esa-sceva/satcom-chunk-collection huggingface dataset:
# Giant Dipole Multi-Resonances Excited by High-Frequency Laser Pulses
## 3 Conclusions and Outlook  
Using the framework of nuclear hydrodynamics for two fluids interacting via nuclear Skyrme and Coulomb forces, the collective dipole response of a spherical nucleus to a strong incoming laser pulse was calculated. State-of-the-art laser pulses are still low in frequency in order to access directly collective states in atomic nuclei. A possible way to overcome the discrepancy between the photon energy of the available high-intensity lasers (\(<\)1 keV) and the energy scale of nuclear processes such as nuclear reactions, single-particle or collective excitations (\(\sim\)100 keV-20 MeV) were proposed by [PERSON] and [PERSON] [44]. They advanced the proposal to accelerate heavy ions at ultra-relativistic energies in the presence of a super-intense counter-propagating laser pulse. Later on, this proposal was discussed in [45] in the context of nuclear dipole mode excitations.  
For example, the Ti:sapphire laser pulse with photon energy \(\hbar\omega=1.5\) eV propagating against a \({}^{208}\)Pb beam of ultra-relativistic energy \(E_{\rm acc}=10\) TeV, is Doppler-shifted with an energy quanta \(\hbar\omega^{\prime}\approx 12.54\) keV and experiences a large amplification of the intensity in the ion-fixed frame: \(I^{\prime}\approx 7\times 10^{7}I\). A better choice would be a beam of hard X-rays with photon energy \(\hbar\omega=9.9\) keV and intensity \(I=\)10\({}^{20}\) W/cm\({}^{2}\) colliding with the same ion beam, this time of energy \(E_{\rm acc}=1\) TeV. In this case, we end-up with \(\hbar\omega^{\prime}\approx 8.3\) MeV and \(I^{\prime}\approx 7\times 10^{25}\) W/cm\({}^{2}\). While the zero-photon absorption cross section displays a maximum at \(\hbar\Omega=13.5\) MeV, the energy centroid of the GDR, the one-photon \(\sigma_{1}\), displays two maxima  
Figure 1: Photon absorption cross-section of \({}^{208}\)Pb in a laser field for three different frequencies: \(\hbar\omega=100\) keV (full line), 1.5 MeV (short dashes) and 3 MeV (long dashes). Calculations are made for the SkM parametrization of \(\mathcal{H}_{\rm Sky}\) and the spreading width \(\Gamma=2.5\) MeV.  
at 5.6 MeV and 22.3 MeV. By using intense laser pulses with photon frequencies shifted in the \(\gamma\)-ray range, nuclear collective modes ranging from low-energy dipole modes (pygmy resonance [46]) to binary fission can be made feasible.  
This work was supported by the Institute of Atomic Physics (IFA) through the national programme PN III 5/5.1/ELI-RO, Project 04-ELI/2016 (\"QLASNUC\") and the Ministry of Research and Innovation of Romania through the Project PN 19 06 01 01/2019.  
Not applicable.  
Data Availability Statement: Data from the study is avaible upon the request from the author.  
The author declares no conflict of interest.
## 3 Results  
### Selection of Optimal Spectral Indices and Texture Features for Identifying Cotton Root Rot  
The RF algorithm was used to select the sensitive spectral indices and texture features for identifying cotton root rot. The results are shown in Figure 5. From Figure 5a, PSRI had the most significant impact on cotton root rot classification, followed by NDVI, NDWI1, MSI, and RDVL Among them, PSRI, which indicates the state of canopy senescence, was the most important spectral index for cotton root rot identification. NDWI1 and MSI provide a measure of the amount of water contained in the canopy foliage. NDVI and RDVI are related to the general quantity and vigor of green vegetation. The first five spectral indices were selected to build the BLR model.  
From Figure 5b, it was obvious that Contrast 1 was the most important texture feature for cotton root rot classification, followed by Dissimilarity 1, Entropy 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2. The remaining features had little impact on cotton root rot identification, therefore, the first seven important texture features were selected to build the BLR model.  
Figure 5: Rankings of (**a**) spectral indices and (**b**) texture features based on their importance for cotton root rot classification through random forest (RF) models.
## 2 Control Strategies
### Sliding Mode Lateral Stability Control
#### 2.2.2 Control Theory: Intermediate-Level Layer  
The Intermediate-level receives the control objectives from the upper controller, along with the driver inputs and vehicle states, to define desired wheel steer demand and equivalent yaw moments to be applied to the vehicle.  
To lead the states \(\beta\) and \(r\) to converge to the target values, a first-order _Sliding Mode Controller_ is proposed. Its action is typically composed of feed-forward impedance control terms (based on model considerations) and feed-back compensation terms (similar to a Proportional-Integral-Derivative control method). Optimal control input \(\hat{u}\) is selected according to (42), ensuring that desired states error \(\hat{x}_{des}=\hat{x}-\hat{x}_{des}\) tend asymptotically to 0, where target states error \(\hat{x}_{tar}=x-x_{tar}*\) is used for the proportional and integral compensation.  
Figure 3: Three DoF Vehicle Dynamic Model.  
\[\dot{u}=\begin{bmatrix}\delta_{cor}\\ M_{yaw}\end{bmatrix}=(B-B_{tar})^{+}\left(\overbrace{\hat{g}-\hat{Ax}}^{\text{Feed- forward}}\overbrace{-k_{p}\hat{g}_{tar}-k_{1}\int\hat{x}_{tar}dt-k_{s}sing(S)}^{\text{Feed-back}}\right) \tag{42}\]  
It is worth nothing to note that matrix \((B-B_{tar})\) of (41) is non-invertible, so a pseudo-inverse operator is used. The Lyapunov stability of the SMC-based ESP is guaranteed by the function candidate (43) [23], whose convergence is ensured by condition (44).  
\[\begin{cases}S=\bar{x}+\bar{\xi}\int\hat{x}dt\\ V(t)=\frac{1}{2}S^{2}+\int e|S|dt\end{cases} \tag{43}\]  
\[\dot{V}(t)=S\dot{S}+\epsilon|S|=S[Ax-g+(B-B_{des})u+\bar{\xi}\bar{x}]+\epsilon |S|\leq 0 \tag{44}\]  
Substituting \(\dot{u}\) into (44) lead to (45).  
\[(\bar{\xi}-k_{p})\bar{x}S-k_{1}\int\bar{x}dtS-k_{S}|S|+\epsilon|S|\leq 0 \tag{45}\]  
Selecting \(k\), \(k_{1}\), and \(k_{s}\) according to (46), the Lyapunov stability is proved. However, to obtain the best stability control performances from this controller, the multiple gains have to be tuned properly. This process was carried out during the preliminary test phases.  
\[\begin{cases}k_{P}=\bar{\xi}\\ k_{I}>0\\ k_{s}\geq\epsilon-k_{1}\int\bar{x}dt\cdot sign(S)\end{cases} \tag{46}\]
## 2 Materials and Methods  
For the fast synthesis of TiO\({}_{2}\) NSs, we used a solution process method. First, we mixed acetonitrile 40 mL, ethanol 50 mL, methylamine and H\({}_{2}\)O solvents with magnetic stirring (Solution A). Ethanol 10 mL and titanium isopropoxide (TTIP) solvents were mixed with magnetic stirring (Solution B). Solutions A and B were mixed together in a 250 mL round bottom flask at room temperature for 1H. TiO\({}_{2}\) NSs were collected by centrifugation and washed with DI water. TiO\({}_{2}\) NSs were then dried in an oven at 80 \({}^{\circ}\)C. The obtained as-synthesized amorphous TiO\({}_{2}\) (Am-TiO\({}_{2}\)) NSs were converted to anatase TiO\({}_{2}\) (An-TiO\({}_{2}\)) NSs through thermal treatment at 450 \({}^{\circ}\)C for 4 h.  
Structural properties of the synthesized amorphous TiO\({}_{2}\) (Am-TiO\({}_{2}\)) and anatase TiO\({}_{2}\) (An-TiO\({}_{2}\)) NSs were investigated by scanning electron microscopy (SEM) (S-4800, Hitachi, Tokyo, Japan), and a high-resolution X-ray diffraction (HR-XRD) system (SmartLab, Rigaku, Tokyo, Japan) equipped with Cu-K\(\alpha\) radiation (1.54 A). Optical properties of the TiO\({}_{2}\) monolayers were analyzed by ultraviolet-visible light (UV-vis) spectroscopy (Lambda 650S, Perkin Elmer, Waltham, MA, USA). To evaluate the light extraction and electrical properties of the LED chips with TiO\({}_{2}\) NSs monolayers, light output power-current-voltage (L-I-V) measurements were conducted (Keithley 2400, Tektronix, Beaverton, OR, USA).  
GaN-based LED epi-structures were grown on a c-plane sapphire substrate via metal-organic chemical vapor deposition (MOCVD). Trimethylgallium (TMGa), trimethylinium (TMIn) and NH\({}_{3}\) were used as the Ga, In, and N precursors, respectively. The LED structures consisted of a 1 \(\upmu\)m thick unintentionally doped GaN layer, a 2 \(\upmu\)m thick n-type GaN layer, six pairs on an InGaN/GaN multiple quantum well (MQWs) active layer, and a 0.3 \(\upmu\)m thick p-type layer. The active layer consisted of six pairs, each with a 3 nm thick InGaN well layer and a 10 nm thick GaN barrier layer in the MQWs [24]. To fabricate the chip, blue LED wafers were partially etched by inductively coupled plasma (ICP) until the n-type GaN layer was exposed. Indium tin oxide (ITO, 100 nm) was evaporated to the surface of p-GaN for current spreading. Cr/Au electrodes (10/500 nm) were subsequently evaporated as both n- and p-type electrodes for contact metal. Finally, LED wafers were divided into chips with an area of 600 \(\times\) 600 \(\upmu\)m\({}^{2}\).
## 2 Materials and Methods
### Data Analysis  
#### 2.5.1 Kinetics Study  
The study of the adsorption kinetics helps to determine the adsorption equilibrium time as a function of the adsorbent used and the operating conditions. It also allows us to understand the phenomena at the interface between the absorbing material and the pollutant molecules in terms of the diffusion and adsorption mechanism [9; 18]. Three kinetic models: the pseudo-first order, the pseudo-second order and the intra-particle diffusion model [26; 27; 28] were used to analyze the kinetic data of MC adsorption on SSS. These models can be written as follows:  
pseudo-first order model:  
\[q_{t}=q_{1}\left(1-e^{-k_{1}t}\right) \tag{8}\]  
pseudo-second order model:  
\[q_{t}=\frac{q_{2}^{2}k_{2}t}{1+q_{2}k_{2}t} \tag{9}\]  
intra-particle diffusion model:  
\[q_{t}=K_{d}\sqrt{t}+c \tag{10}\]  
where \(q_{1}\) and \(q_{2}\) are the theoretical uptakes at equilibrium (mg/g), \(q_{t}\) (mg/g) is the uptake at time t, and \(k_{1}\) (1/min) and \(k_{2}\) (g/mg min) are the pseudo-first-order rate constant and the pseudo-second-order rate constant, respectively. \(K_{d}\) (mg.g.\(\sqrt{min}\)) is the intra-particle diffusion constant and c (mg/g) is a constant related to the thickness of the boundary layer.
# Design of a 2 m Primary Mirror Assembly Considering Fatigue Characteristics
## 2 Mirror Assembly Design
### Design of Flexible Supports  
The initial design of the flexible support structure is presented in Figure 4.  
The support structure is divided into flexible and rigid parts. The flexible part should be able to move radially and rotate within a certain range to reduce the mirror surface error under gravity, temperature fluctuation, and non-flatness error. At the same time, it should balance the stiffness and flexibility to ensure the fundamental frequency and rigid body displacement of the mirror [7,8]. Therefore, the flexible part is designed as a spring leaf to provide increased rotational flexibility without reducing the axial rigidity. The clover flexible structure connected to the rigid connector can provide axial flexibility by grooving along the central axis. Four key parameters affecting the performance of flexible supports include width of the radial flexible sheet \(\mathbf{L_{1}}\), length of the radial flexible sheet \(\mathbf{L_{2}}\), length of the axial flexible groove \(\mathbf{L_{3}}\), and thickness of the clover support \(\mathbf{L_{4}}\). The mirror surface error RMS value and first-order fundamental frequency of mirror assembly are taken as the objective function, and optimal values of the four parameters are determined through size optimization.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 1 Introduction
In addition, they extract two important observations about the special connectivity conditions in urban VANETs from their study. First, the existence of path redundancy is usual: there are multiple ways to connect one vehicle with another. This can help to improve the robustness of routing or dissemination protocols. Second, the broadcast storm and the disconnected network problems are present at the same time. The reason is that vehicles are not evenly distributed in the scenario, forming dense groups disconnected from the others. Urban scenarios are especially sensitive to broadcast storms because of the high density of vehicles in downtown areas. On the other hand, disruptions in propagation caused by buildings and other obstacles accentuate the problem of network partitioning. Broadcast storms are usually avoided by using broadcast suppression schemes. They consist of a set of rules that prevent some nodes from retransmitting the message. In order to combat network partitioning, a popular type of mechanism is store-carry-forward. One or more vehicles get in charge of keeping a copy of the message and forwarding it after some time (for example, when they encounter other vehicles). We use both techniques in our search for a dissemination scheme that is useful in urban settings. In this article, we discuss several alternatives that we have considered and present our conclusions about the results achieved. In particular, we develop our work on urban adaptations to the roadway broadcast suppress scheme, which we presented in the original paper. In addition, we design and evaluate different ways to incorporate a specific store-carry-forward mechanism in our solution. We also compare the resulting formula with a protocol from the state of the art, Urban Vehicular Broadcast (UV-CAST). The article is structured as follows: First, in Section 2, we present some of the most relevant work to our approach, as well as our solution for roadway environments. In Section 3, we discuss the design alternatives and the main parameters that affect their performance. Next, in Section 4, we present the metrics that we use, the simulation configuration and the results of the comparative evaluations that we have carried out. Finally, we summarize our conclusions in Section 5.
# Victim Detection and Localization in Emergencies
## 3 Overview of Location Technologies
### WiFi
#### 3.3.1 WiFi Fingerprinting-Based Localization  
This is one of the most commonly used technologies and algorithms used for indoor localization [36] due to the ubiquity of WiFi networks and its low cost. Fingerprinting consists of two phases: offline training phase and online operating phase. During the offline phase, a radio map divides the scenario into a lattice, and the RSSI of the visible routers at each point of the lattice is stored. In the online phase, the system estimates the UE position, comparing the RSSI information to the most similar entry of the radio map. However, this technique cannot be used in emergency cases due to the changes in the environment, making ranging-location systems crucial for locating victims. The main issue of all fingerprinting techniques in emergency scenarios is the fact that one cannot create a radio map in the offline phase. When the catastrophe comes, such as an earthquake or flooding, the scenario completely changes, and the radio map becomes useless for localization service.
## 4 Experimental Results and Discussion
### Results of the Estimation Obtained at Variable Current without Aging Effect
\begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Model** & **Advantages** & **Disadvantages** \\ \hline DNN & - Ability to model complex relationships between inputs (voltage, current, temperature) and outputs. This is useful for capturing nonlinear battery behavior. & - Massive data requirements: DNN often requires a large quantity of data to generalize correctly, which can be a drawback if the available data are limited. \\ DNN & - Efficient training on big data, an advantage when datasets are large, used to improve the accuracy of SoC estimation. & - Data sensitivity: DNN can be sensitive to the data provided, which means that the quality and representativeness of the data used for training are crucial. \\ DNN & - Data adaptability: the DNN can adapt to different types of data. Lithium-ion batteries vary considerably in terms of manufacturer specifications, operating temperature, charge/discharge cycles, and various conditions of use. & - Similarity to hyperparameters: DNN has many hyperparameters (number of layers, number of neurons for each layer, learning rate, activation function, number of epochs, optimization algorithms like Adam, RMSProp and SGD, regularization to avoid overfitting, etc.). \\ GRU & - Modeling sequences: the GRU is designed to treat sequential data efficiently, making them suitable for SoC estimation, because they can take into account variations in voltage, current, and temperature over a given period. - Less complex than LSTM: the GRU has a simpler structure than LSTM, which can facilitate learning and reduce the risk of overfitting on small datasets. \\ LSTM & - Capturing long-term dependencies: the LSTM network is designed to capture long-term dependencies in sequences, where the behavior of charging and discharging can be influenced by complex factors over long periods; this capability is crucial. - The LSTM network is better equipped to avoid the problem of gradient disappearance over long sequences, which is common in temporal data. \\ LSTM & - Complexity of training: an LSTM network is more complex than a GRU, which can make learning more difficult and require more computing resources. - Risk of overfitting: as a LSTM network has more parameters due to its complexity, the risk of overfitting is higher, especially with limited datasets. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The advantages and disadvantages of the three neural networks for reliable battery SoC estimation.
# Impact of the Integration of the Electric Vehicle on Low-Voltage Rural Networks
## 1 Introduction
They use a routing procedure, assigning a destination to each trip based on evaluating all possible destinations based on trip distance and speed, the attractiveness of car access, and the availability of parking spaces. The model was tested in all districts of Berlin, and the results show that peak energy demand can be reduced by up to 31.7% compared to uncontrolled charging. Adverse effects particularly affect rural grids, as they are generally radial topology grids with a more deteriorated and aging infrastructure compared to urban grids [15]. Rural grids connect consumers spread over a large territory so their cables cross valleys, mountains, and forests. Due to the orography, they are difficult to access for maintenance and repair, which is aggravated by adverse weather conditions. Another problem is that, unlike urban networks, rural networks often lack a communication system. For this reason, distribution system operators cannot remotely solve any eventualities, increasing the time without supply to consumers in the event of a fault [16]. This work aims to study the impact of electric vehicle charging on the low-voltage grid of a Spanish rural community. The study uses actual grid data and consumer demand with an electric vehicle fleet estimated from the forecasts made in the draft of the Spanish National Energy and Climate Plan [17]. Based on the results of the impact on the grid, the work proposes solutions for the mitigation of some of its harmful effects. This paper is organized as follows: Section 2 depicts the works found in the literature related to EVs in rural and low-voltage networks. Section 3 presents the methodology and the different cases studied in the work, Section 4 shows the results of the simulations with the impact of the EVs charging process, Section 5 proposed some improvements contributing to the stability of the grid and favoring the EV penetration. Finally, Section 6 summarizes the contributions and conclusions.
# A Simulation of Tire Hydroplaning Based on Laser Scanning of Road Surfaces
## 2 Background of Research
### Three-Dimensional Road Surface Reconstruction Method  
#### 2.2.1 CT Scan of the Road  
The principle of CT (computerized tomography) scanning is using X-rays to scan the human body or other objects from different angles, and then using a computer to synthesize layer-by-layer slice images, thus reconstructing a 3D image. Specifically, an X-ray tube emits an X-ray beam that passes through the object being scanned, and the X-rays attenuate according to the varying densities of different materials. Industrial CT scanners acquire attenuation data from multiple angles by rotating the object or the X-ray source. The computer uses this data to reconstruct the three-dimensional internal structure of the object through an inverse projection algorithm.  
The most commonly used CT devices are divided into two major categories: medical CT and industrial CT. Industrial CT focuses more on the spatial resolution and density resolution of scanned objects compared to medical CT, allowing for the determination of the position and density differences of internal components based on CT images [25]. Asphal concrete test blocks are materials obtained by mixing aggregates and asphalt paste through stirring and compaction. After formation, they contain three different components: aggregates, asphalt paste, and voids. During the in situ hot recycling heating process of asphalt pavements, the thermal properties of the various components that make up asphalt concrete differ, affecting heat transfer. Therefore, to study the heat transfer patterns within asphalt pavements, CT scanning technology can be used to process asphalt concrete test blocks, obtaining the spatial distribution of each component inside the block [26]. Overall, CT scanning can capture the texture characteristics of the pavement surface, but more importantly, it is better suited for obtaining structural features such as the porosity of the pavement material. Additionally, CT has high costs and requires significant time for scanning and data processing.
# Quiescent Gap Solitons in Coupled Nonuniform Bragg Gratings with Cubic-Quinic Nonlinearity
## 4 Results of the Stability Analysis and Discussion
In addition, it is necessary to analyze how the system parameters (i.e., \(\lambda\) and \(m\)) affect the stability of solitons. To this end, we have carried out the stability analysis for various values of \(m\) and \(\lambda\). The results of these stability analyses are displayed in the stability diagrams of Figure 8. Compared with the model of Reference [47] (i.e., \(m=0\)), the stability diagrams in Figure 8 show that the stable regions are significantly altered due to the dispersive reflectivity. In particular, \(m\
eq 0\) gives rise to the enlargement of the stable regions for asymmetric Type 2 solitons and symmetric Type 1 solitons. Another notable feature of Figure 8 is that for a given \(\lambda\), if \(m\) is varied from 0.1 to 0.5, the sizes of the stable regions for asymmetric Type 2 and symmetric Type 1 solitons progressively increase. In the case of \(\lambda=0.1\), as \(m\) increases in the range \(0.1\leq m\leq 0.3\), the stable region for Type 1 asymmetric solitons becomes larger. However, for values of \(m>0.3\) this trend is reversed (see the third diagram in the first row of Figure 8). Figure 8 shows that, for a fixed \(m\), increasing the coupling coefficient leads to significant enlargement of the stable region for Type 1 symmetric solitons. This, however, results in the shrinking of the stable region for Type 1 asymmetric solitons. In addition, as \(\lambda\) increases, the stable region for asymmetric Type 2 solitons is shrunk. In other words, for the asymmetric solitons of both types, stronger coupling coefficient counteracts the stabilizing effect of the dispersive reflectivity.