esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Through these investigations, the dual adaptive filtering for artificial measurement noise estimation is finally selected and used along with the state-space model. It is also noted that the dual adaptive filtering, which adopts a slave filter to update artificial-measurement-noise of the master filter, requires additional selection of artificial noise covariance matrices for the slave filter. The increased number of covariance matrices of artificial noises makes it challenging to tune dual adaptive filter for various choices of those covariance matrices. It is difficult to select proper artificial noises of slave filter empirically due to the absence of accurate knowledge [13; 15; 18]. Therefore, it is important to construct a general framework for tuning process of dual adaptive filtering. For this purpose, particle swam optimization (PSO), which was utilized in a recent study to tune Kalman filter for mobile robot attitude estimation [14], is adopted for dual filtering with appropriate assumptions to decrease the number of tuning parameters. However, this tuning process should be performed thoroughly because of randomness in the PSO algorithm. This study thus aims to improve estimation of dual filtering by performing PSO again with different boundary conditions. In numerical investigations, the estimation results by the proposed approach will be presented along with several considerations including the use of iterative tuning process. This paper first introduces the algorithm of UKF with its general formulation for dynamic state-space model in Section 2. Various applications of filter methods will be also mentioned together with practical issues. Then, Section 3 introduces the problem statement of dynamic system, which can be damaged during seismic excitations. A proper state-space model is proposed and explored to choose appropriate adaptive filtering techniques. In Section 4, for an actual earthquake excitation,several damage scenarios are assumed based on the calculated time histories of relative displacement between adjacent stories. For these scenarios, accuracy and robustness of the proposed method are tested and demonstrated in comparisons with normal UKF and other adaptive filtering techniques. Lastly, this paper concludes with several insights and future research topics.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 1 Introduction
Our analysis considers the potential for tilting of the radar rotational axis through time, as the observing platform was installed indirectly on soft ground for a total of 26 months, during which time access for maintenance was restricted due to the COVID-19 pandemic. In contrast to the study of [PERSON] et al. [16], the system analysed here was located at a higher latitude (54.5\({}^{\circ}\)N), where wintertime solar interference is restricted to the southern sky (Northern Hemisphere) at lower elevation angles, leading to analytical challenges when considering a tilting platform. This paper begins with a discussion of the NXPol-1 radar system deployment in Cumbria, UK, including factors that may influence its positioning accuracy during the deployment. Then follows a description of the online solar interference methodology applied to the NXPol-1 volume scans, including raw results from the 26-month deployment. This includes a description of the methods applied to derive time-variant positioning offsets from the solar interference data, including the magnitude and bearing of antenna tilt relative to the horizontal plane. We then consider the accuracy of these corrections and how they can be verified using radar and ground observations. A discussion of the impact radar tilt can have on derived radar products (particularly surface precipitation) in regions of complex orography follows, along with a discussion of the wider applicability of the methodology and verification options, considering both radar specification and the location and duration of the radar deployment. Finally, we provide future recommendations focused particularly on short- to medium-term deployments of higher-frequency weather radar systems.
## 4 Methodology
### Methodology Generalization  
The integrated methodology combining Extreme Gradient Boosting (XGBoost) for feature selection and fuzzy-set qualitative comparative analysis (fsQCA) for causal configuration analysis is adaptable to a variety of datasets. In the first step, XGBoost is employed to identify the most influential predictors for the outcome under investigation. Researchers should begin by ensuring the dataset is rigorously pre-processed to address issues of data quality and consistency. Feature selection via XGBoost can be optimized by adjusting model parameters and incorporating context-specific variables that reflect the characteristics of the new dataset. This data-driven approach results in a parsimonious set of variables that are prepared for further causal analysis.  
Following the feature selection, fsQCA is applied to examine the combinations of conditions that lead to the outcome of interest. The selected variables are calibrated into fuzzy sets using established thresholds--full membership, crossover, and full non-membership--that are tailored to the specific data distributions and contextual characteristics of the new dataset. By analyzing the interaction of these calibrated variables, fsQCA uncovers multiple causal pathways, thereby demonstrating the method's ability to handle complexity and equifinality in causal relationships. This sequential approach, beginning with XGBoost and followed by fsQCA, ensures that the methodology is robust and generalizable across diverse datasets.
## 3 Battery Experiments  
### Test Bench  
As shown in Figure 3, an experimental test bench was established to study the characteristics of the lithium-ion battery. The experimental setup consisted of the following: (1) a set of Sony US18650 VTC5a type cells (Sony Corp., Tokyo, Japan) with Li(Ni\({}_{1/3}\)Co\({}_{1/3}\)Mn\({}_{1/3}\))O\({}_{2}\) (NCM) cathode and graphite anode; (2) a thermal chamber with temperature control; (3) current and voltage sensors; (4) a battery test station; (5) a host computer with software to set up the database; and (6) a Matlab 2017b (MathWorks Inc., Natick, MA, USA) to run the model. The cell is a commercial battery that is used for high specific energy demand application with a nominal voltage of 3.7 V and a nominal capacity of 2.5 Ah. The main characteristics of nominal voltage and nominal capacity are shown in Table 1. For the tests, the temperature chamber was set at \(-20\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C, \(10\)\({}^{\circ}\)C, \(20\)\({}^{\circ}\)C, \(30\)\({}^{\circ}\)C, \(40\)\({}^{\circ}\)C, \(50\)\({}^{\circ}\)C, respectively. The battery test station (NEWARE BTS4002, Shenzhen, China) was used for test profile control. Each channel of the BTS was capable of \(\pm 20\) A current and \(+5\) V voltage. The accuracy of current and voltage measurement was 50 mA and 10 mV, respectively.
## 3 Biological Activities
### Antifungal Activity  
Citral stands out for its broad-spectrum antifungal effect (Table 2), and its mechanisms of action have been extensively studied (Table 5, Figure 3). Recently, _Alternaria alternata_, an important foodborne pathogen, was completely inhibited with a citral dose of 250 \(\upmu\)L/L corresponding to the MIC. With a citral dose of \(\frac{1}{2}\) MIC, the biosynthesis of the polyketide mycotoxin (alternariol) and its derivative (alternariol monomethyl ether) was also suppressed up to 97% [79]. The transcriptomic profile revealed that citral provoked cell integrity disturbance, specifically by disrupting fungal spores and inhibiting the biosynthesis of ergosterol, a major structural constituent of fungal cell membranes. Interestingly, 41 over-expressed and 84 repressed proteins of _Penicillium digitatum_ were identified by isobaric tags for the relative and absolute quantitation technique (iTRAQ) when 1.0 \(\upmu\)L/L of citral was applied to the fungus for 30 min [80]. The authors suggested that the citral mechanism involved membrane damage of _P. digitatum_ cells and the disruption of oxidative phosphorylation, which is the first way to produce energy in eukaryotic cells. In another study reported by [PERSON], [PERSON] [81], citral also showed inhibitory action by downregulating the genes related to the sporulation and growth of _Aspergillus ochraceus_ and _A. flavus_. While on _A. ochraceus_, the synthesis of proteins associated with the mycelian growth was altered, achieving a fungal complete inhibition at 200 \(\upmu\)L/L and the reduction of production of mycotoxin ochratoxin A, a 2B-categorized mycotoxin related to cancer in humans, with a dose of 75 \(\upmu\)L/L of citral [54]. Given the broad antifungal activity of citral, its potential as an innovative barrier for controlling fungi in agri-food systems is confirmed. However, further research is needed to explore its incorporation into different products. Factors such as vehicles, specific doses, and bioactivity in each system must be considered during the study. A recent study evaluated the inhibitory effects of citral against several fungal pathogens (_Botryosphaeria dothidea_, _Phomopsis macrospore_, _and Botrytis cinerea_) present in kiwi fruit. In addition, an attempt was made to describe the antifungal mechanism of citral against _B. dothidea_, which is not completely clarified. The antifungal effect was evaluated, determining the inhibitory rate of mycelian growth on potato dextrose agar Petri dishes supplemented
## 1 Introduction
### Installation Process for Offshore Wind Farms
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Operation** & **Base Duration (h)** & **Max. Wind (m/s)** & **Max. Wave (m)** \\ \hline Traveling & 4 & 21 & 2.5 \\ Positioning & 1 & 14 & 2 \\ Jack-up/-down & 2 & 14 & 1.8 \\ Load Tower & 3 & 12 \({}^{\star}\) & 5 \({}^{\star}\) \\ Load Nacelle & 2 & 12 \({}^{\star}\) & 5 \({}^{\star}\) \\ Load Blade & 2 & 10 \({}^{\star}\) & 5 \({}^{\star}\) \\ Load Hub & 1 & 12 \({}^{\star}\) & 5 \({}^{\star}\) \\ Install Tower & 3 & 12 & 2.5 \({}^{\dagger}\) \\ Install Nacelle & 3 & 12 & 2.5 \({}^{\dagger}\) \\ Install Blade & 2 & 10 & 2.5 \({}^{\dagger}\) \\ Install Hub & 2 & 12 & 2.5 \({}^{\ddagger}\) \\ \hline \hline \end{tabular} \(\star\) Omitted using loading bridges. \(\dagger\) Unaffected by wave height due to jack-up.  
\end{table} 
Table 1: Duration of basic operations. a decision-support tool for the operative scheduling has to rely on an incremental approach to be useful in practice. From information gathered in talks with other researchers in this field, it became apparent that in practice, the scheduling is performed more or less ad-hoc. Therefore, the vessels' captains and the port-side operations managers continuously render decisions based on experience, current weather conditions, and using forecasts. [PERSON] provides a review of models and approaches for decision-support systems for the monitoring and control of projects in general. Therefore he defines the main tasks of decision-support systems as the identification and reporting of the project's current status, the comparison with a predefined plan, the identification of and analysis of deviations, and finally, the implementation of countermeasures [19]. A decision-support system provides two main functionalities to achieve this objective: First, a monitoring policy, defining which information must be collected and monitored, also defining the when and how. Second, an intervention and control policy, which clearly states who should counteract deviations from the provided plan, when and in which way.
## 1 Introduction
Although the aforementioned studies undertaken by several researchers assumed that the obstacles' position information is always given to an agent, this study used a 3D scanner to measure the obstacles' point cloud information for practical sensing when the obstacles are within the sensing range and field of view (FOV). In addition, the dynamics of UAV are taken into account to describe the practical behaviors of the UAV. The proposed CA approach is validated at realistic cluttered urban simulation environments generated using real urban datasets. This paper is organized as follows: Section 2 describes the dynamics and kinematic equations, the control logic, and the formulation of the proposed CA approach. Section 3 presents the analysis results for the key parameters of the proposed CA algorithm and the numerical simulation results to validate the performance of the proposed CA algorithm. Finally, the conclusion is described in Section 4.
## 2 Baselines in the Literature
### Baseline Methodologies
#### 2.2.6 Machine Learning  
Machine learning implementations are nowadays also used as a baselining methodology [27; 28; 29; 30]. By using a 'black-box' approach, machine learning eliminates the baseline's proneness to manipulation. Furthermore, it can potentially better describe the behaviour of the more fluctuating flexibility assets, resulting in a higher accuracy. However, there are also disadvantages, namely a lack of transparency and simplicity, which are essential for market parties to accept a baselining algorithm.
## 2 Experiments  
X-ray diffraction of three _TMTe\({}_{2}\)_ single crystals was taken using an X-ray diffractometer (D/MAX-2500/PC, Rigaku, Japan) with Cu K radiation (\(\lambda\) = 1.54718 A) at room temperature. The temperature dependent resistance of three _TMTe\({}_{2}\)_ single crystals was measured by a physical property measurement system (PPMS) (Quantum Design) through direct current (DC) measurements over a temperature range between 1.8 and 300 K.  
For the electrochemical measurement of three _TMTe\({}_{2}\)_ single crystals, a piece of _TMTe\({}_{2}\)_ single crystal was glued to an Au (100 nm)/SiO\({}_{2}\) (300 nm)/Si substrate and connected to the bottom Cu plate. Except for the open area for the HER, all other surface areas were covered by an insulating nail polish to prevent any unexpected leakage current. The open area of samples for the HER was automatically estimated by the Leopard 2.5 program installed in the software for the optical microscope; the current density (_J_) in this manuscript was calculated by the current divided by the open sample area.  
All electrochemical experiments were performed using a potentiostat (VMP3 from Bio Logic, France) and three-electrode electrochemical cell in 0.5 M sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) electrolyte solution. A silver/silver chloride electrode (Ag/AgCl, Qrins, Korea), a platinum wire electrode (Qrins, Korea), and the _TMTe\({}_{2}\)_ were used as the reference, counter, and working electrodes, respectively. Linear sweep voltammetry (LSV) was conducted with a scan rate of 5 mV/s. The electrochemical active surface area (ECSA) analysis with VTe\({}_{2}\), MoTe\({}_{2}\), and WTe\({}_{2}\) was conducted at different voltage range and sweep rates. For impedance measurement, potentiostatic electrochemical impedance spectroscopy (PEIS) technique was used and measurements were carried out at frequency range between 100 kHz to 100 mHz with an AC voltage of 10 mV and a DC voltage, electric potential, of 390 mV (vs. RHE). The results were analyzed by using z-fitting software named EC-Lab.  
First-principles calculations were conducted using a Quantum ESPRESSO package [30]. We used norm-conserving pseudopotentials [31] for W, Te, and H atoms, a plane wave energy cutoff of 80 Ryd, and a Perdew-Burke-Ernzerhof exchange-correlation functional [32]. Equilibrium lattice parameters of a = 6.36 A and b = 3.53 A are used to simulate the orthorhombic MoTe\({}_{2}\) structure.
## 3 Solutions
### Solution to the Complete Model (1)
#### 3.2.3 Approximate Solution Profiles  
With the penetration depth defined by Equation (22), the approximate solution is  
\[u_{a}=\left(1-\frac{x}{\sqrt{\frac{D_{0}}{b}}\sqrt{\frac{N}{M}} \sqrt{e^{MFo}-1}}\right)^{n}=\left(1-\frac{X}{\sqrt{e^{MFo}-1}}\right)^{n}= \left(1-\frac{\eta}{\sqrt{\frac{N}{M}}}\right)^{n} \tag{28}\]  
In (28), we define intrinsically the dimensionless distance (dimensionless spatial variable) \(X=x/\sqrt{D_{0}/b}\) and the similarity variable \(\eta=\frac{x}{\sqrt{\frac{D_{0}}{b}}\sqrt{e^{MFo}-1}}\). From the last version of Equation (28), the front is defined in terms of \(\eta\) for \(\eta_{front}=\sqrt{\frac{N}{M}}\). For short times,it is easy to check, \(\sqrt{\frac{D_{0}}{b}}\sqrt{\frac{N}{M}}\sqrt{e^{MF_{0}}-1}\rightarrow\sqrt{D_{0}t}\) (see the comments after Equation (22)), and then \(\eta=\frac{\chi}{\sqrt{D_{0}t}\sqrt{e^{MF_{0}}-1}}\rightarrow\frac{\chi}{\sqrt{D _{0}t}}\), the Boltzmann similarity variable.  
Profiles of the approximate solutions as a function of the dimensionless spatial variable \(X\) (for different times, represented by the Fourier number) and in terms of the similarity variable \(\eta\) are shown in Figures 2 and 3, respectively. In Figure 3b,c, we present profiles corresponding to some extreme values of the exponent \(m\) appearing in diffusion problems relevant to moisture dynamics in ceramics and soils [35]; the only goal is to demonstrate the feasibility of the solution approach to cover a broad range of degenerate diffusion processes.  
Figure 3: Solution (Equation (28)), with \(p=1\) and \(q=2\), as a function of the similarity variable \(\eta\) (**a**) For short times (\(0<F_{0}<1\)), less than the characteristic time scale; (**b**) For long times (\(F_{0}>1\)), greater than the characteristic time scale; (**c**) For long times (\(F_{0}>1\)) near the front. In all cases, \(n=1/m\). Note: The exponents \(p=1\) and \(q=2\) are chosen arbitrarily only to demonstrate the solution behavior.  
Figure 2: Solution (Equation (28)), with \(p=1\) and \(q=2\), as a function of the dimensionless spatial variable \(X\) (**a**) For short times (\(0<F_{0}<1\)), less than the characteristic time scale; (**b**) For long times (\(1<Fo\)), greater than the characteristic time scale. In all cases, \(n=1/m\). The points where the profiles cross the abscissa define the solution fronts. Note: the exponents \(p=1\) and \(q=2\) are chosen arbitrarily only to demonstrate the solution behavior.  
Now, we refer to the specific case with \(k=3/2\) of [2] discussed in the Appendix. This case corresponds to \(m+1=3/2\Rightarrow m=1/2\). The plots in Figure 4 clearly reveal that there is no degeneracy (\(m=1/2<1\)) of the equation, and the profiles are classic parabolic profiles with \(n=2\); the contrast regarding the results in Figures 2 and 3 is obvious. These plots confirm the results of [PERSON] [2], and the case \(k=1/2\) will not be discussed further in the context of the solutions developed in this work, but we are obliged to present these examples for the sake of clarity and correctness of the expositions, avoiding any doubts.
# Complex-Order Models: A System Identification Point of View
## 2 Operators of Complex Order
### Complex-Order Real-Valued Differintegral
#### 2.2.2 Type 2 CO-RV-1 Integral Operator  
It is possible to define the CO-RV-1 integral operator by repeating essentially the same procedure previously performed for the differential operator: by taking the mean of two integral operators having complex-conjugate orders.  
**Definition 2**.: _Given \(\alpha>0\) and \(\beta>0\), the complex-order real-valued integral operator of the first type (CO-RV-1) is defined as:_  
\[\tfrac{1}{0}\gamma_{t}^{\alpha,\beta}\coloneqq\frac{1}{2}\left(\omega T_{t}^{ \alpha+j\beta}+{}_{0}\mathcal{I}_{t}^{\alpha-j\beta}\right)\,, \tag{14}\]  
_with the associated Laplace domain operator:_  
\[{}^{1}\mathfrak{I}^{\alpha,\beta}(s)\coloneqq\frac{1}{2}(s^{-\alpha+j\beta}+s ^{-\alpha-j\beta}). \tag{15}\]First note that the differential and integral operators of the first type are not reciprocal to one another: \({}^{1}\mathfrak{I}^{\alpha,\beta}(s)\
eq(^{1}\mathfrak{D}^{\alpha,\beta}(s))^{-1}\)! In fact, by repeating exactly the same steps as were taken previously, one readily concludes that:  
\[|^{1}\mathfrak{I}^{\alpha,\beta}(j\omega)| =\omega^{-\alpha}\sqrt{\cos^{2}(\beta\ln\omega)+\sinh^{2}(\frac{ \beta\pi}{2})}\, \tag{16}\] \[\arg^{*\,1}\mathfrak{D}^{\alpha,\beta}(j\omega) =\frac{-\alpha\pi}{2}-\atan^{*}(\tan(\beta\ln\omega)\tanh(\frac{ \beta\pi}{2})). \tag{17}\]  
In other words, the logarithmic slope of the amplitude is indeed negative and equal to the expected \(-20\alpha\) dB/decade; however, the phase slope is also negative, and approximately equal to \(-\beta\) rad/decade.
# Investigation of Rock-Breaking Mechanisms Based on the Adaptive Matching Method for Drilling Loads
## 3 Results and Discussion
### Effect of the Driven Force  
In this study, the driven force represents the cutting force applied to the PDC cutter in the X direction, serving as the primary energy source for rock fragmentation. Figure 10 presents the simulation results on the PDC cutter's kinematic behavior and reaction force characteristics under varying driven forces (450 N to 600 N) during the entire simulation period. These simulations were conducted with a fixed initial axial force of 450 N and a load factor of 0.6. Corresponding rock fragmentation parameters across different driven forces are provided in Figure 11.  
Figure 9: Rock breaking characteristics under different initial WOBs.  
With the initial axial force held constant, maintaining a consistent initial cutting depth, the cutting displacement, axial force variation patterns, and reaction forces on the PDC cutter exhibited significant differences under the action of different driven forces. As shown in Figure 10a, smaller driven forces increased the likelihood of cutter seizure during rock breaking. At a driven force of 500 N, two distinct seizure stages occurred (as seen in Figure 10b), resulting in maximum X direction displacement falling short of the programmed 140 mm. Conversely, when the driven force was set to be 550 N, a single seizure event was induced, while the driven force of 600 N enabled smooth cutter  
Figure 11: Rock breaking characteristics under different driven forces.  
Figure 10: Motion and reaction force of the PDC cutter under different driven forces.  
progression without seizure. With a driven force of 600 N and the applied axial force kept constant at 450 N, the adaptive load-matching mechanism was rendered inactive.  
According to Figure 11 above, the number of total cracks generated, the maximum displacement, and the work power increase during the rock-breaking process with the increase in the driven force, whereas MSE exhibits the opposite tendency though its growth rate gradually decelerates. When the driven force increases from 550 N to 600 N, the PDC cutter achieves the programmed displacement limitation, but increments in total cracks and the work power become negligible. This is because higher driven force enables rapid completion of the simulation without cutter seizure, resulting in elevated cutting velocities that drive increased work power and energy consumption under a constant axial load acting on PDC cutter.  
Consequently, driven force applied to the PDC cutter--or equivalently, torque to the PDC bit during drilling in the bottomhole--should be controlled within an optimal range. Combining the results in Figures 10 and 11, a driven force of 550 N is recommended for this study's rock-breaking process. This setting balances rock fragmentation efficiency, energy consumption, and cutter stability while activating the adaptive load-matching mechanism.