esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
## 3 Numerical Model
### Numerical Scheme  
The finite volume method was used to discretize the fluid governing equations. Semi-Implicit Method for Pressure Linked Equations (SIMPLE) and Pressure-Implicit with Splitting of Operators (PISO) schemes were tested for the coupling between pressure and velocity, whilst first-order and second-order upwind methods were tested for spatial discretization. The underrelaxation factor for the pressure correction using the SIMPLE scheme was set to a value of 0.3. For the PISO scheme, the underrelaxation factor was set to unity, taking advantage of the solutions of the pressure correction in two stages. Owing to the simplicity in the geometry setup and mesh grids, the differences in numerical results between these two schemes were observed to be minimal (\(\approx\)0.1%). For simplicity, the SIMPLE and second-order upwind schemes were adopted. Transient solutions were attained in time by marching the computations with a fixed time step of \(10^{-4}\) s. At each time step, the governing equations were iteratively solved and deemed to be converged until the Root-Mean-Square (RMS) residuals fell below \(10^{-6}\).
## 3 Results
### Characterization of rGO/CeO\({}_{2}\)/Polymer Monoliths
#### 3.2.1 SEM/EDX  
In Figure 7, the images of all samples studied by SEM can be seen in two different magnifications: \(2000\times\) and \(500\times\).  
In Figure 7, it can be seen that all the materials exhibited a different porous morphology not only because of the CeO\({}_{2}\) nanoparticles, but also because of the different polymer brushes. In the case of GCePV and GCePM, white particles dispersed in a gray rGO porous structure could be observed, likely representing the CeO\({}_{2}\) particles. In the case of GCePA, the difference compared with the GCe was based on the edges of the self-assembled rGO sheets that seemed to have a different roughness and compactness. In order to depict these materials better, EDX mapping was performed, and their elemental percentages could be seen. These results are presented in Figure 8 and Table 2.  
It can be seen (Figure 8 and Table 2) that the percentages of the elements changed when the particles with polymer brushes were added to the rGO monolith. The percentage of Ce was in the range of 3.7-6.7%. For the case of S, a small percentage was also detected (0.1-0.7%). The S was found due to synthetic reasons, namely the polymer brushes with the CTA agent grafted on the particles. A slight increase in N was also found for the samples that had N in their molecular formulas (samples with PNVCL and PMETAC). Most important was the increase in the O content, since all polymers had extra O atoms in their structures (PAA had two, PNVCL had one, and PMETAC had two). Therefore,  
Figure 7: SEM images of monolithic samples at \(2000\times\) magnification (**left images**) and \(500\times\) magnification (**right images**).  
11.2% of the O at the initial monolith with CeO\({}_{2}\) was almost tripled for the GCePA sample (31.8%) and doubled for the GCePM sample (21.7%), and it was also relatively higher in the GCePN sample (17.0%). This could also be verified by mapping these monoliths (Figure 8), where the GCePA and GCePM samples showed a greener color (the one attributed to O) in the mappings.
## 2 Materials and Methods
### SEM Evaluation and Statistical Analysis  
After separation, the replicas were gold-sputtered and examined by scanning electron microscopy (SEM Jeol 5400, Tokyo, Japan) in accordance with methodological approaches proposed in previous studies (20; 21; 23).  
After preliminary scaling procedures, each sample was analyzed at 50\(\times\) magnification by two operators in double blind before scaling (T0) and after scaling (T1) to evaluate the surface regularity and the presence of signs and lesions produced by the instrumentation in the cervical third of the root surfaces. These values were measured by using a modification of the \"Modified Loss of Tooth Substance Index\" (17):  
* Regular root surface, without loss of substance and signs of instrumentation;
* Regular root surface with isolated instrumentation marks;
* Widespread presence of signs produced by the instrumentation;
* Irregular surface and/or presence of lesions produced by the instrumentation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Paste name** & **Composition** \\ \hline Stomyprox media® & Glycerin, Pumice, Zinc Hydroxyapatite, Hydrated Silica, Sodium Stearate, Aqua, Aroma, Sodium Saccharin, Benzyl Alcohol, Phenoxyethanol, Cl 77891 and Cl 42090 \\ \hline Ultrapro Tx cool mint medium® & Pumice, Glycerol 30/50, Sodium Fluoride 2/6, Potassium Nitrate 4/6, flavor and water \\ \hline Nupro medium orange® & Pumice, Glycerin, Sodium Silicate, Methyl Salicylate, Monosodium Phosphate, Sodium Carboxymethylcellulose, Sodium Saccharin, FD&C Blue n.1, flavor and water \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of polishing pastes used in the study.  
SEM pictures of replicas of each sample at T1 were then compared by two operators with images of replicas obtained after polishing (T2), both at a magnification of 50\(\times\), in double blind, to evaluate the surface roughness with the assignment of a value, according to the variation of the surface morphology and according to a protocol suggested by [PERSON] et al. [16]:  
1. Smoother surface;
2. Unchanged surface;
3. Rougher surface.  
Data (scores) were expressed as mean \(\pm\) standard deviation and group mean differences and were assessed with Student's \(t\)-test. The chosen level of statistical significance was 0.05. For the statistical analysis, the samples collected before any root scaling treatment (T0) are to be considered as a control group to assess the variation of root surface morphology [16].
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Non-Contact Technology
#### 3.4.2 Ultrasonic Sensor  
Ultrasonic sensors are used in sitting posture recognition systems to detect and measure distances by emitting ultrasonic waves and interpreting the reflections from nearby objects. These sensors are particularly valuable in applications where non-contact, real-time monitoring of posture is desired. They can be strategically placed around a chair (as shown in Figure 9), for example, on the seat, backrest, or armrests. By continuously measuring the distance between the sensor and the user's body (such as the back, shoulders, or head), the system can infer the user's posture. They are relatively inexpensive, making them a cost-effective option for developing posture recognition systems, especially when compared to more complex technologies such as cameras or IMUs. However, they are sensitive to the environment (such as temperature, air currents, and surface reflection), lack precision, and require line of sight.  
[PERSON] (2015) used an ultrasonic sensor placed on a monitor and a wearable speaker and estimated the sitting posture by measuring the space between the computer screen and the eyes of the user, generating auditory feedback when a poor posture is judged. [PERSON] et al. (2016) used ultrasonic sensors to detect various postures with an accuracy of 92.01%. They first extracted the features and input into both static and dynamic-oriented networks to accurately classify five different postures and four activities. [PERSON] (2017) used a circular microphone array together with a modified frequency modulated continuous wave (FMCW) technique for accurate interpretation of the acoustic signals to assess and recognize different sitting postures. LiDAR devices can be expensive, which may impact the feasibility of widespread adoption in various environments. Traditional LiDAR devices are often bulky and not easily portable, which can limit their use in dynamic or mobile settings. Handling and processing large amounts of point cloud data generated by LiDAR devices can be computationally intensive and require efficient algorithms for analysis.  
Figure 8: Posture recognition setup using RFID (2012).
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
###### Abstract  
A full-scale measurement of wind characteristics atop a high-rise building (with a height of 115 m) was conducted during the passage of Typhoon Sarika on 18 October 2016. Wind field characteristics, wind speed, and wind direction atop the building were recorded synchronously, and turbulence intensity, turbulence integral scale, gust factor, and power spectrum were investigated. Meanwhile, the time and frequency domain characteristics of the wind field were analyzed. The stationarity test results of Typhoon Sarika at different time steps are researched in a runs test. And the time-frequency analysis of non-stationary samples of fluctuating wind speed are conducted by wavelet transform, the measured data are valuable for the wind-resistant design of high-rise buildings in typhoon-prone regions.  
full-scale measurement; typhoon; wind characteristics; turbulence intensity; turbulence integral scale; gust factor +
Footnote †: journal: Applied
## 6 Results
### Displacement Measurements  
As a measure of the system performance, the achievable precision was measured. A linear stage with a movable metal plate was placed at a distance of 40 cm in front of the radar system. In order to mimic the reflection properties of the human body in the vital parameter sensing application, RF absorbers were stuck to the metal plate to reduce its radar cross section. After powering up the system until it was thermally stable, a linear movement of the stage which serves as reference for the ellipse reconstruction was performed. Subsequently, the stage was stopped and 100,000 samples were recorded, corresponding to a measure time of 50 s. As can be seen in Figure 9(a), the samples approximately follow a Gaussian distribution with a standard deviation of 1.07 \(\upmu\)m. Drifts of the mean value could not be observed during the measurement.  
In order to quantify the cumulative error of a target displacement around a reference point, the linear stage was moved in steps of 100 \(\upmu\)m. As can be seen in Figure 9(b), small absolute errors of less than 100 \(\upmu\)m occur, if the target is moved by less than \(\pm\)2.5 cm around the reference point. The oscillating errors are caused by linearity errors of the power detectors. The variation of their magnitude is caused by changing operating points of the detectors due to a varying receive power level when the distance between target and system changes. This, however, can be compensated in software by updating the parameters of the fitted ellipse whenever a sudden change in receive power and hence in the operating points is detected.
## 1 Introduction
Although effective, ML models often lack transparency (black boxes), posing challenges for specific applications or when dealing with new data categories. As a result, efficient analytical formulas and algorithms could be preferred [26]. Symbolic regression (SR), a machine learning approach, aims to automatically discover formulas that capture correlations within a given dataset [27], offering models in the form of easily understandable mathematical equations. While it has traditionally been implemented using Genetic Programming [28], alternative approaches also exist. These include deterministic methods that systematically explore the space of mathematical expressions, deep learning-based techniques, transformer-based methods, and hybrid algorithms that combine multiple strategies to enhance the efficiency and accuracy of SR, among others. SR has been used in geological and geotechnical engineering [29; 30; 31; 32] and, specifically, to obtain \(\mathrm{p_{s}}\)[33] with satisfactory results. Despite the increasing number of studies addressing the prediction of \(\mathrm{p_{s}}\) using empirical or machine learning approaches, several critical gaps remain. Firstly, most existing correlations are based on remoulded samples, which fail to reflect in situ conditions and may lead to misleading predictions. Secondly, widely used ML models often operate as black-box systems, lacking interpretability and offering limited insight into the influence of input parameters. Lastly, few studies have explored the use of SR algorithms that produce simple, physically meaningful equations while maintaining high predictive accuracy. In response to these limitations, this study aims to explore the capabilities of SR as a tool to discover patterns in complex datasets in order to synthesize simple equations that allow engineers to estimate the \(\mathrm{p_{s}}\) value in clayey soils with the same characteristics as those studied here, with a high degree of reliability. To achieve this, it is only necessary to know some easily obtainable index properties, such as: liquid limit (LL), plasticity index (PI), dry density (\(\gamma_{\rm d}\)), percentages passing through a 0.075 mm sieve, and natural water content (w). Additionally, the most influential input variables on the final value of \(\rm p_{s}\) were evaluated.
# Research on the Dynamics of the Space Tubular Expandable Structure Driving Deployment Unit
## 2 Methods
### The Spatial Nodal Dynamic Description of the Thin-Walled Flexible Beam Structure
#### 2.2.4 The Dynamic Equations of the Triangular Configuration  
Through the dynamic derivation of the thin-walled flexible beam element in the previous section, to solve the dynamic equation of the triangular configuration of thin-walled flexible beam element combination, first, suppose that each cross section area is A\({}_{1}\), A\({}_{2}\), A\({}_{3}\), each length is \(l_{1}\), \(l_{2}\), \(l_{3}\), and the material is the same material, so the density is \(\rho\), I is the order \(3\times 3\) unit matrix, and the first and second moments of the cross section are, respectively:  
\[\left\{\begin{array}{l}Q_{y1}=\int\limits_{A1}zdA_{1},\ Q_{y2}=\int zdA_{2}, \ Q_{y3}=\int\limits_{A3}zdA_{3}\\ Q_{z1}=\int\limits_{A1}^{A}ydA_{1},\ Q_{z2}=\int\limits_{A2}^{A2}ydA_{2},\ Q_{z3}=\int \limits_{A3}^{A3}ydA_{3}\\ I_{yy1}=\int\limits_{A1}^{A1}z^{2}dA_{1},\ I_{y2}=\int\limits_{A1}^{Z}zdA_{2}, \ I_{y3}=\int\limits_{A3}^{Z}zdA_{3}\\ I_{zz1}=\int\limits_{A1}^{Z}y^{2}dA_{1},\ I_{z2}=\int\limits_{A2}^{A2}ydA_{2}, \ I_{z3}=\int\limits_{A3}^{Z}y^{2}dA_{3}\\ I_{y21}=\int\limits_{A1}^{A1}yzdA_{1},\ I_{y2}=\int\limits_{A2}^{Z}yzdA_{2}, \ I_{y23}=\int\limits_{A3}^{A3}yzdA_{3}\end{array}\right. \tag{22}\]  
Substitute the parameters in Equation (22) into Equation (15), where \(l\) is replaced by \(l_{1}\), \(l_{2}\), \(l_{3}\), and the kinetic energy and strain energy of the system are obtained through Equations (14) and (16). The system dynamics equation can be established by using the traditional finite element assembly method. For the unit mass matrix obtained by substituting Equation (22) into Equation (15), use the direct integration method to solve the dynamic equation [43], as follows:  
\[\mathbf{M}_{v}\ddot{q}_{t}=\mathbf{Q}-\mathbf{K}_{\mathrm{A}}\mathbf{B}_{t- \Delta t} \tag{23}\]  
where, \(\mathbf{M}_{v}\) is the equivalent mass matrix,\(\mathbf{Q}=\mathbf{Q}_{\mathrm{FA}}+\mathbf{Q}_{\mathrm{gA}}\)  
\[\mathbf{M}_{v}=\mathbf{M}_{\mathrm{A}}+\frac{\Delta t^{2}}{6}\mathbf{K}_{ \mathrm{A}} \tag{24}\]  
\[\mathbf{B}_{t-\Delta t}=q_{t-\Delta t}+\Delta t\dot{q}_{t-\Delta t}+\frac{ \Delta t^{2}}{3}\ddot{q}_{t-\Delta t} \tag{25}\]  
Then, multiply the left and right ends of Equation (23) by the inverse matrix \(\mathbf{M}_{v}^{-1}\) of the equivalent mass matrix to get.  
\[\ddot{q}_{t}=\mathbf{M}_{v}^{-1}(\mathbf{Q}_{t}-\mathbf{K}\mathbf{B}_{t- \Delta t}) \tag{26}\]  
After obtaining node acceleration \(\dddot{q}_{t}\) at time \(t\), node velocity and node displacement can be calculated by the following formula:  
\[\dddot{q}_{t}=\dot{q}_{t-\Delta t}+\frac{\Delta t}{2}\dddot{q}_{t-\Delta t}+ \frac{\Delta t}{2}\dddot{q}_{t} \tag{27}\]  
\[q_{t}=q_{t-\Delta t}+\Delta t\dot{q}_{t-\Delta t}+\frac{\Delta t^{2}}{2}\dddot {q}_{t-\Delta t}+\frac{\Delta t^{2}}{6}\dddot{q}_{t} \tag{28}\]
###### Abstract  
The Multiple Symbol Detection (MSD) algorithm can effectively lower the demodulation threshold in Frequency Modulation (FM) technology, which is widely used in aircraft wireless communications due to its insensitivity to large Doppler shifts. However, the high computational complexity of the MSD algorithm leads to considerable hardware resource overhead. In this paper, we propose a novel MSD architecture based on hybrid stochastic computing (SC), which allows for accurate signal detection while maintaining low hardware complexity. Given that the correlation calculation dominates the computational load in the MSD algorithm, we develop an SC-based, low-complexity unit to perform complex correlation operations using simple hardware circuits, significantly reducing the hardware overhead. Particularly, we integrate a flexible and scalable stochastic adder in the SC-based correlation calculation, which incorporates an adjustable scaling factor to enable high distinguishability in all possible correlation results. Additionally, for the symbol decision process of the MSD algorithm, we design a binary computing-based pipeline architecture to execute the computing process serially, which leverages the inherent low update rate of SC-based correlation results to further reduce the overall resource overhead. Experimental results show that, compared to an 8-bit quantization MSD implementation, our proposed hybrid SC-based MSD architecture achieves a comparable bit error rate while reducing the hardware resources to 69%, 45%, and 36% of those required for the three-, five-, and seven-symbol MSD algorithms, respectively.  
m _Article_  
1
## 3 Results and Discussion
### Accuracy  
The accuracy of the method was determined by recovery experiments. The accuracy studies were carried out at three concentrations: 0.0550, 0.1100, and 0.1375 mg/mL for ASA (50%, 100%, and 125%) and 0.0400, 0.0800, and 0.1000 mg/mL for CB (50%, 100%, and 125%). The percentage recovery was calculated from the data obtained. The results are shown in Tables 2 and 3. Mean recoveries of acetylsalicylic acid and clopidogrel were 98.6-101.0% and 100.0-101.6%, respectively, confirming the accuracy of the method.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 5 Details of Prototype System
### Basic Components  
#### 5.3.1 Driver Application  
The Driver application, designed for edge devices, continuously updates the driver's location to simulate real-time movement. In the experimental evaluation described in the next section, we achieve this by generating random geographical coordinates at regular intervals. The application comprises two primary modules:  
* Location Update Module: Generates random location updates for each driver, mimicking continuous position changes through periodic updates. Listing 1 demonstrates a code snippet showcasing this periodic location update functionality.
* MQTT Publishing Module: Publishes location updates as JSON-encoded messages to the MQTT broker under the driver's unique topic. Additionally, it synchronizes all simulated taxis' statuses and publishes pickup requests when drivers become available.  
```
1 defupdate_location(driver_id)do
2:timer.sleep(1000)
3
4 location=generate_random_location()
5
6:ok=publish_location(driver_id,location)
7
8 update_location(driver_id)#Keeptheloopgoing
9 end
10
11 defpgenerate_random_location()do
12%(latitude:rand(0..90),longitude:rand(0..180))
13 end
14
15 defp publish_location(driver_id,location)do
16 event=%(driver_id:driver_id,location:location,timestamp:DateTime.utc.now())
```  
**Listing 1**. Updating the driver's current location in real time.  
* event_binary=Jason.encode!(event)
* * ok=:emqtt.publish(conn,\"driver/#{driver_id}/location\",event_binary)
* end  
The Elixir application concurrently manages hundreds of taxi objects, including their real-time location and state changes, replicating an actual taxi dispatch scenario. This application runs autonomously, simulating multiple drivers updating and publishing their locations indefinitely, demonstrating Elixir's robustness and suitability for long-running edge computing processes.
## 6 Experiment and Result Analysis
### Limitations and Future Research Work  
Although the method in this paper has achieved state-of-the-art performance, there are still two key limitations in our framework worthy of in-depth discussion.
# Potato Leaf Disease Segmentation Method Based on Improved UNet
## 3 Results
### Model Evaluation Index  
Evaluation metrics are a direct reflection of assessing the quality of an algorithm. In this experiment, the primary evaluation metrics employed on potato leaf images include the Dice coefficient, intersection over union (IoU), and, considering the presence of threetypes of potato disease spots, accuracy (Acc) is also used as an evaluation metric. The specific calculation formulas for these evaluation metrics are as follows:  
The Dice coefficient metric is utilized to measure the similarity between predicted results and expert-annotated ground truth labels:  
\[\text{Dice}=\frac{2 TP}{FP+2 TP+FN} \tag{5}\]  
where \(N\) represents the number of potato disease categories, \(TP\) represents the true positive instances of potato disease correctly predicted, and \(FP\) represents the false positive instances of potato disease incorrectly predicted. \(FN\) corresponds to false negative instances of potato disease not detected in predictions.  
Pixel accuracy (\(Acc\)) is the ratio of correctly classified pixels to the total number of pixels.  
\[Acc=\frac{TP+TN}{TP+FN+TN+FP} \tag{6}\]  
where \(N\) represents the number of potato disease classes, \(TP\) stands for truly predicted potato disease samples, \(FP\) represents falsely predicted potato disease samples, and \(FN\) represents the predicted samples of potato disease that were not detected.  
Intersection over Union (\(IoU\)) is the most crucial metric for assessing the effectiveness of semantic segmentation. It calculates the intersection over the union of two sets: the ground truth and the predicted segmentation, respectively. It is a ratio of the intersection to the union of these two sets.  
\[IoU=\frac{TP}{TP+FN+FP} \tag{7}\]  
where \(N\) represents the number of potato disease categories, \(TP\) stands for true positive, which represents the correctly predicted instances of potato disease, \(FP\) represents false positive, indicating the instances that were incorrectly predicted as potato disease, and \(FN\) represents false negative, indicating the instances of potato disease that were not detected by the prediction.
## 4 Parameter Analysis
### Effect of Truncation Thickness on AREM Bandgap Characteristics
Due to the increase in truncation thickness, the ABH effect of the structure is weakened, making the LR effect in the BG weaker and causing the energy band flatness to decrease. At the same time, it also makes the structural stiffness increase, prompting the BG to move to high frequencies. Therefore, the second longitudinal BG also shifts to high frequencies and the bandwidth gradually decreases with the increase in the cutoff thickness. In summary, the smaller the truncation thickness \(h_{0}\) is, the stronger the ABH effect of the structure and the better the BG characteristics of the AREM. Figure 7: Effect of the cutoff thickness \(h_{0}\) on the BG characteristics between AREM energy bands: (**a**) flexural BG; (**b**) longitudinal BG.