esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Imaging correlography, an effective method for long-distance imaging, recovers an object using only the knowledge of the Fourier modulus, without needing phase information. It is not sensitive to atmospheric turbulence or optical imperfections. However, the unreliability of traditional phase retrieval algorithms in imaging correlography has hindered their development. In this work, we join imaging correlography and ptychography together to overcome such obstacles. Instead of detecting the whole object, the object is measured part-by-part with a probe moving in a ptychographic way. A flexible optimization framework is proposed to reconstruct the object rapidly and reliably within a few iterations. In addition, novel image space denoising regularization is plugged into the loss function to reduce the effects of input noise and improve the perceptual quality of the recovered image. Experiments demonstrate that four-fold resolution gains are achievable for the proposed imaging method. We can obtain satisfactory results for both visual and quantitative metrics with one-sixth of the measurements in the conventional imaging correlography. Therefore, the proposed imaging technique is more suitable for long-range practical applications.  
ptychography; imaging correlography; computational imaging; phase retrieval; Fourier optics +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_  
**Keywords:** ptychography; imaging correlography; computational imaging; phase retrieval; Fourier optics +
Footnote †: journal: Applied Physics Letters
## 2 Results and Discussion
### In Vivo Anti-Fatigue Effects of SBP-III-3  
#### 2.4.1 SBP-III-3 Prolonged Exhaustive Swimming Time  
Exercise tolerance assay is the most direct and objective indicators of reflecting physical fatigue. Swimming to exhaustion is an experimental exercise model to evaluate anti-fatigue; it works well for evaluating the endurance capacity of mice, and gives a high reproducibility [29]. The improvement of exercise endurance was the most powerful representation of anti-fatigue effect. In the experiment, the anti-fatigue effect of SBP-III-3 was investigated through the weight-loaded swimming test, and the length of the swimming time to exhaustion indicated the degree of fatigue. As shown in Figure 4, the mean exhaustion time of the SBP-III-3-HG was 33.41 \(\pm\) 2.40 min (107.5% greater than that of NCG); the mean exhaustion time of the SBP-III-3-MG was 28.86 \(\pm\) 1.01 min (79.2% greater than that of NCG); and the mean exhaustion time of the SBP-III-3-LG was 25.43 \(\pm\) 1.91 min (57.9% greater than that of NCG) (\(p\) < 0.05 or \(p\) < 0.01). Therefore, the average loaded swimming time of mice was significantly longer in the SBP-III-3 treatment group (SBP-III-3-LG, SBP-III-3-MG, SBP-III-3-HG) than that of the normal control group (NCG) (16.1 \(\pm\) 1.46 min) (\(p\) < 0.05 or \(p\) < 0.01), and these results indicate that SBP-III-3 has significant effects on movement and endurance in mice, thereby postponing the fatigue.  
Figure 3: DNA damage protective effect of SBP-III-3. Lane 1, the native pBR322 DNA; lanes 2, the DNA treated with FeSO4, H2O2 and SBP-III-3 (3.0 mg/mL); lane 3, the DNA treated with FeSO4, H2O2, and ascorbic acid (1.0 mg/mL); lane 4, the DNA treated with FeSO4, H2O2, and SBP-III-3 (1.0 mg/mL); lane 5, the pBR322 DNA treated with FeSO4 and H2O2.
## 2 Proposed Cone Cell Identification Method
### Identification Based on K-Means Clustering  
\(K\)-means clustering [38] is the most time-consuming operation in the proposed cone cell identification method. To reduce the computational time of \(K\)-means clustering, we divide the bias-field-corrected image into image patches before identification. Then, we separately identify the cone cells on each patch and join the identification results afterward. To accurately identify the cone cells near the edge of image patches, we add extra patch borders of 10% of the length of the image patch before identification. These borders only support identification in the image patches, but the identified cone cells in the borders are discarded from the final identification results.  
For identification on each image patch, we first apply a histogram equalization. Second, the pixels are divided into 3 clusters by \(K\)-means according to intensity. Third, we generate a mask for the cluster that contains the largest number of pixels because this mask represents either cone cells or the background. Fourth, we extract the contours of this mask by using function findContours in the Python implementation of OpenCV. Then, we obtain the centroid of the area inside each contour to identify the cone cells.  
Figure 5 shows an example of cone cell identification based on \(K\)-means clustering on a representative image patch. The cone cells are roughly identified. Nevertheless, overidentification errors occur, as some cone cells are identified more than once.  
Figure 4: Bias field correction. (**a**) Denoised AO-SLO image patch. (**b**) Bias field image. (**c**) Bias-field-corrected image.  
Figure 5: Cone cell identification based on \(K\)-means clustering. (**a**) Denoised AO-SLO image patch. (**b**) Identified cone cells marked in red on denoised AO-SLO image patch.
### The Initial Operation Conditions of the VFD's Supply-Side Rectifier  
As highlighted in the publication [2], to compensate the reactive power provided by the LED, the conventional VFD must operate in the mode shown in Figure 4c. Under the conventional VFD mode shown in Figure 4b, the effect on PDL reactive power will be neutral. In describing this mathematically, the aim is to describe the capacitor voltage \(V_{C}\) at steady state and the angle of displacement \(\alpha\) of the input current pulse.  
The current flows through the diodes of the VFD rectifier at the time interval when the voltage of the PDL \(v(t)\) is higher than the voltage of the capacitor of the VFD DC circuit \(v_{c}\). Assuming that the capacitor capacity is large enough, the \(v_{c}\) = const can be considered [14].  
\[i(\omega t)=\frac{v(\omega t)-v_{c}}{R} \tag{1}\]  
where \(i(t)\)--VFD rectifier input current, \(R\)--PDL resistance.  
The charge \(Q\) applied to the capacitor of the VFD DC circuit from the PDL can be described by the following integral:  
\[Q=\int_{90-\alpha}^{90+\alpha}i(\omega t)d\omega t \tag{2}\]  
The charge applied to the capacitor of the VFD DC circuit from the PDL during the half-cycle is as follows:  
\[Q=\frac{v_{c}\times\pi}{R_{ap}} \tag{3}\]  
Figure 4: Operation modes of VFD from point of view of the input reactive power [2]: (**a**) leading; (**b**) zero; (**c**) lagging.  
where \(R_{ap}\)--resistance of the circuit consisting of the VFD inverter and the induction motor. Equation (2) is integrated:  
\[Q=\int_{90-\alpha}^{90+\alpha}i(\omega t)d\omega t=\int_{90-\alpha}^{90+\alpha} \frac{v(\omega t)-v_{c}}{R}d\omega t=\int_{90-\alpha}^{90+\alpha}\frac{\sin( \omega t)-v_{c}}{R}d\omega t=\frac{2}{R}(\cos(90-\alpha)-v_{c}\alpha) \tag{4}\]  
where \(\alpha\)--angle of displacement.  
Since these loads are equal, the components of the above equations can be equated:  
\[\frac{2}{R}\left(\cos\Bigl{(}\frac{\pi}{2}-\alpha\Bigr{)}-v_{c}\alpha\right)= \frac{v_{c}\times\pi}{R_{ap}} \tag{5}\]  
After transforming the equation:  
\[v_{c}=\frac{2\bigl{(}\cos\bigl{(}\frac{\pi}{2}-\alpha\bigr{)}\bigr{)}}{\frac{ \pi\times R}{R_{ap}}+2\alpha} \tag{6}\]  
The obtained equation is unsolvable, so it is necessary to write another equation for the time interval when the PDL voltage is equal to the voltage of the VFD DC circuit capacitor:  
\[v_{c}=\Bigl{(}\sin\Bigl{(}\frac{\pi}{2}-\alpha\Bigr{)}\Bigr{)} \tag{7}\]  
The internal resistance of PDL is assumed to be \(1\)\(\Omega\), as such resistance of PDL was obtained experimentally in the arena building described above. The experimentally obtained resistance of the circuit consisting of the \(1\) kW VFD inverter and the induction motor is equal to \(80\)\(\Omega\). Thus, a similar resistance ratio will remain with increasing VFD power.  
Mathematically solving these equations gave the following results: \(v_{c}=278\) V (idle voltage \(300\) V) and \(\alpha=21^{\circ}\). These data will be used to solve further equations to assess not only the internal resistance of PDL but also the inductance of PDL.
## 2 Research on 3D Temperature Field Reconstruction Algorithms
### Principles of Tucker Decomposition Algorithm
According to the definition of the mode product, Equation (11) can be rewritten as the multiplication of the vectors and matrices [39]:  
\[t_{x}=\left(f_{1x}\times A_{1 unfold}\right)^{T} \tag{12}\]  
where \(\mathbf{t}_{x}\) represents the first-order representation of the 3D TOF data, and \(\mathbf{A}_{1 unfold}\) denotes the coefficient matrix obtained by unfolding the tensor \(\mathbf{A}\) along the first mode. To establish the relationship between the measured TOF data and the first mode for the reconstruction of the 3D TOF distribution to be solved, the following equation can be applied [15]:  
\[MA_{1 unfold}^{T}f_{1x}^{T}=t_{M} \tag{13}\]  
where \(\mathbf{t}_{M}\) denotes the vector representing the measured TOF data, and \(\mathbf{M}\) is the matrix representing the positions of the actual acoustic temperature measurement paths. In Equation (13), \(\mathbf{t}_{M}\) is obtained through measurements, and \(\mathbf{M}\) is a known condition once the positions of the acoustic transceivers are fixed. Furthermore, the coefficient matrix \(\mathbf{A}_{1 unfold}\) can be obtained through the Tucker decomposition calculation of the prior dataset. By solving Equation (13) for \(\mathbf{f}_{1x}\) and then applying Equation (11), the reconstructed 3D TOF data \(\mathbf{T}_{x}\) can be obtained. Finally, the 3D temperature distribution is obtained by solving the discrete grid algebraic equation set using Equations (4) and (5). A 3D Temperature Field Reconstruction Algorithm for Acoustic Thermometry Based on Tucker Decomposition  
In this study, a novel 3D temperature distribution reconstruction method is proposed based on the Tucker decomposition algorithm, as illustrated in Figure 1. The fundamental calculation approach of the algorithm can be summarized as follows. Firstly, a four-corner cut-circle boiler simulation model is created using Fluent to simulate the 3D temperature distribution under different operating conditions. The TOF data of the sound wave thermometry paths for each temperature distribution plane are computed, forming the prior dataset. Then, Tucker decomposition is applied to the sample tensor of the empirical dataset to obtain the core tensor and the factor matrices along different directions, forming the reconstruction coefficient tensor. Subsequently, the tensor is unfolded along the first mode direction. Figure 1: Flow chart of the 3D reconstruction algorithm. In the experimental setup, an 8-emitter 24-channel sound wave thermometry system is employed to calculate the TOF data for one layer of temperature distribution. Through the algorithm, the 1-mode direction factor vector is calculated to facilitate the reconstruction of the desired 3D temperature distribution. Consequently, the 3D TOF data are obtained. The final step involves solving the equation set, enabling the acqui-sition of the 3D temperature distribution. This study presents the root mean square error (RMSE) as the evaluation metric to demonstrate the reconstruction performance.
## 4 Case Study: Application in Cybersecurity Anomaly Detection
Periodic infusions of new training data serve to align the model with shifting data patterns and evolving cyber threats. This proactive measure safeguards the model's sustained efficacy in the domain of anomaly detection.
## 7 Outcomes
### RQ2: What Is the Performance of Anomaly Intrusion Detection in IoT Using Deep Learning Techniques?
#### 7.3.4 Edge Computing and Security  
An edge computing platform offers better extensibility in data processing and storage for resource-constrained IoT devices. Furthermore, it enables nearby devices located around the data sources to intelligently operate, even if they are far from the center node of infrastructure. The cloud infrastructure stores the IoT devices' data source regarding network computing to provide rational edge services in detecting real-time threats. Unfortunately, IoT as a standalone entity is incapable of storing and analyzing data for any potential threats, due to insufficient resources [63]. Hence, with the aid of edge computing, it will enable multiple resource distribution of data processing over the cloud for analysis [64]. It is convincible to state that the amalgamation of deep learning in IoT helps in facilitating security analytics in providing an enhanced processing system that can detect threats effectively and accurately [54].
# Experimental and Numerical Study on the Low Velocity Impact Behavior of ABR Pipe
## 3 Experimental Results and Discussion  
### Experimental Phenomenon  
In low velocity impact tests, the impact dents on the surface of the pipeline serve dual purposes. On one hand, they indicate the size of the contact area during the impact process, while on the other hand, the trend of these impact dents varies with the changing impact energy. To clearly illustrate this experimental phenomenon, photographs of the impact points on the pipeline after the initial impact of all tests were taken, as shown in Figure 3. The vertical direction in the images corresponds to the axial direction of the pipeline, while the horizontal direction represents the circumferential direction of the pipeline. Additionally, the red arc in Figure 3 outlines the boundaries of the impact marks. It is important to note that, at a drop height of 0.25 m, there were hardly any noticeable impact marks. Thus, the impact dents at this height are not presented in Figure 3.  
The contact region between the hammer and the pipeline exhibits distinct frictional traces. For conditions with smaller drop heights and lower masses, the shapes of these marks are approximately elliptical and nearly circular. However, with the increase in impact energy, the area of these marks enlarges, and their shapes elongate along the axial direction of the pipeline, gradually transforming into more pronounced elliptical shapes. This phenomenon becomes more prominent with changes in height compared to variations in hammer mass. It is worth mentioning that the contact area between the hammer and the pipeline is very small in these experiments, allowing the impact load to be treated as a concentrated load at the impact point in relevant theoretical calculations, following the conventions of impact studies in similar scientific literature.
## 3 Results and Discussion
### Characterisation
#### 3.2.2 PXRD  
The powder X-ray diffraction patterns of UiO-66 and UiO-66-NH\({}_{2}\) exhibited similarity with the calculated PXRD patterns for the corresponding MOFs [26; 46; 51; 52; 53; 54] (Figure 4), confirming the phase purity of both MOFs. The PXRD patterns of UiO-66-MTX and UiO-66-NH\({}_{2}\)-MTX are similar to the pristine MOFs, confirming the retention of the crystallinity of the MOFs upon loading of MTX. A slight shift in peak positions and relative intensities in MTX-loaded MOFs suggests a slight change in unit cell due to possible interaction of the drug with the MOFs [55]. Such interactions were further established by computational modelling (see Section 3.5).  
Figure 3: TGA plots for (**A**) MTX, UiO-66, and UiO-66-MTX and (**B**) MTX, UiO-66-NH\({}_{2}\), and UiO-66-NH\({}_{2}\)-MTX are shown.
# Regolith-Rich PEEK Composite Bricks: Steps Towards Space-Ready Lunar Construction Materials
## 2 The Manufacturing Process and Parameter Refinement
Cooling: After curing, samples are cooled in air at room temperature, as this parameter has no impact on the final properties of the processed parts. PLA-based samples revealed that binder weight percentage (wt%) and compaction pressure (\(p_{c}\)) have a major impact on the selected mechanical properties, specifically bending and compression strength. Therefore, the study with PEEK focuses solely on these two key factors. The levels of binder weight percentage are reduced to three: 5, 10, and 15 wt%. Higher values are not considered, as the ISRU context emphasizes optimizing the use of consumables brought from Earth. Additionally, the properties achieved within the 5 to 15 wt% range for PLA are already satisfactory. Four levels of compaction pressure are selected, instead: 0.5, 2.5, 5.0, and 7.5 MPa. Compared to the previous analysis, the 7.5 MPa value is added to determine whether an asymptote might occur in the mechanical properties at higher compaction pressures. Introducing PEEK into the process also allows for considering two additional variables, both crucial for applying the material in real-world scenarios: vacuum processing and temperature behavior. The former variable is fundamental for assessing whether the material can be processed directly on the Moon's surface. The letter is critical for providing an initial indication of how the material would perform across a range of temperatures. A summary of all the processing parameters is detailed in Table 2; Section 3 details how both the processing parameters and environmental variables are accounted for in the analysis.
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
###### Abstract  
The lunar calendar is often overlooked in time-series data modeling despite its importance in understanding seasonal patterns, as well as economics, natural phenomena, and consumer behavior. This study aimed to investigate the effectiveness of the lunar calendar in modeling and forecasting rainfall levels using various machine learning methods. The methods employed included long short-term memory (LSTM) and gated recurrent unit (GRU) models to test the accuracy of rainfall forecasts based on the lunar calendar compared to those based on the Gregorian calendar. The results indicated that machine learning models incorporating the lunar calendar generally provided greater accuracy in forecasting for periods of 3, 4, 6, and 12 months compared to models using the Gregorian calendar. The lunar calendar model demonstrated higher accuracy in its prediction, exhibiting smaller errors (MAPE and MBE values), whereas the Gregorian calendar model yielded somewhat larger errors and tended to underestimate the values. These findings contributed to the advancement of forecasting techniques, machine learning, and the adaptation to non-Gregorian calendar systems while also opening new opportunities for further research into lunar calendar applications across various domains.  
GRU; forecasting; LSTM; lunar calendar; machine learning; rainfall +
Footnote †: journal: Applied sciences  
Article
## 3 Geometry and Numerical Method
### Model Validation  
#### 3.3.1 Numerical Method Validation  
In this paper, the experiment results of [PERSON] et al. [31] about the leakage flow rate \(m_{\text{se}}\) when sCO\({}_{2}\) passes through labyrinth seal are compared to CFD results to perform computational approach validation. The meridian plane of two-teeth labyrinth seal is shown in Figure 4, and its geometry dimensions and operating conditions are presented in Table 4. Furthermore, the realizable \(k\)-\(\varepsilon\) turbulence model with wall function was used in validation simulations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Case I** & **Case II** & **Case III** & **Unit** \\ \hline Outer radius of impeller back gap, \(R_{\text{o}}\) & 27.8 & 28.3 & 28.8 & mm \\ Radius ratio, \(\eta\) & 0.018 & 0.036 & 0.054 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of the outer radius \(R_{\text{o}}\) and radius ratio \(\eta\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Temperature at inlet, \(T_{\text{in}}\) & 303.15 & K \\ Pressure at compressor inlet, \(P_{\text{in}}\) & 9–12 & MPa \\ Pressure at outlet, \(P_{\text{out}}\) & 2.0 & MPa \\ Rotational speed, \(\Omega\) & \(10^{-1}\)–\(10^{5}\) & r/min \\ Reynolds number, \(Re\) & \(10^{2}\)–\(10^{8}\) & - \\ Pressure ratio, \(\pi\) & 4.5–6.0 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of boundary conditions.  
Figure 4 describes the comparison between the CFD results and experimental data under the different pressure ratio \(\pi_{\text{se}}\) conditions. The deviations between them gradually increase with larger \(\pi_{\text{se}}\), but they are still slight overall. Furthermore, the maximum relative deviation of 2.1% indicates that CFD results closely align with experimental results. These findings strongly suggest that the numerical method can effectively predict \(m_{\text{se}}\) of sCO\({}_{2}\) passing through the labyrinth seal.
## 5 TAFLRLR Protocol  
### Overview of the TAFLRLR Protocol  
Aiming at the characteristics and challenges of UANs mentioned in the previous section, a trust-aware and fuzzy logic-based reliable layering routing protocol is proposed to achieve routing reliability and energy efficiency. The framework of TAFLRLR is shown in Figure 5. The TAFLRLR protocol has the following features.  
1. With the TAFLRLR protocol, a layer is configured for each node, and DATA packets are transmitted layer by layer from a node with a higher layer to the sink node with layer 0, so the reliability of the routing is guaranteed.
2. The TAFLRLR is essentially a single-path routing, in which one path is established between a source node and the sink node. In each hop, the forwarding node is determined by the sender of the previous hop. Compared with multi-path routing, TAFLRLR can decrease the probability of packet collision and effectively reduce energy consumption.
3. The FLTEM mechanism is proposed for the trust evaluation of nodes. Specifically, with the consideration of HELLO packet forwarding behavior, DATA packet forwarding behavior, and residual energy, a fuzzy comprehensive evaluation decision model is introduced to calculate DTV for each node. In addition, combined with the DTV and ITV, the CTV is calculated to identify the MNs in UANs as well as select the best forwarding node.
4. The best forwarding node in the TAFLRLR is decided by the forwarding probabilities (FPs) of candidate forwarding nodes; the candidate forwarding node with the maximum FP is chosen as the best forwarding node. To improve the performance of the routing for UANs, the FP is calculated through a fuzzy control system (FCS) since the FCS has many advantages, such as low computational complexity and excellent adaptability [17; 26]. In the FCS, the NDs and the CTVs of candidate nodes are taken as the input variables of the FCS, and the outputs of the FCS are the FPs of the input nodes.