esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Discussion  
Among all the analysed characteristics of the five algorithms, it can be observed that, according to the parameters explicitly defined to control the learning process, different metrics were obtained that indicate the result of the performance of each one of them. The understanding of data was carried out in each case by testing different configurations at the beginning of the learning process of each model. As a result, the fastest algorithm in its execution was Decision Tree using entropy, with only 0.05444 s for performance. The best precision and F1 score were seen with the Support Vector Machine (SVM) algorithm, using Kernel RBF, C equal to 3, and gamma equal to auto. The multinomial naive Bayes algorithm has the best sensitivity or recall, and the K-Nearest Neighbor (KNN) also stands out in this metric. A comparative benchmark was created between the best values obtained from each forecast model, which was analysed to summarise the data obtained. Table 10 summarises the condensed results among the metrics obtained.  
Accuracy and reliability are essential in studies carried out in health [22; 46]. According to the no free lunch theorem, if an algorithm outperforms another in one metric, it may lose in a different metric, depending on the problem. So, in general, there is no certainty about which algorithm is the best. However, the Multilayer Perceptron (MLP) algorithm, which is extremely fast when performing predictions, obtained the best results for the present study. Training MLP networks with backpropagation took considerable time, despite using the term momentum. Analysing the cost of recognising bold patterns in predicting clinical evolution was prioritised to obtain the best precision at the speed of diagnosis. Next, the predictions made with the winning MLP prediction model are presented.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Comparative Benchmark between Prediction Models**} \\ \hline Metric/Prediction Model & Accuracy & Precision & Recall That & F1 Score & ROC & Time (s) \\ \hline \hline \multirow{2}{*}{_K-Nearest Neighbor—KNN_} & 75.27\% & 74.93\% & 0.9322 & 0.83082 & 0.76202 & 69.995 \\ \hline \hline \multirow{2}{*}{_Naive Bayes_} & 66.62\% & 66.98\% & 0.9618 & 0.78966 & 0.64363 & 0.0826 \\ \hline \hline \multirow{2}{*}{_Decision trees_} & 71.83\% & 74.87\% & 0.8544 & 0.79808 & 0.69686 & 0.5455 \\ \hline \hline \multirow{2}{*}{_Multilayer Perceptron—MLP_} & **76.3\%** & **76.41\%** & **0.7646** & **0.76441** & **0.84300** & **286.023** \\ \hline \hline \multirow{2}{*}{_Support Vector Machine—SVM_} & 75.78\% & 76.61\% & 0.91320 & 0.83318 & 0.73772 & 0.00101 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Benchmark for prediction models.  
Figure 8: ROC curve—SVM.
## 4 Communications
### Communication Primitives  
RDMA operations can be categorized in one-sided and two-sided communications. In one-sided communication (i.e., READ, WRITE, atomic FAA and CAS), the receiving side's CPU is completely passive during the transfer. However, two-sided communication (i.e., SEND) requires the action from both sides.  
**WRITE** is a one-sided operation which has no notification on the remote host. However, sending an immediate data with WRITE notifies the remote host about the operation. The WRITE and immediate notification are an atomic unit, either both arrive or neither arrive. It should be noted that in an immediate operation, a receive request will be consumed and the immediate data will be sent in the message. This immediate data will be available in the CQE that is generated for the consumed receive request in the remote side. The immediate data are racing behind the WRITE, i.e., between the WRITE completing and the immediate data arriving.  
**READ** is a one-sided operation that reads a contiguous block of memory (in virtual address space) from remote side and writes it to the specified local memory buffer. It should be noted that READ does not consume receive request in the remote side.  
**SEND** carries the content of a local memory buffer to be written in a remote memory address that is specified in a pre-posted receive request in the remote side. Since both sides require to send work requests, SEND is counted as a two-sided communication. In the remote side, a work completion will be generated for the consumed receive request. Moreover, SEND supports immediate data as well as WRITE.
## 3 Results
### Design of the Conditioning Sections for Electrochemical Gas Sensors
(V\({}_{\text{WE}}-{\text{V}}_{\text{RE}}\)) remains constant (as \(\mid\)V\({}_{\text{CE}}\mid\) = \(-\mid\)V\({}_{\text{WE}}\mid\) ) so that no current flows through the RE; in practice, when the d.d.p. between CE and WE varies according to gas concentration changes, the opamp Up balances these variations according to:  
\[{\text{V}}_{\text{WE}}-{\text{V}}_{\text{RE}}>\ 0,\,{\text{V}}_{\text{in, U1}}^{-}>\ 0\ \ \ \ {\text{V}}_{\text{out, UI}}={\text{V}}_{\text{CE}}=({\text{V}}_{\text{in, U1}}^{+}+{\text{V}}_{\text{in, U1}}^{-})\ *\ {\text{K}}\ <\ 0 \tag{4}\]  
Figure 11: Schematic (**a**), layout (**b**), and PCB top (**c**) and bottom (**d**) views of the conditioning board of the ME2 family sensors. Diagrams were realized with Eagle CAD software. The gain of the transimpedance stage was sized to meet the concentration limit dictated by the European Union. In particular, the H\({}_{2}\)S concentration in the workplace should not exceed 10 ppm (STEL limit, Table 1). Considering the sensor's sensitivity (0.8 \(\mu\)A/ppm), the output current from the sensor (I\({}_{\mathrm{sense}}\)) will be equal to 8 \(\mu\)A. Equations (5) and (6) allow sizing capacitance C7 and the feedback resistance (R3) of the transimpedance stage based on the opamp U\({}_{t}\) (Figure 12). The V\({}_{\mathrm{oMax}}\) and V\({}_{\mathrm{oMin}}\) voltages indicate the dynamics of the U\({}_{t}\) output voltage; I\({}_{\mathrm{Max}}\) represents the maximum current delivered by the sensor during gas detections, and f\({}_{\mathrm{p}}\) is the pole frequency due to capacitor C7 and the resistor placed in the feedback loop (R3). \[\mathrm{R}_{3}=\frac{\left(\mathrm{V}_{\mathrm{o,Max}}-\mathrm{V}_{\mathrm{o, Min}}\right)}{\mathrm{I}_{\mathrm{Max}}}=\frac{3.3\;\mathrm{V}\;-\;0\; \mathrm{V}}{8\;\mu\mathrm{A}}=412.5\;\mathrm{k}\Omega\;\approx\;400\;\mathrm{ k}\Omega \tag{5}\]  
\[\mathrm{C}_{7}\;\leq\;\frac{1}{2\pi\;\times\;\mathrm{R}_{3}\times\mathrm{f}_{ \mathrm{p}}}=\frac{1}{2\pi\;\times\;400\;\mathrm{k}\Omega\;\times\;10\; \mathrm{k}\mathrm{H}\mathrm{z}}\;\approx\;40\;\mathrm{pF} \tag{6}\]  
Figure 13a shows the circuit diagram, created with Eagle CAD software, of the conditioning section for the electrochemical sensor ME3-H2S. The resistor series placed in the feedback loop was calculated from Equation (6) and is equal to 400 k\(\Omega\). Finally, a 50 k\(\Omega\) (R10) SMD potentiometer was added in series to the feedback loop, which includes the resistors R5-R6, allowing fine-tuning of the transimpedance stage gain to increase the resolution of the measurement of the gas concentration. The layout of ME3-H2S sensor is depicted in Figure 13b, in which the connections and components are placed in the top layer, whereas the sensor lies on the bottom layer. By adopting this solution and optimizing the positioning of the components, the board dimensions were significantly reduced (i.e., 29 mm \(\times\) 33 mm).
# Enhanced Efficiency and Reduced Side Lobe Level Convex Conformal Reflectarray
## 5 Optimized CRA: Numerical and Experimental Characterization
If, as is done in most of the cases, the radiation pattern of the feed horn is approximated in the two principal planes with functions of the type \(\cos^{q}(\theta)\) (where \(q\) can be the same or have different values in the E and H planes), it is possible to determine the value of \(q\), i.e., the feed illumination, that maximizes the RA efficiency. This approach was extended to the case of the CRA here considered, and it was found that the optimal value for \(q\) is 7.9 since it guarantees a theoretical aperture efficiency of about 65%. From the feed radiation pattern obtained in this way, a smooth wall horn was designed and used to illuminate the CRA. The simulated CRA radiation patterns at the design frequency in the two principal planes are shown in Figure 12, where those for the configuration using the optimized feed are also plotted. From the comparison between the two curves, it emerges that the main beam is the same, but the antenna with the optimized horn outperforms the other for what concerns side lobes, which are lower, especially in the H plane. Figure 11: Variation of the gain vs. frequency of the CRA with the standard rectangular horn [20] and the optimal feed horn. For this last configuration, also the simulated values are plotted. The main features of the two configurations are summarized in Table 2: using the optimized feed, the SLL is reduced by at least 2 dB in the E plane (column 2), while in the H plane, the side lobes are up to 6 dB lower (column 4). In the last two columns, the efficiency of the two configurations is reported: that with the optimum feed slightly outperforms the other configuration, even if in this case the feed was actually designed with the aim of maximizing the efficiency; this provides further confirmation of the effectiveness of the design technique here introduced. Finally, the configuration proposed here can be compared with the only other (according to authors' knowledge) convex reflectarray for which experimental results are available in the literature [19], realized using a totally different technique, i.e., making grooves with variable depth in a metal-only structure. The results reported in [19] relative to two different configurations (with different, but in both cases very large, radii of curvature) show that the achieved efficiency is 17% and 27%, with SLL in the plane being more susceptible to the effect of the curvature equal to \(-\)7.68 dB and \(-\)9.52 dB, and confirm that the approach here introduced allows to significantly enhance the performance of the CRA.
## 2 Materials and Methods
### Data Analysis  
The data obtained in this study were analyzed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA). The normality of all variables was first assessed using the Shapiro-Wilk test and visual inspection methods, including histograms and Q-Q plots. For variables that met the assumption of normality, Pearson correlation analysis was performed; for non-normally distributed variables, [PERSON]'s rank correlation analysis was used. Parametric data were presented as mean \(\pm\) standard deviation (SD), while nonparametric data were reported as median and interquartile range (IQR). These analyses were employed to examine the relationships between the eccentric utilization ratio (EUR) and sprint velocity (m/s), change of direction (COD) performance, and vertical jump height.  
In addition, simple linear regression analyses were conducted to determine the extent to which performance variables could be explained by EUR and other biomechanical parameters, such as peak power (PP), reactive strength index (RSI), stiffness, pre-stretch augmentation percentage (PSAP), concentric mean power (CONMP), eccentric peak power (ECCPP), and eccentric mean power (ECCMP). To determine the associations between performance outcomes and predictor variables, each predictor-outcome pair was analyzed using a separate simple linear regression model. This approach allowed for evaluating the individual predictive value of each biomechanical parameter without combining multiple predictors within the same model. In the regression models, the dependent variables were vertical jump height, sprint velocities, and COD times, while the independent variablesconsisted of EUR, RSI, PP, and other strength-power metrics. Assumptions of simple linear regression (normality, linearity, and homoscedasticity) were checked using residual plots, and no major violations were observed. Statistical significance was set at \(p<0.05\) for all analyses.
## 3 Results and Discussion
### Results of the 2 nd Model Using CFD Inputs (Evaluating the 2 nd Model Independently)
While the predictions for the \(y\)- and \(z\)-direction velocities generally follow global trends, they exhibit limitations in capturing local variations and extreme values. Nevertheless, their mean values remain closely matched to the ground truth.
## 2 Material and Methods
### Quantification of Phenolic Compounds Using UPLC-MS/MS  
UPLC controlled by Acquity console software coupled to a mass spectrometer (Waters Corp., Milford, MA, USA) was used to quantify the different phenolic compounds present in the most active fraction. The separation of phenolic compounds was achieved using a Waters Acquity UPLC HSS T3 (100 \(\times\) 2.1 mm, 1.8 \(\upmu\)m), using Milli-Q water containing 0.1% formic acid (mobile phase A) and 0.1% formic acid in acetonitrile (mobile phase B) with a flow rate of 0.5 mL/min. The starting condition for each run was: 0 min, 2% B; 6 min, 10% B; 8.5 min, 50% B; 10 min, 70% and held for 3 min.  
The mass spectrometer was operated in negative electrospray ionisation mode. The source temperature was set at 120 \({}^{\circ}\)C and the desolvation temperature at 350 \({}^{\circ}\)C. The capillary voltage was set at 2.8 kV. The cone voltages and collision energies were optimised for each of the compounds using IntelliStart(tm) software (Waters Corp.). Nitrogen gas was used as both sheath gas and auxiliary gas (800 L/h and 50 L/h, respectively). The detection and quantification of the phenolic compounds in the UPLC-MS/MS were conducted in multiple reaction monitoring (MRM) mode by analysing two or three transitions per compound (Table S1, Supplementary Materials). Analyses were carried out in triplicate, and target compounds were quantified against the standard calibration curves of concentrations ranging from 10 ng/mL to 1 ug/mL. Phenolic compounds in _C. australasia_ L. were expressed as ug/g of d.w. of citrus peel.
## 4 Discussion
### Environmental Risk Assessment
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Pharmaceutical**} & \multicolumn{2}{p{113.8 pt}}{**No. of Studies in Which the PRs Exceeded the High-risk Level (\% of the Total)**} & \multicolumn{2}{p{113.8 pt}}{**Risk Quotient (RQ) 1 for PRs in Effluent WW (Countries)**} & \multicolumn{2}{p{113.8 pt}}{**Highest Detected Average Conc. in WWTPs Effluents (ng/L)**} & \multirow{2}{*}{**Refs.**} \\ \cline{1-1} \cline{5-5} \cline{7-6} Amoxicillin & 1 (12.5) & 6.4 (Kenya) & 1600 & [15] \\ \hline Azithromycin & 3 (37.5) & 0.02–377.8 (China) & 56,666 & [17] \\  & & 3.6–56.9 * (Cyprus, Finland, Germany, Ireland, Portugal, Spain) & 598 & [10] \\  & & 1.3–34.2 (Poland) & 650 & [13] \\ \hline Cefalexin & 1 (12.5) & 1.2–17.0 * (Cyprus, Ireland, Portugal, Spain, Finland) & 308 & [10] \\ \hline Ciprofloxacinacin & 3 (37.5) & 40.6 (Kenya) & 2600 & [15] \\  & & 24.212 a (Spain) & 89 & [37] \\  & & 1.6–19.6 * (Cyprus, Finland, Germany, Ireland, Portugal, Spain) & 589 & [10] \\ \hline Clarithromycin & 3 (37.5) & 16.7 (Switzerland) & 234 & [14] \\  & & 1.9–4.0 (Poland) & 160 & [13] \\  & & 1.8–4.6 * (Germany, Ireland, Portugal, Spain) & 313 & [10] \\ \hline Ofloxacin & 2 (14.29) & 2.6 (Danube river basin) 2 & 3051 & [12] \\  & & 0.02–1.3 (China) & 180 & [17] \\ \hline Norfolkacin & 1 (12.5) & 5.8 (Kenya) & 2900 & [15] \\ \hline Roxithromycin & 1 (12.5) & 0.1–98.2 (China) & 6272 & [17] \\ \hline Sulfathiazole & 1 (12.5) & 0–3.2 (China) & 134 & [17] \\ \hline Sulfamethoxazole & 3 (37.5) & 12.7 (Spain) & 977 & [37] \\  & & 3.5 (Kenya) & 21,400 & [15] \\  & & 1.1–1.3 (Poland) & 770 & [13] \\ \hline Trimethoprim & 1 (12.5) & 1.0 (Kenya) & 500 & [15] \\ \hline \hline \end{tabular}  
* Values did not account for dilution factors. 2 Average of 13 WWTPs in Austria, Croatia, Czech Republic, Germany, Hungary, Romania, Serbia, Slovakia and Slovenia.
* Worst-case scenario, according to the authors. PEC (Predicted Environmental Concentration) values multiplied by the dilution factors to obtain MEC values.  
\end{table} 
Table 6: Environmental risk assessment for PRs that posed a high risk to the environment.
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 2 Implicit-Explicit Coupling-Enhancement-Based 3D Reconstruction
### Implicit Synthetic Co-Visibility-Cluster-Guided Dense 3D Reconstruction  
In this section, we will provide a detailed explanation of the UAV-based large-scale scene dense point cloud reconstruction module, that is guided by implicit synthetic co-visibility clusters. The module initiates by utilizing a generic visual odometry method to rapidly and accurately compute the pose and depth information from input images, which is used for the supervised training of the implicit model. Subsequently, the trained implicit model is employed to render synthesized new perspectives along with their corresponding dense depth data. This process optimizes spatiotemporal co-visible clusters for dense point cloud reconstruction. Finally, robust and accurate reconstruction results were obtained through the explicit model for dense point cloud reconstruction.
## 3 Materials and Methods
### Ship Feature Extraction  
Feature extraction is a key step in ship classification and anomaly detection. Whether the selected features can reflect the differences between different ship types largely determines the classification performance. AIS data contain the ship's length, width, speed, position, heading, and other information. In this paper, ship features were divided into geometric features and behavior characteristics, which were analyzed and extracted. Based on these two types of features, machine learning algorithms were used to classify and identify five types of important ships sailing in the ocean, i.e., cargo ships, tanker ships, fishing ships, passenger ships, and tug ships.
## 4 Discussion  
### Conclusions  
In this study, we propose an adaptive preview distance control method based on the deviation suppression strategy of rice transplanter navigation, and the performance is verified by simulation tests.  
In the deviation pattern recognition test, the two-stage DBN-based model deployment method has excellent feature learning capability for application scenarios with inconsistent feature tendencies. In the DBN performance test, the method shows very excellent prediction accuracy, stability, and fast prediction speed, with prediction accuracies higher than 96% in different scenarios; in the deep learning method comparison test, DBN shows a stronger feature learning capability compared to RNN and LSTM; and in the ablation study, DBN shows a better structural stability. Combining all these experimental analyses, the DBN-based deviation pattern identification method proposed in this study has strong generalization ability, excellent prediction accuracy, good stability, and very fast identification speed, which can satisfy the application scenario of rice-planting machine navigation.  
In the straight path navigation control test, compared with the optimal preview point method [18], both of them are based on the principle of optimizing the preview distance to improve the navigation control performance and have very excellent navigation control performance. However, the optimal preview point method does not analyze the deviation suppression ability in the case of perturbation. Compared with the heading error rate method [29], the control method proposed in this study has faster convergence for transient deviations. Compared with the static preview distance, it improves the path tracking accuracy by 5.79%, 37.07%, and 41.70%, respectively, in different levels of deviation test scenarios.  
In conclusion, the adaptive preview distance control strategy proposed in this study has more significant performance advantages for rice transplanter navigation control with complex perturbation variables.
## 1 Introduction  
Metal oxide materials have attracted great attention from the scientific community because of their important technical applications. One-dimensional (1D) nanorods (NRs), nanowires (NWs) and nanotubes (NTs) formed from metal oxides (such as ZnO, TiO\({}_{2}\) and WO\({}_{3}\)) enable fabrication of some specific nanodevices for optoelectronic applications, for instance, photodetectors [1; 2], light-emitting diodes (LED) [3], and solar cells [4; 5]. This is because of the large surface-area-to-volume ratio, excellent charge carrier transport performance and good crystallization ability shown by these types of nanostructures [6; 7]. Thus far, heterostructure optoelectronic devices have been formed by depositing n-type metal oxides on various p-type substrates, including Si [8; 9; 10; 11], GaN [12; 13; 14], NiO [15], Cu\({}_{2}\)O [16; 17; 18; 19], graphene [20; 21], boron-doped diamond (BDD) film [22; 23; 24; 25; 26; 27], and organic material [28]. Among them, BDD acts as an excellent p-type conductive material for high-temperature, high-power and radiation-proof photoelectronic devices with its large band gap at room temperature (5.47 eV) and high thermal conductivity [29]. When combining 1D metal oxide with p-type diamond, one has to explore the carrier transport behavior of the formed heterojunction devices, which has both theoretical and application importance for designing new photoelectronic devices for extremely harsh environments, such as outer space or nuclear energetics industries. In recent years, p-type BDD has been used in combination with various 1D-structured metal oxides (for instance ZnO [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], WO\({}_{3}\)[34; 35; 36] and TiO\({}_{2}\)[37; 38; 39; 40; 41]) to form heterojunctions demonstrating effects of rectification and negative differential resistance (NDR), which may be widely used in various technologies. However, no comprehensive discussion focusing specifically on electrical characteristic of diamond-based p-n heterojunctions has been published. Therefore, this review summarizes the past progress in the device fabrication, electrical transport properties and NDR-related applications of diamond-metal oxides (ZnO, TiO\({}_{2}\) and WO\({}_{3}\)) heterojunctions. The temperature-driven carrier injection mechanisms in such heterojunctions are describedherein in further detail. The approximately 10 years of research discussed will benefit the next step in the development of high-temperature and high-power optoelectronic devices.