esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theoretical Modeling
#### 2.2.11 Experimental Investigation of Drug Homogeneity in Syringe Pumps  
The experimental study evaluates the stability and distribution of various intensive care unit (ICU) drugs during infusion. Simulated drug injection conditions were established based on clinical protocols, such as furosemide/insulin administered at 2 mL/h. The research team measured the concentration from the three collection points in the syringe body to determine the relative homogeneity of the total mass of drugs administered. Sample collection was performed at the three points of the syringe, top, middle, and bottom, at different time intervals: 2/4/6/8/10/12 h (Figures 7 and 8).  
Samples collected from the middle of the syringe represent the effective drug concentration delivered to the patient. In contrast, samples taken from the top and bottom of the syringe serve to assess the degree of sedimentation and potential concentration gradients within the solution. The sampling needle was inserted carefully so as not to generate homogenization factors by swinging the solution in the syringe. The sample volume was 1 mL from the three parts mentioned above.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Conventional Syringe Pump**} & \multicolumn{3}{c}{**Proposed Syringe Pump—with Homogenization System**} \\ \hline
**Hours (h)** & **Insulin** & **Gentamicin** & **Furosemide** & **Insulin** & **Gentamicin** & **Furosemide** \\ \hline
2 & +0.2\%/0.1\%/0.33\% & +0.2\%/0.1\%/ & \(-0.8\%/-0.6\%/\) & +0.3\%/0.2\%/ & +0.17\%/0.1\%/ & \(-0.8\%/-0.6\%/\) \\  & & 0.33\% & 1.1\% & 0.27\% &
## 3 Results and Discussion
### SEM and EDAX  
The SEM image highlighted that the formed nanoparticles were spherical in shape and were fine and uniform in size (Figure 3A). The biosynthesized AgNPs from various plant extracts have also been described previously [27; 28; 29]. Our results are consistent with those obtained by [PERSON] et al., [25] who showed that the AgNPs prepared from leaf extracts of _Clitoria terntate_ and _Solanum nigrum_ were mostly spherical in shape and 10 to 50 nm in diameter. The elemental composition of Aw- AgNPs was analyzed by EDAX. In the acquired EDAX spectrum, all the elements showed a unique set of peaks with different atomic structures (Figure 3C). The EDAX elemental composition presented in Figure 3B,C highlights strong signal of silver element with weight % of 50. AgNPs have been synthesized from same source in the study by [PERSON] et al. and this is reflected in our EDAX results [2].
## 3 Design and Implementation
### Detection of a Dynamic Objects during Stationary Measurement  
As a first step, we decided to test our proposed algorithm on a stationary measurement, in which the mapped environment was a dynamic object. The main goal was to determine how successful our proposed algorithm would work. The measurement took place in the corridors of our department. Photos of the environment can be seen in Figure 7. This measurement also included objects that could cause noise in the measurement, such as glass doors and flowers, which we considered desirable, as the measurement did not take place in an ideal environment.  
Figure 6: Flowchart for Algorithm 1.  
In the first step, data were collected from a lidar scanner. These data were entered into the SLAM algorithm in which the deviations were calculated and a 2D map was generated. This step was not necessary from the point of view of the whole measurement, as it was a stationary measurement and the position of the scanner did not change, but the output served as a visual check for the detection of dynamic objects. This map can be seen in Figure 8. The dynamic object that we wanted to identify using the proposed methods is highlighted in yellow. The whole map consisted of 16 measurements, while the object of our interest was in it 12 times. The measurement was performed at point [0 0].  
After generating the map, we processed the data into a suitable form for creating a point cloud. After editing the data, we applied the proposed algorithm to identify dynamic objects. As a result, the dynamic objects were displayed on a graph, which can be seen in Figure 9. The number of identified dynamic objects was 11, which was one fewer compared to the original map. The problem with identifying an object was  
Figure 8: Map from SLAM algorithm for static measurement.  
Figure 7: Corridors in the department.  
close to the wall, which resulted in uniting the edge of the wall and the object when creating the segments. This problem can be seen in Figure 10, where each segment is separated by a different color. An incorrectly segmented object is indicated by an arrow.  
Once the dynamic object data were obtained, the data were edited and added to the data to create a navigation map. After creating this map, it was possible to apply navigation algorithms. The addition of a dynamic object to the navigation map was in order to take into account the trajectory of the dynamic object's motion, in finding a suitable path. The application of the navigation algorithm can be seen in Figure 11. The blue line represents the trajectory of the dynamic object and the red line symbolizes the route calculated using the A* algorithm. The time was recorded during the measurement, due to which we had the opportunity to determine the average speed of the object. In this case, it was 3.48 km/h.  
Figure 10: Dynamic object united with wall on one segment.  
Figure 9: Dynamic objects found in static environment.
# Gait Biomechanics for Fall Prevention among Older Adults
## 3 Biomechanical Factors for Falls Risks
### Biomechanics of Tripping and Minimum Foot Clearance (MFC)  
Tripping, the leading cause of falls, is defined as unexpected foot contact with the walking surface or an object upon that surface, which generates momentum sufficient to destabilise the walker [36]. The two key factors for tripping falls are, therefore, (i) physical foot contact and (ii) excessive momentum. Focusing on these two essential conditions for falls by tripping, minimum foot clearance (MFC) is recognised as the critical gait event (Figure 2) [37].  
MFC is the mid-swing phase event, where the vertical swing foot displacement reaches the local minimum while travelling near maximum horizontal velocity [39]. The position of both feet is parallel at MFC (Figure 2, right), leaving the small base of support (BOS) vulnerable to forward balance loss and subsequent falls [40,41]. MFC characteristics can, however, be individual-specific, and some older adults do not experience MFC events, possibly due to a slower gait speed and inadequate joint control [42]. The optimisation of MFC control is most important in preventing falls by tripping [37]. While a higher MFC is fundamental, it is also important to achieve constant swing foot control (i.e., intra-individual gait control consistency), indicated by variability measures such as standard deviation (SD) and interquartile range (IQR) [37,43]. As shown in Figure 3 (left), a lower IQR (or SD) indicates the ability for consistent MFC control [44].  
Figure 2: **(Left) Illustration of minimum foot clearance (MFC) and swing foot clearance graph [38]; (right) frontal plane illustration at MFC, COM = centre of mass, BOS = base of support, BOS boundary indicating the line between the toes (frontal BOS boundary).**  
MFC can be analysed in detail by plotting the data in the histogram to understand the individual-specific MFC characteristics [37]. As highlighted in Figure 3, the area marked 'High Risk' is of particular importance because the risk of tripping increases at occasional low swing foot clearances. While most previous studies have focused on central tendency and its dispersion, such as mean \(\pm\) SD or median \(\pm\) IQR to describe MFC characteristics, for fruitful future directions, lower percentiles (i.e., 0-5%) should be the focus for the most dangerous instances [45].  
Despite the importance of a sophisticated analytical approach for MFC data, the fundamental direction is to provide sufficient MFC height, achieved most efficiently by ankle dorsiflexion (Figure 3, right), such that 1\({}^{\circ}\) of ankle dorsiflexion elevates the MFC by 0.3 cm [46], but ageing is a factor that causes reduced dorsiflexion [47]. Enhanced dorsiflexion at MFC is, therefore, the target for tripping prevention [48; 49]
## 4 Existing Standards  
### Introduction  
The main challenge in designing standards for WBANs is the balance of QoS demands with the low power constraints of battery powered nodes. A number of low power standards have been used in WSN/WBAN research as well as for commercial applications, all of which go some way in partly satisfying the requirements for life signs monitoring. Some low power standards designed to support low power sensing have been adapted for healthcare applications, e.g., ZigBee, while others such as 802.15.6 have been designed specifically for WBANs and cater for personal entertainment applications as well as healthcare.
###### Abstract  
This study examines the security implications of generative artificial intelligence (GAI), focusing on models such as ChatGPT. As GAI technologies are increasingly integrated into industries like healthcare, education, and media, concerns are growing regarding security vulnerabilities, ethical challenges, and potential for misuse. This study not only synthesizes existing research but also conducts an original scientometric analysis using text mining techniques. To address these concerns, this research analyzes 1047 peer-reviewed academic articles from the SCOPUS database using scientometric methods, including Term Frequency-Inverse Document Frequency (TF-IDF) analysis, keyword centrality analysis, and Latent Dirichlet Allocation (LDA) topic modeling. The results highlight significant contributions from countries such as the United States, China, and India, with leading institutions like the Chinese Academy of Sciences and the National University of Singapore driving research on GAI security. In the keyword centrality analysis, \"ChatGPT\" emerged as a highly central term, reflecting its prominence in the research discourse. However, despite its frequent mention, \"ChatGPT\" showed lower proximity centrality than terms like \"model\" and \"AI\". This suggests that while ChatGPT is broadly associated with other key themes, it has a less direct connection to specific research subfields. Topic modeling identified six major themes, including AI and security in education, language models, data processing, and risk management. The analysis emphasizes the need for robust security frameworks to address technical vulnerabilities, ensure ethical responsibility, and manage risks in the safe deployment of AI systems. These frameworks must incorporate not only technical solutions but also ethical accountability, regulatory compliance, and continuous risk management. This study underscores the importance of interdisciplinary research that integrates technical, legal, and ethical perspectives to ensure the responsible and secure deployment of GAI technologies.  
s +
Footnote †: journal: Applied sciences  
article
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Cloudlet Computing
#### 2.2.3 Smart Homes  
A Cloudlet computing approach is used in Wireless Fidelity (Wi-Fi) positioning and navigation in [33]. The authors proposed a Cloudlet computing system that provides accurate position and navigation on a one-hop wireless network. The self-driving indoor cart can make real-time interactions with the accurate point data collected by this system.  
In [34], the authors proposed a delay-aware Cloudlet placement strategy with an enhanced k-means algorithm. This technique enabled a mobility-enhanced environment and calculated the optimal Cloudlet position. The proposed strategy can improve the service quality of healthcare support by minimizing the network access delay between sensors and Cloudlets.  
[PERSON] et al. [35] implemented a floor-based walk detection system with Cloudlet computing support. The sensors are embedded in the carpet to collect the waveform when people traverse the carpet. The measurements are sent to the Cloudlet nearby for future analysis and making real-time decisions with minimum delay.
## 3 Model Development
### Dynamic State Model Development
#### 3.3.2 Open-Loop and Default Controllers  
If the controller output has no feedback from the process and is kept constant until manual interference, the control system is then referred to as an open-loop control (controller off). Many processes are able to maintain a stable operation in the absence of any disturbances if operated in an open-loop control mode. Once a disturbance is inflicted, a deviation will be caused on the monitored variable. Some processes are self-regulated, and the experienced deviations are mild, while others are drastic, requiring quick and effective control action to maintain the process stability [45]. To understand the dynamic response of our carbon capture process, all default controllers were shut-off, and a disturbance was made to the flue gas flow rate. The resulting response is shown in Figure 4.  
A disturbance of 10% decrease in the flue gas flow rate was made at a dynamic simulation time of 2 h. After around 1.5 h, the process stabilizes to an undesired steady-state condition. All the parameters were greatly deviated from the stripper set point. Uncontrolled changes in the temperature and pressure affect the operation of the column and, eventually the capture rate. Similar behavior was noticed in all the other units where the dynamic response towards any disturbance was a deviation from the optimum operating condition, any disturbance greater than 50% causes such severe deviations that the model fails to converge and does not yield a result. Aspen Plus(r) Dynamics set the OP value as the bias value when the error (SP-PV) was equal to zero. A summary of the default control loops that were assigned by Aspen Plus(r) Dynamics to the process according to each unit are listed in Table 4:  
Figure 4: Stripper response to 10% in flue gas flow rate in open-loop mode.  
The sampling period for sending information to the strip-card recorder was set to 36 s (0.01 h) as default, to obtain higher resolution plots the communication time was changed to 18 s (0.005 h) through the run options menu. For every run, an initialization run was a prerequisite to obtaining a successful dynamic run. The initialization run assigns variable values at the initial t = 0 and resets the system to the starting condition. Once the values are reset, the dynamic model can be run for as long as specified in the run option. The steady-state run mode can be completed after the initialization stage to assure the values are correctly imported into the dynamic model, but once the control loops are inserted, the steady-state mode would not necessarily run successfully, and the model may fail to converge since the closed-loop response is not the same as the open-loop one. The solver report level should be set to at least high where the equations are sorted in different sets by APD and then solved, [PERSON]'s method is applied for the set of implicit-type equations that are solved simultaneously.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 4 Discussion
### Performance of PlanetLabs SuperDove Data  
Daily high-resolution observations at \(\sim\)3 m GSD are promising for coastal habitat mapping, but selecting suitable imagery is non-trivial. Commercial sensors, including SuperDove, are not designed for aquatic applications [57]. Nonetheless, a multitude of aquatic case studies have demonstrated that the SuperDove series of sensors can be a satisfactory means for operational monitoring in the marine environment [39].  
In coastal subtidal habitat mapping, several factors influence the quality of the final product, many of which are beyond control during image selection. The only parameters that can be specified are cloud cover thresholds, off-nadir angle, sun elevation, and azimuth. No dedicated filters exist to exclude images affected by sunlight or thin cirrus clouds. Furthermore, oceanographic phenomena such as water haze, often caused by downslope winds disturbing the sea surface, are not detectable via the PlanetLabs Explorer interface, and current automated pipelines lack the capacity to identify and filter such conditions.  
Wave action and whitecaps are also rarely visible in the Explorer, further limiting the effectiveness of automated quality control.  
These challenges arise during data harvesting, retrieval and preprocessing with ACO-LITE to generate aquatic reflectance products. In cases of strong sunlight, the processing resulted in negative reflectance values, leading to the masking of large areas in the analysis. Similar issues were encountered with significant wave action and wavecaps, which rendered several images unsuitable for sease mapping due to severe signal degradation.  
Finally, geolocation among images from the same sensor was not always consistent. In a few cases, shifts exceeding three pixels (>6 m) were observed between images. Although this remains within the sensor's CE90 specification, such shifts, when combined with handheld GPS inaccuracies, can introduce errors in model development and product validation. A thorough investigation [58] identified geolocation errors along multiple axis orientations, based on careful comparison of numerous images from different locations, with field data for geolocation and Copernicus Sentinel-2 imagery for radiometric consistency.
## 3 Computational Methods
### Mesh Strategy  
The STAR-CCM+ mesher is used to generate the computational grids. On the grid arrangement of flow field computation domain, the first layer thickness is 0.05 mm, the geometric grid growth rate is 1.1, and the number of the grid layers close to the train surface is 10 to resolve flow in the boundary layer. The grid size on the pantograph surface and train body surface is 10 mm and 60 mm, respectively, and mesh refinement rear the train body and pantograph is employed to make the computational grid fine enough to capture flow field physics. An isotropic mesh is used in the whole flow field except near wall boundaries. Three sets of grids, i.e., coarse, medium and fine grids, are used to test the grid sensitivity of the simulations. The total number of grid cells in the three grid configurations are 15, 30 and 60 million cells, respectively. Four mesh refinement zones including small, middle, big and wake region are set up, as shown in Figure 5. The mesh size of each refinement zone is listed in Table 1.  
Figure 6 shows the distribution of time-averaged slipstream velocity along a sampling line at a distance of 2 m from the center of the train, 1.3 m above the ground predicted by the coarse, medium and fine meshes. The non-dimensional results of slipstream velocity \(U_{\text{non}}\) is defined as:  
\[U_{\text{non}}=\sqrt{\left(V_{\text{x}}-V_{\infty}\right)^{2}+V_{y}^{2}+V_{z}^{ 2}}/V_{\infty} \tag{9}\]  
where \(V_{\text{x}}\), \(V_{y}\) and \(V_{z}\) are the velocity components along the x-direction, y-direction and z-direction, respectively, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Grids** & **Coarse** & **Medium** & **Fine** \\ \hline Grid size settings (mm) & 18, 18, 36, 9 & 8, 12, 24, 6 & 6, 9, 18, 6 \\ (small, middle, big, wake) & 15 million & 30 million & 60 million \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mesh size of the refinement zone.  
Figure 5: The distribution of refinement zone.  
Figure 6: The distribution non-dimensional time-averaged slipstream velocity.  
Figure 6 shows that the time-averaged non-dimensional slipstream velocity shows larger difference between the coarse and the fine meshes near the train in Region 2 and in the wake propagation Region 3, indicating that the coarse mesh is inadequate analytical accuracy, while the medium mesh exhibits good consistence with the fine mesh. Thus, the medium grid configuration is adopted to calculate characteristics of flow field, as it gives a satisfactory balance between accuracy and computational costs. Figure 7 shows the medium grids around the train surfaces. Because pantograph noise is the main research content, the area near the pantograph is refined. The total number of the computational cells used is about 32 million.
## 3 Results and Discussions
### Simulation Results of Brain Structural Imaging  
In this section, the structural imaging of hierarchies and vascular structures present in the brain tissue was simulated. The parameters of the hierarchical structure [16; 33] and vessel structure [22] in the simulation are listed in Table 1. The \(n\) and \(g\) of tissue were 1.37 and 0.9, respectively. The simulated hierarchy structure consisted of the skull, cerebrospinal fluid (CSF), gray matter and white matter, and its 3D volumetric finite-element (FE) mesh is shown in Figure 5a. Figure 5b shows the 3D volumetric mesh of gray matter with a blood vessel embedded in it, and the position of the blood vessel is indicated on the figure. The numbers in Figure 5a,b represent the number of grids, and the grid resolution was 10 \(\upmu\)m. In the simulation, the center wavelength was 630 nm and \(l_{\mathrm{c}}\) was 10 \(\upmu\)m, which is equivalent to using a light source having a spectral bandwidth of 13 nm.  
First, in order to verify the correctness and effectiveness of the MMC-OCT model in OCT-based structural imaging, all parts of the hierarchy structure were compressed, and compressed sizes are shown inside parentheses in the \"Thickness\" column of Table 1. The backscattered signal of the compressed hierarchical structure (Figure 5c,d) shows the simulation result of the vessel structure obtained with actual tissue parameters. It is clearly seen that the backscattered signals reflect the structural distribution of the two tissues, and the dimensions of each part are identical to the preset structural dimensions. Moreover, the results show that the larger the scattering coefficient, the larger the amplitude of the backscattered signal and the faster the signal attenuates, which is completely consistent with the actual situation [7] and proves the validity of the MMC-OCT used in this paper.  
Figure 4: Three-dimensional (3D) brain atlas (\(181\times 217\times 181\) mm): (**a**) 3D model of brain; (**b**) 3D model of blood vessels segmented from brain model shown in (**a**); and (**c**) 2D section of brain extracted at plane \(x=90\) mm. CSF represents the cerebrospinal fluid.  
Figure 5e shows the simulation result of the brain hierarchy structure obtained using actual tissue parameters shown outside parentheses in the \"Thickness\" column of Table 1. It can be found that the backscattered signal attenuates rapidly with an increase in depth and the light cannot penetrate the skull, which is consistent with reality [4]. The imaging depth of SD-OCT is generally 1-2 mm, owing to the large attenuation coefficient of tissue, which is not enough for human brain tissue [4]. But for some in vitro studies, we may only focus on a part of the brain, such as the cerebral cortex or the vascular network. In addition, brain imaging of small animals also provides significant reference value for human brain research. In the aforementioned applications, the MMC-OCT can be used to simulate their imaging process, thus providing important theoretical support for brain imaging.