esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Robust Principal Component Analysis  
RPCA is an efficient matrix decomposition algorithm capable of extracting the low-dimensional structure of a complex matrix. A complex matrix \(D\) can be decomposed into the sum of matrices \(A\) and \(E\), where \(A\) is a low-rank matrix with a few nonzero singular values and \(E\) denotes a sparse matrix with a few nonzero entries [15]. Their mathematical meanings can be expressed by the following equation:  
\[\left\{\begin{array}{rl}\text{min rank}(A)+\left\|E\right\|_{0}\\ s.t.&A+E=D\end{array}\right. \tag{3}\]  
where rank\((A)\) represents the rank of the matrix and \(\left\|\ \right\|_{0}\) represents the 0 norm, \"min\" is the abbreviation of \"minimum\" and \"s.t.\" is the abbreviation of \"subject to\". This formula can be understood as decomposing a matrix \(D\) into the sum of two matrices A and \(E\), and minimizing the sum of the rank of matrix \(A\) and the 0 norm of matrix \(E\). This is an optimization problem, which can be solved by optimization algorithm. It should be noted that in this method, we do not pay attention to the specific values of rank\((A)\) and \(\left\|E\right\|_{0}\), but how to minimize the sum of them.  
However, the rank function and 0 norm are non-convex, making it difficult to solve Equation (3) directly. In this regard, [PERSON] et al. put forward the use of the nuclear norm instead of the rank function and the 1 norm instead of the 0 norm, thereby defining the so-called RPCA [22]:  
\[\left\{\begin{array}{rl}\text{min }\left\|A\right\|_{*}+\left\|E\right\|_{1} \\ s.t.&A+E=D\end{array}\right. \tag{4}\]  
Figure 1: The schematic diagram of back projection algorithm. The red rectangle is the transmitting antenna and the blue rectangle is the receiving antenna. The black circular is the target object. Point A is a generic point in the detection region. The x-axis is the measuring line, and the y-axis is the detection depth. The background dielectric constant is \(\varepsilon_{r}\)[13].  
where the nuclear norm \(\parallel\parallel_{*}\) is the sum of the singular values of the matrix and the 1 norm \(\parallel\parallel_{1}\) is the sum of the absolute values of the matrix. Compared to Equation (3), the nuclear norm and the 1 norm used here are both convex, so it is more tractable by optimization method [22]. We choose the truncated nuclear norm regularization method to solve this optimization problem (Equation (3)) [23].
## 1 Introduction
When compared to the conventional superheterodyne topology, the direct conversion structure is more appealing as the front-end of multicarrier schemes due to its compact size and ease of integration on a single chip [13]. Furthermore, it offers excellent adaptability to meet the evolving needs of today's communication infrastructures for radio standards. The discrepancy between the in-phase (I) arm and the quadrature-phase (Q) arm, which can occur at both the broadcaster and recipient, is one of the key difficulties with this structure. This IQ discrepancy (IQD) is common in analog computation because of component flaws, which cannot be anticipated or managed and tend to widen as production technologies shrink in size [14; 15]. Tolerating these abnormalities in the analog domain and correcting for them digitally is simpler and more versatile than reducing the IQD with costly analog devices. The IQD, specifically, can be broken down into two distinct types: frequency-flat and frequency-selective IQD [16]. The former is mainly produced by poorly balanced local oscillators, which fail to generate I and Q arms with identical amplitudes and a precise 90 degree phase shift. The latter is normally brought about by other defective analog parts including filters, amplifiers, and converters from digital to analog or analog to digital. Multicarrier schemes are more susceptible to IQDs than single-carrier transmissions are. This is because the sub-carriers in multicarrier schemes have spectral clash and tight separation between them. As a result, sub-carrier orthogonality of multicarrier schemes breaks down, inter-carrier disturbance grows, and the overall efficiency suffers. The impact,assessment, and correction of the IQD for multicarrier communications have been discussed in a significant number of studies, see [17; 18] and the citations therein.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 2 Materials and Methods
### Development of Multiple Regression Models
#### 2.5.2 Variable Selection  
For use in the regression models, the variable was selected based on well-known knowledge regarding the tractor power transmission system and the results of some previous studies. The tractor axle is closely related to the engine load because it is driven by power from the engine. The engine's operating point depends on its speed, which is based on the engine map, and this determines the engine torque. The engine parameters can be collected without a separate sensor when the tractor is equipped with an electronic engine capable of CAN communication, thereby reducing costs. Therefore, the engine speed and engine torque were selected as the parameters of the engine. In addition, variables for predicting axle torque were selected according to previous studies, which mentioned that the parameters of the tillage depth [6; 8; 38], slip ratio [6; 15; 39], and travel speed [5; 38; 40] had significant  
Figure 4: Procedure for the development and verification of the prediction model using regression analysis.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameters** & **Number of****Gear****Pairs** & **Gear****Ratio** & **Efficiency** \\ \hline Front wheel—gearbox output axle & 6 & 15.8 & 0.941 \\ \hline Rear wheel—gearbox output axle & 3 & 22.2 & 0.970 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of gear pairs, gear ratio, and efficiency between each wheel and gearbox.  
effects on tractor load. Therefore, in this study, the engine torque, engine speed, tillage depth, slip ratio, and travel speed were selected as the explanatory variables.
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 3 Methods
### The Improved Ratio Model  
The ratio model was proposed by [PERSON] to eliminate the influence of the vegetation canopy on the radar backscattering coefficient. The model assumes that the ratio of the soil backscatter to the total backscatter is only related to the vegetation indices [22]. Based on the fact that the soil backscatter and the total surface backscatter are affected by both vegetation and soil moisture, the ratio model was modified as a nonlinear function of SM and LAI in this study.  
\[\frac{\sigma_{pq}^{\text{S}}}{\sigma_{pq}^{\text{T}}}=f(\text{SM},\text{LAI}) \tag{3}\]  
where \(\sigma_{pq}^{\text{S}}\) represents the backscattering coefficient of the bare soil, \(\sigma_{pq}^{\text{T}}\) is the total backscattering coefficient of the radar, and the subscripts \(p\) and \(q\) denote the transmitting and receiving polarizations, respectively.  
The MIMICS model can simulate the total surface backscattering coefficient and the backscattering coefficient of different components such as the vegetation, the soil and the interactive term between the two. Based on the simulation data set of the MIMICS model, the correct mathematical expression of the improved ratio model described by Equation (3) needed to be determined. The main input parameters of the MIMICS model listed in Table 2 under the configuration of the Sentinel-1 image were used to produce the simulation data set.  
Figure 3: Simulation results under different LAI and the fitting performance between NDVI and LAI.  
Based on the regression analysis on the simulated data set, the Formula (3) can be well expressed as a nonlinear function of SM and LAI as shown in Equations (4)-(6).  
\[\frac{\gamma_{pq}^{2}\cdot\sigma_{pq}^{\text{S}}}{\sigma_{pq}^{\text{T}}}=\frac{ f_{1 pq}(\text{SM})\cdot\text{LAI}+f_{2 pq}(\text{SM})}{\text{LAI}+f_{3 pq}(\text{SM})} \tag{4}\]  
with  
\[\gamma_{pq}=e^{-k_{pq}\cdot\text{LAI}} \tag{5}\]  
and  
\[\begin{array}{l}f_{1 pq}=a_{1 pq}\cdot SM+b_{1 pq}\\ f_{2 pq}=a_{2 pq}\cdot SM+b_{2 pq}\\ f_{3 pq}=a_{3 pq}\cdot SM+b_{3 pq}\end{array} \tag{6}\]  
The empirical coefficients of \(a_{1}\), \(b_{1}\), \(a_{2}\), \(b_{2}\), \(a_{3}\), \(b_{3}\) and \(k\) obtained by fitting the MIMICS simulation data are listed in Table 3 with the Pearson determination coefficient (R) of 0.99 for both the VV and VH polarizations.
# pFedBASC: Personalized Federated Learning with Blockchain-Assisted Semi-Centralized Framework
###### Abstract
To address these issues, we adopt personalized federated learning (pFL), in which a personalized model is learned for every client. Typically, the final personalized models outperform global models that are trained using normal FL techniques. We propose a new _personalized federated learning with blockchain-assisted semi-centralized framework (pFedBASC)_. This framework focuses on real-world applications, utilizing various types of network connections for collaborative training and leveraging the blockchain to provide comprehensive distributed training support for FL. The pFedBASC aims to minimize communication costs and increase flexibility. The proposed framework allocates aggregation tasks to clients, reduces aggregation time through trusted client connections, and applies a blockchain-assisted approach to optimize the aggregation of untrusted client models, ensuring the local model's adaptability and performance. We embed a new algorithm, _personalized federated learning with hypernetwork (FedHype)_, into pFedBASC. FedHype is designed to address the challenges of pFL training and data heterogeneity. FedHype directs each client to train a small hypernetwork (HN) and a larger local target network (TN) whose model parameters are generated by the small HN. Clients are permitted to perform local aggregation of HN to train a personalized TN. Only the compact local HN is involved in knowledge exchange among clients, which helps mitigate data heterogeneity and reduces communication costs. pFedBASC can utilize FedHype to solve personalized training issues effectively in BFL. The main contributions of this paper are as follows:  
* We propose a blockchain-assisted semi-centralized pFL framework called pFedBASC, which provides a reliable collaborative training environment for distributed data in IoT and offers guidance for designing BFL algorithms. * We design the block structure of the blockchain for pFedBASC. We model and formally describe the semi-centralized framework. Building upon this, we design an FL aggregation method, utilizing loss functions and delayed rounds for weight adjustment. * We propose a pFL algorithm called FedHype, which takes advantage of the hypernetwork's characteristics. FedHype significantly enhances the overall pFL performance and meets the personalized training needs of different users. We also integrate FedHype with other existing algorithms, further extending its functionality.
## 1 Introduction
In addition to GPP estimates provided by various methods, these two remote sensing-based GPP products provide a high time resolution of 8 days. Therefore, analyzing the relationship between tree-ring and 8-day GPP can more intuitively and accurately reflect the relationship between radial tree growth and canopy activity. The Helan Mountains are the demarcation line between arid and semi-arid areas in northwest China, which makes the vegetation in the region very sensitive to climate change [15]. The forest in this region is an important barrier against the advancement of the northwest desert; therefore, studying forest growth strategies is highly significant. Previous studies have focused on the relationship between secondary growth of tree rings and climate, whereas the relationship between primary and secondary growth of the forest itself has rarely been studied. Therefore, we aimed to study the relationship between radial growth and canopy dynamics (tree rings and GPP) of the dominant tree species in the Helan Mountains, spruce (_Pica asgruceerata_) and Chinese pine (_Pinus tabuliformis_), at higher temporal scales to provide a basis for an in-depth understanding of the coordination of various organs in the forest in the dry zone of northwest China and provide some theoretical guidance for vegetation restoration and forest conservation under climate change.
## 3 Methodology and Criteria for a Comparative Evaluation of the Guidelines
### Survey Evaluation
#### 3.3.1 AHP and Weight Calculation
After the comparison matrix is filled with average ratings, the matrix is normalized by dividing each field with the sum of the respective row resulting in Table 3.  
Now for each row the weights can be calculated. Table 4 shows the weights for the main categories calculated using the AHP method. \begin{table}
\begin{tabular}{l l l l l l}  & **C1** & **C2** & **C3** & **C4** & **C5** \\
**C1** & 1 & 1.55 & 0.77 & 0.95 & 1.11 \\
**C2** & 0.65 & 1 & 1.27 & 1.21 & 1.51 \\
**C3** & 1.30 & 0.79 & 1 & 1.55 & 1.60 \\
**C4** & 1.05 & 0.83 & 0.64 & 1 & 1.77 \\
**C5** & 0.90 & 0.66 & 0.62 & 0.57 & 1 \\ \hline \end{tabular}
\end{table} 
Table 1: Aggregated (average) comparison matrix (n = 34). \begin{table}
\begin{tabular}{l l} \hline
**Category Name** & **Short Name** \\ \hline DHCPv6 and Autoconfiguration & C1 \\ IPv6 Specification and Address format & C2 \\ Routing and DNS & C3 \\ Transition Methods & C4 \\ IPvSec and ICMPv6 & C5 \\ \hline \end{tabular}
\end{table} 
Table 2: Categories. As the results show, most of the weights are very close. Only _IPsec_ received a low weight of 14%. _Routing and DNS_ is viewed as most important with 24%. The _IPv6 Specification and Address format_ is ranked second with 22%, closely followed by _DHCP and Autoconfiguration_ (21%) and _Transition Methods_ (20%). _IPsec_ might be rated lower than the other categories because it is implicitly contained in IPv6 and is activated by default when implementing IPv6. Another reason might be that _IPsec_ is already used with IPv4 and most cryptographic methods are already known from the IPv4 world. ICMPv6 does not seem to be as important as other categories, even though the numbering of the messages has changed and new mechanisms such as ND and PMTU have been introduced. _Routing and DNS_ are also known from IPv4, but are more difficult due to the increased length of the addresses. Routing tables are feared to dramatically increase, and it is important to know how to use the routing protocols with the new Internet protocol version. DNS works very similar with IPv6 as with IPv4, but needs to be handled carefully. In particular, reverse DNS can easily lead to faults due to human failure when entering reverse addresses manually. _IPv6 Specification and Address format_ are also important for a secure deployment of IPv6. Since IPv6 addresses are longer and represented in a very different way than with IPv4, practitioners need to become comfortable with the new addresses. The new scope concept for addresses is also an important feature. IPv6 also specifies a new streamlined header and extension header concept, which makes IPv6 more flexible but also more complex. For this reason, probably, this category has been rated this high. _DHCPv6 and Autoconfiguration_ and _Transition Methods_ are also important to be aware of as the results show.
## 1 Introduction
Notably, DESs offer numerous advantages, including cost-effectiveness, non-toxicity, and biocompatibility [26; 27]. Furthermore, they can selectively dissolve lignin from plant materials through acid-based catalytic mechanisms [28]. DESs are also generally less expensive than numerous ILs owing to their low precursor costs, straightforward synthesis methods, and recyclability. Among the wide variety of DESs, metal halide-based DESs, particularly those containing zinc chloride (ZnCl\({}_{2}\)), have emerged as viable and affordable alternatives to conventional solvents. These DESs are particularly valued for their remarkable solvation capabilities, rendering them useful as solvents in biodiesel applications, reusable or homogenous catalysts, and high-temperature electrolytes [29]. Despite the successful applications of ZnCl\({}_{2}\)-based DESs as solvents in biodiesel applications, related fundamental research and further explorations in biomass conversion remain limited. Inspired by previous studies, this study investigated the combined effects of ethanol and (ChCl:ZnCl\({}_{2}\)) DESs on the depolymerization of Kraft lignin (KL). Key variables, including the molar ratio of ChCl to ZnCl\({}_{2}\), reaction temperature, and reaction duration, were examined to assess their impacts on lignin depolymerization. Analytical techniques such as gas chromatography-mass spectrometry (GC-MS), gel permeation chromatography (GPC), and Fourier transform infrared spectroscopy (FT-IR) were employed to analyze the formation of different products and the cleavage of bonds in lignin.
## 4 Electronic Sensing Design for MLE-Based Position Estimation
### MLE Target Position Estimation  
The conceptual test idea for the on-fly UAV position localization is shown in Figure 11. In this case study, the distance from the \(i-\)th base module to the UAV is computed using (8), and then, the position of the UAV \(\mathbf{x}=[x_{1},x_{2},x_{3}]^{\mathrm{T}}\) is estimated using (10) for the fixed position of the GLS. The Cartesian coordinates of each of the base modules located in the GLS are the following: \(\mathbf{A}=(0,0,0)\), \(\mathbf{B}=(0,0.26,0)\), \(\mathbf{C}=(0.55,0.26,0)\) and \(\mathbf{D}=(0.55,0,0)\). The UAV follows a straight trajectory with continuous speed, and at the same altitude, i.e., 0.5 m from the GLS, as illustrated in Figure 11.  
Table 1 summarizes a comparative analysis of the UAV's estimated position with their real position and the resulting error at each Cartesian coordinate.
# Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites
## 1 Introduction
Due to the small thrust, it is difficult to complete high-precision thrust calibration on the ground. The commonly used thrust calibration methods include the parameter calibration method, wheel calibration method, and orbital calibration [22]. However, these methods simplify the thrust to a fixed vector without considering the influence of the error of the vector adjustment mechanism, nor do they consider the coupling effect between the other subsystems and the motion of the vector adjustment mechanism. Therefore, a challenge that needs to be tackled is how to create a hierarchical rule and model updating method to correct the parameters of a satellite with vector electric propulsion. To address the problems mentioned above, this paper aims to develop a hierarchical model updating method for the vector electric-propulsion satellite. Firstly, in order to reflect the coupling effect between different parts, the whole satellite is decomposed into different subsystems and devices to establish mathematical models. The state variables that play a major role in affecting the telemetry variables are selected for updating. Then, a grouping rule is put forward in terms of the relationship of variables. State variables and telemetry variables are grouped and sorted according to the rule, and each group is updated by the least-squares method in sequence. When all groups are updated, the residuals of all groups are recalculated. Repeat this process several times until the mean squared error (MSE) converges to the specified range. This paper is organized as follows. Section 2 presents the mathematical description of the model updating problem and establishes the simulation model of a vector electric-propulsion satellite. A hierarchical method for model updating is developed in Section 3. In Section 4, a simulation test case is investigated, and the results are discussed. Finally, Section 5 concludes this paper.
## 2 Materials and Methods
### Raman Microspectroscopy and Spectra Processing  
In order to perform Raman spectra, the coverslip with the fixed cells was located onto a microscope slide equipped with a well containing PBS solution. In particular, the side of the coverslip with attached cells was immersed in the PBS solution. The laser beam passed through the coverslip and was focused above single cells. We ascertained, both visually and by means of a few Raman spectra subsequently measured on the same sample point, that this configuration did not cause sample damage as a result of photochemical reactions or sample heating.  
Raman spectra were measured with a Raman confocal micro-spectrometer system (Labram from [PERSON]), by using the 514.5 nm Ar+ laser line to excite the sample with 10 mW power. The laser beam was focused by a 100\(\times\) oil-immersion objective (Olympus, NA = 1.3) to excite single PBLM cells. Spectra were measured in the 750-1750 cm\({}^{-1}\) range, by focusing the laser spot above the nucleus in order to decrease the cell-to-cell variability and to collect Raman signals mainly from the nucleus (and also from the cellular material located between the cell surface and nucleus). About 30 randomly chosen single cells for each type of cellular sample (unexposed and EMFs exposed) were measured, with the Raman signal averaged over three acquisitions of 10 s each one. The backscattered light crossed an edge filter (Omega Optical, Inc. Brattleboro, VT, USA) to remove the laser signal, a square confocal hole (400 \(\upmu\)m diagonal) and a spectrometer with a 600 grooves/mm grating. The signal detection occurred by a cooled charge-coupled device (CCD). The spectral resolution was -5 cm\({}^{-1}\)/pixel. Background spectra, related to coverslip and PBS Raman signals, were measured by moving the sample so that the laser was focused to a cell-free area of the coverslip.  
Each Raman spectrum was preprocessed by a subtraction of the corresponding background signal and the cell fluorescence and stray light signal (with an automated baseline removal method by LabSpec 5 software, version 2.02, Horiba Scientific, Kyoto, Japan, 2010). Then, each spectrum was normalized to the total spectral area. Eventually, the normalized spectra of each type of cell sample were averaged to obtain a mean spectrum for the unexposed and exposed cells. The PCA method for the set of cells irradiated for different times and the corresponding control cells was performed with ChemoSpec package of R software (version 3.4.1, R Core Team, Vienna, Austria, 2017) [24].