esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The results show that when the LEF/SEF decreased/increased, the LD operation states were modified from a chaotic state to a CW or periodic oscillation (PO) state. This state is attractive due to its stability and lowest noise level compared with the solitary laser noise level. We anticipated that the LD with OFB would be highly stable and have a descending RIN level at lower/higher values of the LEF/SEF, adjusting the suitable value of the LEF and SEF. The article is arranged as follows. A model of analysis that includes a modified rate equation for a time delay LD with any amount of OFB is presented in Section 2. The procedure of numerical simulation of the rate equations is induced. Section 3 presents the BDs of the peak value of the photon numbers of the LD at various LEF and SEF values, and varying OFB strengths are shown. The corresponding average RIN in the low-frequency region (\(f\leq 10\) MHz) of the LD are presented. The RIN spectrum at several dynamics regimes of the LD, such as the CW, PO, period doubling (PD) and chaos operation states, are introduced. Section 4 shows the conclusion.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Principal Component Analysis (PCA)  
During the preprocessing stage, the often-associated raw multidimensional dataset variables are transformed into a new set of uncorrelated variables, each of which is represented by a collection of principle components, using a statistical approach called principal component analysis (PCA). As a multivariate statistical data compression method, PCA reduces the dimensionality of the data by compressing redundant or correlated data into fewer bands [51]. A PCA produces independent, non-correlated output bands that are frequently easier to understand compared to the original data. As much of the data's variability as possible can be explained by the first principal component, and as much of the remaining variability as possible can be explained by each subsequent component. Data variability is lowest for the first main component and increases for each subsequent component [52]. PCA enhances the spectral features of surface material by lowering the irradiance effects that predominate in all bands, removing duplicate data from several bands, and condensing information to a few significant selected bands. These bands are used as input for further examination by machine learning algorithms.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Process Framework
#### 2.4.2 SAM Module  
Figure 3 provides a visual representation of the SAM module architecture, demonstrating the dynamic feature weighting mechanism based on spatial significance.  
Function: Dynamic feature weighting based on spatial significance.  
Selection justification: Attention mechanisms suppress noise in geospatial data by focusing computational resources on critical regions (e.g., unstable slopes). This adaptive weighting improves robustness against irrelevant terrain variations.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
###### Abstract
Overall, Wi-Senser has three main advantages: firstly, it utilizes WiFi signals to detect head movement during sleep, which can protect the privacy of the user and is noninvasive to the human body; secondly, it directly utilizes existing WiFi routers as transmitters, which are cost-effective and easy to deploy; thirdly, WiFi signals have strong penetrability, enabling the detection of human movement in non-line-of-sight scenarios. The main contributions of this paper are summarized as follows. * To the best of our knowledge, Wi-Senser is the first system enabling the detection of contactless human head movement during sleep by reusing the existing WiFi network. * We propose a new metric to select an optimal subcarrier from candidate subcarriers and designed an algorithm that has the capability to accurately track human head movements from the extracted fine-grained CSI signals. * We implement Wi-Senser with COTS WiFi devices and evaluate the performance with extensive real-world experiments involving 10 volunteers (including 6 adults and 4 children). The results demonstrate that with WiFi signals alone, Wi-Senser is able to achieve higher than 97.95% accuracy in detecting human head movement during sleep.
## 4 Implementation of the System
### Subsystem Implementation
#### 4.2.2 Decentralized Database  
OrbitDB [23] was selected as decentralized database. It is based on IPFS and provides an easy way to store and retrieve information through Create, Retrieve, Update, Delete (CRUD) operators. The decentralized database operates in a similar way to a blockchain, so every peer in the network has a synchronized view of the database. Moreover, the stored data are scattered among the participating peers and are tracked by an address and a hash table. OrbitDB offers developers five predefined types of databases (log, feed, keyvalue, docstore and counter), each with its own API and CRUD operators. The docstore database stores information with JSON format, so it is a good fit for managing structured data. OrbitDB databases also provide a control access system to manage the read/write permissions of the users. However, it must be remarked that decentralized databases such as OrbitDB do not implement privacy by default [83], so developers have to take privacy-protection actions to achieve regulatory compliance.  
It must be indicated that OrbitDB performance can be tested in terms of response time. Although such an evaluation is out of the scope of this article, the interested reader has further information in [93], where OrbitDB was used for a healthcare DApp. In such a paper, OrbitDB nodes were deployed locally and remotely (in Fog and Cloud computing environments), and it was found that response times were in the order of milliseconds, which demonstrates that OrbitDB is suitable even for scenarios where response time is critical.
# Side-Illuminating Optical Fiber for High-Power-Density-Mediated Intraluminal Photoacoustic Imaging
## 2 Materials and Methods  
### Side-Fire Fiber Design  
The forward light propagation in multi-mode optical fibers can be altered by modifying the refractive index or the physical structure of the fiber core or its surface through methods such as chemical etching, mechanical abrasion, UV laser ablation, and femtosecond laser writing [17; 32; 39; 40]. These modifications cause scattering of optical energy and redirection of some light propagation modes, allowing photons to escape the core and be emitted from the fiber's side, such as in circumferential diffusers. In our work, we used UV laser ablation to create corrugated windows along the side of single core 1.5 mm diameter multi-mode optical fibers (FT1500 UMT, Thorlabs, Inc., Newton, NJ, USA), which have a damage threshold of 1 GW cm\({}^{-2}\). We made side-emitting fibers with 10 mm (n = 2), 20 mm (n = 2), and 30 mm (n = 2) long windows. Three successive ablations were performed to enhance the side emission by creating deeper structures in the fiber, enabling more transmission modes to interact with the window. The ablated window was positioned 5 mm from the fiber's distal end, leaving an intact segment for quantifying emission properties, including the side-to-front fire ratio, as described below. The goal of these modifications is to disrupt the total internal reflection at the modified core surface, causing a constant redistribution of the modes traveling in the core.  
Reflective silver coatings were used to further redirect modes that do not satisfy the optical fibers' NA and would not be emitted at the machined window. Silver films are highly reflective across the visible and infrared spectrum down to 400 nm and minimally absorbed in the visible range [41]. While optical properties depend on film thickness, stability in transmittance and reflectance is achieved once a minimum thickness of 12-18 nm is reached [42].
###### Abstract  
This manuscript presents an experimental study of Quadrature Chaos Shift Keying (QCSK) as a means to tighten the physical layer security of Internet of Things (IoT) communication. Our study examines the characteristics and operational aspects of chaos oscillators, prioritizing low-power functionality, resilient chaotic oscillations, and resistance to parameter variations and noise. This study emphasizes the key role of chaos oscillators in enhancing IoT security, showcasing their potential to ensure data integrity. The findings elucidate the dynamics and synchronization stability of the selected oscillators, providing insights into their suitability for secure communication systems. This comprehensive analysis contributes to advancing secure communication methodologies for the expanding landscape of wireless sensor networks in the Internet of Things, underscoring the significance of chaos oscillators in ensuring robust and secure data transmission.  
nonlinear systems; chaos shift keying; chaos oscillator; chaotic synchronization; communication system; signal processing; internet of things +
Footnote †: journal: Applied  
_articles_
## 2 Overall Analysis Approach and Implemented Methods
### Mission Analyses
#### 2.6.2 Mission Description and Simulation
In addition, the mission simulation is prematurely stopped if the pressure inside the tanks drops below the minimum safety pressure. Figure 15: Standard mission simulation scheme. This coupled mission-thermodynamic analysis depends on the strategy which defines the fuel flow supplied by each tank. For example, venting and pressure drop could be actively contrasted implementing a flow control law depending on the instantaneous internal pressure. For the sake of simplicity and to save computational time, in this preliminary study the same fuel flow value \(\dot{m}_{ft}\) was assumed for all the active tanks, i.e., those in which LH\({}_{2}\) is not depleted. At each time step, \(\dot{m}_{ft}\) is calculated as in Equation (48), where \(\dot{m}_{ft}\) is the fuel flow requested from all the engines and \(n_{at}\) is the number of active tanks. This latter is updated at each time step comparing the quantity of LH\({}_{2}\) left in each tank with a minimum threshold value (70 kg, corresponding to 1 m\({}^{3}\)). \[\dot{m}_{ft}(t)=\frac{\dot{m}_{fe}(t)}{n_{at}(t)} \tag{47}\]
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 3 Numerical Simulation
### Calculation of the Setting of Working Conditions  
The foundation pit of this project is excavated in six layers, and the excavation conditions corresponding to the excavation face of each layer are shown in Table 6. In order to verify the influence of prestress loss of anchor cable on the stability of the foundation pit, the prestress loss rate was set as \(\beta=0\%\), \(10\%\), \(20\%\), \(30\%\), and \(40\%\).
## 3 Research Questions and Hypothesis  
The main research questions in this study were:  
* Do the head-centric rest-frames have an effect on the VRISE levels, user experience, presence, and performance?
* Is there a difference between the effects of different types of head-centric rest-frames based on their placement (of the central or peripheral vision) in terms of VRISE levels, user experience, presence, and performance?
* Are there differences between LA and the more provocative HA mode of the game in terms of the VRISE levels, user experience, and presence?  
Based on the background research, we developed hypotheses that state (H1) that the head-centric rest-frames do positively affect VRISE levels that alleviate side effects from VR usage (less sickness). We state (H2) that there is a difference between the effects of different types of head-centric rest-frames in terms of the VRISE levels, user experience, presence, and performance. We state (H3) that the head-centric rest-frames do not affect the user experience, (H4) have a positive effect on presence, and no effect on (H5) performance. We state (H6) that a more provocative HA mode of the game will have a negative effect on the VRISE levels (more sickness) compared to the LA mode of the game and have (H7) a positive effect on the user experience and (H8) on presence.
## 6 Validation of Lateral Pressure Estimation  
To evaluate the proposed techniques, lateral pressure estimations using the constitutive model were compared against experimental OVS and OHS test measurements. To further validate the anisotropy consideration, additional predictions were carried out treating \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Net Normal Stress (kPa)** & \(\mathbf{\kappa}\) & \(\mathbf{\beta}\) & \(\mathbf{R^{2}}\) \\ \hline
0 & 102.502 & 0.747 & 0.9851 \\
12.5 & 19.224 & 0.904 & 0.9983 \\
25 & 13.227 & 0.873 & 0.9986 \\
50 & 11.047 & 0.866 & 0.9982 \\
100 & 10.640 & 0.864 & 0.9960 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fitting results between \(E_{\text{unstat}}/E_{\text{sat}}\) and \(s/s_{0}\). \(H_{\text{0}}\) and \(H_{\text{h}}\) as equal (isotropic assumption) rather than directionally dependent, namely [PERSON]'s nonlinear elastic model. Table 6 summarizes the parameter values obtained for input from the analyses presented in Sections 4 and 5. Elastically isotropic behavior was assumed within the stress range under consideration. Namely, the elastic moduli \(E\) in the vertical and horizontal directions were considered to be the same. All of the test series for the OVS and OHS tests provided lateral pressure datasets covering net normal stresses ranging from 0 to 100 kPa under staged suction reductions. Predictions were generated using Equations (3) and (6) with the appropriate elastic moduli at given stress states calculated using Equations (15) and (16). Figure 11(a) overlays the estimated and measured lateral pressure variations across the decreasing matric suction path for each test. Remarkably close correlations were observed regarding the fact that the datasets are distributed near both sides of the 1:1 perfect prediction line, demonstrating the suitability of the proposed constitutive relationships and prediction methods. However, as shown in Figure 11(b), it presents significantly poorer alignment that would increasingly underestimate the lateral pressure of expansive soil by 5.8-61.0%, which can be a disaster for engineers designing geo-infrastructure in expansive soil distributed areas. In addition, the deviations between the measured and predicted lateral pressures were found to be relatively large at higher stress states, whether using the proposed method or the isotropic model. This may be due to the fact that the expansive soil has already entered the plasticity state under such large stress and even yielded, where the elastic constitutive model can no longer characterize its stress-strain behavior. Therefore, this proposed model and method are suggested for use in designing light structures with lateral confinements at shallow depths. Five statistical indices were used to better evaluate the performance of the proposed method, including the correlation coefficient (\(R^{2}\)), refined Willmott index (RWI), mean arctangent absolute percentage error (MAAPE), root mean square error (RMSE), and mean absolute error (MAE). The model has larger \(R^{2}\) and RWI, and smaller MAAPE, RMSE, and
## 2 Method
### The Magnetic Dipole Model for Tracking System
#### 2.2.3 The Induced Voltage in the Receiving Coil  
Since the PIG with the ELF transmitter is pushed forward by oil or natural gas in the pipeline, its speed is usually slow. Therefore, the induced electromotive force in the receiving coil approximately meets (3):  
\[\dot{e}=-\mathrm{i}2\pi fn\mu_{e}\dot{\mathbf{H}}S_{c}\mathbf{e_{y}}, \tag{3}\]  
where \(n\) is the number of turns of the receiving coil, while \(S_{c}\) is the cross-sectional area. \(\mu_{e}\) is the effective magnetic permeability of the magnetic core in the receiving coil. \(\dot{\mathbf{H}}\) is the magnetic field intensity, and only the vertical component of the magnetic field shown in (1) can induce a voltage in the receiving coils, which are placed vertically. Furthermore, the nomenclature of the variables in the equations is listed in the Appendix A. Since the \(f\), \(n\), \(S_{c}\) and \(\mu_{e}\) can all be regarded as constants, to simplify the model, analyzing the characteristics of the vertical component of the radiated magnetic field is equivalent to analyze the output voltage of the receiving coil.
## 1 Introduction  
A mobile _ad hoc_ network (MANET) is a self-configuring network of mobile devices connected by wireless links. In a MANET, each node acts as a router to establish end-to-end connections, and because the network topology between sources and destinations frequently changes, it is difficult to maintain and restore a route. To deal with these issues, several routing protocols for MANETs have been proposed. However, these protocols are simulated with low numbers of network nodes with low node mobility. Moreover, to track mobile destination, they require large a number of control messages.  
This study suggests a location-based hybrid routing protocol for a large number of high-mobility nodes. The proposed routing scheme effectively manages the maintenance and restoration of a route. The basic operation of the scheme is similar to the _Ad Hoc_ On-demand Distance Vector (AODV) algorithm [1], as the suggested scheme adds a greedy forwarding algorithm, the location information of a Global Positioning System (GPS), and directional forwarding [2] to the AODV algorithm. In addition, the proposed algorithm can reduce routing overhead. The main route nodes broadcast beacon messages, which have the location of the destination, to their one-hop neighbors. This approach provides efficient tracking of a mobile destination and reduces messages for routing.  
To evaluate the performance of the proposed scheme, the data packet delivery ratio and routing control overhead messages are analyzed and compared with existing routing protocols of AODV, Location-Aided Routing (LAR) [3], Zone Routing Protocol (ZRP) [4], and AODV-DFR [5].  
The remainder of this paper is organized as follows: Section 2 reviews the previously known MANET and related algorithms. Section 3 develops the proposed routing scheme. In Section 4, the proposal is verified by simulation. Finally, Section 5 summarizes the contributions of this study.
## 3 System Model
### Network Model  
We use both homogeneous and heterogeneous WSN model that includes \(N\) smart sensor nodes randomly scattered in a region with \(R\times R\) square meter size and a sink device. The sink device does not restrict energy, memory, and processing ability. In the homogeneous WSN model, all sensor devices are the same in proportions of initial energy level and other characteristics, but in heterogeneous WSNs, we assume that \(N\) sensing nodes are only different initial energy levels that are used with three kinds of sensor devices: _normal_, _advanced_, and _super_ sensors. Let \(N_{1}\) and \(N_{2}\) be the coefficient of \(N\) smart sensor nodes of _advanced_ and _super_ nodes that contain \(\beta\) and \(\alpha\) times greater energy levels than the _normal_ one, respectively.  
\[\begin{split}& N_{S}=N\times N_{1}\times N_{2},N_{A}=N\times N_{1}(1- N_{2}),\\ & N_{N}=N(1-N_{1}),\text{and}\ N=N_{S}+N_{A}+N_{N}\end{split} \tag{1}\]  
where \(N_{S}\), \(N_{A}\) and \(N_{N}\) are the respective quantity of _normal_, _advanced_, and _super_ sensor nodes [35; 36]. If we assume \(E_{0}\) denotes the initial energy level of each _normal_ sensor, then \(E_{0}(1+\beta)\) and \(E_{0}(1+\alpha)\) will be the initial energy of each _advanced_ and _super_ sensor node, respectively. Therefore, the total initial energy of WSN containing \(N\) sensor nodes is calculated as in Equation (2).  
\[E_{init}=E_{0}(N_{N}+N_{A}(1+\beta)+N_{S}(1+\alpha)) \tag{2}\]In general, if the \(N\) sensor nodes are distributed uniformly in the square region of \(R\times R\) size, then the probability density function \(\rho(x,y)\) can be computed as follows [21]:  
\[\rho(x,y)=1/R^{2} \tag{3}\]  
The average region size occupied by individual sensor nodes is indicated by Equation (4) [21]:  
\[A_{node}=R^{2}/N \tag{4}\]  
Let \(N_{cell}\) be the number of grid cells (clusters) then the average region size of a cell is equivalent:  
\[A_{cell}=R^{2}/N_{cell} \tag{5}\]  
The average distance of a node to the nearest neighbor in each cell is  
\[d_{t_{0}NB}^{2}=R/\sqrt{N} \tag{6}\]  
Additionally, the longest distance from the CHN to the farthest node in each cell (as shown in Figure 5) is expressed in Equation (7):  
\[d_{max}=R/\sqrt{\pi N_{cell}} \tag{7}\]
## 2 DFIG and Battery Control System
### Battery System Model  
The battery is modeled as being ideal with a 5 MWh capacity. To investigate a grid system with a high-capacity battery, the battery model is assumed to be a series cell-connected configured battery pack [30]. When the battery operates in such conditions, its voltage curve has a non-linear form due to electrochemical characteristics such as electrolyte resistance and charge transfer resistance which causes overpotential [31]. From this point of view, the non-linear voltage curve changes in two steps when the current flows through the battery after enough rest time for eliminating the overpotential factors characterized as the open-circuit voltage (OCV) condition [31]. In the first step, the battery voltage drops suddenly, and which can be directly used to calculate the ohmic resistance. From [PERSON]'s law, the resistance (\(R_{\textit{batter}}\)) is defined as the difference between OCV and dropped voltage divided by current [31; 32; 33]. In the second step, after a sudden voltage drop, the battery voltage draws an exponential decrease form resulting from the charge transfer reaction and ionic diffusion. The exponential voltage curve can be designed with a resistor and capacitor parallel connection. The beginning and end voltage in the exponential curve indicate resistance (\(R_{1}\)) as being the same as in the first step of non-linear voltage curve's change and the degree of exponential change is set by the time constant [34]. In the resistance and capacitor parallel connection, the time constant is defined as one product between resistance and capacitance. In this study, an ideal battery system is applied; thus, 5 MWh can be used whenever the system needs energy from the battery system. Therefore, capacitance (\(C_{1}\)) can be calculated from the time at 63.2% voltage variation. As seen in Figure 5, the lithium-ion battery electrical circuit model is composed of a series resistor and a parallel resistor, as well as a capacitor combination. The battery-model-related equation can be expressed as in Equation (6). This model is composed of a voltage source (\(E_{0}\)) representing the OCV dependent on the SOC, an ohmic resistor modeling the internal resistance (\(R_{\textit{batter}}\)), and an \(RC\) network (\(R_{1}C_{1}\)) that describes the diffusion phenomena. For this investigation, \(R_{\textit{batter}}\), \(R_{1}\), and \(C_{1}\) were assigned values of 0.12 \(\Omega\), 0.09 \(\Omega\), and 473.89 F, respectively.  
\[\begin{array}{l}V_{\textit{battery}}(t)=E_{0}(SOC(t))-V_{1}(t)-I_{\textit{ battery}}(t)R_{\textit{battery}}\\ \dot{V_{1}}(t)=-\frac{1}{R_{1}C_{1}}V_{1}(t)+\frac{1}{C_{1}}I_{\textit{battery }}(t)\end{array} \tag{6}\]