esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation and Discussion
### Materials  
The potential impact of texture, complexity, and color distribution on the quality and processing time of image steganography methods is significant. Images with complex textures can better mask embedded data, reducing noticeable artifacts, while simpler textures might reveal changes more easily. High-complexity images, with many variations and details, can hide data more effectively, whereas simpler images might make hidden data more detectable. Diverse color distributions can disperse hidden data more effectively, making detection harder, while uniform color images might show more noticeable changes. Processing time can also increase with more complex textures and high-complexity images due to the need to preserve intricate details.  
As shown in Table 2, we have chosen a diverse set of secret images to ensure comprehensive evaluation. For example, Rainbow (256 \(\times\) 256 pixels, 0.777 KB) has a wide range of colors and gradients, representing high color variability, and Monarch (768 \(\times\) 512 pixels, 604 KB) has fine textures and intricate patterns, providing high complexity. Plane and Dipxe also feature varied complexities and color distributions, testing the embedding algorithm's robustness.  
Similarly, our cover images shown in Table 3 exhibit a range of characteristics to test the robustness of our method. Lenna (512 \(\times\) 512 pixels, 462 KB) has balanced color distribution and texture, while Malamute (1616 \(\times\) 1080 pixels, 2897 KB) includes both smooth and textured areas. Pepper and Kid also provide varied textures and color distributions, enhancing the testing of our method's ability to handle of different cover image types.
# Flow-Based Programming for Machine Learning
## 5 Conceptual Approach
### Flow Specification and Flow-Checking  
As a second step, we take the components (discussed in Section 5.1) and make them available to the end-user via a programming tool. The programming tool must have an interactive graphical user interface consisting of a palette, a drawing area called canvas, a property pane and a message pane. The palette contains all available modular components which can be composed in a flow confirming to some standard flow composition rules. The actual flow composition takes place on the canvas when the user drags a component from the palette and places it on the canvas. The component, when present on the canvas and when selected, should display all its configurable properties in the property pane. The user can override default settings and provide custom settings for the operation of a component via this pane. The flow, while being composed on the canvas, is captured, converted into a directed acyclic graph (DAG) and checked for the correct order of the connected components. The flow-checking should be done whenever there is a state change. A state change occurs whenever something changes on the canvas. For example, a new component is dragged onto the canvas from the palette or the connection between two already present components change, among other such change possibilities. Flow-checking checks the user flow for any potential irreconcilability with the compositional rules. Such a flow when passed to the back-end for code generation will generate target code which does not produce any compile-time errors. The components are _composable_ in the form of a flow if they adhere to the following compositional rules:  
1. A flow is a DAG consisting of a series of connected components.
2. It starts with a particular component called the \"Start\" component and ends with a specific component called the \"Save Model\" component.
3. Every component has its input and output interface adequately defined, and a component can be allowed to be used in a specific position in a flow if it is compatible with the output of its immediate predecessor and if it is permitted at that stage of processing. For example, the application of the ML algorithm is only possible after feature extraction. Hence, the component corresponding to the ML algorithm and evaluation must be connected after the feature extraction component.  
When the flow is marked complete and flow-checking has completed successfully, it is passed to the back-end to start the code generation process. Figures 1 and 2 depict the flow-checking sequences for a typical ML flow leading to passing and failure of the process respectively. In the figure, it is assumed that the flow-checking begins after the flow is complete to make it easier for illustration purposes. Nonetheless, it is done whenever there is a change detected on the canvas.  
Figure 1: Flow-checking sequences for a valid ML flow.  
Figure 2: Flow-checking sequences for an invalid ML flow.
## 3 Numerical Validation of the Damping Estimation Methodology
### Validation of the Application of the Prony Method
#### 3.3.3 Application of the Prony Method
Figure 9: Results for the Alfa Pendular train for a speed of 135 km/h and irregularity September 2019: (**a**) original record, (**b**) original record and Prony estimates along the LD exponential, (**c**) FFT for the free vibration records, and (**d**) sinusoids that compose the main Prony record. Figure 10: Results for the Alfa Pendular train for speed of 135 km/h and irregularity October 2020: (**a**) original record, (**b**) original record and Prony estimates along LD exponential, (**c**) FFT for the free vibration records, and (**d**) sinusoid that compose the main Prony record. \begin{table}
\begin{tabular}{c c c c c c} \hline \(v\) (km/h) & **Irregularity** & \(f_{1}\) **(Hz)** & \(\xi_{1}\) **(\%)** & \(f_{2}\) **(Hz)** & \(\xi_{2}\) **(\%)** \\ \hline
135 & February 2018 & 10.66 & 2.14 & 11.98 & 2.12 \\ \hline
135 & September 2019 & 10.66 & 2.14 & 11.98 & 2.12 \\ \hline
135 & July 2020 & - & - & 11.98 & 2.13 \\ \hline
135 & October 2020 & - & - & 11.98 & 2.12 \\ \hline
165 & February 2018 & - & - & 11.98 & 2.13 \\ \hline
165 & September 2019 & 10.66 & 2.12 & 11.98 & 2.13 \\ \hline
165 & July 2020 & 10.66 & 2.13 & 11.98 & 2.11 \\ \hline
165 & October 2020 & 10.66 & 2.13 & 11.98 & 2.12 \\ \hline  & & & & \(\overline{f_{1}}=10.66\) & \(\overline{\xi_{1}}=2.13\) & \(\overline{f_{2}}=11.98\) & \(\overline{\xi_{2}}=2.12\) \\ \end{tabular}
\end{table} 
Table 3: Results from the application of the Prony method for validation of the methodology.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 3 A Model of Container-Based Virtual Network and Its Scalability
### The Model  
What follows is the list of the main features of the virtual network we aim to support with our architecture.  
**Interconnected Containers:** A virtual network consists of several interconnected containers each representing a VD and implementing either a complex device (e.g., a router, a switch, a web server, etc.) or a simpler atomic network function.  
**Multiple Interfaces:** Each container has several L2 interfaces. Each interface is assigned to exactly one virtual LAN. Conversely, a virtual LAN can be connected to several interfaces.  
**Traffic Segregation between LANs:** The traffic between interfaces assigned to a certain virtual LAN is kept separated from the traffic of any other LAN, from the cluster traffic, and from the Internet traffic.  
**Traffic Integrity:** Packets sent by an interface must be received by the destination interface with no modification.  
**Partial Virtualization:** For the sake of generality, we assume that part of the network is virtualized while the remaining part consists of physical devices and physical LANs.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Introduction to DL-Based Methods for Wake Detection
#### 3.3.4 Attention Modules  
One of the biggest issues when it comes to accurately detecting ship wakes is the presence of a complex background. Moreover, there is the general issue in object detection that is the unbalance between foreground and background. In object detection tasks, this problems can be mitigated by the use of attention mechanisms [84].  
Attention mechanisms, in general, work by re-weighting a feature map to highlight useful features and suppress background information that is semantically redundant.  
[PERSON] et al. [10] cited several works that exploit interesting attention mechanisms for their object-detection purposes:  
* [PERSON] et al. [85] increased classification accuracy by better utilizing small object contextual semantics through the usage of a global attention module.
* A spatial and scale attention module was developed by [PERSON] et al. [86] to allow varying kinds of objects with varying scale attributes to have varying intensity responses at each layer of the FPN.
* [PERSON] et al. [87] used a supervised semantic segmentation method to change the weights of the features of objects of different classes into different channels and, respectively, enhanced and weakened the object and background features in the spatial domain.  
In the context of ship wake detection, implementing an attention mechanism can significantly enhance a model's precision. By focusing on the wake's characteristics and minimizing the influence of the surrounding water and noise, the model can achieve higher accuracy and reliability.
# Polypphenols: Natural Antioxidants to Be Used as a Quality Tool in Wine Authenticity
## 2 Structural Information
### Non-Flavonoid Polyphenols
#### 2.1.2 Stilbenes  
Stilbenes are polyphenols featuring diverse biological properties and a complex structure that exhibits a limited but heterogeneous distribution in the plant kingdom [30]. Grapes are one of the richest sources of stilbenes that have also been detected in wines and oak wood. They share a common C\({}_{6}\)-C\({}_{2}\)-C\({}_{6}\) skeleton, containing two benzene rings, usually bonded by an ethylene, or ethane chain. Resveratrol as well as specific oligomers collectively mentioned as \"inducible\" phytoalgebras are produced by the grapevine as a defense response against pathogens [31] (Figure 2). Conversely \"metabolized\" viniferins are formed by enzymes released from pathogens in an attempt to eliminate toxic compounds [32]. Stilbenes are present as mono-, di-, and trimers and very complex oligomers. Resveratrol is probably the most studied compound of this subclass as it exhibits various therapeutic effects [33; 34; 35; 36; 37]. Studies have shown that resveratrol mimics effects of caloric restriction, exerts antioxidative anti-inflammatory properties, and is linked with the initiation and progression of many diseases through several mechanisms [37]. However, the major drawback as reported in most of the clinical trials is its poor bioavailability [37]. Resveratrol content is regulated by genetic and environmental factors. Viticultural practices aiming at lower yields as well as soft vitiification protocols can promote/maintain the resveratrol content in wine [31]. Most monomer stilbenes in _Vitis_ species are found as glycosylated or methoxylated derivatives of piceatannol and resveratrol [38]. Apart from _trans_-resveratrol they include _trans_ and _cis_ piceid (3,4'-5-Tribhydroxystilbene-3-beta-D-glucopyranoside), pterostilbene (trans-3,5-dimethoxy-4'-hydroxystilbene), piceatannol (3,4,3',5'-tetrahydro-trans-stilbene), and astringin (piceatannol 3-O-beta-D-glucose). Dimer stilbenes usually contain a monomer resveratrol moiety with the most commonly occurring compound being \(\varepsilon\)-viniferin. Figure 2 depicts a scheme of the formation of resveratrol oligomers and viniferins, initially reported from [PERSON] et al. (2016) [31]. [PERSON] et al. (2016) reported a significant increase of resveratrol (trans monomer, dimers, and two tetramers) and a simultaneous decrease in glycoside derivatives on _Aspergillus_. _Carbonarious_ inoculated Negro Amaro cv grapes [32]. Pentamers of resveratrol have been found present in other plants but not in the _Vitis vinifera_[38]. [PERSON] et al. (2014) reported the presence of the resveratrol hexamer Viniphenol A in vine stalks of _V. vinifera_[39].  
Figure 1: Basic structures of phenolic acids in wine.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 3 Review of Landslide Monitoring Equipment
### Geodetic Monitoring System in Small-Scale Landslide Modeling  
Geodetic monitoring system is based on innovative photogrammetric equipment for multitemporal landslide analysis [51] of image sequences obtained by a pair of high-speed stereo cameras [41]. Terrestrial laser scanner and structure-from-motion (SfM) photogrammetry surveys enabled determination of the surface of the slope model in the stages before and after the sliding [52].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Monitoring** & **Equipment** & **Measurement** & **References** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Geodetic \\ monitoring \\ \end{tabular} } & High-speed & Acceleration, displacement, & [PERSON] and [PERSON] [8], [PERSON] et al. [12], [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [19], [PERSON] et al. [27], [PERSON] et al. [41], [PERSON] et al. [42], [PERSON] et al. [43] \\ \cline{2-4}  & Laser scanner & Displacement, deformation & [PERSON] et al. [44], [PERSON] et al. [45] \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Geotechnical \\ monitoring \\ \end{tabular} } & Accelerometer & Acceleration, inclination, & [PERSON] et al. [19], [PERSON] et al. [41], [PERSON] et al. [42], [PERSON] et al. [43], [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [48], [PERSON] and [PERSON] [49] \\ \cline{2-4}  & Moisture transducer/soil & Moisture/water content & [PERSON] et al. [16], [PERSON] and [PERSON] [23], [PERSON] et al. [25], [PERSON] et al. [26], [PERSON] et al. [26], [PERSON] et al. [46], [PERSON] et al. [50] \\ \cline{2-4}  & \multirow{2}{*}{Pore pressure transducer} & Positive pore water pressure & [PERSON] and [PERSON] [10], [PERSON] et al. [12], [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [19], [PERSON] and [PERSON] [20], [PERSON] et al. [24], [PERSON] et al. [26], [PERSON] et al. [27], [PERSON] et al. [28], [PERSON] et al. [46], [PERSON] and [PERSON] [49] \\ \cline{2-4}  & Soil water potential & Positive pore water pressure, & [PERSON] and [PERSON] [23], [PERSON] et al. [26], [PERSON] et al. [27], [PERSON] et al. [28], [PERSON] et al. [44], [PERSON] et al. [50] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Review of the monitoring equipment for small-scale models.
## 4 Conclusions  
Based on the test results of various durations of UV aging for Nano-TiO\({}_{2}\)-modified SBS/Sasobit compound binders, the following conclusions can be drawn:  
1. With the increase in UV-aging times, the viscosity had a growth trend for all aged binders. However, the viscosity value of asphalt with nanoparticles increased less significantly compared to the blank sample.
2. The nanoparticles showed insignificant effects on the complex modulus of the asphalt binder at different temperatures. However, nanoparticles can enhance the property of resistance of UV aging in terms of phase angle.
3. The binders with nanoparticles after UV aging showed better fatigue-cracking resistance than the aged binders without nanoparticles due to lower fatigue factors.
4. The results show that the 2S2P1D model can accurately characterize the complex modulus of all UV-aged asphalt binders. However, the measured phase angle value is in poor agreement with the main curve, indicating that the asphalt binders after UV aging only met part of the time-temperature equivalence principle. In addition, the \(k\) and \(h\) values, as important model parameters, were almost the same and less than one for all UV-aged binders.
5. According to the black space curves and the master curves of the 2S2P1D model, all aged asphalt binders showed viscoelastic solid mechanical characteristics. Compared with the black space curve, the main curve of the 2S2P1D model could better identify whether the time-temperature equivalence principle was satisfied.
6. The black space diagram and the 2S2P1D model were used to investigate the viscoelastic properties of UV-aged binders. In further studies, validation of the model may require longer UV-aging durations for indoor simulations and longer outdoor exposure for all aged binders.  
Conceptualization, S.Y. and K.Y.; methodology, S.Y.; formal analysis, S.Y. and W.L.; investigation, S.Y.; data curation, S.Y. and W.L.; writing--original draft preparation, S.Y.; writing--review and editing, W.L.; supervision, K.Y.; funding acquisition, K.Y. All authors have read and agreed to the published version of the manuscript.  
This study was financed by the National Natural Science Foundation of China (NSFC Nos. 50808077, 51278188, and 51778224), the Project of Growth for Young Core Teachers from Hunan Province (Project No.: 521107031) and the Project of Development for Young Teachers in Hunan University (Project No.: 5211070313).  
Not applicable.  
The authors declare no conflict of interest.
# Short-Term Load Forecasting Based on Spiking Neural P Systems
## 2 Proposed Prediction Model
### LF-NSNP Model  
To construct a recurrent-like model, we consider a special NSNP system that contains just one neuron \(\sigma\), this is, in Definition 1, \(m=1\). There are no neurons that can send the spikes to the unique neuron \(\sigma\), so in Equation (2), \(n=0\). Therefore, the state and input-output equations are able to be rewritten as below:  
\[u(t)=u(t-1)-g(u(t-1)+x(t)) \tag{4}\]  
\[y(t)=f(u(t-1)+x(t)) \tag{5}\]  
where \(u(t)\) and \(u(t-1)\) denote the states of neuron \(\sigma_{i}\) at time \(t\) and time \(t-1  
Based on Equations (8) and (9), we construct a recurrent-like neuron, called a recurrent-like NSNP neuron. Figure 1 presents the functional construction of the recurrent-like NSNP neuron model. In the recurrent-like NSNP neuron, three nonlinear gates are introduced:  
\[r(t) =\rho(U_{t}y(t-1)+W_{r}x(t)+b_{r}) \tag{10}\] \[c(t) =\rho(U_{c}y(t-1)+W_{c}x(t)+b_{c})\] (11) \[o(t) =\rho(U_{c}y(t-1)+W_{o}x(t)+b_{o}) \tag{12}\]  
where \(r(t)\), \(c(t)\), \(o(t)\) are the reset gate, consumption gate, and generation gate, respectively; \(\rho(\cdot)\) is a nonlinear function, and it is often given by sigmoid; \(x(t)\) is the input of neuron \(\sigma\) at time \(t\), and \(y(t-1)\) are the output of neuron \(\sigma\) at time \(t-1\). Let \(f\equiv g\), and \(a(t)\) is given by  
\[a(t)=f(U_{a}u(t-1)+W_{a}x(t)+b_{a}) \tag{13}\]  
Thus, the state and input-output equations are given by:  
\[u(t)=r(t)\Theta u(t-1)-c(t)\Theta a(t) \tag{14}\]  
\[y(t)=o(t)\Theta a(t) \tag{15}\]  
where \(\Theta\) indicates the inner product of two vectors. Usually, \(f\) is given by the \(\tanh\) function. In Equations (10)-(15), \(U_{r}\), \(U_{c}\), \(U_{o}\), \(U_{a}\), \(W_{r}\), \(W_{c}\), \(W_{o}\), \(W_{a}\), \(b_{r}\), \(b_{c}\), \(b_{o}\), \(b_{a}\) are the parameters to be trained.  
The recurrent-like NSNP neuron model in Figure 1 is expressed by Equations (10)-(15). Figure 2 is the unfolding of the recurrent-like NSNP neuron in time \(t=1,2,\cdots,T\). The unfolding model is called the LF-NSNP model, which is used to perform short-term load forecasting in this study. Therefore, the LF-NSNP model is essentially a recurrent-like model. The advantage of the LF-NSNP model is that it can effectively capture the correlation between the temporal features of the electric load sequence. Moreover, the LF-NSNP model can easily be trained by using the usual BPTT algorithm because it is a recurrent-like model. The flowchart is shown in Figure 3.  
Figure 1: The recurrent–like NSNP neuron model.
# In Vitro Evaluation of a Non-Invasive Photoplethysmography Based Intracranial Pressure Sensor
## 2 Material and Methods
### Intracranial Chamber & Brain Phantom  
The intracranial chamber was designed to mimic the intracranial compartment within the human skull. The chamber was manufactured in Polyvinyl Chloride (PVC) with internal dimensions of 300 mm \(\times\) 85 mm \(\times\) 65 mm (W \(\times\) D \(\times\) H) and a wall thickness of 15 mm. The intracranial chamber had a total of six inlets/outlets for the connection with the rest of the cardiovascular circuit and two additional draining outlets.  
A brain phantom was enclosed in the intracranial chamber to recreate the cerebrovascular tissue within the skull. The brain phantom was developed in silicone gel (Sylgard 527, DOWSIL--Dow Corning, MI, USA), as this material mimics the mechanical properties of the brain under dynamic and static conditions [17]. The casting mixture was prepared by adding the two-part silicone gel in a ratio of 1:1. In addition, as the silicone gel is transparent with minimal scattering properties, Titanium Dioxide (TiO2) particles with a primary crystal size of 550 nm (Altiris 550, Venator Corporation, Teesside, UK) were added to the mixture in a ratio of (_weight/volume_) 0.1 g/100 mL to simulate near-infrared scattering. The solution of Sylgard 527 and Titanium Dioxide particles was then hand-mixed and sonicated before undergoing a degassing cycle into a vacuum chamber (i.e., to remove air bubbles which may cause optical heterogeneities within the phantom).  
The silicon mixture was poured into a cast of dimensions 150 mm \(\times\) 70 mm \(\times\) 50 mm (W \(\times\) D \(\times\) H), with a resulting total volume of 525 mL. The cast pre-contained a net of four silicone rubber vessels with an internal diameter of 1.5 mm and wall thickness equal to 0.5 mm (SP15-0.5-X-CLS, Hilltop Products Ltd., Warrington, UK), representing the superficial cerebral arteries on the surface of the brain. These were positioned at a depth of approximately 2 mm from the surface. Below this vascular net, a single silicone rubber vessel of an internal diameter of 3 mm and wall thickness 0.5 mm (SP30-0.5-X-CLS, Hilltop Products Ltd., Warrington, UK) simulated the basilar cerebral artery and aimed at aiding the mechanical actions of compression/extension of the cerebral tissue during the cardiac cycle. After an initial curing cycle of 48 h at ambient temperature, the silicone phantom was removed from the casting and cured in the oven at 125 \({}^{\circ}\)C for 60 min. Figure 2 shows the intracranial chamber environment, inclusive of the brain phantom. The extracerebral space in the intracranial chamber was filled with deionised water, and the chamber was sealed with a clear acrylic plate.
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Experiment II: IEEE CEC2017 Test Functions
#### 5.1.1 Terrain Constraints  
In various missions, the workspace for UAVs is different. According to the altitude, the terrain is divided into three types of terrain: plain areas, mountainous areas, and hilly areas. One of the most influential factors is the mountain peaks, and the influence of altitude change needs to be considered. Therefore, the model established in this paper is for UAV path planning in complex terrain including mountain peaks. The mathematical expression of the mountain peak model is:  
\[Z(x,y)=h\cdot e^{[-\frac{(x-x_{0})^{2}}{m_{1}}-\frac{(x-y_{0})^{2}}{m_{1}}]} \tag{28}\]  
where \((x,y)\) is the coordinate of the peak terrain in the horizontal plane; \((x_{0},y_{0})\) is the center point coordinate of the peak terrain in the horizontal plane; \(h\) represents the height parameter; and \(m_{1}\) and \(m_{2}\) reflect the steepness of the peak.