esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 2 Multimodal Feature Fusion for Image Preprocessing and Feature Enhancement
### Color Histogram Features  
To preserve global color distribution information and supplement the color cues that a CNN might otherwise overlook, especially for objects with similar appearances yet distinct coloration, we compute a 3D histogram in the LAB color space:  
\[Hist[d_{1},d_{2},d_{3}]=\big{|}\big{\{}(x,y)\big{|}L(x,y)\in B_{d_{1}},A(x,y) \in B_{d_{2}},B(x,y)\in B_{d_{3}}\big{\}}\big{|} \tag{9}\]  
Here, \(L\), \(A\), and \(B\) denote the three channels in the LAB color space. Each channel is divided into eight intervals, where \((d_{1},d_{2},d_{3})\) indicates the corresponding \(bin\) index is indicated, and the total number of counting bins is \(8\times 8\times 8=512\).  
Figure 4: Detailed network structure of the RNAM.  
Next, normalization is performed as follows:  
\[Hist_{norm}[d_{1},d_{2},d_{3}]=\frac{Hist[d_{1},d_{2},d_{3}]}{\sum_{i,j,k}Hist[ i,j,k]} \tag{10}\]  
The resulting features preserve overall color distribution and complement the texture and semantic features extracted by the CNN. As illustrated in Figure 5, the upper row presents our original garbage images, and the lower row shows the 3D histograms in LAB color space. From these visualizations, it is evident that each image contains several aggregation regions in the LAB color space. The position, shape, and size of these regions vividly reflect the image's primary brightness and chromaticity features. Taking the third image as an example, the main subject is a green bottle with a small area of colorful labeling. Most pixels are distributed near negative values on the a axis, indicating green; some pixels appear at positive values on the b axis, denoting a greenish-yellow region. Meanwhile, the L axis predominantly signifies illumination intensity and generally exhibits a lower distribution. The histogram displays one or two relatively concentrated clusters, suggesting that the color is fairly uniform and predominantly green.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 4 Proposed Method  
Despite the widespread adoption of the DCP-based transmission map estimation approach for image dehazing [4] and its various extensions [6; 8; 13], they still exhibit notable limitations in image quality. This paper introduces an image dehazing method inspired by [4], as outlined in Algorithm 1. Our proposed method harnesses multiscale and multiple features to compute the \(1\times 1\) DCPs for better transmission map estimation. Additionally, we incorporate an enhanced airlight estimation method and adaptive gamma correction to refine the estimation of the transmission map, aiming to address the quality issues of the image above.  
```
Input:\(I(x)\)--Hazy Image Output:\(I_{dz(x)}\)--Dehazed Image Procedure DehazingProcess(\(I(x)\)) Step 1: Resize \(I(x)\) to generate \(\times 0.5\) and \(\times 0.25\) of \(I(x)\) Step 2: Estimate an airlight adaptively using Equation (6). Multiscale multiple features transmission map estimation \(\triangleright\)Begin Multi-scale multiple features transmission map estimation Step 3: Generate three \(1\times 1\) DCPs based on \(I(x)\), \(\times 0.5\) and \(\times 0.25\) of \(I(x)\). Step 4: Estimate three intermediate transmission maps based on DCPs and airlight from steps 2 and 3, followed by a Guided filtering. Step 5: Each intermediate transmission map is rescaled to match the original size before being averaged, resulting in a transmission map \(t^{\omega r}\). Step 6: Generate inverted and Euclidean difference images with Equations (2) and (3). Step 7: Repeat step 3 to step 5 to yield \(t^{\omega r}\), and \(t^{\varepsilon}\). \(\triangle\)End Multi-scale multiple features transmission map estimation Step 8: Utilizing \(t^{\omega}\), \(t^{\omega}\), and the image \(I^{\varepsilon}(x)\) derived from Equation (4), an enhanced transmission map \(t^{\text{th}}\) is computed using Equations (7)-(9). Step 9: Refine \(t^{\omega t}\) by adaptive gamma correction using Equation (10). Step 10: Recover the final image, \(I_{dz(x)}\) based on \(t^{\text{th}}\) in step 9.
```  
**Algorithm 1** Enhanced Transmission Map with Multiscale Multiple Features DCP and Adaptive Gamma Correction
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 1 Introduction
### Driving Studies  
The categorization of driving research methodologies into Driving Simulators, Naturalistic Driving (ND) studies, Instrumented Vehicle Studies (IVS), and CDS serve as a critical framework for dissecting and understanding the different aspects of driving behavior, vehicle maneuvering, and interactions within the traffic ecosystem. This categorization is instrumental in integrating ET research.  
Driving simulators offer a controlled environment for safely assessing driver behavior and cognitive abilities [5, 6]. These studies highlight both the absolute and relative validity of simulators in mimicking real-road conditions, albeit with noted limitations in replicatingthe full spectrum of driving errors, particularly those related to vehicle positioning and speed regulation. The controlled nature of simulators allows for the manipulation of specific variables and the safe assessment of driver responses to hypothetical scenarios, which are impractical or dangerous to test in real-world settings.  
ND studies are pivotal for capturing real-world driving behavior by equipping vehicles with cameras and sensors [7]. This unobtrusive data collection approach offers authentic insights into the dynamic interactions between drivers, vehicles, and traffic environments. The strength of ND studies lies in their ability to provide a rich, contextual understanding of driving behavior without the artificial constraints imposed by experimental settings.  
IVS are similar to ND studies but utilize real vehicles outfitted with advanced measurement tools [8]. However, IVS can be more focused in their objectives, often geared towards quantifying specific aspects of driving behavior or vehicle performance under naturalistic conditions. This approach enables a detailed analysis of driver strategy, vehicle usage, and decision-making processes. IVS can serve as a bridge between the authentic environments of ND studies and the controlled conditions of driving simulators.  
CDS involve the operation of real cars within a managed setting, such as a closed circuit [9]. This method allows researchers to study driving behavior under adjustable conditions, facilitating a focused examination of specific hypotheses about driver performance or the effectiveness of interventions. While lacking the ecological validity of ND studies, controlled driving allows for the precise manipulation of experimental variables, making it a valuable tool for testing specific driving aids or interventions under semi-naturalistic conditions.
## 3 Results
### Calculation Method of Wave Crest Height and Panel Impact Force
#### 3.2.1 Calculation Method of Wave Crest Height
\(K_{r}\) is obtained by integrating the average wave energy flow in one cycle \(R\) of the analytical solution; \(A\) is the amplitude (for a regular wave, \(A=H/2\)); \(d\) is the water depth; \(\rho\) is the density of water; \(g\) is gravitational acceleration; \(z\) is the distance between the water quality point and the clean water surface; the coordinate origin is located on the clean water surface; the coordinate upward is positive; \(k\) is the wave number, \(k=2\pi/L\); \(L\) is the wavelength; \(\sigma\) is the wave angle frequency \(\sigma=2\pi/T\); and \(T\) is the period. \(\eta_{0}\) is the maximum height of the incident wave surface from the stillwater surface, which can be calculated by the second-order Stokes wave theory; \(H_{i}\) is the incident wave height. To obtain the change rule of crest height \(\eta\) and influencing factors, we set a certain wave steepness \(H_{i}/L\), change \((1+K_{r})*\Delta h/H_{i}\), and we investigated the change rule of \((1+K_{r})*\Delta h/H_{i}\) and \(\eta\) (see Figure 4). It can be seen from Figure 4 that, under the condition of \(H_{i}/L\), we obtained the relationship of \((1+K_{r})*\Delta h/H_{i}\); \(\eta\) is linear, and the slope of the change trend is basically the same, so it can be considered that \((1+K_{r})*\Delta h/H_{i}\) and \(\eta\) is linear. We also set certain values of \(\Delta h/H_{i}\) and change wave steepness, \(H_{i}/L\), and we investigated the change rule of \(H_{i}/L\) and \(\eta\) (see Figure 5). It can be seen from Figure 5 that wave steepness \(H_{i}/L\) and \(\eta\) also show linear changes, but the slope of the change trend is basically zero. Therefore, it is considered that the influence of crest height is small and can be ignored. To sum up, for Equation (6), only \((1+K_{r})*\Delta h/H_{i}\) is considered between the height of wave crest and each influence factor for its influence, the least-squares method is used to linearly fit the data, and the fitting empirical formula is derived as follows:  
\[\eta=12.75(1+K_{r})*\frac{\Delta h}{H_{i}}-0.56 \tag{10}\]
# Hydraulic Vibration and Possible Exciting Sources Analysis in a Hydropower System
## 3 Materials and Mathematical Model  
### Governing Equations  
For a single pressurized pipe, as shown in Figure 3, the simplified governing equations for the internal steady-oscillatory flow, including the momentum equation and continuity equation, can be deduced by simplifying differential equations of transient flow [33]. The two governing equations are as below,  
\[\frac{\partial H(x,t)}{\partial x}+\frac{1}{gA}\frac{\partial Q(x,t)}{ \partial t}+\frac{fQ^{2}(x,t)}{2 gDA^{2}}=0 \tag{1}\]  
\[\frac{\partial Q(x,t)}{\partial x}+\frac{gA}{a^{2}}\frac{\partial H(x,t)}{ \partial t}=0 \tag{2}\]  
where \(H=\overline{H}+h\), \(Q=\overline{Q}+q\), \(H\) is the instantaneous pressure head, \(Q\) is the instantaneous discharge, \(\overline{H}\) and \(\overline{Q}\) are the average value, and \(h\) and \(q\) are the fluctuation value from the average. \(x\) is length to the inlet, \(t\) is the time, \(g\) is the gravity acceleration, \(f\) is the friction factor, \(A\) is the cross-sectional area of the pipe, \(D\) is the diameter of the pipe, and \(a\) is the wave speed.  
Figure 3: Single characteristic computing pipe.  
Figure 2: Comprehensive characteristic curves of the turbine.  
By substituting the instantaneous items into Equations (1) and (2), and removing the average items, the following form of equations can be obtained and written as  
\[\frac{\partial\Phi(x,t)}{\partial x}+B\frac{\partial\Phi(x,t)}{\partial t}+G\Phi( x,t)=0 \tag{3}\]  
where \(B=\left(\begin{array}{cc}0&L\\ C&0\end{array}\right)\), \(G=\left(\begin{array}{cc}0&R\\ 0&0\end{array}\right)\), \(\Phi(x,t)=\left\{\begin{array}{c}h(x,t)\\ q(x,t)\end{array}\right\}\), \(L=1/(gA)\), \(C=Ag/a^{2}\), \(R=f\overline{Q}/(gDA^{2})\), \(L\), \(C\), and \(R\) are defined as the hydraulic inductance, hydraulic capacitance, and hydraulic resistance of the fluid in the pressurized pipe, respectively.  
Application of the Laplace transformation yields the following subsidiary equation,  
\[\frac{d\dot{\Phi}(x,s)}{dx}+(Bs+G)\dot{\Phi}(x,s)=0 \tag{4}\]  
where \(\dot{\Phi}(x,s)=\left\{\begin{array}{c}\hat{h}(x,s)\quad\hat{q}(x,s)\end{array} \right\}^{T}\) is the transformation of \(\Phi(x,t)\), \(\dot{\Phi}(x,s)=\int_{0}^{\infty}\Phi(x,t)e^{-st}dt\). \(s=\sigma+i\omega\), \(s\) is the complex frequency, \(\sigma\) is coefficient of attenuation, and \(\omega\) is angular frequency.  
Assuming the oscillatory pressure head and discharge at the upstream end are known, the complex oscillatory pressure head and discharge are expressed as  
\[\left\{\begin{array}{c}\hat{h}(x,s)\\ \hat{q}(x,s)\end{array}\right\}=\left(\begin{array}{cc}\cosh\gamma x&-Z_{C} \sinh\gamma x\\ -\frac{1}{Z_{C}}\sinh\gamma x&\cosh\gamma x\end{array}\right)\left\{\begin{array} []{c}\hat{h}(0,s)\\ \hat{q}(0,s)\end{array}\right\} \tag{5}\]  
where \(Z_{C}=\gamma/(Cs)\) is the characteristic impedance of the pipe, \(\gamma=\sqrt{Cs(R+sL)}\) is the complex propagation constant, and \(x\) is the length from the inlet.
# Automotive Cybersecurity Application Based on CARDIAN
## 4 Case Study
### Deployment of Bayesian Network  
The Bayesian network was built after a careful analysis of approximately 300 h of acquisition, distributed across four different vehicles, operating in the same conditions and compatible with the SAE J1939 protocol. From the analysis of the data produced, it emerged that only a small number of parameters are suitable for analysis, as they are sent on the bus at intervals of less than 10 ms. Furthermore, only data presenting at least one variation during the driving and, therefore, acquisition period were used.  
The nodes of the network, and the definition of the classes to which the data belong, have been described in the previous section (Figure 5).  
The following table highlights, explicitly and for each of the nodes, their parent nodes, i.e., those nodes whose state inevitably conditions the state of the node itself. The Bayesian network was then implemented using Vivado software in VHDL language.  
The hardware structure of a Bayesian network [25] is very simple; each node is composed of the following:  
* a memory unit containing the a priori and conditional probability distributions;
* a multiplier, already described by the IPs present within Vivado, for calculating probabilistic inference.  
Nodes that do not have parents implement a ROM memory, so it is possible to treat the input data as an address. However, the nodes which do have parents carry out the calculation of the output probability by applying the previously mentioned Bayes' theorem.  
Figure 5: Structure of the Bayesian network.  
As can be seen from Figure 6, each node receives as input a memory address of variable width, depending on the node, and a probability represented with a 16-bit fixed point coding. Following each multiplication, in order to guarantee the uniformity of the data, a truncation is carried out, taking only the 16 most significant bits. The final attack detection node contains a comparator, so as to provide an affirmed output only if it exceeds the threshold of 0.5.  
Figure 6: Hardware deployment of the Bayesian network.
## 3 Methodology
### Data Processing
#### 3.2.3 Patching Missing Points  
After the removal of anomalies/redundant points from the trajectory collection, there will be a significant number of missing trajectory points. In order to make the trajectory complete, we need to patch in these missing trajectory points. Since the previous processing steps retained enough trajectory points representing the vessel's motion state, the missing trajectory points that need to be inserted are mostly associated with relatively stable uniform motion states. Therefore, a simple linear interpolation method is used to fill in the missing trajectory points with evenly spaced intervals in time. When the time interval between two adjacent trajectory points, \(Po_{i}\) and \(Po_{j}\), is greater than the specified time interval threshold \(T_{interp}\in\mathbb{R}\), the linear interpolation method is used to interpolate five state variables of the missing trajectory points between \(Po_{i}\) and \(Po_{j}\). These variables include the heading angle (\(cg_{i}\)), speed over ground (\(sg_{i}\)), longitude (\(lon_{i}\)), latitude (\(lat_{i}\)), and timestamp (\(t_{i}\)). The number of inserted points is denoted as \(n\in\mathbb{N}\).  
\[n=\left\lfloor\frac{t_{j}-t_{i}}{T_{interp}}\right\rfloor \tag{14}\]  
We use linear interpolation to patch in the missing points for the five state variables of the missing trajectory points. The insertion formula is as follows:  
\[\begin{bmatrix  
between each trajectory point in the trajectory collection is \(T_{samp}\), ensuring uniformity and stability of the trajectory points, ensuring consistency between the data, and making a trajectory prediction analysis easier.
# Measurement of Visible Radiation through a _Sansevieria cylindrica_-Based \"Living Sensor\"
###### Abstract  
This research activity regards the development of a sensor based on a _Sansevieria cylindrica_ plant for the measurement of visible radiation. The proposed solution, based on the adoption of a soil-plant system as a chemo-electrical transducer, goes beyond \"classical\" silicon-based approaches that are not biodegradable nor eco-friendly and that produce CO\({}_{2}\) from the production step to the disposal phase. It is worth noting that no toxicity can be associated with plants and, due to the natural process of photosynthesis, these systems, used as living sensors, are even able to absorb carbon dioxide from the environment. The working principle of the proposed device based on the metabolic processes of the natural organisms present in the living system, soil and plant, as a function of visible radiation will be presented here. Particular emphasis will be also given to the analysis of the visible radiation spectrum, the metrological characterization, the performance of the device, and the analyses in terms of insensitivity to other external physical quantities. The obtained results evince the suitability of the proposed device which presents the prerogative of being environmentally friendly, self-generating, battery-less, simple, mimetic, low-cost, non-toxic, and biodegradable. The aforementioned features pave the road for a disruptive technological approach for an ecological transition which can impact the variegated applied field, including in the security, cultural heritage, smart home, and smart agriculture aspects.  
living sensor; _Sansevieria cylindrica_; soil-plant system; light radiation measurements; environmentally friendly device; ecological transition
## 3 Architecture and Problem Formulation
### System Model  
A single cell is considered with diverse distribution of MTC and HTC devices with differing service requirements coexisting. A base station (BS) is at the center of this cell environment with a massive number of MTC users randomly distributed over the entire cell area as depicted in Figure 1. Consider a total of \(N\) users being present in the BS coverage with \(J\) conventional HTC and \(K\) MTC devices. uRLLC service nodes are provided a direct connectivity to the BS in order to ensure low latency and reliable communication link. Direct access to BS by mMTC devices may cause a serious congestion problem due to massive concurrent access event by all devices. A clustered approach is considered for mMTC devices which are grouped and relayed to counteract any congestion at the BS. An MTC gateway (MTCG) is responsible for relaying data of connected MTCDs to the BS for a seamless access experience. An MTCG can either be an MTC device itself or an HTC device relaying for the connected mMTC devices. Because of privacy and data integrity concerns, an mMTC device is selected as the gateway node such that battery lifetime and energy consumption are improved. A decode-and-forward (DF) protocol [35] is used to relay MTCDs traffic for the proposed cellular environment. The selection of a combined access protocol based on a typical LTE-A time-frequency resource division is considered to provide access to HTC and MTC users. Consider an \(m_{th}\) mMTC device with \(b_{m}\) data bits to be transmitted using a time period -\(T_{i}\). An MTCG has a minimum rate requirement of \(b_{h}\) bits and the transmission times allocated to MTCD and MTCG are denoted, respectively, as \(t_{m}\) and \(t_{h}\). This involvesa two-stage operation in the uplink frame through which each MTCD communicates with the BS via MTCG. The uplink resource is allocated directly to the MTC and HTC devices to comply with the reliability condition and ensure minimum delay.
## 4 Routing Layer  
Routing is an important aspect in any multi-hop network. The routing layer is in charge of selecting the most convenient paths in order to deliver data towards the destination. In some underwater network applications such as rescue missions, short network deployment times are required. Moreover these applications do not allow for previous deployment planning. In such scenarios, routing protocols must be able to determine the best routes to the destination without any pre-existing network knowledge.  
The currently existing routing protocols for terrestrial networks can be classified into two major categories: proactive and reactive. On the one hand, proactive routing protocols have a large signaling overhead every time there is a topology change, which occurs continuously underwater with frequentnode movements. On the other hand, reactive routing protocols, which are designed for dynamic scenarios, have large delays and are heavily aggravated underwater.  
In this section, the main routing protocols for UWSNs are discussed and shown in Figure 6. Efficient routing is a key element in these types of networks. Hence, routing protocols need to take into account their special features and the requisites of new application fields for UWSNs. The presented protocols can be classified in many ways according to different criteria weighting. The classification that has been followed meets the criteria indicated in each sub-section, although many protocols could belong to several classes.
## 3 Results and Discussion
The measurements reported here were made at 633 nm which is near the onset of the surface plasmon resonance. Therefore, \(\chi^{(3)}\) at this wavelength is complex, with real and imaginary parts. The theoretical treatment mentioned above predicts that the maximum value of \(\chi^{(3)}\) is proportional to \(\mathrm{T_{1}T_{2}(1-d/d_{0})/d^{3}}\), where \(\mathrm{T_{1}}\) is the excited state lifetime, \(\mathrm{T_{2}}\) is the dephasing time, \(\mathrm{d_{0}}\) is a characteristic threshold diameter of the system and \(\mathrm{d}\) is the diameter of the nanoparticle. Therefore, \(\mathrm{d}<\mathrm{d_{0}}\), \(\chi^{(3)}\) increases as \(\mathrm{d^{-3}}\). Although the particle concentration was not known for our samples, the optical density (\(\alpha\)) was similar (about 0.05 at 633 nm). The particle concentration would alter \(\alpha\) the same way it would alter K. As long as the optical density was kept the same, we could compare the Kerr coefficients at the fundamental level. As the results show, the Kerr coefficient follows the theory and increases as \(\mathrm{d^{-3}}\). An alternative interpretation of the \(\mathrm{d^{-3}}\) dependence is that for any given excitation the fraction of electrons excited in a nanoparticle is higher in a smaller particle since the total number of electrons in a smaller nanoparticle is lower. Therefore, a larger optical nonlinearity can be expected in a smaller nanoparticle [20]. This report provides the first experimental demonstration of the fundamental correlation between third order nonlinearity and the size of metal nanoparticles. It was not possible to demonstrate this correlation earlier, presumably because of thermal effects, which could be significant in other nonlinear optical measurements that utilized ultrashort intense optical pulses. These measurements include time-resolved four-wave mixing and optical Kerr effects using femtosecond pulses. Those measurements have produced a wealth of information, including excited-state lifetime, dephasing time, multiphoton processes and many others, although the effect of particle size was not clearly delineated. Use of intense optical pulses may excite interband transitions in gold nanoparticles and, according to theory, does not lead to any particle size dependence. Only intraband transitions involving surface plasmons are expected to show a particle size dependence of \(\chi^{(3)}\), as observed here. These metal nanoparticles within glass materials are expected to have various applications in ultrafast electro-optic switching/modulation, Kerr cells and others. Although the present results are for metal nanoparticles in glass, the results can be extended to metal nanoparticles in other transparent dielectric media.