esa-sceva/satcom-chunk-collection huggingface dataset:
# Ecological Dynamics and Evolution of Cooperation in Vehicular Ad Hoc Networks
## 5 Conclusions  
This paper presents a novel approach, the ecological-aware public goods game (PGG), to investigate the evolution of cooperation between vehicles in Vehicular Ad hoc Networks (VANETs). The proposed model incorporates dynamical network topology and varying population sizes, which are two key features of VANETs. Our simulations and theoretical analyses reveal that the promotion of cooperation is influenced by various network parameters. Specifically, a higher birth rate increases the size of local communities and results in a smaller difference in payoff between cooperating and defecting vehicles, thereby reducing the probability of strategy change. On the other hand, lower densities can lead to an increase in defection due to the large difference in payoff between cooperators and defectors. The proposed model provides a useful tool for designing network properties to promote cooperation between vehicles, without the need for any additional mechanisms.  
Furthermore, our results demonstrate that the topological properties of the network, such as the degree distribution and clustering coefficient, also have a significant impact on cooperation dynamics. Networks with higher degrees and clustering coefficients promote cooperation by providing more opportunities for cooperation and increasing the probability of establishing social ties between cooperators. Moreover, the departure rate of vehicles, which reflects the turnover of network participants, is found to play a crucial role in determining the stability of cooperative behavior. In general, a moderate departure rate is conducive to cooperation, as it provides a balance between introducing new cooperators and maintaining existing social ties.  
To extend the findings of this study, future work could explore the effect of other network parameters, such as network size and heterogeneity, on the evolution of cooperation in VANETs. Moreover, empirical studies could be conducted to validate the effectiveness of the proposed approach in real-world scenarios. Overall, our work highlights the importance of designing appropriate network properties to promote cooperative behavior in VANETs, which can have significant implications for the development of intelligent transportation systems.  
Conceptualization, J.S.S.; writing--original draft preparation, J.S.S.; writing--review and editing, F.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All data were presented in main text.  
The authors declare no conflict of interest.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 7 Antimicrobials  
### Antibacterials  
Linezolid (**33**) is a chiral antibacterial with remarkable properties [98]: it is the only chiral antibacterial drug active against the multi-resistant Gram-positive bacteria, also clinically important, with an excellent oral bioavailability and effective penetration at therapeutic concentrations into almost every organ in the body. It is also even able to act against _Enterococcus faecalis_, a commensal and nosocomial pathogen responsible for serious infections in humans [99]. Linezolid is the first antibiotic belonging to the class of oxazolidinones to be approved by the FDA in 1999 and the (_S_)-enantiomer is the eutomer [100,101]. Tedizold (**34**) is a parenteral antibacterial agent belonging to oxazolidinones, approved by the FDA in June 2014 and positively endorsed by the Committee for Medicinal Products for Human Use (CHMP) in January 2015. Tedizold phosphate has a favorable safety profile and has been approved to treat acute bacterial skin and skin structure infections, and the antibacterial activity is due to the (_R_)-enantiomer, while the (_S_)-enantiomer is devoid of this activity [102]. The fluoroquinoline class of antibiotics comprehends chiral drugs, including ofloxacin (**35**), gemifloxacin, lomefloxacin, and gatifloxacin. The eutomer of the ofloxacin is the (_S_)-ofloxacin, which is named and commercialized as levofloxacin, and acts as DNA topoisomerase IV inhibitor [103]. Gatifloxacin (**36**) belongs to the fourth generation of quinolones and shows a stereogenic center on the piperazine ring at C-7, thus, it exists as two enantiomers, (_S_)- and (_R_)-gatifloxacin, with the (_S_)-enantiomer being the eutomer. Recently, complexes of the Schiff base of gemifloxacin have demonstrated high antifungal and antibacterial activity [104,105].
## 1 Introduction
The research includes the following stages: First, the Beidou high-precision navigation system is optimized by designing signal filtering and data redundancy strategies to improve the system's robustness in complex environments. Second, based on binocular vision recognition technology and combining artificial intelligence deep learning algorithms, the real-time recognition and modeling of obstacles and equipment structures in the environment are achieved. Finally, the Unscented Kalman Filter (UKF) is used to intelligently fuse the Beidou positioning data and binocular vision data, dynamically plan paths, and enable the robot to autonomously avoid obstacles and adjust its path in complex scenarios. Through simulation and physical experiments, the effectiveness and feasibility of the proposed method in complex industrial environments, such as substations, are verified. The research in this paper not only provides a new solution for addressing the positioning bottleneck of Beidou systems in substation environments, but also promotes the in-depth application of multi-sensor fusion technology in robot navigation. By achievingintelligent collaboration between Beidou navigation and visual recognition, this research provides theoretical and engineering support for robot autonomous navigation, path planning, and obstacle-avoidance control, with the potential to further improve the operational performance and safety of mobile robots in hazardous and complex environments.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 2 Reinforcement Learning
### The System State Space  
In multi-agent systems, the notion of a \"state\" has a local and a global meaning. Local states refer to individual agents, whereas global or system states include all the individual local states and other relevant parameters (such as the positions of the interest-points in tracking applications).  
A key challenge in multi-agent RL is the exponential growth of the state-space in the number of agents. A single agent system on a 10 \(\times\) 10 grid has at minimum 100 possible states (if each state corresponds to a coordinate position). However, an n-agent system on the same 10 \(\times\) 10 grid has 100\({}^{n}\) possible states.  
Locally interactive structure will often be referenced in this paper to reduce computational complexity (Section 3) and satisfy fundamental communication constraints (Section 4). This property implies that activity occurring in remote areas of the grid relative to any agent, is less relevant to the optimal behavior of that agent than activity nearby [7, 8]. Each agent can then simplify the global state it \"sees\" while staying responsive to global/system-level objectives.  
Another challenge in multi-agent systems is that agents only observe their local states (not the global state). \"Partially-Observable\" MDPs (or POMDPs) allow observations to stochastically depend on the underlying global state. POMDPs are more complicated than MDPs and fall outside the scope of this survey. However, other ways of extending the MDP framework to multi-agent systems are discussed in Section 3.
# A Review of Research on Dropwise Condensation Heat Transfer
## Abbreviations  
\begin{tabular}{l l} \(A_{F}\) & area of filmwise condensation surface, m\({}^{2}\) \\ \(A_{D}\) & area of dropwise condensation surface, m\({}^{2}\) \\ \(g\) & acceleration of gravity, m/s\({}^{2}\) \\ \(h_{F}\) & heat transfer coefficient on surface, W/(m\({}^{2}\cdot\)K) \\ \(H_{fg}\) & latent heat of condensation, J/kg\({}^{1}\) \\ \(h_{i}\) & interfacial heat transfer coefficient, W/(m\({}^{2}\cdot\)K) \\ \(k_{c}\) & thermal conductivity of condensation, W/(m\({}^{2}\cdot\)K) \\ \(k_{caut}\) & thermal conductivity of the coating material, W/(m\(\cdot\)K) \\ \(n\) & population density of small drops, m\({}^{-3}\) \\ \(N\) & population density of large drops, m\({}^{-3}\) \\ \(N_{s}\) & Number of nucleation sites per unit area m\({}^{-2}\) \\ PTFE & Polytetrafluoroethylene \\ \(q\) & heat flux, w/m\({}^{2}\) \\ \(q_{d}\) & heat flux through the drop, w/m\({}^{2}\) \\ r & Droplet radius, m \\ \(r_{min}\) & Minimum droplet radius, m \\ \(r_{e}\) & Effective radius, m \\ \(r_{max}\) & Maximum droplet radius, m \\ \(T\) & temperature, K \\ \(T_{S}\) & vapor saturation temperature, K \\ \(\Delta\)T & surface subcooling temperature, K \\ \(\Delta R_{i}\) & the vapor-liquid interfacial resistance, m\({}^{2}\cdot\)K/W \\ \(\Delta R_{d}\) & the resistance due to the conduction through the drop, m\({}^{2}\cdot\)K/W \\ \(\Delta R_{p}\) & the resistance from the promoter layer, m\({}^{2}\cdot\)K/W \\ \(\Delta R_{c}\) & he resistance due to the curvature of the drop of liquid-vapor interface, m\({}^{2}\cdot\)K/W \\ Greek symbol \\ \(\alpha\) & angle in Figure 1, deg. \\ \(\theta\) & contact angle, deg. \\ \(\theta_{r}\) & receding contact angle, deg. \\ \(\theta_{a}\) & advancing contact angle, deg. \\ \(\sigma\) & surface tension, N/m \\ \(\delta\) & thickness of promoter layer, m \\ \(\gamma\) & surface free energy, J/m\({}^{2}\) \\ \(\Phi\) & Heat transfer rate, W \\ \(\Delta\gamma\) & surface free energy difference, J/m\({}^{2}\) \\ \(\rho\) & density of the condensate, kg/m\({}^{3}\) \\ \(\tau\) & sweeping period, s \\ \multicolumn{2}{c}{Subscripts} \\ \(l\) & liquid \\ \(s\) & solid \\ \(f\) & surface \\ \(d\) & droplet \\ \end{tabular}
## 3 Results  
### Tilianin, Acacetin and Total Polyphenol Content in ARE  
As expected, ARE contains various plant-originated polyphenols, including flavonoids, which have numerous benefits for humans ([PERSON] et al., 2016). The total polyphenol content of ARE was determined to be 38.11 \(\pm\) 0.88 mg/g. This figure means that it contains at least 2.05 times more polyphenols than green tea, a noted plant which contains abundant polyphenols from 1.17 to 18.59 mg/g ([PERSON] et al., 2016).  
The levels and identities of single compounds in ARE were determined using HPLC-UV. Tilianin and acacetin identification was verified by eluting the single standard and spiking methods. The mixture of the standards showed a clear chromatogram. The analysis method was modified from a previously published UPLC method ([PERSON] et al., 2016). Since the standard solution eluted clearly without any issues, this method was deemed verified.  
Acacetin is another main compound in _A. rugosa_. It has numerous effects on human including anti-obesity (Tilianin and acacetin, 2016). Tilianin and acacetin from ARE eluted at average times of 19.42 and 32.82 min, respectively, during a total analysis time of 55 min. ARE contains 21.14 \(\pm\) 0.15 mg/g of tilianin and 9.94 \(\pm\) 0.08 mg/g of acacetin. In addition, a peak, eluted at approximately 23 min showed a similar area to that of tilianin and was identified as acacetin 7-O-(6\"-O-malonyl)-\(\beta\)-D-glucopyranoside.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Target Gene** & **Forward** & **Reverse** \\ \hline C/EBP\(\alpha\) & CCGGAAACGACAACAACTGC & TGTCCAGTTGACGGCTCAGC \\ CPT1 & ACTCCTGGAAAGAAGTTCAT & AGATTCTTGACAGCTGGCAC \\ FABP4 & TGGGAACCTGGAAGCTTCTC & GAATTCCACGCCCGTTGAA \\ FAS & TGGGCATAACGGTCTCTGGGT & TCCATGGCGGTGTGAAAAC \\ PPARy & CGGAAGCCTTGTGACTTATG & CGACGGTGTCTTTGTGATGTC \\ SREBP1 & CCCCTGGTGTACTGGCCTTT & ACGGTGTGTATCCGTAGCAT \\ \(\beta\)-actin & TGAGAGGGAAATCGTGCGTGAC & GCTCGTTGCCAATAGTGATGAC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sequences of primers for mRNA used in RT-qPCR.
## 3 Packet Detection Algorithms
### Energy-Based Algorithms
#### 3.1.1 Received Signal Energy Detection  
The received signal energy detection algorithm detects packets by directly identifying the change in the received signal energy level [14], but using only one sample for energy detection is easily affected by single strong noise. A calculation window containing multiple samples is used. The decision variable \(m_{n}\) is set as the received signal energy accumulated over the window of length \(L\) as Equation (1). A sliding window can be used to simplify the computation, as shown in Figure 5. At every time instant n, one new value enters the window and one old value is discarded, so \(m_{n}\) is calculated by a moving sum of received signal energy by Equation (2). Therefore, the number of complex multiplications per \(m_{n}\) is reduced to one received sample.  
\[m_{n}=\sum_{k=0}^{L-1}r_{n-k}r_{n-k}^{*} \tag{1}\]  
\[m_{n+1}=m_{n}+|r_{n+1}|^{2}-|r_{n-L+1}|^{2} \tag{2}\]  
Simulation. The response of the simulation experiment is shown in Figure 6. The real start location of the packet is at the index of 101. The sliding window length for accumu  
Figure 4: The classification of packet detection algorithms for 802.11 OFDM signals.  
Figure 5: The structure of sliding window.  
lation is set as 16 (\(L=16\)). There is an obvious jump around the index of 101, which is consistent with the real situation. This algorithm is sensitive to noise and is difficult for threshold setting since it directly depends on the received signal energy. The next algorithm reduces reliance on energy power.
## 2 Applications of Artificial Intelligence and Machine Learning
### Healthcare and Ergonomics Applications  
Neuronal Constraint-Handling Technique for the Optimal Synthesis of Closed-Chain Mechanisms in Lower Limb Rehabilitation [4], deals with the automatic generation of rehabilitation routines, adapted for lower limbs. These routines must comply with certain constraints that are very difficult or impossible to meet using conventional computation techniques, so authors [PERSON] et al., have devised a novel hybrid soft-computing system, combining two bio-inspired models, such as differential evolution and artificial neural networks, to propose a system that can achieve state-of-the-art results, in two presented cases (four-bar and cam-linkage mechanisms). The contribution includes a detailed comparison on the performance of this model, with other four well-known techniques for this problem, showcasing an interesting example of the utility of the models for healthcare applications.  
sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network [5], proposes a novel solution to a specific problem, in the area of Human-Robot Cooperation, working through communication with an artificial system, using physiological signals, such as the surface electromyogram (sEMG), generated by action neurons in muscle. In order to do so, [PERSON] et al., proposed an Artificial Neural Network model, inscribed in the field of a Deep Learning denominated large-scale temporal convolutional network (LS-TCN), enabling one to estimate the angles of the main joints of the human hand in real time. The authors include both extensive theoretical explanations and a complete comparison of the presented model, including the source public dataset used for the experiments, as valuable takeaways for practitioners.
## 2 Overview of Compressive Sensing Algorithm
### CS Data Acquisition  
CS theory can be expressed mathematically for signal acquisition, signal x being the original signal can be reconstructed by utilizing m << n patterns, where \(\Phi_{\text{m}\text{n}}\) being the sensing matrix through measurement vector. This sensing matrix is made up of 1 s and 0 s and to generate N-pixel random patterns through either Bernoulli or any other distribution patterns such as Hadamard, wavelet, and speckle which could be employed. Selecting the sensing matrix \(\Phi\) is one of the important strategies in CS research; commonly, a random matrix is considered and the data is represented where the signal is more sparse [38; 39; 40]. Figure 3 gives an idea of the sensing matrices.  
Assuming x to be a large vector of N signal values. Consider \(\Phi\) matrix, having dimension m * n where m \(\ll\) n. The product y = \(\Phi*\times\) yields a much smaller, compressed vector of data y = \(\Phi*\times\), \(\Phi\) is the sensing matrix, and y is the vector of measured values. It should be noted that m much lesser than n and when m is equal to n no compression takes place.  
There are certain considerations in order to achieve faithful reconstruction; hence, the CS matrix should undergo the following properties [41; 42].  
* Null Space Property
* Restricted Isometry Property (RIP) and
* Incoherence  
Figure 4 interprets the sparsifying matrix model which represents the signal of interest sparsely. A signal represented in sparse domain yields more desirable signal compression for efficient storage, data bandwidth and power usage, as it concentrates on the most related quality of data itself and also leads to a more effective signal detection, classification and other pattern recognition objectives. Original and Sparse representation of ECG signal is depicted in Figure 5 [43; 44; 45].  
Figure 3: The CS sensing matrix.
# Design of a Computed Tomography Automation Architecture
## 5 Industrial Applications
### Additive Manufacturing  
As with any method of manufacturing a part, CT also proves to be a useful technique for 3D printed (additively manufactured) parts. As additive manufacturing technologies become increasingly utilized in industry, a majority of the applications can be grouped into either defect detection, dimensional evaluation, density measurement, or roughness analysis [52]. These industrial applications span from musical instruments to historical artifact reconstruction, among others.  
Defect detection is a primary application for CT in general and is no different in the case of additively manufactured parts. As new methods for additive manufacturing are becoming increasingly more implemented into the industry, the role of internal inspection by CT becomes important to check the new manufacturing methods for the potential creation of defects such as pores. The use of defect detection for additively manufactured parts with CT is shown in many areas [53; 54; 55; 56].  
Dimensional evaluation [57; 58] and reverse engineering [59] have been performed with the help of CT. These are important in verifying part tolerances and wall thicknesses and can be directly compared with their respective CAD models [60]. Density and roughness measurements and analysis of additively manufactured parts are also performed by CT scans [61; 62; 63; 64]. The combination of 3D printed parts with CT imaging also intersects in the medical industry [65]. We can see that many areas are using CT scanning to inspect and analyze their additively manufactured components, but a study on titanium and nickel aerospace components made by additive manufacturing concluded that CT is in fact the best technique for complex geometries [66].
## 2 Methodology  
### Precipitation Distribution Estimation Based on Satellite Remote Sensing  
Precipitation is the most important factor affecting the power generation capacity of small hydropower stations. The accuracy of precipitation data directly affects the accuracy of daily power generation forecasts for small hydropower stations. Rain gauges are a traditional way of directly observing precipitation on the ground, but it is difficult to accurately capture the temporal and spatial distribution of precipitation due to the influence of the density of the station network and its spatial distribution. The spatial variations in precipitation are large, and small hydropower stations are scattered and remotely located. Therefore, it is difficult to predict the daily power generation of small hydropower stations with high precision by only using the actual precipitation values observed by rain gauges [12].  
With the development and advancement of satellite remote sensing technology, quantitative precipitation observation (QPE) data based on satellite remote sensing inversion have become a new source of precipitation data. Satellite QPE data usually have broad coverage and high temporal and spatial resolution, which effectively compensates for the shortcomings of the spatial distribution of traditional meteorological station observations and can provide a new data reference for regions lacking meteorological data [13; 14].  
Satellite observations of precipitation are affected by radar sensor designs, weather conditions, and inversion algorithms, and they are not sufficient to accurately and quantitatively represent the precipitation values in the monitored region, but they can approximately reflect the spatial distribution of precipitation. Therefore, it is possible to combine the spatial distribution field of precipitation observed by satellites and the actual values of ground precipitation observed by rain gauges and use the method of linear interpolation to estimate the approximate precipitation values are the locations without rain gauges, as shown in Figure 1.  
First, we define the reference ratio of precipitation for a certain day as:  
\[P_{B}\ =\ \frac{P_{R}}{P_{S}}, \tag{1}\]  
where \(P_{R}\) represents the precipitation value observed by the ground meteorological station and \(P_{S}\) represents the precipitation value observed by the satellite at the longitude and latitude of the aforementioned ground meteorological station. Then, the actual precipitation at any location can be estimated as:  
\[P_{R}(long,lat)\ =\ P_{S}(long,lat)\ \times P_{B}\, \tag{2}\]  
where _long_ and _lat_ represent the longitude and latitude, respectively, corresponding to the estimated position.
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 4 HSV Color-Space-Based Automated Object Feature Extraction
### HSV Color-Space-Based Automated Object Localization Algorithm
#### 4.2.2 HSV Color-Based ROI Random Sampling and HSV Color Similarity Comparison
The following is an algorithm for the HSV color-space-based similarity distance, as in Equation (7):  
\[\mathrm{h}=\frac{\min(|\mathrm{h}1-\mathrm{h}0\mid,360-|\mathrm{h}1-\mathrm{h} 0|)}{180.0} \tag{4}\]  
\[\mathrm{s}=|\mathrm{s}1-\mathrm{s}0\mid \tag{5}\]  
\[\mathrm{v}=\frac{|\mathrm{v}1-\mathrm{v}0\mid}{255.0} \tag{6}\]  
\[\mathrm{distance}=\sqrt{\mathrm{h}^{2}+\mathrm{s}^{2}+\mathrm{v}^{2}} \tag{7}\]  
where H (hue) ranges from 0 to 360\({}^{\circ}\), and the distance between the two colors can be calculated from 0 to 1 in Equation (4). S (saturation) is in the 0-1 range in Equation (5). V (value) ranges from 0 to 255 in Equation (6), and when divided by 255, it has a value of 0 to 1. H1, S1, and V1 are the HSV values of the reference ROI, and H0, S0, and V0 are the HSV values of the randomly sampled ROI. If the distance value of color similarity is close to 0, compared to the threshold, it means that the HSV color value of the reference ROI and the HSV value of the randomly searched ROI are the most similar. If the threshold value of the HSV color similarity is less than or equal to the distance value, the HSV upper and lower limit values of the randomly sampled ROI are added to the temporary ROI buffer list and stored. Otherwise, the similarity of the HSV values is compared by executing the maximum number of randomly sampled ROIs.