esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Monit4 Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system's modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4 Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4 Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments.  
IoT; connected health ecosystems; health monitoring; Monit4 Healthy system; sensor fusion; real-time biomedical signal processing [1][[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 3 Materials and Methods
### Architectures
#### 3.3.1 Wildland Fires Detection and Segmentation using Deep Learning  
[PERSON] et al. propose in [38] the DeepFire model for wildfire segmentation based on the U-Net architecture [69]. The network outputs a single-channel binary mask that represents the fire pixels of the image.  
In this architecture, the images are reduced four times through max-pooling layers in the encoder section and then are expanded through transpose convolution layers in the decoder section to their original size. The last convolution layer of the model employs a \(1\times 1\) kernel and a sigmoid activation function to produce the final binary mask. Figure 7 shows the in-detail architecture of the DeepFire model.  
Figure 6: Sample visible, NIR, fused, and ground truth images employed in the present paper.  
Figure 7: In-detail structure of the model proposed by [PERSON] et al. [38]. The numbers inside the squares represent the number of filters in the corresponding layer.  
This model has the advantage of having approximately two million trainable parameters, a relatively low number for DL standards. The latter allows for faster training and inference times. The authors employ 419 visible RGB wildfire images from the Corsican Fire Database, using 377 for training and 42 for testing. Finally, [PERSON] et al. report good results with the Dice similarity coefficient as the loss function for the model, with an F1-Score ranging between 64.2% and 99% on the test set.
# Defacement Detection with Passive Adversaries
## 2 Defacement Response  
Defacement response is a complex process, that is planned based on risk analysis results and available budget. It normally comprises three phases:  
* _Prevention_. One should in the first place avoid the very possibility of defacement, by protecting the relevant perimeter, including the Content Management Systems (CMS), and the target Web Servers and Proxies. This can be done with conventional security measures, e.g., WAFs, IDS/IPS systems, vulnerability scanning and remediation.
* _Detection_. When defacement occurs it should be immediately detected, yielding low error rates for both false positives (e.g., normal CMS changes, acceptable external feeds, and advertising banners), and undetected defacements.
* _Reaction_. Again, a three-phase process: 1. Alerting: based on detection alarm thresholds, an alert is generated, firing automated reaction or a request for human intervention. 2. Inspection: defacements are rare, yet high priority events; as a consequence, human inspection is needed when an above-threshold alarm is fired. Integration with a Security Operation Center (SOC) and corresponding Security Incident and Event Management (SIEM) software architectures is needed for enterprise-level solutions. The reaction component should be available as a 24/7 service and appropriate information/escalation procedures should be defined.
3. Mitigation: a courtesy page should be displayed, until normal operation is enforced. If proxies or a CDN such as Akamai are involved, this action should be propagated accordingly.  
In this paper, we will concentrate on the detection phase. See [28] for a discussion of the general relationship between _detection_ and _response_ in anomaly detection. We will, however, also address issues that may be relevant for the reaction phase.
# A New Method for Detecting Weld Stability Based on Color Digital Holography
## 2 Methods  
### The Basic Principles of the Color Digital Holography  
Digital holography records the amplitude and phase of the object light in the interference fringes of the hologram. The complex amplitude distribution of object light is:  
\[\mathrm{U_{o}(x,y)=o(x,y)exp(j\varphi_{0}(x,y)),} \tag{1}\]  
The distribution of the reference light complex amplitude is:  
\[\mathrm{U_{R}(x,y)=R(x,y)exp(j\varphi_{R}(x,y)),} \tag{2}\]  
The complex amplitude of the hologram is:  
\[\mathrm{I(x,y)=\left|U_{o}(x,y)\right|^{2}+\left|U_{R}(x,y)\right|^{2}+2o(x,y) R(x,y)cos[\varphi_{0}(x,y)-\varphi_{R}(x,y)],} \tag{3}\]  
where \(o(x,y)\) is the amplitude of object light, \(R(x,y)\) is the amplitude of the reference light, \(\varphi_{0}(x,y)\) is the phase of object light, and \(\varphi_{R}(x,y)\) is the phase of the reference light.  
Single-channel digital holography can obtain the deformation of the measured object along the optical axis direction, while three-channel digital holography (also called color digital holography) can obtain the deformation of the measured object in the three xyz directions. The solution for three-dimensional deformation is as follows [28].  
\[\begin{cases}\lambda_{R}\Delta\varphi_{\lambda R}\\ \lambda_{G}\Delta\varphi_{\lambda G}\\ \lambda_{B}\Delta\varphi_{\lambda B}\end{cases}=2\pi\begin{bmatrix}\cos\!{ \theta_{x}}^{\lambda R}\!\sin\!{\theta_{xx}}^{\lambda R}&\sin\!{\theta_{x}}^{ \lambda R}&1+\cos\!{\theta_{x}}^{\lambda R}\!\cos\!{\theta_{xx}}^{\lambda R}\\ \cos\!{\theta_{x}}^{\lambda G}\!\sin\!{\theta_{xx}}^{\lambda G}&\sin\!{\theta_{ x}}^{\lambda G}&1+\cos\!{\theta_{x}}^{\lambda G}\!\cos\!{\theta_{xx}}^{\lambda G}\\ \cos\!{\theta_{x}}^{\lambda B}\!\sin\!{\theta_{xx}}^{\lambda B}&\sin\!{\theta_{ x}}^{\lambda B}&1+\cos\!{\theta_{x}}^{\lambda B}\!\cos\!{\theta_{xx}}^{\lambda B} \end{bmatrix}\begin{bmatrix}\mu_{x}\\ \mu_{y}\\ \mu_{y}\\ \mu_{z}\end{bmatrix}, \tag{4}\]where \(u_{x}\), \(u_{y}\), \(u_{z}\) are the deformations in the x, y and z directions, respectively. \(\theta_{x}\) represents the angle between the illumination vector and its projection on the xoz plane, and \(\theta_{xz}\) represents the angle between the projection of the illumination vector on the xoz plane and the z-axis. \(\Delta\varphi_{R}\), \(\Delta\varphi_{G}\), and \(\Delta\varphi_{B}\) are the unwrapped phase differences for the red channel, green channel, and blue channel, respectively.
## 4 Study Area and Data Selection
### Input Data
#### 4.2.1 Preprocessing of Data  
The collected data have a missing value and outliers due to a malfunction of the measuring instruments, crosstalk in the radio wave path and changes in the measurement points. Thus, preprocessing of the input data was required to improve the accuracy of the forecasting model for this study. The outliers and the missing value of observed data were corrected and interpolated according to the article 14 of the installation environment, maintenance, and management of hydrological survey facilities and the quality control standard for hydrological data [36]. The interpolation and correction methods are summarized in Table 2.  
The proposed method was used to correct and interpolate outliers and missing values corresponding to about 1% of the total data. Most of the outliers and missing values of hydrology data used in this study occurred in the non-flood season which was not considered in this study.
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 5 Experimental Results
Figure 13: Comparison of synthesis results in the ABC system in terms of the number of LUT blocks (**a**), number of logic levels (**b**), and power (**c**). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Benchmarks** & **Model 0** & **Model 1** & **Model 2** & **Model 3** & **Model 4** & **Model 1–4 Mean** \\ \hline dk15 & 30.07 & 35.51 & 61.82 & 82.94 & 75.48 & 63.94 \\ lion & 3.11 & 5.13 & 8.82 & 8.63 & 7.96 & 7.64 \\ mc & 7.46 & 14.74 & 21.24 & 29.62 & 23.83 & 22.36 \\ s8 & 3.56 & 4.72 & 8.68 & 6.8 & 7.12 & 6.83 \\ tav & 30.75 & 63.76 & 52.71 & 94.8 & 81.6 & 73.22 \\ train4 & 1.98 & 4.81 & 5.06 & 6.71 & 5.69 & 5.57 \\ bbara & 10.6 & 14.84 & 22.54 & 33.52 & 27.73 & 24.66 \\ bbta & 5.74 & 10.78 & 16.75 & 20.15 & 16.26 & 15.99 \\ becount & 8.47 & 12.59 & 20.29 & 22.78 & 21.07 & 19.18 \\ dk14 & 48.35 & 64.76 & 130.51 & 154.42 & 142.01 & 122.93 \\ dk17 & 45.75 & 64.63 & 112.21 & 148.24 & 131.52 & 114.15 \\ dk27 & 27.5 & 52.45 & 80.48 & 109.81 & 67.84 & 77.65 \\ ex5 & 5.58 & 6.1 & 14.46 & 16.52 & 15.96 & 13.26 \\ ex6 & 61.72 & 71 & 128.92 & 150.85 & 156.51 & 126.82 \\ ex7 & 4.86 & 6.69 & 13.79 & 14.38 & 13.38 & 12.06 \\ lion9 & 4.81 & 6.49 & 9.37 & 12.81 & 12.9 & 10.39 \\ s27 & 14.07 & 15.57 & 10.92 & 36.7 & 35.68 & 24.72 \\ shiftreg & 10.04 & 17.46 & 19.64 & 34.8 & 28.36 & 25.07 \\ dk512 & 4.56 & 91.96 & 164.73 & 196.72 & 167.31 & 155.18 \\ train11 & 5.23 & 6.83 & 7.48 & 12.22 & 12.54 & 9.77 \\ bbse & 9.89 & 26.14 & 54.74 & 56.48 & 53.54 & 47.73 \\ cse & 15.35 & 30.29 & 59.85 & 62.32 & 59.87 & 53.08 \\ sse & 11.08 & 29.35 & 59.5 & 59.2 & 54.98 & 50.76 \\ ex2 & 11.05 & 13.89 & 30.06 & 31.65 & 30.91 & 26.63 \\ keyb & 37.46 & 73.08 & 159.53 & 167.43 & 157.8 & 139.46 \\ tma & 9.21 & 11.04 & 19.93 & 18.4 & 18.54 & 16.98 \\ s1 & 26.25 & 72.68 & 157.82 & 155.57 & 145.14 & 132.80 \\ \hline B-mark mean & 16.83 & 30.64 & 53.77 & 64.61 & 58.20 & 51.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Power results for the ABC system. Figure 15: Comparison of power results (mean values) in the ABC system.
## 4 Data Analyses, Discussion and Findings
### Manufacturer and Distributor  
The survey indicates that the information provided by the manufacturers and the early involvement of distributor are beneficial for piping prefabrication (as shown in Figure 5). The  
Figure 5: The degree of influence of building information modelling (BIM) to the piping prefabrication.  
Figure 6: Detailed BIM model of a fire sprinkler system developed by the contractor.  
manufacturer or distributor may provide a product brochure in which vibrant pictures and professional introductions are given for contractors. This information is sufficient when the contractor develops the BIM model based on products in the brochure. However, developing the product brochure based a BIM model is a time-consuming task, especially when many products will be used a project. The interoperability between the software used by the manufacturer and the BIM software in the building and construction industry is a challenge. To achieve this issue, some manufacturers, including the equipment manufacturers and the fitting manufacturers, develop the BIM model that can be used directly by the contractors. For example, Daikin, one of famous HVAC product suppliers, provides the comprehensive library of a high quality BIM models for its HVAC products in support of our building owner, architect, MEP and contractor customers using Autodesk Revit platforms. Another example is Victaulic, the originator and world's leading producer of mechanical pipe joining solutions and grooved pipe joining systems, provides certified content in 21 native software platforms including BIM software such as Revit MEP, Bentley AutoPlant and PipeDesigner 3D. In case studies provides by Victaulic, a company wins the bid for a major hospital expansion project by creating a BIM model using Victaulic grooved mechanical piping systems to save time and costs.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 6 Discussions  
### Code Generation Comparison between LLMs and SCADE  
We compare the code generation process of the proposed LLMs approach with the widely used SCADE software (16.4) development method. The two approaches differ significantly in the design input, development time, code size, learning curve, and verification method, as shown in Table 3.  
Clearly, the language model-based approach for developing safety-critical software has distinct advantages in terms of learning curves and software development efficiency. With further improvements in code quality, this approach will provide a new choice for the development of safety-critical software.
# Shear Resistance Evolution of Geogrid Reinforced Expansive Soil Under Freeze-Thaw Cycles
## 3 Results and Discussion
### Establishment and Validation of the Empirical Formula
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **F-T** & **Geogrid** & **Confining** & **E\({}_{i}\)/MPa** & **(\(\sigma_{1}\)–\(\sigma_{3}\))\({}_{ult}\)/kPa** & **\({\bf R^{2}}\)** \\ \hline GRES-1 & 0 & & & & 63.69 & 925.9 & 0.999 \\ GRES-2 & 1 & & & 37.74 & 892.9 & 0.999 \\ GRES-3 & 3 & 3 & 200 & 36.36 & 833.3 & 0.999 \\ GRES-4 & 6 & & & 36.76 & 813 & 0.999 \\ GRES-5 & 10 & & & 32.05 & 800 & 0.999 \\ GRES-6 & & 0 & & 26.45 & 719.4 & 0.999 \\ GRES-7 & 3 & 1 & & 31.21 & 773.3 & 0.999 \\ GRES-8 & 3 & 3 & 200 & 36.36 & 833.3 & 0.999 \\ GRES-9 & & 5 & & 44.05 & 917.4 & 0.999 \\ GRES-10 & & & 100 & 27.32 & 601.59 & 0.994 \\ GRES-11 & 10 & 3 & 200 & 32.05 & 801.25 & 0.999 \\ GRES-12 & & & 300 & 43.36 & 961.58 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Initial elastic modulus and ultimate strength. where \(P_{a}\) (101.3 kPa) is the atmospheric pressure, \(\sigma_{3}\) is confining pressure, \(N\) is number of F-T cycles, \(C\) is number of geogrid reinforced layers. Figure 11 shows measured and predicted values of the initial elastic modulus and ultimate strength. It is worth mentioning that the initial elastic modulus and ultimate strength predicted by Equations (8) and (9) are in good agreement with the test values, with the inaccuracy of the initial elastic modulus within 25%, and the inaccuracy of the ultimate strength within 20%. This indicates that Equations (8) and (9) can be used as a prediction formula for the initial elastic modulus and ultimate strength for GRES. Substituting Equations (3), (4), (8), and (9) into Equation (2), the Duncan-Zhang modified model describing the mechanical properties of GRES under freeze-thaw cycling conditions can be obtained, and is expressed as Equation (10). \[\left\{ \begin{array}{l}(\sigma_{1}-\sigma_{3})=\frac{\varepsilon_{1}}{ \frac{1}{\varepsilon_{1}}+\frac{\varepsilon_{1}}{(\sigma_{1}-3)_{\text{all}}}} \\ E_{i}=23.56\times N^{-0.13}\times 1.18^{C}\times\left(\frac{\sigma_{3}}{P_{a}} \right)^{0.38}\\ (\sigma_{1}-\sigma_{3})_{\text{all}}=98.45\times N^{-0.15}\times(5.5+0.64C) \times\left(\frac{\sigma_{3}}{P_{a}}\right)^{0.61}\end{array} \right. \tag{10}\]  
The deviatoric stress-strain curve of GRES subjected to F-T cycles was calculated by Equation (10), as shown in Figure 12. Compared to the experimental data, the predicted curves were well fitted to the test values, and the stress-strain deviations at the peak points were within 5.1%. The Duncan-Zhang modified model can be used as a prediction formula for the stress-strain characteristics of GRES under freeze-thaw cycling conditions. The research results can provide important theoretical support for the practical engineering design of GRES in cold regions. Figure 11: Measured and predicted values of initial elastic modulus (**a**) and ultimate strength (**b**). Figure 12: Comparison between the experimental data and predicted results.
## 3 Addressing the Dynamic Traveling Salesman Problem
### Ant Colony Optimization Metaheuristic
#### 3.1.2 Updating Pheromone Trails  
The pheromone update procedure consists of two parts: (1) pheromone evaporation, in which all pheromone trails are reduced by a constant rate; and (2) pheromone deposit, in which ants increase the pheromone trails associated with their constructed solutions.  
In this work, the pheromone policy of the \(\mathcal{MAX}-\mathcal{MIN}\) Ant System (\(\mathcal{MMAS}\)) algorithm is used, which is one of the best-performing ACO variants [28; 29]. Firstly, in the pheromone update procedure of \(\mathcal{MMAS}\), an evaporation is performed on all pheromone trails as follows:  
\[\tau_{ij}\leftarrow(1-\rho)\tau_{ij},\forall\ (i,j)\in A, \tag{5}\]  
where \(0<\rho\leq 1\) is the rate of evaporation.  
Thereafter, the best ant is allowed to deposit pheromone as follows:  
\[\tau_{ij}\leftarrow\tau_{ij}+\Delta\tau_{ij}^{best},\forall\ (i,j)\in s^{best}, \tag{6}\]  
where \(s^{best}\) is the solution generated by the best ant and \(\Delta\tau_{ij}^{best}=1/C^{best}\), where \(C^{best}=\phi(s^{best},t)\) is the quality value of DTSP solution \(s^{best}\). The best ant to deposit pheromone may be either the best-so-far ant, in which case \(\Delta\tau_{ij}^{best}=1/C^{bs}\), where \(C^{bs}=\phi(s^{bs},t)\) is the solution quality of the best-so-far ant, or the iteration-best ant, in which case \(\Delta\tau_{ij}^{best}=1/C^{ib}\), where \(C^{ib}=\phi(s^{ib},t)\) is the solution quality of the best ant of the iteration. By default, the iteration-best ant is used to update the pheromone trails and, occasionally, the best-so-far ant. The pheromone trail values in \(\mathcal{MMAS}\) are kept to the interval \([\tau_{min},\tau_{max}]\), where \(\tau_{min}\) and \(\tau_{max}\) are the minimum and maximum pheromone trails limits, respectively. The overall \(\mathcal{MMAS}\) pheromone update is presented in Algorithm 2, lines 1-20.  
```
1:if\(\mathcal{MMAS}\) is selected then
2:\(\tau_{max}\gets 1/\rho C^{bs}\)
3:\(\tau_{min}\leftarrow\tau_{max}\big{(}1-\sqrt[\rho]{0.05}\big{)}/\big{(}( cand-1)\cdot\sqrt[\rho]{0.05}\big{)}\)
4:for\(\forall(i,j)\in A\)do
5:\(\tau_{ij}\leftarrow(1-\rho)\tau_{ij}\)
6:endfor
7:\(s^{best}\gets s^{ib}\ ||\ s^{bs}\)
8:\(\Delta\tau_{ij}^{best}\gets 1/C^{ib}\ ||\ 1/C^{bs}\)
9:for each arc\((i,j)\in s^{best}\)do
10:\(\tau_{ij}\leftarrow\tau_{ij}+\Delta\tau_{ij}^{best}\)
11:endfor
12:for\(\forall(i,j)\in A\)do
13:if\(\tau_{ij}>\tau_{max}\)then
14:\(\tau_{ij}\leftarrow\tau_{max}\)
15:endif
16:if\(\tau_{ij}<\tau_{min}\)then
17:\(\tau_{ij}\leftarrow\tau_{min}\)
18:endif
19:endfor
20:endif
21:if P-ACO is selected then
22:\(\Delta\tau\leftarrow(\tau_{max}-\tau_{0})/K\)
23:if\(t==K\)then
24:\(pop(t)\gets pop(t)\setminus s^{\prime}\)
25:for each arc\((i,j)\in s^{\prime}\)do
26:\(\tau_{ij}\leftarrow\tau_{ij}-\Delta\tau\)
27:endfor
28:endif
29:\(pop(t)\gets pop(t)\cup s^{ib}\)
30:for each arc\((i,j)\in s^{ib}\)do
31:\(\tau_{ij}\leftarrow\tau_{ij}+\Delta\tau\)
32:endfor
```  
**Algorithm 2** Pheromone Update \((t,s^{ib},s^{bs})\)
# A New Method for Evaluating the Bearing Capacity of the Bridge Pile Socketed in the Soft Rock
## 3 Field Test and Numerical Simulation
### Field Test Results of the Pier's Fundamental Vertical Frequency  
In the natural environment, structures vibrate slightly and irregularly with the surrounding vibration, and the structure's slight vibration is pulsation. The pulsation has various frequencies, and the harmonic quantity of the structure's fundamental frequency is the main component of the pulsation. The vertical fundamental frequency of the test pier was tested by the MGCplus dynamic data acquisition and analysis system. This system is accurate and reliable in dynamic tests and analyses. First, the dynamic signal was collected by the acceleration sensor and amplified by the amplifier. Then, the data was directly collected and recorded, and the acceleration time history curve was observed in real-time on the computer. Finally, with data post-processing software, the amplitude-frequency characteristics could be analyzed. The ultra-low frequency acceleration sensor was placed vertically on the pier side in the field test, as shown in Figure 4. The environmental vibration response of the pier was detected, and the MGCplus dynamic data acquisition and analysis system and computer at the other end read the detected signal. A photograph of the test pier pile is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Silty** & **High-Weathered** & **Medium-Weathered** \\  & **Clay** & **Argillaceous Silstone** & **Argillaceous Silstone** \\ \hline Compression modulus (\(MPa\)) & 5.42 & / & / \\ Cohesion force (\(KPa\)) & 31.5 & / & / \\ Friction angle (\({}^{\circ}\)) & 18.1 & / & / \\ Coefficient of friction on basis & 0.25 & 0.4 & 0.45 \\ Allowable bearing capacity (\(KPa\)) & 160 & 320 & 1000 \\ Standard value of soil friction resistance around the & 40 & 110 & 160 \\ pile (\(KPa\)) & / & / & 11 \\ Uniaxial compressive strength of saturated rock (\(MPa\)) & 1.1 & 5.34 & 8.56 \\ Thickness (\(m\)) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of rock and soil layers.  
Figure 4: The elevation of the test pier. (Units: \(cm\))  
Figure 6a shows the test bridge pier shaft to the acceleration time history diagram; using a filter to remove high- and low-frequency signal components and process the data by fast Fourier transformation, the frequency within the power spectral density curve was obtained, as shown in Figure 6b. The power spectrum peaks were determined by the frequency of each corresponding order of natural frequency. The first peak point was the fundamental frequency of the measured pier structure.  
The pier's measured vertical natural vibration frequencies were 23.282 Hz, 23.758 Hz, and 23.955 Hz. The three results are almost the same and indicate the method is accurate. Their average value is 23.665 Hz, and this was used as the vertical fundamental frequency.