esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The proposed method of obtaining composite hydrogel membranes based on pHEMA-gr-PVP copolymers includes two stages (Scheme 1)--obtaining a hydrogel membrane (Figure 1a) and forming an ultra-thin polymer layer on its surface (Figure 1b). A PA-6/PVP modifying solution in formic acid was used to obtain the reinforcing layer. To obtain composite membranes, the method of forming a polyamide film on the water-containing surface of the hydrogel was used. Deposition of the reinforcing layer based on the PA-6/PVP solution happens in the presence of a water swollen hydrogel membrane-substrate. Deposition is carried out as a result of a decrease in the solubility of the PA-6/PVP mixture caused by the mixing of free water from the volume of the hydrogel with formic acid.  
At the first stage of research, the formation conditions of the hydrogel substrate were established, namely--the water content in the original composition, the temperature regime of polymerization, as well as the hydration conditions of the obtained hydrogel films. The effect of the formulation of the original composition on the physico-mechanical properties of the obtained hydrogel films based on pHEMA-gr-PVP copolymers, which were used as substrates for the preparation of composite two-layer membranes, was established (Figure 2).  
From the presented results, a clear regularity is observed--an increase in the content of polyvinylpyrrolidone in hydrogels is accompanied by an improvement in their elasticity and a deterioration in strength characteristics [31; 48]. Previous studies have established that the effectiveness of polyvinylpyrrolidone grafting decreases with an increase in its content in the original composition [31; 48]. Ungrafted PVP is washed out from the samples during their hydration, as a result of which the number of PVP chains that perceive the load decreases in the swollen polymer, which leads to a decrease in strength. At the same time, as a result of the leaching of ungrafted PVP, additional pores are formed, the presence of which facilitates conformational changes during sample deformation and, thus, the growth of relative elongation.  
Figure 1: Hydrogel membrane-substrate based on pHEMA-gr-PVP copolymer (**a**) and hydrogel substrate with an applied ultra-thin reinforcing layer based on PA/PVP (**b**).  
As can be seen (Figure 2), hydrogel film materials based on pHEMA-gr-PVP copolymers are characterized by insufficient strength characteristics, which significantly limit their suitability for the production of membranes, and their modification is required.
## 3 Results and Discussion
### Positive Axicon Simulated by SLM  
The experimental setup is shown in Figure 5. The laser passed through Mirror 1 and a beam expander (M \(\approx 3\times\)), and after reflection on Mirrors 2 and 3, illuminated on the SLM. A CCD (Charge Coupled Device) was used to receive Bessel beams generated by the SLM, and the images were sent to a computer. Meanwhile, the computer generated the holograms and loaded them into the SLM. Figure 6 presents the 256-level phase holograms for simulating the positive axicon on the conditions of \(d=15\), 20 and 25 pixels (1 pixel = 12.5 \(\upmu\)m).  
We can see that the interval of Bessel rings increased with the extension of the holographic period \(d\). From Equations (3) and (21), it was found that the relationship between the real axicon and the hologram is \(h/d=\tan\,\theta\approx\theta\). When \(h\) remains constant (\(h=0.0012\) mm), increasing the holographic period, \(d\) is the equivalent of decreasing the refracting angle \(\theta\) of the real axicon. It means that changing the period \(d\) can obtain axicons with different refracting angles. In the same optical path, it can change the holograms dynamically to simulate a different axicon. This will be convenient in some applications.
## 4 Experiment Results and Discussion
### Dataset Preparation
#### 4.1.2 Pets2009 Dataset  
The PETS2009 dataset [34] contains three different levels of complexity, namely L1, L2 and L3. Each difficulty level has four different viewpoints with two time segments. In this proposed experiment, we selected the time step data of each level for testing. Several samples of the complexity levels of each data set are shown in Figure 4.
## Abbreviations  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} USV & Unmanned Surface Vessel \\ ROV & Remotely Operated Vehicle \\ UUV & Unmanned Underwater Vehicle \\ UXO & Unexploded Ordnance \\ CPT & Cone Penetration Testing \\ UAV & Unmanned Aerial Vehicle \\ IUU & Illegal, Unreported, and Unregulated \\ SOLAS & Safety of Life at Sea \\ USBL & Ultra-Short Baseline \\ PAM & Passive Acoustic Monitoring \\ MDA & Maritime Domain Awareness \\ ISR & Intelligence, Surveillance, and Reconnaissance \\ \end{tabular}   
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**USV** & **Hull Material \({}^{\star}\)** & **Size** & **Draft \({}^{\star}\)** & **Dry Weight \({}^{\star}\)** & **Payload Capacity \({}^{\star}\)** & **Moonpool** \\ \hline \multirow{2}{*}{Otter} & High-Density Polyethylene (HDPE) & 2.00 m \(\times\) 1.08 m \(\times\) 1.06 m & 0.32 m & 62 kg & 30 kg & \\ \hline \multirow{2}{*}{Mariner} & High-Density Polyethylene (HDPE) & 5.98 m \(\times\) 2.06 m \(\times\) 2.70 m & 0.50 m & 2000 kg & 400 kg & \(1\) (optional) \\ \hline \multirow{2}{*}{Mariner X} & High-Density Polyethylene (HDPE) & 9.00 m \(\times\) 2.50 m \(\times\) 3.00 m & 0.60 m & 5000 kg & 1200 kg & 2 \\ \hline \multirow{2}{*}{Armada 8 (A8)} & High-Density Polyethylene (HDPE) & 7.61 m \(\times\) 2.14 m & 0.99 m & 3700 kg & 1100 kg & \( \begin{tabular}{c} 1 (0.85 m \\ \(\times\) 0.75 m) \\ \end{tabular}  \\ \hline \multirow{2}{*}{Mariner 8 (A78)} & High-Density Polyethylene (HDPE) & 5.00 m & 2,500,000 kg & 1,050,000 kg & \(\ \begin{tabular}{c} 2 (9 m \(\times\) \\ 4 m) \\ \end{tabular}  \\ \hline \multirow{2}{*}{Mariner X} & High-Density Polyethylene (HDPE) & 11.75 m \(\times\) 2.20 m \(\times\) 8.45 m & 0.72 m & & 2000 kg & \\ \hline \multirow{2}{*}{Tupan} & Aluminum & 4.92 m \(\times\) 1.78 m \(\times\) 3.34 m & 0.6 m & & 200 kg & \(1\) \\ \hline \multirow{2}{*}{Shadow Fox C-Cat 3} & \multirow{2}{*}{Polyethylene} & 12.70 m \(\times\) 3.50 m \(\times\) 6.00 m & 0.7 m & & 940 kg & \\ \cline{1-1} \cline{2-6} C-Cat 3 & Polyethylene & 3.00 m \(\times\) 1.60 m \(\times\) 2.30 m & 0.4 m & 320 kg & 70 kg & \\ \hline \multirow{2}{*}{C-Worker 4} & Aluminum & 4.20 m \(\times\) 1.60 m \(\times\) 2.70 m & 0.6 m & 940 kg & 40 kg & \\ \hline \multirow{2}{*}{C-Worker 5} & Aluminum & 5.50 m \(\times\) 1.80 m \(\times\) 3.20 m & 0.85 m & 1250 kg & 40 kg & \\ \hline \multirow{2}{*}{C-Worker 7} & Aluminum & 7.5 m \(\times\) 2.30 m \(\times\) 6.40 m & 1.0 m & 3930 kg & 500 kg & \( \begin{tabular}{c} 1 (2.5 m \(\times\) \\ 1.0 m \(\times\) \\ 1.5 m) \\ \end{tabular}  \\ \hline \multirow{2}{*}{DriX H-8} & Composite construction (vacuum infusion) and Kevlar reinforced
## 1 Introduction
### Motivation
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Ref.** & **Highlight** & **[A]** & **[B]** & **[C]** & **[D]** & **[E]** \\ \hline [21] & Air learning for autonomous UAVs & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [22] & FL for scheduling and power allocation cooperation of UAV swarms & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [24] & Challenges and architecture of decentralized FL in UAV networks & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [26] & Securing data sharing for UAV computing networks & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [27] & Performance of FL for edge-assisted UAV networks with data sharing & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [29] & UAV-enabled edge computing to support IoT devices & ✓ & ✓ & \(\times\) & ✓ & \(\times\) \\ \hline [28] & Combination of FL and Blockchain for drone edge intelligence over & ✓ & ✓ & \(\times\) & \(\times\) & ✓ \\ (2021) & smart environments & & & & & \\ \hline Our work &
\begin{tabular}{l} Evolution of the network performance of UAV computing \\ to expedite SAR missions \\ \end{tabular} & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \end{tabular}  
[A] = AI in UAV, [B] = UAV computing network, [C] = SAR, [D] = Network performance, [E] = UAV computing for disasters and harsh environments.  
\end{table} 
Table 1: Summary of current works.
## 4 Conclusions  
This work addresses static and dynamic adsorption of silica NPs on chalks and their fluid/rock interactions during low salinity flowing tests. Based on the results from this study the following conclusions can be stated:  
1. Silica NPs showed an adsorption affinity to calcite surface. Salinity was shown to enhance the adsorption by about 40%.
2. Dynamic adsorption of the NPs in the chalk core showed high irreversible adsorption at elevated salinity (SSW) and desorption of NPs was observed in low salinity and ion free condition.
3. Adsorption/desorption mechanisms for the NPs have been proposed. Further, it can be concluded that NPs adsorption during these experiments led to significant reduction of calcite dissolution both in DIW and LSW.
4. In spite of the NPs affinity to adsorb on the chalk surface, no pore throat blockage was observed from SEM imaging. The SEM images were done on spots along horizontally cut core. However, they are small fractions of the whole core. Further the differential pressure drop across the chalk core during nanofluid injection also indicated reduced resistance to flow wherein lower pressure drop was recorded, compared to with injection of LSW alone.
5. The nanofluid at 1 g/L prepared in LSW reduced the produced calcium ion concentration by about 30% as compared to the case of LSW alone. This indicates that silica NF hinders calcite dissolution i.e., has less effect on chalk matrix integrity which is one of the major concern in chalk reservoir, if low salinity for EOR is to be employed.
6. The combination of silica NPs with low salinity EOR technique reduces the risk of matrix integrity loss and the subsidence degree of the water flooded chalk.  
[PERSON] and [PERSON] designed the study. The experiments were performed by [PERSON] and [PERSON]. The analysis was done by all three authors. The manuscript was written by [PERSON] and [PERSON].  
This research received no external funding.  
The authors would like to thank the University of Stavanger (UiS), Norway, for funding this work. The authors thank [PERSON] (UiS) for help with SEM imaging, [PERSON] (UiS) for providing access to DLS equipment. The authors would like to thank [PERSON] (UiS) for acquiring chemicals used in this study. In addition, the authors would like to thank [PERSON] and [PERSON] (both UiS) for helping with the flooding setup. Finally, the authors would like to thank NYACOL(r) Nano Technologies Inc for supplying the nanofluid used in this study.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 10: Tentative observation of incremental recovery from SK5-SSW, SK6-LSW and SK7-mixed.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model
### Kinetic Equations
#### 2.4.4 Restoring Forces and Moments  
Considering the center of buoyancy of the ROV, as stated in the BF frame is \([x_{p},0,x_{z}]^{T}\), the restoring forces and moments \(G(\eta)\in R^{6\times 1}\) could be expressed as follows [21; 23]:  
\[G(\eta)=\left[\begin{array}{c}(W-B)\text{sin}\,\theta\\ -(W-B)\text{cos}\,\theta\text{sin}\,\phi\\ -(W-B)\text{cos}\,\theta\text{cos}\,\phi\\ -(y_{G}W-y_{\dot{B}}B)\text{cos}\,\theta\text{cos}\,\phi+(z_{G}W-z_{\dot{b}}B) \text{cos}\,\theta\text{sin}\,\phi\\ (z_{G}W-z_{\dot{b}}B)\text{sin}\,\theta+(x_{G}W-x_{\dot{b}}B)\text{cos}\, \theta\text{cos}\,\phi\\ -(x_{G}W-x_{\dot{b}}B)\text{cos}\,\theta\text{sin}\,\phi-(y_{G}W-y_{\dot{b}}B) \text{sin}\,\theta\end{array}\right] \tag{13}\]  
where \(W\) and \(B\) represent the gravity force and the buoyancy force, respectively.
# Review of Auxetic Materials for Sports Applications: Expanding Options in Comfort and Protection
## 3 Common Auxetic Materials and Structures
### Additively Manufactured Auxetic Materials and Structures  
As alternatives to often inhomogeneous foams or textiles, the creation of auxetic materials with repeatable structures using various AM techniques has been investigated (e.g., fused deposition modelling [137], selective electron beam melting [175], selective laser sintering [247] and lithography-based ceramic manufacturing [248]). AM is a natural extension of FEM, as the existing modelling file can be converted to be compatible with most AM technologies to fabricate the modelled structure [249; 250; 167; 251; 252]. The AM structure can be experimentally tested to validate and potentially improve FEM [253; 254; 162; 255], and key parameters can consequently be tuned and investigated with a particular application in mind.  
Example materials used for AM in auxetic research include compliant rubbers and plastics. Metals and ceramics can also be made via AM but these studies have been omitted due to their incompatibility with sporting PE and PPE. Auxetic 3D chiral lattices have been modelled and produced by AM using TangoBlack(r) (Stratasys, Eden Prairie, MN, USA), a rubber-like AM filament [256]. AM has also combined two materials of different stiffness within; (i) one re-entrant unit cell [257; 258; 202] (Figure 4c); and (ii) novel chiral auxetic structures consisting of four 'base' unit cells surrounding a smaller 'core' chiral unit cell [259]. Using two materials of different stiffness provides a designer with greater control over unit cell deformation. The chiral auxetic has since been developed to use a re-entrant cell as the 'core' unit cell [260], meaning softer hinges are not necessary and the structure can be made from one filament material. The location and amount of each dual material provides additional ways to tune the mechanical properties (e.g., stiffness) of a structure without changing its geometry; as illustrated with the FEM of layered auxetic plates [261]. FEM and AM used in conjunction could facilitate such a parametric study with relative ease, for example by varying repeated structures [262].  
AM structure's Poisson's ratio can be tuned by altering the geometry of a unit cell [263; 264; 265], enabling the design and analysis of novel auxetic structures using FEM [266; 267] to help validate existing analytical formulae. Matlab has also been used to run FEM parametric analysis [167]. NinjaFlex(r), (Ninjatek, Manheim, PA, USA) an elastic and flexible thermoplastic PU, has been used in the AM of auxetics [268; 269; 137; 270], and in impact testing studies [269; 270], with FEM used to highlight auxetic structures' desired shock absorption capacity. Elsewhere, impact testing on non-auxetic NinjaFlex(r) honeycombs demonstrated that increasing strain rates (0.01 to 0.1 s\({}^{-1}\)) resulted in an increase of energy absorbed (0.01-0.34 J/cm\({}^{3}\)) [271]. Both strain rate and energy absorption are key considerations for auxetic material applications in sporting PE and PPE.
## 3 LiCSAR Products: Current State and Future Trend
### LiCSAR Products File Structure
The image is resized to 30% of the original GeoTIFF;  
**yyyymmdd_yyyymmdd.kmz**: an optional Google Earth KMZ output is typically generated from full resolution (0.001\({}^{\circ}\)) raster previews of the generated interferogram products. In addition to the interferograms, georeferenced MLI images for each processed epoch are stored in the epochs folder in directories corresponding to the acquisition date in the format yyyymmdd. The intensity images are produced by space-domain averaging of the SLC images with 4 and 20 as the number of azimuth and range looks, respectively. MLI images are generated without a radiometric calibration and only for a co-polarised channel. Additional files are stored in the metadata folder:  
**OOOP_AAAAA_BBBBBB.geo. {E,N,U}.tif**: these GeoTIFF files contain the E, N and U components of the LOS unit vector for each pixel. They are calculated from the SAR look-vector elevation and orientation angle of each pixel, based on the SAR imaging and DEM geometries with the local topography taken into account. The unit vector information can be used, for example, to project E-N-U modeling results or 3-dimensional geodetic observations like GNSS data onto the LOS vector in order to be able to compare them to the LiCSAR results [43]:  
**OOOP_AAAAA_BBBBBB.geo.hgt.tif**: this image (GeoTIFF) contains the height values extracted from the DEM used in processing;  
**baselines**: a text file containing the temporal and spatial baselines of each acquisition with respect to the master image;  
**metadata.txt**: a text file containing various other information related to the frame (e.g., primary epoch and acquisition time for its center location, etc.) In the near future, the time-series and velocity folders will contain outputs stemming from multitemporal InSAR processing based on the LiCSBAS tool [64]. The main objective of this module (currently still under development) is to generate average displacement velocity maps and time series of displacements for all processed LiCSAR frames. The velocity and time series products will be provided initially in 1 km resolution, with a higher resolution (initially 100 m) over volcanic areas. GACOS tropospheric delay maps are provided per epoch (in the epochs folder) in GeoTIFF format in the same resolution as the other LiCSAR products and in both vertical and LOS directions. The LOS tropospheric delay is stored as a yyyymmdd.sltd.geo.tif file. It should allow the user to readily apply the correction to the LiCSAR phase products, for example using the LiCSBAS software [64]. Additionally, we archive the original GACOS tropospheric delay map as yyyymmdd.ztd.geo.tif and yyyymmdd.ztd.png files. Currently, only a few frames have GACOS products generated during the testing phase.
# A Review of Health Assessment Techniques for Distribution Transformers in Smart Distribution Grids
## 3 Transformer Health Assessment Techniques  
In order to provide information about the transformer's state of health and detect incipient faults, the monitoring system must perform physical measurements and analyze the results in the context of given environmental conditions. Health Indices methods are practical tools to aggregate the results of multiple operating observations, field inspections, and site and laboratory testing into a single objective index that quantifies overall health [26; 27]. They are important for asset management because they help to identify, prioritize, and schedule required investments into capital and maintenance programs. Effective methods of monitoring the condition and health of distribution transformers could help utilities to proactively mitigate failures and degradation. The objectives of the monitoring process are to:  
* Determine the most appropriate measurement techniques to employ for low cost, accurate, and in-situ health monitoring of distribution service transformers
* Synthesize or create methods of determining transformer health from these measurements as well as contextual or environmental data as appropriate
* Implement the system for field validation. Algorithms will be implemented locally and/or centrally (on a server) to measure and analyze the operational characteristics of distribution service transformers and provide an overall health index encompassing the pertinent failure and degradation modes  
Significant deviations or rapid changes in this index or its factors could be used to predict the need for maintenance, reconfiguration, upgrade, or replacement. Ultimately, this will improve reliability and reduce the cost of electric service. It is particularly important with the advent of higher penetrations of distributed PV, electric vehicles, and other energy resources that are rapidly changing the operation of the grid and have the potential to introduce added stress to service transformers. Several studies proposed various methods of establishing health indices for power transformers from the available measurement techniques [28; 29; 30; 31]. Many of these methods can be applied to distribution transformers because the underlying working principles and key components are the same. However, the prioritization and acceptable operation range of these measurements or conditions must be carefully reassessed.
## 3 Selective FMS Filtering with Two Kinds of Measurement Windows  
### Temporary Model Uncertainty and Window Length  
Even if many kinds of dynamic systems can be accurate on a long time scale, they may experience unpredictable situations, such as jumps in frequency, phase, and velocity. These are called temporary model uncertainties since these effects usually occur over a short time interval [13]. Unknown faults, unknown inputs, and modeling errors in dynamic systems can be temporary uncertainties. The FMS filter should be robust to minimize the effects of these temporary uncertainties.  
To deal with temporary uncertainties, how to get a proper measurement window length \(M\) for the FMS filter might be important issue. The window length affects differently the performance of the FMS filter according to presence or absence of temporary uncertainties. The FMS filter is well known to have better noise suppression as the window length grows. In other words, the noise suppression of the FMS filter is closely related to the window length. However, even if the FMS filter can show greater noise suppression as the window length increases, the tracking speed of state estimate for actual state variable worsens in proportion to the window length, which can degrade the estimation performance of the FMS filter. This means that the FMS filter requires a compromise between the noise suppression and the tracking speed of the state estimate.
## 4 Discussion
### Detectability of Corrosion in the Rebar  
Based on Figure 8, the waveforms of the specimen beyond 48 h of electrolytic corrosion differed significantly from the other waveforms. In the time domain, a characteristic tail-like waveform appeared (black arrows in Figure 8a), and the sharp peak around 200 kHz was dominant in the frequency domain, as well as in the naked steel rod and the heated specimen. Concerning waveforms of the specimen with electrolytic corrosion of 40 h or less, sharp peaks were observed in the low-frequency region, the same as in the intact specimen of the previous experiment. The change in the ultrasonic waveforms can be attributed to the point at which the bonding was destroyed by electrolytic corrosion, which indicates that enough reinforced concrete had deteriorated for the dissolving rebar to be detected from the ultrasonic wave propagated on the rebar. The mode transition also changed with the progress of the corrosion. From the early stages of electrolytic corrosion ((b) in Figure 9), the L(0,2) mode appeared in the higher-frequency region. In the middle stage ((d) and after in Figure 9), 400 kHz and 700 kHz of the L(0, 3) mode appeared. The wavelet power spectrum corresponding to the tail-like wavepacket can be seen clearly in the late stage ((g) and after in Figure 9). The L(0,3) mode of 700 kHz shifted toward the lower-frequency region, in response to the time, after (d) in Figure 9. The consistency between the dispersion curves and WT of the signals was not adequate, compared to Figure 7, as the rod became uneven in shape due to the electrolytic corrosion.  
Only the frequency spectrum curve of the electrolytic corrosion at 8 h was missing the sharp peak at around 200 kHz. We believe this phenomenon may be explained by temporary bonding strengthening, which is caused by the volume expansion due to the rust on the rod in the early stages of corrosion. Together with the mode transition seen in Figure 9, this missing peak suggests that our method can be applied to evaluate the degree of the corrosion, which needs further investigation.