esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
###### Abstract  
Object-oriented programming (OOP) is prone to defects that negatively impact software quality. Detecting defects early in the development process is crucial for ensuring high-quality software, reducing maintenance costs, and increasing customer satisfaction. Several studies use the object-oriented metrics to identify design flaws both at the model level and at the code level. Metrics provide a quantitative measure of code quality by analyzing specific aspects of the software, such as complexity, cohesion, coupling, and inheritance. By examining these metrics, developers can identify potential defects in OOP, such as design defects and code smells. Unfortunately, we cannot assess the quality of an object-oriented program by using a single metric. Identifying design-defect-metric-based rules in an object-oriented program can be challenging due to the number of metrics. In fact, it is difficult to determine which metrics are the most relevant for identifying design defects. Additionally, multiple thresholds for each metric indicates different levels of quality and increases the difficulty to set clear and consistent rules. Hence, the problem of object-oriented metrics selection can be ascribed to a multi-criteria decision-making (MCDM) problem. Based on the experts' judgement, we can identify the most appropriate metric for the detection of a specific defect. This paper presents our approach to reduce the number of metrics using one of the MCDM methods. Therefore, to identify the most important detection rules, we apply the fuzzy decision-making trial and evaluation laboratory (Fuzzy DEMATEL) method. We also classify the metrics into _cause_-and-effect groups. The results of our proposed approach, applied on four open-source projects, compared to our previous published results, confirm the efficiency of the MCDM and especially the Fuzzy DEMATEL method in selecting the best rules to identify design flaws. We increased the defect detection accuracy by the selection of rules containing important and interrelated metrics.  
object-oriented metrics; design defect; Fuzzy DEMATEL
# Performance Evaluation of Silane in Concrete Bridge Decks Using Transmission X-ray Microscopy
## 2 Materials and Methods
### Depth of Silane Penetration  
The test procedure recommended by the Alberta Transportation and Utilities (AT&U) was identified as a widely used method for finding the effective depth of sealant penetration [17]. In this study, a simple way was implemented to visually inspect the depth of silane penetration. The sides of all 14 collected samples were prepared for investigation at nine random spots on the perimeter of each sample. Each spot was prepared by polishing the sample with 120 grit sandpaper for 5 min to remove anyitance from the samples, as shown in Figure 1. The exposed surface of all 14 samples was stained with a dye at the nine polished spots. The dye changes the color of the concrete where it penetrates. This was shown to be a satisfactory manner to map the presence of silane in past work [7]. Next, pictures of the samples were taken with a digital microscope at each polished spot, and then images were processed to determine the depth of silane.  
The profile of the silane coating was marked along with the depth of the penetration at five equally spaced locations, as shown in Figure 2. This led to 45 measurements (9 spots \(\times\) 5 measurements at each spot) for each of the 14 cores. The silane penetration depth for each sample was then calculated by finding the mean and standard deviation of the 45 measurements.  
Figure 1: (**a**) Polished spots on a typical sample near the surface to measure the silane penetration depth; (**b**) a cored sample from a bridge deck after polishing.  
Figure 2: Silane coating profile under a digital microscope with measurements at nine polished spots (1 in = 25.4 mm).
## 2 Partial Discharge Signal  
### The Simulation Model of PD Signals  
A large number of papers and field experiments have proved that PD signals can usually be characterized by four kinds of mathematical models [14; 21]. In this paper, three kinds of simulated PD signals are selected: double exponential decay function \(y_{1}(t)\), double exponential oscillation decay function \(y_{2}(t)\) and single exponential decay function \(y_{3}(t)\). The expressions are shown as follows:  
\[y_{1}(t)=A_{1}\Big{(}\mathrm{e}^{-1.3t/\tau_{1}}-\mathrm{e}^{-2.2t/\tau_{1}} \Big{)} \tag{1}\]  
\[y_{2}(t)=A_{2}\cdot\Big{(}e^{-1.3t/\tau_{2}}-e^{-2.2t/\tau_{2}}\Big{)}\cdot \sin(2\pi f_{\mathrm{c1}}t) \tag{2}\]  
\[y_{3}(t)=A_{3}\cdot e^{-1.3t/\tau_{3}}\cdot\sin(2\pi f_{\mathrm{c2}}t) \tag{3}\]  
where \(A_{1}\), \(A_{2}\) and \(A_{3}\) is the signal amplitude. \(\tau_{1}\), \(\tau_{2}\) and \(\tau_{3}\) are the attenuation coefficients, while\(f_{\mathrm{c1}}\) and \(f_{\mathrm{c2}}\) are the oscillation center frequency. And the sampling time is 40 \(\upmu\)s. The specific parameters of the simulated PD signal are shown in Table 1.  
The simulation software used in this paper is MATLAB 2022a. The sampling frequency of the analog signal is 50 MHz. Three types of PD pulse models are introduced, as shown in Figure 1a. \(y_{1}\) appears at \(N_{1}\) = 250, \(y_{2}\) appears at \(N_{2}\) = 650 and \(y_{3}\) appears at \(N_{3}\) = 1325. In order to simulate the noise interference of the PD signal in the actual field, a Gaussian WGN signal with an SNR of \(-1\) dB was superimposed on the pure PD signal shown in Figure 1a.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Amplitude** & \multicolumn{3}{c}{**Attenuation Coefficient**} & \multicolumn{3}{c}{**Oscillation Frequency**} \\ \hline \(A_{1}\) & \(A_{2}\) & \(A_{3}\) & \(\tau_{1}\) & \(\tau_{2}\) & \(\tau_{3}\) & \(f_{\mathrm{c1}}\) & \(f_{\mathrm{c2}}\) \\
3.0 mV & 5.0 mV & 1.4 mV & 0.5 & 0.9 & 0.6 & 3.0 & 8.0 \\  & & & \(\upmu\)s & \(\upmu\)s & \(\upmu\)s & MHz & MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: PD simulation parameter settings.  
Figure 1: Simulated PD signal. (**a**) Pure PD signal; (**b**) narrowband interference; (**c**) noisy PD signal.  
In addition, there is often a problem of narrowband interference and PD signal aliasing in the actual detection of PD signals. The narrowband interference shown in Figure 1b was superimposed on the pure PD signal, as shown in (4). The specific parameters of narrowband interference are shown in Table 2.  
\[S_{\text{noise}}\ =\sum\limits_{i=1}^{5}A_{i}\sin(2\pi f_{i}t+\varphi_{i}) \tag{4}\]  
In (4), \(A_{i},f_{i}\) and \(\varphi_{i}\) represent amplitude, frequency and initial phase angle of narrowband interference.  
The PD signal with superimposed noise is shown in Figure 1c, which shows that the pure PD signal has been greatly disturbed by the noise and cannot be extracted.
# Semantic Structure from Motion for Railroad Bridges Using Deep Learning
## 3 System Development
### Bridge Component Classification Using Semantic Segmentation  
The first step of the proposed system is to provide semantic information of the bridge components to each pixel in the two-dimensional (2D) images. In this study, information on bridge components was assigned by performing transfer learning based on Deeplab-V3+. Transfer learning is a method of constructing a network using a pretrained deep learning model. Transfer learning can result in a high accuracy using a pretrained deep learning model with a high performance. In this study, transfer learning was performed using Deeplab-V3+/ResNet-50 and a network was developed to classify the components of the bridge. The network developed in this study classified each pixel of the image into 10 classes: pole, building, girder, pier, ground, grass, water, sky, car, and road. Eight classes, except girder and pier, which are components of the bridge, were integrated into the background later. A total of 245 photographic data with a resolution of 800 \(\times\) 600 pixels were used as the training data. Among the 245 images, 103 were collected through Google Street View, while 142 images were obtained using unmanned aerial images at the Osong 5 th Test track bridge in Nojang-ri, Jeon-myeon, and Sejong. The equipment for unmanned aerial photography included DJI Inspire 2 and Zenmuse X5S, as shown in Figure 6. The specifications of the equipment are listed in Tables 1 and 2.  
Figure 5: Overview of the proposed SSfM system.  
Figure 6: Unmanned aerial photography equipment. (**a**) DJI Inspire 2; (**b**) Zenmuse X5S.  
To train the semantic segmentation network, a labeling process is required to designate which component information of a bridge is contained in each pixel of a photograph. In this study, pixel label data were generated using MATLAB Image Labeler. The label has 10 classes: pole, building, girder, pier, ground, grass, water, sky, car, and road. Figures 7 and 8 show the photographic data and label data used for learning. In addition, the number of pixels for each class was used as a weight to balance the classes.  
The Holdout method was used, as shown in Figure 9, for network learning and verification. For the training data, 80% of the 245 data obtained with the Osong 5 th test track and Google street view were randomly selected. The remaining 20% of the data were used for network self-validation. The hyperparameters used for the training are listed in Table 3. The batch size was set to 5, while the epoch number was set to 500. The Adam optimizer was used for optimization. The initial learning rate was 0.001. The epoch number was reduced 0.3 times every 10 times. The configuration of the deep learning network used for training is shown in Figure 10.
## 2 Power Internet and High Voltage Transmission Line Condition Monitoring Intelligent Sensor Design
### Smart Grid High-Voltage Transmission Line State Data Acquisition Technology  
#### 2.2.1 Multi-Channel Data Acquisition Technology  
Multi-channel data acquisition technology can significantly improve the speed of data acquisition and communication and enhance the efficiency of the power system. Multiple sources of data collection and data transfer are realized through multi-channel data collectors, and the final collected output data are used for data calculations and analyses through the cloud. Due to the adoption of multi-channel data acquisition technology, the data collection volume increases significantly, improving the overall efficiency of the system operation, and the whole process can be monitored remotely, realizing the data collection and operation of the power networking solution.
# MEMS-Switched Triangular and U-Shaped Band-Stop Resonators for K-Band Operation
###### Abstract  
Triangular resonators re-shaped into Sierpinski geometry and U-shaped resonators were designed, linking them with single-pole-double-through (SPDT) RF MEMS switches to provide frequency tuning for potential applications in the K-Band. Prototypes of band-stop narrowband filters working around 20 GHz and 26 GHz, interesting for RADAR and satellite communications, were studied in a coplanar waveguide (CPW) configuration, and the tuning was obtained by switching between two paths of the devices loaded with different resonators. As a result, dual-band operation or fine-tuning could be obtained depending on the choice of the resonator, acting as a building block. The studied filters belong to the more general group of devices inspired by a metamaterial design.  
Sierpinski triangle; U-shaped resonators; frequency tunability; RF MEMS; metamaterials 2023
## 4 Discussion  
All simulation results for the four case studies demonstrate that MPC outperforms the best of the three PID controllers when the dead-time-to-time-constant ratio exceeds three. For some parameters of the studied systems or tuning, this value changes to around \(\frac{\theta}{\tau}\). This is especially true for the aggressively tuned systems or controllers with \(0.1\leq r_{w}\leq 0.01\) although there is no strict boundary defining the parameter values that define the performance regions. The results can guide practitioners on when it is likely to benefit from replacing PID controllers with MPC. The results obtained are as expected because MPC is seen to avoid excessive movements in dead-time dominant systems and the underdamped system. This could be attributed to MPC's ability to anticipate such systems' behavior because of its predictive capabilities. Hence, the results depend heavily on the availability of fairly accurate models that can provide good predictions. The speed of response is also expected to affect results because MPC implementation may be computationally expensive for very fast dynamics, depending on the computational capabilities of the control algorithm implementation hardware. However, MPC implementations such as explicit MPC could be explored when process speed is a concern. Another factor that may affect the results is how noisy the process is. This is because the derivative action of the PID controllers is likely to amplify any noise. Moreover, using observers or Kalman filters, integral to MPC, can naturally solve the problem of process noise.
# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 4 Results and Discussion
Ensemble Type II models that are made up of different blocks with a possibly different architecture per block can also have different types of inputs per block; in those cases where there are different inputs per block, we have marked that in the tables by an NA in the \(f\) column, reporting the type of input associated with each block by an additional letter within parentheses and immediately after the layer type (LSTM or 1D-CNN). Possible values for these additional letters are: an (L) for inputs formed exclusively by the load values, a (D) for inputs formed by the load plus the date/time features, and a (A) for inputs formed by the load plus the date/time and weather features. Table 1 provides a summary table with a ranking of the three best models for the three most important metrics (\(R^{2}\), RRMSE, and SMAPE) plus training and test times. Similar to Figures 6-8, metric values are provided for the forecasts of the first (T-0) and the last (T-23) time-slots, in addition to the average of forecasts for all (24) predicted time-slots. The main findings of the work, as conclusions from Table 1, are:  
* The best value for parameter \(p\) seems to be 168, i.e., having a rolling window length of 1 week, it appears that shorter and longer values do not generally provide the best results. The exceptions are Seq2 seq models, which prefer a value of 24 (1 day), and some deep learning models that combine a CNN with an LSTM, for which 720 (1 month) is the preferred length. * The best number of features associated with each time-slot seems to be one, i.e., to use the load value exclusively. The exception is again the Seq2 seq models which prefer to have the date features additionally. The behavior of logistic regression is confusing in this regard because they obtain best results with both 1 and 45 features/time-slot.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 2 Related Work
### The Identification of Gluten Presence in Food Using Images  
Several studies have examined the use of deep learning to detect gluten in flour. [PERSON] et al. [32] developed a system that combines near-infrared spectroscopy technology with deep learning and machine learning algorithms to predict whether flour samples contain gluten or not. A total of 12,053 samples were collected from three different types of flour (rye, corn, and oats). The method involves shining near-infrared light on flour and measuring the light reflected back, which provides information about the chemical composition of the flour. Concerning performance, the XGBoost classifier achieved an accuracy of 94.52% and an F2-score of 92.87%, while the deep neural network achieved an accuracy of 91.77% and an F2-score of 96.06%.  
[PERSON] et al. [33] detected wheat flour traces in a gluten-free product, such as chickpea flour. A total of 1400 photographs were taken of pure and mixed flour samples. Using a ResNet50 model, these images were classified into 14 groups based on the concentration of wheat flour, which varied from 0 to 50 parts per million. The overall accuracy of the testing set was greater than 93%. Similarly, [PERSON] et al. [34] identified lentil flour samples containing trace amounts of wheat (gluten) or pistachios (nuts) using the ResNet34 algorithm. Based on 2200 images, the wheat model was trained and achieved 96.4% accuracy on a testing set consisting of 25 wheat flour samples.  
While previous research has focused on identifying gluten using images of various flour types, none have explored gluten content in other foods. Our study focused specifically on bread products, with the aim of developing a model that can accurately identify gluten presence from image data alone. This method offers a novel contribution by offering a non-invasive quick assessment tool that can benefit both individuals with gluten sensitivities, patients with celiac disease, and the food industry.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### Far Field Calculation  
After the iterative process, the entire EM system reaches stable status. Therefore, the far field of the MLFMA region can be obtained by (1), and the far field of the BT region can be obtained by summing up the PO fields scattered from each small hit beam. Thus, the total far field can be obtained as the superposition of contributions from both the MLFMA region and the BT region.
# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 3 Ground Theory
### Data Multidimensionality  
Formally, a multidimensional dataset can be expressed as a set of \(n\) instances \(X=\left\{x_{1},x_{2},\ldots,x_{n}\right\}\), in which each instance contains a vector of \(m\) items, \(x_{i}=\left\{v_{1},v_{2},\ldots,v_{m}\right\}\). Each \(x_{i}\) vector can be seen as a data object and is represented by a subset of attributes of different data types, such as integers, reals, binaries, text, categorical, images, and others. When \(x_{i}\) has more than three attributes, the dataset cannot be represented in simple visualizations such as those based on the Cartesian plane. In this case, the vector produces families of curves in the m-dimensional space [19].
# PUF and Chaotic Map-Based Authentication Protocol for Underwater Acoustic Networks
## 5 Informal Security Analysis  
### Offline Password Guessing Attack  
Since the information in smart cards can be retrieved by side channel attacks, such as power analysis attacks, stolen smart card attacks should be considered when designing authentication schemes using smart cards. In our scheme, if the SC is stolen by an adversary, it can retrieve the information stored in the SC and eavesdrops on the message transferred on the public channel. Though the adversary can guess the user's identity and password and obtain \(M_{2}\), he still cannot know the random nonce \(rug\), and can not verify whether \(M_{4}=h(T_{1}\parallel M_{2}\parallel rug\parallel id_{i})\) is correct or not. Therefore, the adversary cannot know whether his guessed identity and password are correct or not. On the other hand, if an adversary wants to guess \(id_{i}\) and \(pw_{i}\) to satisfy \(M_{3}=h(id_{i}\parallel M_{0}\parallel pw_{i})mod\ n_{0}\), there are \(2^{32}\) candidates for the \((id_{i}\), \(pw_{i})\) pair when \(n=256\). Moreover, the adversary cannot know which pair is correct. Thus, our scheme can withstand the stolen smart card attack and offline password guessing attack.
# Diversity of Physical Processes: Challenges and Opportunities for Space Electric Propulsion
## 2 Variety of Physical Processes to Integrate into a Single Design
When compared to these physically simple direct current systems, thrusters that feature more complex physics (such as Hall-type, radio-frequency etc. systems) involve a much more complex hierarchy of physical processes [49; 51]. However, ignition of the discharge is only the first stage of the complex chain of physical processes for such a simple design. The whole chain of physical processes is much more complex--see Figure 5 which illustrates two main stages, yet not the whole set of processes. Figure 6: Schematic describing the ablation and ion charge states distribution. For a very simple design of an arc thruster, the whole set of involved physical processes is quite complex. Reproduced with permission from Ref. [26]. Copyright 2019, Springer-Nature. The second stage is plasma plume formation and control of ion energies. Traditionally, in the setups of vacuum arc technology an axisymmetric magnetic field was applied to form the shape of the extracted ion flux [93; 94], and the idea was adopted in VACs [95]. The composite cathodes made as alloys of heavy and light metals, allow the expansion of the set of options with respect to the plasma plume. The non-uniformity in the chemical composition causes the re-distribution in the discharge, when the heavier ions concentrate on the discharge axis of the angular distribution, and the lighter ions gain more kinetic energy in comparison with the pure metal cathodes. At the same time, a pulsed mode of electric supply was considered as the best way to obtain the multiple charged ion flux after a number of experiments [96]. The ways to affect the ablation and ion charge states distribution are summarized in Figure 6.  
Apparently, the characteristics of the material flows emitted from the cathode of the arc discharge describe the performance of this type of arc thruster. Vapours, multi-charged ions, neutrals, solid clusters and liquid droplets usually compose the material flux [97; 98], and the cathode erosion rate is considered as the primary parameter describing the arc effectivity with respect to the production purposes (either in space propulsion or coating deposition) [99; 100]. Despite the long history of investigation of the erosion rates for a large variety of the applied materials and discharge currents, the theoretical background is still not completely clear due to the explosive character of the event [101; 102], unlike the evaporation from a steady source [103]. Currently, kinetic models [104] show good agreement with the experimental results. Hence, one of the most significant challenges in advanced space propulsion technology is the complexity of physical and chemical processes, and the necessity to integrate them into a single highly efficient system.