esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## 4 Experiments and Results
### Experimental Platform  
The experimental platform is shown in Figure 4. The axial flow fan is used to generate wind disturbance. The anemometer is used to measure the wind speed. The aerial manipulator system mainly consists of a hex-rotor UAV, a 7-DoF robotic arm and an under-actuated gripper. The main parameters of the aerial manipulator are listed in Table 1. The tuned propulsion system of the hex-rotor is E800 of DJI powered by a 6 cell Li-Po battery. The flight stack of the hex-rotor is an open source controller for autonomous drones named PixHawk. The robotic arm is composed of Dynamixel smart actuators. The computer used as the arm controller is Intel NUC with a core-i7 processor. The under-actuated gripper is designed based on the Yale OpenHand Project [36], with some structures and parameters changed. The experiments are conducted in the OptiTrack, an indoor motion capture system.  
The operational space of the aerial manipulator system is shown in Figure 5. The operational space \(\Omega\) is a space in the front of the hex-rotor, in which the target object is away from the hex-rotor with a safe distance, and is also reachable for the robotic arm. The space is part of the space between two concentric spherical surfaces, whose center is point \(O\) (the coordinate origin \(\Sigma_{B\cdot}\)) and radiuses are \(l_{min}\) and \(l_{max}\), respectively. The area is also limited in four planes. Its projection on the \(X_{B}\)-\(O\)-\(Y_{B}\) and \(X_{B}\)-\(O\)-\(Z_{B}\) planes are shown in Figure 6. Any point \(T\) in the space will satisfy the following inequalities:\[\left\{\begin{array}{l}-\frac{1}{2}\alpha\leq\left(X_{B},Proj_{xoy}(^{B}p_{bt}) \right)\leq\frac{1}{2}\alpha\\ l_{\min}\leq\|^{B}p_{bt}\|\leq l_{\max}\\ \beta_{\min}\leq\left(X_{B},Proj_{xoz}(^{B}p_{bt})\right)\leq\beta_{\max}\\ \end{array}\right. \tag{32}\]  
where \({}^{B}p_{bt}\) is the position of point \(T\) relative to hex-rotor body fixed frame with respect to \(\Sigma_{B}\). _Proj_(.) is a function that projects a vector in the coordinate plane (\(X\)-\(O\)-\(Y\) or \(X\)-\(O\)-\(Z\) plane). \(\alpha\) is the yaw angle range of the space \(\Omega\). \(\beta_{min}\) and \(\beta_{max}\) are the pitch angle range limit of the space \(\Omega\).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Hex-rotor mass & 3 kg \\ Maximum takeoff weight of the hex-rotor & 5.5 kg \\ Hex-rotor diagonal size & 80 cm \\ Flight time & 15 min \\ Arm mass (including gripper) & 1.5 kg \\ Arm length & 53.4 cm \\ Maximum opening of gripper & 12 cm \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of aerial manipulator.  
Figure 4: Experimental platform.  
Figure 5: Operational space of aerial manipulator.
## 2 Materials and Methods
### Instrumentation  
Different electrodes from Metrohm with the same diameter (3 mm) have been used for this study, namely glassy carbon electrode (GCE), gold disk electrode (AuE), platinum disk electrode (PtE), and boron-doped diamond electrode (BDDE). In addition, GCE was modified with a thin layer of MWCNTs (GCE/MWCNTs). Pyrolytic graphite electrodes (PGE) prepared at the Institute of Biophysics of the Czech Academy of Sciences were also tested. All voltammetric measurements were carried out through a common three-electrode system consisting of one of the tested working electrodes, saturated calomel electrode (SCE) with 99.9% MeCN containing 0.1 mol L\({}^{-1}\) LiClO\({}_{4}\) as a salt bridge (reference) and platinum wire (auxiliary electrode) in a conventional electrochemical glass cell. The electrode system was connected to a potentiostat/galvanostat PGSTAT101 operated with Nova 1.11 software from Metrohm. This software enables evaluation of peak-shaped electrochemical signals with several evaluation methods, such as linear and polynomial baseline and reading of peak current from zero base. Moreover, a deconvolution method was performed by OriginPRO\({}^{\text{\textregistered}}\) software version 9.0 from OriginLab Corporation (Northampton, MA, USA). The instrumentation used together with evaluation methods are shown in Figure 1.
## 2 Materials and Methods
### Test Scheme  
The direct shear apparatus was chosen as the test machine. The machine is mainly composed of three parts: a shear box, a loading system, and displacement measurement system. Both normal and shear stress loading systems provide a maximum load of 500 KN with a load accuracy of 0.01 KN. The displacement measurement system consists of a normal displacement sensor and a shear displacement sensor. The loading method of the testing machine includes force loading and displacement loading.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Quartz** & **Feldspar** & **Montmorillonite** & **Calcite** & **Mica** \\ \hline Content & 64.2 \% & 24.4 \% & 5.4 \% & 4.9 \% & 1.1 \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock mineral composition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Compressive** & **Tensile** & **Basic Friction** & **Elastic** & **Density** \\
**Strength (MPa)** & **Strength (MPa)** & **Angle (\({}^{\circ}\)) [53]** & **Modulus (GPa)** & **(kg/m\({}^{3}\))** \\ \hline Dry & 43.81 & 4.01 & 34.3 & 6.08 & 2388 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic mechanical parameters of red sandstone.  
In this study, direct shear tests were used to simulate fault block slip in natural state. The normal stress, surface roughness, and slip displacement were selected as variables for indoor small-scale test research. The shear stiffness of faults under different test conditions was measured. To reduce the cross-influence between different factors, this test adopted the method of controlling a single variable. Only one influencing factor was changed in each group of tests to reduce the interaction between different factors.  
The specific test process is divided into two stages: the pre-loading stage and the formal-loading stage. During the pre-loading stage, a shear force preload is required to eliminate poor interlock between surface asperities and ensure the accuracy of the shear stiffness calculation. The specific steps of the pre-loading stage are to load the normal force to the target value and then load the shear force. After the shear displacement reaches 1 mm (Figure 2b), the shear and normal forces are unloaded. During the formal-loading stage, the normal force is first loaded at a loading rate of 0.5 KN/s. When this normal force is loaded to the target value, it remains constant, and then the shear displacement is loaded at a loading rate of 0.05 mm/s. Until the shear displacement at this stage reaches 4 mm (Figure 2b), the shear and normal forces are unloaded. When the force is completely unloaded, the second stage is repeated until an unstable slip occurs or the fault cumulative slip displacement reaches 41 mm.  
Figure 1: Sample photographs. (**a**) Photograph and dimension of the rock samples. (**b**) Photo of the 3D laser survey of the fault surface, which was roughened with #60 silicon carbide git sandpapers.  
Figure 2: Fault block slip experiment. (**a**) Shear test apparatus (LVDT: Linear Variable Displacement Transducer). (**b**) Schematic diagram of loading method.
## 3 Results
### Optimal Regression Model for the RF, SVR, and MLR  
For the RF model, before selecting the best RF regression model, the mtry and ntree parameters must first be determined. Based on the number of selected variables, an error rate loop algorithm was used to calculate the smallest error rate. In Figure 6a, it is clear that mtry = 5 resulted in the smallest error rate. In Figure 6b, ntree = 257 was determined to be the best parameter.  
In the SVR algorithm, 909 models were trained. The best model that was selected had cost = 8, gamma = 0.125, and epsilon = 0.68. Then, the best SVR regression was used to build the regression model for the FSV prediction. In the MLR algorithm, the results of the MLR model are shown in Table 5.  
A basic hypothesis test was made for the linear model (Figure 7). In Figure 7, the top left graph, the residuals versus the fitted values, was used to test the linearity assumptions. The scatter points were concentrated near a straight line, which indicated that the linear relationship was good. The top  
Figure 6: (a) Distribution of the error rates versus mtry, and the best mtry was 5. (b) Distribution of the error versus the number of trees, and the best ntree was 257.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Estimate** & **Std. Error** & **t Value** & **P** \\ \hline (Intercept) & 5.6320995 & 0.9240733 & 6.095 & 3.17e \(-\) 09 *** \\ B5 & \(-\)0.005478 & 0.0004089 & \(-\)13.397 & \(<\) 2e \(-\) 16 *** \\ MSI & 1.9034603 & 0.4423448 & 4.303 & 2.24e \(-\) 05 *** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Coefficient of the MLR model.  
right graph was a normal Q-Q plot, which was used to test the normality. The scatter points were mainly concentrated along the straight line, which indicated that the residual normality was good. The bottom left graph was the scale-location graph, which was used to test the homoscedasticity. The points were randomly distributed around the curve, which indicated that the homoscedasticity assumption was accepted. The bottom right graph used the residual and leverage to determine the outliers, and no high leverage points or strong influence points were found.
# Recent Advances in Extraction and Stirring Integrated Techniques
## 1 Introduction  
Microextraction techniques are physicochemical processes based on the mass transference between, at least, two different phases. Mass transference is driven by thermodynamic and kinetics factors, both of which have a dramatic impact on the extraction yield [1]. Thermodynamics defines the maximum amount of analytes that can be extracted by a technique while kinetics defines the rate at which this transference occurs. The microextraction thermodynamics is mainly described by the distribution constant and many factors, such as the use of secondary reactions or the proper selection of the working pH, may affect this partitioning equilibrium. However, microextraction techniques usually work under diffusion controlled conditions. This situation, a direct consequence of the size difference between the sample and extractant phases (the diffusion paths of the analytes become larger), is of paramount practical importance. In short, a thermodynamically favored but slow technique is not suitable for analytical purposes, as it would provide a very low sample throughput. Therefore, kinetic factors must be deeply considered in any microextraction development. Among these factors, the extraction surface and the Nernst boundary layer can be highlighted. On one hand, the extraction kinetics are dependent on the area of the interface between the sample and the extractant. This phenomenon is behind the development of dispersive techniques [2] where the liquid or solid extractant is dispersed in the form of fine droplets or small-sized particles into the sample. On the other hand, the thickness of the boundary layer between the bulk sample and extractant also affects the extraction rate. In this case, the higher the thickness, the lower the rate. The boundary layer thickness can be effectively reducedif the sample is agitated, enhancing the mass transference during the extraction. In fact, agitation is common to all of the microextraction techniques.  
Agitation can be done in two different ways. In most cases, the sample is agitated using an internal mechanical element (e.g., inert stir bar) or an external energy source (e.g., ultrasound) independent of the extraction element. However, some techniques are based on the integration of the agitation and extraction elements in the same device. This integration simplifies the extraction to a large extent, avoids analyte losses due to retention on external devices, and enhances the extraction yields. These techniques, that can be named as extraction/stirring integrated techniques, are the topic of this review article. This contribution reviews the recent developments of these techniques rather than focusing on the applications reported in the field.
## 3 Simulation Experiments
### Datasets
#### 3.1.2 Data Preprocessing
The data collection periods span multiple months and seasons, ensuring a comprehensive representation of traffic flow under various conditions. For instance, the dataset includes data from both peak and off-peak hours, as well as data collected during holidays and non-holiday periods. This approach ensures that the dataset reflects a wide range of traffic conditions, making the results of our study more generalizable and applicable to real-world scenarios.
# A Review of Deep Learning Based Speech Synthesis
## 2 An Overview of Speech Synthesis
### Traditional Speech Synthesis Technology  
To understand why deep learning techniques are being used to generate speech today, it is important to know how speech generation is traditionally done. There are two specific methods for TTS conversion: concatenative TTS and parametric TTS. This paper will give a brief introduction to the two kinds of methods in the following sections.
# _remote sensing_
## 3 Results
### Coupling EO Data with Crop Models for Yield Prediction
The yield of the two fields with a shorter growing cycle (Figure 7_f_g) was overestimated, probably due to the rather poor yield (1.1 and 1.4 t-ha\({}^{-1}\)), which is more difficult to simulate for a relatively simple model such as SAFY due to the involvement of various sources of stress (Figure 8). Crop models in general, including SAFY, are reported to perform poorly when yield is greatly limited by severe stress [48]. Lower yields can result from limitations that are rarely simulated by this kind of dynamic crop model, such as weeds, pests and diseases [49; 50]. In another field the model showed poor performance despite the assimilation, simulating 2.7 t-ha\({}^{-1}\) of grain yield with respect to the 5.7 t-ha\({}^{-1}\) measured at harvest (Figure 7). The field was the largest, and showed a slow and uneven wheat growth that may not have been adequately caught with the use of a single LAI curve for the entire field.
## 2 Theory and Experimental Methods
### Experimental Apparatus  
In situ attenuation experiments were conducted in floating fish farm cages in March 2019. These cages were built for research purposes in the Tongyeong area at the Korean South Sea. The sea depth of the experimental site was about 14 m. The attenuation experiments were performed under artificial conditions. Movement in the ocean water was minimized by performing the experiments at near tides for the least influence in the net cage.  
The target fish were live horse mackerels. The total length (\(L\)) of the fish was measured using 12 randomly sampled mackerels. \(L\) ranged from 20 to 35 cm, with a mean of 27.7 cm. \(L\) of the fish showed a Gaussian distribution, and it appeared as a colony similar to actual fish schools. A previous study calculated the mean weight (\(W\)) of fish to be approximately 217 g [24].  
Direct net cage methods were used for quantitative attenuation measurements. The net cage was fabricated using a thin mesh, which was small and rectangular, thereby minimizing its influence on acoustic waves. A detachable door was cut at the top to place the fish in the net cage, which had a volume of 1 m\({}^{3}\) (1 \(\times\) 1 \(\times\) 1 m). The experiments were performed in seven ways: Exp1, non-net; Exp2, only net (individual fish: 0 ind.); Exp3, 100 ind.; Exp4, 200 ind.; Exp5, 300 ind.; Exp6, 400 ind.; and Exp7, 500 ind. in the net cage. Table 1 lists the details of the attenuation experiments according to the number of individual fish. In the attenuation experiment, the fish were released into the net cage and allowed to form schools. An underwater camera was installed to observe the shape and condition of the net cage.
## 5 Applications and Discussion
### Cross-Section Inversion Results  
A comparative analysis of well-linked cross-sections and inversion profiles in the Lower Second Member of the Dongying Formation reveals that, due to boundary constraints in the study area, sandbody boundaries extend outward, and thin sand layers (3-8 m) are stably distributed. The inversion profile clearly delineates effective sandbodies in individual wells, with accurate prediction of sandbody pinchouts (Figure 13).  
Petrophysical analysis and application results demonstrate that the seismic waveform-indicated inversion technique can identify 1.8-3.0 m-thick thin sandstone layers, with a mean match rate of 90.2% (95% CI: 87.5-92.7%, n = 12) for participating wells and 81.5% (95% CI: 76.8-85.3%, n = 11) for validation wells, based on bootstrap resampling with 1000 iterations. Additionally, the waveform-based facies-controlled constrained inversion exhibits robustness to well distribution constraints, as evidenced by spatial cross-validation results, while maintaining geological consistency.  
To address the uncertainty in thin-layer sand body prediction, this study attempts to integrate waveform-indicated inversion with SOM. By employing facies-controlled inversion and uncertainty propagation analysis, the goal is to control reservoir prediction errors within 10%, enhance vertical resolution, improve thin-layer identification accuracy, and reduce ambiguity in thin-layer predictions. However, significant room for innovation remains. Future efforts could further explore deep learning domains and real-time uncertainty feedback mechanisms to enhance exploration reliability under complex geological conditions.  
Figure 12: Verification of Waveform Indication Inversion results against Log-Interpreted Lithology for well C4-3-a (T3u-T3 seismic reflection horizons).
## 3 Application of Drive-by Methodologies on the Bridge Subsystem
### Bridge Damage Identification
#### 3.2.3 Machine Learning Methodologies
Cluster analysis can be seen as an alternative to these techniques as it allows distinguishing different groups in the data without any previous knowledge or known reference baseline [57]. [PERSON] et al. [77] implemented deep learning algorithms to perform damage identification using measurements from an instrumented train. Therefore, a deep CNN was built, optimized, trained, and tested to detect damage based only on acceleration signals acquired from the train. The authors concluded that to harness the power of 2D CNN, time-frequency analyses offer powerful images with more distinctive features for classifying train-borne acceleration signals. Among different approaches, the time-frequency spectrogram of the signals was found to provide the best high-level damage sensitive feature. The hyperparameters of the algorithm were optimized using the Bayesian optimization technique. For validation purposes, the method was applied to measurements acquired on a model instrumented train travelling on a simply supported model steel bridge. The algorithm showed good accuracy when experimentally tested for four damage scenarios (combination of two different locations and intensity) and three different travelling speeds. Figure 15 presents a spectrogram of acceleration responses for the benchmark and four damage scenarios under an average speed of 4 miles/h. In this figure, the far-left column corresponds to the sample of five sets of measurements for healthy scenarios. The other four columns denote samples with different damage intensities and locations. The difference between healthy and the damage scenarios is relatively noticeable; however, the pattern does not appear to be consistent all the time.
## 3 Shortest Queue Length-Cluster-Based Routing Protocol  
In the traditional satellite scenario, one satellite can establish up to four inter-satellite links. But some researchers have proposed encountering inter-satellite links (eISLs), allowing satellites to communicate without predetermined inter-satellite links when they are visible to each other [17]. In this way, one satellite can establish more than four ISLs. Based on the concept of eISLs, the scenario considered in this article is as shown in Figure 1. The satellite nodes are divided into multiple cluster structures, with members within a cluster directly connected to the cluster head. The cluster heads are interconnected to form a satellite self-organizing network. Users connect to the satellite and communicate via the satellite network.  
Based on this scenario, a new routing algorithm called Shortest Queue Length-Cluster-Based Routing Protocol (SQL-CBRP) is proposed. In this algorithm, different nodes perform different functions. The specific functions are as follows:  
* Cluster head: The cluster heads are generally located at the center of the cluster and are responsible for managing the cluster structure, maintaining network topology information, routing computation, and forwarding data packets;
* Cluster member: The cluster members orbit around the cluster head, directly connected to the cluster head. When a node has data to forward, it sends the data to the cluster head;
* Ground control node: The ground control node can communicate with all satellites in the network. When receiving a request to join the network from a satellite node, the ground control node assigns it to the nearest cluster head or creates a new cluster head, and allocates time slots for it;  
Figure 1: Satellite self-organizing network scenario.  
* User: Users on the ground send data to a certain satellite. The data can be forwarded to the destination through the satellite network, and finally arrive at another user.
# Ecological Dynamics and Evolution of Cooperation in Vehicular Ad Hoc Networks
## 3 The Proposed Model Based on Evolutionary Public Goods Game  
The problem of cooperation in social dilemmas has been a challenging issue in evolutionary game theory for a long time. In Vehicular Ad Hoc Networks (VANETs), where effective communication and safe driving depend on vehicle cooperation, this problem becomes even more complicated due to the dynamic and uncertain nature of the network environment. To address this issue, we propose a new model for studying the ecological dynamics and evolution of cooperation in VANETs based on the evolutionary public goods game. The model takes into account the interaction between the payoff structure, spatial structure, and dynamics of network topology. Specifically, the model assumes that vehicles can interact locally and update their strategies based on the payoffs they receive from their neighbors. Furthermore, an evolutionary mechanism allows vehicles to adjust their cooperation strategies according to their own success and the success of their neighbors. The proposed model provides insights into the factors that influence the evolution of cooperation in VANETs. Overall, the proposed model offers a promising approach for understanding the evolution of cooperation in complex and dynamic social networks.