esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation
### Gross Weight Identification
The proposed method was applied to locate the transverse position, and then we could identify the vehicle's gross weight based on the calibrated strain response area. The identified results of the transverse position (denoted as TP) and the gross vehicle weight (denoted as GVW) are shown in Table 3.  
It can be seen that the maximal absolute identified error of the transverse position was 1 cm in all cases, and the identified result was not sensitive to the noise, which indicates that the cosine similarity index has good anti-noise performance. For the identification of the GVW, the error \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Vehicle** & **Number of Axles** & **D2 (m)** & **D3 (m)** & **D4 (m)** & **D5 (m)** & **D6 (m)** \\ \hline V1 & 2 & 2.6 & - & - & - & - \\ V2 & 3 & 3.0 & 1.4 & - & - & - \\ V3 & 4 & 2.0 & 4.0 & 1.4 & - & - \\ V4 & 5 & 3.2 & 1.4 & 6.0 & 1.4 & - \\ V5 & 6 & 3.2 & 1.4 & 7.0 & 1.4 & 1.4 \\ \hline \hline \end{tabular} Note: Dr denotes the wheelbase from the \(i\)th axis to the \((i-1)\)th axis.  
\end{table} 
Table 1: Wheelbase information for different vehicles. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Vehicle** & **A1 (kN)** & **A2 (kN)** & **A3 (kN)** & **A4 (kN)** & **A5 (kN)** & **A6 (kN)** & **GVW** (kN)** \\ \hline V1 & 14 & 14 & - & - & - & 28 \\ V2 & 14 & 14 & 14 & - & - & - & 42 \\ V3 & 14 & 14 & 14 & 28 & - & - & 70 \\ V4 & 14 & 14 & 14 & 14 & 28 & - & 84 \\ V5 & 14 & 14 & 14 & 14 & 28 & 28 & 112 \\ \hline \hline \end{tabular} Note: At denotes the axis weight of the \(i\)th axis. GVW denotes the gross vehicle weight.  
\end{table} 
Table 2: Axle and gross weight for each vehicle. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Vehicle** & **Noise Level** & **Tretical TP** & **Identified TP** & **Absolute Error of TP** & **Identified GVW** & **Error of GVW** \\  & & **(cm)** & **(cm)** & **(cm)** & **(kN)** & **(\%)** \\ \hline V1 & \( \begin{array}{c}0\%\\ 10\%\\ 20\%\end{array} \) & \( \begin{array}{c}-58\\ -57\end{array} \) & \( \begin{array}{c}1\\ -58\\ -58\end{array} \) & \(\begin{array}{c}1\\ 1\\ 1\end{array  
increased with noise enhancement, and the maximal error was about 3% under 20% noise, which can meet the need of practical engineering applications.
## 2 Modeling Methodology for Blade Profile with SV
### Systematic Variation of Chord Ratio  
In a specific combination of different geometry parameters, the chord ratio (CR) was systematically increased while the other parameters were kept as constant values in order to observe the effects of CR  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Splittered Cascades** & **Conventional Cascades** \\ \hline inlet metal angle, \(\kappa_{1}\) (\({}^{\circ}\)) & \(\sim\)63 & \(\sim\)63 \\ camber angle, \(\phi_{\text{pb}}\) (\({}^{\circ}\)) & 16.8–65.2 & 16.8–71.5 \\ stagger angle, \(\chi_{\text{pb}}\) (\({}^{\circ}\)) & 54.5–36.5 & 54.5–36.5 \\ equivalent solidity (ES), \(\sigma_{\text{eq}}=(\text{cp}_{\text{pb}}+C_{\text{sv}})/S\) & 1.21–2.04 & 0.95–2.55 \\ diffusion factor (DF) & 0.26–0.65 & 0.28–0.67 \\ chord ratio, \(\text{CR}=C_{\text{sv}}/\text{Cp}_{\text{pb}}\) & 0.25–0.92 & — \\ percent pitch (PP) & 0.5 & — \\ \hline \end{tabular}
\end{table}
Table 1: Ranges of different parameters for the basic correlations.  
on the reference minimum-loss incidence and deviation angles. The dynamic growth of CR is sketched in Figure 4 where the chord of PBs is kept constant and the pitch is increased to maintain the fixed value of ES. When CR exactly equals to CLR, i.e., when \(C_{\text{sv}}=L_{\text{c}}\), a critical chord ratio (CCR) can be defined as follows:  
\[\text{CCR}=\frac{\sigma_{\text{eq}}-(1-\text{PP})\sin\chi_{\text{pb}}}{\sigma_ {\text{eq}}+(1-\text{PP})\sin\chi_{\text{pb}}}. \tag{2}\]  
This expression can be readily derived by introducing the definition of ES into that of CLR to eliminate the term of pitch-to-chord ratio of PBs. The corresponding chord of SVs and solidity of PBs are defined as critical chord \(C_{\text{sv,cr}}\) and critical solidity (CS) \(\sigma_{\text{cs}}\), respectively. CCR could be an important factor of the basic correlations.
## 3 Results and Discussion
By changing the carrier density in ITO we can further modulate the light transmission of the photonic crystal, since the change in the number of carriers leads to a change in the plasma frequency, and thus the dielectric function of ITO. This results in a simultaneous change of the plasmon resonance and of the photonic band gap. Figure 5 depicts changes to modulation in the number of carriers in ITO for photonic crystals where the ratio in the ITO:TiO\({}_{2}\):air layer is 10:60:30 (Figure 5a), 30:40:30 (Figure 5b), and 60:10:30 (Figure 5c). We change the number of carriers from 2.49 to \(5.49\times 10^{26}\) charges/m\({}^{3}\). This change in the number of carriers can be for example induced via an electrochemical [18] or optical [28] way. If we use the photonic crystal where the ITO:TiO\({}_{2}\):air layer has a ratio 60:10:30 (Figure 5c) as a filter for the Sun irradiation (direct and circumsolar [29]), we see that in the three bands in the range between 0.6 and 1.1 eV the light transmission is switched for two doping levels (Figure 6). Figure 5: Light transmission spectra of the photonic crystal as a function of the doping level (\(2.49\times 10^{26}\) charges/m\({}^{3}\), \(3.49\times 10^{26}\) charges/m\({}^{3}\), \(4.49\times 10^{26}\) charges/m\({}^{3}\), \(5.49\times 10^{26}\) charges/m\({}^{3}\)) for the ITO:TiO\({}_{2}\) ratio (**a**) 10:60, (**b**) 30:40, and (**c**) 60:10. Figure 6: Transmission of the Sun (direct and circumsolar irradiation [29]) without the photonic crystal filter (blue curve), with an ITO:TiO\({}_{2}\) 60:10 sample characterized by a doping level of \(2.49\times 10^{26}\) charges/m\({}^{3}\) (red curve) and \(5.49\times 10^{26}\) charges/m\({}^{3}\) (yellow curve). We need to consider that a layer in which ITO nanoparticles and TiO\({}_{2}\) nanoparticles are in close contact can be seen as a bulk heterojunction, with a large contact surface between the two materials, in which plasmon-induced hot electron generation can occur [30; 31]. In a future work, for a more precise description of this interesting entangled multicomponent nanostructure, we should take into account this type of interaction.
## 3 Results and Discussion  
As mentioned in the Introduction, the potential dietary intake of CM and/or OP residues poses a threat for human health due to their neurotoxicity. However, unless sophisticated laboratory instrumentation is available, then controlling residues is in most cases a rather difficult task. On this account, we focused on the development of a rapid, simple and cost-effective method capable to detect CMs and OPs in food matrices. In this study, critical assay parameters such as enzyme tolerance towards organic solvents (see Supplementary Materials), enzyme concentration and the enzyme substrate were optimized. In addition, AChE specificity towards 11 widely applied OP and CM pesticides was investigated since all these compounds have the same inhibitory mode of action. Based on this information, the assay was validated for carbofuran (a CM compound), carbofuran-3-hydroxy (a carbofuran toxic metabolite) and dichlorvos (an OP compound) in strawberry and lettuce using a fast and simple ethyl acetate extraction. Additionally, the AChE assay results were confirmed by an accredited LC-MS/MS method. Finally, the TU approach was applied to investigate the effect of mixtures containing two pesticide residues, as a simplified analogue towards real life conditions, since samples may be contaminated with multiple residues.
# Wind-Wave-Current Coupled Modeling of the Effect of Artificial Island on the Coastal Environment
## 2 Materials and Methods
### Data Source and Model Setup
#### 2.2.1 Initial Depth Condition  
Based on the coastline data of Hainan Island collected by [PERSON] et al. [19], we defined the calculation domain and generated a triangular unstructured grid in the study area, which was divided into 489,776 units and 249,782 nodes. The triangular grid used different lengths at the river channel, coastline, artificial island boundary line, and offshore boundary. After mesh quality optimization, the triangular mesh with uneven density was able to meet the needs of high-precision water and sediment simulation.  
The topographic survey adopted a single-beam sounder to carry out an intensive survey of the marine area near the study area. The tracked line of the navigation survey is shown in Figure 3a (the \(x\) and \(y\) axes in the figure represents longitude and latitude, respectively, in decimal form of WGS1984 system, and the full text is unified). The intensive topographic survey reflects local topographic features under the influence of the artificial island, as shown in Figure 3b.  
Figure 2: Field survey stations (The \(x\)-axis is the east longitude axis and the \(y\)-axis is the north latitude axis).  
At the same time, we collected the spatial resolution of 2 km of global water depth ETOPO1 figures released by the American NOAA ([[https://www.ngdc.noaa.gov/mgg/global/](https://www.ngdc.noaa.gov/mgg/global/)]([https://www.ngdc.noaa.gov/mgg/global/](https://www.ngdc.noaa.gov/mgg/global/)), accessed on 15 July 2021). Due to the low spatial resolution of ETOPO1 data, which cannot accurately reflect the local topography around the artificial island (Figure 3c), the measured and collected data needed to be fused to form more accurate bathymmetric data (Figure 3d) that can reflect the scour pits and salts around artificial island more clearly, in addition to more detailed siltation patterns at the mouth of Tannen Port. Then, we interpolated the fused water depth data into the grid by using the inverse range interpolation method, which was used as the initial water depth condition for this study and laid a foundation for the numerical simulation of water and sediment in the complex near-shore environment.
# Solar Potential in Saudi Arabia for Southward-Inclined Flat-Plate Surfaces
## 3 Results
### Definition and Variation of the Correction Factor
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\beta\) **(SEZ)** & **Regression Equation** & **R\({}^{2}\)** \\ \hline
20\({}^{\circ}\) S (A) & CF = 8\(\cdot\)10\({}^{-8}\)\(\beta^{3}\) + 2\(\cdot\)10\({}^{-6}\)\(\beta^{2}\) + 1\(\cdot\)10\({}^{-4}\)\(\beta\) + 0.999 & 1 \\
25\({}^{\circ}\) S (B) & CF = 1\(\cdot\)10\({}^{-9}\)\(\beta^{3}\) + 1\(\cdot\)10\({}^{-5}\)\(\beta^{2}\) \(-\) 8\(\cdot\)10\({}^{-5}\)\(\beta\) + 1.0009 & 1 \\
30\({}^{\circ}\) S (C) & CF = 6\(\cdot\)10\({}^{-9}\)\(\beta^{3}\) + 1\(\cdot\)10\({}^{-5}\)\(\beta^{2}\) \(-\) 5\(\cdot\)10\({}^{-5}\)\(\beta\) + 1.0007 & 1 \\ all (all) & CF = 3\(\cdot\)10\({}^{-8}\)\(\beta^{3}\) + 8\(\cdot\)10\({}^{-6}\)\(\beta^{2}\) + 1\(\cdot\)10\({}^{-5}\)\(\beta\) + 1.0001 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Regression equations for the best-fit curves to the correction-factor, CF, values averaged over all respective sites in the period 2005–2016, together with their R\({}^{2}\) values; \(\beta\) is the optimum tilt angle of a flat surface inclined in a SEZ towards south (S). Figure 11: Variation of the correction factor, CF, as function of the ground-albedo ratio, \(\rho_{\rm r}\), for various tilt angles, \(\beta\), for a flat plane in Saudi Arabia inclined towards south (S), averaged over the period 2005–2016. The blue lines correspond to \(\rho_{\rm r}\) = 1 and CF =1. Figure 12: _Cont._  
It is anticipated that the curves of CF vs. \(\beta\) (Figure 9) and CF vs. \(\rho_{\rm r}\) (Figure 11) are universal (nomograms); this remains to be demonstrated at other locations in the world.
## 3 Obtaining Waste-Based Bioadsorbents
### Activated Carbon Production
A wide variety of other agricultural wastes have been tested to produce AC for the removal of nutrients and pharmaceuticals such as sodium diclofenac, diclofenac, carbamazepine, sulfamethoxazole, and ibuprofen, among others. As an example, rice husk and lemon juice residue (solid after juice extraction) were explored as adsorbents for phosphate sequestering. The dried residues were submerged in NaOH followed by H\({}_{2}\)SO\({}_{4}\) for their respective activation; both were then dried, carbonized, and finally sieved to a 250-350 \(\upmu\)m particle size [49]. Likewise, rubber pod husk showed favorable results for removing phosphates through chemisorption and a multilayer process after undergoing chemical treatment with H\({}_{3}\)PO\({}_{4}\) to activate its adsorbent capacity [50]. A low percentage of cacao pod husk generated during chocolate production is recycled as a fertilizer and the rest is discarded. This massive generation facilitates its transformation into AC when treated with H\({}_{2}\)SO\({}_{4}\) for the removal of sodium diclofenac from aqueous solutions [51]. Chemical activation with H\({}_{3}\)PO\({}_{4}\) was further implemented by [PERSON] et al. [52] on a carbon composite based on _Argania spinosa_ tree nutshells and TiO\({}_{2}\). The latter modified the properties of the adsorbent by increasing the surface area and easily retaining diclofenac, carbamazepine, and sulfamethoxazole as well as the photolysis of the pharmaceuticals. [PERSON] et al. [53] and [PERSON] et al. [54] used ACs from _Aegle marmelos_ (wood apple) shells and peach stones with physical and chemical activation, respectively, to remove ibuprofen and acetaminophen from water sources. The pretreatment conditions, including the carbonization and pyrolysis temperatures employed to obtain the different activated carbons from biowastes, are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Biowaste** & **Pretreatment** & **Characterization** & **Contaminant** & **Ref. ** \\ \hline _Acacia eridolous_ seed & Chemical activation (H\({}_{2}\)SO\({}_{4}\)), & SEM, XRD, BET, & MB, iodine & [43] \\  & Pyrolysis (600 \({}^{\circ}\)C) & FTIR, EDX & & \\ \hline _Aegle marmelos_ shell & Carbonization (650 \({}^{\circ}\)C), Steam & SEM, BET, FTIR, PZC & IBU & [53] \\  & activation (800 \({}^{\circ}\)C) & & & \\ \hline _Argania spinosa_ tree & Chemical activation (H\({}_{3}\)PO\({}_{4}\)), & FTIR, SEM-EDS, TGA, & CBZ, SMX, DCF & [52] \\ nutshells & Propagation & XRD, BET & CBZ, SMX, DCF & [52] \\ \hline \multirow{2}{*}{Cacao pod husk} & Chemical activation (H\({}_{2}\)SO\({}_{4}\)), & \multirow{2}{*}{SEM, FTIR, EDX} & \multirow{2}{*}{SD} & [51] \\  & Carbonization (600 \({}^{\circ}\)C) & & & \\ \hline \multirow{2}{*}{Cacao shells and \(\,\
## 5 Research Method
### Standard Performance Metrics  
The study assessed the performance of the AMiDS models according to the applied data sampling and data generative methods using ML standard metrics, i.e., precision (P), accuracy (A), recall (R), F1-score (F1), ROC, and AUC, as well as using the training time and inference time as additional performance indicators.
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## Appendix A Experiment Details  
For the three datasets, the length of each episode is set to 32 and the discount factor \(\gamma\) in RL is searched over \(\{0.6,0.9,0.99,1\}\), and we found that \(\gamma=1\) obtains the best performance for most methods.  
For ATRec, we use a single layer neural network with 64 neurons as the node of the tree policy network. In the attention-based state representation model, the hidden size for the fully connected layer is also set to 64. The learning rate is searched over \(\{10^{-2},10^{-3},10^{-4},10^{-5}\}\). For the matrix factorization method, we decompose the user-item matrix to obtain 128-dimensional user and item feature vectors. Additionally, The user rating (with range \([a,b]\)) is mapped to a one-hot vector with a mapping function as below:  
\[\textit{onehot\_mapping}(rating)=\textit{onehot}(l-floor(\frac{l\times(b- rating)}{b-a}),l)\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Metric** & **All-Shared** & **Layer-Shared** \\ \hline \multirow{8}{*}{Baby} & Reward@10 & **0.07272** & 0.06842 \\  & HR@10 & **0.08944** & 0.08587 \\  & MRR@10 & **0.03883** & 0.03628 \\ \cline{2-4}  & Reward@30 & 0.12885 & **0.13162** \\  & HR@30 & 0.16719 & **0.17207** \\  & MRR@30 & **0.04311** & 0.04106 \\ \cline{2-4}  & Average learning time per-step & **0.04382** & 0.05972 \\  & Average decision time per-step & **0.00814** & 0.01149 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._where \(floor(x)\) returns the largest integer no greater than \(x\) and \(one\_hot(i,l)\) returns an \(l\)-dimensional vector with the \(i\)th elements is set to 1 while others are set to 0.  
For DDPG-KNN, we use a three-layer neural network to represent the actor network and critic network. The hidden size of the neural network is set to 64 for each layer. The learning rates for both actor and critic is searched over \(\{10^{-2},10^{-3},10^{-4},10^{-5}\}\) to obtain the best performance. The size of experience replay is set to 1,000,000 and the batch size of each learning step is set to 64. The soft-update parameter \(\tau\) for DDPG is set to 0.05. To improve the search efficiency of KNN, we use FLANN [52] for implementation.  
For DQN-R, we also use a three-layer neural network to represent the Q-network. The hidden size of the neural network is set to 64 for each layer. The learning rate for Q-network is searched over \(\{10^{-2},10^{-3},10^{-4},10^{-5}\}\) to obtain the best performance. The soft-update period is set to 500 steps. In addition, the size of experience replay is set to 1,000,000 and the batch size of each learning step is set to 64.  
The implementation of TPGR is similar to [5], except some hyperparameters are well tuned. The learning rate for TPGR is searched over \(\{10^{-2},10^{-3},10^{-4},10^{-5}\}\). The state representation model for TPGR is learned offline by supervised learning.
# Low Dose Ionising Radiation-Induced Hormesis: Therapeutic Implications to Human Health
## 5 Implication of Radiation Hormesis for Potential Therapeutic Study  
### Cancer  
The hormetic effect of LDIR has been reported to promote growth and development, also to suppress the ageing process, enhancing immune functions, and delaying cancer progression [133, 134]. The hormetic effect of LDIR on the immune system has a potential positive impact on human health and has attracted the attention of many scientists. Over the past two decades, radiation hormesis has been extensively studied by researchers, both in regard to animal and clinical studies. In the case studies by [PERSON] et al. (2017) [135], two prostate cancer patients who were treated with repeated low-doses of X-ray irradiation resulted in an immediate decrease in the level of prostate-specific antigen. High-dose radiation usually results in localised and sometimes systemic immune suppression [136]. However, LDIR induces a series of immune hormesis, modulates a variety of immune response processes and exerts its hormetic effect in normal stem cells [134, 137, 138]. Evidence has also indicated that LDIR treatment is effective in treating immune-related diseases by increasing the immune defence system in the body [139, 140, 141]. Several studies (at <75 mGy) have also demonstrated that the hormetic effect of X-rays can induce a significant increase in cell proliferation, including rat mesenchymal stem cells, bone marrow hematopoietic progenitor cells and neural stem cells [142, 143, 138]. Furthermore, [PERSON] et al. (2016) have evidence on the hormetic effect of LDIR, in which 50 mGy of X-ray radiation induced cell proliferation in human embryonic lung fibroblasts but not in cancer cells via the ERK1/2 and AKT signalling pathway. This points to the potential significance of LDIR in protecting normal tissues from radiotherapy without diminishing the efficacy of tumour radiotherapy [57].
## 4 Performance Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. of vehicles (N) & \(200,400,600\) \\ Number of MEC units & 7 \\ Number of lanes & 4 \\ Road length & 5 km \\ Density of vehicles (TD) & \(0.1,0.2,0.3\) veh/m \\ MEC placement & _equidistant_ \\ Packet transmission frequency & 10 Hz \\ Simulation time & 150 s \\ Streaming service bandwidth & \(\in[10,2048]\) (KB/S) \\ Channel bandwidth & 10 MHz \\ Packet size & 190 bytes \\ Vehicle task energy & \(\in[20,80]\) (watt/s) \\ Transmit power & 23 dBm \\ Maximum work load (MEC) & 50 events/s \\ Storage/RAM & 2048 MB \\ Storage/HDD & 5 GB \\ Processing/CPU & \(\in[0.7,2.5]\) GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 3: Percentage of the communication overhead with the change in the number of cluster heads, for different values of traffic density. The percentage of blocked tasks compared to the total number of computing tasks was detected for the developed blockchain-MEC V2X system with the developed offloading scheme and for the optimized offloading scheme. Figure 6 provides the average percentage of blocked tasks for the three considered cases, for different values of number of vehicles. Each case represents a system; case (1) represents the system with local execution and no offloading scheme. The second case, case (2), represents the developed system with the developed offloading scheme, while the third case, case (3), was introduced for the developed system with the optimized offloading scheme. The optimized offloading scheme achieved the highest performance of task handling, even with the increase in the number of vehicles. Figure 7 provides the average percentage improvement of latency performance of the developed blockchain-MEC V2X compared with the traditional system, for three values of deployed vehicles. Moreover, the latency performance improvement of the optimized blockchain-MEC V2X is introduced in Figure 7. The optimized blockchain-MEC V2X achieved an average improvement of the latency performance of 39% compared with the traditional systems. The developed offloading scheme with blockchain-MEC V2X improved the energy efficiency of the system by reducing the energy consumption of task handling. Figure 8 provides the average percentage improvement of the energy performance for both systems, the blockchain-MEC V2X and the optimized blockchain-MEC V2X, compared with the traditional systems, for three values of deployed number of vehicles. The optimized blockchain-MEC V2X achieved an average improvement of energy efficiency of 36% compared to the traditional V2X systems. Figure 4: Percentage of the communication overhead with the change in the number of cluster heads, for different values of number of deployed vehicles. Figure 5: Percentage of the communication overhead with the change in the vehicle velocity.