esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 3 Results
### Photoacoustic Detection Experiment
#### 3.4.1 Data Acquisition  
In the experiments, a homemade imaging platform shown in Figure 7 was used to acquire photoacoustic signals. A pulsed laser (MC Inc. Real Light Technology Co. Ltd., Beijing, China) emitting a 532 nm laser and a multichannel ultrasound system (Vantage 256, Verasonics, Kirkland, WA, USA) were used to send synchronized signals, while photoacoustic signals were acquired using an ultrasound array (L11-5, Verasonics). Subsequently, the acquired photoacoustic images were used for the re-construction of PA imaging using both a conventional and an optimized reconstruction procedure, respectively. The experimental dataset comprised 201 photoacoustic images with different numbers of photoacoustic sources (PAS), and the image size was set to 500 \(\times\) 500 pixels (\(N_{x}=500,\ N_{y}=500\)). The modeled light sources for photoacoustic data were included. Finally, the images after artifact removal were compared with the original images by qualitative and quantitative methods.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Precision** & **Recall** & **Accuracy** & **IoU** & **Mean BFScore** & **Dice** \\ \hline \multirow{3}{*}{Artifact Image} & All & 0.915 & 0.852 & 0.961 & 0.773 & 0.863 & 0.864 \\  & Brain tumor & 0.843 & 0.704 & 0.970 & 0.621 & 0.766 & 0.766 \\  & Brain & 0.986 & 1 & 0.952 & 0.924 & 0.961 & 0.961 \\ \hline \multirow{3}{*}{Artifact-free Image} & All & 0.955 & 0.932 & 0.986 & 0.946 & 0.972 & 0.973 \\  & Brain tumor & 0.918 & 0.864 & 0.994 & 0.928 & 0.963 & 0.963 \\ \cline{1-1}  & Brain & 0.992 & 1 & 0.978 & 0.964 & 0.982 & 0.982 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation metrics for brain and brain tumor segmentation in photoacoustic images with artifacts and with artifacts removed.  
Figure 7: The experiment setup of the PA imaging system.  
The experiment involves four mimetic models, each employing a different photoacoustic source configuration. Model I use a pencil core as the photoacoustic source due to its good optical absorption properties. Model II uses a combination of copper and pencil cores to achieve richer photoacoustic effects. Models III and IV use a copper core, a pencil core, and a steel wire as photoacoustic sources, respectively. Figure 8 shows in detail the configurations of Models I to IV and their photoacoustic sources.
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
## 2 Formulation
### aSDG Method  
The _asynchronous Spacetime Discontinuous Galerkin_ (aSDG) method, formulated for elastodynamic problem in [32], is use for dynamic fracture analysis. The _Tent Pitching_ algorithm [53] is used to advance the solution in time by continuous erection of _patches_ of elements whose exterior patch boundaries satisfy a special causality constraint. This results in a local and asynchronous solution process. In addition, since spacetime is directly discretized by finite elements, the order of accuracy can be arbitrarily high both in space and time directions. This is in contrast to conventional finite element plus time marching algorithms where increasing the order of accuracy in time is not straightforward.  
In addition to the displacement field for the elastodynamic problem, the damage field \(D\) is discretized in spacetime. The finite elements solve the weak form of elastodynamic balance laws, cf. Section 2.2, and the damage evolution Equation (6), \(\dot{D}-D_{\text{src}}(D,D_{f})=0\). Since the damage evolution is simply an ODE and maintains the hyperbolicity of the problem, the solution of the coupled elastodynamic-damage problem lends itself to the aSDG method. In addition, the satisfaction of balance laws per element for discontinuous Galerkin methods results in a very accurate discrete solution of the damage evolution equation. We refer the reader to [31] for more details on the aSDG implementation of the problem, including the specification of jump conditions, and initial/boundary conditions for the damage evolution equation.  
Figure 4: Sample quasi-static and dynamic strain versus stress responses.
## 4 Experiments
### Quantitative Evaluation
#### 4.4.2 Ablation Study  
To demonstrate the effectiveness of the proposed PUB-SalNet model, we compare quantitative results of different combinations of modules from our method, as is shown in Table 2. **B** stands for backpropagation from [2]'s work, which serves as our baseline because it is a classic unsupervised image segmentation method using deep learning. The experimental results in Table 2 shows that three parts of our PUB-SalNet functions together and are all indispensable. It is even competitive compared to the supervised method.
# A Congestion Control Strategy for Power Scale-Free Communication Network
## 3 Congestion Control Strategy for the Power Scale-Free Network
### Integrated Routing Strategy
#### 3.3.2 Global Dynamic Routing  
The packets are delivered not only according to the topological structure, but also the queue lengths and capacity of the nodes under the global dynamic routing strategy. Nodes with a large degree and less load can be transmitted more effectively and are less prone to congestion. Conversely, the nodes with a small degree and heavy load have lower delivery efficiency. It is necessary that the heavy load nodes be avoided as much as possible to obtain a large network capacity and reduce the local congestion in the selected path. Because the node queue length changes from time to time, so the computation is consumed to find the global dynamic paths at each time step.  
The routing update module is the procedure for realizing the dynamic and adaptive routing protocol. This module can quickly inform all nodes of the current state in the network. Each node counts periodically the queue length in turn and updates its global dynamic routing table.
3. The routing decision module is the procedure of the global dynamic routing protocol. It is based on Equation (4) and allows the optimal routing to be found by computing the multi-segment map.  
For example, when node i has two neighbor nodes (\(\
u_{1}\) and \(\
u_{3}\)), the costs are \(\mathrm{H}_{1\to\mathrm{j}}=2+\frac{5}{3}+\frac{7}{4}=5.42\) and \(\mathrm{H}_{3\to\mathrm{j}}=3+\frac{6}{9}+\frac{6}{4}+\frac{5}{5}=6.36\). Therefore, node v1 is selected as the next hop node, as shown in Figure 7a. When the load condition of nodes changes, the recalculated costs of the two paths are 5.42 and 5.23, respectively, and \(\
u_{3}\) is selected as the next hop node, as shown in Figure 7b.  
In Figure 7, the delivery capacity and queue length of the power communication nodes are denoted as the parameters in parentheses. The selected path would change when the load condition of nodes changes, which indicates that the shortest path is not necessarily selected under the global dynamic routing. The forwarding of II-level packets is based on the dynamic routing table.
## 3 Results
### High-Resolution Remote Sensing Data  
As described in the previous section, high-resolution remote sensing data has been used to identify topographic changes along the western cliff of Lefkada Island. The processing of the data has been divided into two parts, i.e., (a) optical recognition of the changes and (b) interpretation of the results through statistical analysis.
## 3 Discussion
This slower process results in the continuous release of VOCs over a period of days. Conversely, a sudden, initial burst of \(\beta\)-ionone, followed by rapid deterioration of signal afterwards, is consistent with the rapid release of cellular contents from repeatedly freeze-thawed algal cells, which occurs over the span of minutes or hours. The detection of \(\beta\)-ionone indicates that the formation of this carotenoid breakdownproduct may not be dependent on the rotifer digestion process but may be a product of abiotic processes that are held in common. Palmitoleic acid was detected in the headspace of freeze-thaw cultures only and was likely released as a result of the physical disruption of the algal. Palmitoleic acid, is a monounsaturated fatty acid (MUFA) and an ideal candidate for biofuel production [29]. As a desirable product of algal cultivation, it has been shown that algae generate higher cellular levels of palmitoleic acid under abiotic stress conditions, such as nutrient limitation [30; 31]. _M. gaditana_ is routinely studied as a food source for the aquaculture production of rotifers such as _B. plicatilis_ and it is known that the fatty acid content of the feed algae has a significant impact on the biochemical composition of the rotifers [32]. It seems likely that in the case of rotifer grazing the palmitoleic acid present in the microalgae is utilized by the rotifer rather than escaping to the volatile fraction. Palmitoleic acid derived from algal extract has been used to mitigate inflammation induced within a human macrophage cell line [33]. Several VOCs were detected specifically during the process of rotifer grazing. Along with \(\beta\)-ionone, grazed cultures of _M. gaditana_ released the biomarkers \(\beta\)-cyclodextrin and 4-(2,6,6-trimethyl-1-cyclohex-1-yl)-2-butanone. These structurally related ketones or aldehydes are known derivatives of carotenoid oxidation [34; 35; 36; 37]. We have previously observed carotenoid degradation products as indicators of rotifer grazing of the closely related algal species, _Microchloropsis salina_[24]. We hypothesize that these compounds, that were not detected under conditions of abiotic physical wounding, may represent further degradation of the part of the carotenoid pool. _M. salina_ and _M. gaditana_ are phylogenetically related [5], thus observance of the same putative biomarkers is not surprising. Previously, other microalgal strains have been shown to emit the some of the same VOCs during algal wounding [38]. In addition to the obvious carotenoid breakdown products both 1-pentadecene and 1-heptadecene were detected during rotifer grazing only. The presence of the long-chain alkenes could be due to the oxidation of \(\beta\)-carotene or long-chain fatty acids--both of which are highly abundant in oleaginous algal strains, such as _M. gaditana_[39]. Our results clearly show differences in the VOCs produced by physical disruption and those produced by grazing and contact with the rotifer digestive system. This work indicates that it may be possible to develop biomarkers that are indicative of different algal stress or damage.
# Refined Study on Free Vibration of a Cable with an Inertial Mass Damper
## 5 Conclusions  
In this paper, a refined free vibration analysis has been conducted to evaluate the IMD efficiency in suppressing cable vibration. An analytical model of cable-IMD system has been developed based on finite difference method. The accuracy of the finite difference method has been verified via experimental results from a scaled cable modal with an IMD. The effects of the cable sag, the cable flexural rigidity and the boundary conditions on the cable control performance have been evaluated via three typical cables in actual cable-stayed bridges.  
The theoretically predicted modal damping ratios based on the refined model of cable-IMD system agree well with those identified from experimental results, while they would be often overestimated based on a taut-cable model. Moreover, the experimental modal damping ratios generally fall between those theoretically predicted with fixed ends or pinned ends of the cable.  
The cable sag has significant effects on the first mode whereas it has almost no effects on the higher modes of the cable. Moreover, the influence of the cable sag becomes more remarkable with the increase of cable length. Nevertheless, the cable flexural rigidity has effects on each mode of interest, and it has the most significant impact on the medium cable, followed by the long cable. The effects of cable sag and cable flexural rigidity on the control performance of the short cable with low cable sag and low flexural rigidity can be neglected.  
Considering the combined effects of the cable sag and the cable flexural rigidity, the maximum achievable modal damping ratio of a fixed-fixed cable always provides a more conservative prediction than its pinned-pinned counterpart. Thus, it is recommended to consider the refined model of the cable-IMD system with fixed-fixed ends for predicting the optimum IMD size and corresponding attainable modal damping ratio. However, the taut-cable model is still suitable to rapidly design an IMD and predict the IMD efficiency for short cables with low cable sag and low flexural rigidity.  
[PERSON], [PERSON] and [PERSON] conceived the idea of this research; [PERSON] and [PERSON] wrote the paper; [PERSON] and [PERSON] reviewed and revised the paper.  
Figure 15: The fourth supplemental modal damping ratio of the long cable versus damping coefficient for various inertial masses: (a) \(\overline{m}_{\mathrm{e}A}=0\); (b) \(\overline{m}_{\mathrm{e}A}=0.3\); (c) \(\overline{m}_{\mathrm{e}A}=0.6\); (d) \(\overline{m}_{\mathrm{e}A}=0.9\).  
Funding:The authors greatly acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 51878274) and the National Basic Research Program of China (973 Program) (Grant No. 2015 CB060000).
## 2 Materials and Methods
### Fabrication of Cu/PPFC Nanocomposite Thin Films  
The sputtering chamber was evacuated to a base pressure of 6.6 \(\times\) 10\({}^{-3}\) Pa by a mechanical pump and a cryogenic pump. The partial pressure of Ar during the process was 0.93 Pa. The Cu/PPFC nanocomposite thin films were fabricated using RF (13.56 MHz), MF (40 kHz), and DC power with a sputtering power density of 1.23 W/cm\({}^{2}\). Dual magnetron cathodes were used for MF sputtering to improve sputtering efficiency. Target-to-substrate distance was fixed at 24 cm. A schematic diagram of the sputtering process for each power source is shown in Figure 1b. We used a polyethylene terephthalate (PET, KIMOTO, Japan) film substrate of size 20 \(\times\) 20 cm\({}^{2}\) and thickness 125 \(\upmu\)m. In order to compare the physical properties of the thin films with different Cu composition ratios, pure PTFE, Cu 20, Cu 50, and Cu 80 composite targets were sputtered with an RF power source to fabricate the thin films. Deposition rates for each composite target and sputtering power source were determined by using the Alpha step method (Dektak XT, Bruker, MA, USA). A thickness step is generated by covering a part of the sample for measurement. To investigate the physical properties of the Cu/PPFC nanocomposite thin films according to power source, 100 nm thin films were fabricated by sputtering with RF, MF, and DC power sources using the Cu 80 composite target. The fabricated thin films were named \"target name + power source type\" for clarity and conciseness. For example, a thin film made by an RF power source using a Cu 20 composite target is named \"Cu 20 RF\", whereas that made by a DC power source using a Cu 80 composite target is named \"Cu 80 DC\".
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 4 Simulation and Experimental Results
### LMSED Algorithm Simulation
#### 4.1.2 Analysis of the Effect of Influencing Factors on Demodulation and Calibration Results
\begin{array}{l}U(k)=a_{1}\cdot\sin((2\pi\cdot f_{c}+\mu(k))\cdot k+\varphi)+ b_{1}\\ =a_{1}\cdot\cos(\mu(k)\cdot k)\cdot\sin(2\pi\cdot f_{c}\cdot k+\varphi)+(b_{1}+ a_{1}\cdot\sin(\mu(k)\cdot k)\cdot\cos(2\pi\cdot f_{c}\cdot k+b_{1})).\end{array}  \tag{23}\]  
The amplitude gain caused by the speed disturbance is \(\cos(\mu(k)\cdot k)\). When the scale factor \(m\) is \(-1\), \(-2\), \(-3\), \(-4\), the gain curve is shown in Figure 10.  
When the speed fluctuation is large (\(m=-1\)), the demodulation curve will diverge; when the simulation time is long, the influence of the speed fluctuation on the amplitude becomes obvious, and the parameter demodulation curve diverges as well. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Scale Factor (\(m\))**} & **Maximum Value of the Absolute** & **Maximum Value of the** & **Error Reduction** \\  & **Error without Calibration** & **Angular Error with Calibration** & **Ratio** \\ \hline \(-2\) & \(34.81^{\circ}\) & \(1.12^{\circ}\) & \(3.27\%\) \\ \(-3\) & \(30.58^{\circ}\) & \(0.17^{\circ}\) & \(0.55\%\) \\ \(-4\) & \(30.25^{\circ}\) & \(0.03^{\circ}\) & \(0.09\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of the effect of speed disturbance on the calibration results. Figure 10: Amplitude gain due to velocity disturbance: (**a**) gain curve when \(m=-1\); (**b**) gain curve when \(m=-2\); (**c**) gain curve when \(m=-3\); (**d**) gain curve when \(m=-4\). According to the analysis results, since the noise does not change the parameter values, the parameter demodulation process is not sensitive to noise; while the speed disturbance adds error components to the parameters, so the accuracy of the parameter demodulation is affected, and even the algorithm fails.
###### Abstract
This system utilized a customized slanted-edge target to capture test images via the optical system imaging process. Subsequently, the MTF test was conducted to validate the efficacy of the slanted-edge test method proposed in this paper. The rest of this paper is structured as follows: In Section 2, the foundational concept of the MTF and the procedural aspects of MTF calculation through the slanted-edge method are introduced. This section also delves into the analysis of algorithmic factors affecting the results of the slanted-edge method, presenting processing steps of the algorithm based on a slanted-edge optimal OSR adaptive model. Section 3 scrutinizes the impact of image factors on the results of the slanted-edge method calculation and proposes constraints for this calculation method. Section 4 presents and analyzes the results of the experiments conducted. Finally, Section 5 offers a comprehensive summary of the entire paper and outlines directions for future research.
# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 5 Experimental Studies
### Preprocessing of Dataset  
The dataset comprised 12,400 CT kidney images. Among these images, 3709 depicted cysts, 5031 depicted normal tissue, 1377 depicted stones, and 2283 depicted tumors. During the data preprocessing stage, the file paths of the images associated with each class were identified and lists of these files were generated. Next, every image was appended to a list and assigned the appropriate class label. To accomplish this, distinct labels were assigned to cyst, normal, stone, and tumor instances, respectively. Within the image processing phase, every image was read and scaled to dimensions of 100 \(\times\) 100 pixels. Subsequently, the pixel values were standardized to the range of values between 0 and 1. This procedure organizesthe digital representation of the images for subsequent processing. It was configured to assign 70% of the dataset for training and the remaining 30% for testing.
## 3 Results and Discussion
An NLP packet was generated with the same fundamental frequency as that of dispersion-managed solitons of 15.795 MHz. Mode-locked NLPs were generated in a variety of intracavity polarization states (for different positions on the PC). A change in the PC position led to optical spectrum tuning, which is presented in Figure 5 and is discussed in the next part of this paper. As shown in Figure 4c, the output power of the generated NLP increased linearly with the increase of the pump power. The maximum average output power was 46 mW at an incident pump power of 1.59 W, and the slope efficiency was \(\sim\)4.2%. The minimum pump power required to support NLP generation was 0.78 W. It is believed that by applying a more powerful pump source, the NLP output power can be easily scaled up to hundreds of milliwatts. For the maximum pump power, the oscillator generated NLP packets with a duration of 3 ns and the same fundamental pulse repetition frequency of 15.795 MHz. The calculated maximum NLP energy was 2.91 nJ. At the recorded autocorrelation trace shown in Figure 4b, a narrow peak on the broad pedestal could be observed, which is typical for noise-like generation. The autocorrelation spike had an FWHM duration of 718 fs and a pedestal FWHM of 5.3 ps. The autocorrelation spike could be fitted with a Gaussian pulse profile, and then FWHM spike duration was determined to be 509 fs. The intensity ratio of the pedestal to the peak was approximately 0.5. Figure 4: Noise-like pulse generation characteristics: (**a**) optical spectrum and (**b**) autocorrelation trace for 46 mW of average output power; (**c**) average output power as a function of the pump power launched into the TDF; the inset shows the oscilloscope trace of the NLP train. Figure 5: NLP output spectrum tuning at a pump power of 1.3 W.  
The tuning of the NLP spectrum for a pump power of 1.3 W is presented in Figure 5. The tuning range of 35 nm (for peak wavelength), from 1943.5 to 1978 nm, was obtained by adjusting the PC position. A tunable NLP generation in the TDFL mode-locked by a semiconductor saturable absorber mirror (SESAM) with a tuning range of 47 nm has already been presented [17]. However, this setup was not developed in an all-fiber format, and a filter based on chromatic dispersion of lenses was used for wavelength tuning. In our oscillator, NOLM not only acted as an SA, but also enabled wavelength tuning. The adjustment of the PC position inside the loop changed the polarization states and introduced wavelength-dependent losses into the laser cavity. The developed TDFL exhibits continuously tunable NLPs. The 3 dB spectral bandwidth of 12.9-18.5 nm was maintained throughout the tuning range in NLP mode-locking operation. The broadest NLP spectrum with a 3 dB spectral bandwidth of 17.4 nm was centered at a wavelength of 1961.4 nm. Characteristic dips in the generated NLP spectra, from 1910 nm to approximately 1940 nm, could be observed (especially in the green and red spectra). These dips originated from the absorption of water in atmospheric air.
###### Abstract  
Environment classification is one of the most critical tasks for Unmanned Aerial Vehicles (UAV). Since water accumulation may destabilize UAV, clouds must be detected and avoided. In a previous work presented by the authors, Superpixel Segmentation (SPS) descriptors with low computational cost are used to classify ground, sky, and clouds. In this paper, an enhanced approach to classify the environment in those three classes is presented. The proposed scheme consists of a Convolutional Neural Network (CNN) trained with a dataset generated by both, an human expert and a Support Vector Machine (SVM) to capture context and precise localization. The advantage of using this approach is that the CNN classifies each pixel, instead of a cluster like in SPS, which improves the resolution of the classification, also, is less tedious for the human expert to generate a few training samples instead of the normal amount that it is required. This proposal is implemented for images obtained from video and photographic cameras mounted on a UAV facing in the same direction of the vehicle flight. Experimental results and comparison with other approaches are shown to demonstrate the effectiveness of the algorithm.  
cloud detection; superpixel segmentation; convolutional neural networks; support vector machines +
Footnote †: journal: Applied Physics Letters