esa-sceva/satcom-chunk-collection huggingface dataset:
# XPS on Co\({}_{0.95}\)R\({}_{0.05}\)Fe\({}_{2}\)O\({}_{4}\) Nanoparticles with R = Gd or Ho
## 1 Introduction
We succeeded in synthesizing the Co\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles using a green sol-gel combustion method. Unlike traditional chemical reductions that rely on organic solvents, our approach employs safe chemicals, environmentally friendly solvents, and accessible materials--key elements of green synthesis strategies [53; 54]. This method represents a significant advancement over others, as it is simpler, user-friendly, and typically yields more stable nano ferrites. It was shown clearly that Zn ions preferentially occupy T sites at lower concentrations. The saturation magnetization increases due to the preferential location of the nonmagnetic Zn ion. On the other hand, Co\({}_{1-x}\)Mn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) nanoparticles (\(0\leq x\leq 1\)) were prepared via the hydrothermal method. Doping with Mn in nanocrystalline cobalt ferrite has led to enhanced magnetic properties through alterations in cation distribution across the two sublattices. The increased magnetic moments can be attributed to the preferential localization of Mn\({}^{4+}\) ions on tetrahedral sites, while Mn\({}^{2+}\) ions tend to occupy octahedral sites. Additionally, the high quality and crystallinity of our samples ensure that the nanoparticles are nearly monodomain, further contributing to these improved magnetic characteristics. In this study, we delve into the synthesis and characterization of CoFe\({}_{2}\)O\({}_{4}\) nanoparticles doped with Gd or Ho heavy rare earths. We carried out a comprehensive analysis of the samples through techniques such as X-ray diffraction, transmission electron microscopy, energy dispersive X-ray analysis, X-ray Photoelectron Spectroscopy, and magnetic measurements.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 2 Related Work
### Attention Mechanism  
Self-attention structures have become mainstream systems in natural language processing, computer vision, etc. In particular, the basic principle and variants of self-attention have played an indelible role in image recognition [18; 19; 20; 21] and image captioning [22; 23]. Alternatively, self-attention is a new paradigm that is comparable to convolutions, which can be divided into spatial attention, channel attention, and hybrid attention. Spatial attention [16; 24; 25; 26; 27] can guide models to adaptively explore the most relevant regions via the explicit procedure. STN [25] is the earliest method to explicitly mine important regions and transformation-invaried features. Analogous to STN, Refs. [16; 24] propose a tailored self-attention mechanism for image classification. Channel attention [28; 29; 30] models the topology of different channels and adaptively captures discriminative spatial relations via channel-wise dependencies. Hybrid attention [18; 23; 31; 32] combines the characteristics of channel attention and spatial attention to enhance channel-wise and pixel-wise attention learning jointly. CBAM [33] is a typical prototype, which uses MaxPooling and AvgPooling to realize channel and spatial attention, but the inherited structures of pooling can only mine coarse-grained spatial relations. Inspired by channel-wise attention [28], the proposed Local Focus Scheme aims to capture more fine-grained local features by modeling channel-wise relations, which can improve the classification performance of hard samples.
## 2 Key Steps in a FRB Search Pipeline
### Candidate Classification
#### 2.4.1 Sifting and Clustering  
When time-domain data are searched for FRBs, the search is done over a number of DM and width trials, over all the time samples. Typically, candidates generated by RFI will all have DMs close to zero. Similarly, real astrophysical signals of interest do not last for more than 100 ms; hence, any pulse wider than that would most likely have a terrestrial origin. One can thus introduce cut offs on the DM and widths, such that candidates below a certain DM and above a certain width are automatically ignored by the real-time pipeline. Such sifting has proven to be an effective way to reduce the number of spurious hits. Similarly, any strong signal will be detected above the S/N threshold of the pipeline over a number of time samples, DMs and widths. One can use this property to effectively cluster events and relate them to a single source/candidate. Popular clustering algorithms include the friend-of-friends algorithm [30], whereby candidates within a normalised distance threshold in time, DM, and width parameter space from a given candidate are clustered together (left panel of Figure 5).
## 2 Materials and Methods
### Evaluating Performance of Model  
The performance metrics employed in this study were the AUC, F1, and ACC scores. The AUC value represents the area under the curve for the ROC which is the curve plotted between true positive rate (\(TPR\)) and false positive rate (\(FPR\)) in several thresholds. The \(TPR\) and \(FPR\) are calculated as follows:  
\[TPR=\frac{TP}{TP+FN} \tag{4}\]  
\[FPR=\frac{FP}{FP+TN} \tag{5}\]  
where true positive (\(TP\)) outcomes correctly predict a positive label, true negative (\(TN\)) outcomes correctly predict a negative label, false negative (\(FN\)) outcomes incorrectly predict a negative label, and false positive (\(FP\)) outcomes incorrectly predict in positive label. The F1 score is the harmonic mean between precision (PRE) and recall (REC), described as follows:  
\[PRE=\frac{TP}{TP+FP} \tag{6}\]  
\[REC=\frac{TP}{TP+FN} \tag{7}\]  
\[F1=\frac{2*PRE*REC}{(PRE+REC)} \tag{8}\]  
The accuracy (ACC) is the overall percentage of correct predictions in both positive and negative labels. It can be calculated as follows:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]The F1 and ACC scores are determined based on the criteria of the maximum F1 score. All performance scores ranged between 0 and 1. Scores closer to 1, indicate a higher performance. The measurement metrics have been employed in previous studies and are described in more details which can be found in [29; 30].
## 3 Research Results
### Analysis of Power Supply and Demand at Each Facility
#### 3.1.4 Examination of the Prediction Method for Solar Power Generation Amount  
The amount of solar power generation depends on the fluctuation of solar radiation, which depends on the weather conditions and the time of day. Therefore, in this paper, a method to estimate the amount of solar radiation to predict the amount of power generated in the week when the operation plan was developed. Multiple operators provide services aimed at forecasting the amount of solar radiation. Therefore, this study examined the Japan Meteorological Agency's solar radiation forecast API (Application Programming Interface) and weather forecast, since private businesses can freely use it. In Prediction method 1, the prediction from API was used to predict the amount of solar radiation. On the other hand, in Prediction method 2 prediction from the weather forecast was used. The error that occurred when the prediction was made weekly was evaluated. Solar radiation amount prediction API can predict the amount of solar radiation up to 3 days later in 1 h units and the amount of solar radiation up to 7 days later in 3 h units. Therefore, in Prediction method 1, the weekly solar radiation prediction result for 1 h was calculated by complementing the solar radiation prediction result for 3 h. Additionally, in Prediction method 2, the weather forecast conducted one week in advance, from 11 February to 11 March, 2020, was classified as sunny, cloudy, rainy, or sometimes cloudy (Table 7) and then the average value for each category was calculated. Using the results, each solar  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Facility** & **Power Generation, kW** &
\begin{tabular}{c} **During Soot Blower** \\ **Operation, kW** \\ \end{tabular} \\ \hline Incineration facility B & 530 & 420 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Power generation of incineraor B.  
Figure 12: Incineration facility B.  
radiation pattern was determined. Figure 13 shows an example of the comparison of the measured values of Prediction methods 1 and 2. The average error of the power generation amount calculated by Prediction methods 1 and 2 during the daytime (9:00 to 18:00) from 11 February to 11 March 2020 was 116.0 and 125.3 W/m\({}^{2}\), respectively. Both values accounted for approximately 3% of the total amount. Based on the weather forecast information results, this study used Prediction method 2, which is the easiest to use. However, since the solar radiation pattern varies depending on the season, the solar radiation pattern for each month was created to calculate the amount of power generation. The amount of solar radiation and power generation displayed a positive correlation. The process of calculating the amount of power generation from the amount of solar radiation is described in Equation (3):  
\[P_{solar}=\text{B}\times S_{r} \tag{3}\]  
where, \(P_{solar}\) is the solar power generation [kW], B is the power generation factor (=1.91) [kW/(W/m\({}^{2}\))], and \(S_{r}\) is the predicted amount of solar radiation [W/m\({}^{2}\)].
## 5 Simulation Experiments and Analysis
### Parameter Settings  
In order to compare the algorithms, the Q-learning algorithm, SARSA algorithm, speedy Q-learning algorithm, and the OWS Q-learning algorithm proposed in this paper are unified experimental parameter settings as shown in Table 1.
## 5 Results and Discussion
### Mean-Subtracted DMD
Additionally, the presence of non-growing and decaying modes enhances the stability of DMD for future state predictions in reduced-order modeling. Figure 26: DMD reconstruction relative error convergence of the mean-subtracted data with respect to sampling period for two conditioned signals: (**left**) \(f_{c}=100\) with \(f_{s}=1\) kHz, (**right**) \(f_{c}=400\) Hz with \(f_{s}=2\) kHz; P and WS represent pressure and wall shear stress signals, respectively. Figure 25: DMD reconstruction relative error convergence of the mean-subtracted data with respect to sampling resolution with a 2-s sampling window (\(T_{w}=2\) s) for two conditioned signals: (**left**) \(f_{c}=100\), (**right**) 400 Hz; P and WS represent pressure and wall shear stress signals, respectively. The variation of the modes' energy (\(St=0.08\), 0.13, 0.16, and 0.23) with respect to the sampling period (\(T_{w}\)) is shown in Figure 27. The results demonstrate a reliable convergence in energy levels for all selected modes. Energy convergence is observed after \(T^{*}=250\), corresponding to \(T=1.8\) s. Additionally, modes with lower \(St\) have higher energy levels. For instance, \(St=0.08\) (\(f=11.1\) Hz) converges to an energy level of 1.5% of the energy of the mean mode. The energy level drops for higher frequencies, for example, mode with \(St=0.23\) having an energy level near 0.5% of the mean mode.
## 3 Results
### Human Health Risk Assessment  
The human health risk assessment data presented in Table 3 reveal significant insights into the exposure risks associated with five herbicides under different tillage methods. These calculations were informed by established models and exposure factors recommended by the USEPA, ensuring alignment with regulatory frameworks for environmental risk evaluation.  
The HI values for atrazine vary across different tillage methods. In the CK method, the HI for adults is 5.10 \(\times\) 10\({}^{-8}\). For children, it is 2.21 \(\times\) 10\({}^{-8}\). The SS method shows an adult HI of 3.97 \(\times\) 10\({}^{-8}\) and a child HI of 1.72 \(\times\) 10\({}^{-8}\). In the ST method, the adult HI is 6.87 \(\times\) 10\({}^{-8}\), while the child HI is 2.98 \(\times\) 10\({}^{-8}\). These data indicate that adults face higher risks compared to children in all tillage methods.  
Acetochlor presents distinct HI values. In the CK method, the HI for adults is 1.20 \(\times\) 10\({}^{-6}\). For children, it is 5.18 \(\times\) 10\({}^{-7}\). In the SS method, the adult HI is 1.19 \(\times\) 10\({}^{-6}\), and the child HI is 5.16 \(\times\) 10\({}^{-7}\). The ST method shows an adult HI of 1.43 \(\times\) 10\({}^{-6}\) and a child HI of 6.18 \(\times\) 10\({}^{-7}\). These findings highlight the influence of tillage practices on exposure risks for acetochlor.  
Alachlor's HI values also differ by tillage method. In the CK method, all HI values are ND. In the SS method, the adult HI is 5.14 \(\times\) 10\({}^{-7}\), and the child HI is 2.23 \(\times\) 10\({}^{-7}\). The ST method shows all HI values as ND. Nicosulfuron and mesoritone consistently show ND HI values across all tillage methods for both adults and children. This suggests minimal non-carcinogenic health risks from these herbicides.
## 4 Derived Products and Data Processing
### Dem
#### 4.1.1 Automatic Extraction of the Engraved Stroke  
In this section, the Fourier transform application for image filtering is used to automatically extract engraving strokes. The procedure followed is summarized in two steps: (a) scaling the heights of the DEM to work in microns, using the Image Arithmetic tool (Functions/Arithmetic); (b) application of a high pass filter to the scaled DEM using the Fourier transform. The high-frequency enhancement filters (high pass) are intended to enhance any discontinuity, highlight the contrasts between homogeneous areas, emphasizing the lines. The central part of the Fourier spectrum is shown in Figure 7. A star-shaped spatial distribution is observed and has arms that are perpendicular to the dominant orientations. Low frequencies are grouped in the center. Filtering in the frequency domain is started by selecting Fourier Operations (Functions/Fourier). The next step is the study of the radius of the filter to be applied for visualizing the spectrum (Figure 7). The larger the circle radius, the greater the low frequencies range that are removed, and therefore, the more severe the effect of filtering. The chosen radius was 125 pixels. The result is an image where the dark areas represent deep areas (small microrelief depressions and the engraving grooves) (Figure 5b).  
Figure 7: Fourier spectrum indicating a circle with a radius of 125 pixels used to delimit the low frequencies.
## 2 Materials and Methods  
### Sample Preparation  
Samples of Longjing tea from six different geographical origins in Zhejiang Province, China were collected in the year of 2019, including Bashan Village (in Fuyang, Zhejiang Province, 119\({}^{\circ}\)51\({}^{\prime}\)41.4936\({}^{\prime\prime}\) N, 30\({}^{\circ}\)4\({}^{\prime}\)44.0796\({}^{\prime\prime}\) E), TongWu Village (in Xihu District, Hangzhou, Zhejiang Province, 120\({}^{\circ}\)3\({}^{\prime}\)10.2351\({}^{\prime\prime}\) N, 30\({}^{\circ}\)11\({}^{\prime}\)41.8383\({}^{\prime\prime}\) E), Meijiawu Village (Xihu District, Hangzhou City, Zhejiang Province, 120\({}^{\circ}\)5\({}^{\prime}\)15.6372\({}^{\prime\prime}\) N, 30\({}^{\circ}\)12\({}^{\prime}\)21.8844\({}^{\prime\prime}\) E), Tiedian Village (in Pan'an County, Zhejiang Province, 120\({}^{\circ}\)41\({}^{\prime}\)32.7444\({}^{\prime\prime}\) N, 29\({}^{\circ}\)12\({}^{\prime}\)39.06\({}^{\prime\prime}\) E), Xinxin Village (in Xinchang County, Zhejiang Province, 121\({}^{\circ}\)3\({}^{\prime}\)39.5892\({}^{\prime\prime}\) N, 29\({}^{\circ}\)19\({}^{\prime}\)51.816\({}^{\prime\prime}\) E), and Huijailing Village (in Yuhang District, Hangzhou City, Zhejiang Province, 119\({}^{\circ}\)56\({}^{\prime}\)56.7492\({}^{\prime\prime}\) N, 30\({}^{\circ}\)12\({}^{\prime}\)11.88\({}^{\prime\prime}\) E). Tea leaves were harvested from _Camellis sinensis_ cv. Longjing 43 in the spring growth period before the Qingming Festival in 2019. The fresh green leaves were spread out in a thin layer on bamboo trays and exposed to sunlight or natural warm air for two hours. Then the leaves were processed by a traditional tea making procedure.  
For each geographical origin, 40 g tea samples were collected, and single tea leaves were placed separately on a blackboard for image acquisition. Figure 1 shows the single tea leaves from six different geographical origins. During image acquisition and sample transportation, some tea leaves were broken into two or more leaves. Since these broken leaves can also be used for hyperspectral image acquisition to obtain spectral information, some of these leaves were manually selected for hyperspectral image acquisition as single tea leaf samples. Thus, the number of samples for VNIR and NIR hyperspectral image acquisition is different. To build classification models, the samples were randomly split into the calibration set and the prediction set at a ratio of 2:1. Table 1 shows the number of samples for VNIR and NIR hyperspectral image acquisition
## 4 Conclusions  
In summary, we propose a new decoder based on the Q-M interferometer for QKD systems, which is free of polarization disturbance caused by optical fiber quantum channel and optical devices  
Figure 4: (**a**) schematic diagram of the Q–M interferometer based time-bin phase encoding intrinsic-stabilization QKD system. The Q–M interferometers are shown in red dashed boxes. LD\({}_{1}\)–LD\({}_{4}\): lasers, Cir\({}_{1}\) and Cir\({}_{2}\): optical circulators, PMC\({}_{1}\) and PMC\({}_{2}\): polarization maintaining couplers, BS\({}_{1}\)–BS\({}_{4}\): beam splitters, PS: phase shifter, DWDM: dense wavelength division multiplexer, VOA: variable optical attenuator, SPD\({}_{1}\)–SPD\({}_{4}\): avalanche diode single photon detectors; (**b**) the geographic distribution of the quantum channel with a 60.6 km standard telecommunication optical fiber.  
Figure 5: Temporal fluctuation of the key generation performance. (**a**) the quantum bit error rate as a function of time; (**b**) the safe key rate as a function of time, where every point is the average safe key rate in one minute.  
in the system. Physical and theoretical analysis has been presented. An experimental verification is also implemented by building a Q-M interferometer based time-bin phase encoding QKD system. The experimental result reveals a long-term low QBER with about 1.3% over 60.6 km standard telecommunication optical fiber. All of the components in the Q-M interferometer are conventional commercial passive optical components, and can be easily fabricated. Since the Q-M interferometer is without Faraday magneto-optic components, our scheme can be expected to realize optical integration, which will be shown in our subsequent work, and also be applicable in magnetic environments [21]. The theoretical analysis and the experimental results indicate that the Q-M interferometer based QKD system is immune to quantum channel disturbance, and will be a competitive scheme in practical QKD applications.  
Conceptualization, H.X.; methodology, H.X.; software, Y.H. and S.W.; validation, S.W. and Y.H.; formal analysis, H.X. and C.W.; investigation, H.X., S.W., Y.S. and C.W.; resources, H.X. and C.W.; data curation, Y.H. and S.W.; writing--original draft preparation, S.W.; writing--review and editing, H.X. and Y.S.; visualization, Y.H. and S.W.; supervision, H.X.; project administration, C.W.; funding acquisition, H.X. All authors have read and agreed to the published version of the manuscript. This research was sponsored by the National Natural Science Foundation of China under grants 61705202, the Joint Funds of China Electronics Technology Group Corporation 6141B082902, and the Innovation Funds of China Electronics Technology Group Corporation. The authors wish to thank the anonymous reviewers for their valuable suggestions, and [PERSON] for his helpful discussion. The authors declare no conflict of interest.
applied sciences  
A Comparative Analysis of Active Control vs. Folding Wing Tip Technologies for Gust Load Alleviation  
**[PERSON]**  
Department of Aerospace Science and Technology, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy; [EMAIL_ADDRESS]  
**Abstract: As part of the Ultra High Aspect Ratio Wing Advanced Research and Designs (U-HARWARD) project, funded by CS2 JU, various gust load alleviation (GLA) technologies have been developed and studied. GLA plays a crucial role in the development of new generation ultra-high aspect ratio wings (UHARWs), as it reduces gust loads, thereby decreasing the structural weight of the wing and, consequently, the entire aircraft. This weight reduction enhances overall aircraft efficiency, enabling a higher aspect ratio. GLA technologies are categorized into passive systems, which require no active intervention, and active systems, where control surfaces redistribute the aerodynamic loads. In this study, passive GLA was implemented using a folding wing tip (FWT) developed by the University of Bristol, while active GLA employed a Static Output Feedback controller developed by Politecnico di Milano. Both approaches were compared against a baseline aircraft configuration. A flutter assessment confirmed that FWT does not introduce aeroelastic instabilities, ensuring the aircraft remains flutter-free across its flight envelope. A thorough comparison of load envelopes, based on nearly 2000 load cases across different flight points and mass configurations, was conducted in compliance with CS25 regulations, examining both positive and negative gust conditions. The results show a possible 15% reduction in the dynamic load envelope for both passive and active solutions. Using NeOPT, a hybrid finite element (FE) model was developed, with a detailed global FEM (GFEM) for the wingbox and stick elements for other components. Linear gust analyses in Nastran, with the hinge locked and released, provided high-fidelity results, comparing wing failure indexes and demonstrating the effectiveness of the FWT solution.  
high aspect ratio wings; load alleviation; folding wing tip
## 2 Related Work  
### Two-Step Floating Catchment Method  
The core of the 2 SFCA method lies in evaluating the spatial alignment between service supply and demand through a two-step process. The first step involves determining the service area \(d_{0}\) for each supply point \(j\) (i.e., service facility). This \(d_{0}\), also known as the search radius, is typically defined based on either distance or time. Within this radius, the supply-demand ratio \(R\) for each supply point is calculated based on the potential demand:  
\[R_{j}=\frac{S_{j}}{\sum_{k\in\{d_{kj}\leq d_{0}\}}D_{k}}, \tag{1}\]  
where \(S_{j}\) denotes the scale or capacity of supply point \(j\), \(D_{k}\) represents the demand at demand point \(k\), usually measured by the population size, and \(d_{kj}\) is the distance from the demand point \(k\) to the supply point \(j\).  
In the second step, for each demand point \(i\), the accessibility \(A_{i}\) is calculated by summing the supply-demand ratios \(R_{j}\) of all supply points \(j\) within its search radius \(d_{0}\). Initially,  
\[A_{i}=\sum_{j\in\{d_{j}\leq d_{0}\}}R_{j}=\sum_{j\in\{d_{j}\leq d_{0}\}}\left( \frac{S_{j}}{\sum_{k\in\{d_{kj}\leq d_{0}\}}D_{k}}\right). \tag{2}\]  
The method sets a fixed search radius around the supply point, considering all residents within this radius as potential users to compute the supply-demand ratio. Subsequently, it evaluates accessibility by summing the supply-demand ratios of supply points within the search radius centered on the demand point. The supply-demand ratio reflects the capacity of service facilities to meet the potential user demand within the service area, while accessibility indicates the cost and convenience for users in obtaining these services.