esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinesthetic Learning Based on Fast Marching Square Method for Manipulation
## 4 Experimental Evaluation of FML Algorithm
### Experiments in a Real Indoor Environment  
Once the algorithm has been tested in 2-dimensional environments, it has been decided to test it in a 3-dimensional indoor scenario. The bimanipulator robot described in Section 3.1 is deployed on the test environment, where there is a fixed obstacle (the table) and dynamic obstacles (boxes) which can vary in the environment. For the correct association between the real environment and the validation of the method, a simulated environment was created with the same shape and size as the real one. It was modeled using collisionable objects provided by MATLAB. This environment additionally allows us to check the robotic arm configurations, thus avoiding collisions with the rest of the arm, since even if the end effector correctly follows the calculated path, the rest of the arm may collide with obstacles in the environment or with itself. Both environments can be seen in Figure 22.  
To test the algorithm, different experiments were carried out. First, an experiment was performed in an environment with no obstacles other than the table. The second experiment takes into account one obstacle (box) during data acquisition. The third experiment uses data from the first experiment and adds an object dynamically. The fourth experiment consists of testing the algorithm performance against segregated data. Finally, a fifth test was carried out in which the method has been confronted with a series of incorrect and unconnected data. As an additional resource, in the following link, a video with all the performed experiments can be found: [[https://youtu.be/_sklRg0](https://youtu.be/_sklRg0) NCM8]([https://youtu.be/_sklRg0](https://youtu.be/_sklRg0) NCM8) (accessed on 1 February 2023).  
For all experiments, tests were performed with different values of saturation and areas of influence. For the representation in the paper, a series of fixed values were selected for the tests, which are the following: \(aoi=5\ vx\), \(sat=0.25\) and \(\kappa=0.6\) as the auto-learning limit. It is important to emphasize that the method does not generate any type of position error when working in the simulated environment because the algorithm is complete and always generates a possible solution if it exists. In contrast, when the paths are sent to the real robot, there is a small error due to the minimum resolution for each of the robot's joints. The value of this resolution is \(0.1^{\circ}\) which generates an approximate error in the position of \(\pm 0.05\) cm in each of the coordinates. Since the resolution we are working with is \(1\) cm, this error is assumed and does not generate any type of inconvenience in the generation of the solution trajectories.  
Figure 22: Experimental environment for FML method in 3 dimensions: (**a**) real environment; and (**b**) simulated environment. Obstacles are represented with convex collision object (brown) and the UR3 arm is represented with the same pose as the real one.
# A Review of Blast Loading in the Urban Environment
## 4 Experimental Methods and Instrumentation
### Pressure Measurements
#### 4.2.3 Piezoresistive/Piezoelectric Pressure Sensors  
Piezoresistive-type pressure gauges (along with piezoelectric-type) are widely used for pressure and impulse data acquisition throughout experimental work within the blast engineering field [133; 134]. [PERSON] et al. [135] use such instrumentation to study angle of incidence effects on positive and negative phase blast parameters in the far field. Four Kulite HKM piezoresistive pressure gauges were set flush against small steel plates, mounted within a larger concrete wall. Multiple combinations of charge mass (PE4) ranging between 180 g to 350 g, standoff distance and angle of incidence were tested. This included tests at an angle of incidence of 0\({}^{\circ}\), which can otherwise be treated as standard normally reflected tests. Test data obtained from this angle of incidence were shown to be in very good agreement with semi-empirical values (e.g., ConWep). The normalised peak overpressure was seen to deviate by no more than 4-5%, similar to the confidence levels presented in [PERSON] et al. [13]. However, it was necessary to process the raw data using curve fitting techniques due to \"_the tendency for the signals to display some degree of electrical noise_\" and \"_sensor ringing_\".  
Such pressure gauges have been extensively proven for use in blast experimentation and can therefore be used to verify more speculative and novel diagnostic techniques such as pressure-sensitive paint [136; 137].
## 4 Experiments and Discussion
### Evaluation in Real-World Environments  
To evaluate the effectiveness of the proposed method in real-world environments, we deploy control models trained using FGRL on two TurtleBot3 robots equipped with a 2D LiDAR sensor. The experiment is conducted with four obstacles of varying shapes, while the robots also serve as dynamic obstacles for each other.  
The robots start at opposite ends of the map, facing each other. The target locations are positioned at the lower-left and upper-right corners of the map. A snapshot of the experiment is shown in Figure 10.  
Both robots begin moving from their starting positions. After 5 s, they encounter the obstacles and begin to avoid them. By 7 s, both robots successfully navigate around  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trained Model** & **Success Rate** & **Crash Rate** & **Timeout Rate** &
\begin{tabular}{c} **Average** \\ **Successful Steps** \\ \end{tabular} \\ \hline TD3-env1 & 0\% & 0\% & 100\% & - \\ TD3-env2 & 0\% & 0\% & 100\% & - \\ TD3-env3 & 1\% & 39\% & 60\% & 1449 \\ TD3-env4 & 14\% & 37\% & 49\% & 1238 \\ FL-TD3 & 31\% & 69\% & 0\% & 1158 \\ FGRL & 79\% & 21\% & 0\% & 924 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The evaluation results in unfamiliar factory environment.  
Figure 10: Snapshots of real robot experiments.  
the obstacles and head toward their target locations. By 11 s, both robots reach their respective targets.  
To assess the reliability of the method, we conducted 20 repeated trials using the same setup. The navigation success rate and the time taken for each robot to reach its target location were recorded. Across all 20 trials, the navigation success rate was 85%, with the average time for successful navigation being 11.89 s. These results demonstrate that the proposed method is effective in real-world environments and that the FGRL approach is adaptable and robust in unfamiliar settings.
## 4 Implementation Details  
Since the focus of this paper is on presenting a proof of concept for the design of FROG-MAC, the high-level implementation of the protocol has been developed in MATLAB. It is to be noted that the packets are not actually transmitted or received but the delay and PDR is calculated based on the values stored in arrays. Furthermore, other schemes have also been implemented in order to validate the performance of FROG-MAC. This section presents some details of the algorithms for the interest of reader.
# Configuration of Non-Pumping Reactive Wells Considering Minimum Well Spacing
## 3 Results and Discussion
### Modeling Contaminant Transport through NPRWs
#### 3.2.2 Evaluation of NPRW Performance  
The performance of NPRWs was evaluated quantitatively using the contaminant removal efficiency and the number of wells in the unit segment, respectively (Figure 5). When wells are arranged in a multiple transect configuration considering the minimum separation distance, if the number of wells in a unit segment is small (when the number of transects is insufficient), sufficient contaminant removal performance cannot be expected. Even in a configuration of three or more transects, the contaminant removal efficiency may differ depending on the well arrangement method. These results suggest that the sawtooth array of NPRW could be used effectively in contaminant spreading prevention using a small number of wells while maintaining minimum well spacing for technical safety. In addition, to completely capture the contaminant plume using multiple transects of reactive wells, the sawtooth array could also be the optimal arrangement for the reactive wells considering the removal efficiency as well as the field constraints for the well construction.  
The optimization of the well configuration should be considered based on two criteria--the stability of well construction and contaminant removal. Among these criteria, if the construction stability of the well is prioritized to classify the NPRW, the number of transects can be a major factor in determining the shape of unit transect. For effective arrangement of the reactive wells, the number of transects is equivalent to the number of wells in a unit segment (N). Then, single- and double-transect configurations, shown in Figure 5a,b, could be the practical configurations for the cases of N = 1 and 2. The sawtooth array, shown in Figure 5d, could be a practical configuration for N = 3. Limited to these efficient multi-transect well configurations, there is a proportional relationship between the number of wells in the unit segment (N) and the removal efficiency (Re).
# The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation
## 4 ACO for Scheduling Production and Maintenance Tasks
### Update of the Pheromone Trace for Makespan Optimisation  
The formula for updating the pheromone trace locally is modified in order to perform makespan optimisation. The reduction of the pheromone trace is calculated using.  
\[\Delta\tau_{K}(r,s)=\tau_{0}=\frac{1}{n\cdot C(nn)} \tag{14}\]  
where: \(C(nn)\) is the end date of the last task in the schedule (makespan):  
\[C(nn)=\max\left[tz_{Vj}\right] \tag{15}\]  
\(tz_{vj}\) is the completion time of operation \(v_{j}\) of job \(j\), \(v_{j}=1,\ldots,V_{j}\), \(j=1,\ldots,J\).  
The increase of the pheromone trace is calculated from  
\[\Delta\tau_{K^{**}}(r,s)=\left\{\begin{array}{cl}\frac{1}{C(nn)},&\text{for }(r,s)\in L_{K^{**}}\\ 0,&\text{for }(r,s)\
otin L_{K^{**}}\end{array}\right. \tag{16}\]  
where: \((r,s)\in L_{K^{**}}\)--job sequence belonging to the global best schedule, \(K^{**}\)--index of the ant that discovered the best schedule.  
The steps of the ACO are presented in Figure 2. The next Section presents a job shop (JS) scheduling problem for experimental study to better understood the steps of the ACO.  
Figure 2: The steps of the ACO.
## 1 Introduction
However, faced with a change in the environment, a linear controller cannot actively adjust the control output, which shows a lack of sufficient robustness; (3) Based on the switching of two working modes (water and air), sudden model changes can easily cause system instability. Inspired by the above scholarly works and referencing existing coaxial aircraft, the authors developed a coaxial HAUV system in this work, as shown in Figure 1. The HAUV has two main modes of operation. When carrying an auxiliary float ball, it can be used as a drifting buoy. In this case, it is buoyant enough to float freely and does not need extra energy consumption to maintain the floating state. When the floating ball is abandoned, the gravity of the HAUV will be greater than its buoyancy (negative buoyancy stage). At this time, the HAUV can enter under the water for underwater sampling, underwater maneuvering, and other tasks. In order to collect a large range of hydrological data, the coaxial HAUV designed in this study needs to take off and land repeatedly on the water's surface. To realize repeated cross-domain motion of the coaxial HAUV, a non-singular fast terminal sliding-mode controller (NFTSMC) based on the adaptive law was proposed. Compared with existing controller designs, the controller designed in this study improves response speed and realizes finite-time convergence of control errors. The adaptive law further improves the robustness of the controller to lumped uncertainties. The rest of this paper is organized as follows: In Section 2, the design scheme and operation modes of the coaxial HAUV are explained, the modeling of the coaxial HAUV is analyzed in detail, and the dynamic modeling of the HAUV is presented in combination with our previous work. In Section 3, the design of the adaptive non-singular fast terminal sliding-mode controller (ANFTSMC) is explained, and its finite-time stability is proven using the Lyapunov method. In Section 4, the Simulink simulation results and explanation are provided to demonstrate the effectiveness of the proposed algorithm. Finally, Section 5 offers a brief conclusion and discussion as well as an outlook for future work plans. The acronyms and full names used in this paper are described in Table 1.
# Drowsiness Detection in Drivers Using Facial Feature Analysis
## 3 Methods
### Adaptive Frame Threshold
Here, CE (Consecutive Eye frames) represents the successive frame threshold value for eye closure, while DURATION signifies the elapsed time in minutes. Table 2 illustrates the CE values corresponding to various TIME intervals, measured in 5 min increments. Adaptive Frame Threshold Algorithm  
Table 2 illustrates the implementation of the adaptive frame threshold algorithm. Four 30 min videos underwent processing using this algorithm. For each video, it was ensured that frames were captured and checked every 0.1 s to avoid redundancy by checking similar frames at a rate of 15 frames per second, as depicted in Figures 11 and 12. This setup allows for checking 8 frames within 0.8 s, adhering to the algorithm's criteria. Subsequently, these preprocessed videos are inputted into the algorithm. While streaming, the Dlib face detector scans each frame for facial presence. Upon detection, the detector localizes the face within the frame, and the Dlib shape predictor identifies 68 facial points, primarily focusing on the eyes and mouth areas. The algorithm proceeds to compute the eye closure ratio (ECR) and mouth aperture ratio (MAR) using the coordinates from these regions. The calculated ECR is compared against the predefined threshold of 0.15. If the ECR is below this threshold, a counter initiates. The count increments when consecutive closed-eye frames are below the Continuous Eye frames (CE) threshold. Once the count reaches CE, a drowsy warning message is issued to the driver. Similarly, using markings around the mouth region, MAR is calculated with a threshold set at 0.4. Exceeding this threshold initiates a counter that increases with consecutive yawning frames below the Consecutive Mouth frame threshold (CM). Upon reaching CM, a drowsy warning message is triggered. Furthermore, the algorithm addresses scenarios where the driver yawns with closed eyes. If both ECR and MAR meet their respective drowsiness thresholds, a counter begins counting frames. This count increases while it remains below the average of CE and CM, derived dynamically based on elapsed duration. Once the count equals this mean threshold, a warning message is triggered. It should be noted that these thresholds are dynamically adjusted based on the elapsed duration.
## 4 Experimental Setup and Data Acquisition
The two stations are located where many obstructions are prone to multipath effects. The GNSS data collected between 1 January 2021 (DoY 1) and 5 May 2021 (DoY 125) were used for this study. The multi-GNSS observables were collected with zero elevation angles. The multi-GNSS data were retrieved every 1 s and 30 s epoch in daily RINEX 3.04 files containing raw, dual-frequency pseudorange and carrier phase observations. The post-fit residuals of the two stations were estimated using the GAMIT/GLOBK software package (Ver 10.7) as part of a regional network in a double differencing strategy; see the details in Section 6. We applied all four GNSS observations with an elevation cut-off angle of 3\({}^{\circ}\) together with high-precision GNSS orbits and clock products from the centre for orbit determination in Europe (CODE) in the IGS14 reference frame [54]. Additionally, essential input data from CODE, such as Earth orientation parameters, eclipse shadow events, and external products such as ocean tidal models, were employed. We used the tidal constituents' coefficients of the FES2004 model [55]. For neutral atmospheric delay modelling, we used VMF1 gridded map products from [56] that include both dry and wet delays and coefficients of the mapping functions that provide the variability of the atmospheric delay over the whole elevation range. The VMF1 grid maps were constructed from the European Centre for Medium-Range Weather Forecasts (ECMWF) weather model based on global meteorological data. We also corrected the carrier phase observations for the ionosphere effects through the so-called mathematical linear combinations, eliminating the first-order ionosphere contributions. The higher-order calibrations using CODE global ionospheric maps for the remaining ionosphere effects in combination with the new generation magnetic field model, IGRF12, were implemented. The individual antenna calibration models were used. We also estimated the ambiguity of integer values depending on the baseline length. Furthermore, the atmospheric parameters were estimated every 2 h, including the zenith wet delay and gradient parameters in the east-west and the north-south components, to model azimuthal asymmetries. The zenith wet was constrained delay at 5 cm per square-root hour between the epochs and for the two gradients at 5 mm per square-root hour. We also processed the two close-by stations using GipsyX v1.7 software [57] in a precise point positioning (PPP) strategy. We included all the latest satellite orbit and high rate clock products from JPL. The post-fit residuals from the two strategies produced a consistent estimate.
## 3 Results
### Anticancer Potential of the CH6 Extract  
Cancer cells, including human lung cancer A549, hepatoma HepG2, breast cancer MCF-7, colon cancer HT-29, and non-cancerous HEK-293 cell lines, were utilized to assess the cytotoxic potential of the CH6 extract. HepG2 was the most sensitive to exposure to CH6, with an IC\({}_{50}\) value of 18.0 \(\pm\) 4.1 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, followed by MCF-7 (IC\({}_{50}\) of 22.5 \(\pm\) 4.3 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) and A549 (IC\({}_{50}\) of 53.7 \(\pm\) 6.4 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) (Figure 3A). HT-29 was less sensitive, as indicated by an IC\({}_{50}\) value of 73.4 \(\pm\) 7.8 \(\mathrm{\SIUnitSymbolMicro g}\)/mL. Notably, the CH6 extract showed a low cytotoxicity against normal cells such as HEK-293, with an IC\({}_{50}\) value of 143.0 \(\pm\) 11.2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, which was about 7.9-fold and 6.4-fold higher than that for HepG2 and MCF-7, respectively.  
Upon treatment with 6-24 \(\mathrm{\SIUnitSymbolMicro g}\)/mL extract, HepG2 morphologies changed. Nuclear staining with Hoechst 33342 showed distinct changes to morphology and cell numbers among the treatment groups. Control cells exhibited round, uniformly stained nuclei with a high cell density, consistent with normal cellular morphology. In contrast, cells treated with 6-24 \(\mathrm{\SIUnitSymbolMicro g}\)/mL CH6 showed a gradual decrease in nuclear integrity, with signs of condensation, fragmentation, and reduced cell numbers at higher concentrations, indicating the induction of apoptosis. The results suggest that the CH6 extract induces apoptotic cell death in HepG2 cells.
## 3 Results and Discussion
### Relationship Between Composition and Collection Area
The first principal component analysis (PCA 1) in Figure 9b was positively correlated with five aromatic compounds: three esters, one alcohol, and one aromatic hydrocarbon. These include ethyl 2-methylbutanoate and ethyl butyrate, which are the aroma compounds that contribute most to the fruit's odor, according to Table 5. In the same figure, the AbHb22 and AbHa22 samples were different from the AbM22 sample, showed higher levels of aroma compounds, and therefore may have a more powerful flavor. This may be due to the higher altitude and cooler climate of these two areas, as already noted for grapes [80] and cherries [82]. The second principal component analysis (PCA 2) was mainly linked to one ester and one alcohol too, with fruit from the Malanje and Huambo areas containing higher levels of (Z)-hex-3-enyl acetate and (E)-hex-2-en-1-ol, while fruit from the Huila area correlated better with one alcohol (hexan-1-ol). However, as the latter had a rather low OAV, it is likely that the aromatic differences were subtle between the Huila and Huambo areas. In conclusion, the PCA indicated regional variation in the composition. Nevertheless, multi-year sampling and controlled agronomic trials would help to distinguish between environmental and genetic influences on fruit quality more effectively.
###### Abstract  
Solar resource assessment is fundamental to reduce the risk in selecting the solar power-plants' location; also for designing the appropriate solar-energy conversion technology and operating new sources of solar-power generation. Having a reliable methodology for solar irradiance forecasting allows accurately identifying variations in the plant energy production and, as a consequence, determining improvements in energy supply strategies. A new trend for solar resource assessment is based on the analysis of the sky dynamics by processing a set of images of the sky dome. In this paper, a methodology for processing the sky dome images to obtain the position of the Sun is presented; this parameter is relevant to compute the solar irradiance implemented in solar resource assessment. This methodology is based on the implementation of several techniques in order to achieve a combined, fast, and robust detection system for the Sun position regardless of the conditions of the sky, which is a complex task due to the variability of the sky dynamics. Identifying the correct position of the Sun is a critical parameter to project whether, in the presence of clouds, the occlusion of the Sun is occurring, which is essential in short-term solar resource assessment, the so-called irradiance nowcasting. The experimental results confirm that the proposed methodology performs well in the detection of the position of the Sun not only in a clear-sky day, but also in a cloudy one. The proposed methodology is also a reliable tool to cover the dynamics of the sky.  
Keywords: irradiance nowcasting; Sun detection; sky dynamics; image processing; particle filtering +
Footnote †: journal: Applied Physics Letters