esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
The fast heat conduction rate leads to smaller switch fall time. Figure 12c shows the reflection spectrum of traditional Bragg sampled grating. The SLSR was measured about 6.1 dB. The side lobes near each reflection peak were strong. Figure 12d shows reflection spectrums when applying power on the electrode. When there was no heat, the 3-dB bandwidth, wavelength spacing, SLSR were 4.8 nm, 9.7 nm, and 22.6 dB, respectively. When the temperature on the electrode changed 10 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, the resonance wavelength shifted approximately 1.8 nm and 3.5 nm. Meanwhile, the power consumptions were 42.4 mW and 87.2 mW. It can be calculated the power consumption and wavelength shift has a relationship of 24.91 mW/nm. Table 2 shows the comparison between the proposed polymer apodized waveguide grating and other waveguide gratings. Our device had a larger SLSR for the lateral shift structure to reduce the coupling coefficient at both sides of grating, which is the aim of proposed device. It also had smaller driving power and larger wavelength shift at the same temperature change of the electrode, which could be explained as the electrode heater was directly above the polymer layer and this avoided the upper cladding thermal absorption of traditional waveguide structure containing upper cladding. \begin{table}
\begin{tabular}{c c c} \hline \hline
**SLSR** & **Wavelength Shift and Driving Power (Temperature) Relation** & **Reference** \\ \hline
13.2 dB & / & [18] \\
10.3–13.8 dB & / & [33] \\ \(\sim\)12.0 dB & 0.16 nm/\({}^{\circ}\)C & [34] \\ \(\sim\)8.0 dB & 0.082 nm/\({}^{\circ}\)C & [35] \\ \(\sim\)20.0 dB & 37.7 mW/nm & [36] \\
22.6 dB & 24.91 mW/nm & Present Work \\  & 0.175 nm/\({}^{\circ}\)C & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of other published results for Bragg waveguide gratings.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 2 Bloch Sphere Quantum Genetic Algorithm  
The quantum genetic algorithm is a probabilistic optimization algorithm based on quantum computing principles, aiming to search for the optimal solution through the evolution and mutation operations of chromosome genes. Its advantages include a small population size, fast convergence, and robust global search capabilities. However, the quantum genetic algorithm requires measuring the state of quantum bits to obtain binary solutions, involving highly random and blind probabilistic operations. Comparing with the traditional QGA, an improved one uses Bloch spherical coordinates for encoding. It extends the search on each dimension to a three-dimensional search in space, with each chromosome occupying three positions in space, to increase the randomness and diversity of quantum chromosomes, and it constructs a new update strategy for quantum chromosomes by incorporating the coordinate transformation of the least square principle. Meanwhile, a selection formula for the variation angle is constructed to improve the search capability.
## 3 Results and Discussion
### LMO Monitoring in the Natural Environment for Canola  
The Environmental LMO Monitoring and Post-Management Project is operated by the NIE, which is part of the MOE. LM canola has been monitored in the environment since 2009, in five provinces, including Gyeonggi-do, Gyeongsang-do, Chungcheong-do, Jeolla-do, and Gangwon-do. Jeju-do has also been included as an investigation site since 2015. Over a four-year period, 3121 environmental sites were investigated: 687 in 2014, 787 in 2015, 800 in 2016, and 847 in 2017 (Figure 2; blue). Suspicious LM canola samples were collected from 260 sites: 3 in 2014, 65 in 2015, 78 in 2016, and 114 in 2017 (Figure 2; yellow). Although suspicious samples were only found at three sites in Gyeongsang-do in 2014, there  
Figure 1: Processing scheme for the environmental living modified organism (LMO) monitoring and post-management project. (**A**) Three steps for the LMO monitoring. (**B**) Flowchart to show the efficiency of the multiplex PCR for the LMO detection analysis.  
was a remarkable increase in the suspicious LM canola samples between 2015 and 2017 (Figure 2E). The most suspicious LM canola samples were collected from roadsides along transportation routes for food and feed factories. The number of LM canola volunteers has been increasing annually since 2014. These results suggest the need for a novel multiplex PCR method for Topas 19/2, Rf3, Dp-73496-4, Ms8, GT73, Mon88032, and T45 to identify LM canola of the 260 volunteer samples.  
Figure 2: Living modified organism (LMO) monitoring sites in South Korea from 2014 to 2017. The maps show the monitoring survey sites (blue spot) and collection sites of suspicious canola samples (yellow spot) in 2014 (**A**), 2015 (**B**), 2016 (**C**), and 2017 (**D**). The bar graph indicates the number of survey sites and collection sites for the suspicious samples. The red line showed the ratio of suspicious canola collection sites compared to the survey sites (**E**).
## 6 Conclusions  
This paper proposes a fault-tolerant service migration approach (DRPS) for service migration in the VEC environment. Compared with the traditional service migration method, DRPS firstly uses the sliding window mechanism to predict the failure rate of ESs in VEC, then calculates a set of high-quality migration paths according to the type of migration task, and, finally, combines the replication and resubmission strategies in the process of service migration to ensure the migration efficiency. To verify the performance of DRPS, we conduct simulation experiments based on two real edge computing datasets. The experimental results show that compared with the traditional service migration method, DRPS can significantly improve the task completion rate and the average task completion time.  
The continuous development of artificial intelligence technology and data collaborative processing has become a general trend to apply artificial intelligence technology in VEC. In the next step, we plan to use artificial intelligence technology (e.g., deep learning, reinforcement learning, etc.) with strong robustness and good generalization to solve problems, e.g., load balancing scheduling and ES performance prediction in VEC to further reduce transmission costs to improve the system QoS.  
Conceptualization, J.Z. and Y.X.; Data curation, J.Z., Y.M. and Y.X.; Formal analysis, J.Z., Y.M., Y.X. and M.D.; Software, J.Z.; Writing--original draft, J.Z.; Supervision, Y.M.; Funding acquisition, Y.X.; Validation, M.D.; Methodology, P.C. and S.S.; Investigation, P. C.; Resources and Project administration, T.L.; Visualization, S.S.; Writing--review & editing, F.L., Y.L. and F.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Postgraduate Scientific Research and Innovation Foundation of Chongqing under Grant No. CYB22064; This work is supported by the National Science Foundations under Grant No 62162036.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Instrumental Equipment
#### 2.2.3 Measurement with Laboratory Raman Spectrometer  
The measurement of tissue samples with a DXR SmartRaman Raman spectrometer was performed in macroscopic mode using a sample holder. This instrument is equipped with a DXR 780 nm HP Laser and a diffraction grid comprised of 400 lines per mm. The power of the laser output was \(\sim\)90 mW. The final spectra were obtained by averaging 3-100 scans, each with an exposure time of 20-60 s. The spectra were measured in a spectral region of 100-3409 cm\({}^{-1}\) with a resolution of 2.4-4.4 cm\({}^{-1}\).
## 1 Introduction
### Related Works
The UAV has strong flexibility, convenient deployment, and wide coverage, which can effectively alleviate signal transmission problems in wireless optical communication and improve communication quality and reliability [24]. In practical applications, UAV-assisted FSO communication can be used in fields such as remote communication, monitoring data transmission, and disaster rescue. [PERSON] et al. [25] analyzed and derived the outage probability of a UAV-based FSO dual-hop decode-and-forward system with multiple sources. [PERSON] et al. [26] establishedand investigated a UAV-assisted mixed RF/FSO dual-hop communication system under the amplified-and-forward protocol with variable gain and derived the system metrics.
###### Abstract  
Site-specific pest management (SSPM) is a component of precision agriculture that relies on spatially enabled agronomic data to facilitate pest control practices within management zones rather than whole fields. Recent integration of high-resolution environmental data, multivariate clustering algorithms, and species distribution modeling has facilitated the development of a novel approach to SSPM that bases zone delineation on environmentally independent subfield units with individual potential to host pest populations (eSSPM). Although the potential benefits of eSSPM are clear, methods currently described for its implementation still demand further evaluation. To offer clear insight into this matter, we used field-level environmental data from a Tahiti lime orchard and realistic simulations of six citrus pests to: (1) generate a series of virtual (i.e., controlled) infestation scenarios suitable for methodological testing purposes, (2) evaluate the utility of nested (i.e., within-cluster) partitioning essays to improve the accuracy of current eSSPM methods, and (3) implement two biological clustering validators to evaluate the performance of 10 clustering algorithms and choose appropriate numbers of management zones during field partitioning essays. Our results demonstrate that: (1) nested partitioning essays outperform zoning methods previously described in eSSPM, (2) more than one clustering algorithm tend to be necessary to generate field partition models that optimize site-specific pest control practices within crop fields, and (3) biological clustering validation is an essential addition to eSSPM zoning methods. Finally, the generated evidence was integrated into an improved workflow for within-field zone delineation with pest control purposes.  
Keywords:algorithms; clustering; modeling; pest control; precision agriculture; site-specific; virtual pests +
Footnote †: journal: _Sciences_
## 3 A Double Optimal Solution
### Two Minimized Functions  
Let  
\[\mathbf{y}:=\mathbf{A}\mathbf{x}, \tag{32}\]such that the approximation to \(\mathbf{b}\) in Equation (2) is accounted by the error vector:  
\[\mathbf{e}:=\mathbf{b}-\mathbf{b}^{\mathsf{T}}\bigg{(}\frac{\mathbf{y}}{\| \mathbf{y}\|}\bigg{)}\frac{\mathbf{y}}{\|\mathbf{y}\|}; \tag{33}\]  
the squared-norm of \(\mathbf{e}\) reads as  
\[\|\mathbf{e}\|^{2}=\|\mathbf{b}\|^{2}-\frac{(\mathbf{b}^{\mathsf{T}}\mathbf{y })^{2}}{\|\mathbf{y}\|^{2}}\geq 0. \tag{34}\]  
We maximize  
\[\max_{\mathbf{y}}\biggl{\{}\frac{(\mathbf{b}^{\mathsf{T}}\mathbf{y})^{2}}{\| \mathbf{y}\|^{2}}\biggr{\}} \tag{35}\]  
as the maximal projection of \(\mathbf{b}\) on the direction \(\mathbf{y}/\|\mathbf{y}\|\), such that \(\|\mathbf{e}\|^{2}\) is minimized. Equation (35) is equivalent to the minimization of  
\[\min_{\mathbf{y}}\biggl{\{}f:=\frac{\|\mathbf{y}\|^{2}}{(\mathbf{b}^{\mathsf{ T}}\mathbf{y})^{2}}\biggr{\}}\geq\frac{1}{\|\mathbf{b}\|^{2}}. \tag{36}\]  
Let \(\mathbf{J}\) be  
\[\mathbf{J}:=\mathbf{A}\mathbf{U}\in\mathbb{R}^{q\times m}. \tag{37}\]  
Then, with the aid of Equation (24), Equation (32) can be written as  
\[\mathbf{y}=\mathbf{y}_{0}+\mathbf{J}\boldsymbol{\alpha}, \tag{38}\]  
where  
\[\mathbf{y}_{0}:=\alpha_{0}\mathbf{A}\mathbf{u}_{0}\in\mathbb{R}^{q}. \tag{39}\]  
Inserting Equation (38) for \(\mathbf{y}\) into Equation (36) yields  
\[\min_{\boldsymbol{\alpha}}\biggl{\{}f=\frac{\|\mathbf{y}\|^{2}}{(\mathbf{b}^{ \mathsf{T}}\mathbf{y})^{2}}=\frac{\|\mathbf{y}_{0}+\mathbf{J}\boldsymbol{ \alpha}\|^{2}}{(\mathbf{b}^{\mathsf{T}}\mathbf{y}_{0}+\mathbf{b}^{\mathsf{T}} \mathbf{J}\boldsymbol{\alpha})^{2}}\biggr{\}}, \tag{40}\]  
which is used to find \(\boldsymbol{\alpha}\). However, \(\alpha_{0}\) in \(\mathbf{y}_{0}=\alpha_{0}\mathbf{A}\mathbf{u}_{0}\) is still an unknown value to be determined by imposing the minimization of squared-norm of residuals, shortened to the residual square:  
\[\min_{a_{0}}\{\|\mathbf{r}\|^{2}=\|\mathbf{b}-\mathbf{A}\mathbf{x}\|^{2}\}. \tag{41}\]
## 4 Results and Analysis
### Dataset Pre-Processing  
In order to train ML models for real-time classification in the future, the dataset generated during the network's data collecting phase must first undergo pre-processing. After normalizing all traffic types to the traffic type with the fewest samples, each traffic type has an equal number of samples (i.e., 60,036), which reduces the chances of over-fitting to a particular traffic type. In this model, working on the 12 features was started first, and the feature importance score of all 12 features was estimated using the Random Forest classifier algorithm. Based on this score, the top eight features, which are shown in the Table 3, were selected in order to simplify the next steps and expedite the classification process.  
All subsequent stages of training a classification model employ these attributes. The removal of unnecessary elements from the feature set facilitates efficient model training, resulting in accurate real-time classification of all traffic classifications.  
Although the average values may not be useful for classifying early flow stages, particularly for burst or short-lived traffic, nonetheless they are helpful for classifying long-lived data produced during our model implementation, such as video, lengthy calls,and file transfers. We have not yet taken into account short-lived burst datasets and how they affect the classification model, which could be a smart strategy for improving this research in the future. The incorporation of the \"instantaneous\" and \"delta\" parameters speeds up classification compared to using the \"average\" parameter alone.
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 6 Current Collector Shape and Structure
### Wires  
Wires are the most common choice for current collection and are used in some form in all but three of the publications reported in the literature between 2015 and 2020, whether used for the anode and/or cathode current collection, as measurement probes, as the sole current collector, or in a combination with another design. In the last 5 years, the most common wire choice has been silver, followed by platinum, then gold and nickel. Wires are readily available in most materials and dimensions and when chosen correctly achieve highly efficient current collection/conduction over long distances, thus constituting an ideal candidate for interconnection between adjacent cells. Wires were the first \(\upmu\)T-SOFC current collectors to be used, published in the academic literature of the late 1990s (Ken
## 2 Background Material--Introduction to FL
### Definitions and Alternative Nomenclature in Distributed Learning  
Federated learning is a rapidly evolving topic in machine learning, marked by the emergence of various terminologies that often overlap with or resemble FL. Understanding these terms is crucial for scholars, practitioners, and industry experts to ensure clear communication and accurate knowledge dissemination. By definition, FL is a distributed machine learning approach where models are trained across multiple devices or nodes without the need to centralize data, preserving data privacy and reducing the data transfer overheads, particularly with large, decentralized datasets [7; 8; 9].  
Key related terms include the following.  
* Collaborative Learning: Often used synonymously with FL, emphasizing cooperative model training without sharing raw data.
* Distributed Machine Learning (DML): A broader concept encompassing FL, focusing on distributed computations with FL's emphasis on privacy.
* Edge Learning/Training: Aligns with FL but focuses on training at the network's periphery, such as on mobile devices or IoT endpoints [10].
* On-Device Learning: Highlights training on devices like smartphones without offloading data, central to FL's paradigm.
* Decentralized Machine Learning: Encompasses any machine learning technique without a central authority, with FL focusing on decentralized data and privacy.
* Privacy-Preserving Machine Learning: Includes methods like homomorphic encryption, SMPC, and differential privacy, with FL as a key pillar.
## 5 Future Trends and Opportunities--Modular Multicell Rectifier
Since HFTs/MFTs are to be used in the dc/dc stage, each converter cell can be achieved with a small volume and light weight. Compared with Si IGBTs, Si and SiC MOSFETs are better choices for each dc/dc stage, as a higher MFT/HFT alternating frequency can be easily achieved. Nevertheless, the highly modularized design also brings high control complexity to the system. Each modular converter cell needs to be able to regulate itself and equally share the operating power.
## 1 Introduction
More recently, with first principle calculations, Ti\({}_{2}\)MnAl\({}_{0.5}\)Sn\({}_{0.5}\) and Ti\({}_{2}\)MnAl\({}_{0.5}\)In\({}_{0.5}\) were proven to be half metal and spin-gapless semiconductor, respectively [24]. Magnetic Heusler compounds are widely used in spintronic applications. The Heusler semiconductor can be used as either a spin-conserved tunneling barrier or a spin-transporting spacer in a Heusler ferromagnet-Heusler semiconductor-Heusler ferromagnet heterojunction, which has been extensively investigated in recent years. The magnetic Heusler compound is useful and meaningful for its potential integration into magnetic multilayer devices toward some spin valves, as well as for other opportunities and new phenomena. In addition to these spintronic applications mentioned above, Heusler compounds, with other excellent properties, were studied extensively in the past several years. For instance, several Heusler compounds can be used as solar cell materials, thermoelectric materials, topological materials, magneto-optical materials, magneto-caloric materials, shape-memory materials, heavy-Fermion materials, superconductors, semiconductors, and so on [25; 26; 27; 28]. In this work, the electronic structures and magnetic properties of the Ti\({}_{2}\)CoSi inverse Heusler alloy were calculated when a quarter of Co atoms were replaced by Ti. Then, the effects of hydrostatic strain and tetragonal deformation on the electronic structures and magnetic properties of the Ti\({}_{2.25}\)Co\({}_{0.75}\)Si compound were studied. Meanwhile, half of the Si atoms in Ti\({}_{2.25}\)Co\({}_{0.75}\)Si were replaced by B, Al, Ga, P, As, and Sb, and the results show that doped Ti\({}_{2}\)CoSi with an appropriate concentration of impurities could be a half-metallic ferromagnet, a gapless half metal, or a spin-gapless semiconductor.
## 2 Materials and Methods
### Airborne Dust Concentration and Physical Parameter Measurements  
Particulate matter concentration was measured using a LOOKO2 (ZAS Elzbieta Stochlinska, Wegrzce Wielkie, Poland) laser photometer with a built-in WiFi module, ensuring constant measurement and monitoring of dust particle concentration. After installation and configuration, the device was connected to the LOOKO2 server. The data were analyzed, calibrated, and then presented to the user. All LOOKO2 devices are calibrated before deployment and, immediately after configuration, join the global network of LOOKO2 sensors ([[https://looko2.com/heatmap.php](https://looko2.com/heatmap.php)]([https://looko2.com/heatmap.php](https://looko2.com/heatmap.php)) (accessed on 24 August 2022). LOOKO2 has introduced a system that detects malfunctioning of smog sensors (caused by e.g., flooding with water), which prevents incorrect data on the air condition from being sent to the network. According to the manufacturer's documentation, the sensor measures the concentrations of the following aerodynamic diameters of particles: 0.3-1 \(\upmu\)m (PM\({}_{1}\)), 1.0-2.5 \(\upmu\)m (PM\({}_{2.5}\)), and 2.5-10 \(\upmu\)m (PM\({}_{10}\)) in 0.0001 m\({}^{3}\) of air. The measurement resolution is 1 \(\upmu\)m, and the maximum measurement error in the range of 0-100 \(\upmu\)m is 10 \(\upmu\)g/m\({}^{3}\).  
The measurement was conducted constantly throughout the day when the bioaerosol samples were collected. The device was placed at a height of 1.5 m above the ground to examine the air from the breathing zone of horses and humans. The data were measured once per minute, then logged once per 10 min, and the results are presented as means of six replicates from 1 h intervals.  
The respirable fraction of particulate matter was assumed as smaller than 2.5 \(\upmu\)m (i.e., PM\({}_{2.5}\) and PM\({}_{1}\)). PM\({}_{10}\) was assumed as inhalable dust. Temperature and relative humidity were measured using the Kestrel 400 Weather Meter ([PERSON], Boothwyn, PA, USA).
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 2 System Model
### Network Model
where \(\,n_{k}\cong\,\mathcal{CN}\big{(}0,\sigma_{k}^{2}\big{)}\) is additive white Gaussian noise with a mean of zero and a variance of \(\sigma_{k}^{2},\forall k\in\mathcal{K}\). Hence, the signal-to-interference-plus-noise ratio (SINR) measured at the \(k\)-th UE is calculated as  
\[SINR_{k}= \frac{\Big{|}\,\left(\sum_{l\in\mathcal{L}}\mathbf{h}_{\text{rk}}^{H} \mathbf{\Theta}_{l}\,\,\mathbf{G}_{l}+\mathbf{h}_{\text{dk}}^{H}\right)\mathbf{w}_{k}\Big{|}^{ 2}}{\sum_{j\
eq k}\Big{|}\left(\sum_{l\in\mathcal{L}}\mathbf{h}_{\text{rk}}^{H}\mathbf{ \Theta}_{l}\,\,\mathbf{G}_{l}+\mathbf{h}_{\text{dk}}^{H}\right)\mathbf{w}_{j}\Big{|}^{2}+ \,\sigma_{k}^{2}},\forall k\in\mathcal{K}. \tag{6}\]  
Additionally, the achievable rate is given by  
\[R_{k}=\log_{2}(1+SINR_{k}),\forall k\in\mathcal{K}. \tag{7}\]
## 5 Analysis of Experimental Results
### The First Heating Experiment
#### 5.1.3 Damage Index Curves for Two Methods  
Further investigation into the changes in DI for the two methods used to quantify defects is presented in Figure 15.  
Figure 15 illustrates the trends of DI for both the BSS method and the LPM method over time. Initially, signals are collected at a normal temperature for a period. At the 40 th minute, the pipeline is heated, and after approximately 90 min of heating, the system reaches its highest temperature.  
As shown in Figure 15, DI for BSS remains between 0 and 0.0027, and the DI for LPM stays between 0 and 0.0025. Both damage indices are very low, indicating a healthy system with no defects. However, as the pipeline temperature changes unevenly, DI for BSS begins to increase significantly, with the maximum value reaching 0.027. This rise is attributed to the method's incomplete phase compensation for the NTF, suggesting potential defects in the system. Nevertheless, this increase may lead to false positives.  
In contrast, DI for LPM remains relatively stable, indicating that NTFs do not affect its accuracy. This demonstrates the LPM method's effective temperature compensation capabilities.