esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work
The benefits of the robotic facilitator included time management as an important element in facilitating group activities, accuracy and focus, which increased the efficiency of repeated activities, and the objectivity of the nonjudmental aspect of a robotic facilitator compared with a human facilitator [8]. The key drawbacks of the robotic facilitator were related to the robot's technical limitations in communication and responsiveness [8]. The authors concluded that Robot-Supported Collaborative Learning (RSCL) is a promisingnovel paradigm for higher education and that social robots can be used as efficient, objective, social facilitators that can support small-group activities in the classroom [8]. The study by [21] explored the application of the NAO robot as a facilitator of brain-storming activities in groups of students. The authors conducted a between-subjects experiment with 54 university students, in which brainstorming facilitated by the NAO robot was compared with human facilitation. The study explored whether the robot condition influenced productivity. The results showed that there was no effect on the productivity of the brainstorming activity facilitated by a social robot compared with a human facilitator [21]. Since robotic facilitation did not result in generating more or fewer ideas, the authors concluded that a robot facilitator can be effectively applied to support brainstorming activities, especially in cases when a human facilitator is unavailable or undesirable [21]. The study by [22] used the NAO robot as a teachable, adaptive robot in higher education settings and investigated whether robotic encouragement of group discussions can enhance social engagement and improve learning. The NAO robot was applied as a novice learner and an active member of a team. The robot was designed to influence group dynamics by encouraging and balancing social engagement in pairs of adult participants. In a study with 68 participants, pairs of learners were encouraged to take on the teaching role. The study showed that the robot's adaptive encouragement resulted in more dialogues between pairs of learners and increased both task and social engagement in the group [22]. Nevertheless, possible drawbacks of applying a social robot as a facilitator of collaborative learning activities need to be taken into consideration. These include repetitive and predictive behaviour, which may have a negative impact on interest and motivation [23]. Further drawbacks include issues related to the current technical limitations of social robots, such as limited speech recognition capabilities, and issues related to the limited adaptability of social robots to situations and learners [8]. Moreover, as indicated by a number of studies, positive effects reported by participants interacting with social robots may result from the experience of novelty, which may fade away with time [8].
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 4 Deviation Correction Control Method of Tracked Robot  
### Control Mechanism of Deviation Correction  
The geodetic coordinate system is XOY, the robot body coordinate system is xoy, the current position coordinate of the robot is (\(x_{1}\), \(y_{1}\)), and the target position coordinate is (\(x_{2}\), \(y_{2}\)). \(\alpha\) is the yaw angle, as shown in Figure 6. \(\alpha_{e}\) is the angular deviation. \(x_{e}\) and \(y_{e}\) are, respectively, the deviation of the target position in the \(x\) and \(y\) directions under the robot body coordinate system; \(\Delta X\) and \(\Delta Y\) are, respectively, the deviation under the \(x\) and \(y\) directions under the geodetic coordinate system. Then, the position error \(p_{e}\) in the robot body coordinate system can be expressed as  
\[p_{e}=\left[\begin{array}{c}x_{e}\\ y_{e}\\ \alpha_{e}\end{array}\right]=\left[\begin{array}{ccc}\cos\alpha_{e}&\sin \alpha_{e}&0\\ -\sin\alpha_{e}&\cos\alpha_{e}&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}\Delta X\\ \Delta Y\\ \Delta\alpha\end{array}\right] \tag{11}\]  
Using the trajectory error of the robot, the trajectory tracking control of the robot can be realized by adjusting the speed difference of the tracks on both sides of the robot.
## 3 Results
### Emission Characteristics
#### 3.2.4 Nitrogen Oxide Emissions (NO\({}_{\rm x}\))  
Many studies have shown that biodiesel fuels produce higher engine NO\({}_{\rm x}\) emissions compared to diesel [63; 64; 65; 66; 67; 68; 69; 70]. Figure 8 displays NO\({}_{\rm x}\) emissions of _MOB_ blends and diesel at various engine speeds. Several factors influence the production of NO\({}_{\rm x}\) and one of them is the oxygen content. In general, vegetable oil-based biodiesel have higher oxygen content (with a difference of 12% relative to diesel) as well as low nitrogen content. This results in higher NO\({}_{\rm x}\) emissions when there is an increase in the combustion chamber temperature, which improves the combustion process [66]. The MOB20 blend has the highest NO\({}_{\rm x}\) emissions (416 ppm) at an engine speed of 2400 rpm. _Moringa oleifera_ biodiesel has more oxygen content as compared to neat diesel fuel. Besides, NO\({}_{\rm x}\) emissions increase with an increase in the concentration of biodiesel in fuel blends. Average NO\({}_{\rm x}\) emissions are lower for diesel compared to MOB10 and MOB20 by 4.71% and 8.12%, respectively. [PERSON] et al. [71] found that fuel blends containing 10% and 20% of palm biodiesel reduce NO\({}_{\rm x}\) emissions by approximately 3.3%. [PERSON] et al. [70] discovered that a fuel blend containing 10% biodiesel produces higher NO\({}_{\rm x}\) emissions by 9% relative to diesel. In general, biodiesel have a higher adiabatic flame temperature because of their high unsaturated fatty acid content, leading to more NO\({}_{\rm x}\) emissions [69]. The higher viscosity and density of biodiesel are also responsible for higher NO\({}_{\rm x}\) emissions [33].  
Figure 7: Carbon monoxide emissions of _Moringa oleifera_ biodiesel blends and diesel at various engine speeds.
## 2 Analysis and Design of a Sun Tracking Control System
### Problem Formulation  
The stability problem of the sun tracking system is because the sun position sensor is influenced by the presence of clouds. A method to suppress the influence of the sensor includes the signal filter, LQG control and H\(\alpha\) control. However, in this case, the sun position sensor will be fully shaded from the clouds. Hence, under a sightless condition, it is too hard to apply the controller to the above method to solve the stability problem.  
In the sightless condition, we can stop the tracker tracking or still track according to the expected sun trajectory which stores the equivalent position encoder counts in the memory. In the field, if we stop the trackers, most of the trackers will not be capable of aligning to each other because the sensitivity of the sun position sensor is different and the area of light shaded by cloud is not the same.  
In this case, we apply the concept of LBS control to the sun tracking control system (Figure 4). The concept of LBS control includes controller 1 for tracking and controller 2 for stabilizing trackers. The switch(S) is the output of LBS control for switching between controllers and might be crucial in cloudy conditions. Suppose that the considered sensor of the azimuth/elevation tracking system (controller 1) is modeled as \(\textbf{P}_{1}\)  
\[P_{1}:\dot{x}=A_{x}x+B_{x}u_{1},y_{1}=C_{1}x \tag{3}\]  
where \(\textbf{x}\in\textbf{R}^{\text{n}}\) is the state, \(\textbf{u}_{1}\in\textbf{R}\) is the control input (the voltage to the DC motor), and \(\textbf{y}_{1}\in\text{R}\) is the measured output (the tracking error); \(\textbf{A}_{1}\), \(\textbf{B}_{1}\), and \(\textbf{C}_{1}\) are constant matrices of appropriate dimensions. The design goal is to find a static output feedback controller  
\[u_{1}=F\cdot y_{1} \tag{4}\]  
for the azimuth/elevation tracking system to achieve the minimum error \(\textbf{y}_{1}\).  
The other, sun trajectory elevation tracking system (controller 2) is modeled as \(\textbf{P}_{2}\)  
\[P_{2}\ :\dot{x}=A_{2}x+B_{2}u_{2},y_{2}=C_{2}x \tag{5}\]  
\[u_{2}=F\cdot y_{2} \tag{6}\]  
where \(\textbf{x}\in\textbf{R}^{\text{n}}\) is the state, \(\textbf{u}_{2}\in\textbf{R}\) is the control input, and \(\textbf{y}_{2}\in\textbf{R}\) is the measured output (the tracking error); \(\textbf{A}_{2}\), \(\textbf{B}_{2 find a static output feedback controller for the azimuth/elevation tracking system to achieve the minimum error \(\mathbf{y_{2}}\).  
The signal \(\boldsymbol{S}\): \([0,\,\infty)\rightarrow\boldsymbol{S}\)--called the switching signal--effectively determines which controller is in the loop at each instant of time. The points of discontinuity of \(\boldsymbol{S}\) correspond to a change in the candidate controller.
## 2 Results and Discussion
### Optimization of the Extraction Parameters
In our study, many of the VCs detected in urine samples contained the carboxylic acid group, therefore the acidic pH facilitates their extraction [21]. Another parameter important to achieving a good extraction is the presence of salt. The results of the experiments carried out with and without salt present are presented in Table 1. The presence of salt changes the nature of the molecular interactions between compounds, causing more ionic activity, and consequently affecting the activity coefficient of metabolites. It was found to have great importance in the SPME method's development, where the presence of salt facilitates the transfer of VCs from the matrix to the headspace [33]. For LLE, salt addition may alter the solubility of certain compounds in the matrix, making them more likely to transfer to the solvent. In the present study, as in LLE, the transfer of VCs to the headspace is not needed, and we did not observe a statistically significant difference in the number of peaks between the presence and absence of salt. However, taking into consideration the slightly increased number of peaks detected (even though not significant) and their size (a summarized peak area of \(5.49\times 10^{5}\) vs. \(1.09\times 10^{6}\) for no salt and salt addition, respectively), we decided to carry out the experiments with the presence of sodium sulphate. It is worth underlining that the urine contains salt, the concentration of which may vary from sample to sample, affecting the results. Therefore, it is important to standardize the urine somehow, e.g., by the analysis of the osmolality of urine samples before metabolomics analyses. In our method, the saturation of urine with salt was achieved, minimizing the differences between samples. After the evaluation of experimental conditions, the best conditions for the LLE extraction of VCs from urine samples were as follows: 2 mL of 1 M sulfuric acid and 2 mL of urine was added to a 10 mL glass vial containing 0.2 g of sodium sulphate. The vial was vortexed till complete salt dissolution. Then, 4 mL of DCM was added and mixed again for 1 min. After that, the vial was centrifuged for 1 min at 3500 rpm for emulsion separation, and the DCM layer was collected and dried with anhydrous sodium sulphate. 3 mL of the dried DCM extract was quantitatively transferred to a new 5 mL glass vial for evaporation. The last approximately 100 \(\mu\)L of extract was then transferred to a GC vial, and the previously used 5 mL vial was rinsed three times with approximately 100 \(\mu\)L of fresh DCM. After the evaporation to dryness in the heating dry block at 40 \({}^{\circ}\)C, the residue was reconstituted in 10 \(\mu\)L of DCM, and 2 \(\mu\)L of the extract was injected into the GC injector port. An example of the chromatogram obtained using the optimized method is presented in Figure 1 (red chromatogram).
## Materials and Methods  
### Study Area  
This study was conducted in the alpine grasslands on the Tibetan Plateau and the temperate grasslands on the Inner Mongolian Plateau. The Tibetan region is in the alpine climate zone, where temperature appears as the controlling factor for the grassland ecosystems. With an average elevation of ca. 4000 m, the mean annual air temperature on the plateau ranges between \(-\)5.75 and 2.57 \({}^{\circ}\)C, and the mean annual precipitation varies from 200 to 600 mm. The alpine meadow covers more than half of the plateau, representing not only a typical ecosystem in central Asia alpine environment but also a unique ecosystem within the alpine regions of the world [32]. Located in the arid and semi-arid zone, the grassland ecosystems in the Inner Mongolian region are mainly limited by moisture. With an average elevation ca. 1000 m, the mean annual temperature in this area varies between 3 and 6 \({}^{\circ}\)C, and the mean annual precipitation ranges from 200 to 350 mm. The temperate grasslands are a typical vegetation type under the temperate continental climate and they represent an important component of the Eurasian grasslands [33]. According to the Atlas of China's Grassland Resources(1:1,000,000), we fully considered the spatial representativeness of the flux-tower sites collected for this research and focused on three types: (i) alpine meadow; (ii) alpine meadow steppe (AS); and (iii) temperate steppe. The alpine meadow was further subdivided into three types: (1) alpine _Kobresia_ meadow (KO); (2) alpine shrub meadow (SH); and (3) alpine swamp meadow (SW); the temperate steppe was also subdivided into three types: (1) meadow steppe (MS); (2) typical steppe (TS); (3) and desert steppe (DS). The distribution of these vegetation functional types (PFIs) as well as the typical grassland flux-tower sites is shown in Figure 1.
## 5 Conclusions  
This investigation proposes the integration of a parabolic-trough collector field and an absorption cooling system, developed and evaluated separately. The solar collector field was evaluated in clear and cloudy days and it was shown that it can produce up to 6.5 kW of useful heat at temperatures as high as 105 \({}^{\circ}\)C. On the other hand, the absorption cooling system was developed using plate heat exchangers as the main components. The cooling system operated with the ammonia-lithium nitrate mixture, and was evaluated at different heat-source and condensation temperatures. The meaningful values achieved with the independent systems are the following:  
* The solar system can provide useful heat at rates from 1.9 to 6.5 kW at temperatures suitable for driving a single-stage absorption cooling system.
* The thermal efficiencies for the solar system varied from 8% to 19.8%.
* The cooling powers achieved with the absorption system varied between 0.11 and 1.92 kW, when the supplied heat in the generator was from 0.67 to 3.4 kW, respectively.
* It was demonstrated the exergy efficiency for the cooling system increases when the heating water temperature decreases.
* The external coefficient of performance achieved with the absorption system varied from 0.14 to 0.56.  
Once the integration of both systems is performed, the expected values for the solar coefficient of performance are in the range from 0.023 to 0.070. From the obtained results, it is possible to conclude that the proposed solar absorption cooling system will be able to be applied in air-conditioning applications offering a reasonable performance.  
[PERSON]. & [PERSON] have modeled and evaluated the solar plant for several operating conditions theoretical previously and experimentally in situ absorption cycle assessment. [PERSON]. & J.C.J.G. carried out the design and evaluation of the absorption device. U.D.C. evaluated the solar devices and the instrumentation uncertainty analysis.  
This research was funded by the _fondo sectorial_: SENER-CONACYT \"_Sustentabilidad energetica_\" grant number CeMIE-SOL-09. The APC was funded by the CONACYT through the project \"879 Proyectos de desarrollo cientifico para atender problemas nacionales 2014\".  
The authors express their gratitude to Red SUMAS, project: 293876 funded by CONACYT, Mexico. [PERSON] wishes to thank IMTA for the \"1772 Catedras CONACYT Mexico\" project. [PERSON] appreciates the economical support provided by the project CIC 2018 UNAM. Also the authors express their gratitude to [PERSON] for its help in this work.  
The authors declare having no conflict of interest.
# FD-YOLO: A YOLO Network Optimized for Fall Detection
## 3 Materials and Methods
### YOLOv9  
In this study, we used the YOLO network, which is a widely recognized AI object detection framework, for fall detection. Detecting falls in real time is crucial; however, it is equally important to accurately identify the location and number of falls to ensure timely responses. Therefore, we selected the YOLO network, which is known for its advantages in both object localization and real-time processing. After optimizing various versions of the YOLO network, each of which demonstrated continuous performance improvements  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Total Numbers** & **Train** & **Validation** & **Test** \\ \hline Roboflow & 10,793 & 6493 & 2150 & 2150 \\ Kaggle & 485 & 291 & 97 & 97 \\ CAUCAFall & 6383 & 3812 & 1285 & 1286 \\ total & 17,661 & 10,596 & 3532 & 3533 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets.  
on other open-source datasets, we trained the network on our fall detection dataset to determine the best-performing version.  
We hypothesized that, although a particular network may exhibit strong general performance, its effectiveness could vary depending on the characteristics of the dataset, leading to the expectation that a specific YOLO version would be optimally suited for fall detection. Consequently, we tested all available versions from the earliest to the most recent iterations and selected the network that yielded the highest performance. In this comprehensive evaluation, YOLOv9 delivered the best results. This superior performance can be largely attributed to the integration of the auxiliary stage into its intermediate layers, which helps prevent information loss as it propagates through the network, thus enhancing the test data performance [31]. Additionally, the inclusion of the Repeated Normalized Cross-Stage Partial (RepNCSP) structure, a refinement of the cross-stage partial bottleneck with two convolutions (C2f) structure introduced in YOLOv8, further contributed to improved fall detection accuracy, suggesting that these architectural innovations played a role in optimizing fall detection in our study.
## 3 Modelling of the Proposed Controller and Observer  
### Design of the DSMC  
Generally, the conventional sliding mode controller (SMC) uses a practically high value of gains to slide the trajectories on the predetermined sliding mode surface [15; 16]. The key advantage of the SMC is its robustness, and it does not allow the parametric variations when the system states are on the switching surface. Furthermore, the sliding mode condition can be reached in finite time due to its noncontinuous control law function. However, the SMC at high speeds has drawbacks such as chattering and the steady-state error due to its high switching gains [17; 18]. These particular attributes make the SMC less attractive to the bearingless drives, even though it offers robust and stable activities. To avoid the prominent problems of reaching time and the chattering in the control scheme of the SMC, a new dynamic sliding mode control (DSMC) is implemented. The DSMC guarantees the system robustness and disturbance rejection capability [19; 20]. The main features of the dynamic sliding mode controller are given by:  
1. The system will achieve stability even with incomplete information from the state observer or absence of the state observer. 2. The system will get stability even under the accommodation of unmatched disturbances. 3. The chattering can be reduced to a great extent. Defining the state tracking errors and the switching functions are \(e_{x}\)= x - x\({}_{d}\), \(e_{y}=y-y_{d}\), \(S_{x}=(C_{x}\times e_{x})+\overset{\bullet}{e}_{x}\) and \(S_{y}=(C_{y}\times e_{y})+\overset{\bullet}{e}_{y}\), respectively, where \(C_{x}>0\) and \(C_{y}>0\), and it must be Hurwitz. The first-order derivative of the switching functions is given by:  
\[\overset{\bullet}{S}_{x}=C_{x}\overset{\bullet}{e}_{x}+\left\{\frac{-k_{x}}{ m}X_{1}+\frac{F_{x}}{m}+F_{dx}-\overset{\bullet}{X}_{d}\right\};\overset{\bullet}{ S}_{y}=C_{y}\overset{\bullet}{e}_{x}+\left\{\frac{-k_{y}}{m}y_{1}+\frac{F_{y}}{m}+F_{dy }+g-\overset{\bullet}{Y}_{d}\right\} \tag{9}\]  
where \(\overset{\bullet}{e}_{x}=\overset{\bullet}{x}-\overset{\bullet}{x}_{d}\) and \(\overset{\bullet}{e}_{y}=\overset{\bullet}{y}-\overset{\bullet}{y}_{d}\) (the design of \(C_{x}\) and \(C_{y}\) are given in Appendix A.2.1). The new dynamic sliding mode switching functions are given by \(\sigma_{x}=\overset{\bullet}{S}_{x}+\lambda_{x}S_{x}\) and \(\sigma_{y}=\overset{\bullet}{S}_{y}+\lambda_{y}S_{y}\) (\(\lambda_{x}>0\) and \(\lambda_{y}>0\)). When \(\sigma_{x}=0\) and \(\sigma_{y}=0\), \(\overset{\bullet}{S}_{x}+\lambda_{x}S_{x}=0\) and \(\overset{\bullet}{S}_{y}+\lambda_{y}S_{y}=0\) are asymptotically stable; therefore, the error and its first-order differential functions tend to zero. (The design of \(\lambda_{x}\) and \(\lambda_{y}\) are given in Appendix A.2.2).
## 3 Motion Dynamics Input Search Algorithm
### Understanding Image Changes Based on Drone Movements  
The movement of a vehicle in motion can be described by a combination of linear and angular motion in different dimensions. When it comes to drones, linear motion can be decomposed into velocity vectors, each parallel to one of the three axes (x, y, and z), while the angular motion can be decomposed into three rotational components, each around one of the three axes (Figure 5).  
Figure 4: Simplified diagram of a video coding framework incorporating the Motion Dynamics Estimator Block, illustrating its role in enhancing ME.  
Figure 5: Diagram illustrating drone movement: axes denote the direction of linear motion, and arrows indicate rotational movements (roll, pitch, and yaw).  
Each rotational component of an aerial vehicle has a specific name: the rotation around the Longitudinal Axis of the vehicle (axis X) is called \"roll\", the rotation around the Lateral Axis of the vehicle (axis Y) is called \"pitch\", while the rotation around the axis vertical to the vehicle (axis Z) is called \"yaw\".  
The most common movement in drone scenes is the forward movement, which requires the drone to slightly pitch. When the drone moves forward, pixels tend to spread away from the center of the image (Figure 6). The further away the pixel is from the center, the more it will expand (move toward the corner of the image) in the next frame. An example of a drone forward motion in a simulator can be seen in Figure 7. Similarly, when the drone moves backward, pixels compress toward the center of the image. The next movement type is the yaw rotation movement. When the drone rotates to the right, pixels tend to follow the movement scheme illustrated in Figure 8. Pixels located closer to the top or bottom of the image exhibit a more pronounced arc in their movement, while pixels located in the center of the image move in a straight line. Of course, when the drone is rotating to the left, the pixels move to the right, and vice versa for the right rotation. Additionally, the arc angle depends on the camera lens characteristics.  
Figure 6: Visual representation illustrating pixel displacement towards the edges during forward drone motion.  
Figure 7: Forward drone motion example, highlighting the effect of pixels expanding from the center toward the image edges.  
Pixel movements are much simpler when it comes to left-right and up-down drone motion. When the drone moves to the left it must perform a slight roll rotation first, and afterward, the pixels move straight to the right. If the drone is in an upward motion, the pixels move straight downwards. Hence, no complex search models were created for the roll and throttle movements. Simply, only the diamond locations that match the current direction of the drone motion are searched.