esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Pressurization Mechanisms
### Prototypes Using Squeezing Rollers  
Another prototype (hereafter, MOVE-ROLLER) that worked by squeezing the drip bag using a pair of rollers, was also developed (see Figure 4). The rollers rotated themselves and squeezed a fixed-mounted drip bag while being pulled down by the vacuum piston cylinders (see Figure 4a,b). However, a problem arose in which the drip fluid escaped from the high-pressure side to the low-pressure side inside the drip bag, resulting in leakage through the small gap between the rollers (see Figure 4c,d).  
Figure 3: Prototype UP-PRESS. (**a**) A pressing plate, elevated by the vacuum piston cylinders through rods, exerts pressure on the drip bag. (**b**) The pressing plate is held at the standby position by a stopper. (**c**) Handles can be flipped up for the pressing plate to be reset (pushed down) to the standby position.  
Figure 2: Prototype V-PRESS. (**a**) A piston is at the starting position of cylinder. A pressing plate is at the standby position before moving towards the drip bag. (**b**) The piston is at the end position of cylinder. The pressing plate fully compresses the drip bag. (**c**) The pressing plate is reset to the starting position by pulling the two holders away from each other.  
To solve the problem faced by the MOVE-ROLLER, we developed a modified prototype (hereafter, FIX-ROLLER) as shown in Figure 5, in which a big roller was employed for the purpose of winding up the emptied part of the drip bag, thus, preventing the drip fluid escape encountered in MOVE-ROLLER. The opposite end of the drip bag outlet was attached to the big roller before operation. As shown in Figure 5a, a wire connected to a vacuum piston cylinder pulls and rotates the big roller, driving the whole mechanism. One should take note that, for simple representation, the movement of the piston (horizontally to the right) in Figure 5a differs from the actual movement, which is upwards in Figure 5c. Compared with MOVE-ROLLER, the new features in FIX-ROLLER included all rollers rotating at fixed positions, and the outlet of the drip bag facing upwards. Exterior and interior views of FIX-ROLLER are shown in Figure 5b,c, respectively. The rolling mechanism is unlocked for release in Figure 5d, and then reset back to the standby position in Figure 5e.
## 3 Results and Discussion
### Impact of Vibration Amplitude on Machined Surface  
The machined surface of the Nomex honeycomb core is essential to building the sandwich structures. Its precise optimization is essential to ensure the robustness and durability of these materials in various industrial applications. Typically, imperfections in the machining of NHC structures come in various forms, including burrs, tears of the walls, and uncut fibers. These defects can appear at different stages of the machining process and have the potential to impact the final quality of the structure. Therefore, in-depth analysis is required to adjust manufacturing parameters and minimize these imperfections. To study the effect of vibration amplitude on the machined surface, numerical simulations were conducted by adjusting the vibration amplitudes to values of 0 \(\upmu\)m, 10 \(\upmu\)m, and 25 \(\upmu\)m. The cutting simulation is carried out under the same cutting conditions, notably a feed rate at 3000 mm/min and a spindle speed at 1000 rpm, which were kept constant throughout the milling simulation. The machining defects generated by the numerical model were spotted by visual inspection with the naked eye and are illustrated in Figure 9.  
The main results of this research indicate a gradual enhancement in the quality of the surface with increasing ultrasonic vibration amplitude. This observation highlights a direct relationship between vibration amplitude and surface quality, demonstrating the potential benefits of this technique in machining processes. Furthermore, the numerical results demonstrate that the predominant machining imperfections noticed on the Nomex honeycomb core are wall deformations and tears. Though, the numerical model failed to detect the burrs, characterized by excess material on the cell walls since they were characterized by S4R shell elements without thickness. In fact, at low spindle speeds, the  
Figure 8: Evolutions of the cutting force components (F\({}_{\mathrm{x}}\), F\({}_{\mathrm{y}}\)).  
cutting tool cannot apply adequate force to the walls of the NHC core, which increases the risk of elastic deformation of these walls until their rupture. The use of ultrasonic vibrations highlights the notable benefits they bring to the machining. These vibrations reduce contact between the tool and the thin walls of the honeycomb structure, thereby reducing friction and improving heat dissipation during cutting. The interaction of these parameters facilitates the engagement of the milling cutter with the walls of the honeycomb core, which contributes to the reduction of machining defects and the improvement of the fineness of the machined surface. This finding highlights the effectiveness of ultrasonic vibrations in improving the quality of the machined surface, which allows for improved milling techniques.
# Development of Wireless Power Transmission System for Transfer Cart with Shortened Track
## 3 Thermal Analysis and Pickup Module Fabrication
### Pickup Module Fabrication  
In this section, we discuss the fabrication of a pickup module that can be applied to a shortened track for a wide range of uses. The pickup module prototype production concept is shown in Figure 2.  
In the figure, the \"A\" part is designed to fit into the groove to avoid up and down shake, and it is possible to assemble without additional tape processing, which reduces the assembly time. The bending design of the \"B\" part is implemented to remove the separate module intermediate fixing bracket by supporting the middle part and avoiding up and down shake. The bending design of the \"C\" part is employed to improve the cooling performance by allowing the fan wind to flow smoothly into the empty space and additionally minimize heat generation by the bolts. The \"D\" part is a round treatment to prevent tearing of the wire cloth by the module edge. The \"E\" part is the fitted design to support the module weight. The connection terminal block of the \"F\" part is installed so that the Litz wire connection terminates the assembly without additional fieldwork. The \"G\" part is MC (machining center) machinery to minimize heat generation due to the lower natural cooling inlet and Litz wire. The \"H\" part represents the fan intake hole design, and the hole rectangle design is implemented to prevent shake due to the module tolerance at the \"I\" part. The \"J\" part is a symmetrical design of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Bakelite** & **Ferrite Core** & **Effective Value** \\ \hline Thermal conductivity (W/m-K) & 1.4 & 4.25 & 3.32 \\ Density (kg/m\({}^{3}\)) & 1300 & 5000 & 3794.9 \\ Specific heat (J/kg\({}^{3}\)K) & 1465 & 750 & 982.9 \\ Volume (m\({}^{3}\)) & \(1.58\times 10^{-3}\) (32.6\%) & \(3.27\times 10^{-3}\) (67.4\%) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of L-dummy used in the thermal analysis.  
the side terminal block to make it easy to assemble to the left and right sides depending on the field conditions. The internal layout of the product is shown in Figure 3.  
As shown in the figure, several essential parts, such as the cooling fan, control wiring terminal block, module, and Litz wire connecting block, are installed inside the product.
## 3 Preliminaries
### System Model  
As shown in Figure 1, our system model contains two main entities: the user (or client) (\(U_{i}\)) and the aggregation server (\(S_{j}\)). In Figure 1, the blue lines indicate the registration phase, while the orange lines denote the login and mutual authentication phases.  
In the registration phase, \(U_{i}\) sends a registration request to the aggregation server \(S_{j}\). After receiving the request, the \(S_{j}\) creates a smart card that contains key information, such as the user's identity information, the generated random number, and the aggregation server's public key. Finally, \(S_{j}\) sends the smart card to \(U_{i}\) to enable that \(U_{i}\) can perform subsequent login and mutual authentication operations.  
In the following login and mutual authentication phase, \(U_{i}\) and \(S_{j}\) run the operations to obtain mutual authentication and then a session key will be established between entities.
## 2 Materials and Methods  
### Sample Preparation  
In order to successfully conduct optical characterization of powders, especially via DRS, a suitable preparation technique is required. In the following, a method is presented to produce highly diffusely reflecting and opaque samples, i.e., their surface acts as a Lambertian scatterer while light transmission through the sample is negligible. Both conditions can be met by compressing the powder into a solid pellet as elaborated in references [1; 15].  
Naturally, this preparation becomes difficult or outright impossible when materials do not tend to form aggregates. This may be caused by too large grains on the order of ten micrometers and above if no smaller particles are present to coagulate the powder. Nevertheless, particles sizing down to 1 um may still cause problems if their surfaces are very smooth or silanized. In the latter case, they possess a hydrophobic coating, such that they do not jam and thereby also do not stick together. Apart from these conditions, nearly any material sizing below 1 um can be pressed into a pellet.  
For compression, all components coming in contact with the powder needs to be inert to the substances involved, i.e., their hardness and corrosion resistance are of particular importance. Owing to their low durability upon mechanical stress, glasses and monocrystalline materials are generally unfavorable. Instead, hardened, stainless steels such as DIN X90 CrMoV18 (EN 1.4112-AISI 440B) with a Rockwell hardness above HRC 55 [20] are preferred.  
In order to ensure a matte finish of the pellet, a sheet of paper placed on the front-side of the pellet or a clean ceramic can be used to avoid glossy surfaces as a result of a direct contact to the steel components [1]. Ideally, the powder is directly pressed into a sample holder which may then be immediately placed into the measuring device for further investigations without ever removing the material from the holder during this time. This way, the exact shape of the sample will be preserved while the risk of breaking or scratching of the pellet is minimized as opposed to if it was taken out of the sample holder.  
In the following, two custom methods to prepare powder pellets that primarily differ in their performance and reliability will be presented. While fully automated pellet preparation devices [21; 22; 23; 24] are commercially available at a typically much higher cost that may only amortize upon large-scale production, they may not necessarily be suited for the materials of interest, e.g., very hard and highly abrasive powders such as aluminum oxides or strongly translucent samples that require nonstandard thicknesses. Therefore, this kind of preparation technique will be omitted henceforth.
## 3 Proposed Approach
### AGBCPT Components
#### 3.2.1 Encoding Scheme  
The parameters used in the pairs trading strategy influence the pairs trading return. Because the strategy described here utilizes the correlation coefficient and Bollinger Bands, it takes into account the six parameters--correlation coefficient threshold (\(cLimit\)), entry width of the Bollinger Bands (\(BBentryWidth\)), out width of the Bollinger Bands (\(BBoutWidth\)), correlation coefficient calculation days (\(cDay\)), moving averages calculation days (\(mDay\)), forward observation days (\(oDay\))--and encodes them into a chromosome with real numbers. The correlation coefficient is applied to find potential stock pairs, and the Bollinger Bands are employed to find pairs trading signals. The encoding scheme of a chromosome is shown in Table 1.  
In Table 1, the genes representing \(cLimit\) and \(cDay\) belong to the correlation coefficient calculation, and \(mDay\), \(BBentryWidth\), \(BBoutWidth\), and \(oDay\) belong to the Bollinger Bands.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{_Chromosome C\({}_{q}\)_} \\ \hline \(cLimit_{q}\) & \(BBentryWidth_{q}\) & \(BBoutWidth_{q}\) & \(mDay_{q}\) & \(cDay_{q}\) & \(oDay_{q}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Encoding scheme of chromosome C\({}_{q}\).  
The _cLimit_ value is the threshold of the correlation coefficient set for finding potential stock pairs. _cDay_ represents the days for calculating the correlation coefficient of two stocks. The _mDay_, _BBentryWidth_, _BBoutWidth_, and _oDay_ parameters are used in the Bollinger Bands. _mDay_ represents the days for calculating the moving averages. The Bollinger Bands width of the up and down channels for the entry and exit signals are represented by _BBentryWidth_ and _BBoutWidth_. _oDay_ represents the days of the stock price comparison for a trading signal.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 5 Conclusions  
In this paper, we have proposed a perceptual hash algorithm for multispectral remote sensing image authentication. In order to compactly represent the perceptual features of the multispectral image, we have adopted an affinity propagation algorithm to classify the MS images into several clusters based on the mutual information of the bands of these images. Dividing each band into a grid, the features of the grid cell at the same location within the cluster are extracted and fused based on DWT, while PCA-based data compression on the fused feature helps reduce the influence of noise. The final perceptual hash value can be acquired after the compressed feature has been encrypted by the cryptographic encryption algorithm. Experimental results have shown that the proposed algorithm is robust against normal content-preserving manipulations, such as data compaction and digital watermark embedding, and has good sensitivity to detect local detailed tampering of the multispectral image. Thus, the algorithm efficiently authenticates the content integrity of multispectral remote sensing images, and overcomes the defects of the existing perceptual hash algorithms which do not consider multispectral remote sensing images.  
Figure 11: Tampering Test 3 (changing the object): (**a**) In\(R_{\mathcal{T}}\) of the original image A; (**b**) In\(R_{\mathcal{T}}\) of the tampered image A; (**c**) In\(R_{\mathcal{T}}\) of the original image B; (**d**) In\(R_{\mathcal{T}}\) of the tampered image B.  
Figure 12: Tamper location of Test 1: (**a**) In\(R_{1}\) of the tampered image A; (**b**) Tamper location of Image A; (**c**) In\(R_{1}\) of the tempered image B; (**d**) Tamper location of Image B.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compared Images** & **Figure 8a,b** & **Figure 8c,d** \\ \hline The normalized hamming distance & 0.1016 & 0.1484 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The normalized hamming distance of the tampered grid.  
The aims of future works are outlined as follows: firstly, to improve the robustness against JPEG compression; secondly, to expand the algorithm to include hyperspectral remote sensing images which contain many more bands than multispectral images.  
This study is supported by grants from the State Key Laboratory of Resource and Environment Information System Open Funding Program (Grant No. 201604), the Jiangsu Province Science and Technology Support Program (Grant No. BK20170116), the National Natural Science Foundation of China (Grant Nos. 41601396, 41501431) and the Scientific Research Hatch Fund of Jinling Institute of Technology (Grant Nos. jit-fhxm-201604, jit-fhxm-201605, jit-b-201520).  
The manuscript was written by [PERSON] under the guidance of [PERSON]. [PERSON] participated in designing the experiments. All authors reviewed the manuscript.  
The authors declare no conflict of interest.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
###### Abstract  
Aiming at the problems of brightness attenuation and contrast reduction in the target image caused by underwater low-visibility environments, a metal target detection method based on split-focus plane polarization imaging is proposed. Firstly, a hybrid enhancement method is proposed to clarify the degraded polarization image. In this study, the GrayWorld method is improved to compensate the attenuation difference of the total light intensity of the polarization image. Variational contrast and saturation enhancement algorithms are used to reduce the underwater scattering effect; secondly, a split-focus plane polarization imaging system is built to complete the control of camera parameters, polarization image acquisition and information processing. Under different underwater low-visibility conditions, polarization imaging of targets with different materials can be realized; finally, an image quality evaluation system is constructed to compare the light intensity and degree of polarization images that are collected by the focal plane polarization imaging system. The polarization characteristics of metal and nonmetal target plates are analyzed. The results show that under the condition of low visibility, the obtained polarization image contrast of the metal target is relatively high, and its EME, information entropy and average gradient are increased by 183.82%, 53.46% and 586.22% on average relative to the image of light intensity. In an underwater low-visibility scene, the method of focal plane polarization imaging proposed in this paper can reduce the difficulty of metal target detection.  
underwater detection; low visibility; metal target; focus plane; polarization imaging Article
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
###### Abstract  
The small strain shear modulus is an important characteristic of geomaterials that can be measured experimentally using piezoelectric sensors (bender elements). However, most conventional signal interpretation techniques are based on the visual observation of the output signal and therefore inherently subjective. Objective techniques also exist, like the cross-correlation of the input and output signals, but they lack physical insight, as they rely on the (incorrect) assumption that input and output signals are similar. This paper presents GeoHyTE, the first objective and physically consistent toolbox for the automatic processing of the output signal of bender element sensors. GeoHyTE updates a finite element model of the experiment, iteratively searching for the small strain shear modulus that maximises the correlation between the experimental and numerical output signals. The method is objective, as the results do not depend on the experience of the user, and physically consistent, as the wave propagation process is modelled in full and signals of the same nature (output) are correlated. Moreover, GeoHyTE is nearly insensitive to grossly erroneous input by the user, both in terms of the starting point of the iterative maximisation process and refinement of the finite element model. The results obtained with GeoHyTE are validated against benchmark measurements reported in the literature and experimental data obtained by the authors. A detailed statistical analysis of the results obtained with GeoHyTE and conventional interpretation techniques is also presented.  
bender element test; automatic interpretation; GeoHyTE; small strain shear modulus; geomaterials
## 4 DNN Optimal Predictor
### Sample Generation  
The key to constructing the DNN optimal predictor is to generate a large number of good training samples. In this paper, the SCvx algorithm proposed in [25] is used to generate the sample trajectory efficiently. Then, the state variables and the corresponding optimal terminal time are obtained directly using linear interpolation. The sample trajectory's initial state variables are generated as follows:  
\[\begin{array}{ll}r_{x,i}{=5000+1000\varepsilon\ (\text{m})}&v_{x,i}{=-100+100\varepsilon\ (\text{m}/\text{s})}\\ r_{y,i}{=1000\varepsilon\ (\text{m})}&v_{y,i}{=50\varepsilon\ (\text{m}/\text{s})}\\ r_{z,i}{=1000\varepsilon\ (\text{m})}&v_{z,i}{=50\varepsilon\ (\text{m}/\text{s})}\\ m_{i}{=38+2\varepsilon\ (\text{t})}&\varepsilon\in[-1,1]\end{array} \tag{18}\]  
where \(\varepsilon\) is a uniformly distributed random number with the range of [\(-1\), \(1\)]. First, the corresponding fuel-optimal trajectory is solved by the SCvx algorithm for each pair of initial state variables. Then, 60 state variables and optimal terminal time pairs are uniformly selected as samples on each sample trajectory. Finally, the generated samples are stored in the training set. The flow chart of sample generation is shown in Figure 8.  
A large number of sample trajectories can be generated efficiently using the above algorithms. A total of 20,000 sample trajectories are randomly generated, and the sample trajectories are shown in Figure 9. Because 60 pairs of samples are uniformly distributed on each sample trajectory, the total number of the samples is 1.2 million, which basically covers the whole feasible region.  
Figure 8: Flow chart of the sample generation.  
The distribution of the terminal time of all sample trajectories is drawn, as shown in Figure 10. It can be found that there is a great variant between the optimal terminal time obtained under different initial conditions. Among the 20,000 sample trajectories, the minimum optimal terminal time is 25.1219 s, and the maximum is 57.8355 s, and the sample data is reasonable.
## 6 Analysis and Discussion of the Results
#### 6.1.6 Visibility Distance Measurement  
From Figure 11, it is possible to observe that the BEB fire, in comparison to the ICEB fire, slightly worsens the environmental situations in terms of visibility conditions, in both cases of ventilation investigated. However, no significant differences were found among the scenarios examined because, in all of them, the visibility distance is <10 m (i.e., the threshold value [45]), except near Portal \(B\). Consequently, as Figure 10 shows, the last user exiting the tunnel through Portal \(B\) never has acceptable visibility conditions.