esa-sceva/satcom-chunk-collection huggingface dataset:
The reference periods of the auxiliary system \(T_{aux}\) for which the gain surfaces have the regions with minimum values of \(\eta\) are different between the Kobe (Figure 8) and L'Aquila seismic records (Figure 11). In general, the regions with minimum values of \(\eta\) obtained with the L'Aquila seismic record correspond to smaller values of \(T_{aux}\) compared with those obtained with the Kobe seismic record. This occurrence can be explained by observing the response spectra of the two seismic records (right graphs of Figure 7a,b). The response spectrum of the L'Aquila seismic record is significantly narrower and shifted towards lower values of \(T\) than the response spectrum of the Kobe seismic record. Since the larger reduction in \(\theta_{\text{Max}}\) is obtained for periods of the external excitation close to \(T_{aux}\), in the case of a seismic excitation, \(T_{aux}\) has to be tuned in the range of frequencies where the seismic record has the highest spectral power for the auxiliary system to be effective in protecting the rigid block. This result is confirmed by Figure 12. Figure 12a shows the response spectrum of the Kobe seismic record (top graph) and the frequency-response curve (bottom graph) of a coupled system with a rigid block of dimensions \(2b=1.2\) m and \(\lambda=6\) and characteristics taken from point \(A\) in the gain map of Figure 8b. Point \(A\) is in the region of the gain map with minimum values of \(\eta\) at \(T_{aux}=0.31T_{b}\). Such a value of \(T_{aux}\) is such that the frequency-response curve shows the smallest values of \(\eta\) (i.e., the auxiliary system has the maximum effectiveness) exactly where the response spectrum of the seismic record has the highest spectral energy. In other words, the part of the spectrum of the seismic record above the reference value of \(5\) m/s\({}^{2}\) (horizontal dashed line) is inside the range of frequencies where the frequency-response curve has the smallest values of \(\eta\) (grey region). Figure 12b shows the response spectrum of the L'Aquila seismic record (top graph) and the frequency-response curve (bottom graph) of a coupled system with a rigid block having dimensions and characteristics defined by point \(F\) in the gain map in Figure 11. Furthermore, in this case, such a point is the region of the gain maps with minimum values of \(\eta\) and corresponds to \(T_{aux}=0.16T_{b}\). Since, as already remarked, the  
Figure 12: Comparison between the response spectrum of the seismic records (upper graphs) and the frequency–response curves of coupled systems with rigid blocks of dimensions \(2b=1.2\) m and \(\lambda=6\) (lower graph): (a) Kobe seismic record; (b) L’Aquila seismic record (\(r=0.9595\), \(m=0.05M\), \(u_{y}=1/3u_{cr},\psi=0.2\)).
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 7 Obstacle-Avoidance
### Detect Collision by Using Gradient Descent and Context Analysis Based Obstacle-Avoidance Scheme
If the robot detects the collision, the robot will construct a new path by following these steps:  
* Decide offset angle for a new direction to avoid the obstacle;
* Repeat step 1 until the obstacle has not been observed anymore;
* Interpolate back point which is inside the initial path in order to help the robot get back main trajectory;  
Figure 11: Context analysis flow chart. Construct reshaped trajectory. In step one, we propose the multi-objective optimization problem to decide offset angle.
# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Aircraft Control  
A T2 fuzzy cerebellar model articulation controller (CMAC) was designed for an automatic aircraft landing system [85]. Conventional automatic aircraft landing systems (ALS) are bounded by certain limits, such as wind shear and turbulence. If these are too high, the ALS cannot be used. It is therefore beneficial to introduce an ALS that can handle higher wind shear and turbulence in order to land the aircraft safely. In this paper, T1 and T2 fuzzy CMACs were added in parallel to the PID to control the pitch autopilot. It was found that the adaptive T2 CMAC was able to guide the aircraft under a turbulence strength of 165 feet per second. This was more robust than the PID-only controller, which can only handle 30 feet per second.  
In aircraft control, an IT2 FLC was developed for hypersonic flight, which is defined as speeds faster than Mach 5 [86]. In this study, the generic hypersonic flight vehicle (GHFV) is proposed to cruise at a speed of approximately Mach 15 at an altitude of 33,000 m. This type of flight is highly non-linear, and it includes very large uncertainties. The controller tracks the altitude command signal and was able to maintain the velocity of the vehicle.  
IT2 FLCs are also designed for a micro aircraft vehicle (MAV) [87]. This IT2 FLC approximated the function of the altitude angle of the MAV. It was able to account for the non-linearity and the uncertainty of the dynamic model. It used the Lyapunov stability theorem to track the error of the MAV altitude control system. It was found that results from the IT2 gain adaptive sliding mode controller were favorable compared to the T1 sliding mode controller and the conventional-based sliding mode controller.  
The same authors created an IT2 FLC for quadrotor micro aircraft vehicles [88]. In this study, two IT2 FLCs and one PD controller were designed. They were designed to account for the system uncertainties, variations and external disturbances of the MAV. The IT2 FLCs were self-organizing (IT2 SO); one IT2 SO was made to learn the inverse model of the MAV, and the other was a secondary copy to account for model errors, uncertainties and disturbances. It was found using real-time experiments that the IT2 SO controllers outperformed the PD controller.
## 4 Estimator Definitions
### Critically Sampled Matched Filter  
Consider the baseband received signal \(z\) and a known baseband reference signal \(x\). These signals are sampled by an ADC operating at a sampling frequency \(f_{s}\), such that  
\[\mathbf{z}[m]=z\Big{(}mf_{s}^{-1}\Big{)},\ m\in[0,1,...,M-1], \tag{7}\]  
where \(m\) is the sample index, and \(M\) is the total number of samples [15]. This sampling rate is typically 2 or 4 times the signal bandwidth for basic communications systems. These sampled signals are used to evaluate the cost function  
\[\mathbf{g}[k]=\left|\sum_{m=0}^{M-1}\mathbf{z}[m]\mathbf{x}^{*}[m-k]\right|^{ 2},\ k\in[0,1,...,K-1], \tag{8}\]  
where \({}^{*}\) denotes the complex conjugate, \(k\) indexes integer time shifts, and \(K\) is the total number of tested time shifts [15]. This cost function compares \(\mathbf{z}\) and \(\mathbf{x}\) at each integer shift \(k\), and is maximized for the \(k\) that best aligns the signals in time. The time domain ToA estimate \(\hat{v}\) is therefore  
\[\hat{v}=\hat{k}f_{s}^{-1}\ ;\ \ \hat{k}=\arg\max_{k}\mathbf{g}[k]. \tag{9}\]  
The precision of this technique is limited to the time difference between two adjacent test points \(k\)[15]. For a bandwidth of 10 MHz and a sampling rate of 40 MHz, the time difference between the two samples is 25 ns, which corresponds to 7.5 m for a signal traveling at the speed of light. This technique is computationally efficient and easy to implement but does not provide the desired resolution for the system [82].
## 3 Proposed Work Methodology
### Modeling Framework for Uncertainty and Defuzzification  
This model represents uncertainty in activity costs and durations using fuzzy set (FS) theory, where fuzzy input data are transformed into exact values. This methodology consists of two main phases: fuzzification and defuzzification. Fuzzification refers to the process of converting numerical values into fuzzy values that reflect the degree of uncertainty, while defuzzification replaces the fuzzy variable with a precise numerical value, facilitating data analysis and decision making based on the available information. A fuzzy set is classified as a fuzzy number if it exhibits normalcy, convexity, and boundedness. Fuzzy numbers may be represented using triangles, which are defined by three values used in modeling when the estimate is clearer with uncertainty regarding the maximum values; a trapezoid is used for four values to provide high flexibility when dealing with unconfirmed data; and Gaussian forms, depending on natural distribution, are used when the gradual change in data is more realistic, as shown in Figure 2.  
According to [59], the configuration of the fuzzy membership function has a minimal impact on the majority of fuzzy system applications. As such, to represent the uncertainty when implementing a project's operations, we use triangular fuzzy numbers (TFNs) for simplicity and efficiency. The \(\alpha\)-cut method works, as we can figure out fuzzy membership functions using each \(\alpha\) cut that is associated with a confidence level \(\alpha\), as shown in Figure 3. The code we designed for the uncertainty modeling and defuzzification module utilizes functions from Python Skfuzzy, which we used for implementing fuzzy logic operations. These functions enable the modeling of uncertain data and the transformation of fuzzy values into precise values (defuzzification), thereby enhancing the accuracy and efficiency of the computational processing of ambiguous variables.  
(The details can be obtained through the computational routines developed in Python 3.12.9, which are included in Supplementary S1).  
The concept is based on fuzzy set (FS) theory, enabling users to enter activity costs and durations utilizing triangular fuzzy representations. The triangular fuzzy number  
Figure 1: Proposed method.  
Figure 2: Categories of fuzzy numbers.  
is defined with three values (a\({}_{1}\), a\({}_{2}\), a\({}_{3}\)) denoting the least (minimum), most probable (likely), and highest (maximum) values, respectively. The \(\alpha\)-cut technique is used as a defuzzification tool, facilitating the calculation of project costs and duration according to the user-specified \(\alpha\)-cut value, which indicates the acceptable amount of risk. This approach computes the average of the right and left border values (a1 and a3), which denote the lower and higher boundaries of the \(\alpha\)-cut levels, yielding accurate results.
## 4 Discussion
### Limitations of the Study  
Despite the robust design, several limitations should be acknowledged. First, the 12-week duration may not reflect the long-term sustainability of the observed improvements. Second, the study sample included only young adult women, limiting the generalizability across sexes and age groups. Third, while participants were instructed to maintain their usual diet, no nutritional tracking was conducted, which may have influenced body composition and lipid outcomes. Fourth, although stable body weight was required as an inclusion criterion, it was assessed through self-report at baseline. This approach may have introduced bias or inaccuracies, which could partly explain the statistically significant weight gain observed in the control group over the 12-week period. Such changes are not unexpected in sedentary populations and may result from unmonitored shifts in behavior, such as decreased incidental activity or increased caloric intake. Fifth, although the International Physical Activity Questionnaire (IPAQ) was used to screen participants and exclude those with high activity levels, baseline IPAQ scores were not statistically compared between groups. This may represent a potential confounding factor, as small pre-existing differences in habitual physical activity could have influenced the outcomes. Future studies should include objective or standardized assessments of physical activity as part of the baseline comparison. Lastly, point-intervention follow-up data were not collected, making it difficult to assess the persistence of the effects over time.  
Future research should incorporate longer intervention and follow-up periods, objective dietary tracking, and a more diverse population. Additionally, comparative trials involving combined aerobic and resistance training versus aerobic-only interventions could yield valuable insights for optimizing exercise prescriptions aimed at metabolic health.
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## Nomenclature  
AGR Acid Gas Removal  
CAPEX Capital Expenditure  
CBMH Coal and Biomass to Methanol and Hydrogen  
CBNMH Coal Biomass and Natural Gas to Methanol and Hydrogen  
CTM Coal to Methanol  
GHG Global Greenhouse Gas  
HCR Hydrogen-to-Carbon Ratio  
IPCC Intergovernmental Panel on Climate Change  
NGTM Natural Gas to Methanol  
OPEX Operational Expenditure  
SMR Steam Methane Reforming  
WGS Water-Gas Shift
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 1 Introduction  
A significant challenge in all remote sensing with signals of opportunity, including transmissions from Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS) is the real-time determination of the time and frequency domain characteristics of these signals at the receiving instrument after they have traveled a large distances and been altered by the physical processes they are intending to observe in such a way that traditional signal tracking techniques are not sufficient. Where traditional techniques refers to the well known navigation tracking algorithms used in personal navigation devices (i.e., cell phones, etc.) [1]. For example, in a GNSS receiver radio occultation (RO) instrument, it is required to track GNSS signals as they pass through the lower atmosphere, which significantly alters their structure and prohibits traditional GNSS receivers from useing them for navigation. Hence, a customized open loop tracking algorithm is needed [2]. Similarly, for a GNSS surface reflected signal, the alterations the signal undergoes upon surface reflection, necessitate a customized open loop tracking algorithm which allows GNSS-reflection (GNSS-R) instruments to make real-time estimates of the delay and Doppler of GNSS signals after reflecting from the Earth's surface.  
This manuscript presents and validates a public domain GNSS-R open loop delay and Doppler tracking algorithm. The organization of this manuscript is as follows,Section 2 gives general background on the history of GNSS-R remote sensing and details the reasons why a custom GNSS-R open loop tracking algorithm is required. Additionally, this section also includes an outline of the presented GNSS-R open loop algorithm and how they are organized within the paper.
2. Section 3 presents the algorithm for estimating the geographic location of the point of surface reflection, or specular point (SP), which represents the center of the surface scattering glistening zone. This point provides the center coordinated from which the GNSS-R open loop tracking delay and Doppler estimates are derived.
3. Section 4 details how the open loop estimates of the reflected signal delay and Doppler are calculated using the estimated surface specular point from Section 3.
4. Section 5 analyzes the performance of the algorithm derived in Sections 3 and 4 using two example reflection tracks from the Cyclone GNSS (CYGNSS) GNSS-R receiver. Raw sampled data is analyzed for these tracks and used as a comparison reference for the algorithm performance.
5. Section 6 then performs a statistical analysis, using a much larger set of CYGNSS data, which demonstrates the open loop algorithms performance globally. This section also includes discussion of the algorithms convergence speed and how this was optimized for the case of CYGNSS low Earth orbit GNSS-R reflections.
6. Section 7 provides a discussion of additional algorithm modifications which would potentially further improve the performance (both in terms of accuracy and speed of convergence).
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 4 Experimental Results
### Performance of the Proposed Power Supply
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Output Power**} & \multicolumn{3}{c}{**PF**} & \multicolumn{3}{c}{**THD**} \\ \cline{2-7}  & **CPS1** & **CPS2** & **PPS** & **CPS1** & **CPS2** & **PPS** \\ \hline
34.6 & 0.42 & 0.35 & 0.69 & 26.30\% & 32.00\% & 3.00\% \\ \hline
68.9 & 0.45 & 0.51 & 0.75 & 24.00\% & 11.00\% & 3.00\% \\ \hline
150.3 & 0.52 & 0.72 & **0.82** & 21.00\% & 3.00\% & **2.90\%** \\ \hline
300 & NA & NA & 0.96 & NA & NA & 2.65\% \\ \hline \end{tabular}
\end{table} 
Table 2: Electrical comparative of the different power supplies. PF: power factor, THD: total harmonic distortion; CPS: commercial power supply; PPS: proposed power supply; NA: not applicable. Figure 11: Waveform of the voltage supplied by the grid to the power supply and the resulting current due to the power factor correction. PF = 0.96. THD = 2.65%. datasheet is the best achieved, which coincides with the maximum power at the output), it improves on the PF achievable with passive filters;
* The supplied power was able to reach that value with an efficiency of 93%, but the power consumed from the network was of the order of 1500 VA since the PF was 0.33 due to high harmonic distortion. It was necessary to adopt an automatic control of the PF that substantially improved the PF. As a result, the following benefits were obtained:
* The input voltage has a low effect on the current loop gain;
* The system is not unstable for duty cycle values lower than 0.5;
* The average value of the current is regulated;
* The system has higher noise immunity because the modulator receives an averaged signal;
* The use of a PF correction integrated circuit (IC) ensures that the network current demand remains sinusoidal. This IC automatically corrects the PF within specified limits by sampling the input voltage (from the power grid) and adjusting the circuit's current to align with the input voltage waveform. It primarily reduces harmonics generated during current rectification by shifting their frequency closer to the IC's switching frequency. The inclusion of this IC (in our case, the UC3854) significantly improves the PF compared to a system without such an IC, which exhibits input currents with substantial harmonic distortion. The PF correction circuit fulfills all the necessary conditions for the SMPS to take advantage of the power provided by the network while minimizing the distortion of the network current.
###### Abstract  
The aim of this study is to produce noble-metal-free oxygen reduction reaction (ORR) catalyst via irradiation. Gamma ray irradiation reduction has been utilized to produce N-doped reduced graphene oxide (rGO)-supported iron (Fe)-based catalysts, whose weight percent (wt.%) of Fe loading varies from 10% to 20%. In this study, the physicochemical properties of Fe/N-rGO with various loadings of Fe (10 wt.%, 15 wt.% and 20 wt.%) were explored through X-ray diffraction (XRD), field emission scanning electron microscopy-energy dispersive spectroscopy (FESEM-EDS), Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). XRD showed that a broad-peak nanocrystalline Fe\({}_{2}\)O\({}_{3}\) phase formed. Raman spectroscopy revealed that Fe insertion increased the disordered GO structure. Fourier transform infrared (FTIR) demonstrated that N was functionalized into rGO. FESEM-EDX presented that Fe nanoparticles existed on the wrinkled rGO surface but their amount was low. Linear sweep voltammetry (LSV) was performed with a rotating disk electrode in 0.1 M KOH at a scanning rate of 20 mVs\({}^{-1}\) and revolution rates of 400, 900 and 1600 rpm and the corresponding electron transfer numbers were investigated with a Koutecky-Levich model. This model indicated that the number of electron transfers of 20% Fe/N-rGO was above 2 and its performance toward ORR was higher than those of 10% Fe/N-rGO and 15% Fe/N-rGO.  
alkaline electrolyte; gamma irradiation; Koutecky-Levich relationship; oxygen reduction
# A Review on Hot-IP Finding Methods and Its Application in Early DDoS Target Detection
## 2 Hot-IP Finding Methods
### Application in Finding Hot-IPs  
In this section, we evaluate the performance of the Count-Sketch, Count-Min and Group Testing methods on finding Hot-IPs in IP packet streams extracted from the UCLA (University of California, Los Angeles) data sets [13]. These methods have been widely used to find high-occurrence-frequency elements in the data stream because they have low computational complexity and low space requirements [1; 2; 4; 5; 6; 7].
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 3 Overview of AI Technology
### Application of AI in Architectural Design
#### 3.2.2. Building Industrialization and Intelligent Construction  
In the application of AI in the field of building industrialization and intelligent construction, data serve as a central driver. In prefabricated buildings, a key aspect of this new construction model lies in effective data application. By combining the strengths of industrialization and modularization, prefabricated buildings not only improve the traditional building process subversively but also enable data-driven decision-making and optimization, advancing intelligence across the entire building lifecycle [31].  
At the manufacturing stage, AI algorithms process data into precise instructions. Through big data analysis and machine learning, they enable intelligent production of prefabricated components, optimize cutting routes, refine processes, and enhance quality. In the construction phase, real-time data collection and analysis are pivotal. The intelligent sensor [32] network captures multiple data, transforming it into actionable insights through platform analysis. Intelligent monitoring ensures safety, and predictive analysis adjusts construction plans and resource allocation based on real-time data, demonstrating the efficiency and flexibility of data-driven construction management [33].
# Internet of Things and Distributed Computing Systems in Business Models
## 4 Theoretical Framework
### Discussion
#### 4.5.1 Data Security and Privacy  
The rapid expansion of IoT and Distributed Computing Systems introduces significant challenges. For instance, IoT devices are interconnected and continuously collecting data, increasing the potential entry points for cyber attacks [44]. These devices often handle confidential data, such as health data, financial transactions, or proprietary business data. The sheer amount and variety of data created by IoT machines make it challenging to ensure robust security measures across all network points [87]. In DCS, data is processed across varying nodes, further complicating security management. Ensuring that data is protected in transit between nodes, as well as while stored at various locations requires sophisticated encryption methods and consistent security protocols [25; 88]. The scattered organization of these systems implies that vulnerabilities in one part of the system can potentially jeopardize it overall, making it difficult to implement a unified security strategy for the business. Privacy concerns are also heightened with the extensive data collection capabilities of IoT devices [89]. The constant monitoring and collection of personal data can lead to unauthorized access or misuse if not properly managed. The risk of data breaches or leaks can undermine consumer trust and result in significant legal and financial repercussions for the company.