esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Metal Matrix Nanocomposites
## 5 Strengthening Mechanisms
### Mismatch in the CTE  
When a nanocomposite is produced by stirring nanoparticles into a metallic melt, the material cools down to room temperature after solidification. Since the nanoparticles and the metallic matrix usually have significantly different coefficients of thermal expansion (CTEs), stresses occur at the interface. The more different the CTEs are, the higher the stresses, which then generate geometrically necessary dislocations (GND) with which the differences of the CTEs can be accommodated. Similar to cold-working, GNDs increase the yield strength of the composite which can be calculated using the Equation (12) [151; 152]. The expression below the square root represents the density of the dislocations generated by the CTE difference where \(\beta\) is the dislocation strengthening coefficient, \(\Delta\alpha\) the difference in CTE, and \(\Delta\)T the difference in temperature.  
\[\Delta\sigma_{\text{CTE}}=\sqrt{3}\beta\text{G}_{\text{m}}\text{b}\sqrt{\frac{ 12\text{V}_{\text{p}}\Delta\alpha\Delta\text{T}}{\text{bd}_{\text{p}}}} \tag{12}\]  
It should be mentioned, however, that during the cooling from high temperature, diffusion processes can also compensate the stresses and no GNDs may be generated [153]. Furthermore, there is also evidence that the contribution of very small particles to the increase in strength due to CTE mismatch is rather negligible [153; 154].
# Global IoT Mobility: A Path Based Forwarding Approach
## 5 Simulation and Cost Evaluation
### Signalling Cost Evaluation for Global vs. Micro Mobility Support  
Figure 10 shows the signaling cost (SC) for IP-over-ICN to support micro and global mobility for MNs with different speeds over a simulation run of 1800 s. The figure shows that IP-over-ICN incurs an average SC of approximately \(5.5\times 10^{3}\) Bytes \(\times\) Hops/Sec to support global mobility, compared to \(4.2\times 10^{3}\) Bytes \(\times\) Hops/Sec for micro mobility support. The difference shown in signalling cost is due to the increased distance between the TM/RV and the NAPs/gNBs that manage the signalling on behalf of the MNs in the inter-domain scenario.
## 2 Materials and Methods
### Classification Method
#### 2.6.2 Support Vector Machine  
The support vector machine (SVM) algorithm is a supervised machine learning algorithm based on statistical theory. Its principle is to map the original vector to a higher-dimensional space and establish a maximum margin hyperplane in this space, which separates the middle hyperplane with the hyperplanes on both sides to maximize the distance between these two planes. The kernel function is the inner product of two vectors in a certain feature space, expressed as \(K(Xi,\,Xj)=\) < \(\Phi(Xi)\), \(\Phi(Xj)\)[39]. The introduction of a kernel function can better achieve nonlinear mapping. Among many kernel functions, the Gaussian radial basis kernel function (RBF kernel) is the most widely used one. Therefore, in this paper, the SVM algorithm with Gaussian radial basis kernel was adopted for classification, and the calculation formula is as follows:  
\[K\big{(}x_{i},x_{j}\big{)}=exp\left(-\frac{\|x_{i}-x_{j}^{2}\|}{2\delta^{2}}\right) \tag{11}\]
## 4 Proposed Model
### Proposed Improved Baseline Estimation Model  
Based on the above, we conclude the improved baseline estimation model based on the proportion of local standard deviation to global standard deviation. According to \(\left(\frac{\sigma_{u}}{\sigma}\right)=\left(\frac{Len_{u}}{Len_{u_{\text{ sign}}}}\right)\), we can obtain:  
\[\frac{\left(\hat{r}_{ui}-\mu_{u}\right)}{\left(\tilde{r}_{i}-\mu\right)}=\left( \frac{\sigma_{u}}{\sigma}\right)\text{,} \tag{7}\]  
\[\hat{r}_{ui}=\frac{\sigma_{u}}{\sigma}\cdot\left(\tilde{r}_{i}-\mu\right)+\mu_ {u}\text{.} \tag{8}\]  
Since \(b_{u}\) and \(b_{i}\) are the observed deviations of user \(u\) and item \(i\), respectively, generally \(b_{u}=\overline{r_{u}}-\mu\), \(b_{i}=\overline{r}_{i}-\mu\), so:  
\[\hat{r}_{ui}=\left(\frac{\sigma_{u}}{\sigma}\right)b_{i}+b_{u}+\mu\text{.} \tag{9}\]  
This is the improved baseline estimation with the proportion of the global standard deviation to the local standard deviation, and we call this novel model SDP (standard deviation proportion baseline estimate). For parameters \(b_{u}\) and \(b_{i}\), we can use the following loss function to obtain the optimized \({b_{u}}^{*}\) and \({b_{i}}^{*}\):  
\[\mathcal{L}_{SDP}=\min_{b_{u},b_{i}}\sum_{(u,j,r)\in\mathbb{R}}\left(r_{ui}- \left(\frac{\sigma_{u}}{\sigma}\right)b_{i}-b_{u}-\mu\right)^{2}\text{.} \tag{10}\]  
Since the local standard deviation \(\sigma_{u}\) is usually less than the global standard deviation \(\sigma\), the above SDP can satisfy most situations. However, the sparsity problem appears when the number of ratings is less than 20, where standard deviation does not follow the normal distribution. Thus, we adjusted the SDP into the following:  
\[\hat{r}_{ui}=\left\{\begin{array}{ll}\frac{\sigma_{u}}{\sigma}\cdot b_{i}+b_ {u}+\mu\text{,}&if\ \ \sigma_{u}\leq\sigma\\ b_{i}+b_{u}+\mu\text{,}&if\ \ \sigma_{u}>\sigma\end{array}\right.. \tag{11}\]  
This means that the SDP uses the original baseline estimation with no features of a normal distribution to limit the influence of the sparsity problem when \(\sigma_{u}>\sigma\). In the next section, we introduce how to apply SDP into existing latent factor-based recommendation algorithms.
## 1 Introduction  
The cigarette-related death toll is estimated at around seven million people annually [1]. Hence, the cessation of cigarette-smoking has become one of the critical components in establishing public health policies or associated regulations [2]. Many smoking-cessation tools and devices (e.g., nicotine patch, nicotine inhalator, and nicotine nasal spray) have been developed and introduced into the market; nicotine-free inhalators with aroma essential oils are one particular product available commercially in Korea and some other countries [3; 4; 5; 6; 7; 8]. It was reported that nicotine-free inhalators were free from the addictive effect of nicotine [8].  
The nicotine-free inhalator was developed to offer psychological comfort to curb many smoking-associated habits through the simulation of smoking conventional cigarettes [3; 9]. The nicotine-free inhalator may be beneficial for use in smoking-cessation interventions, particularly in smokers for whom handling and manipulating their cigarettes is an important part of the smoking ritual (e.g., the hand-to-mouth action of smoking) [8; 10]. Therefore, nicotine-free inhalators are expected to be one of the effective options to make smokers refrain from their smoking habit.  
A typical nicotine-free inhalator device consists of an air-breathing system that does not require electronic operation [5; 6; 8]. It is simply a fibrous, sponge-filter plug soaked in naturally extracted herbal oil, which is encased in a plastic cartridge container similarly to a cigarette [8]. The nicotine-free inhalator is different from other cigarette-related products (e.g., traditional cigarettes, e-cigarettes, and nicotine products) in that it does not contain typical cigarette chemical compounds (e.g., nicotine, propylene glycol, or vegetable glycerin). The herbal materials that are infused into the nicotine-free inhalator are made from natural essential oils and herbs [8; 11]. The nicotine-free inhalator can be purchased online [5; 6]. Nonetheless, there have not been sufficient efforts to evaluate the human health impacts of volatile organic compounds (VOCs) released from nicotine-free inhalators. Therefore, many questions arise regarding the safety of these products when used for therapeutic treatment. For this reason, we aim to evaluate the safety and risks associated with VOCs released from nicotine-free inhalators when used by consumers. Herein, we analyzed for the first time the volatile compounds of vapor from marketed nicotine-free inhalators.
## 4 Verification and Validation
### Validation of the Stability Improvement
#### 4.2.3 Bending Rigidity  
The bending rigidity is used to present the material's ability to resist bending, and is indispensable to most elastic materials. Because of instability, the traditional explicit method can only simulate cases with small bending rigidity. In this section, by simulating the benchmark case, we will verify that the AWI approach can improve the upper limit of the bending coefficient.  
Figure 8: Maximal fastening coefficient using the AWI approach with different iteration times.  
Figure 9: Varying histories of \(E_{d,ij}\) for different \(\overline{K}_{f}\).  
A filament is placed vertically in the middle of a rectangular domain with dimensions \(L\times W=400\times 200\). The filament length is \(L_{fi}=100\) and is discretized with \(N_{b}=301\) Lagrangian points. As shown in Figure 10, the filament middle point is fixed to (100, 100). The fluid flows from left to right, the inlet velocity is set to \(U=0.01\), and the fluid viscosity is \(\
u=0.01\). These quantities are expressed in lattice units.  
By selecting \(\rho_{f}\), \(U\), and \(L_{fi}\) as the characteristic density, velocity and length, the bending coefficient is non-dimensionalized according to Equation (15).  
As shown in Figure 11, with the explicit method, the maximal bending coefficient is \(2\times 10^{-5}\), whereas it can increase to \(1.75\times 10^{-1}\) (up to 8000 times) with the proposed method. Figure 12 shows the streamlines after the flow field settles at \(\overline{K}_{b}=1.75\times 10^{-1}\). When the bending coefficient is relatively large, only a small curvature is generated along the filament, and the flow field resembles the one passing a bluff body, where opposite spinning vortices are generated in the wake. Figure 13 shows the vorticity field at \(\overline{K}_{b}=1.75\times 10^{-1}\). The streamlines and vorticity contours are notably smooth and symmetric, which indicates that the results of the AWI approach are notably stable when \(\overline{K}_{b}=1.75\times 10^{-1}\).  
Figure 11: Maximal bending coefficient \(\overline{K}_{b}\) by using the AWI approach with different iteration times.  
Figure 10: Schematic diagram of the fluid that pushes the filament into bending.
## 7 Conclusions  
This work mainly studies an efficient guidance algorithm for fuel-optimal powered landing. The LCvx algorithm is combined with the DNN predictor to construct the DNN-LCvx algorithm, accurately predicting the optimal landing time and efficiently solving the optimal trajectory with various constraints. Open-loop numerical simulation shows that:  
1. The computation accuracy of the proposed algorithm is high, and the error of terminal mass is several kilograms, which can ensure the optimality of the results.  
Figure 23: Closed-loop position profiles with random initial state variables.  
Figure 22: Closed-loop simulation trajectory with random initial state variables.  
2. The computational efficiency of the proposed algorithm is high, and it is about an order of magnitude higher than the traditional CVX-based algorithm. The elapsed time of single trajectory planning is stable between 5 ms and 8 ms.  
The MPC framework is introduced to the closed-loop simulation under the given disturbance, and the simulation results show that the proposed algorithm can achieve accurate landing under various disturbances. The horizontal position error is less than 0.06 m, the horizontal speed error is less than 0.4 m/s, and the vertical velocity is less than 1.7 m/s.  
We noticed that although the DNN-LCvx algorithm has a significant improvement in real-time performance, it also has the following shortcomings that need to be highlighted:  
1. In order to obtain a high-precision DNN optimal predictor, the proposed algorithm requires a large number of training samples (1.2 million in this paper); thus, the offline training cost is high.
2. For different types of vehicles, the DNN optimal predictor needs to be retrained, reducing the algorithm's generality.
3. The initial state of the vehicle must be distributed within a specific range (see (18)). Otherwise, the predicted optimal landing time may be invalid.  
In summary, the proposed guidance algorithm meets the requirements of multi-constraint, optimality, real-time, and high precision. Moreover, it can significantly improve real-time performance compared with other CVX-based algorithms. Thus, the algorithm can be applied to onboard applications. Some shortcomings of the algorithm are highlighted, which are worth further study in the future.  
Conceptualization, W.L. and S.G.; methodology, W.L. and S.G.; software, W.L.; validation, W.L.; formal analysis, W.L.; investigation, W.L.; resources, W.L.; data curation, W.L.; writing--original draft preparation, W.L.; writing--review and editing, W.L. and S.G.; visualization, W.L.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China, grant number 11822205 and 11772167.  
Not applicable.  
The authors declare no conflict of interest.
# Albatross-Like Utilization of Wind Gradient for Unpowered Flight of Fixed-Wing Aircraft
## 5 Simulation Results and Discussion
### Rayleigh Cycle Simulation Results
The phase switches of the continuous GPOPS optimized trajectory are not obvious. The plots for both methods in Figure 16 validate the Rayleigh cycle energy theory discussed in Section 4.1. Energy is harvested during the upwind climb and downwind dive, whereas energy is lost during the low-altitude and high-altitude turns. However, the total energy changes during a cycle for the two methods are positive. For the GPOPS method, the energy increase during a cycle is about 1300 J, accompanied by an altitude increase of about 17 m. In the case of the bio-inspired strategy, the energy and altitude increases during a cycle are about 900 J and 12 m, respectively. The GPOPS method produces an optimized solution, hence the energy increase during a cycle is about 1300 J.  
Figure 16: Time histories of energy and altitude for the Gauss Pseudospectral Optimization Software (GPOPS) and proposed bio-inspired methods. Figure 17: Time histories of the control inputs for the GPOPS and proposed bio-inspired methods. its larger cyclic energy and altitude increases. Nevertheless, the proposed bio-inspired strategy is simpler and affords a significant saving in the computation time. The larger cyclic energy increase of the GPOPS method is afforded by its lower energy loss during the high-altitude turn. Figure 18 shows phase portraits of the GPOPS and proposed bio-inspired methods. The plot for each method covers a cycle in the velocity space. The altitude corresponding to each point is color-coded, and the four phases of the Rayleigh cycle of the bio-inspired strategy are indicated by numbers, where the 1-2, 2-3, 3-4, and 4-1 segments respectively correspond to the upwind climb, high-altitude turn, downwind dive, and low-altitude turn phases. Table 3 summarizes the computation time for GPOPS and the bio-inspired strategy. GPOPS generally consumes about 15 s to compute a dynamic soaring trajectory in a personal computer with an Intel(r) CoreTM i3-2100 CPU running at 3.10 GHz, while the bio-inspired strategy consumes 2 ms because of the simple judging logic (the computation time can be checked in the Simulink report interface). This comparison shows that the bio-inspired strategy is more efficient at real-time applications.
## 4 BER Performance Analysis
The effect of XT on the BER is shown in Figure 10. We have verified that in order to evaluate the effect of XT on the channel of interest it is enough to consider only the effect of the two more adjacent signals. From Figure 10 we observe that the SNR loss of the average case compared to that w/o distortions increases of only 0.2 dB at \(\mathrm{BER}=10^{-2}\) with respect to the situation without XT considered in Figure 8 while it increases up to 1.3 dB at \(\mathrm{BER}=10^{-10}\). The loss in SNR of the best case with respect to the ideal case w/o distortions is comprised now between 4.5 dB at \(10^{-2}\) and 6 dB at \(10^{-10}\). It is worth noting that the SNR gap between the best and the worst case remains the same as that without XT. In Figure 11 the effect of XT on the best and the worse performance is reported for increasing values of the roundtrip losses \(\alpha=0\), 0.2, 0.4, 0.6, 0.8, 1. In contrast to the analogous situation without XT considered in Figure 9, in this case we observe that for the values of \(\alpha\) equal to 0.6, 0.8 and 1 the BER presents an error floor. This effect arises because the presence of some configurations of interference that cause the closure of the eye even in the situation where noise is absent. By comparing the curves reported in Figures 9 and 11 at a given BER it is possible to evaluate the degradation introduced by XT for a fixed value of \(\alpha\). Figure 9: BER _vs._ SNR for transmission at 10 Gb/s of 32 NRZ-OOK channels with increasing roundtrip losses \(\alpha=0\), 0.2, 0.4, 0.6, 0.8, 1. Dashes curves: best channel (#10); dotted curves: worse channel (#7). It is worth observing that for input signals with rates higher that 10 Gb/s, _i.e._, 40, 100, and 160 Gb/s an increase of modulation order is required in order to satisfy the bandwidth limitation imposed by the optical filtering. As is well known, high order modulation formats require coherent demodulation and are more sensitive to signal distortions introduced by optical filters and XT. Therefore, in order to achieve a performance that is comparable with that of 10 Gb/s transmission an appropriate signal processing is required. Figure 11: BER _vs._ SNR for transmission at 10 Gb/s of 32 NRZ-OOK channels with increasing roundtrip losses \(\alpha=0\), 0.2, 0.4, 0.6, 0.8, 1 in presence of XT. Dashes curves: best channel (#10); dotted curves: worse channel (#7). Figure 10: BER _vs._ SNR for transmission at 10 Gb/s of 32 NRZ-OOK channels with roundtrip loss \(\alpha=0\) in presence of XT.
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 2 Materials and Methods  
During the last years, the RF energy harvesting and the design of rectenna systems are gaining popularity and have attracted the attention of several researchers. A bibliographic search using the keyword \"RF energy harvesting\" in the Scopus database shows that there are 1893 conference papers and 1442 journal papers related to RF energy harvesting from 2010 to 2020. Figure 2 displays the number of papers related to RF energy harvesting for the last 10 years. The purpose of this work is to describe in-depth the design process of a rectenna system and to present selected recent works about this scientific topic. Note that we emphasize on the design of the rectifier circuit. In this state-of-the-art work, we mention the most relevant works that had in their title the keywords \"RF energy harvesting \"or \"RF to DC rectifier \"or \"DTMOS RF energy harvesting\" and were published within the last 10 years.  
At first, we introduce few works that are categorized according to the type of receiving antenna. It should be noted that the antennas will not be analyzed in depth but we will only mention some basic designs, because such an analysis is out of the scope of this work. Then we present many works that are divided depending on the type of diode, the topology of the impedance matching network, and the topology of the rectifier circuit. We mention  
Figure 1: Block diagram of a typical RF harvesting system.  
the pros and cons of each topology for both rectifiers and impedance matching networks. Presenting these works in this way, we aim to provide a detailed guide for the process of the rectenna design.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 3 Applications and Real-World Scenarios
### Domain Adaptation in Robotic Vision: Real-World Applications
#### 3.2.2 Agriculture and Farming  
In agriculture and farming, where robotic vision systems are deployed for crop monitoring, disease diagnosis, and precision agriculture, domain adaptation holds tremendous potential. By accommodating shifts in ambient conditions, soil types, and crop varieties, domain adaptation enables agricultural robots to tailor their vision for effective data-driven decision-making. For example, in precision agriculture, robotic systems can analyze multispectral and hyperspectral imagery to detect signs of crop stress, nutrient deficiencies, and pest infestations. Domain adaptation ensures precise and timely interventions, optimizing agricultural productivity and resource utilization by adapting to diverse crop types and environmental conditions.
# An Accurate Probabilistic Model for TVWS Identification
## 2 Background
### Cognitive Radio Fundamentals  
The functionalities of CR [15] are the following. First, spectrum sensing refers to identify the available spectrum channels and detect the presence of PU operating in a licensed band. Second, spectrum management is the possibility to select the best available channel from all the idle channels detected. Third, spectrum sharing refers to coordinate the smart access to a channel assigned to a PU when it is unused. Fourth, spectrum mobility is the option to vacate the channel when a PU is detected.  
According to the definition given by the FCC, A Cognitive Radio is a radio that can change its transmitter parameters based on interaction with the environment in which it operates [16]. From this definition, CR has two characteristics, namely, cognitivity and reconfigurability. **Cognitivity** is the possibility of the device to sense the spectrum. This is not the simple monitoring of the power in a determined frequency band, but rather it requires more elements to identify the temporal and spatial variations in the radio environment, all the time avoiding harmful interference to PU or to another SU. With this, it is possible to identify the unused channels or WS at a specific time or geographical location, giving the possibility to select the best operational parameters [7]. **Reconfigurability** is the capability that permits a dynamic reconfiguration of the operational parameters according to the radio environment. In other words, CR devices can transmit and receive on a variety of frequencies with different access technologies, thanks to their dynamic hardware features [17].  
The cycle of the CR is depicted in Figure 2. A portion of the spectrum in a time period \(t_{i}\) is represented, with the signals of the PUs in orange, and the TVWS in white. The first step consists in sensing the spectrum, then this information is analyzed, and based on the obtained result, the device decides which TVWS is the best option to transmit its information. This research is focused on the spectrum sensing and spectrum analysis stages of the CR cycle, specifically in the composite signal that contains the PU information.