esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
State-of-the-art neural network models are actively used in various fields, but it is well-known that they are vulnerable to adversarial example attacks. Throughout the efforts to make the models robust against adversarial example attacks, it has been found to be a very difficult task. While many defense approaches were shown to be not effective, adversarial training remains as one of the promising methods. In adversarial training, the training data are augmented by \"adversarial\" samples generated using an attack algorithm. If the attacker uses a similar attack algorithm to generate adversarial examples, the adversarially trained network can be quite robust to the attack. However, there are numerous ways of creating adversarial examples, and the defender does not know what algorithm the attacker may use. A natural question is: Can we use adversarial training to train a model robust to multiple types of attack? Previous work have shown that, when a network is trained with adversarial examples generated from multiple attack methods, the network is still vulnerable to white-box attacks where the attacker has complete access to the model parameters. In this paper, we study this question in the context of black-box attacks, which can be a more realistic assumption for practical applications. Experiments with the MNIST dataset show that adversarially training a network with an attack method helps defending against that particular attack method, but has limited effect for other attack methods. In addition, even if the defender trains a network with multiple types of adversarial examples and the attacker attacks with one of the methods, the network could lose accuracy to the attack if the attacker uses a different data augmentation strategy on the target network. These results show that it is very difficult to make a robust network using adversarial training, even for black-box settings where the attacker has restricted information on the target network.  
Keywords:image classification; adversarial example attacks; adversarial training; MNIST +
Footnote †: journal: Applied Sciences
## 4 Empirical Study
### Effect Size Estimation
#### 4.6.1 Wilcoxon Effect Size  
We used the non-parametric Wilcoxon effect size [52] test to examine the magnitude of the prediction performance between the tuned and untuned tree-based ensembles. We selected a non-parametric effect size test because the data were not normally distributed. The normality distribution was checked using the Shapiro-Wilk test. The Wilcoxon effect size test calculates the correlation coefficients using the Z-score employing; then, the following formula is used to estimate the effect size:  
\[r=\frac{Z}{\sqrt{n}} \tag{5}\]  
where \(n\) is the total number of samples on which \(Z\) is based. We used the thresholds proposed by [PERSON] to categorize the magnitude of \(r\) effect size [53]:\[\text{Effect size}=\left\{\begin{array}{ll}\text{negligible}&\text{if }r\leq 0.1\\ \text{small}&\text{if }0.1<r\leq 0.3\\ \text{medium}&\text{if }0.3<r\leq 0.5\\ \text{large}&\text{if }0.5<r\end{array}\right. \tag{6}\]
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Complex Indoor and Outdoor Scene Classification Algorithm Based on Spatio-Temporal Features
#### 2.6.1 Dual-Scale CNN Convolutional Networks  
Convolutional networks are deep learning models designed for processing data with a grid topology. They automatically extract and classify features from data using convolutional, pooling, and fully connected layers. These networks have gained widespread application in fields such as image processing and computer vision. In recent years, researchers have achieved significant advancements in enhancing model efficiency, lightweighting, and task adaptability.  
For instance, [PERSON] proposed the ResNet-RS network, which optimizes the classical ResNet architecture. This enhancement improves performance on large-scale image classification tasks through better data augmentation, optimization techniques, and refined training processes [30]. Similarly, [PERSON] et al. introduced ConvNeXt, which is a modernized version of the classical CNN. ConvNeXt adapts the original structure to close the performance gap with Vision Transformers while maintaining the efficiency characteristic of CNNs [31]. Additionally, [PERSON] et al. developed MS-Net, which is a deep learning model aimed at improving prostate segmentation in MRI scans [32].  
In this paper, we employ a dual-scale convolutional network, as illustrated in Figure 12. This dual-scale neural network captures detailed information at different scales. Compared to traditional single-scale convolutional networks, the dual-scale approach allows for the extraction of feature information changes that may not be evident at a single scale. By integrating both local and global information, this method enhances the model's generalization ability and supports more comprehensive decision making.  
Figure 11: Pearson correlation feature map.  
Figure 12: Schematic of a two-scale convolutional neural network.
## 2 The Matrix Diagonalization
### Householder Bifactorization  
In 1958, [PERSON] introduced a transformative approach [16] for solving systems of equations using a point reflection on a plane. This reflection involves a unit vector \(\mathbf{v}\) orthogonal to the plane. The transformation can be described as the reflection of a point \(\mathbf{x}\) on the plane, resulting in a linear transformation.  
\[\mathbf{x}-2\langle\mathbf{x},\mathbf{v}\rangle=\mathbf{x}-2\mathbf{v}\mathbf{ v}^{T}\mathbf{x}.\]  
In matrix form, this transformation can be expressed as  
\[\mathbf{P}=\mathbf{I}-\frac{\mathbf{v}\mathbf{v}^{T}}{H}, \tag{2}\]  
The Householder transformation matrix, where \(H=\frac{1}{2}|\mathbf{v}|^{2}\) and \(\mathbf{v}\) is a real vector, exhibits the property of being either symmetrical or orthogonal. The key idea behind the Householder transformation is that when applied to a vector \(\mathbf{x}\), it produces the following outcome:  
\[\mathbf{P}\mathbf{x}=||\mathbf{x}||\epsilon_{0}.\]  
To accomplish this, the vector \(\mathbf{v}\) in Equation (2) needs to be \(\mathbf{v}=\mathbf{x}\pm|\mathbf{x}|e_{0}\), where \(e_{0}\) represents the first vector of the canonical base.  
In 1992, [PERSON] discussed in [15] the application of the Householder transformation for the tridiagonalization of a symmetrical matrix. Let \(\mathbf{A}\) be a non-singular symmetrical matrix, and the objective is to obtain a tridiagonal matrix \(\mathbf{T}\) that is similar to \(\mathbf{A}\). To achieve this, we seek an invertible matrix \(\mathbf{P}\) satisfying the following condition:  
\[\mathbf{T}=\mathbf{P}\mathbf{A}\mathbf{P}^{-1}. \tag{3}\]  
The matrix \(\mathbf{P}\) can be expressed as the product of \(k\) matrices of the form \(\mathbf{P}\mathbf{K}\mathbf{P}k-1\dots\mathbf{P}_{1}\), where each matrix \(\mathbf{P}_{I}\) represents a Householder transformation. Additionally, it is worth noting that \(\mathbf{P}^{-1}=\mathbf{P}1\dots\mathbf{P}k-1\mathbf{P}_{K}\).  
Let \(\mathbf{P}_{1}\) be such that  
\[\mathbf{P}_{1}\mathbf{A} = \left[\begin{array}{c|cccc}1&0&0&\cdots&0\\ ine0&\\ 0&\\ \vdots&\\ 0&\end{array}\right]\left[\begin{array}{c|cccc}a_{00}&a_{01}&a_{02}&\cdots&a_ {0n-1}\\ ine0&\\ a_{20}&\\ \vdots&\\ a_{n-1,0}&\end{array}\right] \tag{4}\] \[= \left[\begin{array}{c|cccc}a_{00}&a_{01}&a_{02}&\cdots&a_{0n-1} \\ inek&\\ 0&\\ \vdots&\\ 0&\end{array}\right].\]  
By computing a bifactorization for \(\mathbf{P}_{1}\) applied to \(\mathbf{A}\), we obtain the following expression:  
\[\mathbf{P}_{1}\mathbf{A}\mathbf{P}_{1}=\left[\begin{array}{c|cccc}a_{00}&k& 0&\cdots&0\\ inek&\\ 0&\\ \vdots&\\ 0&\end{array}\right].\]The above process is repeated \(k-1\) times to obtain a tridiagonal matrix. At each iteration, the dimension of the Householder matrix, denoted as \(\mathbf{P}_{i}\), is reduced. After performing the Householder bi-factorization on the original matrix \(\mathbf{A}\), we obtain a tridiagonal symmetric matrix. At this point, we can apply either the QR algorithm or [PERSON]'s algorithm. If the matrix size is small, it is more convenient to use the QR algorithm. However, [PERSON]'s approach is more efficient for larger matrix sizes.
###### Abstract  
Water covers a greater part of the Earth's surface. However, little knowledge has been achieved regarding the underwater world as most parts of it remain unexplored. Oceans, including other water bodies, hold substantial natural resources and also the aquatic lives. These are mostly undiscovered and unknown due to the unsuited and hazardous underwater environments for the human. This inspires the unmanned exploration of these dicey environments. Neither unmanned exploration nor the distant real-time monitoring is possible without deploying Underwater Wireless Sensor Network (UWSN). Consequently, UWSN has drawn the interests of the researchers recently. This vast underwater world is possible to be monitored remotely from a distant location with much ease and less risk. The UWSN is required to be deployed over the volume of the water body to monitor and surveil. For vast water bodies like oceans, rivers and large lakes, data is collected from the different heights/depths of the water level which is then delivered to the surface sinks. Unlike terrestrial communication and radio waves, conventional mediums do not serve the purpose of underwater communication due to their high attenuation and low underwater-transmission range. Instead, an acoustic medium is able to transmit data in underwater more efficiently and reliably in comparison to other mediums. To transmit and relay the data reliably from the bottom of the sea to the sinks at the surface, multi-hop communication is utilized with different schemes. For seabed to surface sink communication, leading researchers proposed different routing protocols. The goal of these routing protocols is to make underwater communications more reliable, energy-efficient and delay efficient. This paper surveys the advancement of some of the routing protocols which eventually helps in finding the most efficient routing protocol and some recent applications for the UWSN. This work also summarizes the remaining challenging issues and the future trends of those considered routing protocols. This survey encourages further research efforts to improve the routing protocols of UWSN for enhanced underwater monitoring and exploration.  
Underwater Wireless Sensor Network (UWSN); routing protocols; acoustic communication; multi-hop communication; energy-efficient; reliable +
Footnote †: journal: Sensors
## 4 Problem Statement
### VNF Placement  
The objective of both the PG and ModPG heuristics is to propose a strategy for efficiently mapping the SFCr on the substrate network. The optimization target is to reduce the total substrate resource cost, defined as:  
\[\begin{split} T_{c}=\alpha*(CPU_{c}+BRC_{c}^{CPU})+\beta*(MEM_{c }+BRC_{c}^{mem})\\ +\rho*Band_{c}+\varsigma*MDC_{c}\end{split} \tag{1}\]  
where the first term corresponds to the CPU resource consumption (\(CPU_{c}\): the total CPU resource consumption of mapped VNFs; \(BRC_{c}^{CPU}\): the total CPU basic resource consumption associated with the required nodes), the second one corresponds to the memory resource consumption (\(MEM_{c}\): the total memory resource consumption of mapped VNFs; \(BRC_{c}^{mem}\): the total memory basic resource consumption associated with the required nodes), the third addend represents the total bandwidth consumption, and finally, the last term corresponds to the total cost of activating MDCs.  
To optimize the total cost, the proposed solution makes use of some strategies:  
1. It is considered that activating an MDC involves a set of additional costs (power supply, hardware equipment, cooling systems, etc). Therefore, the mapping process should activate as few MDCs as possible.
2. VNF combination strategy: If an MDC has enough resources, the maximum number of consecutive VNFs of an SFCr will be mapped on the MDC. With this strategy, the bandwidth cost is reduced.
3. VM reusing strategy: To reduce the CPU and memory BRC, when a VNF is instantiated in an MDC, the VM running the VNF is reused for running VNFrs of the same type.  
The VNF placement problem can be formulated as an Integer Linear Programming (ILP) model, as shown in Appendix A. The obtained ILP model is NP-hard. The Gurobi optimizer [21] was used to solve the VNF placement problem when the number of SFCs is small in [13]. To be able to solve the problem in larger substrate networks with a larger number of SFCs, the priority based greedy heuristic described in Section 2 is proposed.  
In our opinion, the priority-based SFCr mapping algorithm defined in the PG heuristic presents some flaws that affect the performance of the solution. In Section 5, we describe the impact of these flaws, before proposing a modified version of the PG heuristic (called the ModPG heuristic) in Section 6.
# Multi-Objective Optimization Study on Production of AlSi10 Mg Alloy by Laser Powder Bed Fusion
## 2 Materials and Methods
### Relative Density and Surface Roughness Measurement  
The average surface roughness was measured using a TESA Technology Rugosurf 20 (TESA Technology, Renens, Switzerland) device. Measurements were made from the top surface of the sample in the production direction (Figure 2). The surface roughness of each sample was calculated using the arithmetic average of the measurement values taken from three different points.  
The densities of the samples were measured by the Archimedes method using the Shimadzu Analytical Balance, Shimadzu Corporation, Kyoto, Japan (Figure 3). The relative  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Unit**} & \multicolumn{2}{c}{**Variable Levels**} \\ \cline{3-4}  & & & **Low** & **High** \\ \hline
**Laser Power** & LP & W & 200 & 275 \\ \hline
**Hatch Distance** & HD & mm & 0.08 & 0.14 \\ \hline
**Scan Speed** & SS & mm/s & 800 & 1400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters and levels.  
Figure 1: (**a**) Technical drawing of sample (mm), (**b**) manufactured parts.  
density values were calculated as the ratio between the measured density of each 3D-printed sample and the theoretical density of the AlSi10 Mg alloy (2.65 g/cm\({}^{3}\)).  
The experimental design arrangement and the corresponding relative density and surface roughness values are presented in Table 3. Three factors, each with two levels (low and high), were considered in the design. A total of eight experiments were conducted according to these parameter combinations.
## 2 Short-Term Load Forecasting Method Based on Feature Reconstruction
### Data Selection and Preprocessing  
All the analyses in this paper are based on the cities in Shandong Province with large distributed photovoltaic installed capacity. The data used for analysis include five years of electricity load data (2018-2022) and meteorological data. The electricity load data are sampled every 15 min, resulting in 96 data points per day. The meteorological data include temperature, humidity, irradiance, wind speed, and other factors that influence the load. The sampling interval and data volume of the meteorological data are the same as the load data.  
To eliminate the impact of different data scales and ranges on the features, this work adopts the Z-score standardization method. This method ensures that the values of the same feature for different samples are in a standard normal distribution, highlighting the impact of the feature changes on the load forecasting results and improving the effectiveness of the feature selection.  
The formula for the Z-score standardization is as follows:  
\[x^{\prime}=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(x^{\prime}\) is the original value, \(x\) is the standardized value, \(\mu\) is the mean of the corresponding sample, and \(\sigma\) is the standard deviation of the corresponding feature sample.  
To verify the effectiveness of the method proposed in this article, a Bi-LSTM network with three network layers and 250 cycles was used to predict the test days of three types of loads, and the LSTM network was selected as the comparative model. The mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as the evaluation indicators of the prediction results. The calculation formula is as follows:  
\[\begin{array}{l}e_{\text{MAE}}=\frac{1}{N}\sum\limits_{i=1}^{N}\left|y_{i}- y_{i}^{*}\right|\\ e_{\text{MAPE}}=\frac{1}{N}\sum\limits_{i=1}^{N}\frac{\left|y_{i}-y_{i}^{*} \right|}{y_{i}}\times 100\%\end{array} \tag{2}\]  
where \(y_{i}\) and \(y_{i}^{*}\) are the actual and predicted values at the time \(i\); \(N\) is the total number of samples.
applied sciences Article  
A Novel Cooperative Controller for Inverters of Smart Hybrid AC/DC Microgrids  
[PERSON] 1,*(c), [PERSON] 1(c), [PERSON] 2(c) and [PERSON] 3,*  
Electrical Engineering Department, University of Basrah, Basrah 61001, Iraq; [EMAIL_ADDRESS]  
Electrical Engineering Department, Sheffield Hallam University, Pond Street, Sheffield S1 1 WB, UK; [EMAIL_ADDRESS]  
Civil Engineering Department, Hydraulics, Energy and Environment/Professor [PERSON] 3, Universidad Politecnica de Madrid (UPM), CP 28040 Madrid, Spain  
Correspondence: [EMAIL_ADDRESS] (B.N.A.); [EMAIL_ADDRESS] (M.D.E.); Tel.: +964-78090985 (B.N.A.)  
Received: 13 July 2020; Accepted: 1 September 2020; Published: 3 September 2020  
This paper presents a novel cooperative control technique concerning fully-distributed AC/DC microgrids. Distributed generation based on inverters has two types, i.e., Current Source Inverter (CSI), also referred to as PQ inverter, and Voltage Source Inverter (VSI). Both inverter forms have a two-layer coordination mechanism. This paper proposes a design method for the digital Proportional-Resonant (PR) controller that regulates the current inside an inverter. The inverters will improve the voltage quality of the microgrid while maintaining the average voltage of buses at the same desired level. There is comprehensive detail on the computations specific to resonant and proportional gains and digital resonance path coefficients. The paper includes a digital PR controller design and its analysis in the frequency domain. The analysis is based on the w-domain. The main contribution of this paper is the proposed method, which not only focuses on the transient response but also improves the steady-state response which smoothens the voltage; furthermore, all inverters are effectively involved to increase the capacity of the microgrid for better power management. The suggested cooperative control technique is used on an IEEE 14-bus system having fully distributed communication. The convincing outcomes indicate that the suggested control technique is an effectual means of regulating the microgrid's voltage to obtain an evener and steady voltage profile. The microgrid comprises distributed resources and is used as the primary element to analyse power flow and quality indicators associated with a smart grid. Lastly, numerical simulation observations are utilised for substantiating the recommended algorithm.  
IEEE 14-bus microgrid; converters; proportional resonant controller; notch filters; inverters
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 3 Multi-Band Polarization Imaging Experiments  
The SALSA complete Stokes polarization imager from Bossa Nova, United States, is chosen to capture image data in this work. The SALSA polarization camera uses the technique of liquid crystal phase delay to collect the polarization parameters in time, and it can capture and store the dynamics of every pixel point in every frame. Figure 4 depicts the primary polarization imaging device used in this experiment, whereas Figure 4a,b depict the physical and structural diagrams of the SALSA camera, respectively.  
The filters are the supporting equipment of the SALSA camera, and the imaging system is factory-calibrated to assure the excellent quality of the multi-band image effect generated by the optical detecting system. Table 2 lists the pertinent camera characteristics. In this research, three distinct wavelength filters--namely, red (670 nm), green (530 nm), and blue (450 nm)--are used to acquire visible band images.  
The experiment was conducted in Aoshan Bay, the First Institute of Oceanography, Ministry of Natural Resources, Jimo District, Qingdao City, Shandong Province, China. The SALSA full Stokes polarization imager from Bossa Nova, USA, was used in the experiment, as shown in Figure 4; a lens with aperture number 1.4 and focal length 75 mm was selected for imaging during the experiment, and during the experiment of multi-band image acquisition, the filter had to be changed manually, and the filter of the appropriate band was rotated in front of the SALSA camera lens to achieve full polarization measurements of the buildings located on the opposite side of the coast at 670 nm, 530 nm, and 450 nm, respectively. In this article, the three bands were examined independently by switching the filters, and the images remained unaltered, demonstrating that sea fog was the primary source of the experimental findings.  
In order to test the accuracy of SALSA camera imaging, this work calibrated the polarization imaging for three wavelengths of light in clear weather and sea fog conditions, as shown in Figure 5: a. Clear weather, b. Sea fog environment. In contrast, the shift in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters** \\ \hline Camera size & 80 mm \(\times\) 80 mm \(\times\) 100 mm \\ \hline Resolution (pixels) & 1040 \(\times\) 1040 \\ \hline Frame rate at maximum resolution & 12 fps (12 bits), 20 fps (12 bits) \\ \hline Digital & 8/12-bit grayscale imaging \\ \hline Synchronous interface method & USB Interface \\ \hline Spectral bandwidth & 410 nm\(\sim\)685 nm \\ \hline Software & Version: Salsa 2.3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SALSA imager parameters.  
Figure 4: Major polarization imaging devices (**a**) SALSA camera; (**b**) Structure diagram.  
visual contrast was found to be minimal during clear weather and substantial during sea fog. It shows that following experimental findings are mostly influenced by sea fog rather than spectral alterations.
# A Low-Profile Wideband Linear-to-Circular Polarization Conversion Slot Antenna Using Metasurface
## 2 Design and Analysis of the Proposed Antenna
### Mechanism of Circular Polarization
To keep the miniaturization of the antenna, we proposed a new metasurface with seven parasitic square cross gaps shown in Figure 4b. Then, the phase analysis was used to verify the circularly polarized performance of two metasurfaces shown in Figure 4. By simulating these two metasurfaces, we can obtain phases of the \(\mathbf{E}_{1}\) and \(\mathbf{E}_{2}\) waves. The phase difference between \(\mathbf{E}_{1}\) and \(\mathbf{E}_{2}\) is shown in Figure 5. It can be seen that the phase difference of the metasurface with seven parasitic square cross gaps is much closer to \(90^{\circ}\) than that of the metasurface with four parasitic square cross gaps. Therefore, we chose the metasurface with seven parasitic square cross gaps as the final design. Figure 4: Geometry of the metasurface with (**a**) four parasitic square cross gaps; (**b**) seven parasitic square cross gaps.