esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Several factors significantly affect the drying shrinkage of foamed concrete, including the water:binder ratio, foam volume, and foam agent type [16; 23]. The addition of lightweight aggregates is considered to be an efficient method to reduce drying shrinkage [27]. [PERSON] et al. [16] examined the effects of different filler materials on the shrinkage behavior of foamed concrete. They concluded that, as the filler:cement ratio increases, the shrinkage drops significantly due to the restraining influence of the increased aggregate content. Foam volume and cement paste characteristics are important factors influencing the drying shrinkage of foamed concrete [23]. The addition of fibers improves the mechanical properties of foamed concrete and reduces its drying shrinkage [28; 29]. Moreover, the incorporation of mineral admixtures also affects concrete shrinkage significantly [30]. The drying shrinkage mainly depends on the type offine material used; it increases with slag content and can be reduced with the use of fly ash [31; 32]. Silica fume mixes suffer from cracking, which can be reduced significantly through the incorporation of polypropylene fibers. Foamed concretes with a density lower than 600 kg/m\({}^{3}\) are being developed and applied in various applications. However, at a low density below 500 kg/m\({}^{3}\), they tend to be unstable in the fresh state and exhibit high drying shrinkage in the hardened state. Lightweight concrete with a density of 400 kg/m\({}^{3}\) has been produced through a combination of expanded polystyrene and foam [33]. [PERSON] et al. [34] developed foamed concrete with a very low density (<300 kg/m\({}^{3}\)). [PERSON] et al. [35] reported that the addition of chemical stabilizing admixtures improves concrete stability and reduces the collapsing of foam bubbles. Foamed concrete with a density lower than 500 kg/m\({}^{3}\) has been developed, but more investigation is needed to optimize its characteristics. The target of this research is therefore to study the properties of low-density foamed concrete. As such, several foamed concretes were prepared and tested to investigate the influence of various parameters on the properties of foamed concrete, including the incorporation of fine lightweight aggregates and the addition of fly ash as a partial cement replacement material.
# A Review of Time-Scale Modification of Music Signals +
## 5 TSM Based on the Phase Vocoder (PV-TSM)
### Pv-Tsm  
The principle of PV-TSM is visualized in Figure 9. Given an input audio signal \(x\), the first step of PV-TSM is to compute the STFT \(X\). Figure 9a depicts the two successive frequency spectra of the \(mth\) and \((m+1)th\) analysis frames, denoted by \(X(m)\) and \(X(m+1)\), respectively (here, a frame's Fourier spectrum is visualized as a set of sinusoidals, representing the sinusoidal components that are captured in the time-frequency bins). Our goal is to compute a modified STFT \(X^{\text{Mod}}\) with adjusted phases \(\varphi^{\text{Mod}}\) from which we can reconstruct a time-scale modified signal without phase jump artifacts:  
\[X^{\text{Mod}}(m,k)=\left|X(m,k)\right|\ \exp(2\pi i\ \varphi^{\text{Mod}}(m,k)). \tag{25}\]  
We compute the adjusted phases \(\varphi^{\text{Mod}}\) in an iterative process that is known as phase propagation. Assume that the phases of the \(mth\) frame have already been modified (see the red sinusoid's phase in Figure 9b being different from its phase in Figure 9a). As indicated by Figure 9b, overlapping the \(mth\) and \((m+1)th\) frame at the synthesis hopsize \(H_{\text{s}}\) may lead to phase jumps. Knowing the instantaneous frequencies \(F_{\text{coef}}^{\text{IF}}\) derived by the phase vocoder, we can predict the phases of the sinusoidal components in the \(mth\) frame after a time interval corresponding to \(H_{\text{s}}\) samples. To this end, we set \(\varphi_{1}=\varphi^{\text{Mod}}(m,k)\), \(\omega=F_{\text{coef}}^{\text{IF}}(m,k)\), and \(\Delta t=H_{\text{s}}/F_{\text{s}}\) (the synthesis hopsize given in seconds) in Equation (20). This allows us to replace the phases of the \((m+1)th\) frame with the predicted phase:  
\[\varphi^{\text{Mod}}(m+1,k)=\varphi^{\text{Mod}}(m,k)+F_{\text{coef}}^{ \text{IF}}(m,k)\frac{H_{\text{s}}}{F_{\text{s}}} \tag{26}\]  
for \(k\in[0:N-1]\). Assuming that the estimates of the instantaneous frequencies \(F_{\text{coef}}^{\text{IF}}\) are correct, there are no phase jumps any more when overlapping the modified spectra at the synthesis hopsize \(H_{\text{s}}\) (Figure 9c). In practice, we start the iterative phase propagation with the frame index \(m=0\) and set  
\[\varphi^{\text{Mod}}(0,k)=\varphi(0,k)\, \tag{27}\]for all \(k\in[0:N-1]\). Finally, the output signal \(y\) can be computed using the signal reconstruction procedure described in Section 5.2 (Figure 9d).
# An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data
## 2 Literature Review
A hand detection and user determination step were used first to demonstrate a comprehensive system, followed by a real-time spatiotemporal descriptor and gesture classification technique. A collection of 19 gestures was divided into four subgroups for use in diverse interactive applications. The dataset used in the study presented challenges due to variations in hand motions and lighting conditions. To address these challenges, the researchers tested multiple temporal descriptors and discovered that combining RGB and depth information (RGBD fusion) was an effective method for accurately identifying hand gestures. [PERSON] et al. (2017) [48] proposed a computation technique employing the PERCLOS (percentage of eye closure time) parameter based on machine vision to determine whether a driver's eyes were fatigued. The recommended approach for evaluating driver fatigue utilizes image processing and provides rapid calculation, high detection, and precision in recognition. [PERSON] and [PERSON] (2018) [49] designed a safety feature detection system to ensure the safety of long-distance transportation by detecting fatigued driving, distracted driving, and uncontrolled driving. Eigenface, Fisher Face, and LBPH image processing algorithms provided a 100% face recognition rate. Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) algorithms delivered an 80% recognition rate for recognizing drivers' mobile phones.
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
###### Abstract  
Target recovery through scattering media is an important aspect of optical imaging. Although various algorithms combining deep-learning methods for target recovery through scattering media exist, they have limitations in terms of robustness and generalization. To address these issues, this study proposes a data-decoupled scattering imaging method based on autocorrelation enhancement. This method constructs basic-element datasets, acquires the speckle images corresponding to these elements, and trains a deep-learning model using the autocorrelation images generated from the elements using speckle autocorrelation as prior physical knowledge to achieve the scattering recovery imaging of targets across data domains. To remove noise terms and enhance the signal-to-noise ratio, a deep-learning model based on the encoder-decoder structure was used to recover a speckle autocorrelation image with a high signal-to-noise ratio. Finally, clarity reconstruction of the target is achieved by applying the traditional phase-recovery algorithm. The results demonstrate that this process improves the peak signal-to-noise ratio of the data from 15 to 37.28 dB and the structural similarity from 0.38 to 0.99, allowing a clear target image to be reconstructed. Meanwhile, supplementary experiments on the robustness and generalization of the method were conducted, and the results prove that it performs well on frosted glass plates with different scattering characteristics.
# Polymorphic Adversarial Cyberattacks Using WGAN
## 2 Related Works
### Taxonomy of Intrusion Detection Techniques Using ML and DL
#### 2.1.1 Signature-Based Intrusion Detection  
Various signature-based supervised machine learning classifiers, such as Support Vector Machine (SVM) [19, 20], Naive Bayes (NB) [19, 20], KNN [20], Decision Tree (DT) [21], Random Forest (RF) [19, 21], Logistic Regression (LR) [21], and Multi-Layer Perceptron (MLP) [22] are studied by current researchers for intrusion detection. Their results show that these algorithms have higher accuracy on known or similar attack patterns. The authors in [23] pointed towards the recent developments and shortcomings in signature-based IDS. They suggested that the main concern with the latest IDS is that they tend to alarm on fake attack data, which results in high false positives in terms of machine learning. A comparison of the detection rate and accuracy between various machine learning techniques, such as Artificial Neural Network (ANN), SVM, NB, RF, and AdaBoost has been discussed. In [24], the authors provide an overview of several single classifiers, hybrid classifiers, and ensemble classifiers consisting of multiple ML models. The authors have compared their results based on multiple network security datasets. Their research shows that hybrid and ensemble classifiers provide a better accuracy and detection rate as compared to a single classifier. The authors in [25] compared the efficacy of binary and multi-class ML classifiers. These signature-based classifiers are compared based on several performance metrics, such as True Positive Rate, False Positive Rate, Area Under the ROC Curve (AUC), and incorrect classifications. Their results depicted that reducing the number of target classes in training data helps in reducing the class imbalance for minority attack classes, thereby improving the overall IDS performance.  
Likewise, several advanced DL classifiers, such as Deep Neural Network (DNN) [26, 27], Convolutional Neural Network (CNN) [28, 29], Recurrent Neural Network (RNN) [30, 31], Long Short Term Memory (LSTM) [30, 31], and Gated Recurrent Unit (GRU) [32, 33] are employed by current network security researchers as signature-based intrusion detection techniques. Although these supervised DL classifiers provide better attack detection rates as compared to the traditional ML classifiers, they can identify only known attack patterns and do not perform equally well on unknown attacks or variants of known attacks [2, 3].
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
## 3 Materials and Methods  
### Equipment  
The experimental setup included a LoRaWAN network composed of a gateway, a network server, and an end device equipped with GPS tracking. Wanesy Management Center, a cloud-based SaaS provided by Kerlink (Torigne Faillard, France), was used as the network server and the device management platform.  
The gateway consisted of a Kerlink Winter Station (Figure 2a), a LoRaWAN EU868 certified Outdoor Gateway with 8 RX channels of 125 kHz and multi Spreading Factor, 1 RX channel of 500 kHz and mono Spreading Factor, and 1 RX channel with FSK modulation, for a total of 10 RX channels with a sensitivity of \(-\)141 dBm. It also has 1 TX channel configured with a power of 14 dBm, an SoC composed of a Cortex A9 ARM CPU and 256 MB of DDRAM, 8 GB eMMC flash storage, and the backhaul is secured by worldwide 4G module with 3G/2G and Ethernet (RJ45) fallback.  
The gateway is coupled to a Kerlink ACCIOT-KAN01 antenna (Figure 2b) with vertical polarization, 865 MHz +/\(-\) 5 MHz frequency range, 50 Ohms impedance, and a max gain of 6 dBi. Antennas with lower gain (1-2 dBi) are used more frequently, but since the goal was to assess the distance covered on the sea, we used an antenna with higher gain that is still free of license and does not require a special power source.  
The end device (Figure 2c) consisted of a Yabby from Digital Matter, a small battery-powered device that is LoRaWAN EU868 certified with GPS tracking. The device has a uBlox EVA-M8 GPS receiver with 72 channels and \(-\)167 dBm sensitivity. It also includes a 3-axis accelerometer for motion detection. The device was configured with a downlink spreading factor (SF) 12.  
Figure 1: LoRaWAN network architecture [17].
## 6 Discussion
### Triggers  
Deviations from the classical Omori law (1) are caused not only by the nonstationarity of the parameters of the geological environment in the source, which we have expressed in the form of a possible dependence of the deactivation factor on time. Deviations can occur under the influence of so-called triggers, i.e., relatively small disturbances of geophysical fields of endogenous or exogenous origin.  
We point here to two endogenous triggers that we have recently discovered. Both are aroused on the main shock. One of them has the form of a round-the-world seismic echo, and the second represents free elastic oscillations of the Earth as a whole, excited by the main shock. We have described both triggers in detail in a number of papers, so we will restrict ourselves here to references [22; 23; 24; 25; 26].  
Let us dwell on exogenous triggers in more detail. For a long time, the cosmic effects on seismicity have been widely discussed, but there is still no agreement in the geophysical community on the effectiveness of such impacts. The controversy about the influence of geomagnetic storms on the global activity of earthquakes arises especially often (see, for example, [27; 28; 29; 30]). The question is really difficult. On the one hand, observations indicate a correlation between seismicity and geomagnetic storms and a complex of electromagnetic phenomena associated with them (see papers [31; 32; 33; 34; 35; 36; 37] and the literature cited therein). On the other hand, the mechanism of the impact of geomagnetic storms on rocks, leading to modulation of seismicity, is not entirely clear. In this regard, the idea of the magnetoplasticity of rocks [32; 33] seems to us very encouraging, but a discussion of this deep idea would lead us far astray.  
A wide class of exogenous triggers of anthropogenic origin is known. We will restrict ourselves here to an indication of the weekend effect discovered in [38], and the so-called Big Ben effect, or the effect of hour markers [39; 40]. Both effects pose a difficult question for the researcher about the global impact of the industrial activity of mankind on the lithosphere.
## 2 Processes of Semiconductor Manufacturing and Measurement Methods
### Processes of Semiconductor Manufacturing
#### 2.1.3 Electrical Die Sorting and Packaging  
Once the fabrication steps are complete, wafers undergo rigorous testing and inspection to identify any defects that could impact the performance of the final semiconductor devices [27; 28]. Individual chips are then cut from the wafer and encapsulated in protective packaging, which includes attaching the die to a lead frame and connecting it to external pins for integration with other electronic components.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 3 System Design  
In addressing the challenges of delay-constrained multicast routing, this paper proposes a neighborhood state-aware delay-constrained multicast routing method in Information-Centric Networking (ICN). The method presented in this paper makes routing decisions and establishes multicast trees based on foundational routing information and neighborhood state information. We proposed a registration and resolution mechanism for selecting tree nodes. A mechanism for the announcement and maintenance of neighborhood state information is introduced, where dynamically changing link information is only transmitted  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Type** & **Initiator** & **Comments** \\ \hline KMB & Steiner tree & Source & Centralized algorithm. No delay constraints are provided. \\ \hline Jia’s Algo & Steiner tree & Source & Perform a tree search starting from the source. Message processing cost is high. \\ \hline QoSMIC & Minimum spanning tree & Receiver & Perform local search firstly, if fails, perform tree search. It avoids blind search at the beginning, but still has a large message processing cost. \\ \hline QMRP & Minimum spanning tree & Receiver & Perform single path search firstly, if fails, perform multi-path search. Message processing cost is high. \\ \hline PIM-SM & Minimum spanning tree & Receiver & Shortest path tree algorithm. \\ \hline Spanning-Join & Minimum spanning tree & Receiver & Broadcast join request to tree nodes to explore the path. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of related work.  
in real-time among neighboring nodes, thereby avoiding flooding of real-time updated path states throughout the network.  
First, we provide an overview of the system's preparations. The design of our delay-constrained multicast routing system can be categorized into four key components: a system overview, management of local network status, a registration resolution mechanism, and an algorithm for constructing the multicast tree.
## 3 Finite Element Analysis Program for Hybrid Testing and Procedures  
OpenSees, one of the hybrid testing programs, is most widely used for the RTHT in the world. For the replacement of OpenSees, in this study, FEAPH (Finite Element Analysis Program for Hybrid testing) was developed. As shown in Figure 2a, data communication between commands and responses has been optimized to simplify the test execution process of the analysis program (FEAPH) which is developed in C++ language. The program has several advantages as input file, computation time, and operating system. The Lua scripting language was used because of its portability and small capacity when writing input files. Intel Math Kernel Library optimizes matrix operation by minimizing computation execution time and optimizing parallel scalability with multithreading technology.  
The main components of FEAPH consist of two parts; a modeling part and a numerical analysis part, as shown in Figure 2b. The modelling part includes the basic information such as node, section, element, material properties, boundary condition, and loadings. In numerical analysis part, parallel algorithm, and time integration with fixed iteration method are adopted to reduce the computational time for hybrid testing [18].  
In real-time hybrid test, it is necessary to minimize the computational time required for numerical analysis. In order to solve the equations of motion numerically, the time integration algorithm is performed at each time step and the command displacement is calculated through an iterative correction process. In particular, the fixed iterative implicit HHT (Hilber-Hughes-Taylor) time integration method is a very efficient method to reduce the repetitive computational time because of a fixed number of convergence error in real-time hybrid test [14; 28]. In addition, a parallel algorithm using OpenMP (Open Multi-processing) was adopted to reduce the computational time using a processor that performs only inverse matrix and on the other processors, the information of subdomain elements is assigned [18]. As shown in Figure 3, real-time hybrid numerical algorithms used in FEAPH are demonstrated.  
Figure 1: System Configuration for real-time hybrid test (RTHT).
# Experimental Study on Cyclic Shear Performance of the Four-Way Geogrid Reinforcement-Soil Interface
## 3 Results and Discussion
### Shear Stiffness and Damping Ratio
#### 3.2.1 Effect of the Normal Stress  
Figure 10a shows that the shear stiffness of the geogrid-soil interface changes differently under various normal stress levels. When the normal stress level is low, the shear stiffness gradually increases with the number of cycles. The smaller the \(\sigma\), the more cycles are required for the shear stiffness to reach its maximum value. Under high normal stress conditions, the shear stiffness reaches its maximum value with fewer cycles as stress increases. Additionally, the rate of decline is faster with larger values of \(\sigma\). This phenomenon may be due to the wear and deformation of the grating under high stress, which causes particle breakage and a decrease in the interface's ability to resist deformation. The larger the value of \(\sigma\), the greater the degree of wear and tear on the grating and particles, resulting in a faster degradation of the interface's strength [30]. The damping ratio decreases with an increase in cycle times under low normal stress and tends towards a stable value. Conversely, under high normal stress, the damping ratio increases with an increase in cycle times and tends toward a stable value. This suggests that the geogrid is not deformed under low normal stress, resulting in relatively low interfacial energy dissipation. However, under high normal stress, the geogrid is deformed, leading to an acceleration in interfacial energy dissipation [31].  
Figure 9: Correlation coefficient from hysteresis loop.
## 1 Introduction
### Pool Shape Prediction  
In this phase, the pool shape is predicted considering the conditions defined in the previous phase. Spillages that occur on a structure under motion inside a large compartment have a high probability of causing a large-scale pool fire. Thus, the spreading extent should be determined using the proposed method. In these cases, the pool shape can be determined by means of four methods: computational fluid dynamics (CFD) simulations, experiments, empirical formula-based calculations, and analytical equation-based calculations. CFD software capable of providing detailed spread properties of the pool has the advantage of yielding fairly accurate results for numerous scenarios. However, this approach requires considerable computing resources and knowledge. Appropriately set-up experiments yield realistic data but may be limited by resources and time. Empirical formula-based estimations are time-saving and reasonably accurate.  
This approach is suitable for diverse scenarios, and formulae developed using a large amount of data for various conditions are generally more accurate. Analytical equations, on the other hand, describe the spillage and spread phenomenon analytically based on the principles of physics. Existing methods may be suitable for predicting the pool shapes of spillage scenarios which do not satisfy the two application conditions. The pool dimensions are evaluated using models that assume that the substrates are not inclined or that the pool spreads over the entire compartment.  
The defined pool fire scenario satisfies both application conditions. In the present study, the spill shape was predicted using experiments and empirical formulae. Flammable liquid was discharged on an impermeable surface, and the spill shape was experimentally characterised under different substrate inclination angles and fuel discharge rates. Empirical formulae were developed using the experimental results.  
### Pool Shape Prediction  
To analyse the effects of pool fire on the structure of interest, parameters such as the HRR, heat flux, and temperature are required. These parameters can be obtained analytically by solving existing governing equations, CFD simulation, or experimental studies. These approaches have their strengths and weaknesses, as described above.  
Given this study's focus on a specific scenario, existing analytical methods are likely to yield inaccurate results. Additionally, given the numerous fire scenarios that should be considered, the experimental approach is not ideal owing to resource and time limitations. Therefore, the approach utilising CFD simulations was selected. The large-eddy simulation code Fire Dynamics Simulator (FDS) was used for the pool fire simulations. The parameters describing pool fires and the predicted pool shape were modelled.