esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Interfacial Bond Properties between CGM and Existing Concrete
## 3 Results and Discussion
### Failure Modes
#### 3.1.2 Failure Modes of Slant Shear Specimens  
For the slant composite specimens without shear keys, the failure mode observed in the test was always adhesion failure at the interface, as shown in Figure 6a; the slant shear strength mainly depended on the chemical adhesion action between CGM and the concrete substrate. For the slant composite specimens with shear key at the interface, shear key being cut off was observed as the failure mode when the shear angle was \(30^{\circ}\), as presented in Figure 6b; the slant shear strength mainly depended on the shear strength of the shear key's own material. As the shear angle increased to \(45^{\circ}\), interfacial failure with concrete substrate and partial CGM failure was observed, as illustrated in Figure 6c. The slant shear bond strength gradually depended on the properties of the concrete substrate and CGM. Hence, the increased concrete strength of the composite specimens resulted in high shear bond strength of the composite specimens with shear keys.  
Figure 5: Typical failure modes of direct shear specimens: (**a**) pure interfacial failure; (**b**) interfacial failure with partial concrete substrate and CGM failure.
## 3 Results  
### LMD-w Process Parameters  
In a parameter study, going along with visual inspection and optical geometry measurements of the beads, suitable process parameters were identified. In a first step, the laser power was varied while the wire feeding rate and the travel speed were kept constant, at 1080 and 900 mm/min, respectively (ratio vW/vM = 1.2). It was shown that laser powers below 850 W were not sufficient to provide enough energy for complete wire melting. Wire scratching through the melt pool and bonding defects occurred. However, above 1650 W, the high energy input led to fume formation and strong oxidation of the bead surface. In a second step, the laser power was kept constant at 1400 W and the travel speed at 900 mm/min, while the vW/vM ratio was increased from 1.1 to 1.65 (corresponding to wire feeding rates from 990 mm/min to 1485 mm/min). At a ratio lower than 1.1, the fed wire quantity was insufficient to enable a steady state and homogeneous deposition. The beads show discontinuities. Above 1.65, scratching occurred, as the laser power was too low to melt the comparatively high wire volume. High speed imaging of the process allowed us to analyze the melt pool behavior and the interaction between wire and melt pool.  
It was observed that high energy input (either caused by high laser power or low travel speed) leads to a large melt pool. In this case, the randomly occurring melt pool oscillations are more pronounced and cause the wire-melt pool interaction, thereby making the process more unstable.  
After the depositions, the bead cross-sectional geometry was determined by measuring the workpiece with an offline OCT-system. The results are shown in Figure 7. It is concluded that among the varied parameters, the laser power has the strongest influence on the bead geometry. The bead width varies between 1.5 and 2 mm in the considered parameter range. The height changes from 1.1 to 0.75 mm, respectively. For the experiments with the coaxially integrated OCT-system, the following parameters were selected which delivered reproducible and satisfactory results: PL = 1400 W, vM = 900 W, vW = 1170 W.  
Figure 6: OCT measuring signal (**a**) interference spectrum showing the Gaussian shape of the SLD. (**b**) Depth information resulting from the interference via FFT (A-Scan) with a FWHM of \(\sim\)30.6 \(\upmu\)m.
# A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images
## 2 Proposed Method Framework
### Ship Detection  
As the reflectance of water is weakest in the NIR band of the GF-4 satellite image, the contrast between a ship and its surrounding water is strongest among five bands. Therefore, the NIR band is selected for ship detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Frame**} & \multirow{2}{*}{**Acquisition Time (UTC)**} & \multicolumn{2}{c}{**Weather and Sea Conditions**} \\ \cline{3-4}  & & **Wind Scale** & **Sea State Scale** \\ \hline
1 & 2017-03-09 03:47:24 & & \\
2 & 2017-03-09 03:50:30 & & \\
3 & 2017-03-09 03:53:36 & 3–4 & 2–3 \\
4 & 2017-03-09 03:56:42 & & \\
5 & 2017-03-09 03:59:47 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the GF-4 data sets.  
Figure 1: Two ships in five sequential images from the GF-4 satellite and detailed AIS information of ships.  
Figure 2: Proposed method framework.  
The spatial resolution of the GF-4 satellite is much lower than that of LEO remote sensing satellites, so ships can be regarded as low observable targets in the GF-4 satellite images. As the intensity distribution of sea background clutter is not uniform in different regions in the same frame, it is not good to use the global threshold to detect targets directly. Therefore, this paper proposes a local saliency map algorithm for detection based on PSNR. The PSNR quantifies the intensity at the peak of the target's point relative to the noise in the background, so the local saliency value of a pixel \((x,y)\) in an image is defined as  
\[sal(x,y)=\frac{I(x,y)-\mu_{b}}{\sigma_{b}} \tag{1}\]  
where \(I(x,y)\) is the amplitude at \((x,y)\), \(\mu_{b}\) and \(\sigma_{b}\) are the mean and standard variance of pixel intensities in the background around \((x,y)\), respectively. To better calculate the statistical properties of the background, two rectangular windows (an inner window and an outer window) centralized by \((x,y)\) are used, and the statistical characteristics of pixels between two windows are calculated. Considering the resolution of the GF-4 satellite image and the size of the ship, the size of the outer window is set to 20 pixels, and the size of the inner window is set to 10 pixels, empirically. To get the binary image \(D\) of detection results, a threshold \(th\) is used. If the saliency value of a pixel is not less than \(th\), it is determined as a potential target, thus the binary image is gotten as  
\[D(x,y)=\left\{\begin{array}{l}1,sal(x,y)\geq th\\ 0,\text{else}\end{array}\right. \tag{2}\]  
After the threshold segmentation, we use connected component labeling to separate the blobs and filter out ones, which are not likely to be ship candidates based on the size criteria. In this paper, only the blobs whose pixel sizes are between 2 and 50 are kept. A blob's centroid is calculated as the location, and the maximum amplitude of a blob is calculated as the amplitude of the target.
# The Flexural Fatigue Behavior of Honeycomb Sandwich Composites Following Low Velocity Impacts
## 4 Conclusions  
The results obtained after three-point bending, low velocity impact, three-point bending after impact, fatigue and fatigue after impact tests are evaluated below:  
* For three-point bending loads, increasing the core height increased the damage load of the specimen. With increased core height, damage was concentrated on the zone affected by the load. For the three core height values, debonding damage was seen between the core and the face sheet; buckling of the cell walls is the main reason for the damage.
* Increasing face sheet thickness increased the flexural strength of specimens for both face sheet materials. This increase was more apparent in specimens of CFRP.
* While performing fatigue tests, the applied load was determined by using the static damage load of the specimen. This restricted the effect of parameters used in the study on fatigue behavior. Application of the same amount of load to all specimens will clarify the effects of these parameters on fatigue behavior.
* Increasing the core height was found to be the parameter that increased fatigue strength of specimens the most, similar to static loading.
* In fatigue tests performed by three-point bending tests, when the loading ratio decreased, the fatigue lives of undamaged and damaged specimens converged.
* The damping ratio--related to the energy absorbed by specimens during fatigue tests and to rigidity--increased with the increasing impact energy. For undamaged and damaged specimens, the damping ratio approached a steady-state and continued its horizontal trend; it decreased when approaching the damage cycle and became a minimum after permanent damage of the specimen.
* For all cycle numbers, the highest damping ratios were observed in specimens with the aluminum face sheet.
* While normalized fatigue cycle approached the 1, stiffness decreases and dramatical stiffness reduction observed at the end of lifetime for all samples.  
Introduction, Materials and Methods, Fatigue Analysis, Results and Discussion, Writing, Reviewing and Editing, M.Y.S.; Introduction, Materials and Methods, Experimental Study, Results and Discussion, Writing, Reviewing and Editing, T.T. All authors have read and agreed to the published version of the manuscript. This research was funded by [Scientific Research Projects Coordination Unit of Firat University] grant number [MF.16.18.].  
Figure 13: Tan (8) vs. Log (N) graph for the 9 AL10b specimen.  
**Acknowledgments:** This work was supported by Scientific Research Projects Coordination Unit of Firat University Project number MF.16.18. [PERSON] is grateful to The Scientific and Technological Research Council of Turkey (TUBITAK) for Ph.D. scholarship (Grant Number: 1649B031501671). This paper is a part of 479133 numbered and \"Investigation of the fatigue behavior of honeycomb sandwich composites after low-velocity impact damage\" titled Ph. D. thesis of [PERSON], awarded from Firat University Graduate School of Sciences.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 AD and PD and Bioactive Rosemary Compounds with Health-Promoting Effects
### Alzheimer's Disease (AD)
Presently, different NOX inhibitor medications are under investigation as candidate therapeutic agents. While medications for AD do not provide a cure, they have the potential to enhance the quality of life and extend the patient's independence. Two classifications of drugs have been authorized for treating AD: those that offer temporary relief from AD symptoms and those that may downregulate the progression of the disease. The Food and Drug Administration (FDA) has approved two types of drugs, namely cholinesterase inhibitors and mementine. Cholinesterase inhibitors function by augmenting the levels of acetylcholine, a critical chemical messenger involved in alertness, memory, cognition, and judgment, through the prevention of its breakdown in the brain; however, these inhibitors are unable to cure AD or halt the degeneration of neuronal cells. Over time, their efficacy diminishes as the disease causes a decline in the production of acetylcholine by brain cells. Three frequently prescribed cholinesterase inhibitors are galantamine (Razadyne, for mild AD, two times per day), donepezil (Aricept, for all disease stages, oral administration once a day), and rivastigmine (Exelon, for moderate AD, available as a pill or a skin patch for severe cases). Mementine (Namenda), which was also approved by the FDA, is indicated for counteracting moderate and severe AD (syrup or pill administration). The underlying mechanism includes regulating the activity of glutamate, a chemical messenger that contributes to various cerebral functions, such as memory and learning. Additionally, aducanumab is an intravenous infusion therapy, among other medical approaches used in AD therapy, yet this drug is exclusively authorized for patients with mild dementia and cognitive debilitation caused by AD and functions by reducing A\(\beta\) protein levels in the brain. Significantly, in 2023, the FDA approved a similar drug called lecanemab [62; 63; 64]. The frequently observed negative outcomes of cholinesterase inhibitors include diarrhea, nausea, and vomiting, effects that may be minimized by commencing treatment at a low dosage and gradually increasing the dose or taking it with food. At the same time, individuals with cardiac arrhythmias should not consume such inhibitors. The notable effects of other drugs include dizziness, headache, confusion, and agitation, as well as amyloid-related imaging abnormalities-edema (ARIA-E), characterized by cerebral swelling, and amyloid-related imaging abnormalities-hemosiderin deposition (ARIA-H), involving micro-hemorrhages and superficial siderosis [62; 63; 64]. So, it is clear enough that alternative solutions are needed with fewer adverse effects than the established drugs. Scientists have examined the potential of utilizing certain plants suitable for alleviating or even curing NDs, which are widely known as medicinal and aromatic plants (MAPs).
## 5 Experiments  
The main goal of our experiment evaluation is to investigate the following key questions:  
* (Q1): Is MNS-SC more efficient than the state-of-the-art stability calculation algorithms?
* (Q2): Is MNG-SC useful when FCA applied to SNA's downstream tasks, such as abnormal users identification?  
In order to solve Q1, we examine the performance of MNG-SC algorithm with compared to the other three state-of-the-art stability calculation algorithms. In order to answer Q2, a case study in Section 5.3 is presented to illustrate the usefulness of MNG-SC algorithm and potential application scenarios of our research. All experiments are conducted on a Core i7 PC machine with 1.80 GHz 1.99 GHz CPU and 16 GB RAM running Windows 10 system.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 4 Problem Statement
### System Model  
As shown in Figure 2, our PBSL includes three entities: trusted authorities (**TAs**), medical centers (**MC**s), and blockchain (**BC**)-based peer-to-peer network. Each **MC** is equipped with a PC server, and **MCs** are interconnected through blockchain (**BC**)-based peer-to-peer networks. The three entities involved in the system are shown below.  
* **TA** stands for Trusted Authority and is responsible for generating initial parameters and public/secret key pairs and distributing them to **MC**s.
* \(\textbf{MC}_{s}=\{\textbf{MC}_{1},\textbf{MC}_{2},\ldots,\textbf{MC}_{K}\}\) represents \(K\) medical centers participating in swarm learning. In our system, each \(\textbf{MC}_{k}\in\textbf{MC}\)s trains the model using its local medical dataset, encrypts the gradient, shares the encrypted gradient to the blockchain, and cooperatively trains with other **MCs** to achieve a more accurate global model.
* **BC** represents a blockchain platform made up of self-organizing networks of interconnected medical centers (**MCs**). Every node (i.e., **MC**s) on blockchain has the opportunity to aggregate the global model, so **BC** can replace a central aggregator. Smart contract (SC) deployed on the blockchain homomorphically aggregates the encrypted gradients to obtain the global collaboration model.
## 3 The Design of _HeaLow_ and Implementation
### Implementation Details
#### 3.3.1 Buffered Load Management  
By buffering workloads throughout _HeaLow_, we improved the efficiency of _HeaLow_. Especially for networks, buffered load delivery maximizes the network usage. To make use of the buffers, the availability checker returns the number of workloads it can process when other devices request for offload. On the other hand, true or false is returned for its own work which has not been buffered yet. In _HeaLow_, the availability indicates the maximum queue length divided by the processing rate.  
Network transmissions are made with buffers as well. The transmissions are made in the transmission thread with a buffer that other threads can add messages to. In this manner, when the network is available, a device can send multiple messages in the queue that have the same destination with a single access. One prolonged communication has better network utilization than two half-length communications. We named this method, sending multiple messages to the same destination as one, _buffered message method_. One may concern this can cause one device to overtake all network bandwidth, but the only possible source of the unfairness is workload, which is sent when a reply to the request is received.
# Tightly Compacted Perovskite Laminates on Flexible Substrates via Hot-Pressing
## 2 Experimental Methods  
The CsPbBr\({}_{3}\)-based precursor solution was prepared by mixing CsPbBr\({}_{3}\) (200 g/L in dimethyl sulfoxide (DMSO, Aladdin, Shanghai, China), molar ratio CsBr (99.999%, Sigma-Aldrich, Shanghai, China)/PbBr\({}_{2}\) (99.999%, Sigma-Aldrich, Shanghai, China) = 1.1:1) and polyethylene oxide (PEO, Aladdin) (20 g/L in DMSO) with the volume ratio 2:1. By adding PEO, the diffusion of CsPbBr\({}_{3}\) precursors could be limited. Thus, the grain size is significantly reduced, producing high-surface-coverage, pinhole-free thin films with lower surface roughness [25; 26]. The polyethylene terephthalate (PET) substrates, prior to deposition, were sonicated in deionized water, then isopropanol alcohol, for 15 min each, and then UV-ozone treated for 15 min. The thin films were fabricated by spin-coating solutions onto flexible substrates inside an argon-filled glovebox, followed by 70 \({}^{\circ}\)C thermal annealing for 5 min. The fabrication method of pristine CsPbBr\({}_{3}\) thin films was the same as that of the thin films prepared for lamination. To make laminates, the two as-cast precursor films were stacked face to face and subsequently loaded into a custom-made hydraulic press machine with controlled temperature (room temperature, 70 \({}^{\circ}\)C, 90 \({}^{\circ}\)C, 150 \({}^{\circ}\)C) and pressure (5 MPa, 8 MPa, 10 MPa, 12 MPa).  
The morphology of the perovskite films was characterized using a metallurgical microscope (Axio Scope.A1, ZEISS, Germany) and a field emission scanning electron microscope (FESEM, Nova NanoSem 450, FEI, USA). The crystal structures of perovskites were determined by X-ray diffraction spectroscopy (XRD, D8A Advance, Bruker, Germany). The substrates with perovskite films after lamination were physically separated for metallurgical microscope, SEM, and XRD characterization. The absorption spectra were characterized by UV-visible spectroscopy (TU-1900, Purkinje, China). The photoluminescence (PL) spectra and PLQY were measured using a fiber-coupled spectrometer (QE Pro, Ocean Optics, USA), with an integrating sphere system, excited by a 365 nm laser. The PLQY values were calculated using [PERSON]'s method [27]. The time-resolved PL lifetimes were measured using a fluorescence lifetime spectrometer (FLS1000, Edinburgh Instruments, UK) excited by a 360 nm laser, and an optical filter was mounted in front of the detector to remove the excitation signal.
## 4 Simulation and Water Tank Experiment Results
### Results of Water Tank Experiment
#### 4.2.2 Analysis of Network Performance  
As shown in Figure 13, we presented the training results of CPD-Net and CPR-Net when the average number of photons per time slot was different. It can be seen from the figure that as the average number of photons in each time slot increases, the probability of CPD-Net identifying the wrong time slot synchronous clock phase value gradually decreases; the error between the phase value predicted by CPR-Net and the phase value of the ideal time slot synchronous clock becomes smaller.  
Figure 12: Statistical distribution of the number of noise photons in 10
## 1 Introduction
This investigation explored the impact of external forces on the system's dynamics and employed a nonlinear saturation controller to suppress vibrations, focusing on the main and 1:2 Internal Resonance instances [23]. The stability of nonlinear systems under parametric and external excitation forces has also been a subject of extensive study. Researchers have utilized MTSM to analyze the behavior of various systems under such conditions, contributing to a deeper understanding of their stability characteristics [24]. While the Multiple Time Scales Method has proven effective in analyzing the behavior and stability of hybrid electric vehicle generator sets, as demonstrated by [PERSON] et al., previous research primarily concentrated on linear analysis and generating amplitude-frequency response curves. However, the complexities of nonlinear phenomena, particularly super harmonic resonance with 1:1 Internal Resonance, and their impact on vibration control strategies remain largely unexplored. This study aims to bridge this gap by investigating the efficacy of a positive position feedback controller in mitigating vibrations within a nonlinear dynamic model of an electric vehicle generator, specifically at SHR with 1:1 IR. Utilizing MTSM, the research will analyze the system's stability and delve into the influence of various PPF controller parameters on vibration suppression. The findings will provide valuable insights into optimizing PPF for enhanced vibration control in electric vehicle generators, potentially leading to improvements in efficiency, durability, and noise reduction.
## 2 Materials and Methods
### Datasets
#### 2.1.2 Self-Constructed UAV Dataset  
To verify the generalizability of the model in complex scenarios, this study constructed a dataset of UAV aerial water-body images in the Dongfengqu Irrigation District, Sichuan Province, China (30.52\({}^{\circ}\) N, 104.23\({}^{\circ}\) E). The dataset covers various water-body landscapes, such as irrigation canals, farmland water areas, and artificial ponds, with a total survey area of approximately 3,521,585.7 m\({}^{2}\), as shown in Figure 2. The data collection used a DJI Mavic 3E quad-copter (SZ DJI Technology Co., Ltd., Shenzhen, China). The 4/3 CMOS high-performance RGB camera it carries has 20 million effective pixels, a video recording resolution of 4K, a shooting height of 170 m, and a ground sampling distance of 5.01 cm/pixel, ensuring wide image coverage and rich details. In the data pre-processing process, 620 images with a size of 960 \(\times\) 544 pixels were extracted from the UAV videos at an interval of 60 frames. The dataset was divided at a ratio of 9:1, and 10% of the samples were finely annotated.
## 4 Reduced-Order Tikhonov Regularization Imaging Based on Soft-Threshold Segmentation
Compared to the conventional reduced-order Tikhonov algorithm that treats pixels with a grayscale value of 0 in the original image as background and eliminates them, performing soft-threshold region segmentation on the original image before dimension reduction allows for the identification of a wider range of background areas while preserving  
Figure 6: Flowchart of the region segmentation-reduced-order Tikhonov regularization imaging algorithm. the target regions. This effectively reduces the number of unknowns in the solution and improves the ill-posedness of the EIT image reconstruction process. Additionally, as an effective image post-processing approach, performing soft-threshold region segmentation on the reduced-order Tikhonov image produces reconstructed images with clearer boundary contours. This facilitates further parameter calculations and assists medical professionals in identification and judgment.