esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Medicinal Chemistry  
In drug design, the Lipinski Rule, Pfizer Rule, GSK Rule, and the concept of the Golden Triangle are key guidelines for selecting compounds with high potential for therapeutic efficacy and safety. The Lipinski Rule of Five [31] outlines criteria for oral bioavailability, including limits on the number of hydrogen bond donors and acceptors, a molecular weight under 500 Da, and a LogP value below 5. The Pfizer Rule [32], or \"3/75 rule\", focuses on reducing toxicity linked to high lipophilicity (LogP > 3) and low polar surface area (TPSA < 75 A2). The GSK Rule [33] suggests that compounds with a molecular weight over 400 Da and a LogP above 4 may face challenges in pharmacokinetics and safety. Finally, the Golden Triangle [34] concept targets optimal ranges of molecular weight, lipophilicity, and polar surface area, where compounds achieve a balanced profile of efficacy, safety, and bioavailability. Table S4 outlines the medicinal chemistry properties of the lead compounds as predicted by ADMETlab 2.0. Compounds L3, L4, and L5 demonstrate strong drug-likeness with high quantitative estimate of drug-likeness (QED) scores (>0.67). All compounds show favorable synthetic accessibility (SA score < 6), indicating ease of synthesis. L5 meets the criteria of [PERSON], Pfizer, GSK, and Golden Triangle rules, reflecting excellent bioavailability and promising drug-likeness. Compounds L1 and L2 meet all criteria except the GSK Rule due to logP values over 4. Compounds L3 and L4 do not meet the Pfizer Rule, suggesting potential toxicity risks. No compounds raised alerts for PAINS or BMS, indicating a lower risk of false-positive or misleading outcomes.
# The Effect of 3D TVs on Eye Movement and Motor Performance
###### Abstract  
Three-dimensional TVs have been commercialized in recent few years; however, poor visual and motor performances may have an impact on consumer acceptance of 3D TVs. The purpose of this study was to investigate the effects of 3D TVs on eye movement and motor performance. Specifically, the effect of stereoscopic display parallax of 3D TVs and movement task index of difficulty (ID) on eye movement was investigated. In addition, the effect of stereoscopic display parallax of 3D TVs and movement task ID on motor performance was also investigated. Twelve participants voluntarily participated in a multi-directional tapping task under two different viewing environments (2D TV and 3D TV), three different levels of stereoscopic depth (140, 190, 210 cm), and six different Index of Difficulty levels (2.8, 3.3, 3.7, 4.2, 5.1, 6.1 bit). The study revealed that environment had significant effects on eye movement time, index of eye performance, eye fixation accuracy, number of fixations, time to first fixation, saccadic duration, revisited fixation duration, hand movement time, index of hand performance, and error rate. Interestingly, there were no significant effects of stereoscopic depth on eye movement and motor performance; however, the best performance was found when the 3D object was placed at 210 cm. The main novelty and contributions of this study is the in-depth investigations of the effect of 3D TVs on eye movement and motor performance. The findings of this study could lead to a better understanding of the visual and motor performance for 3D TVs.  
3D TV; stereoscopic displays; virtual reality; depth +
Footnote †: journal: Applied Sciences
## 2 Hydraulic Collector
### Operating Principle  
The operating principle of the hydraulic collector in this study is described in Figure 4. The hydraulic collector performs the extraction and transport of the polymetallic nodules from the deep seafloor while moving forward. The pumps continuously supply seawater into the confluence tubes. Due to the minute calibers of the nozzles, the seawater in the confluence tubes sprays out as high-speed jets. The oblique jets ejected from the acquisition nozzles strongly scour the seafloor, thereby causing the polymetallic nodules to loosen and peel from the seafloor. At the same time, these acquisition jets are reflected by the seafloor and converge together, which further lift the polymetallic nodules into the bottom of the conveying channel. Finally, the polymetallic nodules in the conveying channel are transmitted to the top exit of the collector by the upward jets that are ejected from the transmission nozzles.  
When the nodule is in the conveying channel, it will be mainly subjected to a combination of the buoyancy (\(F_{b}\)), gravity (\(G\)), and the drag force under the action of flowing seawater (\(F_{d}\)), as shown in Figure 5. The three forces are expressed as follows:  
\[F_{b}=\rho_{s}V_{n}g, \tag{1}\]  
Figure 4: Schematic of the operating principle of a hydraulic collection.  
Figure 3: Structural sketch of the hydraulic collector.  
\[G=\rho_{n}V_{n}g, \tag{2}\]  
\[F_{d}=\frac{1}{2}\rho_{s}C_{D}A_{n}v_{s}^{2}. \tag{3}\]  
where \(\rho_{s}\) and \(\rho_{n}\) are the densities of the seawater and nodules, respectively; \(g\) is the gravitational acceleration; \(C_{D}\) is the drag coefficient; \(v_{s}\) is the seawater flow velocity in the conveying channel; and \(V_{n}\) and \(A_{n}\) are the volume of the nodule and the projected area of the nodule in the direction of seawater flow, respectively. Assuming that the nodule is a standard sphere with a diameter of \(d_{n}\), \(V_{n}\) and \(A_{n}\) can be expressed as follows:  
\[V_{n}=\frac{1}{6}\pi d_{n}^{3}, \tag{4}\]  
\[A_{n}=\frac{1}{4}\pi d_{n}^{2}. \tag{5}\]  
To ensure that the nodules do not settle and fall under the pull of gravity, the sum of \(F_{b}\) and the component of \(F_{d}\) in the direction of gravity should be greater than \(G\).  
\[F_{b}+F_{d}\sin\alpha\geq G. \tag{6}\]  
Substituting Equations (1)-(5) into Equation (6), as well as transforming it, yields  
\[v_{s}\geq\sqrt{\frac{4(\rho_{n}-\rho_{s})d_{n}g}{3\rho_{s}C_{D}\sin\alpha}}. \tag{7}\]  
Theoretically, the absence of the gravitational settling of nodules is ensured when the seawater flow velocity in the collector conveying channel, \(v_{s}\), satisfies Equation (7). However, due to the narrowness of the conveying channel, the seawater flow rate in the channel should be increased from the minimum value shown in Equation (7) in order to avoid clogging due to the low transport velocity of the nodules in the channel. In this study, a factor of two was taken. As such, \(v_{s}\) was found to be  
\[v_{s}=\sqrt{\frac{16(\rho_{n}-\rho_{s})d_{n}g}{3\rho_{s}C_{D}\sin\alpha}}. \tag{8}\]  
Figure 5: Forces on the nodule.
## 1 Organic Waste Treatment Plants and Gaseous Emissions
\begin{table}
\begin{tabular}{|l|c|c|c|c|c|} \hline
**Compound** & **Concentration** & **Units** & **Waste** & **Type of plant/measurement area** & **Reference** \\ \hline _Hydrocarbons (total)_ & 30\(\times\)560 & \(\mathrm{kg\,m^{-3}}\) & VFG\({}^{1}\) & Effluent of biofilter & [20] \\ \hline Pentene & nd/26.20 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & nd/75 & \(\mathrm{kg\,m^{-3}}\) & Food waste & 2 composting plants-Composting hall & [22] \\ \hline Hexene & 8.02/16.13 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 28\(\times\)5 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\ \hline Benzene & 1.04/17.52 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 3\(\times\)56 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\ \hline Toluene & 5.24/16.90 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 20\(\times\)64 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\ \hline Ethylbenzene & 1.29/6.74 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 62/9 & ppb & Food waste & 2 composting plants-Composting hall & [22] \\ \hline _p,m_-Xylene & 1.95/10.56 & ppb & Food waste & 2 composting plants-Composing hall & [21] \\  & 8\(\times\)46 & ppb & Food waste & 2 composting plants-Composting hall & [22] \\ \hline Styrene & 68.50/113.58 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 291/482 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\ \hline _o_-Xylene & 1.008.01 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 360\(\times\)842 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\ \hline Aliphatic Hydrocarbons (total) & 2.07–30.20.48–14 & ppb & OFMSW & Anaerobic digestion plant F/D\({}^{2}\) & [19] \\ \hline Aromatic Hydrocarbons (total) & 19.3–39.1/22.3–55.1 & ppb & OFMSW & Anaerobic digestion plant F/D\({}^{2}\) & [19] \\ \hline _Ketones (total)_ & nd-2820 & \(\mathrm{kg\,m^{-3}}\) & VFG\({}^{1}\) & Effluent of biofilter & [20] \\ \hline Acetone & 211.17/187.15 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 500\(\times\)443 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\ \hline Butanone & 15.17/10.34 & ppb & Food waste & 2 composting plants-Composting hall & [21] \\  & 45/30 & ppb & Food waste & 2 composting plants-Composing hall & [22] \\  & 0.002–0.56 & \(\mathrm{mg\,m^{-3}}\) & Food waste & Composing plant-Composting hall & [13] \\  & 12–337\(\times\)11–556 & ppb & OFMSW & Anaerobic digestion plant F/D\({}^{2}\) & [19] \\ \hline
2.3-Butanedione & 0.009–1.15 & \(\mathrm{mg\,m^{-3}}\) & Food waste & Composing plant-Composing hall & [13] \\ \hline _Alcohols (total)_ & nd-4150 & \(\mathrm{kg\,m^{-3}}\) & VFG\({}^{1}\) & Effluent of biofilter & [20] \\  & 413–762/38.5–82.6 & ppb & OFMSW & Anaerobic digestion plant F/D\({}^{2}\) & [19] \\ \hline \end{tabular}
\end{table} 
Table 1: VOC emissions from waste treatment plants.
# Study on the Design of the Fear Pair and Flow Characteristics of Circular-Arc Gear Pumps
###### Abstract  
Compared with traditional gear pumps, circular-arc gear pumps have the advantages of silence and small flow pulsation, but the theory of design is underdeveloped. This paper presents a design method for gear pumps with circular-arc helical gear pairs, and the influence mechanism of flow characteristics is studied. First, a model of the gear pair is established, and a design method for the gear pair is proposed. Second, a CFD model is demonstrated, and the influences of the tooth profile parameters (tooth number, modules, and pressure angle) on the flow characteristics are analyzed. Finally, the significance of the influencing factors is analyzed. The results show that when the stagger angle of the two ends of the arc helical gear pair is an integral multiple of \(\pi/Z\), there is no flow pulsation, and there is little noise. The tooth number and modules are positively correlated with the flow rate and flow pulsation, among which the modules have the most significant influence. The flow rate of the gear pump increases by 4-5 L/min for every 0.2 increase in the modules. The pressure angle and flow rate show a negative correlation trend, but the influence is insignificant. The flow rate is less than 1 L/min for every 2\({}^{\circ}\) change in the pressure angle. This paper provides a theoretical basis and reference value for the gear pair design of gear pumps.  
tooth profile design; circular-arc helical gear pump; flow characteristics; parameter influence 2025 15 3911
## Materials and Methods
### Experimental Setup
\(X_{\rm NO}\), \(X_{\rm N_{2}O}\), and \(X_{\rm NO_{2}}\) (%) were used to represent the conversion ratios of fuel-nitrogen to NO, N\({}_{2}\)O, and NO\({}_{2}\) during combustion within \(\Delta t\), calculated as follows:  
\[X_{\rm NO-N} = \frac{\int_{t_{0}}^{t_{0}+\Delta t}F\cdot C_{\rm NO}\cdot 10^{-6} \cdot\mathrm{d}t/22.4}{M\cdot N_{\rm d}/14}\times 100 \tag{1}\] \[X_{\rm N_{2}O-N} = \frac{\int_{t_{0}}^{t_{0}+\Delta t}F\cdot C_{\rm N_{2}O}\cdot 10^{-6} \cdot\mathrm{d}t/22.4}{M\cdot N_{\rm d}/14}\times 100\] (2) \[X_{\rm NO_{2}-N} = \frac{\int_{t_{0}}^{t_{0}+\Delta t}F\cdot C_{\rm N_{2}O}\cdot 10^{-6} \cdot\mathrm{d}t/22.4}{M\cdot N_{\rm d}/14}\times 100 \tag{3}\]  
where \(F\) is the volumetric flow, L\(\cdot\)s\({}^{-1}\); \(M\) is the sample weight, g; \(t_{0}\) is the time at which the measurement begins, s; \(\Delta t\) is the measurement duration, s; \(N_{\rm d}\) is the nitrogen content in the sample, %; C\({}_{\rm NO}\), C\({}_{\rm N_{2}O}\), and C\({}_{\rm NO_{2}}\) are the concentrations of NO, N\({}_{2}\)O, and NO\({}_{2}\) in flue gas, respectively, \(\mu\)L\(\cdot\)L\({}^{-1}\). The volume of each mole ideal gas is 22.4 L at the standard condition (\(t\) = 273.15 K, \(p\) = 101325 Pa), while the atomic weight of nitrogen is 14.
# Numerical Simulation of the Influence of Spur Dikes Spacing on Local Scour and Flow
## 3 Laboratory Experiments  
A series of experiments were conducted on the local scouring around spur dikes at the State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi'an University of Technology, China.  
In this study, three experimental cases were selected to test the performance of the numerical model. The spur dike group was composed of four equal-length and uniformly-spaced spur dikes. The spur dike spacings were 2\(l\), 3\(l\), and 4\(l\) in each of the three cases.  
The flume channel was 10 m long, 40 cm wide and 50 cm deep, and consisted of the entrance tank, fixed bed, movable bed and sand recycle zone (Figure 1). The fixed bed was 200 cm long and was composed of gravel and coarse sand, to prevent water entering the sediment observation area pushing all the sediment to one place, and to thereby ensure the water could flow smoothly. The movable bed was 400 cm long and was covered with 20 cm of sand. In this area, a group of impermeable spur dikes were set perpendicular to the right side of the flume. The spur dikes were 10 cm long, 1 cm thick, and 50 cm high, in order to remain unsubmerged throughout the experiments. The first of four spur dikes was placed 100 cm into the observation area, with equal spacing between all four. The sediment grain size was selected to be D50 = 0.473 mm, with the sediment grading curve as shown in Figure 2. The hydraulic conditions under which the experiments were carried out are listed in Table 1. The experiments started with a flat bed and lasted two hours until a quasi-equilibrium condition was reached, with no further significant change in the bed surface. The experiments were conducted under clear-water scour conditions and no sediment was fed into the upstream area.  
Figure 1: Experimental setup (**a**) plan view; (**b**) A-A sectional view (units: cm).  
Figure 2: Sediment grading curve.
# Human-Touch-Inspired Material Recognition for Robotic Tactile Sensing
## 4 Results
### Property Recognition  
Linear SVM was used to traverse the number of principal components from 1 to 11 for the best dimension. The results shown in Figure 4 illustrate that the features were redundant for property recognition, as the accuracy remained the same when the number reached 8. Therefore, 8 principal components were used for property recognition. The time overheads, accuracies, and other indexes of the classifiers are provided as Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Recognition** & **Performance Indexes** & **Linear SVM** & **RBF SVM** & **kNN** & **LDA** & **NB** & **RF** & **MLP** \\ \hline \multirow{4}{*}{Property Recognition} & Accuracy (\%) & 92.01 & 94.10 & **95.83** & 90.97 & 77.43 & 90.63 & 94.79 \\  & Precision (\%) & 92.25 & 94.25 & **95.75** & 91.75 & 79.50 & 90.75 & 95.00 \\  & Recall (\%) & 92.25 & 94.00 & **95.  
From Table 4, it can be noted that for most classifiers, the accuracy is not below 90%, and kNN provides the best accuracy (95.83%). Figure 5 is the confusion matrix obtained from the kNN property recognition, and the columns of the confusion matrix indicate the predictions, while the rows indicate the real classification. The confusion matrix shows that most errors occur in rigid-rough material recognition. In our experiment, rigid-rough materials were easily recognized as soft-smooth materials. This is because there is great adhesion in the contact between soft-smooth materials and the tactile sensor, and the adhesion will result in vibration, with a low frequency and large amplitude, during slide action, as rigid-rough materials do [40]. In addition, Table 4 shows that our method is a time-saving. The run time of each process was measured, and the results are provided in Table 5. By analyzing Table 5, it is observed that reading the tactile data from the hard disk (Data Reading) takes up most of the run time, and converting data to time sequences is the second most time-consuming process (Dimensionality Reduction). Moreover, pre-processing tactile data took up more than 99% of the total time. This is due to the fact that our method is required to process the huge data produced by the tactile sensor: for each sample, there are 100 tactile images, whose size is \(44\times 44\). However, the overhead is still cheap compared with the overhead of the slide action, which means our method can finish the classification during the next slide action.  
Figure 4: The accuracy changes as the number of principal components increase.  
Figure 5: Confusion matrix of property recognition based on the kNN classifier. The columns indicate the predictions and rows indicate the real classification.
## 3 Results and Analysis
### Specific Surface Area  
The porous structure formed by the decomposition of AP at low temperature will lead to an increase in specific surface area, with the ratio of surface area to volume being used as a parameter to evaluate the specific surface area. Based on the analysis of the established 3D pore model, the specific surface area of porous particles formed during decomposition was calculated using image processing and then compared with the AP specific surface area before decomposition. As shown in Table 2, the \"Ratio\" was the specific surface area of PAP to AP, where PAP is the porous AP formed after thermal decomposition. With an increase in the mass loss, more pores were produced inside the particles, and the specific surface area was greatly increased. When the mass loss reached 29.9%, the specific surface area increased 8.5 times.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mass Loss\%**} & \multirow{2}{*}{**Volume/\(\mu\)m\({}^{3}\)**} & \multicolumn{2}{c}{**Specific Surface Area/\(\mu\)m\({}^{-1}\)**} & \multirow{2}{*}{**Ratio**} \\ \cline{3-3} \cline{5-5}  & & **AP** & & **PAP** \\ \hline
9.7 & 3,158,713 & 0.0517 & 0.153 & 3.0 \\
19.6 & 2,480,330 & 0.0510 & 0.295 & 5.8 \\
27.8 & 2,981,480 & 0.0402 & 0.341 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific surface areas of porous AP.  
Figure 9: Pore size distribution of AP particles with different mass losses.
# Application of Machine Vision Technology in Citrus Production
###### Abstract  
The construction of standardized citizens orchards is the main trend in the future development of modern agriculture worldwide. As the most widely used and mature technology in the agricultural field, machine vision has greatly promoted the industrial development model of the citrus industry. This paper summarizes the application of machine vision technology including citrus pest and disease detection, harvesting identification and localization, and fruit grading. We compare the advantages and disadvantages of relevant research, and analyze the existing problems and prospects for future research. Due to the complex and changeable in-field environment, robots may experience unpredictable interference in the recognition process, which leads to errors in target fruit localization. The lack of datasets also affects the accuracy and stability of the algorithm. While expanding the dataset, it is necessary to conduct further research on the algorithm. In addition, the existing research focuses on indoor monitoring methods, which are not practical for the changeable outdoors environment. Therefore, realizing the diversity of sample datasets, designing agricultural robots suitable for complex environments, developing high-quality image processing hardware and intelligent parallel algorithms, and increasing dynamic monitoring methods are the future research directions. Although machine vision has certain limitations, it is still a technology with strong potential for development.  
citrus production; machine vision; citrus disease; citrus harvesting +
Footnote †: journal: Medical  
and then the signal is transmitted through the corresponding control device to make the executive device start to work.  
The application of machine vision technology in the production process of citrus can effectively improve production efficiency, such as disease and pest detection, harvesting identification and positioning, fruit grading, and other links. Machine vision technology has good application prospects, and, driven by this technology, many aspects of citrus production can be further developed. This article compares the advantages and disadvantages of relevant research through the application of machine vision technology in citrus disease and pest detection, picking recognition and positioning, and fruit grading. It analyzes the current problems and prospects for future research, which can effectively improve the accuracy and efficiency of various stages of citrus production and provide certain reference value for the development direction of machine vision technology in agriculture.
## 3 Classical Routing Protocols  
Due to the network being homogenous, nodes will be identical in terms of hardware complexity and battery energy. In these networks, static clustering has been used only. This implementation utilizes a single network topology, and it is not complicated. However, the most significant drawback of a homogeneous sensor network is that all network nodes may be able to act as cluster heads, which means they must have the hardware capabilities to meet the hardware requirements [64]. Since energy is an extremely critical resource, various routing protocols have been proposed to minimize the energy consumption at different levels of the network [65].