esa-sceva/satcom-chunk-collection huggingface dataset:
# Vortex-Induced Vibration of Deep-Sea Mining Pipes: Analysis Using the Slicing Method
## 1 Introduction
[18] investigated how variations in oscillation frequency impact the vortex-induced vibration characteristics of risers under oscillatory flows, demonstrating how such changes influence the structure of the cross-flow vibration envelope. [PERSON] et al. [19] performed a comparative analysis of the vibrational responses of end-hinged and cantilever risers in deep-sea mining applications, revealing a consistent pattern in the evolution of vibration amplitude and the primary vibration frequencies. [PERSON] et al. [20] considered natural frequency influence and special boundary conditions, as well as tension variation along pipes, proposing five vibration modes. [PERSON] et al. [19] calculated cantilever riser's vortex-induced vibrations using a quasi-three-dimensional coupling algorithm based on DVM and FEM, observing an amplitude hopping phenomenon during continuous principal state transitions. Conclusively, the study of fluid-solid interactions emerges as a pivotal component in the field of vortex-induced vibration analysis. This approach not only forecasts the dynamics of vortex-induced vibrations in deep-sea mining pipes with high precision but also enhances theoretical frameworks that support the design and systematic maintenance of these infrastructures. This document delineates new investigative efforts aimed at exploring the unique characteristics of vortex-induced vibrations affecting deep-sea mining tubes, thereby broadening the understanding of their operational dynamics: (1) The computational model of viscous flow field is constructed by slicing and overlapping mesh methods in the fluid domain, while the finite element method is applied to construct a 3D computational model of the tube in the deep-sea mining tube structure domain. The two-way, sequential coupling between the fluid domain and the deep-sea mining pipe structure is achieved through UDF programming, so that a numerical model of the coupled flow-solidity of the vortex-excited vibration of the mining pipe is established and verified for its accuracy. (2) In this paper, the mechanism of vortex-induced vibration of a deep-sea mining pipe under the action of oscillating flow is revealed.
## 2 Materials and Methods  
The design requirements for the closed impeller took into consideration the main operating specifications of the pump, which are presented in Table 1. In view of the safety classification of AM parts defined by the ESA European Cooperation for Space Standardization (ECSS) ECSS-Q-ST-70-80C standard [18], parts manufactured by AM technology for space applications are classified into four classes according to their function and requirements using safety categories, i.e., Class 1.1--critical and structural, Class 1.2--critical, but non-structural, Class 2--non-critical but structural, and Class 3--non-critical and non-structural. The proposed hydraulic component in this paper falls within Safety Class 2, in accordance with the standard [18].  
The general AM verification plan according to Safety Class 2 [18] is presented in Figure 1. The powder material inspection plan was not part of the current study, but it is presented in other activities by the authors and their results are disseminated in [19; 20; 21]. Figure 1 also shows the main steps of the post-processing activities, while Figure 2 shows the entire verification process for the AM closed impeller in this study.  
It should be noted that, in order to correctly perform the verification processes, two closed impellers had to be fabricated in the same print job. The interior finishing post-processing is currently under study, and it will be presented in a future paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Domain** & **Value** \\ \hline \multirow{2}{*}{**Pump temperature domain**} & Operational: \(-20\)\({}^{\circ}\)C \(\div\) + \(50\)\({}^{\circ}\)C \\ \cline{2-3}  & Non-operational: \(-30\)\({}^{\circ}\)C \(\div\) +\(60\)\({}^{\circ}\)C \\ \hline
**Flow rate domain** & \(500\)\(\div\) 1000 kg/h at \(22\)\(\pm\) 2 \({}^{\circ}\)C \\ \hline
**Inlet pressure** & Between 1.5 and 3.0 (bar) without any impeller cavitation \\ \hline
**Pressure rise** & 2–5 (bar) \\ \hline \multirow{2}{*}{**Fluid**} & HFE 7200 or Galden HT80, or propylene glycol/water solution (PGW) \\ \cline{2-3}  & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pump operating specifications.
# Investigation of Crack Propagation and Healing of Asphalt Concrete Using Digital Image Correlation
## 3 Results and Discussion  
### Full-Field Strain and Crack Detection  
Figure 5 shows the horizontal strain map of the three samples, produced using the same asphalt mixture, at the termination cycle of the first step of the experiment calculated by Istra 4D. As expected, due to the difference between the module of elasticity of the aggregate and binder, the strain obtained within the binders is larger than those within the aggregates. However, after some cycles and before the appearance of any cracks, some locations with higher strain are observed on the surface. By continuing the fatigue experiment, the strain all over the surface increases but the location of the maximum strain remains the same. These are the locations where the cracks will initiate and grow. The increase of strain near the crack location in different steps of the fatigue test can be seen in previous research and is confirmed in this study [38]. It can be seen that the contour line of maximum strains matches very well with the fracture path. Due to the anisotropic characteristics of the asphalt mixture, the strain distribution and crack locations are closely related to the distribution of aggregates and the asphalt mortar (In this research mortar is defined as the mixture of bitumen, filler, and sand). The cracks are normally located at the interface of the aggregate-mortar matrix, but depending on the structure of the mixture, in some cases, the crack passes through the stones as well.
# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 2 Related Research  
### POI Recommendation Methodology Based on Collaborative Filtering  
Collaborative filtering is an algorithm that is widely employed in various recommendation systems. It is categorized into user-based and item-based collaborative filtering [7].  
Figure 1: Relationship of the considered factors of the existing schemes and the proposed scheme.  
User-based collaborative filtering is used to search for users who are similar to the target user of the recommendation and make recommendations based on the item utilization history of comparable users. Table 1 shows the user information matrix for the purpose of analyzing users who exhibit similar characteristics. The user information matrix consists of rows of users and columns of items. Each cell stores a value that denotes the item used by each user or their rating. Based on these values, the similarity is computed. The similarities are combined to provide personalized recommendations.  
The similarity between users is determined based on the social relationships and location information in recommendations in which LBSNs are employed. In contrast, the similarity between items is determined based on the attributes and location information of the items. LBSNs, for example, define similar users as those who frequently visit similar locations or have similar social relationships [8]. This scheme is used to calculate the degree of similarity between items to search for users who share similar characteristics. The final collaborative filtering algorithm is applied by calculating the similarity between users based on the similarity between items. However, the limitations of previous research are that the similarity is assessed by using only data location information, and social relationships and other types of attributes or user behavior patterns are not considered [8].
## Spatiotemporal Variation in Mangrove Chlorophyll Concentration Using Landsat 8  
**[PERSON] 1, [PERSON] 1,2,3,4,\({}^{\dagger}\), [PERSON] 1,\({}^{\star}\) and [PERSON] 5**  
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sensitive to mangroveve leaf CC. At the ESU level Landsat 8 NDVI green, which uses the green band in its formulation explained most of the variation in CC (\(R^{2}>0.8\)). Accuracy assessment between estimated CC and observed CC using the leave-one-out cross-validation (LOOCV) method yielded a root mean squared error (RMSE) = 15 \(\mu\)g\(\cdot\)cm\({}^{-2}\), and \(R^{2}=0.703\). CC maps showing the spatiotemporal variation of CC at landscape scale were created using the linear model. Our results indicate that Landsat 8 NDVI green can be employed to estimate CC in large mangrove areas where ground networks cannot be applied, and mapping techniques based on satellite data, are necessary. Furthermore, using upcoming technologies that will include two bands around the red edge such as Sentinel 2 will improve mangrove monitoring at higher spatial and temporal resolutions.  
Keywords:Landsat 8; mangrove; spatiotemporal; chlorophyll map; vegetation indices
# Photooxidation of \(p\)-Arsanilic Acid in Aqueous Solution by UV/Persulfate Process
## 1 Introduction  
\(p\)-Arsanilic acid (\(p\)-ASA), a kind of organoarsenic feed additives, are utilized worldwide in order to promote the growth of animals and prevent diseases [1; 2; 3]. Though the toxicity of those organoarsenic additives are quite low, disputes about their application still have been aroused in recent years, because of the potential health risks and environmental hazard caused by the residual of arsenicals in livestock products and the discharge of agricultural effluents [3; 4; 5; 6]. In fact, organoarsenic additives (including \(p\)-ASA) might undergo environmental transformation to produce arsenic species of stronger toxicity (i.e., inorganic As(III)) through biological or chemical processes [7; 8; 9; 10]. This environmental behavior of \(p\)-ASA contributes to the elevated level of arsenic around the farms [11; 12], posing a high threat to the environment and human health as a consequence. Hence, great attention should be paid to developing effective and low-cost approaches to the treatment of organoarsenic wastewater.  
Photochemical degradation plays an important role in transformation of arsenic compounds in aqueous systems and efficient decomposition of \(p\)-ASA via UV irradiation has been previously reported [13; 14; 15; 16; 17]. However, rapid degradation of \(p\)-ASA during UV irradiation treatment is accompanied with a weak decrease of total organic carbon (TOC) [13; 14; 15], an indicator of the severity of organic pollution [18], suggesting a negligible mineralization and the accumulation of aromatic by-products [13; 19] with questionable toxicity. Improved photochemical treatments are therefore needed for the \(p\)-ASA containing agriculture effluents.  
Advanced oxidation processes (AOPs) have been successfully applied in the treatment of wastewater, owing to its capability of mineralization and detoxification of many organic pollutants [20; 21; 22; 23]. Recently, sulfate radical (SO\({}_{4}\bullet^{-}\)) based AOPs have gained much more attention [20; 24], among which activated-persulfate (PS) based system is of popularity due to its stability in aqueous solution [21]. PS is generally activated via UV, heat, base, transition metals, ultrasound and some synthetic catalysts [25; 26]. Consequently, the applications of PS based system to numerous pollutants have been studied [27], while the treatment for \(p\)-ASA has not been reported to the best of our knowledge. In this work, photodegradation of \(p\)-ASA in PS/UV system was studied. Most attention has been paid to determination of intermediate products, degradation pathways, and conditions of complete \(p\)-ASA mineralization.
###### Abstract
The main contributions of this article, provided with the proposed solution, are:  
* RRBE approach: One of the primary contributions of our method is the utilization of the RRBE approach. This strategy leverages the data redundancy present in the original image before encryption to create space for data hiding. This approach ensures security as it maximizes the embedding capacity without altering the encryption process. * Integration of pixel prediction: Our method introduces pixel prediction in the RDHEI process. By predicting the error image, the efficiency of data embedding is enhanced. * Quadtree decomposition: Quadtree decomposition is applied to each bit plane of the mapped prediction error image. This step identifies homogeneous blocks within the image, which can be rearranged to create room for data embedding. Quadtree decomposition allows for a more fine-grained allocation of embedding space, potentially increasing the embedding capacity. * Bit plane reordering: The method incorporates bit plane reordering as part of the data hiding process. This technique likely enhances the efficiency of embedding data by optimizing the allocation of space within each bit plane. * Error-free reversibility: One of the most important contributions of our method is its ability to achieve high ERs while maintaining error-free reversibility. This means that the hidden data can be extracted without any loss or distortion, and the original image can be fully restored after decryption. Error-free reversibility is a crucial requirement in RDHEI to ensure that the quality and integrity of the image are preserved. * Privacy preservation: Encryption is applied to all images being transmitted or stored, ensuring the privacy and confidentiality of sensitive information. The rest of this paper is organized as follows. While Section 2 details the proposed separable high-capacity RDHEI scheme, Section 3 presents the obtained results for different image datasets, as well as a comparison with the state-of-the-art RDHEI methods. In addition, the reversibility, security, and embedding capacity of the proposed scheme are analyzed and discussed. Finally, general conclusions of this paper are drawn in Section 4.
## 2 Materials and Methods
### Analytical Methods  
The physicochemical parameters of the leachates were determined according to Standard Methods for the Examination of Water and Wastewater Handbook [21]. The parameters measured were: electrical conductivity (EC), pH, nitrates, K, Na, Ca, Mg, SAR, Total Hardness, Fe, Mn, Cu, Zn and B.  
The chemical parameters of the samples (soil, bio-mixture and bio-mixture/soil) were determined according to Methods of Soil Analysis Handbook [22]. pH was measured by a JENWAY 3520 pH Meter in water-saturated soil paste for soil samples and in 1:2 (\(v/v\)) bio-mixture/water slurry for bio-mixture samples. Electrical conductivity was measured by a CRISON GLP 32 Conductimeter in water-saturated soil paste for soil samples and in 1:2 (\(v/v\)) bio-mixture/water slurry for bio-mixture samples. The Walkley-Black method was used for soil organic matter analysis [23], while Loss-On-Ignition Method was used for bio-mixture organic matter analysis [24]. CaCO\({}_{3}\) was measured with Automatic Digital Soil Calcimeter Fogl. The extractable-available phosphorus and boron were determined colorimetrically using a Perkin Elmer Lambda 35 UV/VIS spectrophotometer. Exchange-able K and Na were determined after extraction with ammonium acetate by a Sherwood M410 Flame Photometer. Exchangeable Ca and Mg were determined after extraction with ammonium acetate with a Perkin Elmer AAnalyst 200 Atomic Absorption Spectrometer. Available Fe, Cu, Mn and Zn were measured after extraction with DTPA with a Perkin Elmer AAnalyst 200 Atomic Absorption Spectrometer. Total N was determined according to ISO 11261:1995 Soil quality--Determination of total nitrogen--Modified Kjeldahl Method [25]. NO\({}_{3}\)\({}^{-}\) measurement was performed after extraction with KCl and by passage through a column of copperized cadmium, based on Methods of Soil Analysis--Part 3--Chemical Methods, 1996, Chapter 38 Nitrogen--Inorganic Forms [26]. Soil texture analysis was performed according to Bouyoucos method [27]. Cation Exchange Capacity was determined according to Ammonium Acetate Method [28]. The exchangeable sodium percentage (ESP), which describes the level of adsorbed Na in soil, was calculated by the following equation: ESP = (exchangeable Na/Cation Exchange Capacity) \(\times\) 100. Concerning physical properties, saturated water content, field capacity, permanent wilting point and bulk density were determined using a soil moisture pressure plate apparatus,according to the standard methods described in Soil Survey Laboratory Methods Manual, Version 4.0 [29].
## 4 Discussion
### Advantages  
The computational results in Section 3 indicate that for MGRIT-based adaptive parallel-in-time Runge-Kutta methods, the relevant quality indicators necessary for computational stability are satisfied for the refinement factors \(r_{mod}\) and \(r\). As we have seen in the case of the PDE test problems in Section 3.3, these methods significantly outperform sequential methods in terms of computational cost beyond a certain number of processors. Furthermore, based on the qualitative results of the different ODE and PDE test problems, as well as the number of time steps in Table 1, the refinement factor \(r_{mod}\) is the preferred approach both qualitatively and quantitatively. Therefore, the approach involving the refinement factor \(r_{mod}\) provides a robust and reliable framework for users utilizing high-performance computing simulations.  
Figure 13: Results for the linear (**left column**) and nonlinear (**right column**) diffusion Equation (10). Upper panel: step-size sequences (\(y\)-axis) over the given time interval (\(x\)-axis). Center panel: achieved accuracy (\(y\)-axis) versus tolerance (\(x\)-axis). Lower panel: runtime in seconds (\(y\)-axis) versus number of processors (\(x\)-axis).
## 4 Sentinel-3A SRAL and MWR Cal/Val Results
### Transponder Calibration Results
Uncertainties were in that manner appraised for every individual constituent, and later, we integrated them to determine the more complex and final uncertainty for the reported calibration results. Table 1 presents such an uncertainty budget for the transponder calibration following this analysis [38]. With those error values presented in Table 1, the combined (root-sum-squared uncertainty) and the expanded uncertainty (standardized at 95% confidence level) were calculated to be \(\pm\)41.5 mm and \(\pm\)81.3 mm (\(k_{[0.005]}\) = 1.96 at 95% with normality, \(k_{[\alpha]}\) is called the 100\(\alpha\) percentage point or coefficient), respectively. These values correspond to the Sentinel-3A Pass No.14 transponder calibration at the CDN1 Cal/Val site. As soon as supplementary knowledge is accumulated later on, this table has to be iterated and updated with improved values for each error constituent and its uncertainty.
# A Phase Correlation Anti-Noise Displacement Estimation Method Based on PSD Phase Screening
## 2 Methods
### Displacement Estimation Based on Straight-Line Fitting  
After the previous section, the phase scatter points used for displacement estimation have been obtained. Now, a straight-line fitting is required for the phase scatter points (as shown in Figure 6, where (a) is the unwrapping phase and straight-line fitting result of left singular \(\mathrm{u_{1}}\), and (b) is the unwrapping phase and straight-line fitting result of right singular \(\mathrm{v_{1}}\)). According to the slope of the fitted line and the image size, the displacement is calculated by Equation (7).  
\[\mathrm{X_{0}}=-\frac{\mathrm{k_{v}W}}{2\pi}\ \ \ \ \ \ y_{0}=-\frac{\mathrm{k_{u}H}}{2\pi} \tag{7}\]  
where \(\mathrm{k_{u}}\) is the slope of the left singular vector phase fitting straight line; \(\mathrm{k_{v}}\) is the slope of the right singular vector fitting straight line; \(\mathrm{W}\) is the image width; and \(\mathrm{H}\) is the image height.
## 2 Materials and Methods  
### ECG Dataset  
The development of robust ECG segmentation algorithms faces challenges, as highlighted in [17], due to a lack of publicly available databases with annotated wearable ECG samples. Furthermore, few datasets incorporate both acceleration data and a comprehensive set of five physiological signals. The inclusion of acceleration data is particularly valuable for identifying periods of high-intensity activity, during which more noise is introduced into the ECG signal. Another challenge arises from the variability in electrode types (dry or gel) and their placement on different parts of the body, which can affect the signal quality and consistency.  
To address this, in this work, we used the ScientISST MOVE dataset (Table 1) [12], a collection of wearable biosignals used to study everyday activities, such as lifting a chair, greeting gestures, jumping, walking, and running. It includes data from three wearable devices: a ScientISST chest band, a ScientISST amband, and an Empatica E4 wristband. These devices monitor physiological signals, including Electrodermal Activity [20], Photoplethysmography [21], Electromyography [22], and ECGs, and also 3-axis accelerometer data.  
The study collected data from 17 healthy individuals, each contributing an average of 37 min of synchronized sensor recordings, with the recordings from 11 participants containing pre- and post-running walking periods. This subset yielded 563.8 min of ECG data, averaging 51.25 min per participant, allowing for an examination of ECG signal variations across different physical activities. In Figure 1, we can observe differences in the ECG signals for these different activities, demonstrating that some activities introduce more noise than others, such as jumping and lifting weights. All data were collected from healthy volunteers, none of whom had a history of cardiovascular disease, such that the noise to be analyzed was primarily caused by environmental and motion-related sources.  
The study used an ECG sensor connected to a Polar chest band with dry conductive textile contacts, mimicking smart undergarments' electrode conditions, enabling increased noise sensitivity in ECG signal capturing. The chest band comprised two electrodes.