esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods  
### New Sensor System  
#### 2.1.1 Hardware Setup  
We developed a sensor system based on structured light illumination for an underwater 3D inspection system for industrial structures according to the requirements of potential users. Its targeted application is the inspection of oil and gas pipelines, offshore windmill fundaments, anchor chains, and other technical structures. It can be mounted to a remotely operated vehicle (ROV), and it can be controlled remotely from a vessel. It captures up to sixty 3D scans per second with a velocity up to 1 m/s. It has a field of view of about 1 m\({}^{2}\) and captures 3D data at distances between approximately 1.6 m and 2.4 m. The system was built as a laboratory setup (see Figure 1). The sensor system, called UWS (underwater sensor), was developed according to the requirements within a funded research project (see acknowledgements).  
The sensor system consists of:  
* Two monochrome measurement cameras arranged in a stereo setup for 3D data acquisition;
* A projection unit producing structured illumination patterns;
* A color camera for navigation and additional visual odometry data capture;
* Two high-power LED flash units, providing synchronized homogenous illumination for the color camera;
* A fiberoptic gyro (FOG) inertial measurement unit (IMU) for motion estimation to support global reconstruction tasks using SLAM algorithms;
* Underwater housings for each camera and the projection unit (IMU is in the housing of the color camera);
* An electronic control and interface box.  
An additional necessary piece of equipment is a PC workstation for control and storage of the measurement data and power supply, which can be positioned on the vessel and is connected to the UWS. The cameras and lenses are commercially available parts. The projection unit is a Fraunhofer IOF development consisting mainly of an LED light source, a rotating slide (GOBO wheel) and motor, a projection lens, and control electronics. The projection unit realizes the generation of a structured light pattern in the form of an aperiodic sinusoidal fringe pattern (see [40]).  
Figure 1: Laboratory setup of UWS.
## 3 Method
### FE Model  
The FE model of the in-house test specimen was created in LS-PrePost 4.6 and simulation was run in LS-Dyna R11.0. The hole geometry was defined according to the specimen coordinates imported from DIC such that the hole shape in FE is not idealized but identical to the real shape and thus the strain concentration can be more effectively evaluated. The loading scheme of the model is shown in Figure 6b. The bottom of the FE model is fixed while the top is loaded by applied displacements in \(X\), \(Y\), \(Z\) directions which values were taken from 10 points DIC displacement measurements at the top of the specimen to reduce the errors due to possible specimen imperfect alignment in the testing machine.  
Mixed quadratic-triangle fully integrated 1 st order SHELL elements with an average element size of \(5\cdot 10^{-4}\) mm over the area of interest were used. The LS-Dyna material model PIECEWISE_LINEAR_PLASTICITY was selected. The material properties were measured according to inhouse tensile testing of the DIBOND composite plate without hole: the effective elastic modulus \(E_{f}=43\) GPa, effective Poisson ratio \(v=0.3\), and yield limit \(\sigma_{y}=100\) MPa. Moreover, the experimental tensile diagram was uploaded to the FE material model by using the LS-Dyna material model curve definition keywords. Nonlinear implicit time integration with Broyden-Fletcher-Goldfarb-Shanno algorithm [48] was used in LS-Dyna.
## 2 Extraction Methods
### Solvent Extraction for Blackcurrant Anthocyanins  
The polarity of the targeted compound is the most critical factor for extraction reagent choice. Given their polarity, anthocyanins are usually extracted by methanol, ethanol, acetone, and/or water [19]. Ethanol and methanol are more efficient at extracting anthocyanins from blackcurrants than water [20]. A mixture of different solvents showed a better extraction ability than a single solvent. In total, 50% methanol showed a better extraction ability than water and methanol alone [21]. When 40%, 60%, and 96% aqueous ethanol were investigated for their extraction capacity, 60% ethanol showed the highest anthocyanin extraction efficiency [22].  
Figure 1: The number of articles retrieved on PubMed using searching terms “blackcurrant” and “anthocyanins” over different periods.  
Although acetone and methanol are effective extraction reagents, their application in foods is restricted due to their toxicity. Ethanol is more suitable for food applications but is more difficult to eliminate during the purification process [23].  
Additionally, the presence of acid improves anthocyanin extractability due to its ability to liberate those bioactive compounds from blackcurrant pomace and skin [24; 25]. It is worth noting that hydrochloric acid may hydrolyze acylated anthocyanins [26]. To avoid the breakdown of acylated anthocyanins, certain organic acids, such as formic, acetic, citric, or tartaric acids, whose elimination is easier during the anthocyanin concentration process, are preferred [24].  
To concentrate anthocyanins furthermore, liquid-liquid extraction was additionally performed [27]. In a sequential extraction of blackcurrant juice residues that used water followed by methanol, anthocyanin detection increased approximately 2-fold in the methanol extract. [28].
## 1 Introduction  
Aircraft noise has been recognized as a significant environmental problem since the 1950s. However, due to the development of quieter engines, Airframe Noise (AFN) has become the dominant source of noise during the landing phase [1]. Among the airframe components, the landing gears (LGs) are considered one of the main sources of noise emitted during the approach-to-land phase of flight. In the last 40 years, aeroacoustics became an important research field to accelerate AFN reduction. Both U.S. and European governments have set stringent targets to minimize the AFN [2]. To fulfill high design standards, new aircraft designs demand efficient AFN prediction techniques to assess the noise impact. Thus, multi-fidelity approaches are required to enable efficient and accurate aircraft noise assessment at different design stages [3]. The accuracy of noise prediction techniques is attributed to various factors, including turbulence modelling and/or acoustic analogy implementation and boundary conditions [4; 5; 6; 7; 8; 9].  
The direct numerical simulation (DNS) of complex three-dimensional (3D) aircraft systems, such as the LGs, is computationally expensive. This is because the 3D model needs high spatial and temporal resolutions to resolve the wide range of energy and length scales between the flow and acoustic fields. Therefore, an efficient two-step hybrid computational aeroacoustics (CAA) approach was proposed, where the flow and the acoustic fields are computed using two independent solvers [8]. In the last decade, noise generated due to flow past a simplified LG geometry has been extensively investigated using hybrid CAA approaches. The numerical results were validated with experiments through benchmark problems for airframe noise computations (BANC) workshops [10; 11; 12; 13; 14]. The BANC workshops focus on improving the far-field noise prediction accuracy of the 3D simulations and reducing the computation time. There are a few semi-empirical tools developed to facilitate the noise prediction of the LG during the design stage [3; 7]. Among these, the Landing Gear Model and AcousticThe Frowcs Williams and Hawkings (FW-H) integration is evaluated over the entire length (SCL) in the depth-wise direction. In that case, the flow is assumed to be fully correlated along the assumed SCL, where the 2D CFD source data is repeated periodically. Both the flow and acoustic results are then validated with the available experimental and numerical data in the literature.
## 1 Introduction
(3) The groundwater area with aggregation characteristics was analyzed by remote sensing the waveband of land type to determine the quantitative relationship between groundwater depth and waveband combination. Based on the results of this study, by obtaining groundwater information, the groundwater depth in the space can be calculated through collaborative kriging interpolation. Without groundwater information, it can be quantitatively retrieved through remote sensing bands, and groundwater depth information with a certain accuracy can also be obtained. This study has certain limitations. The accuracy of the model is higher in farming and pastoral areas with low vegetation coverage and it is also suitable for arid and semi-arid areas. The error is larger in high altitude alpine mountains and dense forests with high vegetation coverage.
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 6 Algorithm Analyses  
For an excellent image encryption algorithm, it can resist several commonly used attacks, such as the brute-force attack and differential attack. This paper analyzes the performance of the proposed algorithm in terms of the key space, histogram, correlation, differential attack, information entropy, and encryption efficiency.
# Monitoring the Joint Area of Composite Membrane Materials
## 4 Conclusions  
The research results presented in this paper prove the feasibility of using electrically conductive textile yarns for the detection of strains in the welded seams of textile membranes. The sensors can be integrated into the welding process and generate reproducible sensor signals under strain, even after undergoing a joining procedure by high-frequency welding.  
Simulation-based statements on the serviceability and residual load-bearing capacity in the area of the joining zone and the textile membrane surfaces are to be derived from analyzing the signals and further used to initialize repair examinations before catastrophic failure occurs. Moreover, the operational safety of textile membrane structures can be significantly improved.  
In further investigations, the sensors will be integrated directly into the textile carrier structure of the membranes (e.g., by weaving or embroidery on the carrier fabric) prior to coating. It is intended to achieve an enhanced connection between sensors and force-absorbing structure and to shorten the manufacturing process chain. Additionally, investigations on the influence of sensor yarns on weld seam strength and the detectability of delamination between HF welded membranes using capacitive sensors will be completed.  
Conceptualization: H.W., O.D.; methodology: H.W., O.D., T.G., A.N.; software: O.D., T.G.; validation: [PERSON], [PERSON]; formal analysis: [PERSON], [PERSON], A.N.; investigation: [PERSON], [PERSON]; resources: C.C.; data curation: [PERSON], O.D.; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], A.N.; visualization: [PERSON], [PERSON], [PERSON]; supervision: A.N.; project administration: A.N., C.C.; funding acquisition: A.N., C.C.  
The IGF research project 19296 BR of the Forschungsvereinigung Forschungskuratorium Textil e. V. is funded through the AiF within the program for supporting the \"Industrielle Gemeinschaftsforschung (IGF)\" from funds of the Federal Ministry for Economic Affairs and Energy (BMWi) by a resolution of the German Bundestag. We thank the aforementioned institutions for providing the financial resources.  
We would also like to thank the companies Sattler PRO-TEX GmbH and Paproth Ingenierientisteinsungen for their technical support and the provision of test material, as well as all other partners who supported us in our research work on this topic.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 16: Comparison of sensor strain values obtained from numerical simulation and experiments.
## 2 Materials and Methods
### Sample Collection  
In 2019, seaweed samples were collected from joint fishery product auction houses (Donghae: Mukho harbor fisheries market; Samchuk: Samcheok fisheries market; Uljin: Jukbyun fisheries market; Youngduk: Donggwang fisheries market; Pohang: Jukdo market; Gyeongju: Yangbuk fisheries market; Ulsan: Agricultural and marine products market; Busan: Jagalchi market; Geoje: Geoje fisheries market; Tongyoung: Chungang market; Yeosu: Yeosu fisheries market; Boseong: Boseong fisheries market; Wando: Wando suhyup fisheries market; Haenum: Maeil market; Jeju: Dongmun fisheries market; Boryoung: Muchangpo fisheries market; Taean: Anmyeondo fisheries market) selling various seaweeds and marine animals in representative coastal cities of ROK, in consideration of the regional production of seaweed (Figure 1). Identification of the seafood samples was confirmed on the basis of the advice of related experts. A total of 37 species were used for analysis of heavy metals and As species in this study. Eleven species of seaweed, including laver (_Pyropia_ sp.), green laver (_Enteromorpha_ sp.), sea mustard (_Undaria pinnatifida_), sea tangle (_Saccharina japonica_), and gulf-weed or hijiki (_Sargassum_ spp.), were collected. In addition, for comparative analysis of levels of As species in seaweeds and those in marine animals, 3 species of crustaceans (_Portunus trituberculosisatus_, _Solenocera melantto_, _Chionecetes opilio_), 4 species of cephalopods (_Octopus ocellatus_, _Todaroldes pacificus_, _Octopus minor_, _Enteroctopus doflein_), 13 species of fish (_Larimichthys polyactis_, _Sebastes schlegglia_, _Lateolabrax jaonicus_, _Gadus macroccephalus_, _Serola quinquateradata_, _Chelon haematocheilus_, _Pagrus major_, _Somber japonicus_, _Trichiurus lepturus_, _Michthys miuuy_, _Dooderleinia beryocoides_, _Thunnus thynus_, _Cololabis saira_), and 6 species of shellfish (_Ruditapes philippinarum_, _Crassostre gigas_, _Meretirix petechialis_, _Argopecten irradians_, _Mytilus galloprovincialis_, _Atrina pectinata_) were collected during the same period. All samples were directly transported to the laboratory in coolers at temperatures below 10 \({}^{\circ}\)C.
## 5 Conclusions  
An artificial neural network-based equation for a corroded pipeline subjected to combined loadings was formulated in this study. An ANN model was trained with the FEA results of mid-to-high strength grade pipelines (API 5L X52, API 5L X65, and API 5L X80) with a single corrosion defect subjected to internal pressure and longitudinal compressive stress. The weights and biases of the network were used to formulate the assessment equation and evaluated with an unseen dataset. The percentage error of predictions based on the assessment equation ranged from \(-6.33\%\) to \(2.39\%\), with a standard deviation of \(2.12\), an \(R^{2}\) value of \(0.9975\), an MSE of \(0.000126\), and an MAE of \(0.00699\). The assessment equation was subsequently used to perform a comprehensive parametric study to investigate the effect of material property, defect depth, defect length, and longitudinal compressive stress on the remaining strength of pipelines with single corrosion defects. In conclusion:  
1. The higher the strength of the pipeline, the higher its failure pressure when subjected to combined loadings. Here, X80 generally had the highest failure pressure, followed by X65 and X52.
2. Defect depth had the most impact on reductions in failure pressure (average reductions of \(-65.90\%\) and \(-66.91\%\)), while longitudinal compressive stress (average reductions of \(-23.99\%\) and \(-20.79\%\)) and defect length (average reductions of \(-21.94\%\) and \(-25.00\%\)) had similar impacts on failure pressure.
3. Across all three materials, the trend for failure pressure against defect depth, defect length, and longitudinal compressive stress exhibited similar patterns.  
Conceptualization, S.K. and M.O.; methodology, M.L. and S.D.V.K.; software, S.K.; validation, M.L., S.D.V.K. and S.K.; formal analysis, M.L., S.D.V.K. and S.K.; investigation, M.L. and S.D.V.K.; resources, S.K.; data curation, M.L., S.D.V.K. and S.K.; writing--original draft preparation, M.L. and S.D.V.K.; writing--review and editing, S.K. and M.O.; visualization, S.K. and M.O.; supervision, S.K.; project administration, S.K.; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported and funded by Ministry of Higher Education, Malaysia (FRGS/1/2018/TK03/UTP/02/1) and Yayasan University Teknologi PETRONAS, Malaysia, grant number 015 LC0-110.  
Not applicable.  
The authors declare no conflict of interest.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 2 Materials and Methods  
### Data Extraction  
Data was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV, [9]), a freely available database of de-identified electronic health records linked to patients admitted to the Beth Israel Deaconess Medical Centre in Boston, Massachusetts. We used version 2.2, released in January 2023, which comprises 299,712 patients, 431,231 hospital admissions and 73,181 ICU stays.  
For this study, we extracted sequences of vitals (e.g., temperature, heart rate, and respiratory rate), lab test results (e.g., glucose, haemoglobin, and platelet count), and other clinical characteristics of the patients admitted to the hospital's ICU (e.g., age, height, and weight). Patients \(<18\) years old, patient admissions with short ICU stays (\(<\)24 h), and patients with multiple ICU stays were excluded from the study. Invalid values of the variables (e.g., heart rate \(<0\)) were marked as not available. Variables recorded with different units were harmonised, e.g., height was present in inches and centimetres (cm), and they were all converted to cm. The data used for modelling was formatted as a three-dimensional array, with dimensions representing patient admissions, time points (hours), and variables.  
As common in health data, extracted data records were frequently incomplete. Records with missing age, haemoglobin, platelet count, or oxygen saturation were removed from the dataset. For the remaining variables, missing values were handled as follows: for gaps in continuous-valued time series, the last observation carried forward (LOCF) method [10] was employed, while the mode was used to impute missing values in categorical variables. Time series with all values missing in a single admission were completed in the way described in [11].
# Flexible, Brain-Inspired Communication in Massive Wireless Networks
###### Abstract  
In this paper, a new perspective of using flexible, brain-inspired, analog and digital wireless transmission in massive future networks, is presented. Inspired by the nervous impulses transmission mechanisms in the human brain which is highly energy efficient, we consider flexible, wireless analog and digital transmission on very short distances approached from the energy efficiency point of view. The energy efficiency metric is compared for the available transmission modes, taking the circuit power consumption model into account. In order to compare the considered systems, we assume that the transmitted data comes from analog sensors. In the case of the digital transmission scheme, the decoded data are converted back to analog form at the receiving side. Moreover, different power consumption models from the literature and the digital transmission schemes with different performance are analyzed in order to examine if, for some applications and for some channel conditions, the analog transmission can be the energy-efficient alternative of digital communication. The simulation results show that there exist some cases when the analog or simplified digital communication is more energy efficient than digital transmission with QAM modulation.  
Keywords:brain inspiration; energy efficiency; analog and digital transmission; power consumption; massive communications; Internet of Things +
Footnote †: journal: Sensors
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Antimutagenic and Cancer-Preventive Effect of Piperine  
Piperine was found to possess antimutagenic potential since it inhibits the mutagenicity of the three food mutagens (3-Amino-1-methyl-5H-pyrido[4,3-b]indole (Trp-P-2), 2-Amino-3-methylimidazole [4,5-f]quinoline (IQ) and 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline (MelQx)) [41]. At a dose of 100 mg/kg, piperine gave a statistically significant reduction in cyclophosphamide-induced chromosomal aberrations in rat bone marrow cells [42]. Hepatic and renal cadmium levels were significantly decreased in mice when treated with piperine, demonstrating its protective action to this type of renal and hepatic toxicity [43]. When orally administered for three days prior the treatment with mitomycin C, piperine inhibited the frequency of sister chromatid exchanges (up to 41.82%), as well as the number of chromosomal aberrations in mouse splenocytes and spermatocytes (50% and 40.78% of inhibition, respectively) [44]. Another study found that piperine inhibits aflatoxin B1-induced cytotoxicity and genotoxicity in V79 Chinese hamster cells [45]. The results of [PERSON] et al.'s [46] in vitro experiments on deltamethrin (DLM, a pyrethroid insecticide, and a potent immunotoxicant)-induced thymic apoptosis in primary murine thymocytes demonstrated the protective role of piperine (1, 10 and 50 \(\upmu\)g/mL). Its administration led to increased cell viability in a concentration-dependent manner and reduced levels of early activated markers of apoptosis (reactive oxygen species and caspase-3), as well as GSH depletion, all induced by DLM.
## 3 Controller Design  
### Robust Control Design for the Nominal System  
For the nominal system (6), we design a \(H_{\infty}\) controller. Let:  
\[\tilde{\eta}(t)=K_{1}\tilde{x}_{n}(t), \tag{22}\]  
where \(K_{1}\) is the gain of the \(H_{\infty}\) controller.  
Define:  
\[\begin{split}\bar{A}&:=A+BK_{1},\\ \bar{C}&:=C+DK_{1}.\end{split} \tag{23}\]  
Here, \(\bar{A}\in\mathbb{R}^{n\times n}\), \(\bar{C}\in\mathbb{R}^{l\times n}\) are constant real matrices. By (23), the system (6) can be rewritten as:  
\[\begin{split}\dot{\bar{x}}_{n}(t)&=\bar{A}\bar{x}_{n }(t)+B_{1}w_{n}(t),\\ \dot{\bar{y}}_{n}(t)&=\bar{C}\bar{x}_{n}(t)+D_{1}w_{ n}(t),\\ \bar{x}_{n}(0)&=x_{n0}-x_{n\text{s}0}.\end{split} \tag{24}\]  
**Theorem 1**.: _Consider System (24); for a given scalar \(\rho>0\), if the matrices \(X=X^{T}>0\) and \(W\) exist, such that:_  
\[\left[\begin{array}{ccc}AX+BW+(AX+BW)^{T}&B_{1}&(CX+DW)^{T}\\ B_{1}^{T}&-I&D_{1}^{T}\\ CX+DW&D_{1}&-\rho I\end{array}\right]<0 \tag{25}\]  
_then we can solve \(K_{1}\), the gain of the \(H_{\infty}\) controller of System (24) [30], as:_  
\[K_{1}=WX^{-1}. \tag{26}\]  
_Then, there is a Lyapunov function of System (24) written as:_  
\[V(\bar{x}_{n},t)=\bar{x}_{n}^{T}P\bar{x}_{n}, \tag{27}\]  
_where:_  
\[P=X^{-1}. \tag{28}\]  
**Remark 2**.: _It is worth noticing that the determinate of the positive matrix \(X\) is greater than zero, which implies that \(X\) is invertible._  
Proof of Theorem 1.: See Appendix A.