esa-sceva/satcom-chunk-collection huggingface dataset:
# Depth Image Super Resolution Based on Edge-Guided Method
## 3 Experiments and Analysis
### Experimental Results and Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**SSIM \(\times\)4** & **Bowling** & **Artl** & **Aloe** & **Cones** & **Indian** & **Venus** & **Warrior** & **Tsukuba** & **Hand** & **Dove** \\ \hline NE + LLE & 0.916 & 0.712 & 0.875 & 0.874 & 0.987 & 0.937 & 0.893 & 0.827 & 0.983 & 0.988 \\ NE + LS & 0.923 & 0.735 & 0.884 & 0.890 & 0.988 & 0.951 & 0.903 & 0.814 & 0.984 & 0.989 \\ NE + NNLS & 0.923 & 0.728 & 0.884 & 0.887 & 0.989 & 0.948 & 0.902 & 0.829 & 0.984 & 0.989 \\ GR & 0.899 & 0.656 & 0.847 & 0.850 & 0.984 & 0.931 & 0.875 & 0.833 & 0.978 & 0.985 \\ ANR & 0.916 & 0.711 & 0.873 & 0.879 & 0.987 & 0.944 & 0.892 & 0.782 & 0.983 & 0.988 \\ AANR & 0.924 & 0.750 & 0.880 & 0.891 & 0.987 & 0.953 & 0.906 & 0.801 & 0.985 & 0.990 \\ CNN & 0.922 & 0.745 & 0.865 & 0.880 & 0.987 & 0.951 & 0.904 & 0.843 & 0.983 & 0.988 \\ Yang & 0.909 & 0.677 & 0.857 & 0.861 & 0.985 & 0.940 & 0.884 & 0.795 & 0.980 & 0.986 \\ Zeyde & 0.925 & 0.740 & 0.885 & 0.893 & 0.988 & 0.950 & 0.905 & 0.839 & 0.984 & 0.989 \\ Xie & 0.946 & 0.791 & 0.908 & 0.916 & **0.992** & **0.971** & 0.931 & 0.855 & 0.989 & **0.993** \\ Ours & **0.953** & **0.804** & **0.912** & **0.917** & 0.991 & 0.968 & **0.936** & **0.879** & **0.990** & 0.992 \\ \hline \end{tabular}
\end{table} 
Table 2: SSIM values on the Middlebury Stereo database with scaling factor of 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**PSNR \(\times\)4** & **Bowling** & **Artl** & **Aloe** & **Cones** & **Indian** & **Venus** & **Warrior** & **Tsukuba** & **Hand** & **Dove** \\ \hline NE + LLE & 42.827 & 39.942 & 40.634 & 45.328 & 49.852 & 51.818 & 36.896 & 38.999 & 42.901 & 48.096 \\ NE +LS & 42.972 & 40.099 & 40.776 & 45.475 & 50.045 & 52.084 & 36.932 & 39.001 & 42.850 & 48.126 \\ NE + NNLS & 42.966 & 40.091 & 40.798 & 45.480 & 50.034 & 52.038 & 36.938 & 38.980 & 42.938 & 48.130 \\ GR & 42.171 & 39.069 & 39.878 & 44.653 & 48.984 & 50.912 & 36.389 & 38.186 & 42.104 & 47.396 \\ ANR & 42.691 & 39.716 & 40.419 & 45.250 & 49.678 & 51.713 & 36.712 & 38.759 & 42.632 & 47.935 \\ AANR & 42.761 & 39.460 & 40.245 & 44.864 & 49.485  
From the above Figures, we can see that, in the SR result of [PERSON] et al. [25], serious zigzag effect exists. [PERSON] [6] and [PERSON] [7] could relieve zigzags using sparse coding method, but still introduced many artifacts around edges. The method of [PERSON] [8] could get good results, but the detail information of edges could not be reconstructed very well compared with our method. In Figures 5f and 6f, we can see clearly that our reconstructed depth images not only avoided blurred edges, but also reduced zigzags near edges and preserved sharpness of edges.
## 1 Introduction
Most NRF2 targets reported to date are proteins involved in decreasing the cellular oxidative level or anti-inflammation, such as NAD(P)H quinone oxidoreductase 1 (NQO1) and heme oxygenase-1 (HO-1) [18; 19; 20; 21; 22]. Proteins involved in other physiological processes such as DNA damage repair are also targets of NRF2, for example, 53 BP1 and 8-oxoguanine DNA glycosylase (OGG1) [23; 24]. In a recently published animal study, [PERSON] et al. reported that a diet supplemented with DNJ extract from mulberry leaves increased the anti-oxidative index in the plasma and intestinal tissues of hens. Their cell studies, using hen intestine epithelial cells, indicated that DNJ increased the level of NRF2 mRNA, and enhanced the expression of reactive species scavenging enzymes including SOD and catalase [25]. [PERSON] et al. observed that mulberry extract as a food supplement upregulated NRF2 protein in the liver tissue of inflammation model rats challenged with lipopolysaccharide [26]. These studies indicated that in addition to inhibiting glycosidase, DNJ can activate NRF2-mediated anti-oxidative responses. It is well-known that high concentrations of glucose induce the production of reactive oxygen species (ROS), which further lead to cellular disorders and cell death. Given the facts that DNJ treatment potentiates NRF2 anti-oxidative signaling [25; 26], in this study, we investigated whether the NRF2 antioxidative response was stimulated by DNJ treatment in high-glucose-challenged cells. Our results showed that DNJ could ameliorate high-glucose-induced oxidative DNA damage and senescence through activating NRF2. We further identified that DNJ activated AKT to stimulate NRF2 anti-oxidative signaling and led to the upregulation of OGG1, which facilitated the repair of oxidative DNA damage in high-glucose-challenged cells. This study provided new insight into the mechanism of DNJ in mitigating cellular damage caused by a high concentration of glucose.
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 3 Electromagnetic Field Simulation of the Designed MR Damper
### Finite Element Analysis (FEA) Setup  
The damping performance of the proposed MR damper is highly dependent on the electromagnetic performance. In this work, the FEA method was used to analyze the electromagnetic performance of the MR damper. Since the flow gaps and the exciting coil of the proposed MR damper were integrated in the piston, and the proposed MR damper is an axisymmetric structure, half of the piston model was selected in the FEA, as shown in Figure 5a. Figure 5b illustrated the closedended magnetic circuit of the MR damper. Most of the magnetic flux passes through three flow gaps multiple times in the same order. Therein, the magnetic flux passed through the outer flow gap more often, which validates the principle presented in Section 2. A small amount of the magnetic flux would not pass through the flow gap because the thin nonmagnetic disks are not completely non-magnetically conductive. Figure 5c shows the contours of the magnetic flux density with a current of 1.5 A, where the magnetic flux density in the inner flow gap is approximately 0.5 T.  
Figure 4: The performance curves of MR fluids: (**a**) \(B\)-\(H\) curve; (**b**) \(\tau\)–\(B\) curve [31].  
Figure 5: _Cont._The magnetic flux densities of the several effective damping gaps were detected with different currents by defining the magnetic-active part of the flow gap. The average magnetic flux density of the initial MR damper was obtained by integrating the magnetic flux density of the gap, as shown in Figure 6. The average magnetic flux density gradually increases with the increase in the current. Then, the average magnetic flux density of the damping gaps increases from the outer gaps to the inner gaps due to the decreasing magnetic flux area from outer gaps to inner gaps with a specific current. This also explains that the magnetic flux densities of the two outer damping gaps are approximately the same. The average magnetic flux densities with a current of 1.5 A are 0.46 T for the inner damping gaps, 0.38 T for the middle damping gaps, and 0.29 T for the outer damping gaps [31].  
Figure 7 shows the damping force against the current magnitude under the frequency of 0.25 Hz sinusoidal displacement excitation with an amplitude of 0.75 mm. The damping force can be controlled by changing the input current, since the damping force increases rapidly with a current augment. The damping force of the MR damper is 367 N with a current input of 0 A. Particularly, the damping force reaches 4585 N with a current of 1.5 A.  
Figure 5: Magnetic characteristics of the initial MR damper [31]. (**a**) Axisymmetric model for the FEA. (**b**) Magnetic flux. (**c**) Contours of the magnetic flux density.  
Figure 6: Average magnetic flux density of the initial MR damper along the detected damping gap with various current inputs [31].
# A Scalable Bidimensional Randomization Scheme for TLC 3D NAND Flash Memories
## 2 Background  
### 3D NAND Flash Memory Architecture and Randomization Fundamentals  
The architecture of a 3D NAND Flash is described in the sketch in Figure 1a. The primary element of the array is the stack of Control Gates (CGs), also indicated as Layers. Associated with each CG, there are several wordlines that depend on the specific integration concept for the memory [17]. The bottom of the memory architecture is represented by the Source Line and the Source Line Selectors of the 3D NAND Flash string. Multiple holes are drilled through the CG stacks and plugged with poly-silicon in order to form a series of vertically arranged 3D NAND Flash memory cells. In TLC architectures, all the cells belonging to a wordline can store up to three bits per cell, defined as Lower Significant Bit (LSB), Central Significant Bit (CSB) and Most Significant Bit (MSB); Bitline selectors and bitline (BL) contacts are on top of the structure.  
The goal of the data randomization is that this operation will scramble the data to be sent for programming in the different memory wordlines after the data input from the host interfaces with the memory, and the de-randomization operation happens before the data output from the memory to the host starts [12; 14]. Figure 1b,c shows the operation flow, considering the case of an on-chip implemented randomizer. The random seed is loaded into an internal circuit of the 3D NAND Flash memory, called a _page buffer_, via the memory data-path. Then, additional circuits take the seed and execute bit-wise XOR of the original data input from the host and random sequence in the page buffers. The program algorithm can then start. On the contrary, de-randomizing operations happen during read mode: first, the data from the memory are sensed, then the seed is loaded into a page buffer and, finally, a bit-wise XOR of sensed data and random sequence is executed making the original data available to the host. In the case of on-chip randomizers, the time for on-chip randomizing is added to the program (\(t_{PROG}\))/read (\(t_{R}\)) time. Off-chip randomizers can help to reduce the former times by providing a scrambled version of the data to be programmed in the memory, but in this case, it is the host that needs to take care of both randomization and de-randomization (see Figure 1d,e).
## 3 Results and Discussion
### Analysis of Adsorption Kinetic Model  
Figure 9 shows the adsorption kinetic curves reflecting the adsorption of iron and manganese ions by manganese sand. The trends of the adsorption rate curves of the two models were basically similar. During the initial 240 min of the adsorption process, manganese sand had many adsorption sites, a large curve slope, and a fast adsorption rate. With the progress in adsorption, the adsorption sites on the surface of manganese sand were gradually occupied by iron and manganese ions, and the adsorption rate slowly decreased until the adsorption equilibrium was reached at 480 min.  
The quasi-primary and quasi-secondary kinetic fitting parameters are shown in Tables 5 and 6. In the adsorption of iron and manganese ions by the unmodified manganese sand and modified manganese sand, the fitting regression coefficients of the quasi-first-order kinetic model were less than those of the quasi-second-order kinetic model, so it can be described by the quasi-second-order kinetic model. Compared with the unmodified manganese sand, the theoretical adsorption capacity of the modified manganese sand was improved. This was because the specific surface area of the modified manganese sand increased, and the adsorption point increased, which was conducive to increasing the reaction residence time of the adsorbent and the adsorbate, and at the same time, increasing the equilibrium adsorption capacity.
## 4 SARA: Main Characteristics and Architecture Components
To do so, SARA keeps a central state of the collaboration session must be maintained and changes on this state can be made at any time. Later, that state may be recovered on demand (e.g., if a participant connects later to the collaboration). 9. **Extensible**: It is possible to add new features to the system easily. This affects both the addition of new services with new functionality to the system, and the adaptation and extension of the associated data model. 10. **Independent of the communication network protocol**: in order to cover as many devices as possible, the system offers a transparent way of establishing the communication. At the moment of truth, there are certain frameworks and platforms that present different difficulties to work with certain protocols, hence the option of choosing is given to the developer. The use of TCP, UDP, Websockets and MQTT is currently included. 11. **Logic decoupled and reusable**: Thanks to SARA, all the logic of the application can be implemented for a single device/platform, which we call the _provider_. This _provider_ will inject AR content to a session and the other participants will send interaction events to content. This second group is labeled as _consumers_. The main core of _consumer_ clients is common to all the applications and hence it can be reused and taken as starting point for development. Over that core, some logic such as interface management may be implemented, but it is not strictly necessary. 12. **Adaptation of non-AR, non-collaborative applications**: by using SARA it is easy to inject the content of applications that were not generated with Augmented Reality in mind and expose it to users. It is also possible to update applications that were not initially developed as collaborative ones to allow that functionality. Figure 3 provides an overall view of the architecture: a set of services will enable the communication among the participants and the management of the collaboration itself. Additionally, several SARA clients  
are depicted in the figure. Each of those clients will be used by the participants to collaborate, either using them one at a time or by combining several of them. Further details are given later in Section 4.2. The architecture has been designed based on the microservice paradigm [33]. This decision has been taken to maintain each service loosely coupled and small in functional terms wanting to assure scalability and reusability. Thanks to this, in general the architecture will keep on working even if a given service is down (only it will not do it in case the service is managing communications, Section 4.1). Figure 4 shows the different services implementing the full architecture. The architecture may be divided into three main building blocks: (a) the _Communication Service_, (b) the _SARA Client_ and its associated services and (c) the _Conflict Manager and Collaboration_ services, to be explained below. Figure 4: Service-oriented architecture diagram for SARA. Figure 3: Overview of the SARA architecture, with its communication and collaboration management microservices and clients.
# Decentralized Documentation of Maritime Traffic Incidents to Support Conflict Resolution
###### Abstract  
For the investigation of major traffic accidents, larger vessels are obliged to install a voyage data recorder (VDR). However, not every vessel is equipped with a VDR, and the readout is often a manual process that is costly. In addition, not only ship-related information can be relevant for reconstructing traffic accidents, but also information from other entities such as meteorological services or port operators. Moreover, another major challenge is that entities tend to trust only their records, and not those of others as these could be manipulated in favor of the particular recording entity (e.g., to disguise any damage caused). This paper presents an approach to documenting arbitrary data from different entities in a trustworthy, decentralized, and tamper-proof manner to support the conflict resolution process. For this purpose, all involved entities in a traffic situation can contribute to the documentation by persisting their available data. Since maritime stakeholders are equipped with various sensors, a diverse and meaningful data foundation can be aggregated. The data is then signed by a mutually agreed upon timestamping authority (TSA). In this way, everyone can cryptographically verify whether the data has been subsequently changed. This approach was successfully applied in practice by documenting a vessel's mooring maneuver.  
decentralized documentation; traffic incidents; conflict resolution; timestamping authority; maritime data +
Footnote †: journal: Marine Science and Engineering  
0
Footnote 0: _J. Mar. Sci. Eng._**2022**, 10, 2011. [[https://doi.org/10.3390/jmse10122011](https://doi.org/10.3390/jmse10122011)]([https://doi.org/10.3390/jmse10122011](https://doi.org/10.3390/jmse10122011))
## 2 Materials and Methods  
### Basin-Scale Water Budget  
Over a contained hydrological basin where continuity equations apply, the natural (non-anthropogenic) water budget can be described by Equation (1) [17; 18]:  
\[\text{P-Ru}=\text{ET}+\text{dS/dt(sw)}+\text{dS/dt(uz)}+\text{dS/dt(gw)} \tag{1}\]  
where P is the precipitation influx, Ru is the streamflow runoff, comprised of surface and groundwater runoff, and their difference (P-Ru) is partitioned into evapotranspiration (ET), change in surface water storage dS/dt(sw), change in moisture storage in the unsaturated zone dS/dt(uz) and change in storage of groundwater dS/dt(gw), respectively. At decadal to longer time scales and over hydrological units undisturbed by anthropogenic activities, an increasing trend in (P-Ru) must therefore be balanced by the net difference of evapotranspiration and storage trends. Over such long timescales many studies have shown that changes in surface water storage, dS/dt(sw), and storage in unsaturated zone, dS/dt(uz), are negligible (e.g., [19; 20]).  
Anthropogenic water use is increasing with increases in population and with increasing agricultural and industrial development. Water use is usually defined and measured in terms of withdrawal and consumption. Withdrawal refers to water extracted from surface or groundwater sources, and consumption refers to the part of a withdrawal that is ultimately used and removed from the hydrological unit, whether by evaporation, transpiration or incorporation into crops or other products. Conversely, the return flow is the portion of a withdrawal that is not actually consumed, and is instead returned to the hydrological unit [21]. Water consumption has been shown to have a non-negligible effect on the hydrological cycle at basin scales [22]. However, consumption data are not available at the basin scale over our study area. Therefore, we analyze precipitation, runoff and groundwater storage at the basin scale to evaluate the inter-basin variation, but only assess the trend in ET as a residual of the components of the water budget, including human water consumption, over the entire eastern United States study area, where anthropogenic water use statistics are available.
###### Abstract  
Understanding the time-lagged response of land subsidence to groundwater level fluctuations and subsurface strain variations is crucial for uncovering its underlying mechanisms and enhancing disaster early warning capabilities. This study focuses on Dangshan County, Anhui Province, China, and systematically analyzes the spatio-temporal evolution of land subsidence from 2018 to 2024. A total of 207 Sentinel-1 SAR images were first processed using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to generate high-resolution surface deformation time series. Subsequently, the seasonal-trend decomposition using the LOESS (STL) model was applied to extract annual cyclic deformation components from the InSAR-derived time series. To quantitatively assess the delayed response of land subsidence to groundwater level changes and subsurface strain evolution, time-lagged cross-correlation (TLCC) analysis was performed between surface deformation and both groundwater level data and distributed fiber-optic strain measurements within the 5-50 m depth interval. The strain data was collected using a borehole-based automated distributed fiber-optic sensing system. The results indicate that land subsidence is primarily concentrated in the urban core, with annual cyclic amplitudes ranging from 10 to 18 mm and peak values reaching 22 mm. The timing of surface rebound shows spatial variability, typically occurring in mid-February in residential areas and mid-May in agricultural zones. The analysis reveals that surface deformation lags behind groundwater fluctuations by approximately 2 to 3 months, depending on local hydrogeological conditions, while subsurface strain changes generally lead surface subsidence by about 3 months. These findings demonstrate the strong predictive potential of distributed fiber-optic sensing in capturing precursory deformation signals and underscore the importance of integrating InSAR, hydrological, and geotechnical data for advancing the understanding of subsidence mechanisms and improving monitoring and mitigation efforts.  
2025
## 3 Results and Discussion
### Fatty Acid Profiles of Oils and Microencapsules  
As a result of fatty acid analysis, saturated and unsaturated fatty acids were determined (Tables 5 and 6). According to these results, unsaturated fatty acids (MUFAs + PUFAs) were higher than saturated fatty acids (SFAs) for both NY and KY oils. The unsaturated fatty acid and SFA values for NY were 73.35% and 26.64%, while the values for KY were 72.62% and 27.37%, respectively. Among unsaturated fatty acids, oleic acid was found to be the dominant fatty acid in both NY and KY oils and the microencapsulated powder products obtained from these oils. This was followed by palmitic and linoleic fatty acids, respectively (Tables 5 and 6). Oleic acid (C18:1) is an important compound in this group and is recorded as having the highest proportion in both oils. The results obtained for fatty acids are consistent within the official limits for extra virgin olive oil (oleic acid, 55.0-83.0; palmitic acid 7.5-20.0%; linoleic acid 2.5-21.0%, and palmitoleic acid 0.3-3.5%) ([PERSON] et al., 2016). A previous study stated that major fatty acids in olive oils are oleic (55-85%), palmitic (7.5-20%), linoleic (7.5-20%), stearic (0.5-5%), palmitoleic (0.3-3.5%), and linoleic (0.0-1.5%) acids, although traces of myristic, arachidonic, and margaric acids have also been found ([PERSON] et al., 2016). These values are consistent with the data obtained in the present study. After the freeze-drying of both oil samples and emulsions with different formulations, the amount of oleic acid (for NY 57.60-58.51%; for KY 57.82-58.64), the most dominant compound, was preserved, and no statistically significant change was observed (\(p>0.05\)). Similarly, the concentration of lauric, palmitoleic, stearic, and arachidonic acids in NY oil and capsules, and only lauric acid in KY oil and capsules, were statistically similar. As a result, when the general profile of fatty acids after microencapsulation was examined, it was found that the concentrations of 14 fatty acids in different formulations were slightly affected by the microencapsulation process. Microencapsulation reduces the oxidation of oils and fatty acids and their exposure to environmental influences. This is particularly important for oxidation-sensitive components such as polyunsaturated fatty acids (PUFAs) ([PERSON] et al., 2016). Similar results were obtained by [PERSON] et al. (2016) in flaxseed oil and [PERSON] et al. (2016) in sacha inchi oil.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
## 2 Models and Methods
### Computational Models  
The SARs in the models were first computed using an FDTD method for electromagnetics. The time course of the temperature elevation was then calculated by solving [PERSON]'s bioheat transfer equation (BHTE) considering the vasodilation. The bioheat modeling has been fully described in our previous studies and is thus reviewed briefly here.