esa-sceva/satcom-chunk-collection huggingface dataset:
## Enhancement of Brillouin Scattering Signal in Perfluorinated Graded-Index Polymer Optical Fibers  
**[PERSON] * and [PERSON]**  
Precision and Intelligence Laboratory, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-45-924-5052; Fax: +81-45-924-5091.  
_Received: 26 December 2011; in revised form: 18 January 2012 / Accepted: 20 January 2012 / Published: 31 January 2012_  
**Abstract:** Perfluorinated graded-index polymer optical fibers (PFGI-POFs), fabricated by replacing the hydrogen atoms of standard polymethyl methacrylate-based POFs with fluorine atoms, have been extensively studied due to their relatively low propagation loss even at telecommunication wavelength. Recently, Brillouin scattering, which is one of the most significant nonlinear effects in optical fibers, has been successfully observed in PFGI-POFs at 1.55-\(\upmu\)m wavelength. The Brillouin Stokes signal was, however, not large enough for practical applications or for detailed investigations of the Brillouin properties. In this paper, we review our recent work on Stokes signal enhancement. First, we induce stimulated Brillouin scattering based on the so-called pump-probe technique, and discuss its applicability to temperature sensors. Then, we investigate the influence of the core diameter and length of PFGI-POFs on Stokes signal, and observe the Brillouin linewidth narrowing effect. We believe our work is an important technological step toward the implementation of practical Brillouin-based devices and systems including distributed strain and temperature sensors.  
**Keywords:** Brillouin scattering; Brillouin gain spectrum; perfluorinated graded-index polymer optical fiber; pump-probe technique; fiber-optic sensing; distributed temperature sensing; nonlinear optics
## 3 Results
### Analysis of WUE in Anhui Province by Time Scale
#### 3.1.2 Multiple Fractal Analysis on Different Time Scales  
Figure 6 shows the multifractal spectra of the WUE in Anhui Province on three time scales: year-averaged, seasonal-averaged, and month-averaged. An effective method for analyzing the time-varying fluctuation trend of the WUE in Anhui Province is to use multiple fractal theory [26]. The difference between years is not significant, with the largest \(\Delta\alpha\) (\(\Delta\alpha=1.1388\)) in 2016 and the smallest (0.9988) in 2019. The fractal characteristics of WUE remained stable during the study period, indicating stability in the WUE of Anhui Province over the 21-year period.  
The parameters related to the specific multifractal spectra in terms of monthly variation are shown in Table 2. The widest \(\Delta\alpha\) (\(\Delta\alpha=1.8590\)) in August indicates strong heterogeneity in the spatial distribution of WUE during this month, whereas the narrowest \(\Delta\alpha\) of 0.8404 in October indicates a more homogeneous spatial distribution. Meanwhile, the lowest f(\(\alpha\)min) of \(-1.4472\) in August indicates the existence of regions with extremely inhomogeneous WUE in August, and the highest f(\(\alpha\)max) of 1.9611 in March suggests an extremely diverse WUE for some regions in March.  
The shapes of the WUE multifractal spectrum curves differ significantly seasonally, with relatively flat multifractal spectra observed in spring and fall as compared to summer and winter. The most significant multifractal characteristics observed in summer, with the largest \(\alpha\) range and the largest variation in f(\(\alpha\)), may be related to complex climatic conditions such as high temperature and rain. Although the fractal characteristics are also pronounced in fall and spring, this is to a lesser degree. The multiple fractal characteristics are complex in winter; however, the f(\(\alpha\)) variation is relatively small at this time.  
Figure 5: Trends in the average monthly WUE in Anhui Province during (**a**) 2001–2011 and (**b**) 2012–2021.  
Figure 6: Multiple fractal spectrum of WUE at different time scales in Anhui Province. (**a**) annual multifractal spectrum (**b**) monthly multifractal spectrum (**c**) seasonal multifractal spectrum.  
In summary, the multifractal spectrum of the WUE in Anhui Province shows significant temporal and spatial heterogeneity, especially in summer and August, which exhibit the strongest multifractal characteristics. This heterogeneity reflects the profound influence of complex climatic conditions on WUE, whereas flat spectral curves in spring and fall illustrate relatively uniform WUE distribution characteristics. In addition, f(\(\alpha\)) varies little in winter despite the complexity of the multifractal characteristics. Overall, the multifractal characteristics of WUE were highly stable on spatial and temporal scales, which provides strong support for further understanding the dynamic characteristics of regional water resource efficiency.
## 3 The Proposed Method
### Data Extraction and Image Recovery
#### 3.4.2 Image Recovery  
The recipient independently conducts the aforementioned procedures on each of the \(r\)-marked encrypted images to isolate the portion \(\text{B}_{\text{k}}\) from the images.  
The identifiers for the data hiders are denoted as \(i_{1}\), \(i_{2}\), \(\ldots\), \(i_{r}\), respectively. Utilizing the key \(K_{p}\), we generate \(\mathbf{X}_{\text{s}}=\mathbf{X}_{1},\mathbf{X}_{2},\ldots,\mathbf{X}_{LB}\), and then select the \(i_{1}\), \(i_{2}\), \(\ldots\), \(i_{r}\) rows from the coefficient matrix \(\mathbf{X}_{i}\) to form the matrix \(\mathbf{X}_{\text{r}}\). The inverse matrix \(\mathbf{X}_{\text{r}}^{-1}\) is then calculated using the following equation:  
\[\mathbf{X}_{\text{r}}^{-1}=\frac{\mathbf{X}_{\text{r}}^{*}}{\det(\mathbf{X}_{\text{r}})} \bmod 256 \tag{13}\]  
where \(\mathbf{X}_{\text{r}}^{*}\) represents the accompanying matrix of \(\mathbf{X}_{\text{r}}\). Next, we utilize the \(\text{B}_{\text{k}}\) part of the \(r\) embedded images \(\mathbf{IA}_{k}\) to decrypt and restore pixel \(\mathbf{IR}(i,j)\). Assuming pixels \(f_{1}(i,j)\), \(f_{2}(i,j)\), \(\ldots\), \(f_{r}(i,j)\) are utilized for decryption, \(\mathbf{IR}(i,j)\) is obtained from the Equation (14):  
\[\left(\begin{array}{c}\mathbf{IR}(i,j)\\ a_{1}(i,j)\\ \vdots\\ a_{r-1}(i,j)\end{array}\right)=\mathbf{X}_{\text{r}}^{-1}\times\left(\begin{array} []{c}f_{1}(i,j)\\ f_{2}(i,j)\\ \vdots\\ f_{r}(i,j)\end{array}\right)\bmod 256 \tag{14}\]  
We conduct the recovery operation on all pixels to obtain the transitional image \(\mathbf{IR}\). Subsequently, we replace the corresponding pixel values in image \(\mathbf{IR}\) with the first row and column pixels of the original image found in the edge information \(\mathbf{O}\). The adaptive parameters \(p_{1}\), \(p_{2}\), and \(p_{3}\) corresponding to pixel \(x^{\prime}(i,j)\) can be obtained from the \(\mathbf{ARP}\) in \(\mathbf{O}\). Starting from pixel \(\mathbf{IR}(2,2)\), the AMED prediction is performed on pixel \(x^{\prime}(i,j)\) in the order of top-to-bottom and left-to-right, and the predicted value \(px(i,j)\) can be obtained. From the pixel label value \(w\) in the edge information, the load capacity \(load(z)\) of the pixel can be obtained so that we can recover the pixel value of the original image because the predicted pixel is the same as the original pixel with the high \(w\) bits, and the \((w+1)\)-th MSB is inverted. The recovery process unfolds in the following manner:  
\[x(i,j)=\left\{\begin{array}{ll}\left\lfloor\frac{px(i,j)}{2^{8-w}}\right\rfloor \times 2^{8-w}+b_{w+1}\times 2^{7-w}+x^{\prime}(i,j)\bmod 2^{7-w},&\text{$i\prime 0\leq w \leq 7$}\\ px(i,j)&\text{$,if\ w=8$}\end{array}\right. \tag{15}\]where \(b_{w+1}\) is the \((w+1)\)-th MSB flip value of the predicted pixel \(px(i,j)\) as shown in the following equation:  
\[b_{w+1}=\left\{\begin{array}{ll}0,px^{w+1}(i,j)=1\\ 1,px^{w+1}(i,j)=0\end{array}\right. \tag{16}\]  
Upon the successful recovery of all pixels, the original image \(I\) can be obtained.
## 2 Materials and Methods
### Data Processing  
Raw data were first converted to mzXML format using the Proteowizard software package (v3.0.8789) and processed using XCMS running under R (V3.3.2) for peak identification, filtration, and alignment. A data matrix, including m/z, retention time, and intensity, was obtained. The metabolites were identified using the databases of Metlin, MoNA, and Panomic's own standard database (Suzhou BioNovGene Biomedical Tech Co., Ltd., Suzhou, China).
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 4 Problems of Measurement and Analysis of Reflectance Indices
### Orientation of Leaves and Parameters of Their Surface  
Errors in the measured reflectance of leaves can also be caused by additional factors, including the variability in the leaves' orientation, roughness at the surface, and specular reflection. These factors cause the appearance of the diffuse component of the reflected light. The direct and diffuse components can be simultaneously detected by a hemispherical spectrometer under laboratory conditions, but they cannot be independently measured by a hyperspectral camera in fields. The plant hyperspectral imaging at multiple angles (e.g., see [143; 156]) can decrease these errors and improve the accuracy of the information about plant physiological processes and biochemical composition. Parameters of plant reflectance can also be reconstructed by using a combination of hyperspectral images and 3D plant reconstruction [144; 145].  
An increase in the quantity of plants and their density per area can make the measurements of the reflected light and interpretation of results difficult. In this case, the reflectance spectra can be interpreted by using radiation transfer models; e.g., PROCOSINE, which is a combination of the PROSPECT and COSINE models, can describe specular reflection and leaf orientation [142]. Potentially, this model can be used for the prediction of the signal/noise ratio and correction of reflectance measurements; however, its application is limited by the leaf scale now [145]. The possibility of its use on the canopy scale requires future investigations.
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
## 2 Materials and Methods
### Marshall Design Test  
According to the Marshall design method in Chinese standard JTG E20-2011 [23] and asphalt adhesion-temperature curve, at the appropriate iso-viscous temperature, standard Marshall Specimens with 63.5 mm\(\pm\)1.3 mm in height and 101.6 mm in diameter were molded by a Marshall compactor through double compaction 75 times. The number of specimens for a set was not less than 4. Then, the specimens were placed to cool at room temperature for 48h. The bulk specific gravity, air voids (VV), voids in the mineral aggregate (VMA), asphalt saturation (VFA), Marshall Stability (MS) and flow values (FL) of asphalt mixture with different dosages of basalt fiber were determined. According to that, the change curves of the Marshall physico-mechanical indexes with the asphalt contents were drawn. Finally, the optimum asphalt content (OAC) was determined.
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Architecture of In-Line Measuring Station
#### 4.2.2 Accuracy  
In the proposed design, this characteristic is achieved by the performance capacities of the fixturing plate, the positioning system, and the laser probe. A positioner with a gantry design provides motion of the laser sensor in a plane. The use of linear guides and  
Figure 6: In-Line Measurement Machine. (**a**) Cartesian 2 axis Positioner, (**b**) machine structure, (**c**) pneumatic part positioning system, and (**d**) control system.  
encoders allows a positioning accuracy of about 0.003 mm and a resolution of 0.001 mm. The laser sensor has a repeatability of 0.012 mm, with a \(\pm\)40 mm measuring range from its focus length of 800 mm. That is, features within 40 to 120 mm distance from the sensor can be measured. The fixturing plate has three pins that define the reference plane (which fixes z and two angular positions), while two pins are used to locate the part in the remaining 3 degrees of freedom (in x, y, and the third angular position).  
The part is made out of aluminum. The surface is generally smooth and reflecting. The measurement tries to identify sections of the part that may be thicker. As seen in Figure 7, flashing or the die's heat checking (surface fatigue) marks may be present, depending on the tooling conditions. These are conditions that affect the quality of the part and the measurement process.
## 3 Microalgae Microbial Fuel Cell (mMFC)
### Scale-Up and Economic Feasibility  
The MFC designs must be scalable to enhance wastewater treatment capacity and improve cell performance in terms of electricity generation. The most commonly employed scaling strategy involves the installation of multiple units connected in series in a modular configuration [167; 168; 169], as output power increases linearly with the number of units [170; 171]. Using this approach, working volumes of up to 850 L have been achieved, reaching power densities of 0.043 W/m\({}^{2}\)[172].  
The main challenges associated with MFC scaling include increased limitations in substrate transfer to the cells and electron transfer to the anode, establishing an appropriate electrode area-to-cell-volume ratio, pH control, developing large-scale materials without significantly increasing costs, and maintaining a stable microbial community in large-scale systems [171].  
Economic feasibility studies on MFC scaling have shown that capital costs can be up to 30 times higher than those of conventional wastewater treatment, with membrane and electrode costs accounting for up to 80% of these expenses [173; 174]. Carbon anodes with a metallic core and stainless steel cathodes with nickel foam have been recommended due to their low cost [171].  
Although knowledge on MFC scaling has been increasing in recent years, studies on the scaling of Microalgal Microbial Fuel Cells (mMFCs) remain very limited [125]. A 10 L-scale study employing polyethylene bags for the cathodic chamber, clay slabs for the anodic chamber and as a membrane, graphite electrodes, and phosphate rock as a phosphorus source reported a microalgal biomass productivity of 0.307 kg m\({}^{-3}\) d\({}^{-1}\) and an energy generation of 11.53 kWh m\({}^{-3}\) with a total cost of $11,225 [98].
## 2 Materials and Methods
### Experimental Set-Up and Performance Tests  
A schematic diagram of experimental set-up of SDCMD process is illustrated in Figure 1. The synthetic wastewater prepared was placed into a feed tank with 5 L of effective volume made by tempered glass and stainless steel, in which a magnetic bar was stirred for generating mixing intensity in bulk wastewater. The feed temperature was controlled by using a digital hotplate (MSH-20D, Daihan Scientific, Wonju, Korea). The hollow-fiber membrane module was submerged horizontally and located at about two third of depth from the free surface of wastewater in the feed tank. Digital pressure gauges were connected at inlet and outlet tubing from MD membrane to monitor hydraulic pressure of fiber lumen. Two thermometers were submerged into both feed and permeate tank to monitor water temperatures. Permeate produced by the hollow-fiber membrane was recirculated into the permeate tank with 0.5 L of volume through a water chiller (RBC-20, JEO Tech, Daejeon, Korea) using a gear pump (WT3000-1 FA, Longer Pump, Hebei, China). Deionized (DI) water was filled initially in the permeate tank. The weight of permeate accumulated into the permeate tank with time was measured by using an electric balance (FZ-5000i, A&D Company, Tokyo, Japan). The SDCMD tests were performed over both the short-term (1 h) and long-term period (180 h) in this study.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 2 Basic Principles of Sound Mixing and Speech Separation
### Dimensionality Reduction  
For a sufficiently populated array, the number of sound sources in the sound field can be assumed to be less than that of microphones, making voice mixing model a non-underdetermined problem. However, for separation algorithms like ICA, the number of separated signals is required to be equal to that of channels especially in the frequency domain, considering the permutation ambiguity. Therefore, dimensionality reduction is urged to be performed effectively. The most common method for dimensionality reduction is the PCA, which can be performed at each frequency bin.  
Firstly, the cross-correlation matrix is calculated as  
\[R_{XX}(f)=\frac{1}{L}\sum_{t=1}^{L}X(f,t)X(f,t)^{\mathrm{H}} \tag{8}\]  
where \(L\) denotes the sample number and \({}^{\mathrm{H}}\) denotes the conjugate transpose.  
Second, the complex domain eigenvalue decomposition of the cross-correlation matrix is applied to obtain eigenvectors and eigenvalues corresponding to the components at the frequency bin. Then, the eigenvalues are ordered in descending order, \(d_{1}\geq\cdots\geq d_{M}\), and \(M^{\prime}\) eigenvalues larger than the threshold [15] are selected to form a diagonal matrix \(D=diag(d_{1},\cdots,d_{M^{\prime}})\). In addition, the corresponding eigenvectors are aligned to constitute the matrix \(E=[e_{1},\ \cdots,\ e_{M^{\prime}}]\). The matrix for dimensionality reduction is given by  
\[V(f)=D^{-1/2}E^{\mathrm{H}} \tag{9}\]  
for which  
\[Z(f,t)=V(f)\cdot X(f,t) \tag{10}\]  
where \(Z(f,t)\) is the dimensionality-reduced signal. Matrix \(V(f)\) whitens the observed signal, resulting in \(R_{ZZ}=I\), and the dimension of signal \(Z(f,t)\) is \(M^{\prime}\), which is the number of the sound components contained at frequency \(f\).  
Normally, there are two important issues required to be considered through using of PCA. The first is the number of the reduced dimension. If the number of sound sources is previously known, the dimension of reduction could commonly be fixed to it. If not, some methods are necessary to calculate it. In this paper, we use two parameters, including threshold and number of sound sources, to determine the signal dimension at each bin. Second is normalization. In some studies, normalization of the observed signal at each frequency bin could be optionally applied before PCA dimensionality reduction, which will allow us to use a fixed threshold for dimensionality reduction at different frequency bins. However, the signal dimension after reduction at some frequency bins may exceed the number of sound sources, especially for the components with low SNR. If normalization is not applied, the process of dimensionality reduction with a fixed threshold could discard some noisy frequency components with low energy. Therefore, normalization before dimensionality reduction depends on the signal characteristics and processing strategies. In this paper, the signal is not normalized before applying PCA.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 3 System Performance Simulation
### System Performance in the Gaussian Channel  
Three watermarking schemes are investigated in the paper. The first one is to hash the user bits with the key, and the intercepted watermark source bits are directly embedded into the phase of DMRS, which is the basic watermarking scheme and comes from the idea proposed in [12]. The second one is to encode the watermark source bits and then embed them into the phase of DMRS after satisfying the symmetry of the watermark. The third one is further improved, in which the pseudo-user-bits replace the user bits as the input of the hash algorithm. The above three watermarking schemes are referred to as Watermarking Scheme I, Watermarking Scheme II, and Watermarking Scheme III in turn. System performance evaluation indexes include the symbol error rate of user data, the bit error rate of watermark bits, the transmission success rate of user data, the transmission success rate of watermark bits, and the authentication success rate of watermark bits. The success rate of watermark authentication is related to two indicators, the transmission success rate of user data (the ratio of data frames in which the user data are transmitted correctly) and the transmission success rate of watermark bits, so the success rate of watermark authentication is judged by these two indicators.
## 5 Results and Discussion
### Concept of Smart Piezolectric Actuator System  
Figure 16 illustrates the potential application of piezoelectric actuator systems in a four-way digital fluid control unit (4 WDFCU) for both the open-loop and closed-loop position control of hydraulic drives. The system uses four piezo valves to replicate the functionality of a four-way, three-stage conventional spool valve. The 4 WDFCU features a modular design, allowing for the replacement of piezo valves and actuator systems. This  
Figure 14: Step response of the piezoelectric actuator system with one PE using PWM control. Overview with varying PWM signal widths (**a**) and detailed view with a 20 \(\upmu\)s PWM signal (**b**).  
Figure 15: Improved step response of the piezoelectric actuator system.  
flexibility enables the configuration of the 4 WDFCU to meet specific hydraulic application requirements, optimizing both static and dynamic performance. Additionally, the integration of wireless 5G technology and RFID is proposed to enable efficient plug-and-play functionality and facilitate data transfer between the actuator, valve, and controller unit [27].  
The Asset Administration Shell (AAS) concept is proposed for use with piezoelectric actuator systems and hydraulic valves. The AAS is a data and information model employed in the Industrial Internet of Things (IIoT) to describe and manage assets. Essentially, the AAS acts as a data plug, enabling the retrieval and storage of all relevant information about a specific asset, device, or component [28]. In this context, the AAS will facilitate efficient connectivity, asset recognition, and data transfer between the piezoelectric actuator system and the valve. This integration will allow for automatic adaptation and parameter setup according to the hydraulic drive application, optimizing the performance of hydraulic components.  
Additionally, real-time data from the piezoelectric actuator system and hydraulic valve can be stored on a local cloud for future use, such as edge computing. These data will enable real-time monitoring to assess component health, static and dynamic performance, energy consumption, and more. It will also be accessible to component developers and manufacturers to predict and implement potential improvements in design or setup.