esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Methodology
#### 2.3.3 Hydrological Simulation Scenarios  
After assessing the accuracy of precipitation, this research used gauge-observed, satellite-based, and reanalysis precipitation products as forcing data in the hydrological model CREST for streamflow simulations in the Beijing and Dongjiang River Basins. In addition, we developed and compared two merging schemes (i.e., precipitation input merging and streamflow output merging) to investigate their capability for improving streamflow and flood simulations. The arithmetic mean method was applied to merge these three uncorrected precipitation datasets (TMPA-3B42 RT, IMERG-cal, and ERA5) as well as another three corrected products (TMPA-3B42V7, IMERG-cal, and GSMaP). To better examine the effectiveness of multi-source precipitation products during the study period, three hydrological simulation scenarios were designed as follows. Considering Scenario I shown in Tables 3 and 4, the values of CC for ERA-Interim, GSMaP-RNL, and PERSIANN-CCS were less than 0.4, and we chose not to use them in Scenario II or III. 1. Scenario I (calibrated by gauge-observed products)  
As shown in Table 3 and Figure 2, this scenario used the CGDPA precipitation data to force and calibrate the CREST model with the observed streamflow data from 2006 to 2010 at a daily scale. Then, based on the same model parameters calibrated by CGDPA  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Statistical Indicators** & **Unit** & **Equation** & **Perfect Value** \\ \hline Correlation coefficient (CC) & NA & \(CC\ =\ \frac{\frac{1}{N}\sum_{i=1}^{N}(P_{i}-\overline{P})(O_{i}-\overline{O})}{ \sqrt{\sum_{i=1}^{N}(P_{i}-\overline{P})^{2}}\sqrt{\sum_{i=1}^{N}(O_{i}- \overline{O})^{2}}}\) & 1 \\ Relative bias (BIAS) & \% & \(BIAS\ =\ \frac{\sum_{i=1}^{N}(P_{i}-O_{i})}{\sum_{i=1}^{N}O_{i}}\times 100\%\) & 0 \\ Mean absolute error (MAE) & mm/d & \(MAE\ =\ \frac{1}{N}\sum_{i=1}^{N}\left|P_{i}-O_{i}\right|\) & 0 \\ Root mean squared error (RMSE) & mm/d & \(RMSE\ =\ \sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left(P_{i}-O_{i}\right)^{2}}\) & 0 \\ Probability of detection (POD) & NA & \(POD\ =\ \frac{H}{H+H}\) & 1 \\ False alarm ratio (FAR) & NA & \(FAR\ =\ \frac{H}{H+H}\) & 0 \\ Critical success index (CSI) & NA & \(CSI\ =\ \frac{H}{H+H+H}\ =\ \frac{1}{1/(1-FAR)+1/POD-1}\) & 1 \\  & & \(\ \for the independent validation period from 2011 to 2018, the model was driven by precipitation inputs from CGDPA, TMPA-3B42V7, TMPA-3B42 RT, IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, GSMaP-RNL, PERSIANN-CCS, and two merged precipitation products, respectively. Scenario II (recalibrated by satellite-based/reanalysis products)  
CGDPA benchmarking in Scenario I was usually applied over gauged areas, while Scenario II could be viewed as a replacement for the utility of remotely sensed precipitation products over data-scare basins that lack rain gauges [10; 31]. During the same calibration period of 2006-2010, this model was recalibrated using TMPA-3B42V7, TMPA-3B42 RT,
# Improved Exponential Phase Mask for Generating Defocus Invariance of Wavefront Coding Systems
## 2 Improved Exponential Phase Mask and Optimization Method
Without considering noise, a wavefront coding system with higher invariance of OTF function in full space-frequency under different defocusing coefficients have better imaging quality. In the whole frequency domain, the Fisher information of OTF with defocusing is determined as follows:  
\[FI(\psi)=\int_{-2}^{2}\left|\frac{\partial}{\partial\psi}H(\rho,\psi)\right|^{ 2}d\rho \tag{8}\]  
For Equation (8), the smaller the value of the function is, the smaller the relationship between the OTF and the defocusing parameters is, which is equivalent to the higher defocusing invariance of the optical system. By setting the maximum defocusing range of the optical system \(\psi_{0}\) the Fisher information of the whole working range of the system is calculated as  
\[F=\int_{-\psi_{0}}^{\psi_{0}}FI(\psi)d\psi \tag{9}\]  
Based on pursuing the defocusing invariance of the system and ensuring the imaging quality of the system, the optimization equation is  
\[ \begin{array}{ll}\underset{Para}{\text{min}}&I(Para)=F(Para,\psi_{0})\\ s.t.&\int_{-2}^{2}|H(\rho,0)|d\rho\geq th\\ &Para>Para_{0}\end{array}  \tag{10}\]  
where \(Para\) is the surface parameter vector of the phase mask to be optimized, and \(th\) is the lower limit of the MTF function area of the system under the non-defocusing condition which can determine different image qualities. \(Para_{0}\) is the lower limit of phase mask surface parameters. The optimization was carried out using a solver that can identify the interior point of a restricted nonlinear multivariable function with the algorithm. When tuning the six phase masks, a unified \(th\) threshold should be utilized to compare the defocusing performance of different phase masks under the same image quality. When the threshold value is set high, the system's imaging quality is high, but the invariance of the optical characteristic function in the expanded range of depth of field is low; when the threshold value is set low, the system's imaging quality is low, but the invariance of the optical characteristic function in the extended range of depth of field is high. As a result, the threshold selection should be considered in the optimization design.
## 5 Concept Validation
### Scenario-4\(\rightarrow\)Irradiance 300 W/m\({}^{2}\), Temperature 30 \({}^{\circ}\)C  
Scenario-4, solar-PV input irradiance level of 300 W/m\({}^{2}\) at a Temperature of 30 \({}^{\circ}\)C, has been considered for illustrating performance of the proposed ICPSO algorithm for tracking of maximum power under high temperature. It has been perceived from Figure 9 that, the expected maximum power of SPV is 29.5 W and obtained power is 27.8 W, which shows that 94.2% efficiency of the proposed algorithm is achieved. The MPP has been tracked at 0.05-s without any fluctuations. This fast tracking has been obtained due to the continuous tracking and particle exploration (Global search) process involved in the ICPSO algorithm. The particle exploration process has been visualized at 0.025-s, which shows effectiveness of proposed algorithm.  
It has been observed from the results and analysis that, scenario-1 to scenario-4 depicts the nonuniform operating conditions (NUOC). The efficiency of scenario-2 and scenario-4 are found less compared to the effectiveness of scenario-1 and scenario-3, which shows, proposed control algorithm provides excellent results, at a fix temperature level with high irradiance. This research paper aims to present the concept of ICPSO for maximum powerpoint. Therefore only 100 iterations and 50 population size have been selected although efficiency would be increased by increasing population data size and number of iterations. The obtained results reveal that the proposed algorithm is efficiently tracked maximum global power and has stabilized, the oscillation around the MPPT mitigates continuously until another variation in irradiance or temperature takes place. The particles in the exploration process not only updated with fixed irradiance and temperature but it also continuous with variable irradiance and temperature of NUOC. It has been perceived that the absence of oscillation is the attractive feature of the ICPSO algorithm.
# Traffic-Predictive Routing Strategy for Satellite Networks
## 6 Simulation Analysis
### Simulation Scenario
#### 6.2.2 Scenario 2: Number of Different Communication Connections
Despite the increased complexity, G-AODV demonstrates significant performance improvement through careful design and the use of popular PSO algorithms. While its complexity is higher, the performance enhancement provides robust support for traffic management in satellite networks, showcasing excellent performance under heavy loads and unstable network conditions. Generally, as the scale of satellite networks increases, routing algorithms often require a greater number of iterations to adapt to the complexity of the network. Throughout the simulation experiments, we closely monitored the convergence performance of the G-AODV algorithm within the context of large-scale satellite networks. After 280 iterations, the algorithm successfully achieved a satisfactory convergence state.
### Related Work  
Several underwater simulations exist. UWSIM [4] is an open source project implemented using Open Scene Graph (OSG) as the graphics engine. Another simulation environment used Gazebo--a robot simulation environment--for the underwater simulation [5]. There are also highly realistic commercial ones, such as Multi-ROV training simulator [6], which are focused on training personnel for underwater ROV missions. The open source simulations, although they are quite flexible, tend to have less realistic features compared to game engine based frameworks where the commercial ones are driven by market demands that are mainly mission training oriented. State of the art game engines today use physically based rendering (PBR), which gives us a more accurate representation of materials, camera models and the light interactions compared to the traditional methods, in addition to a wide range of tools to build a rich underwater simulation similar to what was achieved by Microsoft AirSim [2].  
To validate underwater vision and navigation concepts, researchers usually carry out the experiments in highly controlled environments such as swimming pools [7]. Those environments usually lack the essential effects such as plankton and bubbles [8] that affects the visibility and the overall performance of the vehicle under real conditions.
# Impact of Patient Size and Positioning on Radiation Dose in Multiphase Liver CT Examinations
## 3 Results
#### 3.4.2. Group with Abdomen and Pelvis Imaging  
* Mann-Whitney U statistic: 1564.0
* \(p\)-value: 0.705  
The analysis of the 'Abdomen + Pelvis' group encompassed 178 patient observations. The \(p\)-value is 0.705, which is greater than 0.05, indicating no significant difference in DLP values based on the isocenter location for examinations involving both the abdomen and pelvis (Figure 13).  
Figure 12. Distribution of DLP (Dose Length Product) mean values for abdomen-only cases, categorized by the isocenter location (“yes”, “no”).  
Figure 13. Distribution of DLP (Dose Length Product) mean values for abdomen and pelvis cases, categorized by the isocenter location (“yes”, “no”).  
Does the Patient's BMI, Weight and Height Have a Statistically Significant Impact on X-Ray Tube Output's Operating Parameters?  
In the conducted analysis, we explored the relationship between patient physical characteristics (height, weight, and BMI) and the operating parameters of a medical X-ray tube (kV, mA in phases I, II, and III) across two distinct groups: patients undergoing procedures involving the abdomen and those involving both the abdomen and pelvis.
## 3 Results
With increasing Zn content, \(\mu^{\prime}\) of Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) increases, while the cut-off frequency decreases. Real part of permeability (\(\mu^{\prime}\)) is depends on various parameters such as stoichiometry, grain size, composition, porosity, impurity levels, saturation magnetization, magnetostriction, and crystal anisotropy [37]. Higher permeabilities are favored by large grain size, low porosity, high saturation magnetization, low crystalline anisotropy, low magnetostriction and high purity. In particular, the real part of permeability strongly depends on grain size. As Zn ion increases, the crystalline anisotropy is decreased, but permeability is increased as shown in Table 1. Here, the crystalline anisotropy constant K\({}_{1}\) was calculated using the following relation [38] expressed as K\({}_{1}\) = M\({}_{\mathrm{s}}\)H\({}_{\mathrm{c}}\)/0.96, where M\({}_{\mathrm{s}}\) is the saturation magnetization and H\({}_{\mathrm{c}}\) is the coercivity. The initial permeability varies inversely with magneto-crystalline anisotropy constant, but proportional to grain size according to the relationship [39] represented by \(\mu_{\mathrm{i}}\)' = M\({}_{\mathrm{s}}\)D/K\({}_{1}\) where D is the average grain size. Thus, the formula \(\mu_{\mathrm{i}}\)' = 0.96D/H\({}_{\mathrm{c}}\) is obtained, which means that as the Zn concentration increases, the increase in permeability (\(\mu^{\prime}\)) is related to increased grain size and reduced coercivity. This corresponds to the results shown in Table 1. In other words, an increase in \(\mu^{\prime}\) as the Zn  
Figure 10: Real part (**a**), and imaginary part (**b**) of the magnetic permeability of Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) toroidal samples in the frequency range from 1 MHz to 1 GHz. concentration increases can be attributed to an increase in the domain wall mobility promoted by the larger grains, as well as lower crystalline anisotropy according to Equations (1) and (2).
## 2 System Design  
### Top-Level Design Choices  
To stay within the design and future purchasing budget, we decided to use mostly COTS components. Self-design of all hardware would allow for greater optimization, but design and manufacturing of all components would add cost and complexity beyond the scope of our goals. Only when commercial hardware was not available or sufficient did we create our own. As availability of the chosen components will change in the next few years, special care was taken to implement a modular design in hardware and software. Individual component changes will require updated software/hardware for only that part, while most of the system remains the same. This also facilitates adding extra hardware and capabilities in the future.  
The TRIOS/RAMSES COTS hyperspectral radiometer was chosen because of its relatively high performance, highly robust and mature low-power design, and moderate price. This radiometer provides measurements for a spectral range of 320-950 nm and was used for satellite validation since 2002 [18] by many teams worldwide. The performance of this class of radiometer was extensively studied and characterized with regard to straylight [19], thermal sensitivity [20], polarization sensitivity [21], angular response of the irradiance units [22], and non-linearity. A low-cost, portable, LED-based FieldCAL device is available for rapid checking of radiometric sensitivity in the field.  
The FLIR PTU-D48E COTS pointing system was chosen for the PANTHYR system because of its high performance (including good pan and tilt speeds and pointing accuracy), robust design, and moderate price.  
While COTS rugged PCs are widely available, a key early decision was to use a small low-power embedded computer board with self-design of additional electronics for managing power and connectivity to components such as the radiometers, GNSS (Global Navigation Satellite System) receiver, and auxiliary sensors. This required more development time, but provided more control over power management and greater flexibility in connecting and supporting additional external devices.  
The Linux operating system was chosen to facilitate software portability to next-generation hardware platforms and to improve reliability and security. Linux is open source, easy to customize, and ideal for an offshore system where logistics make on-site intervention difficult and/or expensive.  
Python was chosen as the main programming language as it is available under an open-source license, is well known in the scientific community, and produces clear, readable code.
## 3 Results
### Results of In Vitro Digestion  
The digestibility of HHP-treated (450, 600 MPa) blood plasma suspensions was investigated in vitro digestion simulation using pepsin (gastric) and pancreatin (small intestine) enzymes. The effect of digestion on blood plasma proteins was monitored using SDS-PAGE (Figure 3).  
Figure 3: Monitoring of changes in HHP-treated blood plasma proteins after in vitro gastric (**a**) and small intestinal (**b**) digestion with 6/15% SDS-PAGE (Mw = molecular weight; Std.= standard; 1. 0 MPa; 2. 450 MPa; 3. 600 MPa; 4. pepsin; 5. pancreatin; I. hemoglobin; II. \(\gamma\)- globulin; III. \(\beta\)-globulin).  
Figure 2: SDS-PAGE patterns of HHP-treated blood plasma suspension (Mw = molecular weight; Std = standard; 1. 0 MPa; 2. 300 MPa; 3. 400 MPa; 4. 450 MPa; 5. 500 MPa; 6. 550 MPa; 7. 600 MPa; I. hemoglobin; II. \(\gamma\)-globulin; III. \(\beta\)-globulin; IV. albumin; V. \(\alpha\)-globulin).  
Figure 3a shows that protein degradation has already occurred in the gastric phase. This means that the higher molecular weight proteins (albumin above 70 kDa, \(\alpha\)-globulin) could not be identified in the control sample either, and they were completely degraded. The bands of \(\beta\)-globulin, \(\gamma\)-globulin and hemoglobin (residual) protein fractions identified in the control sample show high color intensity. The \(\beta\)-globulin was no longer identifiable at 450 MPa and was completely degraded. \(\gamma\)-globulin and hemoglobin were not completely degraded in the gastric phase. Their band intensities were reduced by pressure treatment compared to the control sample, indicating that the application of HHP treatment improved protein digestibility. HHP treatment modifies the structural integrity of plasma proteins by disrupting non-covalent interactions and altering secondary structures. These modifications result in protein unfolding and the increased exposure of hydrophobic regions, making them more susceptible to enzyme action during digestion. This phenomenon has been observed in various protein sources (cereals, legumes, nuts and dairy products) [46]. [PERSON] et al. [47] treated red abalone with HHP (200, 300, 400 and 500 MPa for 5 min). They found that HHP treatment improved the digestibility of abalone regardless of pressure level due to the partial unfolding of proteins. However, there is no difference in the gastric phase between samples treated at 450 MPa and 600 MPa, so it can be concluded that increasing the pressure values did not have a greater impact on the digestibility of blood plasma. The digestion of blood plasma in the small intestine (Figure 3b) shows that blood plasma proteins are completely degraded. The bands appearing on the gel image are the protein bands of the enzyme pancreatic used for digestion. Most of the processes that convert food into a form that the body can use occur in the small intestine [48]. Thus, based on the results, blood plasma can be considered an easily digestible source of protein, as evidenced by the following studies [49; 50; 51].
# Transient Current Density in a Pair of Long Parallel Conductors
## 3 Results and Discussion
### Example 3  
The cross section of the two conductors is a circle with the radius \(r=8\) mm. The cross sections \(\mathcal{A}_{1}\) and \(\mathcal{A}_{2}\) have their centres at points \((0,0)\) and \((d,0)\), respectively, \(d\geq 2r\). Both conductors are of copper and they are connected to a sinusoidal voltage source at time \(t=0\), the same as in Example 1.  
The solution to Example 3 allows extending and complementing the results of solving Example 1. With the sinusoidal source connected, the main effect of the transient state is an increase in the maximum value of the current density  
\[J_{\text{max}}=\max_{\forall i,\ t\in[0,T)}|J_{i}(t)|\]  
and in the maximum value of the current  
\[I_{\text{max}}=\max_{t\in[0,T)}|I(t)|\]  
Figure 5: Example 2: The dependence of transient current on time when the source of constant current is connected at time \(t=0\) and is later short-circuited at time \(t_{\text{sho}}=0.005,0.01,0.05\) s. The curves (solid lines) denoted by Cu correspond to copper conductors. The curves (thin lines) denoted by Al correspond to aluminium conductors at a temperature of \(300\) K, \(\varrho=2.733\times 10^{-8}\Omega\cdot\)m [13]; \(t_{\text{sho}}=0.01,0.03\) s.  
with respect to the steady state. The maximum values \(I_{\rm max}\) and \(J_{\rm max}\) are obtained during the first period \(T\). The amplitude of the steady current density attains the maximum  
\[\hat{J}_{\rm max}=\max_{\forall i}\hat{J}_{i}\]  
at the point \((r,0)\). The point at which the transient current density attains the value \(J_{\rm max}\) is on the surface of the conductor \(\mathscr{A}_{1}\) (and at the symmetrical point on the surface of the conductor \(\mathscr{A}_{2}\)) and its position depends on \(d\) and \(f\). The instant at which \(J_{\rm max}\) is attained differs from the instant at which \(I_{\rm max}\) is attained (see Figure 6).  
Table 1 gives the values \(J_{\rm max}/\hat{J}_{\rm max}\) and \(I_{\rm max}/\hat{I}\) in dependences on \(f\), \(d\) and \(\varrho\). The resistivity of Al conductors is \(\varrho=2.733\times 10^{-8}\Omega\cdot\)m [13]. Alloy conductors are of an alloy of 75 mass% Cu + 25 mass% Al, \(\varrho=1.76\times 10^{-7}\Omega\cdot\)m [13].  
The transient current \(I(t)\) can be considered steady for \(t\geq t_{\rm sin}\). The instant \(t_{\rm sin}\) is determined by Formula (10) and depends on the chosen value \(\Delta_{I}\), the distance \(d\) of the axes of conductor cross sections, the voltage source frequency \(f=1/T\) and the conductor resistivity. Table 2 gives the values of \(t_{\rm sin}\) for several values of \(f,\Delta_{I}\) and \(d\). The conductors in Example 3 are of copper and the respective data are given in Table 2, with the exception of the last column, where the data given hold for aluminium conductors (\(\varrho=2.733\times 10^{-8}\Omega\cdot\)m [13]). With increasing conductor resistivity, the time \(t_{\rm sin}\) decreases. This statement is supported by comparing the values in the penultimate and last columns of Table 2.
## 2 System Architecture
Although CPU processing power had increased during the last decade, the bandwidth for data transmission did not [21]. As a result, the network bandwidth has become a bottleneck that affects the performance of the cloud -based architecture. To alleviate this problem, Fog and Edge computing were proposed. In Fog computing, first coined by Cisco in 2014 [22], the processor is brought closer to where data are generated. The data processing is done between the source and the cloud, prior to being pushed to  
Figure 1: General cloud-base architecture of a smart home [3]. the cloud. In a smart home with the fog computing architecture, a gateway will do data processing since the gateway is closer to the data source. Data transmission between end devices and gateways is sped-up. The gateways will compress the data received from various sources and will send the compressed data to the cloud for further computation. Less bandwidth is needed to transmit to the cloud in comparison with the original method [3]. Fog computing offers several advantages such as deduction of data size sent to the cloud, reducing the latency of network, improving the response time of the system and single point of failure avoidance [23]. However, Fog computing has some security weaknesses and privacy issues [24; 25]. In comparison with Fog computing, Edge computing gives each end device the capability to decide if the data need to be stored locally, analyzed locally, or need to be sent to the cloud [3]. Edge computing has a more localized or at-source computation. Edge computing has the similar advantages and disadvantages as Fog computing [26; 27].