esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Reagents  
AAPH (2,2\({}^{\prime}\)-azobis (2-amidinopropane)) (Acros Organics), sodium phosphate dibasic, sodium chloride, potassium dihydrogen phosphate, potassium sulphate, TRIS (ultrapure), glycine (proteomics grade), ortho-boric acid, and methanol (HPLC grade) were obtained from VWR International Eurolab S.L. (Barcelona, Spain). Trizma(r) base, ABTS (2,2\({}^{\prime}\)-azinobis (3-ethylbenzothazoline 6-sulfonic acid)), DTT (DL-Dithiothreitol), Trolox(r) (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid), fluorescein sodium salt, formic acid (reagent grade \(\geq\) 95%), and diatomaceous earth (Hyflo(r) Super Cel(r)) were provided by Sigma-Aldrich (Steinheim, Germany). Sodium hydroxide, glacial acetic acid, and sulfuric acid were supplied by Fisher Scientific (Madrid, Spain). SDS (sodium dodecyl sulfate) and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Salmon**} & \multicolumn{4}{c}{**Heavy Metals (ug/g of Wet Weight)**} \\ \cline{2-5}  & **As** & **Hg** & **Cd** & **Pb** \\ \hline Muscle & 0.5413 \(\pm\) 0.0068 & 0.0238 \(\pm\) 0.0005 & 0.0004 \(\pm\) 0.0001 & 0.0269 \(\pm\) 0.0002 \\ Head & 0.6922 \(\pm\) 0.0072 & 0.0157 \(\pm\) 0.0005 & 0.0011 \(\pm\) 0.0001 & 0.0190 \(\pm\) 0.0001 \\ Viscera & 0.4617 \(\pm\) 0.0055 & 0.0095 \(\pm\) 0.0002 & 0.0044 \(\pm\) 0.0002 & 0.0071 \(\pm\) 0.0001 \\ Skin & 0.4504 \(\pm\) 0.0032 & 0.0077 \(\pm\) 0.0003 & 0.0019 \(\pm\) 0.0001 & 0.0247 \(\pm\) 0.0001 \\ Taiflin & 0.4186 \(\pm\) 0.0054 & 0.0408 \(\pm\) 0.0015 & 0.0104 \(\pm\) 0.0003 & 0.0859 \(\pm\) 0.0016 \\ (Legislation *) & \(<\)13.5 & \(<\)0.50 & \(<\)0.05 & \(<\)0.30 \\ \hline \hline \end{tabular}  
* values refer to fish muscle tissue [23; 25; 35].  
\end{table}
Table 5: Concentration of heavy metals in salmon side streams.  
nitric acid (65% p/p) were purchased from Panreac (Barcelona, Spain). Bromophenol blue indicator (ACS reagent), acetonitrile (HPLC grade), trifluoroacetic acid, acetone, and glycerol were provided by Merck (Darmstadt, Germany). Absolute ethanol was obtained from J.T. Baker (Deventer, The Netherlands), Octadecyl C18 sorbent was obtained from Phenomenex (Madrid, Spain), and anhydrous magnesium sulfate (99.5% min powder) was obtained from Alfa Aesar (Karlsruhe, Germany). Deionized water with a resistivity of \(>\)18 M\(\Omega\)/cm was obtained through a Milli-Q SP(r) Reagent Water System (Millipore Corporation Bedford, MA, USA).
## 2 Experimental Setup  
### Combustion Test Bench and Instruments  
The experiment was performed using a lean-premixed burner with thermoacoustic oscillations. During the experiment, the thermal power of the burner was maintained at 4 kW, and the flame equivalence ratio was set at 0.75. The burner is classified into upper and lower sections (Figure 1). The lower section serves as the inlet and mixing area for the premixed gas, while the upper section functions as the combustion chamber and exhaust outlet. The burner has a total height of 1500 mm, with the flame positioned 750 mm from the bottom. Combustion air is supplied by an air pump, and the airflow is regulated using the glass rotameter. Methane, carbon dioxide, and oxygen are supplied through high-pressure cylinders, and their flows are regulated using an Alicat MC series flowmeter (Alicat Scientific, Inc., Tucson, AZ, USA). The sound pressure fluctuation signal during the thermoacoustic oscillations is captured by a high-frequency dynamic pressure sensor (Kunshan Shuangqiao Sensor, Kunshan, China). The flame heat release pulsation signal is recorded using a photomultiplier tube (PMT, Hamamatsu, Japan), with a flame radical filter (CH*, 430 \(\pm\) 10 nm) positioned in front of the PMT. Both the pressure sensor and PMT are powered by a programmable DC power supply. The collected sound pressure and flame heat release signals are captured using a high-frequency dynamic data acquisition card (NI-USB-6210) with a sampling frequency of 10 kHz. The thermoacoustic oscillation signals are stored on a computer using the LabVIEW 2012 software. Flame images are captured with a high-speed camera, synchronized with the thermoacoustic oscillation signal through a signal synchronizer (DG 645). Flame filters for CH* (430 \(\pm\) 10 nm) and NH2* (630 \(\pm\) 10 nm) are positioned in front of the high-speed camera to capture the distribution characteristics of free radicals in the unstable flame. The Abel inverse transform method was used for flame CH* and NH2* image processing. In this study, the measurement uncertainties of flame free radicals by a high-speed camera was less than 2.23%. The original flame image was captured using a Nikon camera to observe changes in the flame morphology before and after the introduction of argon/helium jets. During the experiment, the temperature field of the flame was measured and reconstructed using high-precision thermocouples. The flue gas generated from the combustion is analyzed using a flue gas analyzer (Testo-350, Black Forest, Germany), and the measured values are corrected to 15% oxygen content. The temperature field data were collected five times, with an error margin below 3%. To analyze the mode transition characteristics of thermoacoustic instability, nonlinear analysis tools, such as phase space distribution diagrams, were used to elucidate the evolution of thermoacoustic instability [30].
# Damage and Failure of a Column-Supported RC Flat Slab Subjected to Impulsive Loading
## 5 Evaluation of Slab Response to Impulsive Loading  
### Impulsive Loading vs. Static Loading  
A flat slab supported by columns that are subjected to static loading may be analyzed by a Finite Element discrete method or by calculating the bending of a multi-span beam representing a typical slab strip that is supported by the columns. In both alternatives, the columns strip, indicates the higher bending moments. The bending moment variation is such as that obtained for a multi-span beam showing a maximum hogging bending moment above the column supports and a maximum sagging bending moment at the span central zone. The deflection line shows a variable curvature that is in accordance with the bending moment variation. In a homogeneous beam made of an elastic-plastic material the bending moment magnitudes increase at increasing load intensity, until a plastic bending moment is developed. It develops first above the columns supports and with further increase of the load intensity plastic hinges are formed in the spans central zone and a mechanism is determined where the corresponding load intensity is denoted as the ultimate load. In reinforced concrete elements, the bending moment capacity depends on the sectional properties, on the concrete strength and reinforcement ratio, and on the load level at which a plastic hinge is formed.  
The transition from elastic to elastic-plastic behavior is continuous and expected, and the design according to the elastic analysis may indicate the margin of safety for the ultimate load.  
This is not the case in the present case, where a slab on columns is considered. The slab design is carried out using the analysis results of an elastic slab supported on columns, which yields the variable bending moments, that determine the reinforcement amount in the slab and their detailing. However, when the slab is subjected to impulsive loading, the failure mechanism turns into a dynamic punching shear failure, that is characterized by severe shear distortions at the slab-column connection zone, accompanied by local reinforcement damage and failure at that zone, whereas the rest of the slab remains almost undamaged. The failure mode is entirely different and cannot be forecasted or extrapolated from the slab analysis and its performance under the design loads. There is no relationship between the elastic-based design and the slab capacity to the impulsive load action. This peculiar situation should be noted, and it deserves special attention in subsequent studies.
## 5 Experimental Progress
### Introduction of the Test Satellite Constellation
#### 5.1.2 Earth Observation Capability of the Satellite  
The camera carried by the satellite can output RGB color images with a resolution better than 2 m@500 km with an imaging range of 60\({}^{\circ}\). The original data volume of a single photo is approximately 2 GB. The satellite supports multiple working modes, such as staring imaging, push-broom, and video imaging. Based on the pre-acquired time and target location information, the satellite controls the camera to switch on and off to complete the imaging task. After imaging, the image data will be stored in the solid-state recorder. According to the pre-embedded program onboard the satellite or the commands from the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Axis** & **Antenna** & **Range** \\ \hline X & both & \(-10^{\circ}\)\(\sim\)\(+63^{\circ}\) \\ \hline Y & Q/V antenna-1 & \(-72^{\circ}\)\(\sim\)\(+63^{\circ}\) \\  & Q/V antenna-2 & \(-63^{\circ}\)\(\sim\)\(+72^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Q/V antenna parameters.  
Figure 6: Illustration of the LEO test constellation for broadband communication.  
ground, the stored information will be downloaded to the ground service station via the data transmission equipment. The phased array antenna is adopted for data transmission with the antenna beam range 0\(\sim\)360\({}^{\circ}\), maximum off-axis angle 60\({}^{\circ}\), half power beam-width 20\({}^{\circ}\). The available range for data transmission can be calculated by the satellite orbit, data-receiving station resources, as well as properties of the phased array antenna listed above.  
During the remote sensing task, the satellite needs to conduct an attitude maneuver according to the communication object or imaging target. The attitude maneuver is usually started 450 s before the task to ensure that the camera captures the target.
## Appendix A  
It can be mentioned that in previous publications [6; 21], the present authors qualitatively evaluated dynamic characteristics of some other components of the studied HEN, namely heat exchangers E11 AB, E15 AB, E30 AB and E35 AB--see Figures 13-14. These HEs were assumed to operate in the control loops similar to that shown in Figure 7a in Section 2.4., that is, in the absence of meaningful interactions with other HEN components. As a complement to the mentioned characteristics, the corresponding values of control-quality indices are shown in Table 11.  
Regarding exchanger E15 AB, the evaluation of its control performance is similar to that discussed for case no. 2 (exchanger E3-18 AB) in Section 3.4. As a consequence of fouling build-up at unchanged PID-controller settings (Figure 12a), settling time \(t_{s}\) increases from 70.9 s to 145 s, for the clean HE and  
Figure 18: Schematic diagram of a PID-controlled HE with the antecedent HEs.  
-fouled HE after 3-year operation (Table A1). Concurrently, peak time \(t_{p}\), delay time \(t_{d}\) and rise time \(t_{r}\) are slightly changed, while overshoot \(M_{p}\) remains nearly constant.  
The indices of control quality of the other HEs affected by fouling build-up are generally deteriorated, however the extent of change is differentiated. At constant PID-controller settings, the indices of E11 AB are changed as follows: overshoot \(M_{p}\) is increased from 47.3% to 73.8%, peak time \(t_{p}\) from 18.7 s do 31.3 s, and settling time \(t_{s}\) from 61.3 s to \(>\)300 s, for the clean HE and fouled HE after 3-year operation, respectively. The remaining indices, that is, delay time \(t_{d}\) and rise time \(t_{r}\) are changed insignificantly (see Table A1 and Figure A1a). Analogous changes determined for E30 AB are: overshoot \(M_{p}\) from 45.5% to 54.3%, and settling time \(t_{s}\) from 76.4 s to 152 s, while peak time \(t_{p}\), delay time \(t_{d}\) and rise time \(t_{r}\) are nearly unchanged (Table A1 and Figure A3a). The situation of heat exchanger E35 AB is different because at constant controller settings, fouling build-up may lead to drastic deterioration of control-quality indices and unstable control performance (see Figure A4a and Table A1). However, if periodic adjustment of the settings of PID-controllers were applied for all the mentioned heat exchangers (E11 AB, E15 AB, E30 AB and E35 AB), then increasing thermal resistances of the fouling layers would not induce deterioration of control characteristics (Figures A1b, A2b, A3b and A4b) and their corresponding quality indices (Table A1). The adjustments of controller settings, optimized using Ziegler-Nichols method [18] or Signal Constraint toolbox in Simulink [21], would result in the elimination of too high values of overshoot \(M_{p}\) (Figures A1c, A2c, A3c and A4c).  
Figure A3: Closed-loop step responses (case in work [6]) under fouling conditions of the HE E30 AB models: without adjustment (**a**) after adjustment (**b**) and after adjustment of optimal (**c**) PID tuning parameters.
## 2 Sentinel-1 SAR Data and the Ancillary Datasets
### Ancillary Datasets
#### 2.2.2. Inundation Maps Derived from MODIS/Terra Data  
In this study, the surface reflectance 8-Day L3 Global 500 m products from MODIS/Terra (MOD09A1) are used to create surface water maps, mainly based on values of the Enhanced Vegetation Index (EVI), the Land Surface Water Index (LSWI), and the difference between EVI and LSWI by a methodology described in [14]. MODIS surface water maps (500 m spatial resolution) over the Mekong Delta will be used to compare to the corresponding surface water maps derived from SAR Sentinel-1 observations for 2015. MODIS/Terra data can be downloaded from [[http://reverb.echo.nasa.gov/reverb/](http://reverb.echo.nasa.gov/reverb/)]([http://reverb.echo.nasa.gov/reverb/](http://reverb.echo.nasa.gov/reverb/)).  
All Sentinel-1, Landsat-8 and MODIS/Terra observations used in this study are listed in Tables 1 and 2. Sentinel-1 and Landsat-8 training observations are used to train the NN (Section 3.2). Sentinel-1 and Landsat-8 test observations are used to test, optimize, and evaluate the performance of the NN (Sections 3.3 and 4.1). NN evaluation is also based on comparisons with MODIS surface water estimates (Section 4.3).
## 3 Background
### Security Requirements
#### 3.3.1 Authentication  
The system should not allow any form of imitation of a vehicle or vehicles trying to send information. The sensor that sends the data must be the vehicle it claims to be. The receiving sensor should also be able to differentiate between the true sender and a fake sender by scanning the ID of the sender. The chances of a masquerading node acting in the same way as a legitimate node are extremely high. As such, communication between nodes must be authenticated. Every sender must have a unique ID that can be verified through keys or passwords [61].
## 2 Damage Detection Using a Stationary Vehicle
### Contact-Point Response of the Stationary Vehicle  
Indirect bridge vibration measurement is conducted by an accelerometer installed on a scanning vehicle that records the vertical vibration of the vehicle. The contact-point response of the scanning vehicle refers to the response of the point below the vehicle's tire, which is in direct contact with the bridge. By calculating the contact-point response, the vehicle's frequency can be eliminated, which would provide a more accurate representation of the bridge response. [PERSON], [PERSON] [19] provided a technique to extract the contact-point response from the vibration signal recorded by a moving vehicle. The same technique could be applied for a stationary vehicle vibration response. Figure 2 shows a stationary vehicle parked at mid-span, where another moving vehicle passes over the bridge as a source of excitation.  
The equation of motion of the stationary vehicle can be expressed in terms of the contact-point displacement response \(u_{c}\), as the following:  
\[m_{s}\ddot{q}_{s}+k_{s}(q_{s}-u_{c})=0 \tag{1}\]  
Figure 1: Schematic representation the proposed damage detection method.  
Figure 2: A stationary vehicle parked in the middle of a bridge while another moving vehicle passes over the bridge.  
where \(m_{s}\) and \(k_{s}\) are the mass and stiffness of the stationary vehicle, respectively. \(\ddot{q}_{s}\) and \(q_{s}\) are the acceleration and displacement responses of the stationary vehicle, respectively. Equation (1) is derived twice with respect to time to obtain Equation (2). By rearranging Equation (2), the contact-point response can be calculated as shown in Equation (3).  
\[k_{s}\big{(}\ddot{q}_{s}-\ddot{u}_{c}\big{)}=m_{s}\frac{d^{2}\ddot{q}_{s}}{dt^{ 2}} \tag{2}\]  
\[\ddot{u}_{c}=\ddot{q}_{s}+\frac{d^{2}\ddot{q}_{s}}{\omega_{s}^{2}dt^{2}} \tag{3}\]  
\(\omega_{s}\) is the stationary vehicle natural frequency and \(\ddot{u}_{c}\) is the contact-point acceleration response. In practice, \(\ddot{q}_{s}\) is the recorded signal by the vehicle and \(\omega_{s}\) can be measured or calculated. Finally, the term \(\frac{d^{2}\ddot{q}_{s}}{dt^{2}}\) can be calculated by the central difference method for the discrete data of the recorded signal as shown in Equation (4), where \(\Delta t\) is the sampling interval and \(i\) denotes the \(i\)th sampling point.  
\[\frac{d^{2}\ddot{q}_{s}}{dt^{2}}=\frac{\left(\ddot{q}_{s}\big{|}_{i+1}-2\ddot {q}_{s}\big{|}_{i}+\ddot{q}_{s}\big{|}_{i-1}\right)}{(\Delta t)^{2}} \tag{4}\]
## 2 Problem Statement
### Decision-Level Fusion Based MUD System Model  
In this paper, we consider the application of decision-level fusion techniques into the field of MUD. The decision-level fusion based MUD system model is constructed as Figure 1 shows. The characteristics of this scheme contain:  
1. The outputs of CD \(y=(y_{1}\), \(y_{2}\),..., \(y_{K})^{\text{T}}\) are passed to the preliminary sub-optimal detectors, including SIC, DEC, and MMSE detectors;
2. The preliminary decisions of SIC, DEC, and MMSE (let \(X_{1}\), \(X_{2}\) and \(X_{3}\)) are sent to the fusion center;
3. The fusion center consists of two parts: committee and fusion criterions. Depending on different decision-level fusion criterions (DFCs), the committee can make a different final decision \(\hat{\mathbf{b}}\) on the basis of these preliminary decisions.
4. This scheme is not distributed in fact, for it only needs one antenna to receive signals. Consequently, the computational complexity and apparatus integration are saved.
## 1 Introduction
A detailed description of the flow field in both qualitative and quantitative manners can thereby be accomplished. With a combination of CFD and the discrete element method, the dynamic behaviors of particles can be elucidated. Furthermore, the stresses in the hydraulic components induced by the impact of particles have been numerically modeled, and the position prone to erosive wear can be predicted [19]. In the present study, a pump assuming dual functions, namely, mixing and transporting water and sand particles, was proposed. The pump is characterized by two inlets, which are designed for sand particles and water, respectively. Sand particles fall into the flow passage of the secondary impeller via the sand inlet, and water enters the passage of the lower part of the primary impeller. The solid volume fraction (SVF) considered was as high as 0.28. Both the pump structure and attainable SVF for a solid-liquid two-phase flow pump have rarely been reported. The CFD technique was used to solve the multiphase flow field in the pump chamber. The effects of the feed rate of sand and the rotational speed of the impeller were investigated. A comprehensive comparison of the results obtained under different operating conditions was implemented. The obtained conclusions are expected to shed light on the relationship between inner flow characteristics and the operating performance of the solid-liquid two-phase flow pump.
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 3 Statistics-Based Approach to MC
### Maximum a Posteriori (MAP) Approach
By assigning unique pilots to every possible modulation type, the modulation information is embedded. BPSK, QPSK, 16-QAM, and 64-QAM are the possible pilot patterns with corresponding modulation types. It is shown by the simulation results that modulation types are successfully identified by the proposed adaptive pilots, while no effect is introduced to the channel estimation process. For the application of the proposed algorithm, pilots can be located at different locations with different values. However, when more number modulation formats are involved in the communication, more adaptive pilots may be required, which degrades the spectrum efficiency of the transmission. The MC approach enables the estimation BAT technique in adaptive OFDM systems [60]. The authors analyze a less computationally complex MAP-based MC algorithm. They derive an estimation of the probability of classification error of a MAP-based classifier. Moreover, based on the derived estimation, a rate-adaptive (RA) BL algorithm is developed. The findings of the simulation reveal that the proposed RA algorithm greatly improves the accuracy of modulation classification. Furthermore, it is also shown that, in comparison to traditional RA methods, the proposed BL approach improves classification performance for SNR above 15 dB.
## 6 Conclusions and Future Perspectives
### Challenges
#### 6.2.5 Poorly Modeled Ocean Processes  
While DL has demonstrated strong performance in various oceanographic tasks, several key physical processes remain poorly modeled. Subsurface dynamics, such as thermocline variability, are particularly challenging due to the limited high-resolution in situ measurements like Argo profiles. Although some studies have attempted to estimate thermocline depth using DL [339], accurate reconstruction of thermocline structures remains a challenge. Similarly, ocean carbon sequestration processes are difficult to model. While DL has been applied to estimate related variables such as surface pCO\({}_{2}\), these studies capture only part of the process. Full carbon sequestration involves complex interactions among physical, chemical, and biological components, which are difficult for purely data-driven models to learn without incorporating domain knowledge.
# Slug Regime Transitions in a Two-Phase Flow in Horizontal Round Pipe. CFD Simulations
## 1 Introduction
### The Objectives of the Study  
The main objective of the study is to propose a technical solution integrated into the pipeline for the flow regime's transition from slug to bubbly two-phase flow. The resulting solution will increase the service life and improve the reliability of process pipelines used in power plants in the oil and gas industry.  
Research objectives:  
* Reproduce the slug flow regime of a two-phase flow in a horizontal circular smooth pipeline using the CFD simulation method.
* Determine the location of the slug.
* Propose a technical solution that can be integrated into the pipeline to ensure a change in the flow regime.  
The research object is isothermal two-phase gas-Newtonian-liquid flow in a horizontal circular smooth pipeline.  
The research subjects are flow rate, pressure drop, phase composition of the flow, and flow regime.