esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Alfa Fiber-Based Mortar with Improved Thermo-Mechanical Properties
## 3 Results and Discussions
### Thermophysical Performance Analysis
#### 3.2.5 Thermal Diffusivity  
The thermal diffusivity variation of AFRM composite as function of mass AF ratio is displayed in Figure 10. The thermal diffusivity is reduced by a factor of about 51% for 5% of AF mass content in the mixture. The heat transfer in the AFRM sample is damped due to the porosity growth within the sample with AF mass content increase, and to the alveolar arrangement of AF which resists the heat diffusion. It is observed from Figure 7 that the density, which was obtained from an independent measurement, decreases with AF content. Calculated specific heat (Figure 11) decreases as well for AF content below 1%. Thus, this decrease in thermal diffusivity is mainly driven by the thermal conductivity.  
Figure 11: Specific heat variation with alfa fiber mass content. Measurements uncertainty is below 7%.  
Figure 10: Thermal diffusivity (primary axis) and its reduction rate (secondary axis) as function of AF mass content. Measurements uncertainty is below 5.22%.  
In addition, the thermal diffusivity of mortar, with no AF, (\(4.04\pm 0.0886\)\(10^{-7}\) m\({}^{2}\cdot\)s\({}^{-1}\)) is greater by about four times than the measured one of AF (\(0.98\pm 0.04\)\(10^{-7}\) m\({}^{2}\cdot\)s\({}^{-1}\)), which obviously impacts the thermal diffusivity of the composite material.
# Dynamic Analysis and Security Characteristics of Carrier-Based Aircraft Arresting in Yaw Condition
## 5 Calculation and Analysis
### The Influence of Yaw Angle on the Performance of the Arresting System  
The response of aircraft speed, displacement, acceleration, and arresting force when the yaw angle changes was then analyzed, as shown in Figures 15-18.  
The calculation results of the arresting system and aircraft response under different yaw angles are listed in Table 4 for comparative analysis.  
It can be seen from the comparison results that the peak value of the arresting force increases slightly with the increase of the yaw angle, and has little influence on the arresting distance and the peak value of the aircraft acceleration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Centering** & **Yaw 3\({}^{\circ}\)** & **Yaw 5\({}^{\circ}\)** & **Yaw 7\({}^{\circ}\)** & **Yaw 9\({}^{\circ}\)** \\ \hline Maximum arresting & 698.78 & 698.76 & 698.72 & 698.68 & 698.61 \\ force (kN) & & & & & \\ Maximum & 30.81 & 30.81 & 30.81 & 30.80 & 30.80 \\ acceleration (m\(\cdot\)s\({}^{-2}\)) & & & & & \\ Arresting distance & 86.76 & 86.62 & 86.39 & 86.06 & 85.38 \\ (m) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of performance of the arresting system of different eccentricity.  
Figure 17: Longitudinal acceleration–time.  
Figure 18: Arresting force–displacement.
## 1 Introduction
Under unbalanced or harmonic distorted voltage conditions, the power commands will contain both DC and AC components for additional control targets. In [24], it was pointed out that ISMC has limited effect to suppress the tracking error for the AC tracking system. It implies that the tracking errors may exist in the DFIG's ISMC system under less than ideal grid voltage conditions in [22; 23]. For removing the tracking errors, reference [24] presented a multi-resonant based sliding mode control (MRSMC) scheme for a single-phase grid connected voltage source inverter, and the suppression of the current's total harmonic distortion (THD) was achieved. In order to eliminate the tracking errors under unbalanced and harmonic distorted grid voltage conditions, this paper presents a MRSMC strategy for a DFIG-based wind power system. In this strategy, multi-resonant items are added to the conventional integral sliding surface to provide sufficient tracking precision on ac references. Thus, the system total current harmonics and most part of the DFIG's electromagnetic torque pulsations can be removed. The tracking behavior of ISMC and MRSMC for the DFIG system under unbalanced and harmonic distorted grid voltages is analyzed in this paper. And the proposed strategy is implemented in the \(\alpha\beta\) frame, the PLL can be avoided. The rest part of the paper is organized as follows. In Section 2, gives dynamic behavior of DFIG system in the \(\alpha\beta\) frame, and deals with sliding model control of a DFIG system. In Section 3, the comparison and analysis on the ISMC and MRSMC are given. In Section 4, some detailed simulation results are carried out to validate the validity of the control strategy. In Section 5, gives some conclusions.
###### Abstract  
This study investigates magnetic flux density (B) and radiofrequency electromagnetic field (RF-EMF) measurements on electric buses operating in Samsun, Turkey, focusing on two bus routes (called E1 and E4) during the morning and evening hours. Measurements were taken under diverse operational conditions, including acceleration, cruising, and braking, at locations of peak passenger density. Along the E1 route, the magnetic field intensity varied significantly based on the bus position, road slope, and passenger load, with notable increases during braking. In contrast, the E4 route showed a lower magnetic field intensity and RF-EMF values due to its straighter trajectory and reduced operational stops. The highest RF-EMF measurement recorded was 6.01 V/m, which is below the maximum levels established by the ICNIRP guidelines. In 11 out of the 12 different band-selective RF-EMF measurements, the highest contribution came from the downlink band of the base stations, while in only one measurement, the highest contribution originated from the uplink bands of the base stations. All data were subject to the Anderson-Darling test, confirming the generalized extreme value distribution as the best fit for both B and RF-EMF measurements. Additionally, the study assessed B levels inside and outside the bus during charging, revealing heightened readings near the pantograph. These findings significantly contribute to our understanding of electromagnetic field exposure in electric bus environments, highlighting potential health implications and informing the development of targeted mitigation strategies.  
m pacs: 24 July 2024
## 4 Discussion
### Horizontal Movements, Habitat Use, Revisitations, and Home Ranges
The lack of movement observed in the satellite-tracked individuals between Nusa Penida and Komodo in our study may be attributed to the relatively small sample sizes (6-8 rays in these regions) and the limited tracking durations (<57 days in Nusa Penida and <105 days in Komodo). Variations in the localized movements that were observed from most individuals as well as the extended home ranges exhibited by some individuals found in our study regions suggest that reef manta rays are perhaps best described as partial migrants [60]. These partial migrants can undertake occasional long-distance dispersal in search of food, moving over deep water and acting as transient individuals visiting an area for a short period. The variations in the movement patterns and home ranges of reef manta rays also seem to suggest that each individual might have different preferences, generating individual differences in movement patterns. For instance, in Sudan, a 220 cm DW male reef manta ray (presumably a juvenile) showed an extensive home range (95% UD) of 2456.9 km\({}^{2}\), while a 300 cm DW male (presumably an adult) only recorded a relatively restricted 95% UD home range of 387.2 km\({}^{2}\)[17].
# Machine Learning-Based Classification of Mushrooms Using a Smartphone Application
## 3 Results
### Inter-Phone Comparison  
In addition to the inter-platform comparison, an inter-phone comparison was conducted to determine whether there was a brand and/or operating system version dependency. While all of the tested smartphone brands utilized the Android operating system, some were more recent than others, including Google's Nexus 5, Pixel 2, Samsung's Galaxy S8, and Samsung's Galaxy Note 20. Tables 2-4 show the results of Equations (1)-(3). While the two-class model showed above 90% accuracy for all brands, there were some performance variations between different smartphone brands. For example, sensitivity for _Gyromitra_ ranged from 100% (Note 20) to 91.6% (S8 and Nexus 5), while specificity was in the range of 92.3%-91.8% on all four models. For the three-class model, sensitivity for _Clavulina_ varied from 100% (S8 and Note 20) to 94.5% (Nexus 5), _Inocybe_ resulted in 99.2% (Note 20) to 93.7% (Nexus 5), and _Marasmius_ reported 97.6% (Note 20) to 89.9% (Nexus 5). For the five-class model, _Agaricus_ showed a large variation in PPV, from 92.3% (Note 20) to 85% (Pixel 2). _Amanita_ reported variations of specificity from 94.8% (Note 20) to 92.5% (the other three models). The rest of the three classes showed variations of about a 2% difference depending on the smartphone model.
## 3 Microwave Photonics Applications of DFT
### DFT for Photonic Analog-to-Digital Conversion (ADC)  
High-speed analog-to-digital converters are of paramount importance in modern signal processing and communications systems. The ever-increasing speeds of digital electronics and power of digital signal processing (DSP) units have challenged the operational bandwidth of the state-of-the-art electronic ADCs. Photonic technologies have been widely investigated to enhance the performance of the electronic ADCs [25], through high-speed and low-jitter optical sampling [127], and optical quantization [128,129].  
Figure 11: Compressive sensing improves the operational bandwidth of the DFT-based microwave spectrum sensing system. (**a**) Schematic of compressive sensing module based on optical random mixing using a reflective spatial light modulator (R-SLM) with a pseudo-random bit sequence (PRBS) input. (**b**) Illustration of sample-and-hold integration by spectral filtering in the optical channelizer.  
DFT technique has also been employed to boost the bandwidth of electronic ADCs [24, 130]. The main efforts here are not to optically sample and quantize the fast analog signal. DFT slows down the analog signal prior to sampling and quantization by an electronic digitizer based on a photonic time stretch process. As illustrated in Figure 12, the analog signal is first modulated onto a dispersed optical pulse thanks to the DFT process achieved in the first dispersive element. A second dispersive element further stretches the modulated optical pulse with its temporal shape being maintained. The original fast analog signal is then slowed down thanks to the DFT-based photonic time stretch process, and can be captured by a low-speed digitizer, which would otherwise have insufficient sampling rate. Photonic time stretch ADC acts as a front-end signal stretcher which enhances the effective sampling rate of any back-end electronic ADCs. The stretch factor is determined by the ratio of the dispersion values of two dispersive element. For a time-limited input signal, a single channel photonic time stretch ADC would suffice, as shown in Figure 12. In order to capture a continuous-time input signal, a multichannel photonic time stretch system involving a segmentation unit can be used [24]. The most recent advances in photonic time stretch ADC include all-optical signal mixing for higher sampling speed, and digital linearization for enhanced dynamic range [131].
## 2 Materials and Methods
### Mathematics Foundations
#### 2.1.5 Svm  
The mathematical foundations of SVM [22; 23] are as follows:  
_Hyperplane:_ A hyperplane is a subspace that is one dimension \"flatter\" than the space it lives in. In 2D, a hyperplane is a line; in 3D, a hyperplane is a flat plane. In an \(n\)-dimensional, a hyperplane is an \((n-1)\)-dimensional subspace.  
_Equation of a hyperplane:_ in an \(n\)-dimensional space, a hyperplane can be defined by the equation:  
\[w\cdot x+b=0\]  
where:  
\(w\) is the normal vector of the hyperplane.  
\(x\) is a point on the hyperplane.  
\(b\) is the hyperplane's offset.  
\(w\cdot x\) is the dot product of \(w\) and \(x\).  
_Separation:_ SVM seeks to find the optimal hyperplane that effectively separates data points into different classes. This hyperplane acts as a decision boundary, with data points on one side classified as belonging to one class and those on the other side belonging to a different class.  
\[w\cdot x+b>0\]  
_for points in one class._  
\[w\cdot x+b<0\]  
_for points in another class._  
_Margin:_ The margin in SVM is like a buffer zone around the decision boundary (the hyperplane). It is the distance between the hyperplane and the nearest data points from each class. SVM aims to find the hyperplane that maximizes this margin.  
_Margin calculation:_ the distance between a point \(x\) and the hyperplane \(w\cdot x+b=0\) is given by:  
\[distance=\mid w\cdot x+b\mid/\mid\mid w\mid\mid\]  
where:  
\(\mid\mid w\mid\mid\) is the norm of \(w\).  
_Maximizing the margin:_ SVM tries to find the flattest possible street (smallest \(\mid\mid w\mid\mid\)) that still keeps the buildings (data points) on the correct sides with enough space (margin) in between.  
_Kernel function:_ The kernel function directly computes the dot product in that higher-dimensional space without explicitly performing the transformation. This saves computational cost and allows SVM to work in very high-dimensional spaces efficiently:  
\[K(x,x^{\prime})=\Phi(x)\cdot\Phi(x^{\prime})\]  
where:  
\(\Phi(x)\) maps x to a higher-dimensional space.
# A Hybrid-Cryptography Engine for Securing Intra-Vehicle Communications
## 1 Introduction
In this paper, we tackle this research challenge by proposing a novel solution to secure intra-vehicle networks aiming to provide the least possible impact on communication latencies: the _Crypto-Engine_. Born in the frame of the EMERGE project (_Veicoli Commerciali Leggeri_ & Tecnologie EMERGEnti per operativita di tutti i giorni e di ausilio nelle EMERGEnze) described in the next sub-section, the Crypto-Engine is a comprehensive solution for securing intra-vehicle networks of connected vehicles via hybrid-cryptographic techniques. The _Crypto-Engine_, which is presented as the core contribution of this paper, can be intended as a logical component that provides authentication of the communication sources and encryption/decryption of data streams with a reduced overhead. As additional optional services, the Crypto-Engine can provide in-vehicle storage protection, aggregation of intrusion detection probe points, and other application-specific cryptographic services (e.g., auditing, log management). Although the Crypto-Engine is generally able to secure any type of intra-vehicle communication link, the solution illustrated here has been designed specifically to secure the links carrying sensor data streams towards the main vehicle On-Board Unit (OBU).
## 5 Topologies in NCSs  
Basically, there are three types of network topologies available in NCSs, namely centralized topology, decentralized topology, and distributed topology. A brief discussion on these topologies is given below.
## 1 Introduction
They concluded that the controlling factor of NPP change shifted from the temperature during 2000-2009 to the precipitation during 2010-2019. [PERSON] et al. [32] used the CASA model and correlation analysis to analyze the NPP change on the Tibetan Plateau from 2001 to 2015, and suggested that the effect of the solar radiation was greater than the temperature and precipitation to the NPP change of the TP. [PERSON] et al. [33] used CESM 1.0.4 to explore the impact of the different concentrations of CO\({}_{2}\) and climate change on the vegetation on the TP. They suggested that the increasing CO\({}_{2}\) concentration increased the values of GPP, NPP and other parameters, but climate change had a negative impact on the vegetation structure and productivity in the TP. Previous research analyzed the response of NPP to the meteorological factor, but it can be seen that obvious contradictions and controversies existed in these researches. As one of the most powerful models with the strongest simulation capability for land surface, CLM is well suited for the simulation of NPP. In particular, the latest generation of the CLM 5.0 model improves the accuracy of soil hydrothermal simulation in the permafrost zone, which makes it more suitable for the Tibetan Plateau. The objective of this study is twofold: (1) to investigate the spatial and temporal trends of NPP and quantify the effects of meteorological factors on the NPP in the Tibetan Plateau and (2) to indicate differences in NPP changes and driving factors over the grasslands with different vegetation cover. This paper is organized as follows. The methods and data are introduced in Section 2. The main results are presented in Section 3. Discussion and conclusions are presented in Sections 4 and 5.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 3 Proposed Kalman Filter Based on TOT Decomposition
As a result, grouping the components \(\widehat{\mathbf{h}}_{2}^{l}(n)\) into \(\widehat{\mathbf{h}}_{2}(n)\) [similar to (13)], while using (21) and (22) in (3), the a priori error equivalently results in  
\[e(n)=d(n)-\widehat{\mathbf{h}}_{2}^{T}(n-1)\mathbf{x}_{12,11}(n), \tag{25}\]  
where \(\mathbf{x}_{12,11}(n)=\widehat{\mathbf{H}}_{12,11}^{T}(n-1)\mathbf{x}(n)\), with  
\[\widehat{\overline{\mathbf{H}}}_{12,11}(n-1)=\left[ \begin{array}{cc}\widehat {\mathbf{H}}_{12,11}^{1}(n-1)&\widehat{\overline{\mathbf{H}}}_{12,11}^{2}(n- 1)&\cdots&\widehat{\overline{\mathbf{H}}}_{12,11}^{L_{2}}(n-1)\end{array}  \right]\]  
and \(\widehat{\overline{\mathbf{H}}}_{12,11}^{l}(n-1)=\sum_{p=1}^{p}\widehat{ \mathbf{H}}_{12,11}^{lp}(n-1)\), for \(l=1,2,\ldots,L_{2}\). Similarly, grouping the components \(\widehat{\mathbf{h}}_{12}^{lp}(n)\) into \(\widehat{\overline{\mathbf{h}}}_{12}(n)\) [as shown in (14) and (16)], while using (21) and (23) in (3), we obtain a second equivalent expression for the a priori error, i.e.,  
\[e(n)=d(n)-\widehat{\overline{\mathbf{h}}}_{12}^{T}(n-1)\mathbf{x}_{2,11}(n), \tag{26}\]  
where \(\mathbf{x}_{2,11}(n)=\widehat{\overline{\mathbf{H}}}_{2,11}^{T}(n-1)\mathbf{x }(n)\), with  
\[\widehat{\overline{\mathbf{H}}}_{2,11}(n-1)=\left[ \begin{array}{cc}\widehat {\mathbf{H}}_{2,11}^{1}(n-1)&\widehat{\overline{\mathbf{H}}}_{2,11}^{2}(n-1) &\cdots&\widehat{\overline{\mathbf{H}}}_{2,11}^{L_{2}}(n-1)\end{array} \right]\]  
and \(\widehat{\overline{\mathbf{H}}}_{2,11}^{l}(n-1)=\left[ \begin{array}{cc} \widehat{\mathbf{H}}_{2,11}^{l1}(n-1)&\widehat{\mathbf{H}}_{2,11}^{l2}(n-1) \end{array} \right.\cdots&\widehat{\mathbf{H}}_{2,11}^{lp}(n-1)\end{array}\right]\), for \(l=1,2,\ldots,L_{2}\). Finally, grouping the components \(\widehat{\mathbf{h}}_{11}^{lp}(n)\) into \(\widehat{\overline{\mathbf{h}}}_{11}(n)\) [as shown in (15) and (17)], while using (21) and (24) in (3), leads to the third and final way to equivalently express the a priori error, which becomes  
\[e(n)=d(n)-\widehat{\overline{\mathbf{h}}}_{11}^{T}(n-1)\mathbf{x}_{2,12}(n), \tag{27}\]  
where \(\mathbf{x}_{2,12}(n)=\widehat{\overline{\mathbf{H}}}_{2,12}^{T}(n-1)\mathbf{x }(n)\), with  
\[\widehat{\overline{\mathbf{H}}}_{2,12}(n-1)=\left[ \begin{array}{cc}\widehat {\mathbf{H}}_{2,12}^{1}(n-1)&\widehat{\mathbf{H}}_{2,12}^{2}(n-1)&\cdots& \widehat{\mathbf{H}}_{2,12}^{L_{2}}(n-1)\end{array} \right]\]  
and \(\widehat{\overline{\mathbf{H}}}_{2,12}^{l}(n-1)=\left[ \begin{array}{cc} \widehat{\mathbf{H}}_{2,12}^{l1}(n-1)&\widehat{\mathbf{H}}_{2,12}^{l2}(n-1) \end{array} \right.\cdots&\widehat{\mathbf{H}}_{2,12}^{lp}(n-1)\end{array}\right]\), for \(l=1,2,\ldots,L_{2}\).
# Optimizing Investment Portfolios with Bacterial Foraging and Robust Risk Management
## 2 Bacterial Foraging Optimization (BFO)
### Principles of BFO  
At the heart of BFO lies the concept of bacterial foraging. In nature, bacteria exhibit a collective foraging behavior to seek optimal nutrient concentrations in their environment. This behavior involves the motion of individual bacteria, their interactions, and adaptation over time. BFO emulates this process to optimize solutions in complex search spaces [7].  
The BFO algorithm encompasses the following key principles:Chemotaxis: In the context of BFO, chemotaxis refers to the movement of artificial bacteria in response to chemical gradients in the search space. Bacteria move toward regions with higher nutrient concentrations, which corresponds to better solutions in optimization problems.
2. Swarm behavior: BFO models the collective behavior of bacteria. Artificial bacteria collaborate and exchange information with their peers to collectively explore and exploit the search space efficiently.
3. Reproduction: Just as bacteria reproduce to increase their population in response to environmental conditions, BFO introduces the concept of reproduction for the artificial bacteria, leading to the survival and propagation of the fittest solutions.
4. Elimination-dispersal: BFO incorporates an elimination-dispersal strategy, simulating the removal of poorly performing bacteria and the dispersal of more effective bacteria to explore new areas of the search space.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 3 Materials and Methods
### Incorporation of the Communication System  
The autonomous mobile station integrates a dual wireless communication architecture optimized for long-range alarm signaling and high-bandwidth video transmission, ensuring operational continuity in mining environments characterized by geographic dispersion and limited infrastructure. The system consists of two independent channels:
# Identification and Optimization Study of Cavitation in High Power Torque Converter
## 3 Identification of Cavitation in Torque Converter  
### Cavitation Identification Method Based on Flow Difference  
The studies in [9; 10; 11; 12] show that when cavitation occurs in the torque converter, it will produce a large number of cavitation bubbles, some of the cavitation bubbles will disappear and burst inside the torque converter, and some of the bubbles will flow away with the circulating fluid of the torque converter. This part of the bubbles will cause a change in the oil flow rate from the torque converter, so this paper proposes using the cavitation identification method based on the difference between the inlet and outlet flow rate of the torque converter.  
Define the flow difference between the inlet and outlet of the torque converter during operation as \(\Delta L\), then  
\[\Delta L=L_{in-let}-L_{out-let} \tag{9}\]  
\(L_{in-let}\) Torque converter inlet flow (L/min).  
\(L_{out-let}\) Torque converter outlet flow (L/min).  
Under normal working condition, due to the factor of high pressure in the cavity, the torque converter leaks part of the torque converter oil from the matching gap of the oil seal, rotary shaft seal, locking clutch piston and other parts during the working process, so the general \(L_{out-let}\) is smaller than the \(L_{in-let}\), and the \(\Delta L>0\). When cavitation occurs, a large number of cavitation bubbles flow out with the circulation circuit, which causes the outlet flow rate to increase and leads to a decrease or even negative value of the \(\Delta L\). If the value of \(\Delta L\) decreases or becomes negative, cavitation can be judged by the change in \(\Delta L\).
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 2 Basis and Method of Design for the LUT Airfoil  
### Design Basis  
Figure 1 shows the structure and operation of an H-type VAWT (H-VAWT). The rotational axis of H-VAWTs is always perpendicular to the incoming airflow \(\mathrm{V_{in}}\), which results in a constantly changing local angle of attack and a readily occurring dynamic stall [24], especially with a rotor at low speed. Moreover, the VAWTs typically operate in low-\(R_{e}\) environments. Nevertheless, reasonable design of special airfoil for VAWTs is one of the effective methods to solve the above problems. Therefore, the characteristics of an airfoil at a low \(R_{e}\) and a wide range of angles of attack (\(\alpha\)) were also considered factors in the design of the LUT airfoil.