esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Plasmon Nanolaser: Principle, Structure, Characteristics and Applications
## 3 Structures and Characteristics of Plasmonic Nanolasers
### Nanolaser Based on Whisper-Gallery Effect  
The whisper-gallery (W-G) effect is often applied to photonic crystal microcavities and disc-shaped microcavity nanolasers [64; 65; 66; 67]. In recent years, this effect has also been used to match the surface plasmons provided by metals to fabricate nanoscale laser devices.  
Figure 10: (**a**) Schematic of the LSP nanolaser, (**b**) emission spectra [63].  
Figure 9: (**a**) Structure of the localized surface plasmon (LSP) nanolaser, (**b**) transmission electron microscope image of Au core, (**c**) scanning electron microscope image, (**d**) lasing mode with \(\lambda\) = 525 nm and Q = 14.8, and (**e**) spectroscopic results [62].  
In 2011, [PERSON] et al. of the University of California at Berkeley first demonstrated experimentally that a semiconductor surface plasmon nanolaser based on the W-G effect can be realized at room temperature, which can compress light to 1/20 th of the wavelength at a vertical scale [68]. The preparation method of this plasmonic nanolaser is as follows: firstly, the CdS nanobelt is prepared by chemical vapor deposition (CVD). Then, the CdS nanobelt is cleaned by an ultrasonic wave in the solution matrix. Finally, 5 nm thick MgF\({}_{2}\) and 300 nm thick silver are deposited on the nanobelt. A Ti sapphire laser is used to pump the laser with a wavelength of 405 nm, a repetition rate of 10 kHz and a pulse width of 100 fs. A 20x objective lens is used to focus the pump source on a spot with a diameter of only 5 \(\upmu\)m. The entire experiment was operated at room temperature.  
Figure 11 shows the surface plasmon laser at room temperature and its SEM. The MgF\({}_{2}\) layer can not only reduce the loss of metal, but also provides mode limitation beyond the diffraction. The refractive index difference between CdS and MgF\({}_{2}\) makes it possible for the structure to confine the optical mode mainly to the MgF\({}_{2}\) layer, which provides a good mode limitation. At the same time, the metal absorption loss is greatly reduced by coupling the electric field from the metal surface to the MgF\({}_{2}\) layer. This coupling is very strong, which makes the momentum of the optical modes in the square CdS cavity increase greatly. That is, the effective refractive index increases greatly, meaning that the TM mode can obtain a sufficient internal reflection. For lasing light with wavelengths of 495.5 and 508.4 nm, the Q values are 97 and 38, respectively. The optical pump power densities of the threshold are 3070 and 4020 MW/cm\({}^{2}\), respectively. In this design, the shortcomings of the square structure are obvious. Because of the high symmetry, there are more modes, and so there are many lasing peaks, as shown in Figure 11c.
# State of Charge Estimation for Lithium-Ion Power Battery Based on H-Infinity Filter Algorithm
## 3 SOC Estimation Based on the HIF Algorithm
### SOC Estimation Accuracy Verification  
After describing in detail the principle of the HIF and the specific calculation process, in order to verify the robustness of the algorithm under the three conditions of uncertain measurement accuracy, initial SOC value uncertainty, and uncertain application conditions, this section uses a file in MATLAB software to write the HIF and EKF running procedures, and carries out simulation operations in Simulink. Based on the obtained experimental data, accuracy verification and comparative analysis are conducted on the estimation results of the SOC of the lithium-ion battery using HIF and EKF.  
In the current literature, the reference values used for battery SOC estimation algorithm verification are all obtained by the ampere-hour integration method [34]. In order to eliminate the measurement error of the current sensor as far as possible, a high-precision battery testing equipment with a stability of 0.1% of FS is adopted, and the battery is fully placed before the test. Therefore, this study uses the approximate SOC calculation value obtained by the ampere-hour integration method as the real SOC value.
## 2 Materials and Methods
### OCT Data Acquisition and Image Processing  
For weld seam analysis with OCT, a Telesto II OCT (Thorlabs GmbH, Bergkirchen, Germany) with an acquisition rate of 76 kHz and a central wavelength of 1.3 \(\upmu\)m was used with the ThorImage 5.3.1.0 software in combination with the LSM02 objective (Thorlabs), giving a depth or so-called axial (\(z\)) resolution of 5.5 \(\upmu\)m and a lateral resolution of 7 \(\upmu\)m (1/e\({}^{2}\) beam diameter in \(xy\)). For each weld seam, a volume (C-scan) of 5.0 \(\times\) 1.5 \(\times\) 2.4 mm\({}^{3}\) (\(x,y,z\)), consisting of 400 \(\times\) 500 \(\times\) 1024 data points, was recorded within 18 s. The distance in the \(x\)-direction between two B-scans (\(yz\)-planes) was 12.5 \(\upmu\)m. This is more than 1.75 times larger than the lateral resolution of the OCT, and the B-scans are therefore independent of each other and do not share any information. Within a B-scan, the pixel sizes of 3 \(\upmu\)m in the horizontal (\(y\)) and 2.3 \(\upmu\)m in the depth (\(z\)) direction are below the resolution of the OCT with the used objective as recommended by Thorlabs. The dispersion compensation was quadratic with a dispersion factor of 63. The A-scan averaging was set to 5, and the refractive index of the base material was assumed to be 1.5.  
Figure 3a shows an example of a volumetric view of the recorded data in the ThorImage OCT software. After the acquisition, the data were transferred directly to ImageJ using the interface implemented in ThorImage. The subsequent image processing was performed automatically using self-written scripts. In ImageJ, the data were converted to 8-bit and saved as a TIFF image stack first. Figure 3b shows the first B-scan of the stack, and Figure 3c shows the final processed B-scan, including a sketch of the weld seam measurements. After processing all B-scans, they were concatenated into a 3D volume (Figure 3d).  
During image processing, the margins of the upper and the lower joining partner and the joining zone between both partners were determined first. Secondly, the weld seam and its dimensions were identified. Both steps were performed with separate scripts. Details of the image processing are explained below.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 5 Summary and Outlook
### Future Research Directions
#### 5.1.1 The New Method Brings a New Dawn to the Optimization Design of Sparse Area Arrays  
At present, in the satellite communication scenario, with the continuous expansion of the scale of the array, for sparse area arrays, the efficiency of some methods, such as the traditional intelligence method, will continue to decline with the expansion of the array scale, and relying on a single method is obviously no longer up to the standard at the engineering application level, while the accuracy requirements of the method have also been increased. Therefore, the use of hybrid methods becomes even more important. In future research, artificial intelligence algorithms such as deep learning, reinforcement learning, and various neural networks (ANN, LMS, etc.) have better generalization ability and better robustness for complex arrays, providing more degrees of freedom to break through the performance boundary of existing technologies and create new functions for antenna design, which will become a hotspot in future research for sparse array optimization design or auxiliary sparse array optimization design. The optimization design method based on compressed sensing shows many excellent properties, such as high matching accuracy, but its own mesh mismatch problem limits the performance of the algorithm in a sparse environment. Thus, how to effectively solve this problem is a research direction that needs addressing. It is worth mentioning that, in order to reduce the software and hardware complexity of the system, increase the computing speed, accelerate convergence, and improve the performance of the array, the combination of sub-array division technology and various methods can also become a new research direction in the future, such as in the distributed constellation. The array of satellites in the constellation can be regarded as different sub-arrays, forming a multi-star joint array, which has important research significance in the optimization design of sparse arrays in the future.
## 3 Algorithms  
### Basic Algorithm for \(F_{1}\) and \(F_{2}\)  
For the objective function \(F_{1}\), we propose an algorithm similar to topological sorting. Let \(G=\textit{(}V,E\textit{)}\) be a directed acyclic graph (DAG) where \(V\) is the set of vertices, \(E\) is the set of edges and _w(u, v)_ is the weight of edge \(u\)\(\rightarrow\)_v_. In this graph, the longest path will go from a node that has no edge going in (initial vertex) and those nodes that are connected to it. Notice that there might be several \"initialvertices\", and that there is no single privileged node. Let \(S\subseteq V\) be the set of vertices with in-degree \(d_{in}(v)=0\) and let \(Q\) be a stack. The algorithm to determine the longest distance from a given vertex to each of the other vertices is the following (Algorithm 1):  
```
1.  For each vertex \(v\in S\) with \(d_{in}(v)=0\) \(1.1.\)\(push(Q,v)\) \(d(v):=0\) \(1.3.\)\(origin(v):=v\) \(2.\)  While \(Q\) is not empty \(2.1.\)\(u:=pop(Q)\) \(2.2.\) for each vertex \(v\)\((u,v)\in E\) \(2.2.1.\)\(d_{in}(v):=d_{in}(v)-1\) \(2.2.2.\) if \(d_{in}(v)=0\) \(2.2.2.1.\)\(push(Q,v)\) \(d(v)=\max_{(x,v)\in E}(d(x)+w(x,v)\) \(2.2.2.3.\)\(father(v):=x\)
```  
**Algorithm 1**Longest distance from initial vertices  
This algorithm only works if the graph is acyclic. In real life DNA fragment assembly problems, there are cycles. When this algorithm is applied to a graph with cycles, neither the cycles nor the nodes that are further from the cycles are detected. Later we will discuss how to take care of cycles.
## 3 Results
### Comparison of the Results with Previous Studies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Study Overview** & **Input Data** & **Models** & **Measures** \\ \hline [PERSON] et al. (2018) & A yellow rust detection system was developed, using multispectral UAV imaging & RVI, NDVI, and OSAVI vegetation indices & Random Forest classifier & Precision, recall, and accuracy of 89.2\%, 89.4\%, and 89.3\% \\ \hline  & UAV-obtained hyperspectral images and deep learning were used for yellow rust detection & Hyperspectral images & A DCNN with multiple Inception-Resnet layers & Accuracy of 85\% \\ \hline  & UAV-obtained hyperspectral images and VI and TF-based models were used for early-to-late hyperspectral images & Vegetation indices and texture features extracted from the hyperspectral images & TF-based, VI-TF-based and models & R\({}^{2}\) between 0.55 and 0.88, depending on the stage of the infection \\ \hline  & UAV-obtained hyperspectral images and an NDVI-based model were used for yellow rust detection & Multispectral imaging & NDVI-based model & An R\({}^{2}\) of 0.514 \\ \hline  & UAV-obtained visible spectrum imaging and deep learning were used to identify yellow rust & RGB imaging & PSPNet neural network & Accuracy of 98\% and Kappa of 0.96 \\ \hline  & Different combination of UAV-obtained spectral maps was used with deep learning for yellow rust identification & RGB, NDVI, and NIR maps & UNetFormer2 neural network & F1 score of 78.4\%, 66.6\%, and 82.2\% for RGB, NDVI, and NIR data \\ \hline  & Yellow rust was identified using UAV imaging and deep learning & RGB images with applied “Histogram equalization” filter & UnetClassifier + ResNet34 & Precision, recall, F1 score, accuracy, and Kappa of 99.1\%, 99.0\%, 99.0\%, 87.4\%, and 0.977 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the achieved results with previous studies.
## 3 Results  
### Characterization of Brick Composite  
#### 3.1.1 XRD Analysis  
The XRD patterns of raw brick and alkali-activated brick presented in Figure 1a,b revealed that quartz was the major crystalline phase, as were, to a lesser extent, illite and rutile. In the XRD patterns of alkali-activated brick (see Figure 1b), additional peaks with lower intensities appeared in the diffractogram.  
Their positions revealed the occurrence of zeolitic crystals: Na-A (or LTA; an abbreviation meaning _Linde type A_), and Na-P. The characteristic peaks observed for LTA crystals were 20 = 7.2\({}^{\circ}\), 10.2\({}^{\circ}\), 12.5\({}^{\circ}\), and 21.7\({}^{\circ}\), which correspond to lattice plans of (200), (220), (222), and (600 and 442), respectively [66]. The characteristic peaks observed for Na-P crystals were 20 = 12.5\({}^{\circ}\), 17.7\({}^{\circ}\), 21.7\({}^{\circ}\), 28.1\({}^{\circ}\), 33.4\({}^{\circ}\), and 46.1\({}^{\circ}\), which correspond to lattice plans of (101 and 110), (200 and 002), (211, 112 and 121), (310, 301, and 103), (132, 123, 231, 213, 312, and 321), and (134), respectively [66].  
It is worth noting that the micro-porosity technique used herein did permit us to assess the pore size contributions provided by LTA and Na-P zeolites to the overall surface area. Indeed, the dimension for nitrogen gas to penetrate the pores at 77 K is limited to 5 A [67], whereas for pore sizes in the LTA zeolite, it is \(\leq\)4.1 A [68], and dimensions of intersecting channels in the Na-P zeolite (i.e., 0.31 nm \(\times\) 0.44 nm and 0.26 nm \(\times\) 0.49 nm) are too weak [69; 70; 71].  
Overall, XRD characterizations performed on alkali-activated brick revealed the appearance of new reflections, which were ascribed to two crystalline zeolitic phases: zeolite Na-A and zeolite Na-P. This study confirmed the occurrence of crystal nucleation of the amorphous geopolymeric (aluminosilicate) gel phase (knowing that this gel was intermediately generated through brick-metakaolinite dehydroxylation under fixed thermal/alkaline conditions).
###### Abstract  
In this paper, we propose an adaptive single differential coherent detection (SDCD) scheme for the binary phase shift keying (BPSK) signals in IEEE 802.15.4 Wireless Sensor Networks (WSNs). In particular, the residual carrier frequency offset effect (CFOE) for differential detection is adaptively estimated, with only linear operation, according to the changing channel conditions. It was found that the carrier frequency offset (CFO) and chip signal-to-noise ratio (SNR) conditions do not need a priori knowledge. This partly benefits from that the combination of the trigonometric approximation \(\sin^{-1}\left(x\right)\approx x\) and a useful assumption, namely, the asymptotic or high chip SNR, is considered for simplification of the full estimation scheme. Simulation results demonstrate that the proposed algorithm can achieve an accurate estimation and the detection performance can completely meet the requirement of the IEEE 802.15.4 standard, although with a little loss of reliability and robustness as compared with the conventional optimal single-symbol detector.  
Keywords:single-symbol noncoherent detection; adaptive estimation; IEEE 802.15.4; wireless sensor networks +
Footnote †: journal: Sensors
## 5 Performance Evaluation
However, when monitoring a patient in intensive care, the outage probability of OCC is low because the patient is static and the NLOS caused by the patient's movement is non-existent. In addition, the outage probability can be minimized by increasing the number of cameras. As is evident in Figure 8, integrating OCC with BLE considerably reduces the outage probability. Network switching becomes important when monitoring mobile patients in these scenarios. When patients move, the changing \(\mathrm{d}_{\alpha,\beta}\) alters the OCC performance (as discussed above), and hence the possibility of outage in OCC. When an outage occurs, the communication must switch to BLE. Figure 9 plots the OCC-to-BLE handover probabilities in the hybrid system as functions of \(\mathrm{d}_{\alpha,\beta}\) for varying \(\partial_{\mathrm{AOV}}\) and \(\xi\), in the scenario of Section 4. The outage probability significantly decreased with increasing \(\partial_{\mathrm{AOV}}\) and \(\xi\), and increased with increasing \(\mathrm{d}_{\mathrm{overlap}}\). Expressed another way, decreasing the \(\partial_{\mathrm{AOV}}\) and \(\xi\) increases the handover probability from BLE to OCC. Figure 8: Comparison of the outage probabilities in the OCC, BLE, and proposed schemes. Figure 7: Variation of OCC selection probability for the single- and multiple-camera scenarios. As discussed earlier, reducing the error in the healthcare information is more important than improving the data transmission rate. Therefore, an excellent SINR is imperative. When the system meets the data rate requirement, its QoS is dominated by the error amount in the received information. In the performance evaluation, we set the target data rate and SINR and estimated the QoS values of the OCC, BLE, and hybrid systems. The cumulative QoS distribution functions in the three systems are compared in Figure 10. The cumulative distribution function (CDF) was calculated over the distance range 0.5-4 m. The CDF of the QoS increased with increasing difference between \(\phi_{\text{tar}}\) and \(\phi_{\text{ins}}\) and with increasing distance between \(\eta_{\text{ins}}\) and \(\eta_{\text{tar}}\). As indicated in Figure 10, the QoS was higher in the proposed scheme than in OCC or BLE. In all the simulations, the spectral density of the OCC noise was assumed to be constant and equal to \(10^{-21}\).
## 1 Introduction
Diatoms also produce long-chain polyunsaturated fatty acids (LC-PUFA) such as eicsapentaenoic acid (EPA) and docosahexaenoicacid(DHA) (omega-3 [u-3] class) [33]. Vertebrates cannot synthesize essential fatty acids and the ability to produce LC-PUFA is also limited; they do not obtain these through their diet [34,35]. Furthermore, LC-PUFA plays key roles in animal and human health, being major components of neurological tissues [36]. Photosynthesis has been shown to be impaired by glyphosate-based herbicides within wide ranges of concentrations, including in cordgrass _Spartina densiflora_ from spray application at concentrations of 720-7200 g ha\({}^{-1}\)[37], to direct contact of 10-80 mg L\({}^{-1}\) in _Anabaena fertilissima_[38]. In both cases, there was inhibition of CO\({}_{2}\) assimilation and depletion of intermediates from the photosynthetic carbon reduction cycle due to deregulation of the shikimate pathway [39,40]. In such scenarios, non-invasive high-throughput bio-optical screening tools, such as Pulse Amplitude Modulated (PAM) fluorometry, emerge as invaluable techniques to evaluate ecotoxicity in photosynthetic organisms [12,41,42]. Through PAM it is possible to translate the fluorescence signals as proxies to the bioenergetics involved in photosynthesis in a non-destructive form [13,41,42,43,44] to efficiently assess primary productivity [45,46] and the physiological effects in plant material at different concentrations of contaminants [12,41,42,44]. The half-life of glyphosate in water varies between 45 and 60 days [47] and phytoplankton have a fast response in the presence of contaminants [13,25]. Thus, it is of the utmost importance to evaluate the potential effects of glyphosate in the environment, namely on primary producers. The present study aimed to correlate bio-optical data with the ecotoxicological effects of the exposure of the glyphosate-based herbicide Roundup(r) (i.e., currently found in estuarine and marine systems) [48, 49, 50, 51], with the working hypothesis that, similarly to other photosynthetic organisms, this herbicide will have a negative impact on primary productivity, antioxidant enzyme activity, energy balance, pigment and fatty acid composition of the model diatom _Phaeodactylum tricornutum_, and its potential implications on estuarine or coastal marine ecosystems.
## 3 Present
### CAD in Lung Cancer Diagnosis
#### 3.1.2 Algorithms Proposed for Nodular Classification  
The classification process although similar to the one in detection presents two major differences: the first one is that information about nodule location is supplied to the algorithm (usually databases used for this purpose provide coordinates about interesting findings in the image), and the second is that the model does not differentiate between nodule and non-nodule findings, but rather differentiates between malignant and non-malignant nodules. Of all the models found in Table 1, only [PERSON] et al. [19] propose the use of conventional CAD for classification purposes, using two models: one volume based and one diameter based with similar results, although slightly higher in the volume one. The rest of the CAD models make use of DL, such as [PERSON] et al. [20], who propose a semi-supervised model considering that the use of unlabeled data is not only effective for the training of the DL models but also critical to carry out a hypothetical lung nodule screening system. [PERSON] et al. [21] compare 11 different algorithms based on DL-CNN and propose one based on Transfer Learning after showing that it showed promising results. [PERSON] et al. [22] study three different CNN architectures of which the third one obtains the best results and encourages finding new applications of Deep Learning in computer-assisted diagnosis. [PERSON] et al. [23] tested 36 combinations of which the algorithm that combines the Support Vector Machine (SVM) classifier with an Extended Histogram of Oriented Gradients (ExHOG) and CNN achieves the highest accuracy when classifying. [PERSON] et al. [24] test different methods from which a 50-layer Residual Network based model obtains the best performance, and its main defect is that it requires a long period of training when it has to deal with a large number of pulmonary CT images, something they will try to solve in future works. [PERSON] et al. [25] propose two models, a Multi-Channel Multi-Slice 2D CNN and a Voxel- Level 1D CNN, the latter is especially useful in small and poorly balanced data sets. At the same time, they question the validity of these openly used databases used to discern between malignant and benign nodules, such as that of LIDC-IDRI, as the Standard of Reference in the latter is not a pathology study but a consensus among four radiologists in which global consensus is not necessary, and therefore, only 25% were classified as nodules by all four while the rest were not. As a solution to improve this type of bias that may affect the design of the classification models, it is proposed to use data sets whose reference standard is pathology study. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline
**CAD Model** & **Method** & **Function** & **Data Base** & **N\({}^{\star}\)Sanners** & **N\({}^{\star}\)N Modules** & **Sensitivity** & **Specificity** & **PP/Scan** & **Accuracy** & **AUC** & **Year** \\ \hline [PERSON] et al. [8] & CAD-conventional & Detection & ELCAP & 40 & NA & 100\% & 99.20\% & NA & 99.60\% & NA & 2019 \\ \hline [PERSON] et al. [9