esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, these situations are common in that the prediction results based on blasting parameters cannot directly give reference values, especially since the adjustment of blasting design parameters still needs to rely on the engineers' experience. To improve the prediction accuracy, metaheuristic algorithms are utilized in hybrid models for predicting blast vibration [20; 30; 33]. Additionally, metaheuristic algorithms are also widely applied in mine blasting optimization, e.g., the grasshopper optimization algorithm (GOA) is employed to identify the blasting solution that minimizes dust generation [34]. Meanwhile, multi-objective particle swarm optimization (MOPSO), developed from PSO, is capable of handling multiple metrics in mining applications [35]. Therefore, establishing a highly reliable prediction model and giving reference values of blasting design parameters is particularly important for engineers in controlling blasting vibration hazards and guaranteeing blasting effectiveness. This study proposes a hybrid model of PSO and least-squares support vector machine (PSO-LSSVM) to predict the PPV of a sand and gravel mine. Furthermore, the LSSVM, GA-BP, and BP were considered for comparison purposes. In addition, the optimization strategy of the blasting design parameters is proposed based on multi-objective particle swarm optimization, and a blast vibration control scheme for sensitive areas is given.
## 4 Results and Discussion
### Experimental Findings
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Time Execution of Vectorization**} \\ \hline
**Vectorization Techniques** & **English Dataset** & **French Dataset** & **Arabic Dataset** \\ \hline \multirow{3}{*}{MX330 GPU} & TF-IDF & 2.919 s & 0.159 s & 0.142 s \\  & Word2 Vec & 47.679 s & 1.597 s & 1.229 s \\  & FastText & 15.188 s & 39.534 s & 13.332 s \\ \hline A100 GPU & BERT & 1029.873 s & 470.829 s & 106.331 s \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Execution time comparison of vectorization methods: TF-IDF, Word2 Vec, FastText, and BERT. The evaluation of execution time reveals that while transformer-based models offer higher accuracy, they demand substantially more computational resources. This trade-off between performance and computational efficiency must be considered when designing real-world applications, especially those requiring real-time analysis or deployment in resource-constrained environments. For experts and developers working on multilingual sentiment analysis, especially in fields like digital marketing, customer relationship management, and social media monitoring, the research's findings provide insightful information. The comparative framework presented here can help them choose the best embedding and classifier combinations based on computing limitations and language-specific requirements. This study provides a foundation for real-time sentiment monitoring systems capable of capturing and interpreting public opinions expressed in multiple languages. Such systems can be particularly beneficial during critical periods marked by intense public engagement, such as political events, public health emergencies, or large-scale commercial campaigns, by offering timely insights into public perception and sentiment dynamics.
# A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load
## 3 Numerical Examples  
### A Simply Supported Beam  
Consider a simply supported beam with a rectangular section as shown in Figure 1. The length of the beam \(l=6.0\,\mathrm{m}\), the elastic modulus \(E=2.8\times 10^{10}\,\mathrm{Pa}\), the sectional area \(A=0.3\,\mathrm{m}\times 0.8\,\mathrm{m}\), and the sectional inertia moment \(I=0.0128\,\mathrm{m}^{4}\). The simply supported beam is divided into six elements with seven nodes, and each node includes the vertical deflection and rotation. A concentrated load \(P=200\,\mathrm{kN}\) is applied in the middle of the beam. The rotational degrees of freedom at all nodes are eliminated using the static condensation method.  
It is assumed that for the elastic modulus of the 4 th element of the beam, a 10% reduction happens, which is due to the discreteness of the elastic modulus of concrete. According to the deterministic mechanical parameters given, the mean values of the assumed measurement deflections are simulated in this case. Considering the uncertainty of the measurement deflections, it is assumed that the coefficients of variation of the measured displacements at all nodes are 0.01 according to our experimental results which are 0.01\(\sim\)0.03, and the displacements are of Beta distributions.  
By the simulated measurement data, the proposed HP-SMUM and the Monte Carlo simulation method with 100,000 samples are used to solve the statistical model updating equation to modify the original simulation model. The statistical results of the element updated factors are plotted in Figure 2. From Figure 2, it is found that the means and standard deviations of the element updated factors from the proposed method are in good agreement with those of the Monte Carlo simulation method. Furthermore, the mean value of the element updated factor of the 4 th element indicates that the updated result consists of the assumed case where the 10% reduction of the elastic modulus of the 4 th element is produced. Especially, from Figure 2c, it is observed that the probability density functions of the 4 th element selected from the two calculation methods coincide with each other very well. These results illustrate that the proposed HP-SMUM is a very accurate solution for the statistical model updating equation. Meanwhile, by using the Monte Carlo simulation method to solve Equation (18), the means and standard deviations of the node deflections of the updated beam structure are obtained and shown in Figure 3. From Figure 3, it is found that the first two moments of deflections and the probability density function (pdf) of the deflection in the mid-span determined by the updated finite element model match with the assumed measurement values very well. For the mean of deflection, actual calculated relative errors are less than 1.38%. On the other hand, for the standard deviation of deflection, the relative errors at all nodes are close to zero. These results demonstrate that the proposed method has very good updated effectiveness.  
Figure 1: A simply supported beam.
## 2 Materials and Methods
### Solid-phase Microextraction Gas Chromatography-mass Spectrometry (SPME GC-MS)  
Samples (4 g) were added to 20-mL screw glass vials, and headspace SPME was performed using an autosampler (TriPlus RSH Autosampler, Thermo Scientific, Waltham, MA, USA). Internal standard 1,2-dichlorobenzene-d4 (1 \(\mu\)L/g of sample) was added. An SPME fiber (65 \(\upmu\)m polydimethylsiloxane/ divinylbenzene (PDMS/DVB) 23 Ga, Supelco Co., Bellefonte, PA, USA) was used for extraction. Vials were incubated for 10 min PDMS/DVB at 60 \({}^{\circ}\)C in an autosampler with agitation. Volatile compounds were extracted by placing SPME fiber into the vial and exposing it to the headspace for 30 min at 60 \({}^{\circ}\)C [15].  
A gas chromatography system (TRACE 1310, Thermo Scientific) attached to triple quadrupole MS (Thermo Scientific) was used. The injection port used was in split mode with a flow rate of 20 mL/min and a surge pressure of 400 kPa. Helium, as the carrier gas, was employed at a constant flow rate of 2 mL/min. A DB-WAX capillary column (60 m \(\times\) 0.25 mm, film thickness 0.5 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA) was used for volatile compound separation. The initial oven temperature was held at 40 \({}^{\circ}\)C for 2 min. It was then raised at 4 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C and finally at 10 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C, where the temperature remained constant for 5 min. The volatile compounds were identifiedby comparing the results with mass spectra and retention indexfrom a database developed by NIST library guidelines. The values were quantified by calculating the ratio of the peak area of each volatile compound to the peak area of the internal standard.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was carried out in SPSS software (version 25.0; SPSS Inc., Chicago, IL, USA). Descriptive statistics were calculated and reported as mean \(\pm\) standard deviation. The normality of the data distribution was verified with Shapiro-Wilk tests (all \(p\leq 0.110\)) and, consequently, parametric statistics were used. A one-way repeated measures analysis of variance (ANOVA) was run to explore the differences among consecutive steps in the sprinting task or consecutive hops in the horizontal hopping task. The effect sizes were expressed as partial eta-squared (\(\eta_{\text{p}}\)\({}^{2}\)). The correlations between the sprinting and hopping tasks were assessed with Pearson's correlation coefficient and interpreted as negligible (<0.1), weak (0.1-0.4), moderate (0.4-0.7), strong (0.7-0.9), or very strong (>0.9) [20]. The effect sizes (\(\eta_{\rm p}\)\({}^{2}\)) were interpreted as small (<0.13), medium (0.13-0.26), or large (>0.26) [21]. Effect sizes >0.13 were considered as practically relevant [21]. For all analyses, statistical significance was accepted at \(\alpha\) < 0.05.
# A Study on the Movement Characteristics of Fuel in the Fuel Tank during the Maneuvering Process
## 3 Results and Analysis
### Analysis of Fuel Velocity Field Characteristics  
Figure 8 illustrates a segment of the speed field for the fuel free surface during the maneuvering process, depicting the transition from relative calmness to significant motion. In general, the flow field within the fuel tank exhibits turbulence, and as the fuel moves, it impacts the upper wall of the tank, resulting in a phenomenon known as \"sloshing\". The free surface appears as a steep slope for a certain duration. During the initial stage of the maneuver, the fuel tends to gather and move backward. The combined effect of acceleration and pitch coupling, along with inertia, leads to significant stratification of the free surface, and the fuel velocity rapidly decreases at the flow-through holes of the transverse bulkhead and longitudinal walls. Temporal variations of fuel motion characteristics inside the tank are provided in Table 3.  
Figure 7: Variation of fuel pressure field in the fuel tank. (**a**) \(t=0.24\) s; (**b**) \(t=0.51\) s; (**c**) \(t=0.99\) s; (**d**) \(t=1.50\) s; (**e**) \(t=2.52\) s; (**f**) \(t=2.97\) s.
# Event-Triggered Fault-Tolerant ADRC for Variable-Load Quadrotor with Prescribed Performance
## 2 Modeling of Variable-Load Quadrotor
### Design of an Event-Triggered Cascade Active Disturbance Rejection Controller
First, subsequent proofs demonstrate that the estimation error is uniformly ultimately bounded, ensuring the estimated values effectively approximate the true states of the quadrotor. Second, the fundamental objective of event-triggered control lies in minimizing unnecessary computations and communications. Requiring real-time value computation within the controller event-triggered mechanism would contradict this design principle._The attitude channel sub-controller of the inner loop controller is designed similarly to the position channel. Taking the roll channel as an example, the controller event-triggered mechanism for this channel is  
\[ \begin{cases}\sigma_{2}(t)=U_{2}(\tau_{q}^{2}),\quad t\in[\tau_{q}^{2},\tau_{q+1} ^{2})\\ \tau_{q+1}^{2}=inf\bigg{\{}t>\tau_{q}^{2}\bigg{|}|U_{2}(t)-\sigma_{2}(t)|>M_{1} ^{2}e^{-M_{2}^{2}|\dot{\mu}_{q1}-\dot{\sigma}_{q}|}\bigg{\}}\end{cases} . \tag{36}\]  
**Remark 5**. : _This study employs a fixed threshold in the event-triggered mechanism of the observer while adopting a dynamic threshold in the controller. This clearly divided design ensures that the observer rapidly and stably estimates system states and disturbances, with the fixed threshold guaranteeing consistent response speed unaffected by dynamic parameters._  
_The controller utilizes a dynamic threshold to balance tracking error and computational overhead. This approach ensures adaptability to varying performance demands during different flight phases. The fixed threshold of the observer provides reliable estimated inputs for the controller, while the dynamic threshold of the controller further optimizes overall resource efficiency._
## 5 Performance Analysis
### Random Topology
RA has a smaller average transmission delay for data transmission than OPP. However, compared to the grid topology, the random distance affects the setup of the route in RA as well. Although the distance was considered using RA algorithm, this case is slightly different to the effect of random distance. If a target node is out of range from the sender, the node cannot be a candidate node in the sender's alternate route. In this algorithm, candidate nodes within the communication range of the sender node are only considered as alternate routes, accounting for the low performance of random topology compared to grid topology. Figure 8: The average transmission delay for each error rate in random topology (5%, 10%, 15%, 20%).
# Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation
## 2 Methods
### Attention Segmentation with Dual-Branch(ASDB)  
Semantic segmentation in ultrasound images is affected by ambiguous boundaries and speckle noise. The multi-scale attention mechanism [26; 27] has been proven to perform well on ultrasound image segmentation. The ASDB (Figure 3) is inspired by the structure of the Deep Attention Feature (DAF) [26; 27] and obtains both the global and local context from the features of the multiple levels. The layer-wise attention mechanism can assign the attention weight of individual layers to comprehensively optimize the segmentation model. The two branches of the ASDB are the output mask predictions (\(P_{X}^{m}\)) and boundary predictions (\(P_{X}^{b}\)), respectively. The dual-branch structure learns from the multi-scale features for the robust segmentation.  
Figure 3: The architecture of the Attention Segmentation with the Dual-Branch (ASDB) module. The encoder extracts multiply single-layer feature maps. The multi-layer feature is the fusion of these feature maps. Single-layer features and multi-layer feature pass by the deep attention model (DAM) for the refined feature. The dual-branch structure generates the prediction of the mask and boundary.  
Figure 2: Overview of the proposed MACS. The MACS consists of an attention-based dual-branch segmentation module (ASDB), a discriminator in feature space, and a discriminator in the output space. Source domain images (\(X^{S}\)) and target domain images (\(X^{T}\) ) are segmented by the ASDB which is optimized using only source domain samples. The output of the segmentation network contains predicted masks (\(P_{X}^{m}\)) and high-level intermediate features (\(F_{X}\) ). The entropy map \((E_{X})\) is calculated from the segmentation prediction \(P_{X}^{m}\). and \(E_{X}\) from two different domains are forced to be aligned by discriminators, making the ASDB output accurate segmentation on unseen target domain dataset.  
The ASDB extracts four feature maps of different scales using the continuous ResNeXt. The low-level and high-level features contain detailed and semantic information, respectively. The layers of features are refined by the deep attention model (DAM). Specifically, the multi-level features are fused and reweighted to form the multi-layer feature (MLF). The MLF and single-layer feature maps are fed into the DAM for the refined feature maps. The mask branch combines the original feature maps and the refined feature maps from all levels and averages them for the final segmentation prediction. The boundary branch learns from the refined feature maps of the lowest two feature layers.  
The loss function of the ASDB module is:  
\[\mathcal{L}_{seg}=\sum_{i=1}^{n}\mathcal{L}_{bce}^{i}+\mathcal{L}_{mse}^{b}, \tag{1}\]  
where \(\mathcal{L}_{bce}^{i}\) represent the binary cross-entropy loss of the \(i\)-th layer prediction, \(\mathcal{L}_{mse}\) is the mean square error (MSE) loss for the boundary regression.
## 2 Materials and Methods
### Evaluation Metrics  
Model performance was evaluated using the following:  
* Dice Similarity Coefficient (DSC): Primary metric for segmentation accuracy.  
Sensitivity (Recall): Measure of correctly identified adrenal tissue.
* Specificity: Measure of correctly excluded non-adrenal tissue.
* 95 th Percentile Hausdorff Distance (HD95): Assessment of boundary delineation accuracy.
* Time-To-Model-Integration-in-Production (TTMIP): Deployment time for inference models.
* Time-Of-Training-Models (TOTM): Mean training time per epoch.
* Time-Of-Preprocessing-Input (TOPPI): Time required for image preparation per dataset.  
Statistical significance between model performances was assessed using paired _t_-tests with a significance threshold of \(p\) < 0.05.  
It is important to note that the precision, sensitivity, specificity, and HD95 values reported in this study were approximated based on expected patterns from the Dice similarity coefficient and typical performance behavior observed in small-organ segmentation tasks. These metrics were not directly computed from the raw segmentation outputs due to computational constraints and workflow limitations. This approximation approach, while providing valuable insights into model performance, represents a limitation of our current methodology that will be addressed in future work.
# Evaluating Satellite Sounding Temperature Observations for Cold Air Aloft Detection
## 2 Data and Methods
### COSMIC GPS-RO Technical Description  
The distribution of radiosonde launch sites in Figure 2 shows that while radiosondes provide high quality measurements, their value is primarily over land. Instead, we perform comparisons with Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, which provides roughly 2000 temperature soundings per day using radio occultation (RO) from global positioning system (GPS) satellites [21; 22; 23]. While still spatially limited, COSMIC profiles are somewhat randomly distributed and thus provide greater variety in scenes than radiosondes. The COSMIC data used in this study was obtained from UCAR's COSMIC Data Analysis and Archival Center [24].  
For this study, we examine the COSMIC dry temperature profiles (that have filenames beginning with atmPrf*). The dry temperature profiles are calculated from the observed refractivity assuming the water vapor contribution to the density is zero. The atmosphere typically has very little water vapor in the upper troposphere and stratosphere, so the dry temperature profile is appropriate for examining the upper troposphere and lower stratosphere. While studies have shown that there may be a high signal-to-noise ratio for occultations closer to the surface, roughly 90% of occultations reach above 5 km, well within commercial flight zones. To control for failed retrievals, we only used profiles that passed quality control (where the \"bad\" flag is 0 in the profile metadata). Previously, [25] used COSMIC air temperature retrievals alongside those from sounding datasets to monitor CAA. The authors compared both COSMIC and sounding datasets with NWP models and radiosondes and found low bias and RMS for both datasets, thus demonstrating utility for CAA detection. While COSMIC has scientific value for detecting CAA, COSMIC is not available to operational NWS forecasters in real-time or via the SBN. In contrast, NUCAPS is operationally available. So, for this study, we will take advantage of COSMIC's higher vertical resolution (0.1-1 km) to evaluate NUCAPS (1-3 km).
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Prospects for the Practical Hardware Deployment of VLC MFR
This iterative process will ultimately drive the transition of MFR technology from algorithm simulation to product deployment.
# FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
## 1 Introduction
The implementation performs poorly since it employs fixed high and low threshold settings for all pictures, but it performs better in terms of timing than all other implementations. In this paper, an improved Canny edge detection algorithm is proposed with adequate accuracy, reduced computational complexity, and self-adaptive threshold calculation. We calculate the threshold using [PERSON]'s algorithm [18]. Several works have dealt with the FPGA-based [PERSON]'s algorithm for threshold calculation [19; 20]. The approximate method and logarithm operation are introduced to reduce the complexity of computation. Our contributions in terms of the proposed design strategy are given below. 1. Computation of the gradient magnitude and orientation using the approximate method. 2. Using [PERSON]'s algorithm for adaptive threshold calculation. 3. The design of a 32-bit logarithmic arithmetic unit to reduce the complexity of [PERSON]'s algorithm.
## 3 Results and Analysis
### Relationship between Chlorophyll and Spectral Reflectance in Peanut at Different Densities  
#### 3.3.1 Effect of Planting Density on Chlorophyll Content in Peanut Growth Period  
Figure 3 shows that, under different peanut seeding densities, chlorophyll content of peanut leaves during the flowering stage is obviously lower than in the other two growth stages and the peanut leaf had the highest chlorophyll content in the pod bearing stage, followed by the mature stage. As an example, for treatment D1 (double grain, 16 cm) measured on May 18, peanut chlorophyll content first increased and then decreased during the growth period, reaching its maximum in the pod bearing stage, mainly because peanut plants flourished, the number of leaves increased and leaf area increased during this stage. As plants reached the mature stage when leaves and stems gradually age and turn yellow, photosynthesis weakened and chlorophyll content declined.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Number** & **Min** & **Max** & **Mean** & **SD** & **CV** \\ \hline Training sample & 32 & 35 & 49.35 & 44.62 & 3.38 & 0.0758 \\ Verification sample & 8 & 36.1 & 43.3 & 40.1125 & 2.87 & 0.7155 \\ Overall sample & 40 & 35 & 49.35 & 43.57 & 3.69 & 0.0843 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of peanut canopy single-photon avalanche diode (SPAD).  
Figure 2: Spectral reflectance curve of peanut canopy leaves.  
For the three single-seeding density treatments (S1, single grain, 8 cm; S2, single grain, 10 cm; S3, single grain, 12 cm), chlorophyll content in the peanut leaves first increased and then decreased with decreasing seeding density (Figure 3). Chlorophyll content of peanut under density treatment S2 was the highest, reaching 47.68. Similarly, for the three double-seeding densities (D1, double grain, 16 cm; D2, double grain, 20 cm; D3, double grain, 24 cm), chlorophyll content in the peanut leaves first increased and then decreased with decreasing seeding density. Peanut chlorophyll content under density treatment D2 was the highest, reaching 47.76 (Figure 3). These results showed that chlorophyll content in peanut leaves is affected by too high and too low seeding densities. In the present study, a single-seeding density of 10 cm and double-seeding density of 20 cm provided the optimal seeding density and the maximum chlorophyll content. Therefore, employing an appropriate seeding density is beneficial to the rational regulation of the relationship between individual plants and populations, the construction of a reasonable population structure and full exploitation of the high yield potential of crops. When comparing the three different sowing treatments (single-grain, double-grain and three-point), differences in chlorophyll content between S1 and D1, S2 and D2, S3 and D3, T and D1 were not obvious, because the plot densities were similar. In addition, the similar density of S1 and D1 can better explain the difference between S and D.