esa-sceva/satcom-chunk-collection huggingface dataset:
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 2 Related Works  
Recent works on underwater sensor networks have indicated the importance of power efficiency. Some works focused on MAC protocols while others focused on routing protocols. A good protocol should take MAC and routing protocols into account because power consumption is influenced by both of them.
## 2 Structure and Model
### Model Validation  
To verify the model, the results obtained using the model are compared with those found in the literature [29]. The dimensionless load capacities of bearings with different eccentricity ratios under different numbers of bearings are shown in Figure 3. The calculation results presented in this paper are in good agreement with the data presented in the literature. The maximum relative error is less than 10%.
## 4 Discussion
For example, variations in the electrochemical gradient occur via mechanosensitive ion channels [62] and phosphorylation cascades such as the Hippo pathway, which regulates nuclear regulators YAP/TAZ (Yes-associated protein/transcriptional coactivator with PDZ-binding motif) [66]. Additionally, traction forces are generated and transmitted through the integrin-cytoskeleton axis to the nuclear membrane, where various cytoskeletal components physically interact with the LINC complex (linker of nucleoskeleton and cytoskeleton) [67]. Once in the nucleus, different biochemical and/or mechanical signals induce changes in chromatin organization, due, among other factors, to the interaction between the nuclear lamina and the lamina-associated chromatin domains (LADs) [68], creating specific gene expression programs and modulating aspects such as cell differentiation and proliferation [69]. Furthermore, the mechanoregulation of transcription factors through their differential nuclear translocation plays an important role in modulating gene expression. An example of this is YAP/TAZ, which, when phosphorylated through the Hippo pathway, continues to be retained in the cytoplasm, preventing its translocation to the nucleus and its interaction with members of the TEAD family (Transcription Enhanced Associate Domain) [70]. However, in its dephosphorylated state, it undergoes nuclear internalization, helping to promote the expression of anti-apoptotic genes such as _COX-2_, _BIRC5_, and _Glut1_[71]. In summary, although this is a preliminary study that requires an expanded sample size, the hypothesis that emotions could act as biophysical agents influencing cellular processes through the physical and acoustic characteristics of the human voice is a tangible possibility. Further exhaustive research is needed.
## 3 Materials and Methods
### Nanoindentation and Elasticity Analysis  
AFM nanoindentation of collagen fibers were carried out on a PicoScan 3000 AFM (Molecular Imaging Co., Tempe, AZ, USA). Gold-coated silicon nitride AFM cantilevers with resonance frequency of 80 kHz, spring constant of 3.1 N/m, and tip radius of curvature R \(\approx\) 20 nm (Nanosensors Co., Neuchatel, Switzerland) were used for indentation. Collagen fibers were rastered in tapping/contact mode first, and then the regions of interest were chosen to perform nanoindentation in force mode withforce-distance plots. All experiments were performed in the air (55% relative humidity) and at room temperature, and all data were shown as mean \(\pm\) SD. Transverse elasticity and adhesive force at these locations were calculated from the recorded force-distance curves, as shown in Results. The indentation force (\(F_{S}\)) applied to the sample was calculated by the cantilever deflection (\(D_{C}\)) and its spring constant (\(K_{C}\)). The sample deformation (\(D_{S}\)) is equal to the piezoelectric extension (\(L_{P}\)) from the original sample surface minus the cantilever deflection (\(D_{C}\)) [26]. Adhesion force (\(F_{A}\)) was read out directly from the force-distance plots, as the absolute maximum indentation force (\(F_{C}\)). The elasticity coefficient of the sample (\(K_{S}\)) is determined using the indentation force (\(F_{C}\)) divided by the sample deformation (\(D_{S}\)).  
\[K_{S}\ =\ \frac{F_{C}}{D_{S}}\ =\ \frac{K_{C}D_{C}}{L_{P}-D_{C}} \tag{1}\]  
\[F_{A}\ =\ \left|\left(F_{C}\right)_{max}\right| \tag{2}\]  
The Young's modulus of the sample (\(E_{S}\)) was calculated using the Hertz formula which assumed a normal interaction of an infinitely solid spherical surface with a soft planar surface. These equations are generally better in understanding the structural and mechanical stability of soft biological samples at cellular and molecular levels. Hertz equations are as follows:  
\[E_{S}\ =\ \frac{3\left(1-\mu^{2}\right)F_{C}}{4R^{1/2}D_{S}^{3/2}}\ =\ \frac{3\left(1-\mu^{2}\right)K_{C}D_{C}}{4R^{1/2}D_{S}^{3/2}} \tag{3}\]  
where \(\mu\) is the Poisson's ratio of the sample, which is the ratio of transverse contraction strain to longitudinal extension strain in the direction of the force and R is the radius of curvature of the AFM tip.  
The data from the compression elasticity and adhesion force experiments were analyzed using SPSS 20.0 (IBM Co., Chicago, IL, USA). One-way analysis of variance (ANOVA) and multiple comparisons between and within sample groups were used to identify differences (see Supplementary Information). A difference was recorded as significant if \(p<0.05\).
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
## 2 Sampled Phase-Only Hologram (SPOH)  
### Generating a Phase-Only Hologram with Direct Magnitude Removal  
Generating a digital hologram from a three-dimensional (3-D) object is commonly referred to as computer generated holography (CGH). The process of generating a digital Fresnel hologram is briefly outlined as follows. There are polygon-based [27] and point-based [28] methods. In this paper, we deal with the point-based method. In point-based CGH, the object space is a set of object points. Each point emits a spherical wave with an intensity proportional to its irradiance. The phase of all the object points are assumed to be zero. The hologram is a planar surface located at certain distance along the \(z\) (axial) direction from the object space. The value of each pixel in the hologram is the accumulative sum of the waves from all the object points. For an object space comprising a single image layer \(I(m,n)\) (\(m\) and \(n\) being the horizontal and vertical coordinate axes) that is parallel to, and at an axial distance \(z_{0}\) from the hologram plane, its hologram can be obtained with Fresnel diffraction as given by  
\[H(m,n)=I(m,n)*f(m,n;z_{0}),f(m,n;z_{0})=exp\Big{(}\frac{i2\pi}{\lambda}\sqrt{m ^{2}\delta^{2}+n^{2}\delta^{2}+{z_{0}}^{2}}\Big{)} \tag{1}\]  
where '\(*\)' denotes convolution, is the free space impulse response [1], with \(\lambda\) and \(\delta\) being the wavelength of the optical beam, and the sampling interval. Equation (1) can also be expressed in the frequency space. Let \(\overline{\mathbf{H}}(\omega_{m},\omega_{n})\), \(\overline{\mathbf{I}}(\omega_{m},\omega_{n})\), and \(\overline{\mathbf{f}}(\omega_{m},\omega_{n};z_{0})\) denote the Fourier transform of \(H(m,n)\), \(I(m,n)\), and \(f(m,n;z_{0})\), respectively, we have  
\[\overline{\mathbf{H}}(\omega_{m},\omega_{n})=\overline{\mathbf{I}}(\omega_{m},\omega _{n})\times\overline{\mathbf{f}}(\omega_{m},\omega_{n};z_{0}). \tag{2}\]  
If the object space contains multiple image layers, the hologram is simply the superposition of the hologram of each image. For simplicity of explanation, we hereafter assume that the object space to be a single image layer, but it can be easily extended to multiple image layers based on the superposition principle. The 3-D image represented by a hologram can be reconstructed with a pair of spatial light modulators (SLM), each displaying one of the two components (magnitude and phase, or the real and imaginary parts). This setup requires precise integration of the SLMs and associated optical components, which is complicated, bulky, and difficult to implement in practice. In a simplified optical reconstruction method, either the phase or amplitude of the hologram is displayed with a single SLM. Displaying the phase component is preferred to the amplitude as higher optical efficiency can be achieved. Although, a phase-only hologram can be obtained by simply discarding the magnitude component, as we shall show later, the reconstructed image suffers degradation.
## 2 Background Concept and Problem Modeling
### Long Short-Term Memory (LSTM)  
Since it is difficult for the RNN (recurrent neural network) to remember the input information when there is a large time difference between output and input information (called the long-term dependence problem), the LSTM is devised to overcome this problem. To solve this disadvantage, the LSTM uses three gates and modifies information by adding a cell state to the internal structure of the neural network. More specifically, the LSTM has three main gates. The first is a forget gate, which determines how much information is discarded and stored in the cell state. The second is the input gate, which decides how much of the new information coming to the input gate is to be stored in the cell state. The third is the output gate, which determines how much information the cell state has to output. It has the ability to preserve long-term memory by using the gates that are not in RNNs. Therefore, the LSTM is mainly used for time series data processing or natural language processing [47, 48, 49, 50]. In this paper, the LSTM is used to predict the future position of a moving obstacle, and the SAC agent uses the predicted position of the moving obstacle for the proposed SAC-based path planning.
## 5 Results and Discussion
### Sound Transmission Analysis  
The experimental and numerical results of the STL property of the two types of corrugated panels are plotted in Figure 6. Firstly, the results of the experiment and simulation had good consistency in the trend, and the maximum error was about 3 dB. The discrepancies between the simulation and experiment may be attributed to the fact that the boundary condition in the simulation is simply supported, but in the sound insulation tests it may not exactly be simply supported [49].  
Regarding the evolution of the STL property as a function of the sound frequency, the entire curves of the corrugated composite panels can be divided into four phases based on the investigations conducted on the traditional homogeneous panels [49], as indicated in Figure 6.  
Figure 6: The experimental and numerical results of the STL of different types of corrugated composite panels.  
In Stage I (<200 Hz), which is named the Stiffness Controlled Zone, the STL is proportional to the stiffness of the corrugated panel, showing a monotonous decrease as the frequency increases. After that, the curve steps into Stage II, which is called the Damping Control Zone. The dip that occurs at around 200 Hz corresponds to the resonant frequency of the corrugated panel (f\({}_{\rm r}\)), and f\({}_{\rm n}\), characterized by the curve depression points, represent the other resonant frequencies of the corrugated panel corresponding to different orders. One should note that the resonant frequencies of the GFCP and CFCP are almost the same, which indicates that they have similar mode frequencies.  
In Stage III, which is named the Mass Control Stage, the discrepancy of the STL between the two types of composite panels results from the difference of their densities. The surface density of the GFCP (5080.9 g/m\({}^{2}\)) is higher than that of the CFCP (4602.8 g/m\({}^{2}\)), which leads to relatively higher STL values. Due to the complex structures compared to homogeneous panels, the STL curves of the sandwiched panels containing different fibers present a nonlinear behavior in this stage.  
In Stage IV, which is named the Coincidence Effect Zone, the STL decreases at a critical frequency value and then increases with the increasing frequency. The critical frequency, f\({}_{\rm c}\), shown in Figure 6, can be obtained by [34]:  
\[\rm{f}_{\rm c}=\frac{c^{2}}{2\pi}\sqrt{\frac{M}{D}} \tag{8}\]  
where c is the sound velocity, and M and D are the surface density and stiffness of the panel, respectively.  
As depicted in Figure 6, it was found that the critical frequencies of the CFCP and GFCP were almost similar. Additionally, the surface density of the GFCP was slightly higher than that of the CFCP. Therefore, considering Equation (8), the stiffness of the GFCP was also slightly higher than that of the CFCP, which finally resulted in a higher STL of the former in Stage I (Stiffness Control Zone).
## 3 Performed Full-Scale/Model Test Comparisons
### Low-Rise Buildings
#### 3.1.4 Silsoe Full-Scale Measurement Program 2: The 6 m Cube  
In order to provide a facility for comparisons between the full-scale and the previously published model test data, a full-scale 6 m cube was constructed at Silsoe Research Institute in open-country terrain. Surface pressure measurements were made on the vertical and horizontal central-line sections, with additional taps on one-quarter of the roof. Measurements of wind velocity were also made in the region around the cube, using ultrasonic anemometers.  
Based on the full-scale measurement campaign, [PERSON] et al. (2018) compared the mean pressure coefficients, measured on location, with published wind tunnel data. The results suggested that when the wind was perpendicular to one face, there was good agreement between the full-scale and the wind tunnel's windward wall pressures. However, the full-scale and the wind tunnel's wind pressures, as measured on the roof and leeward wall, appeared to be different. [PERSON] and [PERSON] (2018) studied the effects of reattachment length on pressure distribution on the 6 m cube and found that the effects could not account for the differences between the full-scale and the typical wind-tunnel data. According to [PERSON] and [PERSON] (2018), some wind tunnel experiment studies showed that the extreme values of some local pressures and global forces followed a type III distribution; a type III distribution could be fitted to the full-scale data from the 6 m Silsoe cube.
## 2 Related Work on Protection against Bogus Attacks
One of the solutions that could prevent the sending of false information is RABTM [21] (road-site unit and beacon-based trust management) model--a framework based on BTM (beacon-based trust management) and RSU (road-site unit) [22]. It requires the use of two distinct confidence methods: indirect and direct. In order to determine the location, speed, and the direction in which the signal transmission object travels, the indirect approach uses beacon signals [23]. Trust coefficient is determined, which is then compared with the current data and then decides its credibility [24]. Another approach named the misbehavior detection system (MDS) was developed by [PERSON] et al. [25] to identify adjacent attackers who are disseminating false details. It is believed that there is a certificate authority (CA) offering one public/private key pair and certification in each car [26]. They often use entropy, a standard measure of data, to give the opportunity for the vehicle to view and evaluate normal and irregular actions in order to identify the intruder [27]. [PERSON] et al., in [28] suggested a comprehensive scheme to identify malicious vehicles for the post-crash warning application. The method used, in the first place, observes the driver's behavior after a crash warning call [29]. The observed mobility and predicted trajectory of the vehicle with the crash mobility model is measured. If the gap between the two reaches a certain threshold value, the alarm is assumed to be incorrect [30]. [PERSON] et al., in [31] suggested a novel credibility management system focused on misbehavior (MBRMS) that involves three components: (a) misbehavior identification, (b) rebroadcast occurrence, and (c) global eviction algorithms for the identification and filtration of incorrect facts in VANETs. Each vehicular node manages an incident information system and related activities to detect misbehavior of the node [32]. The described method used the outlier identification strategy and misbehavior of the risk value of the poor node to calculate the risk level. MBRMS easily recognizes and eliminates misbehavior nodes [33]. As it could be seen, the authors in their findings indicated multiple solutions based on the improving authentication security. They also analyzed the drivers' behaviors on the road (Table 1).
# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 3 Methodology
### Model Learning
#### 3.3.1 Pseudo-Label Generation  
The pseudo-label generation process is based on cosine similarity [42; 43]. First, we obtain the feature representations of the unlabeled data for the two modalities \(\mathbf{F}_{x}^{u}\) and \(\mathbf{F}_{y}^{u}\), as well as the feature proxies \(\mathbf{P}^{f}\). Then, we calculate the cosine similarity between the feature representations of the two modalities, \(\mathbf{F}_{x}^{u}\) and \(\mathbf{F}_{y}^{u}\), and the feature proxies \(\mathbf{P}^{f}\) using the pseudo-label generation module, as shown in Equation (1):  
\[\mathbf{S}_{x}^{u}=\frac{\mathbf{F}_{x}^{u}}{\|\mathbf{F}_{y}^{u}\|_{2}}\cdot\left(\frac{ \mathbf{P}^{f}}{\|\mathbf{P}^{f}\|_{2}}\right)^{T},\mathbf{S}_{y}^{u}=\frac{\mathbf{F}_{y}^{u}} {\left\|\mathbf{F}_{y}^{u}\right\|_{2}}\cdot\left(\frac{\mathbf{P}^{f}}{\left\|\mathbf{P} ^{f}\right\|_{2}}\right)^{T} \tag{1}\]  
where \(\|\cdot\|_{2}\) is the \(L_{2}\) norm, \((.)^{T}\) is the transpose of vector(or matrix), and \(\mathbf{S}_{x}^{u}\), \(\mathbf{S}_{y}^{u}\in\mathbb{R}^{N_{u}\times C}\) represent the similarity matrices between the image and text unlabeled data features and the feature proxies, respectively. Then, compute the average similarity \(\mathbf{S}^{u}\) by calculating the following Equation (2):  
\[\mathbf{S}^{u}=\frac{\mathbf{S}_{x}^{u}+\mathbf{S}_{y}^{u}}{2} \tag{2}\]Hence, we generate the pseudo-labels \(L^{u}\) using \(S^{u}\) and a threshold, as shown in Equation (3):  
\[L^{u}=\left\{\begin{array}{ll}S^{u}&\text{if }S^{u}\geq\text{ {threshold}}\\ 0&\text{otherwise}\end{array}\right. \tag{3}\]  
In this paper, we set the **threshold** to 0.5. If \(S^{u}\geq\text{{threshold}}\), we retain the continuous values in \(S^{u}\). If \(S^{u}<\text{{threshold}}\), following [30], we set it to 0.
# Research on the Evacuation Characteristics of Cruise Ship Passengers in Multi-Scenarios
## 2 The Description of Research Problems  
### The Cruise Ship Evacuation Scenarios  
Passenger safety has always been one of the most important indicators for cruise ships. The International Maritime Organization issued the _Guidelines for Evacuation Analysis for New and Existing Passenger Ships_ (The IMO Guidelines) [47], which provided relevant provisions for passenger evacuation. The evacuation analysis of passenger ships is stipulated in the guidelines, including two main scenarios, which are day and night scenarios. In the day scenario, passengers are distributed in public areas, and they are inside cabins under the night scenario. The objective cruise ship for this paper is shown in Figure 1.  
According to the fire safety and evacuation requirements of cruise ships, there are several main vertical zones (MVZ) along the longitudinal length of the cruise ship. The red line in Figure 1 indicates the location of the main fire barriers (MFB), which divide the ship into six MVZs. Passengers are distributed between MVZ 1 and 5. There are several staircases along the longitudinal length of the cruise ship. Stairs 1-5 are the main evacuation staircases that connect multiple decks. Stairs 6 connects Deck 1 to 2, and stars 7 and 8 connect Deck 9 to 10. Stairs 1, 2, 5 and 6 are double-run staircases, stairs 3 and 4 are double-divided staircases, and stairs 7 and 8 are single-run staircases. The structure and dimensions of these staircases are shown in Figures 2-4.  
The activity space of passengers is distributed from Deck 1 to Deck 10. As shown in Figure 1, yellow areas are the public zones and green areas are cabin zones. According to the IMO guidelines, all passengers are distributed in cabins under the right scenario. Each cabin holds two passengers on this cruise ship. In the day scenario, passengers are distributed in public zones, and the number of passengers in each zone can be calculated according to the area of the zone [48]. The passenger capacity of the cruise ship is 948, and the initial positions of passengers in the two main scenarios are shown in Table 1.  
At the beginning of the evacuation, all passengers are in their initial positions, and the destinations of the evacuation are embarkation stations where lifeboats are located. The embarkation stations are located on two sides of Deck 2, and passengers from other decks move to Deck 2 through staircases.
# Practical Approach for Developing Lateral Motion Control of Autonomous Lane Change System
## 3 Simulation Results  
The proposed algorithm is designed and validated via MATLAB/Simulink. A C-class hatchback model in CarSim from Mechanical Simulation is also used to solve the vehicle dynamics. From the vision sensor in the CarSim, one can obtain the state of vehicle such as lateral offset and heading offset with respect to the road. The overall simulation-in-the-loop architecture of the proposed ALCS is shown in Figure 4.  
Two lane change scenarios are considered in this study for the validation:  
* Formal sine and cosine trajectory-based lane change to the right within 5 s;
* Human driver trajectory-based lane change to the left within 3 s.  
One of the basic methods of changing lanes is by setting a reference using sine and cosine components, as presented in this study. However, the reference that appears when a human driver changes lanes does not perfectly follow sine and cosine [21]. Therefore, the second scenario is conducted to verify the proposed algorithm under a non-sinusoidal reference trajectory.  
In both cases, the vehicle travels a straight lane at 60 km/h. The nominal parameters of a C-class hatchback in CarSim are used for the proposed ALCS.