esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Related Work  
Earlier studies mainly focused on the electrical power system. Research on the cascading failures of the electrical power system has a long history. Over the past two decades, the research on cascading failure propagation modes in the electrical power system has focused on two aspects: one is the single network cascading failure propagation modes [9; 11; 13; 28] and the other is the interdependent cascading failure modes between a PN and a CN [16; 22; 23]. The theories for analyzing cascading failures of large blackouts include self-organization theory [28], percolation theory [15; 17; 21], power flow model [14; 22; 24], and so on.  
Research on cascading failures in a single network, including random failure modes, with single or multiple nodes removal, the following problems are specifically solved: (1) The impacts exerted  
Figure 12: \(R_{PF}(\infty)/R_{IF}(\infty)\) of different tolerance parameters in the PN and CN.  
on cascading failures are analyzed when the power overloads lead to nodes or edges failures [11]. (2) Considering the intermittent and random nature of new energy generation, the impacts exerted on the cascading failures are analyzed in a single PN [9; 11; 13].  
Research on the CCF of interdependent networks between a PN and a CN includes: (1) The characteristics of the CCF across the PN and CN are summarized: (a) The accidents caused by the CCF have the characteristics of power law distribution [6], and the probability distribution of large-scale blackouts caused by the increasing load or unbalanced power flow changes from the exponential tail to the power law tail. (b) Total demand or pressure leads to the operation of the interdependent network under extreme conditions, which is one of the main factors leading to the CCF. (2) After analyzing the CCF propagation mechanisms of different kinds of networks, the conclusions are as follows: (a) Scale-free networks for a single network are robust to random attacks, but two interdependent scale-free networks are sensitive to random attacks [24]. (b) Even if the small disturbances in one network can lead to failures in another network, many clusters can be isolated from the whole network after the CCF stops [15; 17; 21; 23]. (c) From the security point of view, an interaction model is established for the importance of the coupling relationship between a PN and a supervisory control and data acquisition (SCADA) system. (d) A model has been established to analyze the process of cascading failure propagation between the PN and the CN [8; 19; 21; 25]. Its basic idea is to calculate the power flow redistribution after cascading failures occur by the dynamic power flow. (e) A topological model of a CN and a PN is established by the graph theory, and their transmission characteristics are analyzed. The ability of different topologies to resist the CCF under different coupling conditions is analyzed and compared [20; 24].  
However, the above current research work does not comprehensively consider the impact of the distribution of distributed generators, network topology, interdependence, and load redistribution on the robustness of the ADN against the CCF.
# Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral Gradient
## 3 Materials and Methods
### Original Datasets  
To evaluate the proposed methodology, three datasets of wide recognition in the world of machine learning and computer vision were used: MNIST, CIFAR-10, and CIFAR-100. However, this methodology can be applied to datasets with different image sizes, total number of images in the dataset or different number of samples per class.  
MNIST is a reference dataset due to its simplicity and widespread availability. It includes grayscale images of handwritten decimal system digits (ten classes). MNIST  
Figure 1: Outline of the proposed adversarial validation methodology.  
images are low-resolution (\(28\times 28\)), with a total of 70,000 images, distributed in 60,000 for training and 10,000 for testing, with an equal number of samples per class [25]. Moreover, the MNIST dataset constitutes a subset extracted from the NIST's Special Database (SD)-1 and Special Database (SD)-3, both of which include binary images of handwritten digits. The digits in the MNIST dataset have been standardized in size and centered within fixed-size images. Specifically, the training set comprises 30,000 patterns from SD-1 and an additional 30,000 from SD-3, while the test set consists of 5000 patterns from each of these databases. The training dataset incorporates samples contributed by approximately 250 different writers. The MNIST dataset is publicly available from [[https://yann.lecun.com/exdb/mnist/](https://yann.lecun.com/exdb/mnist/)]([https://yann.lecun.com/exdb/mnist/](https://yann.lecun.com/exdb/mnist/)) (accessed on 4 April 2024).  
CIFAR-10 is a low-resolution (\(32\times 32\) pixels) color image dataset that has been widely used for the validation of new object classification and recognition algorithms. This dataset comprises 60,000 images, each with a single label of 10 mutually exclusive classes. It also presents a distribution of 50,000 images for training and 10,000 images for testing, with an equal number of images in each class in both the training and test sets [26].  
On the other hand, CIFAR-100 is a dataset similar to CIFAR-10, but differs in two fundamental aspects: the number of classes and the number of samples. In this case, 20 super classes and 100 different classes have been defined (i.e., each super class groups five classes). Each of the samples in the dataset has been labeled both with the superclass to which it belongs and with its particular class. As for the number of examples, the difference lies in the number of samples per class (600 images per class) and not in the total number of samples (60,000 images), showing a distribution similar to that of CIFAR-10 [26]. Both CIFAR-10 and CIFAR-100 datasets are publicly available from [[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)) (accessed on 4 April 2024).
## 5 Performance Comparison
### Comparison to Benchmark DL Models
#### 5.2.2 Performance of Inception and GoogLeNet  
The concept of _Inception_ for very deep CNNs was introduced in [66] with the GoogLeNet model. The GoogLeNet model is based on a block of parallel convolutional layers with differently sized filters (\(1\times 1\), \(3\times 3\), and \(5\times 5\)) and a max-pooling layer \((3\times 3)\). Then, the results of inception networks are composed by chaining.  
In our experiments, we explored several architectures based on the inception model. The effect of the pooling layer on the output of the model was studied as well. The input shape for the Naive Inception model and Inception2 model is \((128,128)\), while the spectrogram image is resized to \((299,299)\) to fit the very deep convolutional Inception-V3 architecture, which is developed in [66]. The performance of Inception-V3 with global average pooling (GAP) in the output layer is better than other inception models as listed in Table 3. We can summarize the result by concluding that, despite the inception model's depth, very deep inception networks feature low performance if one considers the associated cost of implementation.
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
## 3 Results
### Performance of the Homogeneous Model for All Designs
#### 3.1.1 Specific Absorption Rate (SAR) of the Homogeneous Model for All Designs  
In this analysis, we examine the SAR distribution of all applicators using the same homogeneous model (Figure 3). According to IEEE norm [51], SAR should be measured or defined as a mean value averaged over either 1 g or 10 g of mass, namely, corresponding to 1 cm\({}^{3}\) or 10 cm\({}^{3}\) of tissue. However, the numerical simulator estimates the \"point SAR\" in order to be accurately transferred to the Pennes bioheat diffusion equation solver. This approach may involve overestimation of point defined power absorption around metallic edges and particularly near metallic tips and corners. In turn, this may yield unexpected, overestimated local temperatures. In Figure 3, we present SAR contours at levels of 2.5, 5, 7.5, and 10 W/kg.  
Figure 3: Specific Absorption Rate (SAR) of the different applicators embedded in a homogeneous tissue with \(\epsilon_{\mathrm{r}}\) = 46.8, \(\sigma\) = 0.861 at 915 MHz. Monopole antenna (**a**), capacitive capped monopole antenna (**b**), multi-slot coaxial antenna (**c**), dual-slot antenna (**d**), triaxial antenna (**e**), choke dipole antenna (**f**), balun-free base-fed monopole (**g**), floating sleeve dipole antenna (FSD) (**h**), balun-free helical dipole antenna (**i**), helical open waveguide structure (**j**) at one model (healthy tissue). The blue, purple, red, and yellow colors indicate SAR contours of 2.5, 5, 7.5, and 10 W/kg, respectively.  
The 2.5 W/kg contour represents a low exposure level, generally regarded as safe according to norm [51]. The 5 W/kg contour indicates moderate exposure, which may still fall within safe limits for healthy tissue. The 7.5 W/kg contour approaches levels where caution may be necessary, depending on the duration of exposure. Finally, the 10 W/kg contour reflects a high exposure level that could increase the risk of thermal damage, particularly with prolonged exposure. Regarding the shape of the treated area, the same observation noted for Figure 2 also applies to Figure 3 exhibiting comet-tail, nearly spherical, or nearly rectangular shapes.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 3 Complexity Analysis  
We will compare the complexity of HABO-OFDM with HACO-OFDM and PHO-OFDM in this section. It is known that the complexity of the transmitter in the conventional optical OFDM scheme is determined by the number of Inverse Fast Fourier Transform (IFFT) operations [20]. In this way, we can evaluate the system complexity by using the number of the real multiplication operation in IFFT [21]. As for HABO-OFDM, the transmitter of it requires an \(N\)-point IFFT and another \(N\)-point IFFT used on the imaginary value, which yields \(3N\log_{2}N-3N+4\) times of the real multiplication operation. Similarly, \(3N\log_{2}N-3N+4\) times of the real multiplication operation are required for the HACO-OFDM transmitter, which is the same as PHO-OFDM.  
As far as the complexity of the receiver, only one \(N\)-point FFT and one \(N\)-point real-valued FFT are included in HABO-OFDM. Thus, it results in \(3N\log_{2}N-3N+4\) times of the real multiplication operation for the HABO-OFDM receiver. By contrast, the HACO-OFDM receiver needs an \(N\)-point FFT and two \(N\)-point real-valued FFT, which causes \(4N\log_{2}N-6N+8\) times of the real multiplication operation. In addition, two \(N\)-point FFT and one \(N\)-point real-valued FFT are needed for the receiver of PHO-OFDM, which leads to \(5N\log_{2}N-3N+4\) times of the real multiplication operation. The comparison of the complexity of the superimposed optical OFDM schemes is shown in Table 1. It can be clearly observed that the complexity of the receiver of the proposed HABO-OFDM scheme is relatively reduced compared with the other two superimposed schemes. Compared with HACO-OFDM and PHO-OFDM, the complexity of the receiver of the proposed scheme is reduced by \(N\log_{2}N-3N+4\) and \(2N\log_{2}N\), respectively.
## 3 Passive Systems for the Stadium's Envelope--Heating and Cooling Loads
### Heating and Cooling Loads Calculation
#### 3.3.1 Existing Envelope's Condition
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**Final Thermal**} & \multicolumn{2}{c}{**Electricity**} & \multicolumn{2}{c}{**Average**} \\  & \multicolumn{2}{c}{**Energy (kWh)**} & \multicolumn{2}{c}{**Consumption (kWh)**} & \multicolumn{2}{c}{**Efficiency**} \\ \cline{2-5}  & **Heating** & **Cooling** & **Heating** & **Cooling** & **COP** & **EER** \\ \hline January & 99,736 & 0 & 6946 & 0 & 2.01 & \\ February & 92,735 & 0 & 6345 & 0 & 2.05 & \\ March & 64,843 & 0 & 4250 & 0 & 2.14 & \\ April & 16,827 & 0 & 1026 & 0 & 2.30 & \\ May & 3350 & 19,194 & 191 & 8348 & 2.46 & 2.30 \\ June & 0 & 82,464 & 0 & 37,749 & 2.18 \\ July & 0 & 124,415 & 0 & 58,272 & 2.12 \\ August & 0 & 124,495 & 0 & 57,250 & 2.17 \\ September & 0 & 66,566 & 0 & 28,387 & 2.34 \\ October & 3022 & 19,177 & 182 & 7749 & 2.32 & 2.47 \\ November & 26,514 & 0 & 1664 & 0 & 2.23 & \\ December & 74,513 & 0 & 4986 & 0 & 2.09 & \\ Annual totals & 381,539 & 436,311 & 25,591 & 198,204 & 2.09 & 2.20 \\ Heating \& cooling total & 817,849 & & 223,795 & & \\ Annual primary energy consumption (kWh) & 649,007 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Monthly summarized heating and cooling loads and the corresponding electricity consumption in the Pancreatic Stadium with the existing envelope’s condition.
# A Dynamic Grid Index for C\(k\)NN Queries on Large-Scale Road Networks with Moving Objects
## 3 Methodology Overview and Problem Definition
### Methodology Overview  
Unlike some existing methods that use tree-like indexes and create rules to optimize the search efficiency, we identified some issues with this approach. First, numerous leaf nodes cannot be efficiently pruned, which leads to numerous unnecessary vertices in these nodes.  
Second, not all leaf nodes in the index are used for location-based services, and some of them have a minor effect, but still require significant costs for the construction and updates.  
Based on the abovementioned problems, our scheme adopted the overall idea of parallel and balanced computing to build high-quality indexes. As shown in Figure 1, we first recursively partitioned the whole graph into multiple-grid subgraphs according to the quadtree structure, and then constructed new grid indices to take over these subgraphs. The moving object can determine the grid unit to which it belongs according to its position and compile it into the index of the corresponding grid unit. The granularity of meshing is an important factor that directly affects the performance of index building and updating behaviors. If the granularity is too large, there are too many nodes in the grid, and the function of dividing the units is lost; if the granularity is too small, the index needs to be frequently updated, which affects its overall performance.
# Deep Learning System for COVID-19 Diagnosis Aid Using X-ray Pulmonary Images
###### Abstract  
The spread of the SARS-CoV-2 virus has made the COVID-19 disease a worldwide epidemic. The most common tests to identify COVID-19 are invasive, time consuming and limited in resources. Imaging is a non-invasive technique to identify if individuals have symptoms of disease in their lungs. However, the diagnosis by this method needs to be made by a specialist doctor, which limits the mass diagnosis of the population. Image processing tools to support diagnosis reduce the load by ruling out negative cases. Advanced artificial intelligence techniques such as Deep Learning have shown high effectiveness in identifying patterns such as those that can be found in diseased tissue. This study analyzes the effectiveness of a VGG16-based Deep Learning model for the identification of pneumonia and COVID-19 using torso radiographs. Results show a high sensitivity in the identification of COVID-19, around 100%, and with a high degree of specificity, which indicates that it can be used as a screening test. AUCs on ROC curves are greater than 0.9 for all classes considered.  
COVID-19; pandemic; deep learning; neural networks; X-ray; medical images +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Modulus Degradation  
Material degradation is reflected as a decrease in the structure's elastic modulus [46]. Dislocations move and microcracks grow such that the material becomes less stiff and will elongate more for the same applied load as damage progresses. The initial elastic modulus close to 67.8 GPa as noted in Table 1 and a consistent modulus degradation trend between specimens is expected. The elastic modulus for each fatigue cycle is approximated by dividing the difference between the maximum and minimum applied nominal stress in the cycle by the difference between the strain at the maximum and minimum stress. While the stress at the notch tip is estimated to be either elastic or plastic between the experiments depending on the applied loading, the nominal stresses for all experiments (between 180 and 270 MPa) are below the yield stress (538 MPa) suggesting that the use of modulus degradation, which is only applicable for the elastic region, is valid. The raw modulus trends can then be normalized and are referred to as _modulus degradation damage_ (MDD). The correlation between AE parameters and this statistic ensures that the variations between the initial and final modulus of the experiments are reduced and allows data from all tests to be analyzed together. An example Matlab script that calculates the elastic modulus for each fatigue cycle is provided in the Supplementary Materials as well as a script that matches AE features to MDD for each cycle. In this case, MDD is calculated based on the following equation:  
\[\text{Modulus degradation damage}\left(MDD\right)=\frac{E_{i}-E_{0}}{E_{f}-E_{0 }}, \tag{3}\]  
where \(E_{i}\) is the modulus at each cycle, \(E_{0}\) is the initial modulus, and \(E_{f}\) is the final modulus. An MDD value of 0 refers to a pristine specimen, while 1 is associated with specimen fracture.  
Numerous experiments were performed to refine the experimental setup and procedure. In the end, eight experiments with consistent experimental setups and acceptable AE signal behavior were analyzed in regards to modulus degradation and AE signal features. The entire experimental setup is depicted in Figure 4 as previously mentioned.
# Radio Signal Modulation Recognition Method Based on Deep Learning Model Pruning
## 5 Experimental Analysis
### The Effect of LSTM Layer Number  
The accuracy will improve by adding LSTM layers. In Figure 6, it can be seen that the two-tier LSTM network has about a 3% accuracy improvement, compared to the one-layer network. After continuing to increase the number of LSTM layers, the results do not change a great deal, so the two-layer LSTM is set.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Iterative Decoding Feedback-Based MP Detector  
The method proposed in this study introduces an OTFS system detection approach based on an iterative decoding feedback message passing detector (IDF-MP). This method enhances the detection performance through an iterative process that enables soft information exchange between the MP detector and a low-density parity check (LDPC) decoder. It utilizes symbol-level soft information output by the detector and bit-level soft information output by the LDPC decoder to improve the information transmission efficiency.  
As shown in Figure 4, the process commences with the utilization of the channel equivalent matrix obtained from the preceding channel estimation results. Subsequently, the OTFS DD domain signal and the channel equivalent matrix are fed into the MP detector. The MP algorithm utilized in this study, which has been previously detailed in [15], is specifically designed to leverage the sparse characteristics of the channel matrix \(\mathbf{H}\) in the DD domain. We do not further elaborate on this algorithm in this paper.  
Figure 4 illustrates the interconnection between the MP detector and the LDPC decoder within the iterative structure. The conversion of symbol-level soft information and bit-level soft information between the MP detector and LDPC decoder is facilitated by log-likelihood ratio (LLR) converters, as well as interleaving and de-interleaving sections.  
In the iterative structure of the IDF-MP algorithm, there are three forms of loop counts:  
* The outer-loop iterations \(n_{out}\).
* The inner-loop iterations of the MP detector \(n_{in,MP}\).
* The inner-loop iterations of the LDPC decoder \(n_{in,LDPC}\).  
The specific information transfer process between the MP detector and LDPC decoder is described in the following.
## 2 Materials and Methods
### Data Observation
#### 2.2.3 Observation of Solar-Induced Chlorophyll Fluorescence (SIF) and Canopy Spectra  
The AutoSIF-1 (Bergsun Inc., Beijing, China), an automatic fluorescence observation system, was installed on a observation tower platform 20 m above the ground, 10 m above the canopy, with an observation radius of approximately 2.2 m. This system is equipped with a high-precision spectrometer (QE65, Ocean Optics Inc., Dunedin, FL, USA) with a spectral resolution of 0.31 nm and a working spectral range spanning 650 to 800 nm. An electronic switch was employed to connect two optical fibers to the spectrometer. At the terminal of one optical fiber, a cosine corrector (CC3-3-UV-S, Ocean Optics Inc., Dunedin, FL, USA) was mounted. The other bare optical fiber possessed a field of view (FOV) of 25\({}^{\circ}\). These components facilitated the measurement of downward solar radiation and upward-reflected radiation from the canopy. Measurements were conducted in so-called \"sandwich\" mode. The spectrometer initially records solar radiation, swiftly transitions to capture canopy radiation, and then reverts to solar radiation measurement, effectively reducing errors stemming from radiation variability. The integration time for each measurement was adjusted to optimize the performance according to the intensity of ambient light. Using the spectral fitting method (SFM), we derived the red and far-red band canopy SIF signals (SIF). The observation system provided canopy reflectance data in the 650 to 800 nm range.
## 1 Introduction  
The fractionation and concentration of particles is used in many industries [1; 2; 3], for which different methods are available that mostly depend on the size of the particles that need to be separated. [PERSON] et al. (2017) distinguish three different categories in their recent review: particles that are smaller than 10 nm that can be separated using adsorption or ultrafiltration, particles in the range of 1-10 \(\upmu\)m that can be separated by microfiltration or specific microfluidic devices, and particles larger than 10 \(\upmu\)m that can be separated by centrifugation or settling when having a sufficient density difference [4]. In the current paper, we are interested in separating particles in the range of 1-10 \(\upmu\)m and focus on a concept that combines insights that were obtained for microfiltration and microfluidic devices.  
In conventional microfiltration particles are carried towards a membrane via convection, where they are then retained creating a concentration polarization layer, and, in due time, a cake layer. These layers will have an influence on the trans-membrane pressure, which results in a reduction of the flux [5; 6], and changing retention or selectivity of the membrane as a function of time [5; 6]. In micro-structured devices and channels, particle behavior in flow can also be used to achieve separation or fractionation. For example, deterministic ratchets make use of the displacement of particles around precisely placed objects [7]; based on a critical size, particles will follow a particular streamline or be displaced [7]. Fluid skimming makes use of fluid flow to carry a particle across a pore (similarly to microfiltration at high cross flow), and is termed an industrially promising technique because of its scalability by some authors [4; 8].  
In the current paper, we focus on shear induced diffusion based segregation that takes place in relatively narrow channels, and has been suggested for particle fractionation that is based on experimental observations [9]. Here, we use modelling studies to chart process parameter effects (e.g., channel dimensions, flow velocity, concentration profiles, and restoration thereof after a disturbance) and achieve systematic insights in process behavior.