esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Influence of Structural Parameters
#### 4.2.2 Center Distance between the Front and Rear Acquisition Confluence Tubes  
With the other parameters being set constant, the velocity contours inside the hydraulic collector at different center distances between the front and rear acquisition confluence tubes (\(L\)) are shown in Figure 18. Moreover, how the collection efficiency (\(\eta\)) and average outlet flow velocity of the conveying channel (\(v_{ao}\)) vary with \(L\) is shown in Figure 19.  
Figure 18 together with Figure 12a indicate that, as the center distance between the front and rear acquisition confluence tubes (\(L\)) increased from 610 mm to 690 mm, the reflected upflow formed by the two acquisition jets scouring the ground increased, and the lifting effect on the nodules was enhanced. When \(L\) was greater than 690 mm, the upflow was slightly weakened with the increase in \(L\). From the red curve in Figure 19, it can be seen that, with the increase of \(L\), the average outlet flow velocity of the conveying channel (\(v_{ao}\)) keeps decreasing, which weakens the nodule transport capacity in the channel. However, at the \(L\) of 730 mm, \(v_{ao}\) was still about 2.9 m/s, which was larger than the required 2.85 m/s.  
Figure 17: Nodule velocity distribution at 24.3 s when \(H\) is 180 mm.  
Figure 18: Velocity contours of the hydraulic collector at different center distances between the front and rear acquisition confluence tubes (\(L\)).  
The above phenomena eventually lead to the collection efficiency (\(\eta\)) of the hydraulic collector first increasing with \(L\), but slightly decreasing after \(L\) was greater than 690 mm, as shown by the black curve in Figure 19. Therein, \(\eta\) was found to be greater than 90% when the \(L\) was between 650 mm and 730 mm, which is the recommended range for designing the hydraulic collector.
## 2 What Is a Smart Grid  
The smart grid is a complex system. As such, it can be described from various points of view. Here we report two different definitions. The first one sums up the \"European\" view of the smart grid:  
_\"A smart grid is an electricity network that can intelligently integrate the actions of all users connected to it--generators, consumers and those that do both--in order to efficiently deliver sustainable, economic and secure electricity supplies. A smart grid employs innovative products and services together with intelligent monitoring, control, communication, and self-healing technologies. Smart grids development must include not only technology, market and commercial considerations, environmental impact, regulatory framework, standardization usage, ICT and migration strategy, but also societal requirements and governmental edicts\"_[2].  
The second one, written in the Statement of Policy on the Modernization of Electricity Grid of the United States Government [3], characterizes the smart grids by means of a list of achievements. The most relevant are:  
* the use of digital information to improve reliability, security and efficiency;
* integration of distributed resources and generation;
* \"smart\" technologies for metering, communication and automation;
* deployment of energy storage technologies (_i.e._, electric vehicles).  
According to professionals in the energy industry [4], it is clear that both definitions combine two dimensions: kWh and bytes. It is not argued the key role of ICT in developing a smart grid, and both viewpoints recognize the growing role of renewable technologies, distributed generation and energy storage.  
The smart grid is not simply a technological innovation. It also involves an accurate economic and financial planning, in order to be realized successfully and in an efficient way. A visualization of this concept is given by [PERSON] [5]. He depicts the smart grid as a pyramid, in which the asset management is the base for the realization of a smart grid infrastructure, as can be seen in Figure 1.  
The development of a smart grid does not involve replacing the existing electricity network. Such a process would be impossible for technical and economical reasons. Instead, the smart grid development is an enhancement of the existing network, by means of implementing new services and features, while maintaining, as much as possible, the old physical infrastructure. We have to define what functions a smart grid must provide. According to the United States Department of Energy's Modern Grid Initiative report [3], these functions are:  
* Self-healing;
* Consumer participation;
* High quality power;
* Support for different types of storage and generation;
* Higher efficiency.  
We will analyse how these functions can be provided from both the economical and technological point of view, focusing on the latter.
###### Abstract  
A novel re-entrant honeycomb metamaterial based on 3D-printing technology is proposed by introducing chiral structures into diamond honeycomb metamaterial (DHM), named chiral-diamond-combined honeycomb metamaterial (CDCHM), and has been further optimized using the assembly idea. Compared with the traditional DHM, the CDCHM has better performance in static and vibration isolation. The static and vibration properties of the DHM and CDCHM are investigated by experiments and simulations. The results show that the CDCHM has a higher load-carrying capacity than that of the DHM. In addition, the vibration isolation optimal design schemes of the DHM and CDCHM are examined by experiments and simulations. It is found that the vibration suppression of the CDCHM is also improved greatly. In particular, the optimization approach with metal pins and particle damping achieves a wider bandgap in the low-frequency region, which can strengthen the suppression of low-frequency vibrations. And the introduction of particle damping can not only design the frequency of the bandgap via the alteration of the dosage, but also enhance the damping of the main structure. This work presents a new design idea for metamaterials, which provides a reference for the collaborative design of the static and vibration properties of composite metamaterials.  
metamaterial; re-entrant honeycomb; static properties; bandgap; vibration isolation +
Footnote †: journal: Applied Sciences
# FPGA-Based Pulse Compressor for Ultra Low Latency Visible Light Communications
## 4 FPGA Implementation
### Implementation Performance  
The transmitter and receiver described above are implemented in the 10M50 DA FPGA of the MAX10 family produced by Intel/Altera. Table 2 reports the resources required by each of the main blocks described in the previous sections for the implementations based on chirps with \(N_{S}\) = 56, 112 and 224 samples. The FPGA resources are subdivided in logic cells (LCs), 9k-bit memory blocks (M9 ks), and Digital Signal Processors (DSPs), which are hardware multipliers in the 18 \(\times\) 18 configuration. In the transmitter, the packet formation and serializer blocks require just few LCs, whereas the modulator, as expected, is by far more demanding for resources. Each chirp table, implemented in logics, needs from 120 to 220 LCs, depending on the chirp length. Since in the configuration with \(N_{S}\) = 56, 112, and 224 are implemented 7, 14, 28 tables, respectively, the total LCs required by the look-up tables are 840, 2520, 6160 for the 3 different configurations. The complete modulator requires a bit more LCs than those occupied by the look-ups, namely 1137, 4127,  
Figure 8: Architecture of the packet reconstruction block integrated in the receiver.  
and 8120, respectively. Alternatively, we could have implemented the look-up tables in M9k memory resources. However, this is not an efficient solution, since each table stores at maximum 224 \(\times\) 14 = 3136 bits, whereas the M9k includes 9216 bits with an apparent waste.  
In the receiver, the compressor FIR requires from 438 to 1226 LCs, from 3 to 9 memory blocks and from 6 to 24 DSPs. The memories are needed for the realization of the \(N_{S}\)-tap data shift register of the FIR; the DSPs are configured like 18 \(\times\) 18 multipliers. The FIR clock is 125 MHz, i.e., 10-fold the data clock that is 12.5 MHz. The filter has 10 clock cycles for each input data, thus with 6 parallel multipliers it can perform up to 60 multiplications, which is sufficient to cover the 56 multiplications needed for 6. \(N_{s}\) = 56. A similar reasoning applies to the other \(N_{S}\) values. The threshold, the sequencer, and the packet reconstruction blocks need few resources. The latter requires a M9k block as well, which implements the shift register.  
In summary, the transmitter and receiver require from 2000 to LCs, 4 to 10 memory blocks, and 6 to 24 DSPs, depending on the configuration. The last row of Table 2 shows how these figures compare to the resources of the 10M50 FPGA.
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 2 Background
### Signals for Measuring Knock
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Instrument** & **Advantage** & **Disadvantage** & **Usage** \\ \hline Pressure Oscillations & In-Cylinder Pressure Transducer & High sensitivity & Expensive & Knock Studies \\ Engine Block Vibrations & Accelerometer & Easy to mount & Less accurate & Knock sensors in automobiles \\ Engine Noise & Microphone or Human Ear & Cheap & Arbitrary and not accurate & Engine calibrations \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The most common methods for measuring knock in SI engines.
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
## 2 Hyperlipidemic Rabbit Models  
### Diet-Induced Hyperlipidemic Rabbits  
#### 2.1.1 Cholesterol-Fed Rabbits  
Rabbits are the first models for the study of lipoprotein metabolism and atherosclerosis. In 1913, a Russian experimental pathologist, [PERSON], described that feeding cholesterol dissolved in sunflower oil to rabbits elevated blood cholesterol levels, and within several weeks, rabbit arteries showed atherosclerotic lesions [11]. As herbivores, laboratory rabbits including New Zealand white (NZW) and Japanese white (JW) rabbits, on a normal standard diet, have relatively low plasma total cholesterol (TC) levels (30-90 mg/dL) at the age of 3-4 months compared with humans. When rabbits are fed a cholesterol diet, they rapidly develop hypercholesterolemia [12]. [PERSON] et al. tested low dietary cholesterol (0.05% to 0.25%) with 6% peanut oil for 30 weeks to explore a response of plasma TC levels in rabbits. Dietary cholesterol in the range of 0.05% to 0.15% resulted in a less than 2-fold stepwise increase in plasma TC, whereas rabbits receiving 0.20% and 0.25% dietary cholesterol showed significantly higher (4- to 5-fold) plasma TC compared to those rabbits fed a diet containing 0.05% to 0.15% cholesterol [13]. Cholesterol diets contain more than 1% cholesterol usually causes extraordinary elevation of plasma TC levels in rabbits, exceeding 2000 mg/dL. Such high level of plasma TC is never seen in human hypercholesterolemiaemia. Therefore, it is generally recommended to use a 0.3-0.5% cholesterol diet, which results in hypercholesterolemia comparable to human familial hypercholesterolemia (FH) [8]. The major elevated lipoproteins in cholesterol-fed rabbits are those lipoproteins (called \(\beta\)-VLDL) derived from both the intestine and liver and they are quite atherogenic because they are rich in cholesteryl esters and can induce macrophages to transform into foam cells [14].  
Currently, we recommend a diet supplemented with 0.3-0.5% cholesterol and 3% soybean oil fed either ad libitum or restricted for most rabbit experiments. Representative hypercholesterolemia in cholesterol-fed rabbits is shown in Figure 1A. Plasma TC levels start to rise within one week and remain at high levels (\(\approx\)800 mg/dL) thereafter. Hyperlipidemic rabbits can develop aortic lesions as early as four-six weeks after cholesterol diet feeding, but at 16 weeks, 20-40% of the aortic surface is covered by atherosclerosis which can be stained with Sudan IV (Figure 1B). The age of rabbits should be considered because young rabbits are more susceptible to aortic atherosclerosis than old rabbits even though there are no differences in plasma TC levels [15].
## 4 Test Results and Analysis
### Stiffness Degradation  
The secant stiffness was used to evaluate the stiffness degradation of two sets of specimens under low-cyclic reciprocal loading, and the corresponding expression is:  
\[K_{j}=\sum_{i=1}^{n}P_{j}^{i}/\sum_{i=1}^{n}U_{j}^{i}, \tag{2}\]  
where \(P_{j}^{i}\) is the peak point load of the \(i\)th loading cycle at the \(j\)th loading displacement (\(\Delta/\Delta y=j\)), \(U_{j}^{i}\) is the peak point displacement of the \(i\)th loading cycle at the \(j\)th loading displacement (\(\Delta/\Delta y=j\)), and \(n\) is the number of cycles.  
A comparative analysis of the stiffness degradation of the two groups of specimens has been shown in Figure 18.  
Figure 18 shows that the slopes of the two sets of members are constant and linear at the early stage of loading. The value of the SF-1 set is slightly higher than that of SF-2 after the line bouncing stage, which is due to the slip of the specimen at the long circular hole, effectively releasing the energy and avoiding damage to the wall plate. At the end of the loading period, the slope of SF-2 decreased significantly less than that of SF-1, indicating that the joint could provide good stiffness after the bolts reached the limit. The damage stiffness values of specimens SF-1 and SF-2 are approximately 25.55-27.62% and 26.36-30.36% of their initial stiffness, respectively. At smaller displacements, the stiffness degradation is more minor. The stiffness degradation of SF-1 is significantly accelerated after the loading displacement exceeds 30 mm. After displacement loading exceeds 60 mm, the stiffness of SF-1 becomes lower than that of SF-2. The hook bolts only allow the AAC wall panel to rotate at the nodes, while the NEDC allows relative displacement of the nodes and the AAC wall panel. This reduces the damage caused to the AAC wall panels under more significant displacement and stabilizes structural stiffness. Overall, although the initial stiffness of specimen SF-1 is greater than that of specimen SF-2, the residual stiffness of SF-2 is significantly higher than that of SF-1 after loading.  
Figure 17: Strength degradation curves of SF-1 and SF-2.
## 4 Results and Discussion
### Effect of Vibration on the Apparent Viscosity
#### 4.3.2 0.3 g Vibration at Different Frequency  
Figure 13 shows the comparison of apparent viscosity between DMMFB and FEA of the applied vibration is 0.3 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz. From the comparison, it can be seen that the results predicted by DMMFB agree well with those obtained by FEA whenever vibration is applied or not.  
Table 10 shows the effective value of the apparent viscosity for each condition. The results show the same trend as above, that is, the apparent viscosity is decreased with the application of vibration, and it is further decreased with the increase of applied vibration frequency. The reason for this phenomenon is the same as that discussed above.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Experimental Results
#### 4.5.3 Comparison between Proposed Descriptor and State of the Art
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Descriptor**} & \multicolumn{2}{c}{**RGB-NIR**} & \multicolumn{2}{c}{**MSD**} \\ \cline{2-5}  & **SIFT Detector** & **Harris Detector** & **SIFT Detector** & **Harris Detector** \\ \hline SIFT & 52 & 43 & 3 & 1 \\ SURF & 25 & 21 & 1 & 0 \\ ORB & 59 & 53 & 2 & 1 \\ BRISK & 51 & 43 & 1 & 1 \\ GOM-SIFT & 57 & 45 & 3 & 2 \\ OR-SIFT & 57 & 53 & 3 & 2 \\ EOH & 42 & 23 & 2 & 2 \\ LBPG & 50 & 52 & 1 & 1 \\ LS-SIFT & 61 & 54 & 1 & 1 \\ DE-SIFT & 63 & 55 & 1 & 1 \\ NG-SIFT & 61 & 54 & 6 & 5 \\ MN-SIFT & **70** & 57 & 9 & 8 \\ LSS & 48 & 49 & 3 & 2 \\ VAR-EOH & 43 & 24 & 2 & 2 \\ LSSF & 53 & 46 & 10 & 8 \\ MFD & 65 & 57 & 19 & 14 \\ HoDM & 68 & **60** & **23** & **18** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average precision score (%) achieved by different detector-descriptor pairs on RGB-NIR and MSD datasets for image matching. Maximum value in each column is highlighted with bold face font. Figure 6 shows a comparison of proposed descriptor with seventeen different state of the art descriptors. It can be seen that the proposed descriptor obtains average matching score of 60% and 27% on RGB-NIR and MSD datasets respectively, and outperforms all other descriptors. Similarly the average precision scores shows that the proposed descriptor achieves 81% and 37% scores on RGB-NIR and MSD dataset respectively, and outperforms all other descriptors. Compared to HoDM, the proposed descriptor demonstrates 15% and 13% better matching and precision scores on RGB-NIR dataset. Whereas on MSD, it performs 11% (matching) and 14% (precision) better than HoDM, respectively. The experimental results show that state of the art descriptors do not perform well on VS-LWIR images. This is due to very high textural and intensity changes between VS-LWIR compared to RGB-NIR images. SIFT, GOM-SIFT, OR-SIFT, LC-SIFT, NG-SIFT and MN-SIFT, all obtain less than 7% matching scores whereas LSSF, MFD and HoDM demonstrates better matching scores compared to them. Similarly, the precision scores show that descriptors also under perform on VS-LWIR images. Compared to them the proposed Reg-SIFT descriptor achieves better results.
## 6 Conclusions  
The MAC delay is one of the error sources in underwater localization. Nevertheless, this problem is ignored in the localization literature, even though localization of moving platforms like AUVs that is widely used in various underwater applications. We investigate the impact of MAC delay on AUV localization and suggest the collaborative operation with multiple user system based on the HFM signal. The proposed system decouples the localization performance from the MAC delay. Our achievements can be further applied in all kinds of localization regardless of ranging techniques.  
Figure 11: The anchor node diversity gain is maintained in the proposed localization.  
Figure 10: The impact of MAC delay and AUV movement on the localization, network size is 1500 m.  
This paper uses a simple receiver architecture that just considers a direct path and the Doppler effect only caused by the AUV movement. For the more practical system, the multipath fading, which significantly distorts the acoustic signal in underwater channel, and Doppler effect caused by the surface movement should be considered also. Accordingly, the corresponding receiver architecture has to be developed to guarantee the simultaneous transmission in the harsh environment.  
This work (2016R1A2B4016588) was supported by Mid-career Researcher Program through NRF grant funded by the MEST.  
[PERSON] investigated the theoretical basis for this work, implemented the acoustic channel for the experiments as well as numerical analysis, and wrote the manuscript. [PERSON] supervised the entire work and helped to write the manuscript, and revised the manuscript.  
The authors declare no conflict of interest.
## 2 System Architecture  
### Hardware  
The underwater intervention system consists of a robotic vehicle equipped with a visual light communication (VLC) modem, enabling wireless data transmission between the robot and the ground control station (GCS). The ground-side VLC modem connects to the GCS via an Ethernet link, allowing for real-time teleoperation and supervision of the robot during inspection or intervention missions. The system is designed to ensure high reliability in challenging underwater environments. Additionally, the GCS incorporates a human-robot interface (HRI) customized to each specific use case, enhancing operator control and situational awareness for mission success. In Figure 1, a diagram of the hardware and connections is shown.
## 2 Materials and Methods
### Ablation Zone Extraction  
Images of the dissected porcine liver were captured using a 4128 \(\times\) 2322 resolution camera. These were taken following an ablation experiment. A stand was used to maintain approximately the same height and angle throughout all experiments. However, variations were inevitable, since the liver specimens were wobbly and irregularly shaped. As shown in Figure 5, images were taken with the antenna in place and a steel ruler in parallel along the ablation zone used as a reference. NIH-ImageJ 1.54f was used to extract the ellipticity of the ablation zone [36]. This was done with two different methods, as shown in Figure 5. Method one: an ellipse was best fitted using the bottom part of the ablation zone as a reference, ignoring the tailing section. Method two: the contour of the ablation zone was traced, including the tailing section. In both cases, the lesion created by the antenna was traced, following the contrast between the dark red colour of fresh liver and the pinkish colour where protein denaturation had occurred [38]. An inbuilt software option, 'fit ellipse', was used, which returned the major and the minor axes in pixel count with both methods. The ellipticity or flattening, \(f\), was calculated using Equation (2) [39].  
\[f=\frac{a-b}{a}, \tag{2}\]  
where \(a\) is the length of the major axis and \(b\) is the length of the minor axis. Since ellipticity is a ratio, pixel-to-metric conversion and calibration were not necessary. Ellipticity varies from 0 to 1, where 0 is a perfect sphere.  
Figure 5: A dissected liver with a typical ablation zone, antenna, and a traced lesion pattern post-processed using Image J 1.54f: (**a**) fitted ellipse and (**b**) traced region.
## 2 Moon Raw Material Surveying Strategy  
The surface of the Moon is 37 million km\({}^{2}\), which is larger than the surface of Africa. This provides enormous operational depth. It is necessary to significantly narrow down the search area for raw materials and carefully select places for future commercial space missions. Satellite data on the lunar surface can provide a lot of information about mineral resources, but without in situ research it is not possible to accurately estimate their size and whether their exploitation will be economically profitable. Until the deposit is confirmed in situ, e.g., by geological drilling with core collection, only probable deposits can be considered. In the case of satellite imaging of the Moon's surface, a big problem is that the space missions to date have been mainly scientific missions and the resolution of the obtained images (in the order of tens of square kilometers per pixel) does not meet industrial requirements. However, currently available data obtained by remote methods (e.g., spectroscopic measurements, neutron spectroscopy, SAR imaging, gravitometry, magnetometry) (lunar missions, e.g., Chang'e 1, Chandrayaan-2, Grail A, Grail B, Lunar Reconnaissance Orbiter, LCROSS, Korea Pathfinder Lunar Orbiter (Danurii)) can significantly narrow the area of further searches, enabling the selection of the most prospective places for future industrial mining space missions. However, a slightly different strategy for searching for such places should be used for each type of deposit.