esa-sceva/satcom-chunk-collection huggingface dataset:
# Copper- and Iron-Based Nanoflowers in Cancer Theranostics
## 2 Synthesis Methods for Copper- and Iron-Based Nanoflowers
### Microwave-Assisted Method  
Microwave irradiation offers a fast and efficient environment for synthesizing nanomaterials due to its uniform and controlled heating properties. Consequently, microwave-assisted synthesis methods have garnered significant attention in the literature [33].  
Microwave-based synthesis enhances nucleation and growth through microwave irradiation, enabling swift nanoflower production [32]. When combined with hydrothermal synthesis, the microwave process requires significantly less power and time, allowing for the synthesis of nanomaterials at lower temperatures compared to traditional furnace heating. The conventional hydrothermal method for nanomaterial synthesis typically involves long reaction times and high energy consumption, whereas the microwave-assisted approach provides faster heating, higher yields, and an improved reproducibility of the nanocrystals. Thus, utilizing microwave energy for chemical reactions offers substantial advantages, as it is a straightforward, highly efficient, and environmentally friendly process [84].  
[PERSON]. [PERSON] et al., studied the microwave-assisted polyol method to synthesize \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) nanoflowers. By adjusting the amount of sodium acetate as an alkali source, they were able to modify the structural and magnetic properties, leading to nanoflowers with excellent heating performance in magnetic hyperthermia therapy (MHT) and photothermal therapy (PTT). Photoluminescence spectroscopy revealed the role of defects, such as oxygen vacancies and surface states, in enhancing the performance. The \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) nanoflowers exhibited improved crystallinity, a higher coercive field, and an intrinsic loss power value of 15.21 \(\pm\) 0.34 nHm\({}^{2}\)/kg, which was significantly higher than commercially available ferrofluids and other nanoflowers. During PTT, a therapeutic temperature of 42 \({}^{\circ}\)C was reached at a concentration as low as 100 ug/mL, demonstrating the effectiveness of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) nanoflowers as a PTT agent [85].
## 1 Introduction
Usually, the effect of thickness stretching can be disregarded for thin isotropic plates, but due to the specificity of the structure of the functionally graded materials, ignoring the effect of thickness stretching in the analysis of mechanical behaviors may lead to inaccurate calculation results [42]. For moderately thick and thick plates, neglecting the effect of thickness stretching produces non-negligible errors, which is evident in both multilayered composite plates and metal-ceramic functionally graded plates. To the authors' knowledge, few researchers have further studied the effect of thickness stretching on the mechanical behaviors of FG-GRC plates in detail. In the present paper, attention is focused on the effect of thickness stretching on the static and dynamic behaviors of FG-GRC plates. The novelty of this paper is reflected in the fact that the importance of the thickness stretching effect is investigated by comparing the numerical results based on two plate theories, with or without taking into account the thickness stretching effect, respectively. In this paper, RPT and IRPT are used to study the bending and free vibration behaviors of FG-GRC plates. The properties of composite materials are estimated by the modified Halpin-Tsai model and rule of mixture. [PERSON]'s principle is used to construct its governing equation, and the Navier solution method is used to find the closed solution. The effects of thickness stretching, distribution modes of GPLs, and some parameters, such as the total number of layers \(N_{L}\), and weight fraction \(f_{\rm G}\) of GPLs on frequencies and deflections of the FG-GRC rectangular simply supported plates are discussed in detail.
## 2 Materials and Methods
### Centroid Downshift Method
The power spectrum of RF signals reflected at a given depth demonstrate a downshift to lower frequencies with respect to the position of the center frequency (or centroid), since higher-frequency components attenuate faster than lower-frequency components. Previous research in the literature has found that the attenuation coefficient in soft tissue is almost linearly proportional at frequencies up to 10 MHz. Due to this linearity of the attenuation at higher frequencies, the downshift in center frequency with respect to depth is proportional to the attenuation coefficient, \(\beta\). Assuming that a Gaussian-shaped transmit pulse is propagated, the relationship between the slope of this downshift with respect to depth and the attenuation coefficient is given by [19]:  
\[\beta=-\frac{8.686}{4\sigma^{2}}\cdot\frac{df_{c}(z)}{dz}, \tag{8}\]where \(z\) is the distance from the transducer to the ROI, \(\sigma^{2}\) is the variance of the transmit pulse, and \(f_{c}(z)\) denotes the center frequency of the power spectrum at depth \(z\).
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Simulation
#### 2.3.3 Simulated ITF Signals  
Given a captured lightning signal of antenna \(B\) (located at Cartesian coordinate 0 of Figure 2), Figure 6 illustrates the proposed simulation steps, including windowing, calculating the amount of temporal shift for each particular window, and using Equations (7) and (8) to produce corresponding simulated step-wise signals for antennae \(C\) and \(D\), respectively. The simulated sliding windows are then concatenated to form a full track of VHF-ITF lightning signals.
## 3 Flow Rate Characteristics of the Valve  
To evaluate the flow rate characteristics of the servo valve, the relationship of the effective cross-sectional area with respect to the cam rotation angle is measured. The effective cross-sectional area was obtained from the flow rate measured by a method using an isothermal chamber [15]. The measurement was performed by changing the cam rotation angle in increments of 5 degrees. Figure 11 shows the results of the measurement. A cam angle of 0 degrees corresponds to the neutral position as shown in Figure 4. It was divided into the supply port and exhaust port with 0 degrees as the border. To verify fluctuation in the flow rate and hysteresis, measurements were conducted thrice in each rotation angle direction. Hence, a total of six rounds of measurements for an angle was  
Figure 10: Flow characteristics of previous prototype pinch valve.  
Figure 9: Cam design of previous prototype.  
performed. Although it was confirmed that the degree of opening saturated at 50 degrees or more and \(-\)50 or less. Moreover, we showed flow characteristics of a previous prototype pinch valve [15] in Figure 10. By comparison of Figures 10 and 11, the developed valve in this study exhibited a V-shaped characteristic compared to a previous prototype pinch valve. When fitting is conducted in range of 0 to 20 degrees, the difference between fitting line and experimental data of the valve developed in this study is revealed as 10% while that of previous prototype is 20%. Additionally, as we proved in [15], the spool-type valve does not have V-shaped characteristics. It means our newly developed pinch valve does not have a dead zone, which is observed in a previous prototype pinch valve and spool-type valve, around 0 degrees. Additionally, its leakage is much smaller than that of spool-type valve. Hence, this indicated that the zero overlap characteristic of the spool-type valve was realized. This suggested an improvement in the linearity of the characteristics around the neutral position compared to the previous prototype pinch valve. However, it was observed that the flow rate characteristic was asymmetric. Moreover, the graph shapes are not the same between the two graphs, especially at exhaust port. These can cause nonlinearity in the valve, and it is not desirable for the control. These are considered as caused by the cam shape and eccentricity of the camshaft. It is extremely important to ensure that the cam shape is considerably accurate. Hysteresis of the effective area was almost absent in the area where the cam angle was \(\pm\)40 degrees. However, hysteresis exists in a high flow rate range (and especially \(-\)70 degrees or less and 80 degrees or more). For now, it is rare to use a high flow rate to the actuate system. Thus, the hysteresis obtained in the result does not constitute a significant problem.
## 3 Proposed FEQS Framework and Research Conception  
In view of the suitability between the counting FEQS scenario of huge earthmoving demands, uniform uncovered trucks, equipped camera system, and vision-based deep learning, the authors posit that deep learning models of CNN or CNN-TL can achieve full/empty-load truck classification and contribute to unmanned and non-contact FEQS in earthmoving operations. Thus, the FEQS framework of this vision-based conception is proposed and is shown in Figure 2.  
The framework first acquires vision information from the surveillance system and establishes data sets for deep learning by manual labeling. It then applies deep learning CNN-related models for  
Figure 2: Proposed Field Earthmoving Quantity Statistics (FEQS) framework.  
full/empty-load truck classification judgment. Finally, it combines the necessary information about trucks, truck identification, and the earthmoving project with the truck classification results and adopts automated counting to implement the automated non-contact FEQS. Under this framework, a partially loaded truck will be detected by the deep learning as it is not visually similar to full-load trucks and will be considered as empty as partially loaded does not qualify for counting as one instance of earthmoving work.  
As vision-based judgement of the truck load condition is the core work of the proposed FEQS framework, this paper thus seeks to first verify the feasibility of CNN and CNN-TL models in solving full/empty-load-truck classification, and then evaluate the efficiency and ability of different models and identify the well performing learning models among the tested models for application suggestions. These are the premises of feasible and better implementation of the proposed FEQS. Hence, a comparison study is performed wherein multiple open-source CNN models and their TL forms are evaluated in a suitable counting FEQS scenario. The comparison study is the important research part of this paper, which provides a reference and support for the FEQS framework application. Three main works are performed in the comparison study: (i) Collecting empty-load and full-load truck images from a surveillance video source to form the training, validation, and testing data sets for deep learning; (ii) adopting 4 classical CNN models and 2 TL methods to construct 12 deep learning models for the comparison study; and (iii) testing the full/empty-load truck classification effect of each deep learning model, and further discussing the results.
## 3 Research and Development Methodology
### Real-Time Notifications and Actions Using Microsoft SignalR Hub  
SignalR, developed by Microsoft, patch 2.4, as depicted in Figure 6 for current research, facilitates real-time communication between clients and servers in web applications. It is used for interactive applications, providing live updates for chats, notifications, and online games.  
Core Technologies SignalR utilizes WebSocket [39] for efficient communication. In the absence of WebSocket, it adapts other technologies such as Server-Sent Events (SSEs) and Long Polling, ensuring extensive compatibility.  
Key Features:  
* Simplified API: sending and receiving messages is easy to implement.
* Multi-Platform Support: compatible with various platforms and languages, including _NET and JavaScript.
* Connection Management: automates reconnections and redirects for reliability.
* Integrated Security: supports authentication and authorization, protecting against unauthorized access.  
Common Uses:  
Figure 6: SignalR architecture for current research.  
* Chat and Messaging Applications: provides instant updates and bidirectional communication.
* Live Notifications: sends real-time notifications for news and social media platforms.
* Real-Time Monitoring: displays updated data in monitoring applications.
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 3 Proposed Method
### Prior-Aided VAMP Detection  
Considering the explicit definition of the sparsity of OFDM-IM symbols in the frequency domain, it can be predicted that the posterior estimation of symbols based on the optimal MMSE criterion can be achieved. \(\mathbf{x}\) has sparse characteristics and non-zero symbols are drawn from the constellation \(\mathcal{S}\). For each element in \(\mathbf{x}\), the prior of \(x\) is given by  
\[p(x)=\frac{\rho}{|\mathcal{S}|}\sum_{s\in\mathcal{S}}\delta(x-s)+(1-\rho)\delta (x), \tag{17}\]  
where \(\rho=\frac{k}{n}\) is the signal sparsity and \(|\mathcal{S}|\) is the size of constellation map \(\mathcal{S}\). The constellation point \(s\) has normalized power, i.e., \(\frac{\sum_{s\in\mathcal{S}}|s|^{2}}{|\mathcal{S}|}=1\).  
As shown in step (2), the marginal belief \(b_{sp}(\mathbf{x})\propto\mu_{\delta\to x}(\mathbf{x})\mu_{p1\to x}( \mathbf{x})\), which is given by \(b_{sp}(\mathbf{x})\triangleq\frac{\rho(\mathbf{x})p(\mathbf{r}\mathbf{x})}{ \int p(\mathbf{x})p(\mathbf{r}|\mathbf{x})dx}\), where \(p(\mathbf{r}|\mathbf{x})\propto\mathcal{CN}(\mathbf{x};\hat{\mathbf{r}},v^{ \prime})\) with mean \(\hat{\mathbf{r}}\) and variance \(v^{\prime}\). The MMSE denosing function is element-wise given by \(\hat{x}=\mathbb{E}\big{[}x|b_{sp}(x)\big{]}=\frac{\int xp(r|x)p(x)dx}{\int p(r| x)p(x)dx}\). Expand the denominator as  
\[\begin{split}\int p(r|x)p(x)dx&=\mathcal{CN}(x; \hat{r},v^{\prime})\bigg{[}\frac{\rho}{|\mathcal{S}|}\sum_{s\in\mathcal{S}} \delta(x-s)+(1-\rho)\delta(x)\bigg{]}\\ &=\frac{\rho}{|\mathcal{S}|}\sum_{s\in\mathcal{S}}\frac{\gamma_{w }}{\pi}\exp\Bigl{\{}-\gamma_{w}|\hat{r}-s|^{2}\Bigr{\}}+\frac{\gamma_{w}(1-\rho )}{\pi}\exp\Bigl{(}-\gamma_{w}|\hat{r}|^{2}\Bigr{)}.\end{split} \tag{18}\]  
The numerator is given by  
\[\begin{split}\int xp(r|x)p(x)dx&=\int x\mathcal{CN}(x ;\hat{r},v^{\prime})\bigg{[}\frac{\rho}{|\mathcal{S}|}\sum_{s\in\mathcal{S}} \delta(x-s)+(1-\rho)\delta(x)\bigg{]}dx\\ &=\frac{\rho}{|\mathcal{S}|}\sum_{s\in\mathcal{S}}\frac{\gamma_{w }}{\pi}\exp\Bigl{\{}-\gamma_{w}|s-\hat{r}|^{2}\Bigr{\}}.\end{split} \tag{19}\]  
The derivative of MMSE denoiser is required to be calculated, which needs the key term \(\mathrm{cov}\big{[}x|b_{sp}(x)\big{]}=\mathbb{E}\big{[}x^{2}|b_{sp}(x)\big{]}- \hat{x}^{2}\). \(E\big{[}x^{2}|b_{sp}(x)\big{]}=\frac{\int x^{2}p(r|x)p(x)dx}{\int p(r|x)p(x)dx}\), where the numerator is given by \[\int x^{2}p(r|x)p(x)dx =\int|x|^{2}\mathcal{CN}(x;\hat{r},v^{r})\bigg{[}\frac{\rho}{| \mathcal{S}|}\sum\
olimits_{s\in\mathcal{S}}\delta(x-s)+(1-\rho)\delta(x)\bigg{]}dx \tag{20}\] \[=\frac{\rho}{|\mathcal{S}|}\sum\
olimits_{s\in\mathcal{S}}\frac{ \gamma_{w}|s|^{2}}{\pi}\exp\Big{\{}-\gamma_{w}|s-\hat{r}|^{2}\Big{\}}.\]  
With the equation from (18) to (20), the MMSE denoiser and its divergence are achieved, which are utilized to replace step 5 and step 6 in Algorithm 1.
## 2 Materials and Methods
### Modeling Approach
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline
**Iterations** & \multicolumn{4}{c}{**Plackett–Burman Analysis of Nine Criteria for Rising Water Level**} & \multicolumn{4}{c}{**Plackett–Burman Analysis of Nine Criteria for Falling Water Level**} \\ \hline
**ID** & **H** & **v** & **k** & **c** & \(\Phi\) & **\(\mu\) & **E** & \(\rho_{\mathbf{s}}\) & **L** & \
\begin{tabular}{c} **SRF** \\ **Rising** \\ \end{tabular} & **H** & **v** & **k** & **c** & \(\Phi\) & **\(\mu\) & **E** & \(\rho_{\mathbf{s}}\) & **L** &
\begin{tabular}{c} **SRF** \\ **Falling** \\ \end{tabular} \\ \hline SR 1 & + & \(-\) & \(-\) & \(-\) & + & + & \(+\) & \(-\) & + & 2.155 & + & + & \(-\) & + & \(+\) & \(-\) & \(+\) & \(-\) & \(-\) & 1.986 \\ SR 2 & + & + & \(-\) & \(+\) & \(-\) & \(-\) & + & + & + & 2.066 & + & \(-\) & \(+\) & \(-\) & \(-\) & \(-\) & \(-\) & \(+\) & \(+\) & 1.782 \\ SR 3 & + & \(-\) & + & \(-\) & \(-\) & \(-\) & + & + & + & + & 1.827 & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & 1.78 \\ SR 4 & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & 1.825 & + & \(-\) & \(-\) & \(-\) & \(+\) & \(+\) & \(-\) & \(+\) & \(-\) & \(+\) & \(1.931\) \\ SR 5 & \(-\) & \(+\) & \(-\) & \(-\) & \(-\) & + & + & + & \(-\) & 1.891 & + & + & \(-\) & \(-\) & \(+\) & \(-\) & \(+\) & \(-\) & \(+\) & \(-\) & \(-\) & \(1.852\) \\ SR 6 & \(-\) & \(+\) & \(-\) & \(+\) & \(-\) & \(-\) & \(-\) & + & \(-\) & + & 2.031 & + & \(-\) & \(-\) & \(-\) & \(-\) & \(+\) & \(+\) & \(-\) & \(-\) & \(-\) & \(-\) & \(1.703\) \\ SR 7 & \(-\) & \(-\) & + & + & + & \(-\) & + & + & \(-\) & 2.28 & \(-\) & + & + & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(+\) & \(1.916\) \\ SR 8 & + & \(-\) & \(-\) & \(-\) & \(-\) & + & + & + & \(-\) & + & 2.131 & + & \(-\) & + & \(+\) & \(-\) & \(+\) & \(-\) & \(-\) & \(-\) & \(-\) & \(-\) & \(1.846\) \\ SR 9 & + & \(-\) & + & + & \(-\) & + & \(-\) & \(-\) & 2.07 & \(-\) & \(-\) & \(-\) & + & + & + & \(-\) & \(+\) & \(+\) & \(2.011\) \\ SR 10 & \(-\) & + & + & + & \(-\) & + & + & \(-\) & + & + & \(-\) & + & \(-\) & \(-\) & \(+\) & \(+\) & \(+\) & \(1.681\) \\ SR 11 & + & \(-\) & + & + & \(-\) & + & \(-\) & + & \(-\) & 2.156 & \(-\) & + & + & + & \(-\) & \(+\) & \(-\) & \(+\) & \(-\) & \(1.826\) \\ SR 12 & + & + & + & \(-\) & + & + & \(-\) & + & \(-\) & 2.133 & \(-\) & \(-\) & + & + & + & \(-\) & \(+\) & \(+\) & \(-\) & \(2.009\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results and scenarios of Plackett–Burman analysis of nine criteria for rising and falling water level.
## 3 Results
### Driving Force Analysis for Internunual GPP Dynamics
#### 3.3.2 The Climate Change Effect on GPP from 2001 to 2016  
We further evaluated the climate change effect on the interannual GPP trends using the \"CLM1\" (accounting for combined effects of changes in DSR, TEMP, and VPD), \"CLM2\" (only accounting for only DSR change), \"CLM3\" (only accounting for only TEMP change), and \"CLM4\" (only accounting for only VPD change) simulations.  
The DSR showed large interannual variations according to the GLASS DSR product, with an overall positive (but not statistically significant) change trend from 2001 to 2016 (Figure 6a). Model simulations indicated that radiation brightening (i.e., increasing DSR) played a minor role in driving the GPP trend (+2.9 Tg C yr\({}^{-2}\), \(p\) > 0.05; \"CLM2\"). In contrast, the mean air temperature (TEMP) (+0.029 \({}^{\circ}\)C yr\({}^{-1}\), \(p\) < 0.01) (Figure 6b) and VPD (+1.61 Pa yr\({}^{-1}\), \(p\) < 0.01) (Figure 6c) both increased significantly during the study period.  
Figure 5: Land use and land cover type across Europe and LULCC and greening effects on GPP. **(a)** Spatial pattern of LULCC from MCD12Q1 in 2001 and relative area proportions of different land-use types in 2001 and 2016. All permanent ice/snow are masked out. **(b)** Effects of land cover change and LAI change (i.e., greening effect) on the interannual GPP trend constrained by the “All”, “LULCC1”, “LULCC2”, and “LULCC3” simulations. Model simulations are shown in Table 1.  
The warming effect (i.e., increasing TEMP) led to a small but significant increase in GPP (+3.7 Tg C yr\({}^{-2}\), \(p\) < 0.05; \"CLM3\"), while the enhanced VPD had an even stronger negative impact (\(-\)5.6 Tg C yr\({}^{-2}\), \(p\) < 0.01; \"CLM4\") than the warming effect (Figure 6d). The warming effect and radiation brightening effect marginally offset the GPP decline caused by the enhanced VPD (Figure 6d). Consequently, climate change had a small positive impact on GPP (1.2 Tg C yr\({}^{-2}\), \(p\) > 0.05; \"CLM1\") due to the combined effects of changes in climatic factors.
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 3 Sharing of Optical Access Networks
### Wavelength Division Multiplexing PON (WDM-PON)  
Another approach to PON is to split the distribution network in wavelengths rather than in power. This requires the use of wavelength demultiplexers in the Optical Distribution Network (ODN) in place of optical splitters, so that each end-user can be connected to the CO through a dedicated wavelength. In this case, the fiber network remains as a point-to-multipoint structure, although the connectivity is provided as a point-to-point wavelength channel overlay. This architecture goes under the name of WDM-PON. The main advantages are that it provides isolation between channels and does not require upstream bandwidth scheduling algorithms. This second point is an advantage for providing services such as fronthauling of Cloud-RAN, as it provides low and deterministic latency [39; 40]. In addition, sharing can be achieved by allocating wavelength channels to different operators, according to their customer connection. Nonetheless, this isolation comes at the cost of dedicating a wavelength and a termination port at the central office to a specific end-user, and it requires every end device to operate tunable lasers. In addition, static allocation of wavelengths to end-users is inefficient, since similar to PtP fiber, any unused capacity cannot be assigned to other users.  
In the past, however, researchers have proposed solutions to tackle such inefficiency. For example, in [41], a purpose-built WDM-PON is introduced in which service providers can transport residential and business traffic as two virtual networks on one physical PON.  
In [42] three different unbundling strategies for realizing multi-operator GPON were compared, with respect to ease of market entrance for new network operators, based on their deployment costs. The first approach is moving back the PON splitter into the CO to mimic an architecture similar to a P2P in which a single fiber is dedicated to each customer. The second strategy is replicating the access network, i.e. replicating the fiber deployment in the drop segment by dedicating a splitter to each operator. The last option is upgrading an already-deployed TDM PON to WDM PON. The cost analysis was done for scenarios with a low to high density of customers per square kilometer, and the conclusion was that upgrading to WDM is the most efficient strategy to perform LLU using GPON. Indeed, if we assume the constraint that the sharing needs to be achieved through channel unbundling, WDM-PON can be seen as a natural solution for multi-tenant PONs due to the ease of sharing it provides. Each VNO can operate on one of the wavelengths and serve its users with absolute isolation from the other VNOs. However, the same limitations of PtP fiber discussed above apply, including additional cost, footprint, energy consumption and inefficiency of capacity allocation.