esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 4 Results and Discussion
### CNN + GNN  
To address the limitations of local feature modeling, we augmented the CNN with a Graph Neural Network (GNN) in Stage 2. This hybrid CNN-GNN architecture used a grid-based graph structure to model spatial relationships among regional features. In the Tuberculosis dataset, the CNN-GNN model achieved better performance, with an accuracy of 92.14%, a precision of 95.69%, a recall of 76.43%, and an F1-score of 82.28%. For the Pneumonia dataset, the model achieved 85.25% accuracy, 80.60% precision, 81.04% recall, and 80.81% F1-score. The improvement, especially in F1-score and recall, suggests that the GNN component improves global context understanding, allowing the model to detect subtle, spatially diffuse patterns commonly seen in respiratory infections.  
Our proposed ViTGNN model, a novel hybrid architecture that combines a Vision Transformer (ViT) with a Convolutional Neural Network and a Graph Neural Network, demonstrated consistently superior performance on both the Tuberculosis and Pneumonia datasets (see Table 3). This architecture effectively uses the ViT's global self-attention mechanism along with the CNN's local feature extraction and the GNN's capacity to model spatial relationships among image regions. Specifically, on the Tuberculosis dataset, ViTGNN achieved an accuracy of 98.81%, a precision of 99.30%, a recall of 96.43%, and an F1-score of 97.79%. On the Pneumonia dataset, it recorded 95.59% accuracy, 92.68% precision, 97.04% recall, and a 94.52% F1-score. Remarkably, although ViTGNN was trained exclusively on datasets, it maintained high performance on both target datasets without requiring additional fine-tuning. This suggests a substantial degree of feature generalizability and robustness to domain shifts, emphasizing the model's potential for broader clinical applicability across diverse respiratory pathologies and imaging conditions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Tuberculosis Dataset**} \\ \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-score (\%)** \\ \hline ViTGNN & **98.81** & **99.30** & **96.43** & **97.79** \\ CNN & 91.67 & 95.45 & 75.00 & 80.95 \\ CNN-GNN & 92.14 & 95.69 & 76.43 & 82.28 \\ \hline \multicolumn{4}{c}{**Pneumonia Dataset**} \\ \hline ViTGNN & **95.59** & **92.68** & **97.04** & **94.52** \\ CNN & 86.02 & 82.14 & 80.33 & 81.16 \\ CNN-GNN & 85.25 & 80.60 & 81.04 & 80.81 \\ \hline \multicolumn{4}{c}{**SARS-CoV-2 Dataset**} \\ \hline ViTGNN & **95.98** & **96.07** & **96.01** & **95.98** \\ CNN & 81.93 & 82.83 & 82.03 & 81.83 \\ CNN-GNN & 81.93 & 82.51 & 82.01 & 81.87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proposed model on Tuberculosis and Pneumonia datasets.
###### Abstract  
In reliability studies of isolated energy supply systems for residential buildings, supply failures due to insufficient generation are generally analysed. Recent studies conclude that this kind of analysis makes it possible to optimally design the sizes of the elements of the generation system. However, in isolated communities or rural areas, it is common to find groups of dwellings in which micro-renewable sources, such as photovoltaic (PV) systems, can be installed. In this situation, the generation and storage of several houses can be considered as an interconnected system forming a cooperative microgrid (CoMG). This work analyses the benefits that sharing two autonomous installations can bring to each one, from the point of view of reliability. The method consists of the application of a random sequential Monte Carlo (SMC) simulation to the CoMG to evaluate the impact of a simple cooperative strategy on the reliability of the set. The study considers random failures in the generation systems. The results show that the reliability of the system increases when cooperation is allowed. Additionally, at the design stage, this allows more cost-effective solutions than single sizing with a similar level of reliability.  
microgrid; cooperation; energy; reliability; Monte Carlo; smart energy control +
Footnote †: journal: Applied Sciences
## 3 Results  
### Basic Characteristics  
#### 3.1.1 Spatial Resolution  
The measured imaging condition and magnifications approximately corresponded to those of calculations as described in Reference [29]. After the band-pass filter in Figure 1a, the peak wavelength of a light, central wavelength \(\lambda_{0}\), and bandwidth (full width at half maximum, FWHM) were 739 nm, 784 nm, and 132 nm, respectively. Assuming a Gaussian spectrum, the axial resolution was calculated to be 2.06 \(\upmu\)m. The axial resolution was measured to be 2.14 \(\upmu\)m with dispersion compensations.  
The lateral resolution was evaluated using the edge of test pattern (TP) from the OCM images as shown in Figure 2a. The width of 10% to 90% of the edge response function corresponds to the  
Figure 1: (**a**) Experimental setup; (**b**) photograph of short multimode fiber (SMMF); (**c,d**) photographs of facets of SMMF.  
FWHM of the line spread function [42]. The intensity profiles indicated by the solid line in Figure 2a are shown in Figure 2b to show the lateral resolution of 2.3 \(\upmu\)m. The spot size of the OCM images was calculated by 0.52\(\uplambda_{0}\)/NA, using the wavelength, denoted by \(\uplambda_{0}\), and the numerical aperture, denoted by NA. It was calculated to be 2.01 \(\upmu\)m with the calculated NA of 0.203 and wavelength of 0.78 \(\upmu\)m. This calculated spot size almost corresponds to the measured value of 2.3 \(\upmu\)m.  
The V parameter and the number of modes M were calculated to be 40.7 and 414 at the grade profile parameter \(p=2\)[43], respectively, indicating that many spatial modes could propagate in the SMMF. As many LP modes make the point spread function converge into the Airy disk [44], any image can be composed of LP modes. Therefore, an SMMF can transfer any image using LP modes.
# A Novel Dual Authenticated Encryption Scheme Suitable for Social Networking Services
## 2 Preliminaries
### Concrete Construction  
**Setup:** Given a 512-bit security parameter \(k\), the system authority first chooses an additive group \(\mathbf{G}_{1}\) and a multiplicative group \(\mathbf{G}_{2}\) of the same prime order \(q\). There is a generator \(P\) of order \(q\) in \(\mathbf{G}_{1}\) and a bilinear map \(e\) satisfying that \(\mathbf{G}_{1}\times\mathbf{G}_{1}\rightarrow\mathbf{G}_{2}\). Some utilized collision-resistant hash functions are defined below.  
\(h_{1}\): \(\{0,1\}^{k}\times\mathbf{G}_{1}\to Z_{q}{}^{*}\),  
\(h_{2}\): \(\mathbf{G}_{1}\times Z_{q}^{*}\)\(\mathbf{G}_{1}\rightarrow\{0,1\}^{k}\),  
\(h_{3}\): \(\mathbf{G}_{2}\rightarrow\mathbf{G}_{1}\).  
The public parameters _params_ include \(\{\mathbf{G}_{1}\), \(\mathbf{G}_{2}\), \(q\), \(P\), \(e\), \(h_{1}\), \(h_{2}\), \(h_{3}\}\).  
**Keygen:** Given an index \(i\), a party \(U_{i}\) runs this algorithm to obtain the corresponding key-pair (\(x_{i}\)\(\in_{R}Z_{q}\), \(Y_{i}=x_{i}P\)) along with a public key certificate \(\textit{Cert}_{i}\).  
**AEncrypt:** Assume that \(U_{a}\), \(U_{b}\) and \(U_{c}\) are engaged in a three-party communication environment. To deliver a message \(m\) designated for \(U_{b}\) and \(U_{c}\), \(U_{a}\) runs the algorithm choosing an integer \(t\in Z_{q}^{*}\) to compute  
\[R=tP, \tag{1}\]  
\[\sigma=t-x_{a}h_{1}(m,R), \tag{2}\]  
\[Z=h_{3}(e(tY_{c},Y_{b})), \tag{3}\]  
\[r=m\oplus h_{2}(R,\sigma,Z). \tag{4}\]  
Then, the generated authenticated ciphertext \(\delta=(R,\sigma,r)\) is returned and sent to \(U_{b}\) and \(U_{c}\).  
**ADecrypt:** Upon receiving \(\delta=(R,\sigma,r)\), \(U_{b}\) and \(U_{c}\) can employ his/her own private key to run this algorithm which first computes  
\[Z=h_{3}(e(x_{i}R,Y_{b}))\text{ if }x_{i}=x_{c}, \tag{5A}\]  
\[Z=h_{3}(e(Y_{c},x_{i}R))\text{ if }x_{i}=x_{b}, \tag{5B}\]  
and decrypts the original message \(m\) as  
\[m=r\oplus h_{2}(R,\sigma,Z). \tag{6}\]  
With the redundancy embedded in \(m\), it is able to check the validity of the recovered message. Moreover, the corresponding signature can be verified by checking if  
\[R=\sigma P+h_{1}(m,R)Y_{a}. \tag{7}\]  
If the above equality holds, the algorithm returns the message \(m\) and its signature \(\Omega=(R,\sigma)\); else, a symbol \(\bot\) is outputted to denote invalid ciphertext.  
We prove that Equations (6) and (7) work correctly. From the right-hand side of Equation (6), we have  
\(r\oplus h_{2}(R,\sigma,Z)\)  
\(=r\oplus h_{2}(R,\sigma,h_{3}(e(x_{c}R,Y_{b})))\) (by Equation (5A))  
\(=r\oplus h_{2}(R,\sigma,h_{3}(e(x_{c}tP,Y_{b})))\) (by Equation (1))  
\(=r\oplus h_{2}(R,\sigma,h_{3}(e(tY_{c},Y_{b})))\) (by Equation (3))  
\(=m\oplus h_{2}(R,\sigma,Z)\) (by Equation (4 which leads to the left-hand side of Equation (6).  
If an authenticated ciphertext \(\delta=(R,\sigma,r)\) is correct, it should pass the test of Equation (7). From the left-hand side of Equation (7), we have  
R  
(by Equation (1))  
(by Equation (2))  
which leads to the right-hand side of Equation (7).
# Research Progress and Effects of Light on Poultry Circadian Rhythm Regulation Based on CiteSpace
## 1 Introduction  
Circadian rhythms are endogenous autonomous oscillators of physiological activities and widely exist in humans, plants, animals, and even some bacteria, especially in poultry [1, 2]. Previous research showed that circadian rhythm plays a key role in hormone secretion, metabolism, immunity, musculoskeletal growth, and so on [3]. It is reported that light regimes can affect the frequency and duration of circadian behavior in Beijing You Chicken laying hens [4]. The disturbed circadian clock plays a role in the development of feather pecking [5]. Feeding chickens based on their biological rhythms reduces aggressiveness and can help increase the efficiency of the production of poultry products [6].  
Light is one of the important environmental factors for poultry growth and reproduction; moreover, light intensity, wavelength, photoperiod, and spectral composition all contribute to animal welfare, yield, immune function, poultry growth and development [7, 8, 9, 10]. Suitable light sources provide a new alternative strategy for improving the welfare, production performance, and commercial returns of poultry. Light is also a key factor affecting circadian rhythm and plays an important role in it [11]. A reasonable lighting schedule can regulate the circadian rhythm of poultry by regulating the level of melatonin and clock gene expression, whereas unreasonable lighting can disturb the circadian rhythm [12].  
To explore relevant research on light and circadian rhythm and to analyze the research frontiers in this domain, CiteSpace was used to draw knowledge maps and systematically expounded the subject distribution, research status, and research frontiers in the domain of light and circadian rhythm on the basis of relevant documents and reviews, which were retrieved from the core collection of Web of Science (WoS), in the domain of light and circadian rhythm in the past five years (2018-2022). We also summarized the key contentsaccording to keywords induced by CiteSpace, providing a theoretical basis for follow-up research on light and circadian rhythm.  
Based on CiteSpace, this work highlights the regulation of light on the circadian rhythm of poultry. This review can clarify the research progress and frontiers in light effect on circadian rhythm and provides new insights into the effect of light on poultry growth performance and its regulation mechanism on the circadian rhythm of poultry.
## 4 Results and Discussion  
### Variation of Micro-Layer Radius and Thickness  
The variation of the micro-layer beneath the growing bubble is a research emphasis in this study. According to Equation (14), the heat flux transferred through the micro-layer was proportional to the micro-layer's occupied area, while it was inversely proportional to its thickness. The last three validation simulation cases in Section 3 are further studied in this part.  
Figure 16 indicates distributions of micro-layers' thickness in different cases at different times, where \(r_{L}=0\) is the position of the nucleation site. With the bubble growth, the dry area beneath the bubble increased. The micro-layers' max thickness in the different cases all increased first and then decreased. At the beginning of the bubble growth, the bubble footprint expanded sharply, and new micro-layers were continuously generated. According to Equation (12), the initial micro-layer's thickness was proportional to the distance between the current position and the nucleation site; thus, the max thickness of the micro-layer increased in the first stage of the bubble growth. At the final stage of bubble growth, the bubble started to float, the bubble footprint stopped expanding, and the maximum thickness of the micro-layers decreased due to the evaporation.  
Comparing sub-plots in Figure 16, one can find the influence of the wall superheat on the variation of the micro-layer. The micro-layer evaporated faster at a higher wall superheat, and the dry area was larger at the same time. However, the bubble growth rate was also larger at a higher wall superheat, leading to the thicker, newly generated micro-layer. Thus, the micro-layer dried up later on the higher-temperature surface.
## 5 System Servo Performance Evaluation and Verification
### Experimental Verification
#### 5.2.2 The Influence of Road Disturbance Torque on Servo Performance  
In the simulation experiment of road disturbance torque, the random disturbance torque caused by the road is also used as the reference value. The test results of the sinusoidal following response of the system under different road disturbance torques are shown in Figure 9. It can be seen that as the road disturbance torque increases, the fluctuation of the system speed is more severe, and the equivalent stabilization accuracy and speed stability of the system are worse.  
The gain of the road disturbance torque is set in seven equal parts in the range of 50-200% of the reference value. The relationship between the road disturbance torque and speed stability is shown in Figure 10. The greater the road disturbance torque, the worse the servo performance of the system. For every 25% increase in the road disturbance torque, the speed stability is reduced by 12%. The experimental results verify the simulation analysis results and conclusions.  
Figure 10: The relationship between the road disturbance torque and speed stability.  
Figure 9: The influence of road disturbance torque on the sinusoidal following response.
## 2 Data and Method
### Buoy Winds  
In this paper, we use the wind vectors measured by the National Data Buoy Center (NDBC) buoys as a reference data source to validate the retrieved winds from SAR observations. Note that the majority of NDBC anemometers record wind speeds at an elevation of 2.5-4 m above the sea surface. In order to align with SAR-measured neutral winds at 10 m, all buoy wind speeds are converted to 10 m neutral winds. This conversion is carried out using a log-profile relationship based on the assumption of a neutrally stable boundary layer. The reference height is set as 10 m, and the sea surface roughness length is 0.0002 m with a negligible temperature gradient. For the remainder of this paper, the wind speed refers to the neutral wind speed at a height of 10 m unless explicitly stated and denoted as \(U_{10}\). Given the limited coverage of SAR images, four buoys are finally chosen as shown in Figure 1 to provide wind data for validation. The closest wind pixels to the buoy locations are used in space, and the temporal collocation criterion is a duration of less than 30 min.
## 4 Discussions
### Optimization (O) System  
Once the function, noise, and control measurement are done, the optimization stage takes place. \"Act\" stage in C sub-system acts to activate the \"Plan\" stage in O sub-system. \"Plan\" in Optimization sub-system is done by correctly choosing the orthogonal array based on noise and control factor strategies. Orthogonal array is chosen based on number of control factor and noise factor and their levels. Orthogonal array is the design space. It is a balance set of experimentation run. Every pair of columns, all combinations of levels occur in equal number of times. One two-level factor and six three-level control factors are used in L18 that result for 108 experimental runs. Outer array of N1 and N2 with one three-level of signal factor is used. Dynamic SNR is employed. The relationship between the mean response and the levels of signal factor is linear. In \"Do\" stage, experiment is implemented based on the setting of levels in orthogonal array. The measurement data are recorded and further analysis on SNR is done.  
Figure 14: Control (C) sub-system of F-N-C-O.  
In the \"Study\" stage, the criticality of data and the assumption is analyzed to ensure the measured data is genuine from extraneous variation that is not in the measurement system. The spread of measured data is checked through linear regression plot for abnormality checking. Next, the confirmation test is done for reproducibility checking. When estimation SNR gain is not comparable with confirmation gain resulting more than 30% difference, the measured data is not reproducible and investigation needs to be done. If this mistake is realized for more than three months, repetition of the experiment of that particular point is needed. However, if the mistake is realized in less than three months, the abnormal data can be replaced with regression point by treating it as missing data. Confirmation experiment is done once again to ensure the reproducibility is less than 30% or 3 dB. Either to repeat or replace with regression point in a linear relation, specimen condition must be considered. This is done to minimize the outer noise and inner noise due to environmental condition and deterioration of elements in the sample, respectively. Thus, the optimum condition is accepted in \"Act\" stage. Figure 15 summarizes the optimization measurement system:
# Analysis of the Impact of Multipath on Galileo System Measurements
## 4 Multipath Analysis
### Positioning Time-Series Analyses

Table 5: Maximum double-difference phase observation multipath errors determined from the decomposition of signals for specified satellites. What is distinctive is that, for both systems, similar systematic error was observed, as the determined heights are shifted in relation to the reference value by approximately 10 mm. Scalograms (see Figure 14c,d) clearly show that, for the height series, the main power of the signal is concentrated on low-frequency components, below 5 mHz. For higher frequencies, the signal amplitude is very low. Figure 15 shows the decompositions of the signal of the \(u\)-coordinate series into components of frequencies typical for the multipath effect. These diagrams confirm that the main disturbances in the determined values are linked with low frequencies of changes. The individual maximum errors of coordinates and the range of amplitudes of these errors are summarized in Table 7. For the low-frequency range, related to specular reflections of the signal, the characteristics of disturbances for both systems are very similar. It can be noticed, however, that the amplitude of fluctuations for Galileo observations is minimally larger than that for GPS. For the higher frequency range, from 3 mHz to 20 mHz, except for the final part of the series, the amplitude of the fluctuations related to the diffuse reflections of the signal is smaller for the Galileo system. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\delta n\) **[mm]** & \(\delta e\) **[mm]** & \(\delta u\) **[mm]** \\ \hline
**Galileo** & 2.1 & 1.9 & 10.7 \\ \hline
**GPS** & 2.4 & 1.8 & 7.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMS of horizontal coordinate errors. Figure 14: Vertical components (**a**), skylot (**b**) and scalograms for Galileo (**c**) and GPS (**d**).
# NICE: Superpixel Segmentation Using Non-Iterative Clustering with Efficiency
## 5 Conclusions  
In this paper, an accelerated non-iterative clustering framework that combines two strategies is proposed for an efficient and accurate generation algorithm of superpixels. The first strategy referred to as Elimination of Inspection Redundancy (EIR) deals with the redundant creation during neighbor inspection via the inter-pixel correlation between adjacent pixels. It also alleviates the inevitably shape regulation in complex boundaries thus efficiently generating desirable superpixels in both size and shape. The second strategy Accelerated Implementation based on Recursion (AIR) carries out the non-iterative clustering framework in a recursive manner. It introduces a subtle LIFO stack to directly assign the elements whose cluster distances are monotonic decreasing. Therefore, a large number of redundant sorting operations can be avoided without apparent deterioration of accuracy. A more efficient framework is integrated by EIR and AIR in a subtle way. The resultant NICE algorithm generates a synergetic effect and performs significantly better than conventional SNIC as well as the combination with any one of the strategies. Experimental results demonstrate that NICE is 40% faster than SNIC with comparable quantitative metrics on the BSDS500 dataset.  
For further improving the performance of non-iterative clustering framework, more effective initialization methods are desired, which would overcome the limitation of grid sampling and cover more small objects and strip areas. It is also worth exploring size and compactness adaptive segmentation algorithms, which could conform to the varying content of images more reasonably.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{8}{c}{**User-Expected Number of Superpixels**} \\ \cline{2-11}  & **50** & **100** & **150** & **200** & **250** & **300** & **350** & **400** & **450** & **500** \\ \hline SLIC & 123 & 124 & 124 & 124 & 126 & 126 & 128 & 129 & 130 & 130 \\ FLIC & 75 & 77 & 77 & 79 & 79 & 80 & 80 & 82 & 83 & 84 \\ SNIC & 69 & 71 & 73 & 75 & 76 & 76 & 77 & 80 & 80 & 81 \\ NICE & 62 & 63 & 64 & 65 & 66 & 66 & 67 & 68 & 69 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of four algorithms in terms of ET (msec) on NYUv2.  
Figure 10: Performance comparison of four algorithms in terms of three quantitative metrics on NYUv2. (a) BR; (b) UE; (c) ASA.  
**Funding:** This research is supported financially by National Natural Science Foundation of China (Grant No. 61571346 and 51805398).  
**Acknowledgments:** The authors would like to thank the editor and anonymous reviewers for their valuable comments on this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.