esa-sceva/satcom-chunk-collection huggingface dataset:
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 2 Materials and Methods
### Classifier Description and Experimental Setup
```
function[accuracy]\(\leftarrow\)SVM_Classification(a)
1:Define the acquisition parameters and RF signal dataset
2:root \(\leftarrow\)Dataset path
3:\(f_{s}\gets 5\)GHz
4:\(N\gets 150\)
5:\(D\gets 24\)
6:\(K\gets 30\)
7:Devices \(\leftarrow\)GetDir(root)
8:database \(\leftarrow\)GetDataBase(root, \(f_{s}\)) [Load RF signal data]
9:
10:Dataset preparation
11:Initialize empty arrays dataframeTrain and dataframeTest
12:for\(i\gets 10\)do
13:subdata \(\leftarrow\) extract N samples from database for device \(i\)
14:r \(\leftarrow\)randperm(\(N\))
15:for\(j\in\)ro do
16:ifout \(\leq K\)then
17:dataframeTest \(\leftarrow\)dataframeTest \(\cup\)subdata(j)
18:else
19:dataframeTrain \(\leftarrow\)dataframeTrain \(\cup\)subdata(j)
20:endif
21:count \(\leftarrow\)count + 1
22:endfor
23:Update indices:dim \(\leftarrow\)dim + \(N\), dim \(\leftarrow\)dim + \(N\)
24:endfor
25:dataframe \(\leftarrow\)dataframeTrain \(\cup\)dataframeTest [Concatenate and shuffle]
26:
27:Build training and testing sets
28:xtrain,ytrain \(\leftarrow\) features and labels for training set (120 Bluetooth signals/device)
29:xtest,ytest \(\leftarrow\) features and labels for test set (30 Bluetooth signals/device)
30:
31:Classifier training
32:t \(\leftarrow\)templateSVM(Standardize=True, Kernel=polynomial, Order=4)
33:SVMModel \(\leftarrow\)ftecoco(xtrain,ytrain, Learners=t)
34:
35:Classifier evaluation
36:testpredict \(\leftarrow\)predict(SVMModel,xtest)
37:
38:Compute the confusion matrix and classifier accuracy
39:ConMat \(\leftarrow\)confusionmat(testpredict, ytest)/\(K\)
40:accuracy \(\leftarrow\)sum(diag(ConMat))/\(D\)
41:
42:return accuracy
```  
**Algorithm 1** SVM-based classifier
## 3 Results and Discussion
### Analysis of cotA and mhqO Gene Expression in B. safensis CN12 in the Presence of 4-n-NP
For example, this gene has been predicted to exhibit similarities with _linE_, which encodes chlorophylloquinone/hydroquinone 1,2-dioxygenase found in _S. paucimobilis_. This enzyme is responsible for cleaving substrates resembling hydroquinone [65,66]. [PERSON] et al. [67] described a degradation pathway of 4-n-NP where hydroquinone appears as an intermediate transformation product in the presence of _Sphingobium xenophagum_. Later, this route was also observed for _Sphingomonas_ sp. strain NP5 [58]. However, the _mhqO_ gene has never been specifically described in the degradation of 4-n-NP. There are few studies in the literature on the genes involved in the degradation pathway of 4-n-NP. Therefore, by suggesting a relation between the transcription of _mhqO_ and the decay of 4-n-NP concentration in CN12 cultures (Figure 3), these results represent an advancement in the field of genetic research on 4-n-NP bacterial degradation. In addition to the _cotA_ and _mhqO_ genes, which have shown a significant correlation with nonylphenol (NP) degradation, other genes, such as _sMO_ (single-component monooxygenase), _alkB_ (alkane monooxygenase), _mPH_ (multi-component phenol hydroxylase), and _C12O_ and _C23O_ (catech 1,2- and 2,3-dioxygenases), also play key roles in NP degradation pathways. The involvement of these genes has been reported in various studies. For example, [PERSON] et al. [68] identified the role of _sMO_ in the degradation of 4-n-NP by _Aspergillus_ strains, highlighting its function in initiating the breakdown of the aromatic ring. Similarly, [PERSON] et al. [69] demonstrated that _Pseudomonas putida_ TX2 and _Pseudomonas_ sp. TX1 employ _mPH_, _C12O_, and _C23O_ to degrade long-chain alkylphenols, including NP, via hydroxylation followed by ring cleavage. Despite these insights, the literature on the genetic basis of NP degradation remains limited. Therefore, adopting a broader genetic perspective in future investigations could provide a more comprehensive understanding of microbial catabolic potential and significantly contribute to the development of more effective bioremediation strategies for NP-contaminated environments.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 3 Simulation Scenarios
### Scenarios  
To achieve comprehensiveness on SVS phenomenas, six different scenarios are considered that are representative of different categories of dynamic transient conditions.  
Six scenarios are generated for conditions given below:  
1. In this case, the power system is considered to be at the basic condition and 70% of loads are set as dynamic loads. A sudden outage of lines 16-17 at time \(t=0.2\) s is taken into account for generating the required data.
2. Active and reactive powers consumed by loads and generated by generators are adjusted to 90% of the basic condition. Dynamic proportion of loads is taken equal to 20%. A three-phase fault at time \(t=0.1\) s occurs on lines 16-17 near bus 17 and cleared by the outgoing line at time \(t=0.2\) s.
3. This case is related to an occurring fault on lines 21-22 near bus 21 at time \(t=0.1\) s. Consumed and generated power are 90% of basic condition and a proportion of dynamic loads are set to 45%. Fault is cleared at time \(t=0.2\) s.
4. This scenario is similar to (b), but the load's dynamic part is increased to 45%.
5. This scenario is similar to (c), but active and reactive power of loads and generators are increased to 110% of their amount in basic condition, and the dynamic part of loads is increased to 50%.
6. A sudden outage of lines 21-22 is taken into account at time \(t=0.2\) s. The consumed and generated power of loads and generators are adjusted at 120% of the basic operating point while a dynamic part of loads is equal to 60%.  
By simulating these scenarios on a 39-bus IEEE test system, the variation of voltage magnitude at buses 6, 8, 14, 16, 21, and 27 are shown in Figure 2. As shown in this figure, this scenario includes three stable and three unstable cases from the perspective of SVS. In case (a), voltage stability is maintained without any sag and swell. Although in cases (b) and (c) voltage stability is maintained, voltage age and swell are observed. Case (c) is related to a significant voltage dip. In cases (d), (e), and (f), voltage magnitude in buses drops after outgoing faulty line at time \(t_{cl}=0.2\) s, and this condition has led to short-term voltage instability. Although these three cases are related to instability, but time and pattern of voltage magnitude falling and oscillation are different.
# Aerodynamic Characteristics When Trains Pass Each Other in High-Speed Railway Shield Tunnel
## 2 Numerical Model  
### Governing Equations  
To numerically simulate the problem of high-speed trains meeting in a tunnel, the conservation of mass, momentum, and energy must be satisfied. Since the values of the Reynolds number in the aerodynamics of high-speed railways exceed \(10^{6}\), the air moves in a turbulent state in the tunnel. Therefore, the \(k-\varepsilon\) turbulence model and the Navier-Stokes equation were used in this study, combined with the ideal gas equation of state [29].  
The formulae of the Reynolds-averaged Navier-Stokes equations can be expressed generally as follows:  
\[\frac{\partial\rho\psi}{\partial t}+\text{div}(\rho\overset{\rightarrow}{V} \psi-\Gamma_{\psi}\times\text{grad}\psi)=q_{\psi}, \tag{1}\]  
where \(\rho,\ \psi,\ \overset{\rightarrow}{V},\ \Gamma_{\psi}\) and \(q_{\psi}\) represent the density, a general variable, the velocity vector, the generalized diffusion coefficient, and the generalized source term, respectively.  
For the continuity equation, the terms in Equation (1) take the following values:  
\[\psi=1,\Gamma_{\psi}=0,q_{\psi}=0. \tag{2}\]  
For the momentum equation in the \(x\)-direction, the terms in Equation (1) are as follows:  
\[\psi=u,\Gamma_{\psi}=\mu_{eff},q_{\psi}=-\frac{\partial p}{\partial x}+\text{ div}(\mu_{eff}\frac{\partial\overset{\rightarrow}{V}}{\partial x}), \tag{3}\]  
where \(u\) is the velocity in the \(x\)-direction, \(\mu_{eff}\) is the effective viscosity coefficient, and \(P\) is the pressure. The momentum equation in the \(y\)- and \(z\)-directions had similar expressions.  
For the energy equation, the terms used in Equation (4) are as follows:  
\[\psi=T,\Gamma_{\psi}=\frac{\lambda}{C_{P}},q_{\psi}=\frac{S_{h}}{C_{p}}, \tag{4}\]  
where \(T\) is the temperature, \(C_{p}\) is the specific heat capacity, \(\lambda\) is the thermal conductivity, and \(S_{h}\) is the internal heat source term.  
For the turbulent kinetic energy equation,  
\[\psi=k,\Gamma_{\psi}=\mu+\frac{\mu_{t}}{\sigma_{k}},q_{\psi}=G_{k}-\rho\epsilon, \tag{5}\]  
where \(k\) is the turbulent kinetic energy, \(\mu\) is the laminar flow viscosity coefficient, \(\mu_{t}\) is the turbulent viscosity coefficient, \(\sigma_{k}\) is the pulsating kinetic energy Prandtl number, \(\epsilon\) is typically the turbulent dissipation rate, and \(G_{k}\) is the generation term.  
For the turbulent dissipation rate equation,  
\[\psi=\epsilon,\Gamma_{\psi}=\frac{\mu_{eff}}{\sigma_{\epsilon}},q_{\psi}= \frac{\epsilon}{k}(C_{1}G_{k}-C_{2}\rho\epsilon\frac{k}{k+\sqrt{\
u\epsilon} }), \tag{6}\]where \(\sigma_{\epsilon}\) is the Prandtl number of the dissipation rate, \(C_{1}\) and \(C_{2}\) are empirical constants, and \(v\) is the viscosity coefficient of the fluid's molecular motion.  
The fluid is assumed to be an ideal gas, whose equation of state is given as follows:  
\[P=\rho RT, \tag{7}\]  
where \(R\) is the molar gas constant.  
The above equations form a closed equation system that can be solved numerically.
# Effect of Calined Marble Powder and Magnetized Water on the Performance of Cement-Based Composites
## 3 Results and Discussion
### Consistency  
The experimental findings for the flow diameters of fresh mortars with RMP and CMP replacement, along with the percentage changes in flow diameter compared to the reference samples, are presented in Figure 6. Each test was repeated three times, and the average of these values was calculated.  
As observed in Figure 6a, the flow diameters increase with higher replacement ratios of RMP and CMP, regardless of the type of mixing water used. When MW is used as the mixing water, the increase in flow diameters becomes more pronounced. The flow diameters of mortars coded as R_/TW, C_/TW, and C_/MW range between 145-175 mm, 145-185 mm, and 170-215 mm, respectively.  
Compared to the reference samples, the highest increases in flow diameters were observed in mortars coded as R25/TW, C25/TW, and C25/MW, with increase rates of 20.7%, 27.6%, and 26.5%, respectively. The observed increases in flow diameters with higher CMP and RMP replacement ratios can be attributed to the larger particle size and lower surface area of RMP and CMP compared to cement, as discussed in the section explaining setting times. The enhancement in workability due to marble powder is also consistent with findings in the literature [41; 42].  
When evaluating the effect of mixing water on flow diameter, it was observed that MW significantly increased the flow diameter. The flow diameters of mortars coded as C0/MW, C5/MW, C10/MW, C15/MW, C20/MW, and C25/MW increased by 17.2%, 16.1%,  
Figure 6: Flow diameter values (**a**) and change in flow diameter (**b**) of fresh mortars.  
18.8%, 18.2%, 13.9%, and 16.2%, respectively, compared to those coded as C0/TW C5/TW, C10/TW, C15/TW, C20/TW, and C22/TW. This can be explained by the breakdown of water clusters and the increased activation of water after magnetization, allowing more water molecules to surround the binder material. This improvement in workability can be attributed to the physical changes in the structure of magnetized water. The breakdown of water clusters into smaller, more active molecules enhances the wetting and dispersion of cement particles, reducing interparticle friction and improving flowability. Although MW accelerates hydration reactions, the initial stages of hydration involve only limited water consumption, allowing the mix to retain its fluidity during the fresh state. This interaction between water and binder enhances workability. The results obtained are consistent with findings in the literature [16; 43].
# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
## Materials and Methods
### Data Acquisition
The highest values of temperature were obtained in the summer months, from June to September, and the lowest values corresponded to the winter months, from December to March, while the cycle of ETo was slightly displaced, with the highest values from May to July, and the lowest from November to February. Regarding the water supply, it was mostly uniform in the study period, although it decreased around the spring season, with irrigations of less than 7 mm and only one every two days. The total rainfall in this period was 269 mm. To measure the soil moisture in the study plot, commercial frequency-domain reflectometry (FDR) sensors were used. These FDR sensors consist of several cylindrical rings located at four depths (10, 20, 30, and 40 cm) in an isolated gauge, with a polyvinyl chloride (PVC) access tube of type EnviroSCAN(r) (Sentek Sensor Technologies, Stepney, Australia). Four of these gauges were installed spaced in the plot during the experiment. Soil moisture data were collected every 5 min from 30 May 2015 to 30 September 2016. The sensors were firstly installed in the laboratory, where they were tested and calibrated. Afterward, they were installed in the experimental plot. The data obtained from the sensors were stored in data loggers, and then they were collected weekly. \begin{table}
\begin{tabular}{c c c c} \hline
**Property** & **0–10 cm** & **10–20 cm** & **20–30 cm** \\ \hline Sand (\%) & 45 & 61 & 75 \\ Silt (\%) & 30 & 22 & 16 \\ Clay (\%) & 25 & 17 & 9 \\ Water field capacity (m\({}^{3}\)/m\({}^{3}\)) & 0.27 & 0.22 & 0.18 \\ Permanent wilting point (m\({}^{3}\)/m\({}^{3}\)) & 0.15 & 0.12 & 0.08 \\ \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of the soil in the experimental plot at different depths.
Among different technologies, imaging approaches have the advantage of evaluating the organ/tissue noninvasively with adequate spatial resolution. Moreover, being a widely available imaging modality with relatively operating low cost, CT is one of the most popular diagnostic tools in therapy response assessment [8]. However, the current criteria requires both pre-therapy and 6-8 week follow-up examinations, and the diameter-based evaluation causes low association with the clinical outcome [9]. Meanwhile, radiomics is an emerging technology that extracts a large amount of features from tumors segmented from various medical images [10; 11; 12]. These radiomics features are able to quantify the complex textures, shapes, as well as other tumor characteristics that are highly associated with carcinogenesis [13]. Therefore, this information can be beneficial in determining treatment options and predicting the tumor's response to therapy [14]. For this technique, identifying effective radiomics features is critically important: 2D features are easier and faster to calculate but provide less information, while the 3D features use the entire 3D tumor volume and intuitively contain more comprehensive information. To this end, many studies have been conducted to explore the effectiveness of 2D and 3D features in different applications. The prognostic performance of 2D and 3D radiomics features in CT images of non-small cell lung cancer (NSCLC) was investigated in [15], which demonstrated that 2D features exhibited better performance, although both feature types had strong prognostic capability. One study compared the 2D and 3D radiomics features in characterizing gastric cancer [16], which revealed that models constructed with 2D radiomics features had comparable performance with those constructed with 3D features. In another study, [PERSON] et al. [17] evaluated the prediction performance of 2D and 3D radiomics features in a multi-organ, multi-modality cancer study, and the authors concluded that 3D radiomics features were more effective. [PERSON] et al. [18] compared the 2D and 3D texture features for discriminating between gastric cancer and normal gastric mucosa on CT images, and they revealed that 3D texture features were more effective than 2D features. Despite their clinical usefulness, these studies only compare features from the single-slice-based 2D tumor and the full-volume 3D tumor. Very few studies have been focused on the effectiveness of incomplete 3D tumors features, which contain only a portion of the image slices. In this investigation, we hypothesized that the inclusion of multiple tumor slices would lead to a substantial enhancement in the predictive accuracy of our model, but it may also introduce more uncertainties that may adversely impact model accuracy. To verify this hypothesis, we extracted 2D radiomics features from a single slice of the tumors and then generated incomplete 3D features by sequentially adding the adjacent CT slices. Based on each individual feature pool, we optimized the same kind of predictive model and evaluated their performance, respectively. The details are presented in the following sections.
# Flexural Behavior of Self-Prestressed RC Slabs with Fe-Based Shape Memory Alloy Rebar
## 4 Conclusions  
In this paper, an experimental study was conducted to evaluate the flexural behavior of self-prestressed concrete slabs with Fe-SMA rebar, and the following conclusions were drawn:  
1. Activation of the Fe-SMA rebar caused camber in the concrete member by applying an eccentric compressive force due to the recovery stress on the cross-section. This camber  
Figure 14: Comparison of load–deflection relationship between SL-3A and SL-3R.  
Figure 13: Time–displacement relationship of SL-3R according to number of activations.  
increased by an average of 0.096 mm when the amount of Fe-SMA reinforcement increased by 100 mm\({}^{2}\).
* The initial cracking load of the specimen with the activated Fe-SMA rebar was 40-101% greater than that of the non-activated specimen due to the action of the eccentric compressive load.
* The theoretically calculated initial crack load was 23% larger on average compared with the experimental value. This was considered to be due to the reduction of the recovery stress resulting from the thermal expansion at the Fe-SMA and concrete interface and the concrete hydration heat. Therefore, future studies are needed to quantitatively evaluate the effect of thermal expansion and hydration heat of concrete on the recovery stress of Fe-SMA rebar.
* The effect of the Fe-SMA rebar activation on the ultimate load of the concrete member was negligible. Therefore, it was confirmed that the introduction of prestress by activation of the Fe-SMA rebar, like traditional prestressed concrete, mainly increased the usability of the concrete member.
* The load-displacement relationship of the specimen with Fe-SMA rebars activated four times was similar to that of the specimen with Fe-SMA rebars activated once. Therefore, it was considered that the prestress force of the concrete reinforced with Fe-SMA rebar could be recovered through reactivation even if the prestress force was reduced due to various reasons, such as drying shrinkage and relaxation, unlike the conventional bonded prestressed concrete.
* Through this study, it was confirmed that the concrete prestressing method using the Fe-SMA rebar could solve several disadvantages of conventional prestressed concrete. Therefore, the self-prestressing method using Fe-SMA was expected to have high potential in the field of prestressed concrete for new construction.  
[PERSON] and [PERSON] conceptualized the study; [PERSON] obtained field data; [PERSON] and [PERSON] implemented data processing under the supervision of [PERSON]; the original draft of the manuscript was written by [PERSON] and [PERSON] with editorial contributions from [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT), grant number 2020R1A2C1003197.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2020R1A2C1003197).  
The authors declare no conflict of interest.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
## 2 Development of AI-FEM
### Crack Tip Meshing Criteria
#### 2.2.3 The Effect of the SIF Calculation Method  
In this section, the SIF was calculated using the domain integral method and interaction integral method [21] provided by Abaqus. The elastic \(J\)-integral (\(I_{e}\)) can be obtained through the domain integral method, and the SIF for each plane stress and strain condition can be calculated through relations based on the fracture mechanics theory, respectively. The relation is as follows in Equation (1):  
\[\begin{array}{ll}&K_{FE}=\sqrt{I_{e}E^{\prime}}\\ E^{\prime}=E&\text{for plane stress}\\ E^{\prime}=\frac{E}{(1-E^{\prime})}&\text{for plane strain}\end{array} \tag{1}\]  
where \(K_{FE}\) is the SIF calculated through FEA.  
Figure 3: FE model of a flat plate with semi-elliptical crack: (**a**) Boundary and loading condition; (**b**) Detailed crack tip mesh and shape variables.  
The interaction integral method is a function that can directly calculate the SIF for each failure mode (mode I, II, III). To see the effect of the SIF calculation method the SIF for mode I was calculated. In both methods, the number of elements in the contour region should be determined in the FE model, and 5 contour elements are considered. The SIF was defined as the average value of contours 2 to 4 excluding the results from the first and last contours.  
Figure 4 shows the difference in SIF according to the SIF calculation methods along the crack tip. In the domain integral method, the SIF values converted through the assumption of plane stress and plane strain were expressed as \(J\)-plane stress and \(J\)-plane strain, respectively, and the SIF value calculated through the interaction integration method was expressed as \(K\). The SIF results were compared with the theoretical solution proposed by [PERSON] [22] and the results of the theoretical solution are denoted as Ref. In Figure 4, \(J\)-plane strain and \(K\) show SIF values similar to those of Raju-Newman solution and \(J\)-plane stress slightly smaller. However, the plane stress and plane strain conditions can be changed depending on the shape and constraint of the structure. Therefore, in this study, the interaction integral method reflecting the constraint effect was selected as the SIF calculation method.
## 4 Applications of Intelligent Acoustic Monitoring
### Public Safety and Security
#### 4.1.4 Drone Detection  
In [102], [PERSON] et al. presented a new approach to drone detection using fibre-optic acoustic technology. They developed FOAS sensors, which, as part of the DAS system, allowed for highly sensitive recording of airborne sounds, including drone signals. Unlike traditional microphones, the system provided synchronisation and centralised data processing on a large scale. In field tests, detecting and locating a drone with high accuracy (RMSE of 1.47\({}^{\circ}\)) was successfully achieved, confirming the potential of this technology in security and urban monitoring applications.  
These works show the increasing effectiveness of acoustic threat detection, thanks to the combination of sensors, sound processing methods, and neural networks. An interesting trend is the use of common devices, such as smartphones, and the development of low-cost, easy-to-implement systems.