esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 4 Classification Algorithms
### Artificial Neural Networks (ANN)
The results of the classifier were verified using the confusion matrix, providing a means to double-check its performance. Support Vector Data Description (SVDD), which is a single-class Support Vector Machine, was utilized to categorize the data without relying on linearity and separability. Several enhancements were proposed to improve the accuracy and training speed of the traditional SVDD. The classification results obtained were then used to train an Artificial Neural Network (ANN), which was utilized for data categorization. This approach did not affect the classification accuracy but significantly improved the processing speed. To form a classifier ensemble, six different kernels were employed, and the dataset was subjected to an ANN-based voting method for classification. Overall, the research introduced an ANN-based Support Vector Data Description approach for anomaly detection in hybrid electric vehicles. The combination of SVDD, ANN, and classifier ensembles provided improved accuracy and processing speed for categorizing data. Ref. [16] proposed a wireless-sensor-network-based anomaly detection system for smart cities. Smart cities heavily rely on the Internet of Things (IoT), a relatively new technology that enables detailed monitoring of various physical products and environments through low-cost, low-power sensor and communication technologies. Although the IoT has gained significant interest for smart cities, there are limited systematic studies demonstrating how valuable insights can be extracted from real-time IoT data using advanced data analytics techniques like anomaly detection. In this research, the authors conducted a case study in a smart environment using real-time data collected by the city of Aarhus, Denmark. They specifically utilized the Air Quality Index (AQI) to investigate and analyze the levels of different contaminants in the air, aiming to identify potentially hazardous or anomalous areas. To achieve this, they employed a machine learning architecture that incorporated a neural network, a Neuro-Fuzzy approach, and Support Vector Machines. These techniques were applied to a pollution database to detect anomalous locations, addressing both binary and multiclass classification problems. Based on the MATLAB simulation results, the machine learning algorithms demonstrated reliability in terms of accuracy and computation time for smart city applications. This indicates their effectiveness in identifying anomalies and highlighting potentially problematic areas in pollution data.
## 3 A Worked Example
### Results
\begin{table}
\begin{tabular}{l r r r r r} \hline Parameter & \(QQ\) & \multicolumn{2}{c}{\(Qq\)} & \(qq\) \\ \hline \multicolumn{5}{c}{**Chromosome 6**} \\ Location, cM from the first marker & 82.68 (67.77, 92.96) & & \\ \(\alpha\) & 36.09 (35.20,37.04) & 34.94 (34.36,35.52) & 33.12 (32.36,33.93) \\ \(\beta\) & 11.93 (11.44,12.45) & 11.58 (11.16,12.03) & 11.07 (10.65,11.51) \\ \(\gamma\) & 0.65 (0.64, 0.66) & 0.65 (0.64, 0.66) & 0.65 (0.64, 0.67) \\ \multicolumn{5}{c}{**Chromosome 7**} \\ Location, cM from the first marker & 46.84 (38.80,56.02) & & \\ \(\alpha\) & 36.55 (35.50, 37.73) & 35.61 (34.56,36.50) & 33.38 (32.54,34.33) \\ \(\beta\) & 11.83 (11.43, 12.34) & 11.27 (10.90, 11.73) & 11.25 (10.76, 11.70) \\ \(\gamma\) & 0.65 (0.63, 0.66) & 0.64 (0.63, 0.65) & 0.65 (0.63, 0.66) \\ \multicolumn{5}{c}{**Chromosome 10**} \\ Location, cM from the first marker & 77.78 (68.75,80.96) & & \\ \(\alpha\) & 35.41 (34.33, 36.52) & 34.71 (33.67,35.70) & 33.59 (32.61,34.42) \\ \(\beta\) & 11.67 (11.23, 12.19) & 11.47 (11.23, 11.81) & 11.01 (10.61, 11.44) \\ \(\gamma\) & 0.65 (0.64, 0.66) & 0.64 (0.63, 0.66) & 0.65 (0.63, 0.66) \\ \hline \end{tabular}
\end{table} 
Table 1: Bayesian estimates of QTL locations and genotype-specific growth curves for the QTLs detected on mouse chromosome 6, 7 and 10. Numbers in parentheses are the \(95\%\) equal-tail confidence intervals.
However, in order to convexify the true VU definition, a minor relaxation of the definition is required. Most of the articles employed nominal voltage value as a substitute for positive sequence voltage. It is a decent relaxation due to safer overestimation of the VU, and is a justified sacrifice to reach convex formulation. However, in real life, the positive sequence voltage fluctuates and leaves varying margin for VU. Instead, the utilization of measured positive sequence voltage would provide slightly more accurate relaxation of the VU definition. In the light of aforementioned concerns, this paper presents a convex VU optimization method that is paired with the Monte Carlo simulation. The model is based on bus injection transfer impedance model, and as in [3], does not require load flow calculations, making the presented approach fast and suitable for repetitive calculations. The convexity of presented VU definition is achieved by substituting positive sequence voltage by corresponding measured weekly data. Furthermore, VU calculation incorporates 5% violation tolerance during a week on 10-minute resolution. Moreover, the effect of background VU fluctuation on the HC is analyzed with respect to fixed and variable phase selection of single-phase PV plants. The hosting capacity of this article is found solely with respect to VU. The rest of the paper is structured as follows. Data utilized and its modification is explained in Section 2. Monte Carlo simulation is described in Section 3. Initial formulation of the problem and its convex reformulation are presented in Sections 4 and 5. Results of the simulations are shown in Section 6, followed by conclusions in Section 7.
## 6 Actor-Critic Methods
### Asynchronous Advantage Actor-Critic (A3C) Method
#### 6.2.2. A3C Algorithm for Robot Navigation in Unknown Rough Terrain  
This representation is passed through an LSTM layer to capture temporal dependencies in the robot's navigation states. The output from the LSTM layer is used to compute the policy \(\pi(a_{t}|s_{t};\theta)\) and the value \(V(s_{t};\theta_{v})\). The policy network computes the probability distribution over navigation actions \(a_{t}\), including moving forward, backward, or turning left or right. The policy is updated using the A3C loss function:  
\[\Delta\theta=\
abla_{\theta}\log\pi(a_{t}|s_{t};\theta)A(s_{t},a_{t};\theta_{ v})+\zeta\
abla_{\theta}H(\pi(s_{t};\theta)), \tag{110}\]  
where \(A(s_{t},a_{t};\theta_{v})\) is the advantage function, computed as follows:  
\[A(s_{t},a_{t};\theta_{v})=\sum_{i=0}^{n-1}\Gamma^{i}r_{t+i}+\Gamma^{n}V(s_{t+n };\theta_{v})-V(s_{t};\theta_{v}), \tag{111}\]  
and \(H(\pi(s_{t};\theta))\) is the entropy of the policy, encouraging exploration, with \(\zeta\) as a scaling factor.  
The value network minimizes the squared advantage:  
\[\Delta\theta_{v}=A(s_{t},a_{t};\theta_{v})\
abla_{\theta_{v}}V(s_{t};\theta_{ v}). \tag{112}\]  
The reward function is tailored to the rough terrain navigation task. It assigns positive rewards for reaching the goal within a certain tolerance and penalizes undesirable states such as collisions or exceeding the maximum steps per episode:  
Figure 6: A block diagram showing the process flow of the method proposed in [34].  
\[r_{t}=\begin{cases}\mu\frac{d_{\min}-d_{t}}{\Delta t},&\text{if }d_{t}<d_{\min},\\ 0.5+r_{v},&\text{if }d_{t}\leq d_{g},\\ -0.5,&\text{if in }s_{\text{tu}},\\ 0,&\text{otherwise}.\end{cases} \tag{113}\]  
Here, \(d_{t}\) is the distance to the goal at time \(t\), \(d_{\min}\) is the closest distance observed so far, \(d_{g}\) is the goal tolerance, and \(s_{\text{tu}}\) represents undesirable terminal states like flipping or getting stuck.  
The network is trained in a simulated environment with high-dimensional sensory inputs. Nine agents operate in parallel, each updating the global policy and value functions asynchronously to accelerate convergence. The block diagram shown in Figure 7 visualizes how architecture extends A3C for navigation in unknown rough terrain by incorporating multiple input branches for depth images, elevation maps, and 3D orientation. These inputs are processed through separate branches to extract features (\(f_{d}\), \(f_{e}\), \(f_{o}\)) which are merged and concatenated to form a unified representation (\(f_{\text{total}}\)). Temporal dependencies are captured using an LSTM layer, and the outputs are used by the policy and value networks to compute navigation actions.
## Factors Influencing the Modulation Bandwidth
### Quantum Confined Stark Effect and Carrier Localization Effect
The optical power for LEDs with QW thickness of 2, 3, 5 nm has also been calculated. Due to the fact that thinner QW has a higher carrier density and more electron-hole wave functions are overlapped by QCSE, the optical power of 2 nm QW LED at a low current density is greater than that of 3 and 5 nm QW LED, resulting in an improvement in the radiative recombination rate and optical power. However, at higher current density of 3 nm QW LED has the largest optical power. The reduction of power in 2 nm QW LED at a high current density is due to Auger recombination and carrier overflow, but for 5 nm QW LED, the reduction can be explained with reduced carrier localization effects leading to Shockley-Read-Hall (SRH) recombination. Figure 1b displays the external quantum efficiency (EQE) for LEDs with different QW thickness. It can be seen from the figure that the efficiency droop effect for 5 nm QW LED is very small compared with 2 nm and 3 nm QW LEDs, which is consistent with the fact that 5 nm QW has a low carrier density in the individual QW, resulting in low Auger recombination or carrier overflow. Figure 1c describes the 3-dB modulation bandwidths for LEDs with different QW thicknesses of 2, 3 and 5 nm, and for certain QW thickness, the modulation bandwidths are almost similar at the same current density, suggesting that the modulation bandwidth is constrained by the carrier lifetime. To further confirm this, using Current-Voltage (I-V) and Capacitance-Voltage (C-V) characteristics, the RC-limited bandwidths have been calculated, i.e., 4111 MHz, 804 MHz, and 992 MHz for LEDs with 2 m, 3 nm, and 5 nm QW thickness, respectively. This calculated bandwidth is much larger than the optical modulation bandwidth and further confirms that the lifetime of the carrier is affecting the modulation bandwidth. It has been found that at 425 A/cm\({}^{2}\), the modulation bandwidth for 5 nm QW LED reaches 700 MHz, which is much larger than that of 2 and 3 nm QW LEDs, and at such a low current density, it is also the highest modulation bandwidth. QCSE cannot explain this since the polarization field in this current density region has already become weak, as there is a decrease in the carrier localization effect, leading to an increase in carrier lifetime. As discussed above, the localization effect for 2 nm QW LED is the largest, but with increasing QW thickness, the localization effects decrease, which will reduce carrier lifetime and increase modulation bandwidth. It can be concluded that QCSE dominates the carrier recombination at lower current density, and, therefore, the rate of radiative recombination decreases. However, with an increase in current density, QCSE becomes less effective, thus increasing optical power and modulation bandwidth. In addition, weaker localization effects will result in a high bandwidth modulation.
## 4 Simulation and Experimental Results
### Experiment  
#### 4.2.1 System Setup  
Figure 13 is the operation flowchart of the hardware system. The solid and dashed boxes represent hardware and software components, respectively. As we can see, the  
Figure 12: Simulated detection probabilities for different signals.  
system consists of two universal software radio peripherals (USRPs), a rubidium atomic clock, transmitting and receiving terminals (Tx & Rx computers), an RF attenuator, and a low noise amplifier (LNA). The workflow of the hardware system is as follows. First, the software signal generator generates the discrete-time baseband transmitted signal files and stores them in the Tx computer. The USRP hardware driver (UHD) reads the file and transmits it to the USRP via a network cable; the motherboard and daughterboard in the USRP perform digital-to-analog conversion and up-conversion, respectively. Another USRP receives the RF signals which pass through the RF attenuator and the LNA. The UHD in the Rx computer saves the down-converted and analog-to-digital converted signals as discrete-time signal files via a network cable. Finally, the software receiver post-processes the saved signal file. The RF attenuator can be adjusted to obtain the desired \(C/N_{0}\). The rubidium atomic clock provides a high-precision frequency reference for the two USRPs to eliminate the frequency offset between the received and transmitted signals. A photograph of the hardware system based on SDR is shown in Figure 14.  
The carrier frequency of the RF frequency is 2 GHz. The master clock rate of USRP N210 is 100 MHz, and the sampling rate supported is limited to dividing the master clock rate by an integer factor. Therefore, the sampling rate is set to 20 MHz, the delay bins \(M=1023\), and the Doppler bins \(N=10\); thus, the duty cycle of the Pulse-OTFS and Pulse-BPSK is set to 0.1.  
We have developed software receivers for Pulse-OTFS(10,1023), OTFS(10,1023), Pulse-BPSK(5), and BPSK(5). They use the same tracking loop as the traditional navigation receivers. The difference is only in the local replica generation. Due to the short period of the pulsed signals, the closed-loop estimator is still able to track the signals stably. The \(C/N_{0}\) setting for the received signal is 25 dB-Hz to 45 dB-Hz (10 ms coherent integration time) and 35 dB-Hz to 55 dB-Hz (1 ms coherent integration time). The interval is 5 dB-Hz.  
Figure 14: Photograph of the experimental platform.  
Figure 13: Flow chart of the experimental platform.  
The equivalent noise bandwidth of DLL is 1 Hz, the early-late spacing of the correlator is 1 chip, and the phase lock loop (PLL) bandwidth is 10 Hz.  
The experimental parameters are listed in Table 4. The first five items are signal parameters, the last four items are software receiver configuration parameters.
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 2 Related Work
### Graph Neural Networks  
Graph Neural Networks (GNNs) extend the thought of deep learning to data with graph structure, and they have been successfully applied in various applications such as node classification [18], graph classification [19], traffic prediction [20], and recommendation systems [21]. Generally, GNNs can be unified under the message-passing framework, which propagates information among nodes and their neighbors and then updates node representations by aggregating the received messages. GNNs can be categorized as spectral methods or spatial methods. GCN [4] is one of the most popular spectral methods and simplifies the operation of graph Laplacian smoothing by proposing a localized first-order approximation. GraphSAGE [5] is a pioneer of spatial methods that makes GNNs inductive by introducing neighbor sampling strategies and utilizes flexible aggregating functions. GAT [6] based on the spatial domain adopts the attention mechanism to adaptively assign different importance to one-hop neighbors and get weighted aggregation of node features. However, most existing GNNs are only suitable for homogeneous graphs and cannot deal with the complexity of various types of nodes and edges in heterogeneous graphs.
## 3 System Model
### Implementation
#### 3.4.2 Handover Triggering Model Using UE-Cell Center Distance and Elevation Angle  
In the case of the handover triggering prediction model, the model was trained by labeling the overlapping area of the cells on both sides as a region where handover is possible based on the cell center distance. The classification was performed using XGBClassifier, and hyperparameter values were adjusted using hyperopt. Table 14 shows the variables and ranges for XGBOOST hyperparameter tuning.
## 3 Results and Discussion
### Performance Evaluation Results
#### 3.2.6 Underwater Loss Resistance  
The value of the test result of underwater loss resistance was calculated by the formula in Equation (2):  
\[\text{Mass change rate}\left(\%\right)=\frac{(b-c)-(a-c)}{(a-c)}\times 100 \tag{2}\]  
where N is the nonvolatile content (%); \(W_{s}\) is the sample weight before drying (g); and \(W_{d}\) is the mass in the dry condition (dish \(+\) nonwoven fabric \(+\) rubber band) (g).  
The results of the underwater loss resistance test showed that samples A, B, and C could secure higher performances than the quality standard, as shown in Figure 11. In the case of samples B and C, there was a slight decrease in the mass change rate, but the samples were not separated and lost and no suspended matter was identified. Thus, it was confirmed that they can be applied not only to ground structures but also as water leakage repair materials for underground structures by securing a performance that is not separated or lost, even in an underwater environment where a flow rate is generated [21; 22].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Test Item**} & **Waste Oil/Waste** & \multirow{2}{*}{**Test Result**} & \multirow{2}{*}{**Quality Standard**} \\  & **Rubber (Weight Ratio)** & & \\ \hline \multirow{6}{*}{Structural responsiveness} & \multirow{2}{*}{
\begin{tabular}{c} Sample A \\ (2:1) \\ \end{tabular} } & No Leakage & \\ \cline{2-3}  & & No Leakage & \\ \cline{2-3}  & & No Leakage & \\ \cline{2-3}  & & No Leakage & \\ \cline{2-3}  & & No Leakage & \\ \cline{2-3}  & & Leakage & \\ \cline{2-3}  & & Leakage & \\ \cline{2-3}  & & Leakage & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Structural behavior responsiveness by sample of W-SPRG.  
Figure 11: Underwater loss resistance by sample of W-SPRG.
## 4 Results and Discussion  
### Leakage Prediction  
#### 4.1.1 Influence of GMB Thickness, Head Loss, and Cut-Off Wall Hydraulic Conductivity on Leakage  
Increasing the GMB thickness and decreasing the head loss and cut-off wall hydraulic conductivity can decrease the leakage (Figure 4). For example, the leakage for \(T_{G}\) = 0.002 m is 1.52 \(\times\) 10\({}^{-10}\) m\({}^{3}\)/s while it for \(T_{G}\) = 0.001 m is 1.85 \(\times\) 10\({}^{-10}\) m\({}^{3}\)/s when \(k_{G}\) = 1 \(\times\) 10\({}^{-9}\) m/s, \(r_{G}\) = 0.005 m, and \(H_{G}\) = 10 m. The values of the leakage and head loss are linearly related. For example, increasing the \(H_{G}\) from 1 m to 10 m leads to a 10-time increase in the leakage. Experiments conducted by [PERSON] and [PERSON] [25] have also proved this conclusion. Additionally, it can be concluded that changing the \(T_{G}\) has an insignificant influence on the leakage when the \(k_{G}\) is small. For example, the leakage for \(T_{G}\) = 0.002 m is 1.22 times smaller than that for \(T_{G}\) = 0.001 m when \(k_{G}\) = 1 \(\times\) 10\({}^{-9}\) m/s, \(r_{G}\) = 0.005 m. However, the difference decreases to 1.02 times when \(k_{G}\) = 1 \(\times\) 10\({}^{-11}\) m/s. This is mainly because the SBCWs are the dominant parts to prevent leakage, though increasing the GMB thickness can lead to a decrease in the hydraulic gradient [25].
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Long Range (LoRa)  
The long range (LoRa), as the name implies, is a proprietary long-range wide area wireless communication technology with a coverage range of up to 3 km in suburban areas with dense residential dwellings, as evaluated in [93]. LoRa operates in the unlicensed ISM band and is a physical layer protocol, suitable for low power, low throughput, and long-range network applications, like the IoT. It is based on the LoRaWAN, an open standard media access control (MAC) layer protocol that adds a network layer for network congestion handling between the end and central nodes [10]. LoRa utilizes the star-to-star network topology, in which the packets are forwarded by the gateways from the nodes to the network server. The chip spread spectrum (CSS) protocol is used for modulation with different bandwidths, which spread the signal over a wider channel bandwidth. The utilization of CSS reduces the interference from other signals, protects the signal against jamming, and cancels out multipathing and fading effects [10]. Moreover, the number of chirps determine the spreading factor, i.e., a high spreading factor achieves long distances and low data rates, whereas a low spreading factor shorter distances with high data rates. LoRa can achieve localization with TDoA and ToF measurements, due to the higher bandwidth provided [6].
# Wind Load Distribution in Float Photovoltaic System
## 3 Proposed Modeling Strategy  
### Analysis and Design of PV Power Plants  
In designing the foundation of a PV power plant, it is necessary to consider the structural behavior of the power plant and the effects of the loads applied to it. In this paper, we investigated the design and load derivation methods for PV power plants through relevant design criteria and the existing research literature. In addition, the load effects required for the design of the foundation of the PV power plant were predicted.  
The structural design of floating solar power plants in Korea is derived through the method proposed by the American Society of Civil Engineers (ASCE) provisions 7-16 and KDS 41 10 15 [8; 9]. In the design of solar PV power plants, wind loads, snow loads, and live loads are considered and applied to the structural design, and structural safety is not evaluated independently from these loads but through load combinations for independently derived loads to consider all possible load effects. The load combinations proposed by ASCE 7-16 and KDS 41 10 15 are given in Table 1 [8; 9].  
The seismic, rainfall, and temperature loads are not considered in the design of PV power plants as lightweight structures, and the load effects of static, live, snow, wind, and fluid pressure are considered. In addition, fluid pressure is considered for floating PV power plants and not for ground PV power plants. Fluid loads in floating PV are mostly predicted by flow analysis, and there is no simple way to evaluate these loads. As the range of variables for the size of the plant, wave conditions, etc., is very large, it is not included in the scope of this research.