuver biomechanics
# Numerical 3D Modeling: Microwave Plasma Torch at Intermediate Pressure
## 2 Model Description
### Plasma Chemistry  
When an electromagnetic wave propagates in the plasma, there will be some reactions between the particles. The main reaction types include:  
1. Elastic collision and inelastic collision: such reactions will lead to energy exchange in particles; the difference between them is that the elastic collision only leads to the transfer of kinetic energy between the colliding particles, but does not change internal energy.
2. Excitation and ionization: such reactions result in growth in the number of free electrons or a change in the energy level of an atom. The probability of their counter occurrence is related to the collision cross-section.
3. Charge transfer: this type of reaction results in the equivalent transfer of charge between particles. The kind of reaction mainly occurs in the collision process of ions and neutral particles.
4. Charge recombination: there are two forms--diffusion and recombination. Diffusion is the process by which the charged particle reaches the wall and electrode to disappear. Recombination is a process by which positive ions capture a free electron and combine with electrons or negative ions to form new neutral atoms.  
Table 1 shows the main reaction and collision processes [30; 35; 36].