esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification
### Result of High-Speed Tactile and Vision Sensor Network System  
To demonstrate that the data-acquisition synchronization in the sensor network system is successfully achieved, we developed a high-speed tactile and vision sensor network system. Figure 3 shows the system structure of tactile and vision sensor network and the photograph of the experimental setup. In the experiment, a ball is moved above or on a table, as shown in Figure 3.  
Figure 3: Overview of experimental system setup: (**a**) shows system structure and (**b**) shows experimental environment. Adapted from [14].  
Figure 2: Clock synchronization: (**a**) shows overall result and (**b**) shows enlarged result.  
In the tactile sensor node, when the force acted on the tactile sensor, the sensor output corresponding to the force was generated. Subsequently, the AD board converted the sensor output to digital values.  
In the vision node, the ball tracking using the method described in Section 3.2 was performed in real time, and the highly accurate position of the ball was obtained by the high-speed vision system within 2 ms.  
For this sensor network system, we conducted two experiments as follows:  
* Experiment 1: A ball is moved above or on a table by a human. If the ball is above the table, the output of the tactile sensor is zero and the ball position is measured by the vision. Meanwhile, if the ball is on the table, the output of the tactile sensor is obtained and the ball position is also measured by the vision.
* Experiment 2: A ball bounces on a table. If the ball touches the table, the impact force is obtained by the tactile sensor. Otherwise, the force is not obtained by the tactile sensor. In both cases, the ball position can be obtained by the vision.
### FDIA Detection Based on a Model-Driven Approach  
Reference [9] proposes a false data static detection method based on the similar characteristics of buses at a certain moment. This method can detect the false data injection problem that may occur in the power system. Enhanced sensitivity to dynamic changes in the power system is achieved, albeit with the possibility of certain attacks being missed due to the primary focus on static features. Reference [10] proposes an event-triggered fully distributed algorithm, which can effectively reduce communication time. In that study, for FDIAs, the authors constructed a specific model that considers the attacker to maximize the loss and the defender to minimize the loss. The proposed methods require complex communication architectures, and their applicability is constrained by the specific characteristics of the system. Reference [11], on the other hand, proposes an FDIA detection method that combines model predictive control and artificial neural networks. Through the incorporation of neural networks, a better adaptation to nonlinear system characteristics is realized to enhance the detection efficiency of the security control layer. However, a substantial volume of data is needed for neural network training, imposing significant computational resource requirements. To achieve FDIA attack detection, in [12], an interval observer was used to accurately estimate the state values of the internal system; then, an isolation algorithm was constructed, and an interval residual detection criterion was established based on a logical judgment matrix of attack characteristics. Reference [13], on the other hand, proposes a model for the detection and defense of FDIAs in load frequency control systems by combining an evolutionary game model with a Kalman filter algorithm. In practice, the implementation of this approach entails the utilization of more intricate models and algorithms, potentially leading to heightened complexity. Reference [14] proposes an improved detection method based on principal component analysis, which introduced a mathematical transformation principal approach to improve the detection performance, such as the detection rate and false alarm rate. Nonetheless, specific prerequisites regarding data preprocessing and computational resources exist, rendering the method potentially inapplicable to all datasets.
## 1 Introduction
reported the use of plasma-excited NH\({}_{3}\) to deposit GaN films with TMG and NH\({}_{3}\) precursors at near RT in 2015. The process temperature of 115 \({}^{\circ}\)C was in the nitridation step. Adsorption and nitridation models at that process temperature were proposed [32]. [PERSON] et al. reported electron-enhanced ALD grown GaN film using the same precursors in 2016. Crystalline GaN thin film can be grown on Si substrate at RT using electron enhancement [30]. ALD techniques using plasma [33; 34; 35; 36] or a hot filament [37] were developed to fabricate the GaN film at low temperature [11]. Although the processing temperature could reduce to near RT, the quality of the film needs to be considered for qualities such as conformality, which is a very complicated area dependent on the recombination tendency. A new precursor is key to developing a process. The reaction mechanism in the ALD process with any precursor must be understood. A Ga source of GaCl\({}_{3}\) had several drawbacks, such as low vapor pressure, releasing of corrosive by-product (HCI), and residual Cl in the film [35]. These problems can be solved by utilizing TMG or TEG. In the current study, TMG was used as the Ga source because of its higher vapor pressure [30]. Quantum mechanical simulations serve as a powerful tool for gaining profound insights into the underlying mechanisms of the ALD process. A report of ALD simulationsin 2022 has been produced [38; 39; 40]. For theoretical study, the growth processes were investigated using computational fluid dynamics and kinetic Monte Carlo simulation methods [41; 42]. The surface reactions using TMG and NH3 were investigated based on ab initio simulations [43; 44; 45]. However, the effectiveness of these methods is limited by the ability to provide accurate kinetic data [46]. Density functional theory (DFT) is a well-known computational method that provides accurate kinetic information of ALD processes. [PERSON] et al. [47] reported first-principle calculations of GaN (0001) growth using TMG and NH\({}_{3}\) versus Treg and N\({}_{2}\)H\({}_{4}\) in 2015. DFT calculation in the growth of GaN has been widely reported [48; 49; 50; 51; 52; 53; 54; 55; 56]. The sequence of chemical bond breakage during the adsorption is the key to growing the ALD film at RT. This paper identifies the initial surface reaction of semiconductor GaN semiconductor film formation in the ALD process using TMG and NH\({}_{3}\). NH\({}_{3}\) has the potential to serve as the nitrogen source in the GaN deposition process [57]. The effects of Si-NH\({}_{2}\) and Si\({}_{2}\)-NH substrates on the initial surface adsorption were the focus of this study. The optimization of chemical structures and energy profiles was reported, using Si clusters as the substrates, such as Si\({}_{15}\)H\({}_{18}\)-(NH\({}_{2}\))\({}_{2}\) and Si\({}_{15}\)H\({}_{20}\)-(NH)\({}_{2}\), respectively.
###### Abstract
Therefore, the Euclidean distance loses its discriminatory power, impacting the effectiveness of learning algorithms because distances between instances become less informative [27]. In this paper, we hypothesize that the use of fractional norms, norm-p, can alleviate the effect of the high dimensionality in a well-known data-level solution. In particular, we employ a dissimilarity approach to mapping the dataset into a lower dimensionality, in which the dimensions are defined by vectors measuring pairwise dissimilarities between two examples, commonly given by the Euclidean distance [28; 29]. We note that data-centric solutions for class imbalance in Big Data scenarios have been less explored. Additionally, some approaches address data issues in an isolated manner. Therefore, we propose a scalable hybrid approach that combines renowned data-level solutions based on distance metrics. Initially, the dissimilarity approach maps the original feature space representation into a low-dimensional dissimilarity space using a fractional norm. In this transformed space, represented by dissimilarity vectors, we then apply SMOTE to achieve class balance. We conducted a comprehensive experimental study on nine imbalanced Big Data datasets with high dimensionality to evaluate the proposal's performance. The datasets were adapted to two-class problems, comprising 24,832 features and 21,025 instances. The decision tree employed was taken from Spark's MLib toolkit. The proposal was compared against the application of SMOTE in the original space and in scenarios without any balancing technique. A nonparametric statistical test was used in our analysis to determine whether our proposal statistically outperformed the other methods. In summary, the contributions of this paper are as follows:  
* We address the problem of class imbalance in the presence of high dimensionality and class overlap. * We explore the suitability of fractional norms as an alternative to the problem of Euclidean distance in high-dimensional datasets. * We employ a dissimilarity-based representation to mitigate high dimensionality and class overlap issues. * We implemented a hybrid approach in Spark. The rest of the paper is organized as follows. Section 2 describes the methods and techniques used in this work. Section 3 presents the experimental setup. Section 4 discusses the results. Finally, Section 5 remarks on the main findings and outlines further research.
# Full-Field Super-Resolution Terahertz Imaging Based on Rotating Coherent Scattering Microscopy
###### Abstract
The reconstruction method was acquired by incoherent superposition of the images illuminated by the evanescence wave from 72 different directions to achieve an imaging resolution beyond the diffraction limit. In 2016, the direct intensity imaging method under different transverse wave vectors were recorded in a single image acquisition time by rotating the incident beam direction at a high speed, which has the advantage of real-time recording without the reconstruction process [39], which is named as the rotating coherent scattering (ROCS) microscopy. Recently, this super-resolution imaging method has received some attention in the field of THz imaging. In 2021, [PERSON] et al. numerically simulated one-dimensional THz spoof surface plasmons (SSP) illuminating a one-dimensional sample [40], and presented the scattered field distribution located at one wavelength distance behind the sample, and obtained high-resolution images by the iterative reconstruction algorithm based on a series of scattered field distributions with nine different frequency shifts. Since the SSP provides only a single direction surface wave, multiple wavelengths are required to fill the Fourier spectrum to reconstruct the complex electromagnetic field distribution. However, a THz far-field and full-field direct intensity imaging method based on the evanescent wave (or the surface waves) illumination has not been proposed yet. In this paper, we designed and built a THz-ROCS microscopic imaging system. The incident THz divergent beam is transformed by the spherical refractive surface of a hemispherical lens and produces the plane wave inside the lens. Then, the plane wave incidents on the planar interface of the hemispherical lens satisfies the condition of TIR, which generates the evanescent wave. The evanescent wave illuminates the sample immediately adjacent to the interface, producing the scattered waves that carry high-frequency information to propagate to the far-field. An objective collects the scattered waves to form the high-resolution image at its output plane. By rotating the hemispherical lens, multiple images under different high transverse wave vector illuminations can be obtained. Finally, through the incoherent superposition of these images, the full-field image of a 2D grating with a period of 150 \(\upmu\)m was achieved experimentally beyond the diffraction-limited resolution.
## 1 Introduction
[40] examined the influence of several machining parameters (spindle speed, feed rate, and number of passes) on the mechanical properties of aluminium nanocomposites utilising a long short-term memory (LSTM) model. The model accurately predicted the ultimate tensile strength, yield strength, intrinsic frequency, and damping ratio of the material, yielding R\({}^{2}\) values of 0.912, 0.952, 0.951, and 0.987, respectively. The findings indicated that LSTM is proficient in evaluating the mechanical properties of aluminium nanocomposites. The studies mentioned above suggest that machine learning techniques, including RSM, RF, and LSTM, can proficiently forecast the mechanical properties of nanocomposites; yet, there is a paucity of research focused on predicting the electrical properties of flexible sensing materials utilising these models. This study employs CNTs, MLGs, and magnetic nanoparticles of Fe\({}_{3}\)O\({}_{4}\) as fillers, with RTV silicone rubber serving as the substrate. Flexible sensing materials are synthesisedthrough mechanical blending, and the optimisation of hysteresis and sensitivity is accomplished using machine learning techniques. The hysteresis mechanism and sensitivity of the sensing material and its affecting elements are examined, and a matching mathematical model is developed. The Box-Behnken Design (BBD) methodology examines the impacts of four variables, Fe\({}_{3}\)O\({}_{4}\), CNT, MLG nanoparticle concentration, and MT, on the hysteresis and sensitivity. Predictive models correlating these four factors with output performance are developed utilising the RSM, RF, LSTM, and HKOA-LSTM algorithms. Ultimately, the Pareto-optimal frontier solutions are identified through the MORIME algorithm, and the method's practicality is experimentally validated.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Analysis of Proposed Architecture
#### 3.6.3 Effectiveness of Frequency Decomposition  
This section shows the effectiveness of using decomposed image for training the network. In Section 2.3, we proposed a frequency-based framework to improve degraded images using guided filters. As mentioned before, FHRR-Net has the advantage that parameter optimization is easier than learning immediately by the original input. This allows to take faster learning times and obtain better improved images.  
Experiment on test dataset implies the effect of preprocessing in Table 6. One is learned without frequency-based image decomposition which only takes the raw image and the second is proposed frequency-based method. Table 6 shows experimental results for two methods. As seen in the table, preprocessing gives an easier optimization for the network.
# Detection of Malicious Websites Using Symbolic Classifier
## 2 Materials and Methods
### Evaluation Metrics and Methodology
#### 2.8.1 Evaluation Metrics  
To evaluate each symbolic expression obtained after training of GPSC algorithm the accuracy, precision, recall, area under the curve (AUC), and F1-score were used. The classification accuracy of any ML model is the ratio between the number of correct predictions and the total number of predictions made by the ML model. The classification accuracy, according to [41], is calculated using the expression  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN}, \tag{18}\]  
where \(TP\), \(FP\), \(TN\), and \(FN\) are truly positive, false positive, true negative, and false negative, respectively. In binary classification, if two classes are positive and negative then precision can be described as the ability of the classifier to correctly label positive samples [42]. The precision value for the ML model can be determined as the ratio between true positive and the sum of true positive and false positive values. The precision score is calculated using the formula:  
\[Precision=\frac{TP}{TP+FP}. \tag{19}\]  
The range of the precision score is between 0 and 1 where 0 is the worst value and 1 is the best value. On the other hand, recall is an evaluation method that provides the information if the ML model found all the positive samples. According to [43] recall is the ratio between the true positive and the sum of the true positive and false negative and can be written as:  
\[Recall=\frac{TP}{TP+FN}, \tag{20}\]  
where \(TP\) and \(FN\) are truly positive and false negative, respectively. To calculate the F1-score precision and recall scores are needed. The F1 score [43] is the traditional F-measure or balanced F-score and it is the harmonic mean of the precision score and recall score. The F1 score can be calculated using the following expression:  
Figure 5: The schematic view of random hyperparameter search with 5-fold cross-validation process.  
\[F_{1}=\frac{2*precision*recall}{precision+recall}. \tag{21}\]  
The _AUC_ or ROC_AUC_SCORE is an evaluation method used to compute the area under the receiver operating characteristic curve from prediction scores.
# Flavi Classification Algorithm for Heat Exchanger
## 1 Introduction  
Nuclear power plants consist of many heat ex-changers (HXs), such as a steam generator (SG), moisture separator and reheater (MSR), condenser (CDS), and feedwater heat exchanger (FWH). Among them, the high pressure (HP) FWH is made of welded austenitic stainless steel tubes (ASTM A688-TP304L). A unit of HP FWH contains three heaters and consists of more than 13,000 tubes. The tubes have small bores and significant length, with an outer diameter (OD) and thickness of 15.87 mm (0.625 in) and 1.27 mm (0.05 in), respectively. Few nondestructive testing (NDT) methods exist which can quickly assess the integrity of this material and its structure, namely paramagnetic, narrow, long, and large number of tube bundles.  
Eddy current testing (ECT) can be applied to small-bore tubes, does not require a couplant between the sensor and the specimen, and can inspect a large number of HX tubes within a short time. Additionally, it has the advantage of the detection of various types of flaws easily, including volumetric flaws [1]. However, it poses a challenge when trying to distinguish crack-like flaws from volumetric flaws, and multisite damage is difficult to evaluate.  
Furthermore, if the NDT results show the presence of any crack in the tube, the tube must be plugged regardless of the repair criteria; however, tubes with volumetric flaws should be plugged only if its thickness exceeds the criteria. Therefore, recognizing the shape of the flaw and quantifying its depth is important so that a crack is not mistaken for a volumetric flaw.  
[PERSON] et al. developed bobbin-type magnetic cameras to quickly access the large number of HX tubes [2; 3; 4; 5; 6; 7; 8]. In order for magnetic camera technology to be effectively applied to industrial field,an algorithm should be provided to make it easier for inspectors to identify and evaluate flaws. This study is a follow-up project of the bobbin-type magnetic camera technology development by [PERSON] et al. It proposes an algorithm to identify the location, recognize the shape, and quantitatively evaluate the depth of the flaws when inspecting HP FWH tubes of austenitic stainless steel using a bobbin-type magnetic camera. The effectiveness of the proposed algorithm was evaluated using three test specimens that represent different types of flaws, ASME test standard, and multisite damage.
# Impact of Iterative Bilateral Filtering on the Noise Power Spectrum of Computed Tomography Images
## 5 Conclusions  
The impact of bilateral filters in changing the noise texture and spatial resolution in CT images has been characterized. After five iterations, the bilateral filter shows its effectiveness in filtering noise at high frequencies as indicated by the flattening of the NPS curve on images produced with tube currents of 77, 154, and 231 mAs. The peak frequency shifts from about 0.2 to about 0.1 mm\({}^{-1}\) for these tube currents and the noise magnitude was reduced by more than 50%. The spatial resolution does not change with increasing iterations of the filter, which is confirmed by the constant values of MTF50 and MTF10. The test results on an anthropomorphic phantom image show a similar pattern, with noise reduced by up to 60% and object edges remaining sharp. The impact of bilateral filters on clinical images and CT raw data still needs to be investigated in further studies.  
Conceptualization, C.A.; methodology, C.A. and H.S.; software, C.A., A.N. and K.A.; writing, C.A., A.N. and G.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Riset Publikasi International Bereputasi Tinggi (RPIBT), Diponegoro University, No. 569-187/UN7.D2/PP/VII/2022.  
Not applicable.  
The authors declare no conflict of interest.
# Measurement of the Speed of Induction Motors Based on Vibration with a Smartphone
## 3 Results
### Motor Supply from Mains with a DC Generator as Variable Mechanical Load  
In this configuration, a two-pole induction motor was directly fed from the supply mains at 400 V, 50 Hz. The motor shaft was mechanically coupled to the rotor of a DC generator. The armature of the DC generator was used to feed a resistance, variable at steps, which allowed the adjustment of the mechanical load torque and, consequently, the speed of the induction motor. The available resistance assembly allowed the rotor speed to vary from 2857 r/min (47.62 Hz) to 2990 r/min (49.83 Hz). Figure 8a shows the setup of the tests, and Figure 8b shows eight RMS spectra of the vibration signals of the motor, \(\mid\)_F(j\(\cdot\omega\))_\(\mid\)_, for the indicated range of rotor speeds.  
Table 1 summarises the results for each value of the load resistance, R0-R7. The second and third columns shown, respectively, the actual values of the rotor speed, \(\Omega_{m}\), and the corresponding mechanical frequency of the mechanical frequency of the rotor shaft, \(\omega_{m}\), calculated from (4). Additionally, the rotor speed, \(\Omega\), and rotor shaft frequency, \(\omega_{F}\), both obtained by the algorithm, are shown in the fourth and fifth columns. Finally, the relative errors, \(\varepsilon\), are calculated in the sixth column. As can be seen, the relative errors are very small. Indeed, in the worst case, the relative error is under 0.09%.  
Figure 8: Motor fed from the mains at 400 V, 50 Hz, and a coupled DC generator: (**a**) test setup; (**b**) RMS spectra of vibration signals for motor speeds ranging from 2857 r/min to 2990 r/min.  
Figure 7: Smartphone locations for the different tests.  
Table 2 shows the relative errors in the estimation of the rotor speed for different time recordings of 5, 10, and 15 s and different locations of the smartphone on top of the motor (_Pos1_, _Pos2_, and _Pos3_, see Figure 7). In general terms, although _Pos1_ with longer recordings (15 s) often led to the better speed estimation, the error always remained rather low, ranging from 0.0131% to 0.1311%.  
The results in Table 2 prove that changes in the position of the smartphone or the length of time recordings do not seriously affect the estimations of the rotor speed since the relative errors are similar.