esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction
## 4 Simulation and Results
### Discussion  
From our observation, ODE and Bullet showed good performance for a simple scenario with primitive-shape objects, and Bullet and Vortex showed good performance for multipoint contact scenarios including mesh objects depending on the situation (Vortex for multipoint contact on simple surface, Bullet for the peg-in-hole scenario). The reasons for these results are that, in the case of ODE and Bullet, the contact simulation algorithms are basically similar, so they showed similar performance for simple scenarios, but in the case of multipoint contact scenarios, the method of reducing the number of contact points and the default option of contact solver were different, resulting in a difference in performance. In addition, since ODE uses maximal coordinates, in the case of scenarios involving multilink robot, simulation must be performed while satisfying the constraints between links compared to generalized coordinates that operate as one object, which can cause errors and reduce simulation accuracy. In the case of Vortex, it is difficult to know the exact reason, because most of the algorithms are not open to the public, but considering  
Figure 11: Snapshot of bolting experiment: when the nut is rotated and pressed down using the robot manipulator, it is fastened with the bolt over time and goes down (**1**\(\rightarrow\)4).  
Figure 10: Snapshot of bolting scenario: (**first row**) ODE; (**second row**) Bullet; (**third row**) Vortex; (**fourth row**) MuJoCo.  
that the performance was relatively good for multipoint contact scenarios between complex objects, it seems that the algorithm had been tuned or improved, so that multipoint contact simulation of complex objects could be performed well. MuJoCo showed relatively poor performance in terms of contact force and penetration compared to other physics engines because it calculates the contact force by relaxing the nonpenetration constraints. However, this can greatly improve the simulation speed when multipoint contacts occur, so it is thought to be useful when it is necessary to quickly perform various scenarios such as reinforcement learning for tasks where contact force is not crucial rather than when simulating accurate behavior between complex objects.  
These analytical results can be helpful reference materials for selecting a suitable physics engine for a specific project to be simulated. Furthermore, by identifying the limitations of the currently developed simulators, it would be possible to suggest points to be improved and future research directions in the field of simulation research.
# Vector Control of PMSM Using TD3 Reinforcement Learning Algorithm
## 3 Establish Simulation Model  
### Create Environment  
A Simulink model of the FOC control architecture is constructed, as shown below in Figure 9, which includes two control loops: the external speed loop and the internal current loop. The outer ring is realized in the speed control subsystem, while the current ring subsystem mainly changes the speed and torque of the motor by controlling the current of the two axes. Using the output current signal corresponding to the output voltage, the appropriate PWM signal is generated to adjust the semiconductor switch of the inverter, thereby driving the PMSM to achieve the required torque and flux.  
Figure 8: TD3 network update process.  
The current loop consists of the following components: three-phase motor current acquisition; Clarke transformation; Park transformation; and a current loop controller (RL controller). This research focuses on the RL controller (shown below in Figure 10), which is primarily made up of external and RL environments. Figure 11 depicts the vector observer (a), reward function (b), and cutoff conditions (c).  
The PMSM model's related parameters are listed in Table 2 below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Term Name** & **Symbol** & **Value** \\ \hline Pole pairs & \(p\) & 7 \\ Torque constant & \(K_{t}\) & 0.0583 N-m/A \\ Friction coefficient & \(B\) & 7.01 \(\times\) 10\({}^{-5}\) Kg-m\({}^{2}\)/s \\ Rate current & \(I_{r}\) & 7.26 A \\ Stator resistor & \(R_{s}\) & 0.293 \(\Omega\) \\ D-Axis inductance value & \(L_{d}\) & 0.877 mH \\ Q-Axis inductance value & \(L_{d}\) & 0.777 mH \\ Inertia & \(J\) & 0.0083 Kg-m\({}^{2}\) \\ Max speed & \(V_{max}\) & 4300 RPM \\ Position offset & \(P_{o}\) & 0.165 \\ QEP encoder slits & Q\({}_{s}\) & 4096 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main parameters of PMSM model.  
Figure 10: RL framework.  
Figure 9: PMSM RL-FOC control program construction.  
The motor module is simulated and analyzed. The motor characteristic curve is shown in Figure 12 below.  
Figure 11: RL learning and training structure. From left to right, (**a**) Reward function; (**b**) Observation vector; (**c**) Termination condition.  
Figure 12: Motor characteristic curve. From left to right, (**a**) Torque–Speed characteristics; (**b**) Power–Speed characteristics; (**c**) \(I_{dq}\)–Speed characteristics; (**d**) \(I_{par}\)–Speed characteristics.
## 5 Data Description and Preprocessing
### Data Preprocessing and Transformation  
To provide accurate prediction results for any machine learning algorithm, data preprocessing and transformation are very essential steps that have to be considered. Removing null values and detecting outliers will impact algorithm's accuracy dramatically. Traffic data usually suffer from class imbalance issues, which can be fixed by applying over-sampling or under-sampling techniques such as SMOTE, Near-Miss, and ROSE [56, 57, 58]. More details on preprocessing and transformation for our data-sets are available in our previous studies [59, 60]. This research proposes a scalable distributed machine learning-based scheme for the Internet of Vehicles to predict highway traffic average speed and  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \# & **Parameters** & **Description** \\ \hline
1 & Detector ID & The ID number of the detector \\ \hline
2 & Total Volume & The total number of vehicles detected within a 15-min interval that was recorded by the available detector. \\ \hline
3 & Average Speed & The average speed of vehicles that are calculated based on a 15-min interval for all the lanes. \\ \hline
4 & Archive Lane ID & ID of each lane on each bound of the highway. Each lane on each segment has a different lane id. \\ \hline
5 & Average Occupancy & Average number of vehicles that occupy the segment. \\ \hline
6 & Detector Lat and log & Latitude and Longitude of the detector. \\ \hline
7 & Traffic Timestamp & The time-stamp of the traffic record (Year-Month-Day, Hour:Minute:Second). \\ \hline
8 & Average Latency & The average latency that recorded by a detector. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Important Parameters Of Traffic Data.  
detect abnormal traffic flow patterns. We trained and validated our proposed model using seven years of traffic data from 2008 to 2014, which constitutes 99.8% of the total dataset. The training-validation split was 80% for training and 20% for validation. We then tested our model on one week of traffic flow patterns from 2015, which accounts for 0.2% of the original dataset.
# Coordination of Macro Base Stations for 5G Network with User Clustering
## 2 Energy Management Model of 5G Macro Base Station Network  
The 5G macro BS homogeneous network is shown in Figure 1. The main energy-consuming equipment in a macro BS include the communications equipment, an AC, a backup battery, and a renewable generation unit. The communication equipment consists of an active antenna unit (AAU) and a baseband unit (BBU), which are responsible for signal transmission and baseband signal processing, respectively. The coverage area of an active 5G macro BS is called a cell, which is a regular hexagonal area. The architecture of the 5G macro base station is shown in Figure 2. The AAU and renewable generation units are installed outside the base station cabinet. The remaining equipment is installed in the cabinet.  
The two-step energy management model for the 5G macro-BS network is illustrated in Figure 3. First, the energy consumptions of the AAU and BBU are minimized by optimizing the on/off state of the BSs and user allocation in the cellular network during each time period. Given the power profile and on/off state of each BS, the injected power of each BS, the on/off state of ACs, the charge/discharge power of backup batteries, and the power of renewable generation units during each time period are jointly optimized to achieve the goal of the economic operation of the 5G macro BS network.  
Figure 1: Schematic of a 5G macro BS network.  
Figure 3: Framework of the energy management model of the 5G macro BS network.  
Figure 2: Diagram showing the structure of a 5G macro BS.
## 2 Materials and Methods
### Classification and Performance Assessment  
To evaluate the informative content of the designed multi-time-scale features and thus to assess to which extent the performance depended on the feature representation or the classification models, we compared the performance of several classification methods. We used two state of the art classifiers: Random Forest (RF) [40] and the Support Vector Machine (SVM) [41]. Additionally, we explored the use of both an artificial neural network [42] and a fully-connected deep neural network [43].
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 4 Performance Metrics and Analysis  
### Performance Metrics  
The selected metrics to evaluate the performance of SOSRP are energy consumption, end-to-end delay, hop count and number of paths (to the sink). In order to test a stable behavior, two different parameters will be swept: range of transmission and size (number of nodes).  
Figure 4: INFO Message Format.  
A node dissipates energy in a sensor network while performing the operations necessary for the collection of data required by the application. Such operations include processing, listening to the channel, transmitting, and receiving the data. Energy consumption is the sum of energy dissipation by a node during the process of performing different operations, whereas the accumulation of energy dissipated by each node defines the total energy consumption of network.  
The end-to-end delay refers to the time taken to transmit the data packet from source to sink, irrespective to the number of intermediate nodes. It is the sum of transmission, propagation, queuing, and processing delay in a network.  
The hop-count quantity is the measure of number of intermediate nodes between source and destination. In a multi-hop approach, the higher the hop count, the higher will be the energy consumption and end-to-end delay. Therefore, it is essential to keep the hop counter small to lower the delay and energy consumption. Similarly, the hop distance is measured between two neighboring nodes. It is one of the key parameters used to select the next node in the path. Depending on hop distance, the energy is dissipated. Therefore, the smaller the hop distance, the less energy is consumed in delivering the data.  
In WSNs, transmission range has significant impact on several parameters of network. It defines the coverage area of a sensor node. If the range of the sensor nodes are large enough to directly reach the sink node (in a hop), they will consume more energy to communicate due to the large distances. However, to converse the energy it is essential to keep the transmission power to a minimum, for which multi-hop (more than a single hop) communication is the best approach. Based on this, transmission power control mechanism is implemented to conserve the energy by calculating the power required to send the packet hop to hop, considering the changing distance between the nodes and receiver's sensitivity. Different transmission range is tested to evaluate its effects on the performance of network, as can be seen in Section 5.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Network Function Virtualisation (NFV)
#### 3.2.5 Current Solutions of NFV  
Several solutions are being implemented to address the security challenges associated with NFV deployment. Standardising the virtual hardware abstraction level during NFV deployment is essential to separate network functions from proprietary and physical hardware, thus reducing the risk of vulnerabilities [43]. Additionally, deploying proper virtualisation technologies to isolate VMs from one another helps mitigate the impact of crashes, hangs, loops, or compromises in one VM on others, enhancing the overall system resilience [47]. Testing and evaluation procedures are crucial to validate the applicability of NFV in different deployment scenarios and ensure its effectiveness in meeting performance and security requirements [43]. Furthermore, leveraging virtualisation methods in NFV contributes to better mobility management, policy creation, and the deployment of billing systems, enabling more efficient and secure network operations [44].
## 2 Results and Discussion
### High Temperature-Triggered Regulatory Events in Koshiiukari Rice Seeds of Grades 2 and 3
#### 2.4.6 Hormone Regulated Gene Expression  
Hormones play a major role in regulating stress-responsive mechanisms in plants. Several hormones are involved in the activation or inactivation of specific genes related to stress tolerance. In this experiment few hormone-regulated genes were found to be differentially expressed in Y2 and Y3. Two different SAUR class proteins OsSAUR18 (Os04g43740.1) and OsSAUR23 (Os04g56690.1) were observed in both Y2 and Y3, respectively (Supplementary Table S4, Abiotic-Biotic stress; and Figure 6). In general SAUR class genes were induced in the presence of auxins. The reduced expression of OsSAUR genes 18 and 23 in Y2 and Y3 could indicate low levels of endogenous auxins. A decrease in endogenous auxins and SAUR expression was previously observed in barley and _Arabidopsis_[78; 79]. This could be due to the disruption of the auxin metabolism caused by heat stress. Previously, such effect due to heat stress was identified in rice under heat stress which could lead to inhibition of pollen tube elongation resulting in decreased spikelet fertility [80]. Further, ABA-induced GRAM domain-containing proteins like FIP1 were also found to be upregulated, indicating possible accumulation of ABA. ABA is known to regulate starch biosynthesis genes and plays a key role in grain filling under high temperatures [81]. The induced expression of the FIP1 genes could therefore suggest a heat-responsive mechanism in rice. Two F1P1 genes (os10g34730.1 and Os04g44500.1) were found to be similarly regulated in both Y2 and Y3. Additionally, genes os02g42430.1 and Os04g44510.1 were observed to be upregulated in Y3 alone indicating a stronger response to high temperatures. In addition to the above findings, unique changes in genes related to jasmonate synthesis were detected. Expression of genes related to the JA biosynthesis pathway, like lipoxygenase 2 (Os03g52860.1), 12-Oxo-PDA-reductase family genes OPR (Os06g11200.1) and OsOPR5 (Os06g11210.1) is also observed in our study. Between Y2 and Y3 the expression of these genes is opposite. Lipoxygenase-2 and OPR were downregulated in Y2, while in Y3 the lipoxygenase-2 and OsOPR5 are upregulated and OPR is not. Previously JA levels and these genes were identified to be upregulated under drought stress and downregulated under heat stress in rice [82]. Accordingly, from the current results, it can be understood that Y2 is experiencing heat stress. Whereas, Y3 might be experiencing both drought and heat stress, as downregulation of the OPR gene was also observed here.
###### Abstract  
Developing a photocatalyst system for solar energy conversion to electric energy or chemical energy is a topic of great interest for fundamental and practical importance. In this study, hydrogen production by a new Z-scheme photocatalysis water-splitting system was examined over Rh-doped SrTiO\({}_{3}\) (denoted as Rh:SrTiO\({}_{3}\)) with Ru nanoparticle as cocatalyst for H\({}_{2}\) evolution and BiVO\({}_{4}\) photocatalyst for O\({}_{2}\) evolution under visible light irradiation, where Co(bpy)\({}_{3}\)\({}^{2+/3+}\) was used as electron mediator. The catalysts were characterized by powder X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), and Ultraviolet-visible spectroscopy. We present a fast and efficient method to synthesize Rh-doped SrTiO\({}_{3}\) photocatalyst via microwave-assisted hydrothermal method. Our results showed a significant effect of Ti precursor on morphology of Rh:SrTiO\({}_{3}\) prepared by microwave-assisted hydrothermal synthesis. The Ru/Rh:SrTiO\({}_{3}\) prepared by TiCl\({}_{4}\) precursor showed a nanoporous structure and high photocatalytic activity. The combination of Ru/Rh:SrTiO\({}_{3}\) with BiVO\({}_{4}\) achieves a high H\({}_{2}\) evolution rate (317 \(\mu\)moL g\({}^{-1}\) h\({}^{-1}\)) and O\({}_{2}\) evolution rate (168 \(\mu\)mol g\({}^{-1}\) h\({}^{-1}\)) in 0.5 mM Co(bpy)\({}_{3}\)\({}^{2+/3+}\) solution under visible light irradiation.  
Keywords:Rh-doped SrTiO\({}_{3}\); hydrogen generation; Z-scheme overall water splitting +
Footnote †: journal: Applied Physics Letters
## 2 Related Works
However, the latency and other issues resulting from real-time variations in traffic scenarios were not taken into consideration. The use of machine learning algorithms in vehicular networks addresses issues related to latency, ensuring the smooth flow of traffic and enhancing road safety, which is addressed in [39]. These algorithms were applied within the context of real-time VANET scenarios to assess their feasibility and effectiveness. Additionally, this approach considers future directions and challenges associated with implementing machine learning techniques. Utilizing machine learning in Vehicle-to-Infrastructure (V2I) communication within VANETs involves employing software comprising both static and mobile agent approaches. Decision tree and Q-Learning algorithms are utilized to detect potential serious incidents, aiming to enhance bandwidth, PDR, and E2E delay [40]. In [41], a method was proposed for selecting the optimal relaying set during the broadcast procedure. This approach employs supervised learning to classify candidate nodes based on various factors, followed by sorting them according to relay quality to select the best ones. Moreover, the method assumes perfect channel knowledge for all nodes and overlooks the effects of node failures on dissemination efficiency. The issues of coverage to enhance the receiving information probability at multiple receiver units were covered in [42] and resolved through the theory of entropy. To the best of our knowledge, the group- and subgroup-based relay selection that is specifically based on a machine learning model was utilized for the first time in the VANET environment using our suggested method as a dissemination protocol. In addition, we suggested creating threshold-oriented subgroups based on the size of the monkey population for scenarios that are grounded in reality.
###### Abstract  
Smoothed particle hydrodynamics (SPH) is a state-of-the-art numerical simulation method in fluid mechanics. It is a novel approach for modeling and comprehending complex fluid behaviors. In contrast to traditional grid-dependent techniques like finite element and finite difference methods, SPH utilizes a meshless, purely Lagrangian approach, offering significant advantages in fluid simulations. By leveraging a set of arbitrarily distributed particles to represent the continuous fluid medium, SPH enables the precise estimation of partial differential equations. This grid-free methodology effectively addresses many challenges associated with conventional methods, providing a more adaptable and efficient solution framework. SPH's versatility is evident across a broad spectrum of applications, ranging from advanced computational fluid dynamics (CFD) to complex computational solid mechanics (CSM), and proves effective across various scales--from micro to macro and even astronomical phenomena. Although SPH excels in tackling problems involving multiple degrees of freedom, complex boundaries, and large discontinuous deformations, it is still in its developmental phase and has not yet been widely adopted. As such, a thorough understanding and systematic analysis of SPH's foundational theories are critical. This paper offers a comprehensive review of the defining characteristics and theoretical foundations of the SPH method, supported by practical examples derived from the Navier-Stokes (N-S) equations. It also provides a critical examination of successful SPH applications across various fields. Additionally, the paper presents case studies of SPH's application in tunnel and underground engineering based on practical engineering experiences and long-term on-site monitoring, highlighting SPH's alignment with real-world conditions. The theory and application of SPH have thus emerged as highly dynamic and rapidly evolving research areas. The detailed theoretical analysis and case studies presented in this paper offer valuable insights and practical guidance for scholars and practitioners alike.  
smooth particle hydrodynamics; computational fluid dynamics; computational solid mechanics; tunnel and underground engineering