esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis on Driving Factors of TSS Change  
#### 3.3.1 Changes Characteristic of the Concentration of TSS in Flood Season and Dry Season  
In addition to the overall analysis of the concentration of TSS in the reservoir area for the five years, the temporal and spatial variation characteristics of TSS in the reservoir area were analyzed by the dry season and flood period, and we produced spatial distribution maps in different seasons (Figure 7). It can also be observed from Figure 5b that water quality in the Hechun Town area is good during the five years, and the averaged concentration of TSS is stable at about 13.16 mg/L. Therefore, the Shijiao Town area was taken as the key research object and analyzed in depth. Comparing the changes in the concentration of TSS in Shijiao Town in different seasons (Figure 8), in general, the concentration of TSS in the dry season with an average of 32.86 mg/L was higher than that in the flood period (23.45 mg/L). Especially in 2015, there was the largest difference of 17.8 mg/L in the average TSS concentration for dry and flood seasons. Within the five years, the differences in averaged TSS between dry and flood seasons were 9.52 mg/L. Despite some increase in the dry season before 2015, the TSS concentration decreased by 24.58 m/L, from 42.86 in 2015 to 18.28 mg/L in 2018. On the contrary, during the flood period, the TSS concentration maintains about 25 mg/L in the three former years and then decreases to 16.45 mg/L in 2018.  
Figure 6: Percent distribution of annual average concentrations of TSS in Shijiao Town from 2014 to 2018, including levels with thresholds of 20, 30, 50 and 80 mg/L.  
Figure 7: _Cont_.  
Figure 7: The spatial distribution map for the estimated concentration of TSS on an annual average in dry and flood seasons of Hedi Reservoir from 2014 to 2018 is mapped as shown in the figure, among which (**a**,**c**,**e**,**g**,**i**) are in the dry season within 5 years, and the rest (**b**,**d**,**f**,**h**,**j**) fall into the flood season. The color scale is the legend of the average TSS concentrations, in mg/L.
## 4 Factors Affecting Pesticide Degradation
### Light  
Chlorine dioxide has good absorption of light in the wavelength range of 220-240 nm and 300-420 nm, and when chlorine dioxide absorbs the energy of light, it undergoes the breaking of the CI-O bond, generating chlorine-oxygen radicals (CIO-), trilinear oxygen atoms (O(3p)), and chlorine radicals (CI-) [77; 78; 79]. These produce reactive substances that can react with a number of pollutants (Figure 5). In the degradation reaction of pesticides, some appropriate increase in light can improve the degradation rate of pesticides. In the degradation with the organophosphorus insecticides azamethiphos and dimethoate, studies have shown that the degradation efficiency is higher under light conditions than under dark conditions [36]. In addition, in a study using light activation of chlorine dioxide in the reaction with the triazine herbicide atrazine, the results showed that the degradation rate of atrazine was significantly enhanced under light activation and that \(\cdot\)OH and chlorine radicals (CI\(\cdot\)) generated by chlorine dioxide after absorbing light played a dominant role in the degradation of atrazine [80].  
The technology of ozone combined with ultraviolet radiation is a common advanced oxidation technique, which is applied to degrade difficult-to-degrade organic compounds in wastewater. This technology can also degrade disinfection by-products [81]. In the solution, ozone undergoes photolysis under UV irradiation to produce oxygen and hydrogen peroxide, and hydrogen peroxide photolysis produces \(\cdot\)OH (Figure 6) [82; 83]. In addition, ozone in solution also produces \(\cdot\)OH. In the degradation with the carbamate insecticide carbofuran, ozone combined with UV was more effective than ozone [84]. In the degradation study with the benzimidazole fungicide carbendazim, UV combined with ozone gave better degradation with 98% degradation [85].  
The action of light enables chlorine dioxide and ozone to decompose and produce decomposition products. These decomposition products play a major role in the degradation process with pesticides, increasing the reaction rate with pesticides and improving the degradation rate of pesticides. However, there are differences in the wavelengths of light exposure for chlorine dioxide and ozone, which produce different degradation products.  
Figure 5: Mechanism of light affecting chlorine dioxide degradation efficiency.  
Figure 6: Mechanism of light affecting ozone degradation efficiency.
## 3 Results and Discussion  
### Welding Defects and Hardness  
Figure 3 illustrates the macrograph analysis of the welded series, where a different morphology in each series was obtained. The main imperfections noted were: excess weld in the bottom side of the S1, S2 and S3 series and in the top side of the S4 and S5 series; undercuts at the weld root in the bottom side of the S1 and S3 series and underfill and porosity in all series. In [27] it was reported that the series presented quality levels B and D according to the ISO 13919-1 welding quality standard and the imperfection sizes and morphology can be considered as crack-like imperfections. More details about the weld profile and imperfections of the welded series can be found in the previous reference. Due to the S4 series not reaching full penetration it will be omitted in the next sections.  
The fatigue cracks occurred in the FZ and the HAZ, for this reason the microstructure, hardness and morphology need to be examined in these zones. Figure 2 presents a representative example of metallographic analysis where Figure 2b corresponds to underfill defect, Figure 2c shows the toe of the weld root and Figure 2d illustrates an undercut defect. For the heat input range used in the present work, Figure 2b showed that the HAZ was divided into two important zones: softened ferritic-bainitic with fine grain (FG) and bainitic-martensitic with coarse grain (CG) as was found in [29]. Figure 2b also shows the macrostructure of the FZ and the BM.  
According to the fracture analysis, the fatigue starts in the weld root and in undercuts located in the boundary between the columnar grains of the FZ and the CG, as well as in the underfill located in the columnar grains of the FZ but very close to the HAZ and in pores located in the FZ. If fatigue crack paths depart from defects as underfill, undercuts and weld root (toe), they could enter the FG that is generally a soft zone.  
The microhardness presents similar profiles for the different series; a very small softening in the FGHAZ was verified as illustrated in Figure 4 with the hardness profiles of the S2 and S3 series. The hardness increased with the heat input decrease at the FZ and the CGHAZ, but the difference in hardness is minimal in the sites where the fatigue started. For the fatigue start locations, the approximated hardness values were: 335, 345, 350 and 370 HV for the welded series S1, S2, S3 and S5 respectively, and approximately 295 HV for the BM. This increase in hardness can be explained by the quick cooling in the FZ and CG which allowed the bainitic-martensitic microstructural transformation while the FG zone cool down slower modifying the BM microstructure to a softened ferritic-bainitic microstructure.  
Figure 3: Cross-sections macrographs of the welded series.  
Although each series presented several fatigue failure modes due to the presence of different imperfections in the top and bottom sides, as well as on the lateral side and inside the weld bead due to the presence of pores; a dominant failure mode, strongly related to the imperfection with the highest stress concentration factor, was observed for each series.
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 2 Finite Element Model of Pipe-Soil System
### Selection of Material Constitutive Model  
The classical Mohr-Coulomb yield criterion is used to describe the soil media around the pipeline. Due to the long distance and large span of the buried pipeline, there may be differences in the properties of the soil around the pipeline in both the horizontal and vertical directions. It is difficult to accurately simulate the properties of soil, and this study simplifies it appropriately without considering the spatial changes in soil properties [27]. The relevant parameters of the soil around the pipe are listed in Table 1.  
When the pipe undergoes the action of active fault dislocation, it has to withstand not only the small deformation caused by the internal pressure of normal operation of the pipeline, but also the large deformation generated by the action of fault dislocation, so pipe with a good deformation performance should be used. In this study, the large-deformation pipeline steel pipe X80 HD2 is chosen as the research object, and the relevant parameters are mainly obtained from the relevant standards and specifications [28], as shown in Table 2 below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil** & **Modulus of** & **Poisson’s Ratio** & **Cohesion/kPa** & **Friction** & **Shear** \\
**Density/(kg\(\cdot\)m\({}^{-3}\))** & **Elasticity/MPa** & **Poisson’s Ratio** & **Cohesion/kPa** & **Angle/Degrees** & **Angle/Degrees** \\ \hline
1900 & 33 & 0.3 & 35 & 22 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the soil around the pipeline.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nominal** & **Nominal Strain** & **True Stress/MPa** & **True Strain** & **Plastic Strain** \\
**Stress/MPa** & **—** & 552 & **—** & 0 \\ \hline
560 & 0.00887076 & 565 & 0.008831646 & 0.006102334 \\
580 & 0.01396761 & 588 & 0.013870962 & 0.011029893 \\
600 & 0.02272401 & 614 & 0.022469666 &
## 2 Thermal Barrier Coating
### Thermal Properties of Thermal Barrier Coatings  
#### 2.4.1 Thermal Conductivity of TBC  
Because TBCs and superalloys are made of different materials, the mechanism of thermal transport is also different. The lattice vibration, e.g., phonons, is the dominant source of thermal transport in TBC, whereas free electrons, e.g., electrons, contributes to the majority of thermal transport in superalloys. This is caused by the difference between nickel, the main component of the superalloys, which is a metal where thermal transport is dominated by electrons, and zirconia, the main component of the TBC, which is a non-metal. In particular, phonons are scattered owing to the cracks and pores of the TBC, and the mean free path is shortened, thereby lowering the thermal conductivity.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 7 Results and Discussion
### Handover Performance
#### 7.2.2 Ping-Pong Handovers  
Figure 6a,b shows the number of ping-pong handovers for both algorithms, and their proportion as a percentage of the total handovers, respectively. The results show the proposed algorithm has fewer ping-pong handovers than the 3 GPP scheme and much fewer when considered as a percentage of the total handovers. The key feature that contributes to this outcome is the averaging of the CQIs over seven timestamps, therefore mitigating potential ping-pong effects and providing a more stable transition to the desired BS.
# Design and Implementation of 24-GHz and 48-GHz VCOs Using Noise Filtering Technique in 90-nm CMOS
## 2 Design and Analysis  
A standard 90-nm CMOS process offers one poly layer and nine metal layers, as shown in Figure 1. The thicknesses of the top metal layers (M\({}_{9}\)) and (M\({}_{8}\)) are 0.85 \(\upmu\)m, the thicknesses of the metal layers (M\({}_{2-7}\)) are all 0.31 \(\upmu\)m, while the metal layer (M\({}_{1}\)) is 0.24 \(\upmu\)m. Typically, the M\({}_{1}\) layer is also known as the bottom metal layer. Each layer of metal can be connected by Via layers. The poly layer is for the transistor's gate and thus above the substrate.
# A Federated Generalized Linear Model for Privacy-Preserving Analysis
## 4 Conclusions  
In this paper, we presented our federated GLM, which allows generating models of different families (linear, Poisson, logistic) in a privacy-preserving manner. The algorithm can be implemented in the platform of choice of the user or it can be utilized out-of-the-box using the provided implementation in our infrastructure for FL, vantage6. We validated its performance by comparing it with its centralized counterpart, which can be considered the gold standard. Given the mathematical equivalence of the two algorithms, our federated GLM reproduced outputs that are practically identical to those obtained when all the data were pooled together. We also validated its execution time as a function of the number of records and number of parties. Both validations demonstrated the usability of our federated GLM for analyzing horizontally partitioned data without disclosing information at a record level. Further development of this type of algorithm has the potential of making privacy-preserving analyses methods, such as FL, a much more common practice among researchers.  
Conceptualization, M.C., A.J.v.G. and A.M.-T.; methodology, M.C., A.J.v.G., H.A. and A.M.-T.; software, all authors; validation, M.C., A.J.v.G. and A.M.-T.; formal analysis, M.C., A.J.v.G. and A.M.-T.; investigation, M.C. and A.M.-T.; resources, F.M.; data curation, A.M.-T.; writing--original draft preparation, M.C. and A.M.-T.; writing--review and editing, all authors; visualization, A.M.-T.; supervision, A.M.-T.; project administration, F.M., A.M.-T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this study were generated programmatically as described in the text (Section 2.3). The corresponding scripts are publicly available in our GitHub repository.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ FL & Federated learning \\ GLM & Generalized linear model \\ ML & Machine learning \\ MLE & Maximum likelihood estimation \\ \end{tabular}
# The Effect of Heat Sink Properties on Solar Cell Cooling Systems
## 3 Brief Algorithm
### Navier-Stokes Equation  
This equation is the momentum equation for viscous, incompressible, and constant velocity flow, where density is \(\rho\).  
\[\rho\bigg{(}\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+v\frac{ \partial u}{\partial y}+w\frac{\partial u}{\partial z}\bigg{)} =-\frac{\partial p}{\partial x}+v\bigg{(}\frac{\partial^{2}u}{ \partial x^{2}}+\frac{\partial^{2}u}{\partial y^{2}}+\frac{\partial^{2}u}{ \partial z^{2}}\bigg{)} \tag{8}\]  
\[\rho\bigg{(}\frac{\partial v}{\partial t}+u\frac{\partial v}{\partial x}+v \frac{\partial v}{\partial y}+w\frac{\partial v}{\partial z}\bigg{)} =-\frac{\partial p}{\partial y}+v\bigg{(}\frac{\partial^{2}v}{ \partial x^{2}}+\frac{\partial^{2}v}{\partial y^{2}}+\frac{\partial^{2}v}{ \partial z^{2}}\bigg{)} \tag{9}\]  
\[\rho\bigg{(}\frac{\partial w}{\partial t}+u\frac{\partial w}{\partial x}+v \frac{\partial w}{\partial y}+w\frac{\partial w}{\partial z}\bigg{)} =-\frac{\partial p}{\partial z}+v\bigg{(}\frac{\partial^{2}w}{ \partial x^{2}}+\frac{\partial^{2}w}{\partial y^{2}}+\frac{\partial^{2}w}{ \partial z^{2}}\bigg{)} \tag{10}\]  
This study used the ANSYS Fluent program. This program can model cases of natural convection heat transfer using the finite volume method in a closed system, which means that mass is constant. Steady-state modeling was used to study free convection flow in the heat sink model. This program can also simulate solar radiation acting on solar panels using the Solar Ray Tracing tool [29]. The existing equation shows that increasing the efficiency of solar cells can be achieved by adding a cooling system. High efficiency can provide a good cooling system. Increasing cooling capability can be achieved by adding a heat sink. The heat sink geometry also affects cooling capability. The material and number of fins used in the heat sink need to be considered. The solar cell working temperature will affect performance and efficiency [15; 16; 17; 18]. Increasing temperature can be caused by a reverse saturation current. The increase in temperature can be countered by the heat transfer system [30; 31; 32; 33]. The material used will affect the cooling performance [15; 16; 17; 18]. Increasing the number of fins and holes will have a considerable effect on cooling capability [34; 35].
## 1 Introduction
### Challenges in Underwater Localization in Confined Tanks  
Outdoor robots usually rely on Global Navigation Satellite Systems (GNSS) for localization, while robots that are operated indoors, such as aerial vehicles, are localized with external camera systems. In contrast, submerged robots cannot use GNSS, because the signal is attenuated in water. Camera systems are also problematic, because image recognition underwater is difficult due to the demand for good visibility and ambient illumination. Furthermore, in order to obtain a global position based on vision, image-processing has to be performed off-board, and the pose has to be sent to the robot, as presented in [8]. Hence, communication bandwidth and computing capabilities limit the amount of robots that can be localized simultaneously with external camera systems. Off-board localization also introduces latencies and, thus, degrades closed-loop controller performances. One desirable capability of \(\mu\)AUV localization systems is therefore the ability to perform on-board self-localization. In [9], a vision-based SLAM algorithm is used to reconstruct 3D underwater environments. It is, however,  
Figure 1: Photo of HippoCampus. A \(\mu\)AUV which is 35 cm long and suitable for multi-robot operations in liquid-filled tanks [2].  
Figure 2: Illustration of an application scenario for a fleet of \(\mu\)AUVs. A confined tank contains a liquid with an underlying concentration field of interest. The \(\mu\)AUVs perform adaptive measurements to collect information and to infer the state of the concentration field.  
still subject to the challenges of underwater camera systems. Furthermore, \(\mu\)AUV fleets with many members require a localization framework that is not affected by the number of fleet members. This can be achieved with a passive localization approach, i.e., an ambient reference signal that is observed without active transmission to beacons.
# Thermodynamic Analysis of Partitioned Combined Cycle using Simple Gases
## 3 Model Description
### Pressure/Temperature Ratios for the Rankine Cycle  
It must be noted that the partitioned Rankine cycle operating conditions are different from the main Rankine cycle mainly because of different temperature gradients. The overall efficiency depends on the rate of heat transfer to Rankine cycle and its efficiency. The temperature of the steam must be kept above 325 \({}^{\circ}\)C, or preferably above the critical temperature (374 \({}^{\circ}\)C). At temperatures below 325 \({}^{\circ}\)C the optimum pressure is greater than the saturation pressure, thus seriously compromising the steam turbine efficiency and possible high condensation in turbine exhaust. Although screw expanders are reported to safely handle a greater degree of sub-saturation [59; 60], for large turbines the volume of steam is too large to be economically depressurized in these expanders. Higher temperatures are preferable as both the heat transferred and the Carnot efficiency increases. However, the maximum steam temperature is dictated by the practical limitations of the waste heat boiler surface area and pressure drop. The local Rankine cycle efficiency maxima are shown in the Figure 4. In order to provide maximum advantage, the turbine exit pressure was maintained at 10 kPa. The figure can be generated at different overall adiabatic efficiencies. The optimum pressure curves can be regressed to the resultant Equation (3), where P and T are in 'kPa' and 'K', respectively. However, the water cannot be pressurized to optimum value as it results in excessive condensation. So, it is adjusted to keep the steam quality above 98% before the condenser.  
\[P=\left[0.0153\eta_{A}^{2}-3.0128\eta_{A}+420.18\right]exp(0.0064T)\quad(Regression\ Error\ \pm 0.54\%) \tag{3}\]  
The overall efficiency equation is directly proportional to heat transfer from exhaust turbine gases in HRSG and does not specifically represent the maximum achievable temperature as in the case of the Carnot cycle. Therefore, HRSG design limitations will largely dictate the optimum point. High steam temperature reduces the heat transfer rate and low steam temperature reduces the turbine efficiency for a HRSG with fixed heat transfer area. Therefore, there is a trade-off. As previously described, in PCGT partitioned working fluid temperature can be reduced much lower than the CCGT fluid, thus the same HRSG (same area) is used to transfer heat between partitioned fluid and steam, as simulated in CCGT. However, the area of HRSG used to exchange heat between main exhaust gases and steam in PCGT is smaller with same ratio to keep the exhaust temperature above 171 \({}^{\circ}\)C.  
Both the pressure and temperature ratio of the individual Brayton cycle and Rankine cycle in CCGT and PCGT have their local efficiency maxima. Care must be taken to avoid simulation at these conditions as the overall efficiency has a global maximum value at other different conditions.
# Optimised Tuning of a PID-Based Flight Controller for a Medium-Scale Rotorcraft
## 4 Simulation Results and Discussion
### Controller Implementation Results
#### 4.2.2 Forward Speed  
A well-known effect of the PID-controlled system is its loss of performance away from the trimmed condition. The tuned system was tested on how well it withstands the translation from hover to 10 m/s forward speed. Because the optimally tuned PID controllers can give performance of up to 2 m/s, after which the rotorcraft becomes unstable, a new trim position is required for forward speed. The four controllers were retuned for the 10 m/s forward flight to be the new trim condition. This trim condition is also based on Equation (32), with the exception that the \(\mathbf{v}^{I}=[10\ 0\ 0]\).  
The best gains returned the values of the objective function and the running times given in Table 8.  
The PID controllers' gains obtained using the proposed optimisation algorithms for 10 m/s forward flight conditions are given in Table 9.  
Figure 11: The four rotorcraft inputs to maintain trim hover flight.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PID Gains** & **PSO** & **GA** & **ACO** & **CS** \\ \hline Best fitness & 1.334 \(\times\) 10\({}^{3}\) & 2.772 \(\times\) 10\({}^{6}\) & 1.221 \(\times\) 10\({}^{6}\) & 1.372 \(\times\) 10\({}^{6}\) \\ Running time (min) & 92.11 & 90.72 & 81.39 & 130.02 \\ \hline Mean & 1.676 \(\times\) 10\({}^{3}\) & 2.776 \(\times\) 10\({}^{6}\) & 1.234 \(\times\) 10\({}^{6}\) & 1.382 \(\times\) 10\({}^{6}\) \\ Standard deviation & 308 & 3.336 \(\times\) 10\({}^{3}\) & 1.707 \(\times\) 10\({}^{4}\) & 8.590 \(\times\) 10\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: The fitness, the running times of the four PID controllers’ gains optimisation algorithms and the mean and standard deviation of the ten experiments conducted for forward speed.  
The newly tuned PID controllers are able to keep the flight path by following the reference signal. Figures 12 and 13 show the position and the velocity time history of the rotorcraft. The velocity is kept constant and the \(x\) translation increases steadily. In this experiment, the GA-PID proved to track the velocity better than the other controllers. Even though it is the best, the velocity overshoots by \(11\%\) and settles quicker than others, in less than \(4\) s. The rest of the controllers also traced the ramp displacement, but this was at the expense of the height of the rotorcraft. Also noteworthy is that the GA-PID did have worse performance for \(y\) direction regulation. The multi-axis and underactuation problem becomes prominent in this part of the numerical experiment as optimising one output variable comes at the expense of the other variable.