esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel 4 \(\times\) 1 MISO-VLC System with FBMC-OQAM Downlink Signals
## 3 Results and Discussion
In practical applications, OFDM modulation is chosen due to its low cost, despite its poor reception performance. On the other hand, FBMC/OQAM multi-carrier modulation has a higher cost but provides higher-quality received signals. Therefore, in practical applications, we can choose the appropriate signal modulation types based on specific needs. Figure 8: BER vs. SNR comparison of OFDM and FBMC-OQAM signals in 1 \(\times\) 1 SISO and 4 \(\times\) 1 MISO downlink channels.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 2 Additional Attenuation of Vegetation
### Tree Alley Perspective  
The radiative transfer equation can be considered with the assumption that the entire signal is transferred _through_ the foliage with its absorption and emission as stated in Equation (10). However, as depicted above, the incoherent component may cause a change in direction, and the signal may go out from the trees with a randomized direction, unlike the coherent component. Then, as shown in Figure 3, diffracted or reflected components may emerge.
## 5 Experiments
### Multi-Density Clustering  
The results show that DBSCAN and K-means clustering did not focus on the factor of noise spots, which included noise points and boundary points. However, the SAMD model with timespan processing not only dealt with the problem of excluding the noise points due to the threshold of the set of MITs but also classified a few of the closest noise points into sparse clusters.  
Normally, two widely used measures are chosen to evaluate clustering accuracy. One is normalized mutual information (NMI) [32] and the other is the NCI Cluster. The computation of NMI consists of two main components: mutual information (MI) and entropy. By standardizing the value of MI, it provides an easy-to-understand and comparable measure of how well the clustering results match the true labels. The advantage of NMI is that it combines precision and recall, making it a comprehensive and commonly used metric for clustering evaluation. The value of NMI equaling 1 represents that the clustering result exactly matches the labels, and it being 0 signifies that the result is completely irrelevant. The performance results of the SAMD, DBSCAN, and K-means models are shown in Table 2; the SAMD model was more accurate than the other methods.  
Running Time Efficiency: We discuss the changes in the running time regarding an increasing dataset. The general trend was that the running time continued to grow as datasets were added. However, our proposed multi-density method showed a more stable state. Our proposed SAMD model was more efficient than the other DBSCAN and K-means algorithms, as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline
**Statistic** & **PJM** \\ \hline Mean & 0.018 \\ Standard Deviation & 24.143 \\ Skewness & 0.512 \\ Peakedness & 14.975 \\ JB & 2289.879 \\ \(Q^{2}(10)\) & 8.971 (\(<\)0.01) \\ \hline \end{tabular}
\end{table}
Table 1: Statistical table of real-time electricity price characteristics in electricity market.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Algorithm** & **SAMD** & **DBSCAN** & **K-Means** \\ \hline NMI & 0.925 & 0.820 & 0.723 \\ \hline \end{tabular}
\end{table}
Table 2: Clustering accuracy evaluation results.  
Figure 7: Real-time electricity price fluctuations.
###### Abstract
Another work [20] proposed RNN with different architecture, but this work was mainly focused on recovering the forces on beam structure excited by harmonic, impact, and random forces. In another work by [PERSON] et al. [21], the authors implemented a deep learning based autoencoder for load identification, for structural health monitoring, from multivariate structural vibration data. They employed residual learning and inception modules in their autoencoder network. Ref. [22] proposed an ANN based on Bayesian Probability Framework to estimate the forces from the displacement responses. In spite of the massive success of deep learning techniques in tackling a variety of problems owing to their ability to explore vast design space and to manage ill-posed problems, deep learning predictions are oftentimes physically inconsistent and generalize poorly [23]. However, this behavior can be alleviated to some extent by embedding various biases; one way of achieving this is by infusing the governing equation in the loss function of a neural network as proposed by [24], known as a \"physics-informed neural network (PINNs). Recently, PINNs have been used to solve inverse source problems; one such account is the paper by [PERSON] et al. [25]. In this work, the author utilized PINNs to predict the spatially and temporally varying heat source from the simulated temperature data with good accuracy. In this work, we use the PINNs approach for estimating the forcing function of one degree of freedom system. Recently, two studies [26; 27] have been published where the authors utilized machine learning and physics information to solve the vibration problem. The former used the Hilbert transform and a variant of the least-squares method to estimate the non-linear restoring force in a bi-stable structure, and the latter used PINNs to solve forced vibration and plate vibration problems. [PERSON] et al. [27] also used PINNs to solve forced spring mass damper systems similar to ours, but their work was mostly about predicting the displacements for a future time step and natural frequency, whereas our approach is more focused on estimating the excitation forces. We propose PINNs to estimate harmonic or non-harmonic and periodic or aperiodic forcing functions for systems with one degree of freedom subjected to various initial conditions. Although in this work our attention is on oscillators as a mechanical system, to our understanding, this work also has the potential to be applied to any systems governed by linear or non-linear ordinary differential equations in different domains. The remainder of the paper is organized as follows: In Section 2, we talk about the mathematical model of duffing's equation followed by Section 3 where we discuss the structure of our neural network and share details about the training process. Later, in the following Section 4, we share our findings and finally conclude this paper with Sections 5 and 6 with discussions and conclusions, respectively.
###### Abstract  
Hip fracture is a major health issue that accompanies community aging. The most critical time after a hip fracture should be the first year. Care systems and surgical techniques for hip fractures have improved, so the trend of mortality in elderly hip fracture could be changed with them. Therefore, we observed the changes in the trend and critical factors for first-year mortality for the hip fractures in an elderly population in Taiwan, and mortality of prognosis prediction model was developed for the early diagnosis using a population-based database in Taiwan (National Health Insurance Research Database, NHIRD). A total of 166,274 elderly subjects with an age greater than 60-years-old from 2001 to 2010 were collected for this study. Cox proportional-hazards (PH) regression and logistic regression were calculated to odds ratio and hazard ratio for mortality of those patients and compared it. Data mining algorithms were also used to generate a risk stratification prediction model. The first-year mortality rate of the overall study group was 21.5% in 2001 and 15.0% in 2010 (\(p\) for trend \(<\) 0.001). In the male subgroup, the first-year mortality rate was 29.3% in 2001 and decreased to 17.3% in 2010; the trend of standardized mortality ratio was significantly decreased from 4.4 to 2.6 (\(p\) for trend \(<\) 0.001). By logistic regression, mortality significantly increased with age and male gender. Furthermore, gender, age, patients with diabetes mellitus (DM), cardiovascular (CV), and renal comorbidity, and surgical intervention can be variables for constructing the risk stratification model. The findings of the study will be used for helping related field physicians to predict the prognosis risk of hip fracture patients, and provide evidence-based tailored treatment recommendations for those patients. It may consider to build various models for predicting the prognosis of hip fracture or integrating prediction algorithms into the computerized physician order entry system, thus creating a practical clinical decision support system with warning functions.  
Keywords:hip-fracture; decision tree; elderly; first-year mortality; national population database +
Footnote †: journal: Medical Informatics
## 6 Results and Discussion
### Dependence on Labelled Samples  
The CAE_CNN model structure is simple and requires fewer labelled samples. To verify that the CAE_CNN model can decrease the dependence of labelled samples, we trained the network using the same number of iterations by reducing the number of training set samples gradually and applied the classification to the same testing set. The results are shown in Figure 9.  
As shown in Figure 9, the CAE_CNN model can still achieve a classification accuracy above 0.93 when the number of training sets is reduced to 1000, and the accuracy remains higher than the  
Figure 8: Loss function values during model training.  
classification accuracy of the Best_CNN model, even while the training set of the Best_CNN model has 2000 samples. The results show that the CAE_CNN model can effectively reduce the use of manually labelled samples and thus the cost of manually marking samples.  
As shown in Table 5, the number of parameters utilized drops from 5780 in the Best_CNN model to 4247 in the CAE_CNN model, while the number of parameters in the fully-connected layer remains similar. The number of parameters in the convolution layer decreases from 2895 to 1242 because the number of convolutional layers is reduced from 8 to 3; that is to say, the CAE_CNN model needs fewer parameters to extract features, so the CAE_CNN model can reduce its dependence on the number of labelled samples.  
In summary, the hybrid deep neural network model is able to achieve a higher efficiency and can significantly improve the accuracy of remote sensing image classification by taking advantage of a simpler structure. Furthermore, the number of labelled samples required is greatly reduced, which suggests that the interference of the subjectivity inherent in experts' domain classification on the classification accuracy can be overcome to some extent.
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 2 Background  
### Security and Privacy Overview  
In the last twenty years, the potential applications of IoT have been steadily multiplying across various sectors paving the way for new business prospects [2; 37; 38]. Yet, the emergence of IoT has simultaneously presented manufacturers and consumers with new challenges [2; 3; 39]. One of the principal challenges lies in safeguarding the security and privacy of both the IoT objects and the data they produce. Ensuring the security of IoT networks is a complicated and arduous task due to the inherent intricacies within the IoT network characterized by the interconnection of multiple heterogeneous devices from different locations and exchanging information with each other through various network technologies. As a result, IoT systems are notably vulnerable to privacy and security threats.  
Before delving into those security threats in the IoT landscape, it is pivotal to explore its security and privacy features. Overlooking these security measures can introduce vulnerabilities into the framework. Through a thorough review of the literature on IoT security [40; 41; 42; 43], these features have been pinpointed. Figure 3 encapsulates the key security and privacy features of the IoT infrastructure.  
Traditional security methods, which employ a predefined set of strategies and rules, have exhibited several drawbacks when implementing specific features. They often overlook new varieties of attacks and are restricted to pinpointing certain types of threats. Hence, the emergence of advanced security solutions such as solutions powered by artificial intelligence. The utilization of ML algorithms has the potential to offer security solutions for IoT networks, ultimately improving their reliability and accessibility. ML-based security models can process large amounts of data in real time and continuously learn from generated training and test data, which increases their accuracy as well as enables them to proactively anticipate new attacks by drawing insights from previous incidents. Our survey will limit the study to contemporary research on the vulnerability of three ML-based IoT security systems: Intrusion Detection System (IDS), Malware Detection System (MDS), and Device Identification System (DIS).
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
###### Abstract  
In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder-decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder-decoder model structure. However, conventional models have large sizes and slow computation speed and, in vision transformer models, the computation amount sharply increases with the image size. To overcome these problems, this paper proposes a model that combines Swin transformer blocks and a lightweight U-Net type model that has an HarDNet blocks-based encoder-decoder structure. To maintain the features of the hierarchical transformer and shifted-windows approach of the Swin transformer model, the Swin transformer is used in the first skip connection layer of the encoder instead of in the encoder-decoder bottleneck. The proposed model, called STHarDNet, was evaluated by separating the anatomical tracings of lesions after stroke (ATLAS) dataset, which comprises 229 TI-weighted MRI images, into training and validation datasets. It achieved Dice, IoU, precision, and recall values of 0.5547, 0.4185, 0.6764, and 0.5286, respectively, which are better than those of the state-of-the-art models U-Net, SegNet, PSPNet, FCHarDNet, TransHarDNet, Swin Transformer, Swin UNet, X-Net, and D-UNet. Thus, STHarDNet improves the accuracy and speed of MRI image-based stroke diagnosis.  
ATLAS; HarDNet; Swin transformer; segmentation; U-Net +
Footnote †: journal: Applied  
19 November 2021  
Accepted: 1 January 2022  
Published: 4 January 2022
## 3 Algorithm Implementation
### Watermark Extraction  
The process of watermark information detection is illustrated in Figure 5.  
Watermark extraction is the inverse process of watermark embedding. Firstly, the BIM data are preprocessed as described in Section 3.2 to obtain the sets of movable elements \(S_{1}\) and immovable elements \(S_{2}\). The watermark information is then extracted based on the local features of the BIM data. The extraction steps are as follows:  
**Step 1**: The elements are iterated in the set \(S_{1}\), and the coordinate position carrying the watermark information is obtained based on _Key_. The watermark information is extracted using Equation (7), \(I_{Index_{i}}^{\prime}\) is updated with the detected watermark value, and the number of times the watermark value is written at that location is increased.  
\[I_{Index_{i}}^{\prime}=\left\{\begin{array}{l}0+I_{Index_{i}},\mod(X_{i} \times 10^{k},2)=0\\ 1+I_{Index_{i}},\mod(X_{i}\times 10^{k},2)=1\end{array}\right. \tag{7}\]  
where \(Index_{i}\) is the index of the sequence corresponding to the element, and \(I_{Index_{i}}\) and \(I_{Index_{i}}^{\prime}\) are the number of times this location maps a watermark value of 1 before and after the update, respectively. \(X_{i}\) denotes the coordinates with watermark information, and \(k\) is the watermark information embedding location.  
**Step 2**: The invisible characters \(U\) and the attributes of the elements carrying watermark information are obtained based on _Key_. Then, the elements are iterated in the set \(S_{2}\), and the watermark information is extracted using Equation (8). The detected watermark value updates \(I^{\prime}_{Index_{i}}\) and increases the number of times the watermark value is written at that location is increased.  
\[I^{\prime}_{Index_{j}}=\begin{cases}0+I_{Index_{j}},C_{j}\;containsU\\ 1+I_{Index_{j}},C_{j}\;containsU\end{cases} \tag{8}\]  
where \(Index_{i}\) is the index of the sequence corresponding to the element, and \(I_{Index_{i}}\) and \(I^{\prime}_{Index_{i}}\) are the number of times this location maps the invisible character \(U\) before and after the update, respectively.  
**Step 3**: Based on the majority principle, all watermark bits are traversed and the watermark information is updated using Equation (9) to obtain the watermark sequence \(I\). Then, the one-dimensional watermark sequence \(I\) is dimensioned and inverted to obtain the BIM data watermark information image.  
\[I_{i}=\begin{cases}0,\frac{I_{i}}{Sum_{i}}>0.5\\ 1,\frac{I_{i}}{Sum_{i}}\leq 0.5\end{cases} \tag{9}\]  
where \(i(i=1,2,3\ldots)\) represents the index of a watermark sequence and \(Sum_{i}\) denotes the number of times the watermark information is written to this index of sequence \(I\)  
Figure 5: The watermark information extracting process.  
Here, the majority principle is used to judge the watermarking bit and watermarking information, making it resistant to changes in the spatial location or attribute information of some elements.
## 3 Application of Uncertainty Quantification Methods and Results  
### Application Outline  
#### 3.1.1 Data  
Table 1 provides the descriptions of the weather radar precipitation cases used in this study; the application area covers the entire Korean Peninsula. Table 1a lists details of the 11 single-polarization weather radars with maximum scan ranges of 200 km (C-band) and 240 km (S-band), and gate size of 0.25 km, operated by the Korea Meteorological Administration (KMA). It is possible to monitor the entire Korean Peninsula and nearby seas areas using the merged coverage of the 11 radar scan ranges. As planned, all 11 weather radars have recently been replaced by dual-polarization radars, which are currently undergoing test-operations. Table 1b lists details of the 18 precipitation events that occurred in summer (Changma, typhoons, and local precipitation) from June to September 2012-2016.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & \\ \hline
**Item** & & & & \\ \hline \multicolumn{5}{c}{11 single-polarization radars operated by the Korea Meteorological Administration} \\ Radar &
\begin{tabular}{c} Baegryeonko (BRL S-band), Kvanakshan (GWK, Sband), Oeersman (RSN-S-band), Jindo (INL, S-band), Cosan (GSN, S-band), \\ Seongan (SSP; S-band), Gudeeksan (SSN, S-band), Josephson (MYN, C-band), Gangueung (GNG, S-band), GWnagdeoksan (GDK, S-band), [PERSON] (IIA, C-band) \\ \hline Calibration & & & & \\ data & & & & \\ \hline \multicolumn{5}{c}{18 Precipitation cases from 1 June 2012 to 31 September 2016} \\ \multicolumn{5}{c}{(b) Precipitation cases (* represents the observed spatial-averaged precipitation intensity from automatic weather stations (AWSs)). LST: local} \\ \hline
## 5 Simulation Results and Discussion
### Simulations and Analysis Under Different Circumstances  
For the full testing of the effectiveness of the proposed adaptive wide-area damping control scheme, the following two representative disturbances are considered:  
Case A: large disturbance--at t of 10 s, a three-phase short-circuit fault occurs at Bus 7, and the fault is removed at 10.1 s.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Modulation Type** & **Channel** & **Gain** & **T\({}_{1}\)** & **T\({}_{2}\)** & **T\({}_{3}\)** & **T\({}_{4}\)** \\ \hline \multirow{2}{*}{DCPM} & active & 10.2 & 0.079 & 0.22 & 0.081 & 0.21 \\  & reactive & 48.3 & 0.081 & 0.21 & 0 & 0 \\  & active & 50 & 0.078 & 0.21 & 0.081 & 0.21 \\  & reactive & 50 & 0.081 & 0.21 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of different modulation damping controller. DCPM: dual-channel power modulation; APM: active power modulation; RPM:  
Figure 5: The simulation diagram of 2-area 4-machine system with DFIG wind farm.  
Case B: small disturbance--the active power load at Bus 6 is increased by 20% at t of 10 s and then restored back to the original value at t of 11 s.  
The system has several oscillation modes: (1) local electromechanical oscillations (1.2 Hz) due to one synchronous machine swinging against a local area; (2) the inter-area mode (0.65 Hz) due to Area 1 swinging against Area 2; and (3) the wind turbine shaft mode (1.82 Hz).
## 2 Methods
### Practical OTA-Based Gyrator
#### 2.2.3 Dynamic Properties  
Next to the non-ideal frequency analysis, it is also important to investigate the dynamic properties of the gyrator such that the amplifiers operate in linear mode and the maximum voltage and current values of their terminals are not exceeded. As far as common OTAs are concerned, the most limiting is the non-linearity of their input transistor stage, which takes effect already at differential input voltages of several tens of millivolts. Let us denote the maximum allowed amplitude of OTA input voltage for linear operation \(V_{\text{OTA,MAX}}\). The output drive current of OTA is also limited to a maximum value \(I_{\text{OTA,MAX}}\); whereas, it holds \(I_{\text{OTA,MAX}}=g_{\text{m}}V_{\text{OTA,MAX}}\). The value of \(V_{\text{OTA,MAX}}\), and also maximum allowable transconductance \(g_{\text{m}}\) can be determined from the respective OTA documentation. The following two conditions must be met to avoid the non-linear operation of the gyrator:  
* The gyrator input voltage \(V_{\text{IN}}\) must not be higher than the allowed input voltage range of OTA\({}_{1}\)\(V_{\text{OTA,MAX}}\) \[V_{\text{IN}}\leq V_{\text{OTA,MAX}}.\] (13)  
Figure 6: Circuit with grounded negative conductance at terminal COMP.  
* The second condition for \(V_{\text{IN}}\) provides that the voltage swing across the gyrator loading admittance \(Y\) does not exceed the maximum possible OTA\({}_{2}\) input voltage \(V_{\text{OTA2,MAX}}\) \[V_{\text{IN}}\leq\frac{g_{\text{m2}}V_{\text{OTA2,MAX}}}{|Y_{\text{IN}}|}.\] (14)  
Apparently, the allowed \(V_{\text{IN}}\) according to (14) changes with frequency. It reaches the minimum value at the lowest operating frequency where \(\mid Y_{\text{IN}}\mid\) is maximal. It can be concluded that the gyrator input voltage \(V_{\text{IN}}\) must meet both the conditions (13) and (14) simultaneously when the linear operation of both OTAs is required. If the conditions for maximum gyrator input current \(I_{\text{IN}}\) are required, they can be easily derived from (13) and (14) by substituting \(V_{\text{IN}}=I_{\text{IN}}/\mid Y_{\text{IN}}\mid\).