esa-sceva/satcom-chunk-collection huggingface dataset:
# Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety
## 2 Proposed Vulnerable Road Users Protection Scheme
#### Scheme Overview and Network Model  
Our scheme consists of two phases, where vehicles first estimate the degree of threat by measuring the signal strength of nearby VRU smartphones as depicted in Figure 2. A lightweight collision detection algorithm (CDA) is then executed to confirm or refute a collision.  
Our V2P network model consists of vehicles, VRU smartphones, and a synchronization server. The designated app on the VRU smartphone is responsible for creating WIFI hotspots to connect with nearby vehicles as well as running the CDA. The server is an additional asset that could or could not exist. Depending on the situation, it is advantageous in terms of its large storage and computation capabilities, but our system could run without it. Finally, vehicles move with different trajectories and speeds and are equipped with OBUs that communicate via cellular or DSRC communications.  
In the first phase of our scheme, a VRU smartphone creates a WIFI hotspot, and nearby vehicles search for available hotspots and attempt to connect to them using their OBUs. Once a pedestrian-vehicle connection is established, the signal strength is measured to estimate the distance between the two parties. If the signal strength exceeds a certain threshold, suggesting that the distance between them is dangerously small, a lightweight CDA is executed to determine whether or not there is a real threat of collision. The vehicle and the pedestrian could connect directly via WIFI/DSRC or communicate via cellular network through the synchronization server, which increases system latency.  
The second phase of our system is executed in instances where signal strength exceeds a certain threshold and involves the implementation of a CDA to detect potential collisions. The smartphone app at the VRU end requires the two most recent GPS locations of the incoming vehicle and uses them to construct a line representing its path. The VRU is located at the center of a circle of configurable radius; then, a circle-line intersection is calculated using analytic geometry, as shown in the efficient detection Algorithm 1.  
The potential scenarios of collision detection after executing the detection algorithm are shown in Figure 3.  
Our scheme is summarized in the flowchart depicted in Figure 4.  
Figure 2: Network model.  
```
1:Input Radius of Circle R. Pedestrian location (\(X_{ped}\), \(Y_{ped}\)), Most recent GPS coordinates of a Vehicle (\(x_{1},y_{1}\)). (\(x_{2},y_{2}\))
2:while true do
3:\(d_{x}=x_{2}-x_{1}\)
4:\(d_{y}=y_{2}-y_{1}\)
5:\(A=d_{x}^{2}+d_{y}^{2}\)
6:\(B=2\) ( \(d_{x}(x_{1}-x)+d_{y}(y_{1}-y)\))
7:\(C=\)( \(x_{1}-X_{ped})^{2}+(y_{1}-Y_{ped})^{2}-R^{2}\)
8:\(\beta=B^{2}-4\)AC
9:if\(\beta>0\)then
10: Potential_Collision = true
11: Issue_Appropriate_Warning()
12:endif
13:endwhile
```  
**Algorithm 1** Efficient Collision Detection Algorithm (CDA)  
Figure 4: Our proposed collision detection methodology flowchart.  
Figure 3: Three different scenarios of pedestrian vehicle collision detection.
## 3 Results and Discussion  
### Nanoparticle Dispersion Characteristics  
As shown in Figure 4, a peak was observed at 2950 cm\({}^{-1}\) in the spectra after surface modification, corresponding to the symmetric C-H stretching vibrations attributed to the hydrocarbon chain in GPTMS [19]. The C-H stretching vibration indicates silane grafting on the silica surface. When GPTMS was added to the feed at concentrations of 1.0 mmol/g, symmetric C-H stretching vibrations were observed in the FTIR spectrum, indicating the completion of the grafting [20].  
Figure 4: FTIR spectra for unmodified and surface-modified SNPs.  
Figure 3: Schematic of the core flooding system for the EOR process using nanofluid.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Amount of GPTMS in the feed & 1 mmol/g SiO\({}_{2}\) \\ Particle concentration (wt.\%) & 1.0 \\ Salinity (salt concentration) & 100,000 mg/L (NaCl:CaCl\({}_{2}\), 8.2) \\ Reaction time with nanoparticle (h) & 12, 24, 48, 72 \\ Temperature (\({}^{\circ}\)C) & 80 \\ Flow rate (mL/min) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental design parameters for the core flooding test.
# Sample, Scalable Route to Produce Transparent Superhydrophobic/Hydrophilic Film Surfaces
## 4 Conclusions  
This paper investigated a simple method of producing coatings with different wettability and transparency. Firstly, different silica-based mixtures were investigated. The sol-gel + f-SiO\({}_{2}\) gives the best WCA and transparency results. The XPS measurement demonstrated the high content of the functionalized silica in the sol-gel + f-SiO\({}_{2}\) coating. The superhydrophobic behavior of this composition is primarily reported. In the second section of the paper, the spraying parameters of the ultrasound spray coater, namely standoff distance, shaping air pressure, and area spacing, were investigated. As a result, coatings were applied to the glass substrate during the cold-spraying process while varying the spray-coater parameters. The findings indicate that increasing the distance between the nozzle and surface has a favorable impact on the coating attributes, particularly coating transparency and WCA. Increasing shaping air positively affects the coating qualities, especially the WCA. Lastly, increasing the area spacing results in better WCA and transparency until a critical value is reached, where transparency is improved but hydrophobicity is lost. The findings of this research can be considered as a step toward reaching a superhydrophobic self-cleaning surface that could be used in different applications such as: PV panels, aerospace, biomedical applications, and buildings. For future research directions, studies concerning surface ageing and UV resistance should be applied to upscale these results.  
Conceptualization, S.A. and Z.M.; methodology, Z.M.; software, S.A. and A.A.; validation, Z.M., S.A. and M.G.; formal analysis, Z.M.; investigation, Z.M.; data curation, Z.M.; writing--original draft preparation, S.A. and Z.M.; writing--review and editing, S.A., Z.M., N.R., M.M., A.A. and M.G. supervision, M.G. and Z.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available upon request.  
The authors declare no conflict of interest.
## 5 Discussion  
Previously, there have been many cases of predicting construction costs based on general influencing factors using machine learning. However, the total construction cost consists of sub-cost, and sub-cost is often required along with the total construction cost. Using ANN, it becomes possible to predict the values of several variables at once through multioutput regression.  
In this study, after predicting sub-construction costs using multioutput regression, the method of predicting construction costs and the predictive performance were analyzed through the sum thereof. In case analyses, the deviation of input and output data was minimized through Min-Max and Z-Score scaling to improve the predictive performance of construction costs, and the L1 and L2 regularization methods were applied to prevent overfitting in the machine learning process. To analyze their performance, six test cases were defined by combining them with each other, and their performance was compared and analyzed. In Case 1, in which scaling was not applied, the R-square value was negative, making it difficult to use it to predict construction cost. Case 3 with Min-Max scaling showed better performance than Case 4 with Z-Score scaling. In the prediction of the Total Cost, the R-square value for using L1 regularization was 0.81, and the R-square value for using L2 regularization was 0.82, indicating that the performance of L2 regularization was slightly better. In the case of total cost prediction and construction cost prediction by the sum of the sub-construction costs, the R-square values in Case 6 were 0.82 and 0.75, respectively, indicating that the prediction performance of the total cost was slightly better.  
In the case of construction costs, there was a problem that many data could not be secured due to limitations in data collection. If data can be continuously secured in the future, it is expected that further improvement in construction cost prediction will be possible through multioutput registration. In addition, when using a method such as Ensemble, which uses a combination of various prediction models, the prediction performance of construction costs can be improved.
## 4 Simulation Analysis
### Analysis of Anti-Noise Performance  
To quantitatively evaluate the anti-noise performance of different entropy methods, the power-varying Gaussian white noise signal is compounded with a sinusoidal signal. The expression of the sinusoidal signal is \(s=\sin(2\pi\cdot 2t)\). The sampling frequency is 512 Hz, and the sampling time is 31 s. The composite signal is noiseless in the first 1 s, and Gaussian white noise is added after 1 s, with the noise power increasing every 1 s. The time  
Figure 5: Analysis results of the Mackey–Glass series for different \(\tau\) values with different sequence lengths using (**a**) RSDE; (**b**) SE; (**c**) FE; and (**d**) PE.  
domain waveform of the composite signal is shown in Figure 6. The parameter settings of the four entropy methods are the same as in the previous section. The number of slide windows is changed to calculate the entropy value of the overall signal in different noise environments. After completing this step, the increase rates \(I\) of the four types of entropy sequences of RSDE, SE, FE and PE can be obtained, as shown in Figure 7.  
The results in Figure 7 visually show that RSDE has better noise immunity than other entropy methods in the synthesized signal. In the first half of the noise increase, RSDE possesses a much smaller \(I_{\max}\) than other methods, and its maximum increase rate is only 172%. The RSDE also fluctuates little and shows a stable nonlinear downward trend. The increase rate of the four types of entropy methods gradually stabilized in the second half of the composite signal, and the increase rate of RSDE still remained at the lowest level, between approximately 20% and 35%. This fluctuation range is also the smallest among the four methods. The simulation results show that the strategy of multiple symbolic encoding in RSDE can effectively improve the anti-noise performance of the entropy method and effectively enhance computational robustness.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossy Data Reduction Algorithms
#### 8.1.6 Simple Random Sampling  
We took a straightforward approach to using the sampling algorithm. The solution was based on column weights, using a random projection to compress the original dataset and generate a representative sample. It is worth noting that our current implementation involves grouping by the primary column, which may not be the most optimized application of this technique. The effectiveness of the Simple Random Sampling algorithm focuses on well-distributed data, making it particularly powerful in scenarios where the data distribution is uniform across the dataset. A more refined approach that considers alternative grouping strategies and optimizes the application of column weights could improve the performance of the algorithm, ensuring its efficiency across diverse datasets and contributing to a more robust data sampling methodology.  
In Table 11, the Simple Random Sampling algorithm demonstrates its effectiveness on a variety of datasets. While it does not produce dramatic reductions, its fast processing makes it a promising choice, especially when tailored to specific data types. By adapting to specific data characteristics, the sampling technique becomes an attractive solution for scenarios in which maintaining a compact yet representative dataset is critical. Its ability  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data Type** & **Name** & **Original Size (MB)** & **Final Size (MB)** & **Percentage Variance** & **Execution Time (ms)** & **Processing Time per MB** & **MSE** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Temporal, \\ Numeric \\ \end{tabular} } & Daily Minimum & & & & & **\begin{tabular}{} \\ \end{tabular}** \\  & Temperatures in & 0.0558 & 0.0401 & \(-28.14\%\) & 0.370 & 6.6308 & 0.623 \\  & Melbourne & & & & & & \\  & Electric Production & 0.0730 & 0.0430 & \(\mathbf{-41.10\%}\)1  & 0.160 & 2.1918 & 0.315 \\  & Monthly Beer Production & 0.0690 & 0.0502 & \(-27.24\%\) & 0.170 & 2.4638 & 0.328 \\  & in Austria & & & & & & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Numeric \\ \end{tabular} } & Accelerometer & 3.7000 & 3.7700 & +1.90\% & 24.404 & 6.5957 & 0.849 \\  & Smoke Detection & 5.8000 & 5.5090 & \(-5.02\%\) & 16.107 & 2.7771 & 0.674 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Temporal, \\ Text \\ \end{tabular} } & IoT Temperatures & 6.7000 & 5.9060 & \(-11.85\%\) & 13.918 & \(\mathbf{2.0773}\) & 0.574 \\ \hline \hline \end{tabular}
\end{table}
Table 10: K-Means results.  
to balance speed and reduction makes it a compelling option in the landscape of efficient data reduction strategies. The algorithm reduced more data, in percentage, on the Monthly Beer Production in Austria dataset, composed of temporal and numeric data, reducing the original dataset size by \(-13.04\%\) in just \(0.002\) ms. When analyzing the time taken to process \(1\) MB per ms, the algorithm performed better in the Smoke Detection dataset, containing numerical data, processing \(1\) MB of data in \(0.0093\) ms.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 4 Applications
### Food Applications  
Among food applications, [PERSON] et al. recently studied the release of catechin from SNPs to retain the properties of the bioactive compound throughout simulated gastric and intestinal conditions. The SNPs protected catechin against the hostile gastric environment and helped to retain its bioactive properties during the in vitro digestion process [57]. [PERSON] et al. also performed in vitro studies on the viability of probiotic-loaded SNPs. The study was carried out under simulated gastrointestinal and processing conditions, using both gastric juice and simulated intestinal fluid. However, in this case, it was concluded that SNPs are not good vehicles for probiotics since it was shown that the particles were too small to encapsulate the probiotic cells within them and, therefore, they were immediately released into the simulated gastric or intestinal solutions that lead to cell death due to the harsh conditions of these environments [53]. In another study carried out by [PERSON] et al. recently, different food simulators were used to evaluate curcumin release profiles, where some intended to simulate highly hydrophilic foods and others intended to simulate highly lipophilic foods. The authors concluded that the released curcumin was favored in lipophilic substances and can be controlled by reducing the polarity of the SNPs [18]. Also, [PERSON] et al. investigated the in vitro release of SNPs loaded with polyphenols in simulated gastric and intestinal juices. This study showed sustained release profiles of polyphenols from SNPs and suggested that SNPs may be promising and effective nanocarriers to protect bioactive compounds against sensitive environments and control their release [21].
# Text Detection and Recognition for X-ray Weld Seam Images
## 2 X-ray Weld Seam Image Text Recognition Model Framework
### A Text Recognition Model Based on MATRN
#### 2.2.2 The CBAM Module  
The CBAM module (Figure 5) is a feature-enhancing module that consists of two sub-modules: channel attention and spatial attention.  
Figure 4: The structure of the CA module.  
1. Channel attention: This part determines which channels are more important to the current task by learning the correlation between different channels in the feature map. Initially, the spatial information of the feature map is aggregated through adaptive average pooling and adaptive max pooling operations to generate average-pooled feature \(F_{avg}^{c}\) and max-pooled feature \(F_{max}^{c}\). These two features are then individually passed through the same MLP structure, and the merged output feature vector produces channel attention map \(M_{C}\). \[\begin{split} M_{C}(F)&=\sigma(MLP(AvgPool(F))+MLP( MaxPool(F)))\\ &=\sigma\Big{(}W_{1}\Big{(}W_{0}\Big{(}F_{avg}^{c}\Big{)}\Big{)}+W_ {1}(W_{0}(F_{max}^{c}))\Big{)}\end{split}\] (14) \(\sigma\) represents the sigmoid function, and \(W_{1}\) and \(W_{0}\) are the weights of the MLP.
2. Spatial attention: This part focuses on the correlation between different spatial positions in the feature map. It calculates the maximum and average values for each channel in the feature map, aggregating channel information to obtain two 2D feature maps \(F_{avg}^{s}\) and \(F_{max}^{s}\). These two feature maps are then concatenated and passed through a standard convolutional layer with a kernel size of \(7\times 7\), resulting in spatial attention map \(M_{5}\). This concentrates attention on the most important regions in the image, enhancing the representation of the regions of interest.  
Figure 5: The structure of CBAM module.  
\[M_{S}(F)=\sigma\Big{(}f^{7\times 7}([AvgPool(F);MaxPool(F)])\Big{)}=\sigma\Big{(}f^{7 \times 7}\Big{(}\Big{[}F^{s}_{avg};F^{s}_{max}\Big{]}\Big{)}\Big{)} \tag{15}\]  
\(\sigma\) represents the sigmoid function, and \(f^{7\times 7}\) represents the convolution operation with a \(7\times 7\) kernel size.  
After extracting the visual features, the CBAM module is applied to the high-level features, performing channel and spatial attention operations. This enables the network to focus more on important channels and spatial locations at higher levels of the feature representation, facilitating optimization of the feature representation. By enhancing the expressive power of the features, the network can more effectively handle text lines in welding images, leading to more accurate recognition and understanding of text lines in welding images. This enhances the accuracy and robustness of the text recognition.
## 2 Materials and Methods
### Statistical Analysis  
A parsimonious approach was applied to maintain statistical power and avoid overfitting, prioritizing age and sex as presurgical predictors in our logistic regression models. This decision intentionally excluded other potentially relevant factors, such as activity level, weight, body mass index (BMI), and comorbidities, thus restricting the number of model variables [26]. This strategy ensured the findings are easily interpretable and applicable in diverse clinical settings, given the universal availability and clinical significance of age and sex [27].  
Logistic regression was used to quantify the effect of age and sex on the frequency of medial meniscal damage, lateral meniscal damage, and associated knee chondropathy. Dependent variables (outcomes) were coded as 1 for presence and 0 for absence. Sex was binary-coded, with men as the reference group (0) and women (1) compared against this baseline. Age was included as a continuous predictor in this regression and was not categorized. The direction and strength of association between significant predictor variables and outcomes were assessed using odds ratios.  
The medical literature supports the existence of a non-linear relationship between age and knee joint lesions [28; 29; 30]. We therefore performed stratified analysis based on age quartiles to reveal potential age-dependent patterns in these outcomes. This approach allowed us to identify potential non-linear relationships between age and outcomes while avoiding the Simpson's paradox--a statistical phenomenon where a trend appears in different groups of data but disappears or reverses when the groups are combined [31]. We chose to use four age quartiles of similar size to optimally balance statistical power, granularity, and interpretability. This reduced the potential risk of bias that might arise from underrepresented age groups while controlling for age as a confounder and addressing non-linearity. Differences in the frequencies of individuals with and without meniscal damage/chondropathy were analyzed using Chi-square tests with 4 \(\times\) 2 contingency tables. For significant outcomes, paired comparisons using Chi-square tests (with Bonferroni correction) based on 2 \(\times\) 2 contingency tables against the lowest quartile were run as post hoc testing [32]. Each comparison was performed against the lowest age quartile (Q1) as the reference group. Finally, phi coefficients were used to measure the strength of association between medial meniscal damage, lateral meniscal damage, and associated knee chondropathy across different age quartiles. The phi coefficients are recommended to be used for measuring associations between two binary variables [33]. A \(p\) value less than 0.05 was considered significant. Data analysis was conducted using the Statistica 10 software (StatSoft Inc., Tulsa, OK, USA).
## Materials and Methods
### Operating Conditions  
#### 2.5.1 Membrane Selection  
Trials were carried out in two different experiments for membrane selection; (1) exclusive membrane filtration (Figure 3) and (2) post-DAF membrane filtration (Figure 4). In membrane filtration, three of 200 kDa cut-off membrane and 20 kDa cut-off membrane were put into the six slots of the membrane housing. The membranes were cleaned by the CIP procedure first, before testing. The piston pump was operated at 20 Hz, which is equivalent to a flow rate of 1135.62 L/h. The pressure was set to 6.89 bar and samples of the feed and the permeate out of each membrane were collected and analyzed for the various water quality attributes. Volumetric flow rate tests were carried out for each membrane to determine the permeate flow rate. The membranes were discarded and then the next two were put into the housing and the whole procedure was repeated, but at different pressures. Table 3 states the pressure at which these membranes were operated. After all six different membranes were tested, the whole experiment was repeated two more times for replication. The same methodology was followed for post-DAF membrane filtration, with the feed in to the membrane filtration test being the DAF effluent water. In order to calculate the separation efficiency of the membrane, the reduction percentage was calculated using:  
\[Reduction\ in\ COD,\ \%=\ \left(1-\ \frac{C_{p}}{C_{f}}\right)100\% \tag{1}\]  
where \(C_{p}\) and \(C_{f}\) are the concentration of the permeate and the feed, respectively.