esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Simulation of Laser Heating Processes on Thin Metal Plates with FFT Using CPU/GPU Hardware
###### Abstract  
In flexible manufacturing systems, fast feedback from simulation solutions is required for effective tool path planning and parameter optimization. In the particular sub-domain of laser heating/cutting of thin rectangular plates, current state-of-the-art methods include frequency-domain (spectral) analytic solutions that greatly reduce the required computational time in comparison to industry standard finite element based approaches. However, these spectral solutions have not been presented previously in terms of Fourier methods and Fast Fourier Transform (FFT) implementations. This manuscript presents four different schemes that translate the problem of laser heating of rectangular plates into equivalent FFT problems. The presented schemes make use of the FFT algorithm to reduce the computational time complexity of the problem from \(\mathcal{O}(M^{2}N^{2})\) to \(\mathcal{O}(MN\log(MN))\) (with \(M\times N\) being the discretization size of the plate). The test results show that the implemented schemes outperform previous non-FFT approaches both in CPU and GPU hardware, resulting in 100\(\times\) faster runs. Future work addresses thermal/stress analysis, non-rectangular geometries and non-linear interactions (such as material melting/ablation, convection and radiation heat transfer).  
spectral method; Fast Fourier Transform; laser heating; GPU; rectangular metal plate; industry 4.0
# Vulnerability Assessment of Sensor Systems
## 3 Sensor System Vulnerability Assessment Method (SVAM)
### The Solution
#### 3.2.1 Elementary Evaluation Processes  
The main AVA_VAN process was decomposed to the following elementary evaluation processes:  
* For the AVA_VAN.1.1E, AVA_VAN.2.1E, AVA_VAN.3.1E, AVA_VAN.4.1E elements:
* EEP1 Checking the TOE configuration and installation;
* For the AVA_VAN.1.2E, AVA_VAN.2.2E, AVA_VAN.3.2E, AVA_VAN.4.2E elements:
* EEP2-1 Identification of vulnerability-related issues;
* EEP2-2 Search of public domain sources to identify potential vulnerabilities and attacks;
* For the AVA_VAN.2.3E, AVA_VAN.3.3E, AVA_VAN.4.3E elements:
* EEP3-1 Areas of concerns identification;
* EEP3-2 Vulnerability analysis based on evidences;
* EEP3-3 Identification of the candidates for testing;
* For the AVA_VAN.1.3E, AVA_VAN.2.4E, AVA_VAN.3.4E, AVA_VAN.4.4E elements:
* EEP4-1 Analyzing candidates for testing;
* EEP4-2 Devising penetration tests;
* EEP4-3 Producing penetration tests;
* EEP4-4 Conducting penetration tests;
* EEP4-5 Penetration tests summary.  
The preliminary research results on EEPs have been discussed previously [51] and are expressed there in a pseudocode. The presented method can be applied for any kind of evaluated IT product, but it should be interpreted and refined for the given kind of product.  
The TOE and all its evaluation evidences are used as the AVA_VAN process input. The result of the AVA_VAN evaluation is placed in the evaluation technical report (ETR) as the process output.  
EEP1 checks if the TOE is properly configured and installed, i.e., is ready for evaluation experiments and testing.  
EEP2-1 identifies the TOE-specific characteristics to orientate the potential vulnerability search in public information assets. The search is performed in EEP2-2.  
EEP3-n concerns searching for vulnerabilities by the analysis of evaluation evidences (omitted for the AVA_VAN.1 component) and involves the identification of areas of concern (vulnerability-prone parts of the TOE design), thus orientating the analysis. The analysis is performed in EEP3-2. EEP3-3 checks whether the given potential vulnerability is applicable to the TOE in its operational environment. Such vulnerabilities are subjects of further analyses and testing.  
EEP4-1 preselects the candidates for testing by a deeper analysis, e.g., it checks whether the TOE is resistant to the required attack potential (AP). For the preselected pairs, i.e., attack scenario-vulnerability, penetration tests are devised (EEP4-2), developed (EEP4-3), performed (EEP4-4), concluded, and finally reported in the ETR (evaluation technical report) in EEP4-5. As a result, exploitable and residual vulnerabilities are identified (exploitable, but only by attacks with potential higher than that considered for the claimed EAL).
###### Abstract  
The aim of this study was to investigate the differences in the volatile profile of meat from chickens fed with alternative protein diets (such as algae or insect) through two different trials. In Trial 1, broiler chicken at one day of age were randomly allocated to three experimental groups: a basal control diet (C) and two groups in which the soybean meal was replaced at 75% (in the starter phase) and 50% (in the grower phase) with partially defatted _Hermetia illucens_ (HI) larvae or _Arthospira platensis_ (SP). In Trial 2, broiler chickens were housed and reared similar to Trial 1, with the exception that the experimental diets replaced soybean meal with either 100% partially defatted HI or 100% SP. In both trials, chickens were slaughtered at day 35. Per group, 10 chickens were submitted to volatile analysis by using solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) analysis. Results in both trials showed that levels of several lipid-derived compounds were found to be lower in chickens fed an HI diet, which could be linked to a possibly lower level of polyunsaturated fatty acid content in HI-fed chicken. In addition, the dietary treatments could be discriminated based on the volatile profile, i.e., the substitution of soy with HI or SP distinctively affected the levels of flavor compounds.  
volatile compounds; sustainable feeds; spirulina; _Arthospira platensis_; insect; _Hermetia illucens_
## 5 Spatially-Resolved Spectroscopic Technique for Optical Property Measurement
### Algorithms and Instrumentation
#### 5.2.2 Instrument Development
The precision of the OPA was assessed by the coefficient of variation (CV) in the absorption peak at 555 nm, which was less than 10% and 4% for \(\mu_{\mathrm{a}}\) and \(\mu_{\mathrm{s}}^{\prime}\), respectively; and the minimum detectable value of \(\mu_{\mathrm{a}}\) was 0.0082 cm\({}^{-1}\).
###### Abstract  
In this study, a post-tensioning method using externally unbonded steel rods was applied to pre-damaged reinforced concrete beams for flexural strengthening. Nine simply-supported beams, three reference beams and six post-tensioned beams, were subjected to three-point bending. The design parameters observed in this study were the amount of tension reinforcements (3-D19, 4-D19, and 2-D22 + 2-D25; \"D\" indicates the nominal diameter of the rebar) and the diameters of the external rod (\(\varphi\)22 mm and \(\varphi\)28 mm). A V-shaped profile with a deviator at the bottom of the mid-span was applied to the pre-damaged beams, and a post-tensioning force was added to overcome the low load resistance and deflection already incurred in the pre-loading state. The post-tensioning force caused by tightening the nuts at the anchorage corresponded to a strain of 2000 \(\upmu\varepsilon\) in the external rods; this value was approximately equal to the strain caused by torque that two adults can apply conveniently. The post-tensioning system increased the load-carrying capacity and flexural stiffness by approximately 40-112% and 28-73%, respectively, when compared with the control beams. However, the external rods did not yield in the post-tensioned beam with larger steel reinforcements and external steel rods. The external rod with the larger diameter increased the flexural strength more effectively.  
post-tensioning system; damaged reinforced concrete; flexural strength; external rods Article Post-Tensioning Steel Rod System for Flexural Strengthening in Damaged Reinforced Concrete (RC) Beams
## 2 Research Area and Data
### Hyperspectral Data Source for the Research Area
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Name** & **Train.** & **Val.** & **Test.** \\ \hline
1 & Strawberry & 2236 & 2237 & 40,262 \\
2 & Cowpea & 1137 & 1138 & 20,478 \\
3 & Soybean & 514 & 515 & 9258 \\
5 &orghum & 268 & 267 & 4818 \\
5 & Water spinach & 60 & 60 & 1080 \\
6 & Watermelon & 227 & 226 & 4080 \\
7 & Greens & 295 & 295 & 5313 \\
8 & Trees & 899 & 899 & 16,180 \\
9 & Grass & 473 & 473 & 8522 \\
10 & Red roof & 526 & 526 & 9464 \\
11 & Gray roof & 845 & 846 & 15,220 \\
12 & Plastic & 184 & 184 & 3311 \\
13 & Bare soil & 456 & 456 & 8204 \\
14 & Road & 928 & 928 & 16,704 \\
16 & Bright object & 57 & 57 & 1022 \\
16 & Water & 3770 & 3770 & 67,861 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detailed breakdown of the labeled sample distribution for the WHU-Hi-HanChuan dataset. aspects. On the one hand, by reducing the number of training/validation samples, we can mitigate the risk of overfitting. Overfitting can cause the model to perform well on training data but experience a decline in performance when encountering unknown data in real-world applications. On the other hand, expanding the number of samples in the test set helps to more accurately assess the model's generalization capabilities, thus providing a robust guarantee for the model's application in real-world scenarios. Additionally, a larger test set can more comprehensively reflect the model's performance in practical applications, offering valuable guidance for subsequent model optimization. In summary, our data partitioning method takes full account of the quality and representativeness of the samples, aiming to improve the model's generalization ability in practical applications. Through this strategy, we anticipate constructing machine learning models with greater practical value and robustness, thereby providing strong support for the development of related fields. overall, our data partitioning method fully considers the quality and representativeness of the samples, aiming to improve the model's generalization ability in practical applications. Through this strategy, we hope to build machine learning models with greater practical value and robustness, offering significant support for the advancement of related areas.
## 5 Proposed Method
### Data Collection  
For the data collection phase, we used three well-known benchmark datasets (i) NSL-KDD, (ii) UNSW-NB15, and (iii) CICIDS2017. These datasets are described in detail in Section 6.2. Each dataset has two subsets of data instances: the training set and the testing set. In addition, we set aside 1000 randomly chosen data instances to be used as verification data to simulate real-time traffic. The verification data were not used before either for training or for testing purposes.
## 4 Proposed Model: RBF-Refined SVM Model
### Classification: Training and Testing Data  
At any point in time, each observation of the captured HETVNET traffic had many features and one class label, referred to as the target variable. The goal of the SVM was to produce a model that could predict the target variable's value for the test data, based on the training dataset. In our investigation, two different approaches of classification patterns were deployed. Initially, the traditional classification task pattern was applied to divide the traffic data into training and testing datasets. Henceforth, the traffic data was divided accordingly: laps one to three were used for training, and laps four and five were used for testing. The traffic dataset classification was done for both north- and southbound directions.  
The classification was applied using RBF kernel tuning parameters (C, \(\gamma\)) to achieve the best rate of prediction accuracy. Here, it was noted that the prediction accuracy was directly proportional to the classification procedure/pattern being used. Identifying the perfect (C, \(\gamma\)) parameters may lead to overfitting in certain cases. To avoid issues of overfitting and to get a better prediction accuracy of the test data, we proposed a cross-validation procedure instead of our traditional classification task pattern. The traditional classification pattern was hence twisted further, so as to improve the prediction accuracy. The improved version of classification was termed the cross-validation procedure.  
The cross-validation classification pattern involved both training and testing sets, unlike the traditional classifier. The variation was that the training set was kept intact, while the testing set was divided into \(n\) subsets of equal size. This was done to ensure cross-validation accuracy, where one subset was tested over the other \(n-1\) subsets. Researchers propose cross-validation when the available data are limited. In our case, the dataset was not big enough, and hence, may produce a lot of variation in performance estimation. As a result, cross-validation classification was recommended as it reduced the variance by averaging, and the performance remained unaffected due to partition of the data. This pattern of classification also guaranteed the prevention of overfitting of the testing data.  
Figure 2: Action stream architecture for the vehicular ad hoc network (VANET) prediction.
# Noise Reduction Using Synthetic Microjet Excitation in Supersonic Rectangular Jets
## 4 Conclusions  
Rectangular jet nozzles, noted for their potential advantages over traditional round nozzles, i.e., reduced drag, simpler design, and improved exhaust mixing, are becoming increasingly relevant for both civilian and military applications [33]. In the current work, a high-fidelity investigation of supersonic rectangular jet flow and noise was conducted. The impact of the synthetic microjet actuation exciting the jet shear layer with the frequency mode that appeared to have the maximum effect on large-scale structure evolution and nonlinear energy transfer (as suggested in the previous study [27]) was examined.  
The flow and acoustic characteristics of the rectangular jet were analyzed, focusing on the effects of unsteady microjet excitation on both near-field and far-field noise. The time-averaged flow results reveal that the applied excitation altered the jet spread and the potential core while enhancing the jet turbulence kinetic energy, particularly in the lip-line regions. The instantaneous flow field confirmed that the jet excitation modifies the propagation and structure of the acoustic waves, reducing the generation of noise sources while slightly shifting the preferred acoustic directivity angles. The SPOD analysis further highlighted that the applied excitation reduced the Kelvin-Helmholtz instability at the peak frequency and the overall energy of the coherent structures throughout the spectrum.  
In conclusion, the obtained results suggest that the investigated unsteady microjet excitation approach presents a promising solution for the rectangular jet noise control strategy. Indeed, the observed noise reduction of up to 4 dB in OASPL in the \(St_{F}=0.30\) case highlights such potential, and is primarily attributed to the impact of the jet excitation on the overall broadband noise reduction across the acoustic spectrum.  
Methodology, M.M.; Formal analysis, S.S.; Investigation, M.M.; Writing--original draft, M.M.; Writing--review & editing, V.G., S.S. and A.L.; Visualization, M.M.; Supervision, V.G. and A.L. All authors have read and agreed to the published version of the manuscript.  
The current work was partially supported by the Office of Naval Research under Grant 482 No. N00014-21-1-2102 monitored by [PERSON]. Computations were carried out 483 using Embry-Riddle's VEGA and DoD HPC systems.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
[PERSON] acknowledges partial support provided by the Ministry of Science and Higher Education under Grant Agreement No. 075-15- 2022-1023.  
The authors declare no conflict of interest.
# Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning
## 3 SVM-Based Decoding Method for Image Recognition and Classification  
The proposed optical encoding model takes as input a 4-bit code defined by the variable \(X\). In addition, a signal noise ratio (SNR) is used to emulate the noise in the communication channel that is given in decibels. The encoding starts with the definition of the variables \(\ell_{1}\), \(\ell_{2}\) and \(p\) to establish the intensity profile, which is executed by the function \(selectCode\). Then, two different intensity profiles are generated using the mathematical formulation given in Equations (1) and (2) and declared in \(functionLG\). This is followed by the representation of the intensity profile in terms of Cartesian coordinates \(x,y\) and the intensity of the resulting beam profile declared in variable \(I\). Next, with a view to emulate a real communication, signal noise is added to the transmitted intensity profile stated in the function addNoise. Later, the \(extractHOGFeaturesFromIntensity\) function is used to extract useful patterns for information recognition through histogram of oriented gradients (HOG) detection [14]. Finally, the function \(predict\), which is based on a linear regression model, is used as a 4-bit classifier through a multiclass error-correcting output codes (ECOC) model using SVM binary learners. For more details about the followed process, Algorithm 1 is presented. As the process involves training procedures, the decoding processing at the receiver side of the encoder is based on SVM. More details on this SVM algorithm can be found in the Appendix A.  
```
1:Input X, SNR
2:Transmission side:
3:\([\ell_{1},\ell_{2},p]\)=\(selectCode(X)\)\(\triangleright\)Give \(\ell_{1},\ell_{2}\), and \(p\) values according to Code Table
4:\([x_{1},y_{1},z_{1}]=functionLG(\ell_{1},\theta_{o}=0,\lambda,z=0,p)\)\(\triangleright\)Generate 1 st Intensity Profile accord. Equation1
5:\([x_{2},y_{2},z_{2}]=functionLG(\ell_{2},\theta_{o}=0,\lambda,z=0,p)\)\(\triangleright\)Generate 2 nd Intensity Profile accord. Equation1
6:\(x\gets x_{1}\)
7:\(y\gets y_{1}\)\(\triangleright\)Generate Value for Cartesian coordinates
8:\(I\gets z_{1}+z_{2}\)\(\triangleright\)Superposition of Intensity Profile accord. Equation2
9:Communication Channel:
10:\(n=addNoise(I,SNR)\)\(\triangleright\)Add Noise to the intensity in order to simulate real communication signal
11:Receiver Side:
12:\(TestFeatures=extractHOGFeaturesFromIntensity(n)\)\(\triangleright\)Extract HOG features for the Intensity profile with noise
13:\(Y=predict(classifier,testFeatures)\)\(\triangleright\)Use model from SVM-ECOC Multiclass Training
14:Output Y
```  
**Algorithm 1** Pseudocode for decoding processing using an SVM-ECOC model
## 3 Control Strategy and Optimization Function
### 2 Dof-Pidf Controller  
The term \"degree of freedom\" (DOF) in a control system pertains to the quantity of distinct closed-loop transfer functions that can be autonomously adjusted. It is a measure of  
Figure 3: Structure of the examined controllers: (**a**) PIDF, (**b**) FOPID, (**c**) 2 DOF-PID.  
the control system's ability to make changes to the system's behavior. A two-DOF (2 DOF) control system has benefits over a single DOF control system when it comes to meeting the performance requirements during design. The 2 DOF controller generates a signal based on the discrepancy between the reference signal and the measured output of the system. In Figure 3c, the parallel 2 DOF-PIDF controller's structure is depicted, and \(r,y\), and \(u\) stand for the reference signal, measured system output feedback, and output signal, respectively. The transfer functions for the 2 DOF-PIDF controller are provided by [50; 51]:  
\[K_{p}(br-y)+K_{i}\frac{1}{s}(r-y)+K_{d}\frac{N}{1+N\frac{1}{s}}(cr-y) \tag{19}\]  
where \(b\) and \(c\) are the proportional and derivative set point weight, respectively. The optimization of the fitness function in the case of the 2 DOF-PID is given by:  
\[\begin{array}{c}J_{\min}(K_{p},K_{i},K_{d},N,b,c)=\int\limits_{0}^{Tsim}t| \Delta f|dt\\ \text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
where \(n\)_jel_ is the population of jellyfish; \(Xc\) indicates the jellyfish's current best position in the swarm; \(af\) signifies the attraction factor; \(m\) is the average position of all jellyfish. The difference between the interest jellyfish's current best location and the swarm's mean location is denoted by \(df\). A distance of \(\pm\beta\sigma\) around the mean position contains a given likelihood of all jellyfish, where \(\beta\) is a distribution coefficient and \(\sigma\) is the standard deviation of the distribution, which is based on the assumption that jellyfish have a normal spatial distribution in all dimensions (see Figure 4). Thus,  
\[d_{f}=\sigma\times rand^{\varpi}(0,1)\times\beta,\ \ \ \sigma=rand^{y}(0,1)\times m \tag{22}\]  
where \(rand(0,1)^{w}\times rand(0,1)^{y}=rand(0,1)\). Hence, flow can be represented as:  
\[\overrightarrow{flow}=X_{c}-m\times rand(0,1)\times\beta \tag{23}\]  
Each jellyfish's new position is given by  
\[X_{j}(t+1)=X_{j}(t)+rand(0,1)\times\overrightarrow{flow} \tag{24}\]  
where \(X_{j}(t)\) denotes the location of the \(j\)th jellyfish at time \(t\)[52].
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
## 3 Application of Synthetic Data
### Imaging Results for Flat Terrain  
Figure 4a shows the Bouguer gravity anomaly data generated using the single model, Model A (shown in Figure 3a), for flat terrain. The cross-section is extracted using the model at \(y=60\) km. Figure 4b presents the imaging result of the DEXP method [9]. The extreme point at \(x=60\) km with a depth of 10 km coincides with the theoretical center of the sphere model. However, in Area I, delineated by the black line, significant \"zero lines\" are observed. These lines occur when the denominator in the DEXP method becomes zero. The presence of these \"zero lines\" is undesirable as they can be mistaken for additional extreme points, compromising the accuracy and reliability of the imaging result. In contrast, Figure 4c presents the imaging result obtained using the improved DEXP method proposed in this paper. The extreme point is located at \(x=60\) km with a depth of 10 km, coinciding with the theoretical center of the sphere model. The improved method ensures the reliability of inverted results. Furthermore, it is evident that the effect caused by the \"zero line\" can be suppressed, which will be beneficial in enhancing the quality of the imaging result.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Location** & **Depth** & **Radium** & **Residual Density** \\ \hline A & \(x=60\) km, \(y=60\) km & 10 km & 5 km & 1.0 g/cm\({}^{3}\) \\ B1 & \(x=20\) km, \(y=60\) km & 2 km & 1 km & 1.0 g/cm\({}^{3}\) \\ B2 & \(x=60\) km, \(y=60\) km & 20 km & 5 km & 0.3 g/cm\({}^{3}\) \\ B3 & \(x=80\) km, \(y=60\) km & 10 km & 2 km & \(-1.5\) g/cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of the synthetic models.  
Figure 4: The forward Bouguer gravity anomaly of Model A and imaging result of the main profile (\(y=60\) km) through Model A. (**a**) The forward Bouguer gravity anomaly observed for flat terrain. (**b**) The imaging result of the DEXP method. (**c**) The imaging result of the improved DEXP method.  
Figure 5a shows the Bouguer gravity anomaly data generated using the combined model, consisting of Models B1, B2, and B3 (shown in Figure 3b), for flat terrain The cross-section is extracted through the models at \(y\) = 60 km. Figure 5b represents the imaging result obtained using the DEXP method, as shown in Area II. It is unfortunate that the center of Model B3 is not well recognized. This disturbance is caused by the empirically given threshold value, which is a constant value that cannot be adjusted automatically. Figure 5c presents the imaging result of the improved DEXP method proposed in this paper. This method, enhanced by the adding constant method, proves to be effective in obtaining reliable imaging results. Furthermore, the depth extreme points (\(x\) = 60 km, \(y\) = 60 km, depth = 10 km) accurately indicate the locations of the combined models.