esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture
### Dual-Mode Internal Combustion Engine  
The internal combustion engine in this powertrain system is an SI/HCCI dual-mode combustion engine for which stable HCCI combustion and SI/HCCI mode transition control was developed in our previous study. It is a 2.0 L, four-cylinder GDI engine equipped with an EVVT system. In the SI combustion mode, the peak power is 125 kW and the maximum torque is 256 Nm. In SHEVs, the internal combustion engine is connected to the generator and the output of the engine-generator set is electric energy instead of mechanical energy; therefore, here, we used the brake-specific fuel consumption (BSFC) map of the whole engine-generator set to interpret the engine's fuel efficiency, and the \(y\)-axis of the map is chosen to be DC power, as shown in Figure 2. The optimal engine operation curve is shown by the solid red line.  
Figure 2a shows the BSFC map of the engine-generator set with SI combustion. The optimal operating point of the engine-generator set is at 2900 rpm, 59 kW, with a fuel consumption rate of 263 g/kWh. From the BSFC map, it can be observed that the most efficient range is located around 50 to 80 kW, with speed around 2500 rpm-3200 rpm, where the NVH issue should not be neglected. When the output power is lower than 20 kW, the efficiency drops significantly and the engine should stop operating. Figure 2b shows the BSFC map of the same engine-generator set with dual-mode combustion. The HCCI operating range is relatively small, with a maximum speed of around 2000 rpm and power between 5 kW and 22 kW. The optimal operating point is at 1800 rpm, 20 kW, with a fuel consumption rate of 264 g/kWh. The optimal engine operating curve in HCCI mode is shown by the dashed red line. The two optimal engine operating curves meet at 1800 rpm, 20 kW; thus, this point is chosen as the combustion mode transition point.  
Figure 2: Brake-specific fuel consumption (BSFC) maps of the engine: (**a**) BSFC of the SI combustion engine; (**b**) BSFC of the SI/HCCI dual-mode combustion engine.  
Obviously, within the HCCI operating range, the difference in fuel efficiency is more than 20% between the SI mode and the HCCI mode. Therefore, if the engine operates in this range, dual-mode combustion can significantly improve fuel economy.
# Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
## 4 Experimental Results and Analysis
### Performance Comparison of Measurement Matrices
Figure 3: _Cont._  
Figure 4: _Cont._  
Figure 1:0 Comparison of reconstruction of the target image with the TVAL3 recovery algorithm under different measurement matrices: (**a**) analysis of reconstruction results by using peak signal-to-noise ratio (PSNR), (**b**) analysis on reconstruction results by using structural similarity (SSIM), and (**c**) target image. The experimental results show that the improved LCS-PRC-MM has better image restoration effect than LCS-CMM, with average increase of 2-3 times. This finding is approximately the same as the recovery results of random Bernoulli matrix, especially under the sampling rate of 40% or 60%. The improved deterministic measurement matrix has better recovery effect than the random Bernoulli matrix. The present application scenario of single-pixel camera is generally static and simple. LCS-PRC-MM can be favorably compared with the Bernoulli matrix under a specific scene, and has advantages in hardware and storage, which makes it suitable for single-pixel imaging equipment.
## 4 Methodology
### Calibration of the AR Environment
#### 4.4.1 Manual Two-Step Calibration  
The manual two-step calibration method relies on users performing the HoloLens's \"air tap\" gesture to mark the drone's location to the HoloLens app.  
In the generic case, the HoloLens app will launch with the user some distance away from the drone and facing a random direction, forming an angle \(\phi\) with the north. Figure 8 shows the two different coordinate systems and the devices' starting location with different colors. In order to translate coordinates from one system to the other, we need to calculate the coordinates of the origin of one with respect to the other (translation vector) and the angle \(\phi\), which can form the rotation matrix.  
During the first step of calibration, the drone has not yet moved, and the user air-taps its present (starting) position, as shown in Figure 8 on the left. That position's HoloLens coordinates \((x^{H}_{0},y^{H}_{0})\) are then captured and stored and form the translation vector.  
Figure 8: Axis translation and rotation via the two-step manual calibration process. Left: with the first tap on the drone, the translation offset is captured by the HoloLens. Right: after moving the drone a small distance, the second tap captures the drone’s position in both coordinate systems (HoloLens and Drone), allowing the calculation of rotation angle \(\phi\).  
To capture the rotation angle, the drone is moved from its starting position. The new position's HoloLens coordinates \((x_{1}^{D},y_{1}^{D})\) are captured with a second air-tap. At the same time, the new position's Drone coordinates \((x_{1}^{H},y_{1}^{H})\) are calculated based on the aggregated IMU readings. Figure 8, on the right, shows this second step. Hence, Equation (4) yields:  
\[\begin{bmatrix}x_{1}^{H}\\ y_{1}^{H}\end{bmatrix}=\begin{bmatrix}cos(\phi)&sin(\phi)\\ -sin(\phi)&cos(\phi)\end{bmatrix}\begin{bmatrix}x_{1}^{D}\\ y_{1}^{D}\end{bmatrix}+\begin{bmatrix}x_{1}^{H}\\ y_{0}^{H}\end{bmatrix} \tag{5}\]  
The only unknown being \(\phi\), the above can yield:  
\[\begin{array}{c}cos(\phi)=\frac{x_{1}^{H}-sin(\phi)y_{1}^{D}-x_{0}^{H}}{x_{1 }^{D}}\\ sin(\phi)=\frac{x_{1}^{H}y_{1}^{D}-x_{1}^{H}y_{1}^{H}-x_{0}^{H}y_{1}^{D}+x_{1}^ {D}y_{0}^{H}}{(x_{1}^{D})^{2}+(y_{1}^{D})^{2}}\end{array} \tag{6}\]  
Therefore, Equation (6) can be used to obtain rotation angle \(\phi\), given any non-zero displacement \((x_{1}^{D},y_{1}^{D})\). Knowing both translation and rotation, any future drone-system coordinates can be translated to HoloLens system coordinates to allow visualization in AR.
## 4 Experimental Results  
### Experimental Conditions  
The present method is applied to the Lung Image Database Consortium (LIDC) [19] dataset, where nodules are annotated by four radiologists. We select \(50\) CT scans that contain 104 nodules from \(4\) to \(10\) mm in diameter. \(29\) / \(22\) / \(31\) / \(22\) nodules are contained at the agreement levels 1 / 2 / 3 / 4, respectively. The slice numbers are from \(102\) to \(371\), and the pixel sizes are from \(0.508\) to \(0.762\) mm. The reconstruction intervals are from \(0.75\) to \(3.0\) mm. The parameters are set as follows: the radius of the spherical VOI at the initial detection phase is \(15\) voxels, and the radius and height of the cylindrical VOI at the false positive reduction phase are \(10\) voxels and \(31\) voxels, respectively. A feature vector used in the k-means clustering is generated from the values of voxels in the cylindrical VOI, and therefore the dimensionality of the feature vector is \(9734\). The numbers of clusters (i.e. the \(K\) values in the k-means clustering method) are set to be \(15\) / \(20\) / \(25\). \(500\) / \(1000\) / \(1500\) / \(2000\) FP VOIs are randomly selected at the training phase of the FP reduction. These parameters are determined experimentally.
# Application of Machine Learning in the Identification and Prediction of Maritime Accident Factors
## 2 Materials and Methods
### Clustering  
To run several artificial intelligence algorithms, not only a workstation with adequate RAM memory and cores, in consequence, a HP Workstation 2820 Intel Xeon 24 core 2.7 GHz 128 GB RAM laptop (Palo Alto, CA, USA) was employed in this case study. At the same time, the software employed for curve fitting was Matlab 2024 in its most recent version due to its capability to interconnect the sampling of data (like the Internet of Things) with the artificial intelligence toolboxes (AI). Similar open-source software resources like Python can be employed to develop this work, but the possibility to create a posterior control system with Matlab Simulink is another advantage that guided us to select Matlab for this analysis.  
Once the software resource and the workstation were selected, different AI analyses were done. The first one was clustering, which can be defined as one method of grouping several similar objects into one cluster and different objects into another [26]. In particular, clustering is a machine learning tool that lets us identify groups of data that have a minimum Euclidian distance to a centrode. In consequence, these groups of data, in a posterior analysis, could be associated with some of the analysed variables, highlighting their relevance.
## 1 Introduction
However, detailed investigation needs to be conducted to identify the mechanism that contributes to these losses. Besides, buoyancy forces may also contribute to the nonlinearity of flow in addition to turbulence and streaming [8]. The current understanding of thermoacoustic processes is based on the assumption that the linear acoustic theory developed by [PERSON] [19] is reliable for the flow with the drive ratio (the ratio of oscillating pressure amplitude to mean pressure) of up to 2-3% [1]. However, turbulence may occur at lower drive ratios particularly within the internal structures of thermoacoustic devices, and hence, numerical modelling needs to be improved. There are quite a number of turbulence models available for solving fluid dynamical problems [20]. Unfortunately, there are no strict guidelines as to which model is best for a given case. Modelling turbulence, among others, requires knowledge about the flow physics and the established practice for a specific problem. The correct choice of turbulence model is important to ensure that the physics of flow within practical thermoacoustic systems is properly presented. The objective of this study is to test the performance of widely-available RANS (Reynolds-Averaged Navier-Stokes) turbulence models for the prediction of flow behaviour in oscillatory flow conditions in the presence of the wall temperature gradient, which is a typical situation in heat exchangers of thermoacoustic devices. In addition, studies of viscous dissipation, related to pressure losses in real systems, are carried out, together with surface heat flux calculations to provide additional validation against available experimental results.
# Biophotons: A Hard Problem
## 5 Biophotons in Living Materials
### Food Quality  
Observation of biophoton emission is widely used in this field because it is a fast and noninvasive technique. Recently, the Iranian-Brazilian group headed by [PERSON] produced an extended list of studies on food quality and food production quality [67]. For example, biophoton emissions coming from chicken eggs were used regarding the possibility for quality verification [68], while the emission from wine has been used to test the different winery practices in France [69] and in Hungary [70] to improve the quality of production.  
Nowadays, food is stored for several days before arriving in our houses. Biophotons can help in this case too. Several groups have used this emission to understand the degree of freshness of several types of food in many different conditions [59; 61; 71] and the security of storage of food, as in the study by [PERSON] [72].  
Pulsed electric field technology is an important emerging modality for both biomedicine and the food industry, e.g., in medicine (electrochemotherapy, tissue ablation, novel methods for drug and gene delivery and therapy), in the food industry (pasteurization, food compounds extraction), and in biotechnology. Biophotons are used in this case for sensing the protein oxidation generated by pulsed electric fields [73].
## 3 Results and Discussion  
### Effects of the Nickel Coating on the Interfacial Microstructures  
Figure 1 shows the results of various analyses performed to investigate the effects of the nickel coating on the Al/Fe alloys hybrid structure. Figure 1a shows SEM micrographs of the Al/Fe alloys hybrid structure's interface with nickel coating. The interfacial bonding exhibited superior physical bonding performance and was free from defects such as gaps and oxides. In conventional dissimilar casting processes used to fabricate Al/Fe alloys hybrid structures (such as diffusion bonding), solid-state steel is combined with a liquid-state aluminum alloy [21]. Considering the substantially different physicochemical properties of Fe and Al, however, severe oxidation occurs on the surface of steel during the pre-heating stage, thus hindering the bonding of solid-state steel and liquid-state aluminum alloy [5; 6; 7; 12]. The oxide film prevents direct contact during the casting process, reducing the wettability between the metals, and thus results in poor bonding [6]. The nickel-coated steel in this study, however, enabled improved interfacial bonding by preventing oxide formation.  
To examine the effects of the nickel coating in more detail, XRD patterns were obtained from the nickel-coated steel before and after the pre-heating step at 300 \({}^{\circ}\)C (Figure 1b). The XRD pattern from the surface of the nickel-coated steel did not change even after preheating at 300 \({}^{\circ}\)C, and no nickel oxide peak was detected. This shows that the nickel coating successfully suppressed the formation of an oxide film on the steel surface during preheating, and improved wettability between the aluminum melt and steel, leading to excellent physical bonding of the Al/Fe alloys hybrid structure [22]. It was therefore confirmed that preventing oxidation on the steel surface using a nickel coating is an effective way to improve the interfacial bonding of the Al/Fe alloys hybrid structure fabricated by dissimilar casting.  
Figure 1c shows the chemical element quantitative maps of the Al/Fe alloys hybrid structure with a nickel coating. The boundaries between each metal, along with their morphological characteristics, were apparently discernible and indexed using different colors. No apparent distribution of Fe and Ni in the Al matrix was detected. The Si elements were located solely at the grain boundary of the Al matrix, and elements in the aluminum alloy did not move into the other metal matrix. These results indicated that the chemical bonding of the interface by elemental diffusion did not occur during dissimilar casting, implying that the nickel coating could theoretically have contributed to the physical bonding of the Al/Fe alloys hybrid structure.
# Voltage Distribution on Transformer Windings Subjected to Lightning Strike Using State-Space Method
## 2 Network Elements
### Modeling of Power Transformer Winding
#### 2.3.4 Penetration Depth and Ferromagnetic Effects  
Ferromagnetic materials, such as iron, exhibit high magnetic permeability, resulting in a very small penetration depth. Consequently, magnetic fields penetrate only the surface region of the iron core and do not reach its deeper layers. This phenomenon is known as the skin effect. At very high frequencies, the penetration depth becomes significantly smaller than the physical dimensions of the iron core. Under these conditions, the magnetic flux is concentrated near the surface, rendering the inner regions of the core ineffective and non-contributory to the magnetic flux. Therefore, magnetization within the inner part of the iron core can be neglected at high frequencies.  
The relationship between penetration and the iron core determines how electromagnetic waves or magnetic fields propagate within ferromagnetic materials, such as iron. This relationship is crucial for transformer design and for understanding the effects of core losses in high-frequency applications. The penetration depth is expressed as follows [33; 34]:  
\[\delta=\sqrt{\frac{2}{\omega\mu\sigma}} \tag{20}\]  
where \(\omega=2\pi f\) is the angular frequency, \(\mu\) is the magnetic permeability of the iron core, and \(\sigma\) is the electrical conductivity.  
In this study, the lightning source is in the range of \(\mu\)s, which corresponds to the levels of MHz in the frequency domain. Therefore, it is considered that penetration depth is  
Figure 6: Equivalent circuit of transformer winding capacitance.  
low and magnetization effect can be neglected in the proposed method and in the ATP-EMTP simulation. This is mainly due to the very low magnetic penetration depth of power transformers at high frequencies [35], as described above. Flux penetrations differ at different frequency values such as 20 Hz, 500 kHz, and 1 MHz. At 500 kHz and 1 MHz, the flux penetration decreases significantly. As the frequency increases, the magnetic field strength decreases and eddy currents are induced in the conductor (core and winding) [36]. In transformer _modeling_ studies, assumptions have been made that the core effect can be neglected, especially at frequencies above 10 kHz [37].
# Greening the NetFPGA Reference Router
## 4 Frequency Control Policies for Dynamic Frequency Scaling  
The dynamic frequency scaling on the FSR is implemented by reading and writing to the relevant memory-mapped I/O registers introduced in the FSR, which adaptively control the operating frequency in response to the instantaneous traffic load. Since the NetFPGA packages (NFPs) Release 2.0.0, a register system has been introduced in the reference pipeline. The registers in the register system can indicate the status information and set the control signals for each separate module.  
As shown in Figure 3, statistics monitoring and preset thresholds are adopted in the design of the frequency control policies of the proposed energy-efficient FSR. The statistics monitoring is designed to indicate the current core logic frequency, the total number of bytes received from all receive queues (byte_counter_received) and the total number of bytes dropped from all output queues (byte_counter_dropped) in a certain sampling period. The sampling period is extremely sensitive, because it directly determines the delay between the request for a new frequency and the actual frequency transition. Experimental results indicate that 10 ms is a reasonable interval time for the sampling period and is consistent with that used in implementing ALR [1]. The preset thresholds are adopted to divide the routing capacity into five grades in response to the incoming traffic load in the previous 10-ms sampling period. Different frequency control policies may have a different set of rules for setting the thresholds.  
Frequency control policy is crucial in the FSR because it manages the operating frequency of the router, which in turn affects the power consumption and the performance of the router. An effective frequency control policy should manage the router to operate at the lowest frequency for as much time as possible without affecting user-perceived QoS. Significant increases to packet delay and loss could degrade the user experience, which is unacceptable. Three different frequency control policies are designed based on statistics monitoring and preset thresholds. These policies are used to determine when to initiate a frequency switching. The three implemented frequency control policies are: single-threshold policy (STP), double-threshold policy (DTP) and packet loss-aware policy (PLAP).
## 4 Results and Discussion
### Potentiostatic Tests
#### 4.2.3 Unimpregnated Carbon with Sizing (UC)
Figure 12: SEM images of UC: unpolarized reference (**a**), after polarization at level 1 (**b**), level 2 (**c**) and level 3 (**d**). Figure 14: EDX analysis in the marked area 1 (**a**) and EDX analysis in the marked area 2 (**b**). Figure 13: SEM image of unimpregnated carbon. 1 and 2 EDX analysis in the marked area. (3) Investigation of an unim impregnated carbon without sizing (UC_WS)  
Thermal gravimetric analysis (TGA)  
First, TGA measurements were performed to be sure that the two unim impregnated samples with and without sizing were different and to identify whether the sample UC really has a sizing. Since the differences cannot be seen from the SEM, TGA measurements were performed to see if the weight changes with increasing temperature. From the Figure 15 it can be seen that the sample UC_WS, which has no sizing, shows no change in mass at elevated temperature. The curve of UC shows that at 300 \({}^{\circ}\)C the weight decreases. This indicates that the sizing begins to dissolve. The comparison of the two curves shows that the sizing has about 1 wt% of the total weight of the carbon filament. After 450 \({}^{\circ}\)C, it stabilizes to 600 \({}^{\circ}\)C and then the curve drops again. From there the carbon textiles could start to decompose. It can be assumed that the examined UC samples with sizing really have a sizing and that the destroyed layer in Figure 12d is the sizing including deposited film. The decrease in the weight of sample UC-WS up to 600 \({}^{\circ}\)C could be due to the combustion of impurities and dirt.
###### Abstract
[22] investigated the clutch coupling vibration of a belt-pulley system under multiple excitation sources, but the analysis was performed for a transmission ratio of 1 and neglected the electromagnetic effects. As belt drives are subject to multiple excitation sources, the current analysis of nonlinear vibrations does not consider the coupling between them [23, 24, 25]. Additionally, no relevant literature was found to clarify how the coupled modulation of the frequency amplitude affects the condition monitoring results when the belt is worn or improperly tensioned. Figure 1: Interaction for modulation current and rotor load and speed. Figure 2: Vibration types of a belt transmission system: (a) transverse vibration, (b) longitudinal vibration, (c) rotational vibration. Therefore, an electromechanical coupling model with integrated parameters of the belt transmission is necessary to study the behaviour of the current modulated signal for machining condition monitoring. An analysis of the motor's current and speed modulation must be performed under various conditions associated with belt transmission, considering both the inappropriate deformation of the belt and pulley and the motor's electromagnetic characteristics. Accordingly, in this study, we developed a comprehensive model of a belt-driven electromechanical spindle system, in which the equations of motion associated with the mechanical components (belt drive) were solved in conjunction with the equations of the electromechanical response. By applying the Galerkin truncation method to the analytical solution of the differential equations of motion, we examined the modulation of the motor current and speed under the vibrations of the belt and pulley. The remainder of this paper is organised as follows: Section 2 presents the model of a belt-driven electromechanical spindle system, along with the formulation and solution of the equations via the Galerkin truncation method; Section 3 discusses the examination of the modulation of current and speed signals in a belt drive system with multiple excitation sources, using the developed model. To assess the validity of the developed model, an experimental test system was developed based on a CNC lathe. Lastly, the conclusions are summarised in Section 4.