esa-sceva/satcom-chunk-collection huggingface dataset:
# Initial Structure Design and Optimization of an Automotive Remote Head-Up Display
###### Abstract  
Aiming at the problems of difficult construction and occlusion of the initial structure of an automotive augmented reality head-up display (AR-HUD), a method to quickly build the initial structure of an automotive AR-HUD is proposed. Firstly, the position of the mirrors in the initial structure is calculated based on the Rodrigues rotation formula. Secondly, the position of the mirrors is restricted by constraints during optimization to prevent the problem of structural occlusion. Finally, a virtual image display system with a visual distance of 7.5 m and a field of view angle of \(10^{\circ}\times 5^{\circ}\) is designed. The image quality analysis of the optimized system shows that the light spots in each field of view are all within Airy spots. At the spatial cutoff frequency of the virtual image plane of the optical system, the modulation transfer function (MTF) value of the full field of view is basically greater than 0.5, and the distortion is less than 1%. Finally, using the image for simulation, the results of the simulation image are satisfying, which proves the validity and feasibility of the structural design. It provides a useful reference for the structural design of the remote head-up display system.  
automotive head-up display; initial structural design; free-form surface; off-axis reflection Article
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Delay Functions
#### 3.5.3 Exponential Delay Function  
The last suggested function delays RREQ messages more for the nodes with small estimated \(PDR\) and less for nodes with higher estimated \(PDR\) in comparison with the Linear Delay function. The differences are clear from Figure 2. The shape of the curve is determined by the following function depending on three parameters, \(BASE\), \(MD\), and \(PDR\) (Algorithm 3).  
```
0:\(BASE\geq 2\); \(MD>1\); \(PDR\in[0,1]\)
0:\(D\geq 0\)\(D\gets MD*(BASE^{1-PDR}-1)/(BASE-1)\) return\(D\)
```  
**Algorithm 3** Exponential Delay function specification  
As we can see from the function specification, the \(BASE\) value influences the bend of the curve, and the \(MD\) value scales the curve in the vertical direction. We expect that this function could have better results than the Linear Delay function because it penalizes more the nodes with worse \(PDR\). We also expect that the \(RDD\) metrics values could be smaller in comparison with the Linear Delay function for the similar \(PDR_{avg}\) values.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 3 Problem Definition  
Before delving into the description of our methodology, we first present the definition of an unsupervised EA. In this study, we concentrate exclusively on the relation structure of KG; thus, a KG can be represented as \(G=(E,R,T)\), where \(E,R\) denote the sets of entities and relations, respectively. The relation structure is defined as \(T\subseteq E\times R\times E\), with each relation triple \(\langle e_{h},r_{k},e_{t}\rangle\in T\) signifying the relationship between two entities. Given two KGs \(G^{1}=(E^{1},R^{1},T^{1})\) and \(G^{2}=(E^{2},R^{2},T^{2})\), the EA task aims to identify a set of aligned entities that share equivalent meanings across these two KGs, denoted as \(S=\{(e_{i}^{1},e_{j}^{2})\mid e_{i}^{1}\in E^{1},e_{j}^{2}\in E^{2},e_{i}^{1} \leftrightarrow e_{j}^{1}\}\), where \(\leftrightarrow\) indicates alignment between a entity pair. In supervised EA tasks, there exist seed alignments as training sets. This work emphasizes unsupervised EA method, so seed alignments are not addressed in this study.  
During both method description and experimentation, it is customary to consolidate two KGs within an encoder framework as a primal graph represented by \(G=(E,R,T)\). Formally, we utilize boldface letters for embedding vectors; for instance, let **E** represent the embedding matrix corresponding to the entity set \(E\).
## 3 Results
### Effect of CBD on Phosphorylation Levels of NF-kB and MAPKs  
To evaluate whether CBD has therapeutic effects on COPD through the MAPKs and NF-kB signaling pathways, the levels of both the phosphorylated and non-phosphorylated forms of proteins belonging to these pathways were measured in lung tissues. The phosphorylation levels of ERK1/2 (\(p\) = 0.009), p38 MAPK (\(p\) < 0.001), and NF-kB (\(p\) = 0.004) were higher in the COPD group than in the control group, but were significantly lower in mice treated with RO (ERK1/2: \(p\) = 0.009, p38 MAPK: \(p\) < 0.001, and NF-kB: \(p\) = 0.014), CBD 100 (ERK1/2: \(p\) = 0.006, p38 MAPK: \(p\) = 0.001, and NF-kB: \(p\) = 0.012), or CBD 200 (ERK1/2: \(p\) = 0.004, p38 MAPK: \(p\) < 0.001, and NF-kB: \(p\) = 0.021) than in the COPD group (Figure 6).
## 2 Analytical Framework
### Topological Parameters and Their Characteristics  
Topological measures have been widely used to capture various network characteristics in complex urban environments [25; 26]. Recent studies highlight their applicability in assessing transportation network vulnerability, particularly when critical links are disrupted. These measures help evaluate system resilience, identify critical road segments, and assess network performance under disruptive conditions. For instance, [PERSON] et al. [27] evaluated the vulnerability of the Shanghai Freeway Network and proposed indices to quantify topological vulnerability and identify critical roads. Similarly, [PERSON] and [PERSON] [8] studied the network characteristics during flooding, highlighting the usefulness of topology metrics for evaluating infrastructure performance and risk identification. Collectively, these studies underscore the significance of topological parameters in evaluating transportation network performance and vulnerability, providing critical insights into their role in assessing impacts under various disruption scenarios.  
This study employs four key topological parameters--street connectivity, closeness centrality, betweenness centrality, and eigenvector centrality--to assess transportation system vulnerability from each parameter's perspective when critical links are disrupted. These parameters have been extensively used to capture the complex properties of topological networks [28]. For instance, recent studies highlight the effectiveness of topological measures in assessing diverse network characteristics, such as traffic flow patterns [25; 29], accessibility [30], origin-destination trip distribution [31], and urban development patterns [26; 32], utilizing topological representations of transportation networks. Such analyses are critical for understanding human activity distribution and transportation flow dynamics within urban transportation networks as interconnected and complex systems [33]. Each topological parameter used in this study, along with its formula and characteristics, is described below.
## 3 Physics-Informed Neural Networks (PINNs)
### PINNs in Binary Classification Problems  
While PINNs are often used for regression problems, they can also be adapted for binary classification tasks by employing a loss function that combines a data-driven term, \(\mathcal{L}_{data}\), with a physical loss function capturing the difference between predicted and physically derived classes. Recent work has demonstrated the application of PINNs in binary classification scenarios, such as distinguishing between earthquakes and explosions. By integrating a convolutional neural network (CNN) with physics-based empirical constraints, these models enhance generalization to new regions [20]. The combined loss function includes a sparse categorical cross-entropy term and a physics-informed term derived from empirical relationships, such as the P/S amplitude ratio or the discriminant of earthquakes and explosions ratio (4), where \(P\) and \(S\) represent seismic event wave amplitudes. This method underscores the potential of physics-informed constraints to improve robustness and performance by faithfully representing the underlying physical phenomena.  
\[\mathbf{\theta}^{*}=\underset{\mathbf{\theta}\in\Theta}{\arg\min}\left(-\lambda_{d} \sum_{i=1}^{N}y_{i}\log(f(x_{i};\mathbf{\theta}))-\lambda_{F}\sum_{i=1}^{N}\hat{u} _{F}(x_{i})\log(f(x_{i};\mathbf{\theta}))\right), \tag{4}\]  
where both data and physics-informed losses are sparse categorical cross-entropy standard loss functions, \(\lambda_{data}\) and \(\lambda_{F}\) are the weights of the data loss and physics base loss, \(y_{i}\) represents the actual target values, \(f(x_{i};\mathbf{\theta})\) is the estimation of the model over the input \(x_{i}\), and \(\hat{u}_{F}(x_{i})\) is the class based in the empirical relationship of certain features of \((p_{i},s_{i})\in x_{i}\) bounded by \(h\), such that:  
\[\hat{u}_{F}(x_{i})=\begin{cases}\text{Explosion class},&\text{if }\frac{p_{i}}{s_{i}}>h\\ \text{Earthquake class},&\text{if }\frac{p_{i}}{s_{i}}\leq h,\end{cases} \tag{5}\]  
where \(h\) is the \(P/S\) amplitude ratio threshold that can separate explosions and earthquakes. As can be observed, \(\hat{u}_{F}\) acts as an oracle for the network through a loss function that, given an empirical physical condition, determines the occurrence of an earthquake of seismic or explosive origin. Likewise, in the proposal Section 5, we describe the use of binary cross-entropy as a model-data loss function (7) and a novel way to attend a set of metallurgical rules to create a physics-informed loss function (8) for our binary prediction problem.  
Figure 3: Influence of \(\mathcal{L}_{F}\) and \(\mathcal{L}_{B}\) in a physics-informed neural network architecture. In the figure, \(\sigma\) represents the activation functions, \(\ell_{i}\) different rules, physical equations, and \(b_{i}\) initialization rules or boundaries.
# The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations
## 3 Challenges for Risk Assessment and Mission Control in SAR Operations in Post-Disaster Scenarios
**Computer vision for information extraction**: AI and computer vision enable people detection, position detection and localization from image and video data;
3. **Smart data filtering/aggregation/compression**: a large amount of data is collected from sensors, robots, and cameras in the intervention area for several services (e.g., map building, scene and action replay). Some of them can be filtered, and others can be downsampled or aggregated before sending them to the edge/cloud. Smart policies should be defined to also tackle the high degree of data heterogeneity;
4. **Device Management**: some application functionalities can be pre-deployed on the devices or at the edge. The device management should also enable bootstrapping and self-configuration, support hardware heterogeneity and guarantee the self-healing of software components;
5. **Orchestration of software components**: given the SAR application graph, a dynamic placement of software components should be enabled based on service requirements and resource availability. This will require performance and resource monitoring at the various levels of the continuum and dynamic component redeployment;
6. **Low latency communication**: communication networks to/from disaster areas towards the edge and cloud should guarantee low delays for a fast response in locating and rescuing people under mobility conditions and possible disconnections.
## 5 Simulation Results and Discussion  
To assess and evaluate the effectiveness of the suggested approach for coordinating the controller coefficients of PSSs and UPFCs in power systems, considering both economic and technical factors, this study employs the IEEE 9-bus and IEEE 39-bus test systems. In this investigation, a synchronous reference frame is employed to model these systems, and it assumes that the speed and rotor angle of each generator are compared with the reference generator (slack) angle.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 4 Experiments and Results
### Analysis of Results
#### 4.1.5 Number of Base Stations  
For a rectangular disaster affected area, the base stations can be placed only in the corners, as ground-level access inside the terrain may not be possible. Therefore, the number of base stations can be one, two, three, or four. Figure 18g shows the relationship between the number of drones required to cover the region and the number of base stations used. From the graph, we can see that the number of drones required decreases with an increasing number of base stations. We then compare the average leftover energy with the number of base stations; the graph is shown in Figure 18h. The curves in these two plots lead towards the solution of a double optimization problem (maximizing the average leftover energy while at the same time minimizing the number of drones required), and in our case, a value of two for the number of base stations is found to be optimal.  
We further consider the corners at which the base stations can be placed. They can be placed either in adjacent corners or diagonally opposite to each other. As explained in the description of the region-filling algorithm, the first base station is always placed at (0, 0). A plot showing \(H_{max}\) vs. the number of drones required when the second base station is placed at various corners is shown in Figure 19a. A similar plot for the average leftover energy is shown in Figure 19b. These plots suggest that placing the second base station diagonally opposite to the first yields the maximum efficiency.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results
### Link Budget
#### 5.1.1 Effect of Weather Attenuation or Received Power  
To study the effect of attenuation due to weather, the received signal was analyzed for a fixed-point drone. The received signal strength is noted for every elevation angle the satellite achieves with the drone. This can be visualized using Figure 5.  
This analysis was carried out for three cases, namely the following:  
* Free space.
* Atmospheric attenuation only.
* Atmospheric and rain attenuation.  
For this analysis, the UAV was considered to be at a height of 500 m above the Earth's surface. The power received for each of the three cases mentioned above at different elevation angles from the drone can be seen in Figure 6. Since the drone has an elevation mask of 60\({}^{\circ}\), the elevation angle varied from 60\({}^{\circ}\) to 120\({}^{\circ}\).  
From Figure 6, it can be inferred that the received power is reduced significantly due to atmospheric attenuation compared to the one received in free space. Rain attenuation further reduces the received power. There is a negligible loss in signal strength without  
Figure 5: Visualization of analysis setup.  
attenuations, and hence, the received power is approximately constant with varying elevation angles. Similarly, the carrier-to-noise ratio or SNR variation due to various attenuation is shown in Figure 7. From the results, we can infer that weather effects significantly impact the signal quality. Bad weather may result in a lower range for the satellite. The signal quality would be best when the elevation angle would be 90 degrees, i.e., the satellite, the UAV, and the center of the Earth would fall in a straight line.  
Latency and FSPL do not vary with the attenuation. This is because these parameters are functions of distance only. The distance between the UAV and the satellite would vary with changes in the elevation angle. The plots showing their variation with the elevation angle are shown in Figures 8 and 9, respectively.
# Bee Swarm Metropolis-Hastings Sampling for Bayesian Inference in the Ginzburg-Landau Equation
## 4 Bayesian Inference for the Ginzburg-Landau Equation
### The GL Equation and Its Soliton Solution  
Consider the complex GL equation with initial and boundary conditions as presented in [27]:  
\[i\frac{\partial u}{\partial t}+\frac{1}{2}\frac{\partial^{2}u}{\partial x^{2}}+ \frac{1}{2}(\beta_{0}-i\beta_{1})\frac{\partial^{2}u}{\partial y^{2}}+(1-i \gamma_{1})|u|^{2}u+iu=0, \tag{1}\]  
\[u(x,y,0)=\varphi(x,y),\quad(x,y)\in\mathbb{R}^{2},\]  
\[u(x,y,t)=g(x,y,t),\quad(x,y)\in\Gamma,\quad 0<t\leq T,\]  
where the system can be viewed as a controllable optical pulse model:  
\[u(x,y,t)=N(\beta_{0},\beta_{1},\gamma_{0}).\]  
By adjusting the parameters \(\beta_{0}\), \(\beta_{1}\), and \(\gamma_{0}\), we can simulate the propagation of optical pulses in mode-locked lasers, thereby studying the evolution of spatiotemporal dissipative optical solitons. Here, \(\beta_{0}\) denotes the group velocity dispersion coefficient, which influences pulse broadening: \(\gamma_{0}\) is the third-order nonlinear coefficient related to self-phase modulation effects; and \(\beta_{1}\) represents the gain coefficient of the amplifying fiber.  
Particularly, when the selected optical parameters balance dispersion, fiber nonlinearity, laser gain saturation, and gain bandwidth filtering effects, the governing equation (1) admits an exact solution [27]. Define  
\[a=\sqrt{\frac{\beta_{1}}{\gamma_{0}}-\beta_{0}},\quad b=\frac{2-\gamma_{0}}{2 \gamma_{0}}.\]  
Then, Equation (1) has the analytical solution  
\[u(x,y,t)=\sqrt{\frac{2}{\gamma_{0}}}\exp[i(x+bt)]\operatorname{sech}\left( \sqrt{\frac{2}{\beta_{1}}}[a(x-t)+y]\right). \tag{2}\]  
In particular, for the parameter set \((\beta_{0},\beta_{1},\gamma_{0})=(-3,1,1)\), the soliton solution profile at time \(t=0.5\) is illustrated in Figure 4.  
Given observed data \(u(x_{i},y_{j},0.5)\), we employ the BeeSwarm-MH sampling method to infer the parameters \((\beta_{0},\beta_{1},\gamma_{0})\).
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Electrochemical Sensors  
Electrochemical sensors are divided into potentiometric (voltage measurement), amperometric (electric current measurement), or conductometric (conductivity or resistivity measurement) [143]. Analyte detection occurs on appropriate electrodes on which a chemical reaction (oxidation or reduction) takes place. Electrochemical sensors typically consist of a sensing (working) electrode and a counter electrode separated by a thin layer of electrolyte [51]. The sensing electrode, on which the reaction occurs, is characterized by high surface-to-volume ratio (for signal enhancement) and is composed of catalytic materials, e.g., platinum, palladium, or carbon-coated metals [143] that are specific for the desired analyte [51]. Analyte-electrode reaction generates a sufficient electrical signal [51] measured with respect to the counter electrode [143]. A schematic representation of electrochemical sensors is presented in Figure 4.
## 4 Test Case: A Two-Link Flexible Robot Arm
### Numerical Assessment
#### 4.2.2 Test Case 2: Improving the Speed of Response  
In the second example, the state feedback controller is tuned to modify the poles associated to the first vibrational mode. It is wanted to simultaneously increase its damping and its natural frequency, and hence the absolute values of both the real and the imaginary parts, to speed up the transient response. By means of example, the pair of poles is assigned to \(-10\pm 20j\). Robustness is obtained by imposing \(M_{s}=2\).  
The pole placement task is correctly satisfied through \(\mathbf{k_{0}}\), with \(\mathbf{f_{0}}=\left[-0.0883\quad 0.0114\right]^{T}\) and \(\mathbf{g_{0}}=\left[-0.0011\quad-0.0098\right]^{T}\), obtained by solving Equation (8) through the _pinv_ function. In this case, as corroborated by the closed-loop poles listed in Table 3 and shown in Figure 5, the closed-loop system is unstable due to the spillover on the second pole pair that lies in the right-hand half of the complex plane.  
The method proposed in this paper tackles this problem by leading to \(\mathbf{k}\), with \(\mathbf{f}=\left[-0.0641\quad-0.0049\right]^{T}\) and \(\mathbf{g}=\left[0.0041\quad-0.2466\right]^{T}\), that simultaneously satisfies the pole placement requirement and stabilizes the closed-loop system additionally ensuring the required robustness. The Nyquist curves of the loop gain \(L(j\omega)\) of the closed-loop systems with \(\mathbf{k_{0}}\) and \(\mathbf{k}\) are shown in Figure 6 and clearly prove these aspects.  
Figure 5: Test case 2: closed-loop poles.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Open-Loop Poles** & **Desired Poles** & **Poles with \(\mathbf{k_{0}}\)** & **Poles with \(\mathbf{k}\)** \\ \hline \(-1.197\pm 7.122j\) & \(-10\pm 20j\) & \(-10\pm 20j\) & \(-10\pm 20j\) \\ \(-1.037\pm 38.177j\) & - & \(1.370\pm 45.847j\) & \(-3.930\pm 40.452j\) \\ \hline \end{tabular}
\end{table}
Table 3: Test case 2: open-loop and closed-loop poles.
## 1 Introduction  
The Junggar Basin is one of the large petroliferous basins in northwestern China [1, 2]. More than sixty years of petroleum exploration show that the Junggar Basin is rich in oil and poor in natural gas [3]. Currently, twenty-seven oil fields and five gas fields have been discovered, and the proven ratio of natural gas discoveries is only 5.3%.  
In the Junggar Basin, it had been found that the natural gas mainly comes from Carboniferous, Permian, and Jurassic source rocks ([1]). The Carboniferous source rocks were deposited primarily on residual sea and its distribution was limited [4, 5], which makes the gas generation potential of the Carboniferous source rocks relatively minor. Due to the shallow burial depth, the Jurassic source rocks have low organic maturity (R\({}_{0}\) < 0.7%) and limited gas generation potential. Neither the Carboniferous nor the Jurassic source rocks have the potential to generate a large gas field, the gas generation potential of Permian source rock are therefore the critical factor to natural gas exploration in the Junggar Basin.  
Natural gas sourced from Permian source rocks has been found in the Mahu, Penyijingxi, and Shawan sags located in the Western region of the Junggar Basin. There are still no large-scale natural gas reservoirs that have been discovered. It is uncertain whether the Permian source rock has natural gas generation potential, although it is the primary oil source rock in the Western Junggar Basin [6; 7]. The geochemical characteristics and distribution of the Permian oil-prone source rocks and the potential of natural gas exploration were discussed. Recently, the potential of natural gas exploration has been proved partly by the exploration wells.
## 5 Performance Evaluation
### Results and Discussion
This is explained by the efficiency of data processing offered by the Arduino platform and by the reduction of the packet size, which leads to the reduction of the dropping of packets in the router nodes. Figure 12: First node dead and the network’s lifetime according to hardware and topology. Figure 13: Packet loss ratio according to hardware and topology. We also note from Figure 13 that the MSCT topology reduces the packet loss ratio; this can be explained mainly by the minimal cost of transmissions and also because most of the parent nodes have a maximum of two children. In summary, we deduce from the above experiments that the developed system significantly improves the energy consumption and the network's lifetime. The combination of effective hardware architecture and MSCT topology has led to the minimization of the communications cost and the increase of the network's performances.