esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Kinetics and Equilibrium Models
#### 3.4.2 Adsorption Equilibrium Models  
Langmuir and Freundlich adsorption isotherms were used to determine the equilibrium type of adsorption and the maximum adsorption capacity of the biosorbents (Figure 9). The parameters for these isotherms were calculated from their respective linear equations (Equations (6) and (7)) and are presented in Table 4. The analysis of the equilibrium models revealed that LD and HH fitted the Langmuir model favourably to the Freundlich model. This implies that the adsorbent surface is energetically homogeneous [6], which is in line with the MB removal results with _Caulerpa lentiillifera_[9]. On the other hand, RS and CH fitted the Freundlich model best, indicating that the MB dye adsorption occurred on a heterogeneous surface [28].  
Figure 9: Experimental and estimated non-linearised adsorption isotherms of MB on (**a**) LD, (**b**) RS, (**c**) CH, and (**d**) HH.  
The \"n\" value in the Freundlich adsorption models reflects the quality of adsorption, with an \"n\" value less than 1.0 indicating poor adsorption characteristics [6]. The maximum saturated adsorption capacity (Q0) of LD feedstocks was found to be up to 500 mg/g (Table 3), which is relatively higher compared to many other biosorbents [4; 8; 17; 68; 69; 111; 112; 113]. The maximum adsorption capacities for CH and HH were 137 mg/g and 117 mg/g, respectively, while RS showed a reasonable maximum adsorption capacity of 85 mg/g.
## 1 Introduction
A number of previous studies investigated the ability of ComBat harmonization to remove the effects of different batches on the values of RFs on various phantom datasets on patient data [16; 17; 18; 41]. Other studies investigated the change in the predictive ability of RFs following ComBat harmonization [37; 38; 39; 41]. Nonetheless, the reported effects of ComBat harmonization on RFs varied across different studies, with no consensus on how to apply it in radiomics analysis. This study investigates the reproducibility of RFs extracted from renal lesions segmented three different contrast enhancement phases (non-contrast, arterial and late phases). In addition, we assess the agreement in renal lesions' RF values across the non-contrast, arterial, and late phase scans following the application of ComBat harmonization. The aim is to examine and ultimately guide the ability to combine scans acquired in different imaging phases in radiomics analyses. Furthermore, we investigate the differences in the ability to predict overall survival using RFs extracted from the different imaging phases, which could help identify the preferential imaging phase for predicting outcomes of renal lesions.
## 1 Introduction
### Vector Tracking Techniques for GNSS Receiver Design  
The concept related to the vector delay lock loop (VDLL) which is assumed as the prototype of the VT structure for the tracking loop towards the GNSS receiver design was initially presented by [PERSON] [47]. Over the past several years, the VT technique has been accounted for many researchers' interests embracing the objectives to enhance the ability of the receiver on weak and dynamic signal tracking [48; 49], interference anti-jamming [50; 51] as well as the multipath mitigation [52; 53]. The researchers from the Position, Location And Navigation (PLAN) group of the University of Calgary have gained many achievements of working on the VT technique [50; 54; 55; 56]. The strengths and drawbacks of the VT technique have been thoroughly analyzed in their publications, and many practical applications based on the VT architectures have been realized with the GNSS software-defined receiver (SDR) [57; 49; 58]. Many academic works have been published by [PERSON] who was from University of Minnesota, and these publications have provided the exhaustive analysis towards the receiver autonomous integrity monitoring (RAIM) algorithm for the VT loop [59; 60; 61]. The random noise in the navigation solutions produced by the VT architecture is the sum of the statistics data associated with the current epoch plus several past epochs. It has been mentioned in her dissertation that the time correlated errors related to the atmospheric delay residuals, ephemeris, multipath as well as the local clock estimations have to be appropriately modelled, and then it will be exploited to compensate such bias error present in the positioning solutions [59]. Accordingly, the researchers from the PLAN group propose an algorithm to improve the Kalman filter (KF) to deal with the time-correlated error in terms of the multipath interference coupled in the measurements [62]. Assuming that the time-correlated error due to the multipath error can be removed by the improved KF technique, there are still remained time-correlated errors in the VT loop caused by the statistical data from the local clock estimations, and these sorts of errors will be not present in the traditional scalar tracking (ST) loop. There are few papers published dealing with such time-correlated error.
###### Abstract  
The composite electromagnetic (EM) scattering characteristics from a target above a canyon/valley environment are significant. Aiming to acquire the composite EM scattering efficiently and accurately, the framework of the canyon/valley environment modeling method and modified shooting and bouncing rays (SBR) hybrid with facet-based small slope approximation (FBSSA) algorithm is investigated. Firstly, the canyon/valley environment containing two slopes and a bottom modeling method is proposed. Then, considering the environment's roughness, the modified SBR algorithm introduced by the high-order reflection model is proposed. Combined with the FBSSA, the modified SBR-FBSSA algorithm is an efficient and accurate method to predict composite EM scattering based on numerical verification. Finally, the effects of different surface types, roughness, slope angles, and incident-pitch and azimuth angles on the composite EM scattering characteristics are further analyzed. The work presented in this article provides a way to study the composite EM scattering from a target above the canyon/valley environment. Meanwhile, the complex scattering mechanism is revealed, and some valuable conclusions are put forward based on the physical phenomena.  
canyon/valley environment modeling; modified shooting and bouncing rays; high-order reflection model; facet-based small slope approximation; composite electromagnetic scattering
## 1 Introduction
In this study, the dynamic characteristics of turboexpander were resolved into the energy consumption and energy recovery. The expansion process was simulated using Ansys CFX. The matching characteristics of turboexpander with a motor were analyzed in detail, which was compared with the characteristics of energy consumption. Through analysis, different matching modes were unified, and the turboexpander matching model was modified and improved. Then based on the transient model, a dynamic model of different operation modes was modified via MATLAB code. The quantitative evaluation method of required power in the cooling process was given. The energy efficiency and economic costs under varied modes were clarified. The starting characteristics of turboexpander together with the cooling performance of the refrigerator were simulated and analyzed. The changing input power of the refrigerator was calculated and evaluated under different rotating speeds of turboexpander. The dynamic simulation conclusion was applied in the experiment and simulated results were compared with experimental data. Test apparatus of the refrigerator were improved with a separate brake cycle. The aims of this work were to simulate and evaluate the dynamic matching and refrigerating performance of the cryogenic turbo-based reverse Brayton refrigerator, provide a basis for the evaluation and operation of refrigerators, and allow for improving the energy and economy efficiencies.
# Centralized MPPT Control Architecture for Photovoltaic Systems Using LoRa Technology
## 3 Experimental Setup
### Control System and Wireless Implementation
#### 3.3.4 LoRa Configuration  
LoRa technology provides flexibility in adopting various network topologies. For the focus of this article, we opted for a point-to-point communication setup that involved the sensor and actuator nodes, as well as the computation node. In this configuration, greater control over communication parameters was achieved. To enhance message management, each node was assigned a unique identifier. Messages lacking the appropriate identifier for the intended recipient node were promptly discarded.  
The Time on Air (TOA) was contingent upon several factors, as outlined in Table 4. Operating within the 868 MHz band, which is specific to Europe, a spread factor of 7 and a code rate of 4/5 were selected to minimize the TOA. A code rate of 4/5 allows using Forward Error Correction (FEC) techniques, effectively reducing the number of packet losses with the least impact on the data rate. The standard 125 KHz bandwidth was selected for signal broadcasting, resulting in a data rate of 5470 bps.  
The information transmitted by the sensor and actuator node was sent in 40-byte packets, encompassing the essential variables from the PV panel. This 40-byte payload corresponded to a Time on Air (TOA) of 69.63 ms [20]. Furthermore, an 8-byte preamble was essential for synchronization, adding an extra 12.54 ms to the TOA. Consequently, the  
\begin{table}
\begin{tabular}{l r} \hline \hline
**Parameter** & **Value** \\ \hline Frequency & 868 MHz \\ Spreading factor & 7 \\ Channel frequency & 125 kHz \\ Code rate & 4/5 \\ Preamble & 8 symbols \\ Output power & 17 dB \\ \hline \hline \end{tabular}
\end{table}
Table 4: LoRa communication configuration parameters.  
total TOA for the sensor and actuator node messages amounted to 82.18 ms. It is worth noting that the default output power for MKR WAN 1300 boards was configured to 17 dB.
## 2 Materials and Methods
### Numerical Models, Solution Methods and Boundary Conditions
#### 2.2.4. Boundary Conditions  
The slip mesh divides the computational domain into two regions, rotation and stationary, and uses the interface boundary surface, which is suitable for more complex rotating flow. Then, combined with the flow state between the rotor and stator in the hydrodynamic cavitation generator, the mesh motion model is chosen as the MRF. The boundary conditions are set as follows: The inlet is set as a velocity inlet, while the outlet is selected as a pressure outlet. The boundary condition of a no-slip wall is used. The discrete formats of the pressure and momentum terms are staggered pressure (PRESTO) and first-order windward format, respectively. The PISO algorithm is employed for the coupling of the pressure-velocity field, where the first-order implicit scheme is used to time advance. The fluid is a mixture of gas and liquid water, with a controllable actual flow rate. The turbulence intensity is set to 5%, the rotor speed is stabilized at 1500 rpm, the rotation period is 0.03-0.04 s, the step size is taken as 0.001 s, and the number of iteration steps is 2000. The calculations are initialized so that the computational domain is filled with a stationary fluid, by which two-phase flow calculations are performed. During the calculation process, the cavitation bubble volume fractions on the surface of the rotated and fixed teeth are monitored. The initial parameters were set according to Table 2.  
The transient gas-phase cloud diagrams of the 2-row teeth rotary hydrodynamic cavitation generator were analyzed in Fluent. The time points of 0.01 s, 0.10 s, 0.20 s, 0.50 s, 0.60 s, 0.70 s, and 0.80 s were selected as the analysis nodes.  
In the gas phase contour plots at each time node as shown in Figure 3, cavitation occurs on the trailing edge of the rotated tooth. With the numerical simulation time progressing, the cavitation occurrence area on the trailing edge of the rotated tooth gradually expands and stabilizes after 0.60 s. Therefore, the gas volume fraction on the trailing edge of the rotated teeth is selected as the standard to measure the cavitation effect with the steady state time set at 0.80 s.
## 2 Materials and Methods
### Experimental Design of Adsorption Isotherm  
In 750 mL glass bottles, 100 g of adsorbent material (i.e., CG-Z, FG-Z and CG-G) and 500 mL of NH\({}_{4}\)Cl solution containing different initial NH\({}_{4}\)-N concentrations (i.e., 20, 30, 40, 50 and 60 mg/L) were added. In the glass bottles with CG-G as porous media, the initial NH\({}_{4}\)-N concentrations ranged from 2 to 60 mgN/L (2, 5, 10, 20, 40, and 60 mgN/L). For the adsorption isotherm experiments on orthophosphates, the same procedure was followed as in the case of NH\({}_{4}\)-N, with the difference being that an aqueous solution of orthophosphates with a concentration ranging from 2.5 to 20 mgP/L (i.e., 2.5, 5, 10, 15 and 20 mgP/L) was added to the bottles with the adsorbent material (i.e., CG-Z, FG-Z, and CG-G). The experiments were carried out at two temperatures, 10 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. Samples from the bottles were taken on the 6 th day, which was considered the threshold where, as shown by the kinetic adsorption experiments, substrate saturation occurs.  
The experimental data were fitted to Langmuir and Freundlich isotherm models, which are frequently used in the literature [43; 44]. The Langmuir and Freundlich models (linear form) are given by Equations (10) and (11), respectively:  
\[\frac{\mathrm{C_{e}}}{\mathrm{q_{e}}}=\frac{\mathrm{C_{e}}}{\mathrm{q_{m}}}+ \frac{1}{\mathrm{k_{L}}\mathrm{q_{m}}} \tag{10}\]  
\[\log\mathrm{q_{e}}=\log\mathrm{k_{F}}+\frac{1}{n}\log\mathrm{C_{e}} \tag{11}\]  
where \(\mathrm{q_{m}}\) (mg/kg) is the theoretical maximum adsorption efficiency, \(\mathrm{q_{e}}\) (mg/kg) is the amount of substance (i.e., ammonium or orthophosphates) adsorbed on the porous media at equilibrium, \(\mathrm{C_{e}}\) (mg/L) are the equilibrium concentrations of the solute (i.e., ammonium or orthophosphates) in the solution, \(\mathrm{k_{L}}\) (L/mg) is the Langmuir adsorption constant and represents the adsorption energy, \(\mathrm{k_{F}}\) (mg/kg) is the Freundlich adsorption constant, and n is the adsorption intensity constant. The n value determines the type of adsorption as follows: if n = 1, the adsorption is linear; if n < 1, the adsorption process is chemical; and if n > 1, the adsorption is a favorable physical process [45; 46]. The coefficient of determination (R\({}^{2}\)) was used to evaluate the fit of the isothermal models to the experimental data.
## 2 Statistical Analyses of Individual Hazards
### Assessing the Stability of Landslide Dams
#### 2.2.1 The Landslide Dam Stability Index  
[PERSON] et al. [16] established a database for rapid predictions of dam stability. In the database, 50 documented historical landslide dams with complete data were chosen for assessing the stability of landslide dams, as listed in Table A2. Based on [PERSON]'s model and the database, a new index named the landslide dam stability index (LDSI) was proposed, which is expressed as follows:  
\[\text{LDSI}=\frac{\log_{10}\frac{100H_{d}L_{V}V_{S}^{1}\frac{1}{S_{C}^{2}}}{ W_{d}V_{D}}}{M} \tag{6}\]  
where \(W_{d}\) is the dam's width (m), \(L_{d}\) is the dam's length (m), and \(M\) is the material coefficient, which describes the influence of the material on the dam's stability. If the geometries of two landslide dams are exactly the same, a landslide dam formed by rock is more stable than one formed by earth, so the deposited material significantly affects the stability. Different values of \(M\) have been suggested for various landslide materials according to the results of analyzing the database. The suggested values of \(M\) are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Rock** & **Rock and Debris** & **Debris** & **Debris and Earth** & **Earth and Debris** & **Earth** \\ \hline
1.0 & 0.9 & 0.8 & 0.7 & 0.6 & 0.5 \\ \end{tabular}
\end{table}
Table 2: Suggested values of \(M\).  
Figure 4 shows that when LDSI \(<2.7\), the landslide dam is stable, and when LDSI \(\geq 2.7\), the landslide dam is unstable. When the number of correct cases was divided by the total number of cases, the formula's accuracy reached 86%.  
Before a landslide occurs, it is difficult to precisely evaluate the dam's length, height, width, and volume; the reservoir capacity of barrier lake; catchment area; etc. Thus, to assess the risk of the hazard chain triggered by a landslide, it is necessary to estimate these parameters in advance. Some empirical methods are introduced in the following section.
## 2 DEOS Mission Scheduling Model
### Problem Description

Table 1: Parameter and label definitions. Considering the actual DEOS system, this paper makes the following reasonable simplifications and assumptions for the mission scheduling process:  
1. The observation task involved in this paper refers to the observation of point targets on the ground by satellites using different types of payloads, and a target only needs to be observed once. 2. A single satellite carries only one payload. This paper considers two types of payloads, visible light and synthetic aperture radar (SAR). The satellite only executes one mission at one time. Once the mission starts, no interruption is considered. 3. Each satellite has computing and processing capabilities, and there is a real-time communication link between the satellites, which can meet the needs of mutual communication and information transmission at any time. 4. The situation of satellite orbit maneuver is not considered.
## 3 Results
### TLC Analysis of the Hydrolysate Produced by CelA\({}_{93}\)  
TLC analysis of the CMC hydrolysate produced by CelA\({}_{93}\) revealed that the enzyme can decompose cellulose into cellobiose (Figure 5A). Cellooligosaccharides larger than cellobiose were also generated by CelA\({}_{93}\), indicating that the enzyme is an endo-type \(\beta\)-glucanase, i.e., endo-\(\beta\)-1,4-glucanase.  
Among the commercially available cellooligosaccharides tested, CelA\({}_{93}\) could not hydrolyze cellobiose but decompose cellotriose into cellobiose. Moreover, it decomposed cellotetraose, cellopentaose, and cellohexace to produce common products cellobiose (major product) and cellotriose (minor product). From these, CelA\({}_{93}\) is expected to break down cellooligosaccharides to produce cellobiose (major) and cellotriose (minor), and subsequently to break down cellotriose into cellobiose and glucose. However, spots corresponding to D-glucose produced by the degradation of cellotriose on TLC were detected very weakly. These results are consistent with the results reported in several scientific papers [23; 24], and are interpreted to be attributed to the relatively low degradation activity of the enzyme toward cellotriose (Figure 5B). In addition, the enzyme activity against CMC was very weak compared to the enzyme activity against cellooligosaccharides, probably because the three-dimensional structure of crystalline cellulose, such as CMC, makes it difficult for the enzyme to access the substrate. These results clearly indicate that CelA\({}_{93}\) is endo-\(\beta\)-1,4-glucanase that can hydrolyze CMC and cellooligosaccharides into cellobiose.
# Numerical Simulation of T Joints Constructed from Hollow Steel Sections
The limit carrying capacity values that were established experimentally were up to 2.5 times more than those that were calculated in accordance with EC 3 using nominal stretching limits, taking experimentally determined stretching limits into account as well. After taking seam effects into consideration, the authors empirically arrived at a limit bearing capacity value that was almost 25% higher than the results obtained numerically. Because of the post-elastic stiffness of the material resulting from plastic deformations and membrane effects, the calculated limit bearing capacities for the experimentally tested joints were very conservative. The good coherence between the experimental data and numerical and analytical approaches was established as the paper's conclusion. In [21], the authors studied \"T\" connections of cold-formed RHS sections and concluded that the corners of cold-formed RHS sections should be considered in predicting both web buckling strength and chord flange strength. In the publication [22], the study included the hollow section, as well as the fatigue loading. Some recommendations for the advanced design of the RHS section connections are also given in [23]. The appropriate material parameter selection is another important part of FEM modeling. An approximate stress-strain relationship is frequently used in place of an accurate one in order to speed up computations. Usually, in FEM analyses, the stress-strain relationship is defined by a bilinear model with isotropic hardening. Ref. [24] discusses the limitations of this kind of material model. The advantage of non-linear FEM modeling compared to analytical solutions was pointed out in [25; 26; 27]. The results obtained from the theory of limit states show that the behavior of these connections at the fracture state is dependent not only on the base material quality and the geometric properties of the connected element, but also on the configuration, or more specifically, the shape of the connection, and the kind and size of the load applied to a chord. In order to account for material non-linearity, this study presents the use of a non-linear numerical analysis for \"T\" connections and makes comparisons with the findings of experimental research carried out under quasi-static loading [28]. This methodology applies a mixed incrementally iterative strategy based on the so-called Total Lagrange formulation [29]. This method divides the overall load into multiple steps, or increments, and performs iterations at each step to equalize the residual load. This is the basic formulation used in numerical approaches to solve non-linear issues [30]. To validate the presented modeling method under the limit states of these connections, a comparison analysis of the outcomes from the analytical solution and the numerical and experimental findings was conducted [28]. The software tool ANSYS R14.5 was utilized to conduct the numerical non-linear analysis [31].
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing
### Data Preprocessing
#### 3.2.4 Extraction of Drop-Off and Pick-Up Points  
Since trajectory data are different from other types of relational data, with obvious spatio-temporal attributes, it is necessary to make trajectory processing for taxi GPS positioning data [30]. The characteristics of the passenger field of the data in this paper are shown in Figure 3.  
Based on the characteristics of the passenger status field, the process of the taxi pick-up and drop-off point extraction algorithm designed in this paper is as follows:  
Step 1: Initialize the preprocessed trajectory data; the set of boarding points is noted as \(O\), and the set of alighting points is noted as \(D\); execute Step 2.  
Step 2: Iterate over the trajectory dataset's passenger-carrying status column Statei, notate the serial number as i, and execute Step 3.  
Step 3: Calculate the difference of \(State_{i}-State_{i+1}\), denoted as \(C\). If \(C=-1\), then put this record into the set \(O\); if \(C=0\), then the taxi is in the traveling stage, without identifying whether it is carrying a passenger or not. If \(C=1\), then put this record into the set \(D\) and execute Step 4.  
Step 4: Extract the set of boarding points as \(O\) and the set of alighting points as \(D\), respectively.