esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Coherent fiber-optic communication systems are limited by the Kerr-induced nonlinearity. Benchmark optical and digital nonlinearity compensation techniques are typically complex and tackle deterministic-induced nonlinearities. However, these techniques ignore the impact of stochastic nonlinear distortions in the network, such as the interaction of fiber nonlinearity with amplified spontaneous emission from optical amplification. Unsupervised machine learning clustering (e.g., K-means) has recently been proposed as a practical approach to the blind compensation of stochastic and deterministic nonlinear distortions. In this work, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed, for the first time, for blind nonlinearity compensation. DBSCAN is tested experimentally in a 40 Gb/s 16 quadrature amplitude-modulated system at 50 km of standard single-mode fiber transmission. It is shown that at high launched optical powers, DBSCAN can offer up to 0.83 and 8.84 dB enhancement in Q-factor when compared to conventional K-means clustering and linear equalisation, respectively.  
Keywords:fiber optics communications; coherent communications; machine learning; clustering; nonlinearity cancellation +
Footnote †: journal: Applied Physics Letters
# A Dynamic Numerical Simulation on the Grouting Timing in Retained Rib of Pillarless Mining
## 1 Introduction  
The retained roadways often experience various types of serious problems with large surrounding rock deformation in pillarless mining [1; 2; 3; 4; 5]. A number of field cases show that the deformation of retained coal ribs is one of main causes behind the rib-to-rib convergence. During this process, the retained rib with loose and fragmented coal becomes the weakest segment of the roadway with a limited bearing capacity [6; 7; 8; 9], which often undergoes severe deformations, and may even affect the stability of the entire roadway [10; 11].  
Grouting is considered as an effective support method to reinforce the fragmented coal and restore the load-bearing capacity of the retained coal rib. Under the long-term impact of the mining-induced stress, the fragmented zone in the retained coal rib gradually develops and expands deep into the coal rib. Along with this process, the shallow coal permeability improves while the roadway surrounding rock stability gradually decreases [12; 13; 14]. For the retained roadway in pillarless mining, the slurry diffusion range and the integrity of the coal rib are dependent on each other in a contradictory and unified manner. In order to maintain the stability of the roadway, a balance must be achieved between the two aspects.  
FLAC3D is an effective tool suitable for simulating the nonlinear and large deformation behavior of geotechnical materials and their supporting structures, especially for simulating the plastic flow of materials after yielding, which has been widely used in the stability analysis of the surrounding rock of the retained roadway [15; 16]. In this study, the FLAC3D was adopted based on multiple constitutive models such as the double yield, plastic softening, Mohr-Coulomb and corresponding algorithms to construct a dynamic numericalmethod to simulate the stability of retained roadway surrounding rock. In addition, the plastic zone development in the roadway during the process of roadway deformation and the optimal grouting timing of the fragmented coal rib were studied.  
The study of rock mass control primarily focuses on understanding the movement patterns of coal and rock layers influenced by mining activities. As the coal is extracted from the longwall mining face, the surrounding rock layers shift and fill the void left behind by the coal extraction. This process results in a series of intense movements, including the movement of rock layers between the coal seam roof and the surface, leading to surface subsidence [17; 18]; the development of support pressure on both sides of the working face; and the convergence of the goaf and the roof/floor of the working face [19; 20]. The basic principles of the dynamic model for coal mining proposed in this study can be applied to various aspects of ground control, such as roadway deformation, roof stability and surface subsidence above the coal mining face. Other researchers in this field can use this dynamic model in combination with the actual geological conditions of their projects to conduct research on ground control.
## 2 Theoretical Background
### Computational Thinking  
Computational thinking is a problem-solving approach that utilizes concepts from computer science and it has become increasingly important in modern education. It involves decomposing problems, focusing on core elements through abstraction, recognizing patterns, designing algorithms, and automating processes [8]. Computational thinking goes beyond programming. It emphasizes cultivating the ability to systematically solve complex problems.  
Enhancing computational thinking in computer vision education helps students understand and apply technology effectively [27]. Decomposing problems in image or video processing and logically solving each part exemplify computational thinking. Additionally, designing algorithms and automating processes to solve computer vision problems are key elements that contribute to developing creative and logical thinking necessary for complex problem solving [28]. As shown in Table 3, the core elements of computational thinking, such as problem decomposition, abstraction, pattern recognition, algorithm design, and automation, are essential for breaking down and solving complex problems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Computer Vision Stage** & **Key Elements** & **Description** & **Related Research** \\ \hline \multirow{3}{*}{Image Recognition} & Image Classification, Object Recognition & Identifying and classifying objects or patterns within images. & CNNs; Models like ResNet and VGGNet [21]. \\ \hline \multirow{3}{*}{Image Segmentation} & Segmentation, Semantic Segmentation, & \multirow{3}{*}{Partitioning images into meaningful regions.} & \(\bullet\) U-Net; Fully Convolutional Networks (FCNs); Mask R-CNN [19; 22]. \\ \hline \multirow{3}{*}{Object Detection} & Bounding Boxes, & Identifying and marking the locations of specific objects within an image. & \(\bullet\) YOLO; SSD [23]; Faster R-CNN [18]. \\ \hline \multirow{3}{*}{Video Analysis} & Object Tracking, Action Recognition, & Analyzing movements and actions of objects in sequential frames. & \(\bullet\) Optical Flow; LSTM; 3D CNN [20]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stages of computer vision.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Element** & **Description and Related Research** \\ \hline \multirow{3}{*}{Problem decomposition} & Breaking down complex problems into smaller, manageable parts. Decomposition is essential for handling complex problems [8]. \\ \hline \multirow{3}{*}{Abstraction} & Focusing on important information while ignoring irrelevant details. Abstraction is the key in understanding and managing complex systems [29]. \\ \cline{1-1}  & & \\ \cline{1-1}  & & \\ \cline{1-1} \cline{2-3} \cline{4-
# A Novel Scheme for MIMO-SAR Systems Using Rotational Orbital Angular Momentum
## 2 An OAM-Based MIMO-SAR System Model
\[s_{p}\big{(}t_{m},t,l_{p}\big{)} \approx N\exp\{il_{p}\pi/2\}I_{l_{p}}\big{[}ka\sin\theta_{p}(t_{m}) \big{]}\omega_{a}[t_{m}-x/v+(p-1.5)L_{a}/v]\omega_{r}\big{[}t-r_{p}(t_{m})/c\big{]} \tag{1}\] \[\exp\Big{\{}i\pi K_{r}\big{[}t-r_{p}(t_{m})/c\big{]}^{2}\Big{\}} \exp\{i2\pi f_{c}\big{[}t-r_{p}(t_{m})/c\big{]}\}\exp\{il_{p}\varphi_{p}(t_{m})\}\]  
where \(t\) is the range time variable; \(t_{m}\) is the azimuth time variable; \(\omega_{r}(t)\) is the range envelope; \(\omega_{a}(t_{m})\) is the azimuth envelope; \(v\) is the platform speed; \(L_{a}\) is the aperture size of each UCA antenna; \(K_{r}\) is the LFM ratio; \(f_{c}\) is the central frequency of the signal; \(c\) is the speed of light; \(l_{p}\) is the mode number of OAM beams transmitted by the \(p\)th UCA antenna; \(\varphi_{p}(t_{m})\) is the target azimuth angle history, which can be expressed as:  
\[\varphi_{p}(t_{m})=\tan^{-1}\bigg{\{}\frac{y\cos\beta-z\sin\beta}{x-vt_{m}-(p- 1.5)L_{a}}\bigg{\}}; \tag{2}\]  
where \(\beta\) is the antenna look-down angle; \(J_{l}[ka\sin\theta(t_{m})]\) denotes the \(l\)th order Bessel function of the first kind; \(k\) is the wave number; \(a\) is the radius of the UCA antenna; \(\theta_{p}(t_{m})\) is the target polar angle history, which can be expressed as:  
\[\theta_{p}(t_{m})=\sin^{-1}\Bigg{\{}\frac{\sqrt{\left(x-vt_{m}-(p-1.5)L_{a} \right)^{2}+\left(y\cos\beta-z\sin\beta\right)^{2}}}{\sqrt{y^{2}+z^{2}+\left(x -vt_{m}-(p-1.5)L_{a}\right)^{2}}}\Bigg{\}} \tag{3}\]  
where \(r_{p}(t_{m})\) is the target range history, which can be expressed as:  
\[r_{p}(t_{m})=\sqrt{\left(x-vt_{m}-(p-1.5)L_{a}\right)^{2}+y^{2}+z^{2}}. \tag{4}\]  
The target azimuth position with respect to the \(p\)th (\(p\) = 1, 2) Tx antenna and the \(q\)th (\(q\) = 1, 2) Rx antenna is \(x-(p+q-3)L_{a}/2\), and the received signal \(s_{p,q}\) can be expressed as:  
\[s_{p,q}\big{(}t_{m},t,l_{p}\big{)} \approx N\exp\{il_{p}\pi/2\}I_{l_{p}}\big{[}ka\sin\theta_{p}(t_{m}) \big{]}\omega_{a}[t_{m}-x/v+(p+q-3)L_{a}/2v]\omega_{r}\big{[}t-2r_{p,q}(t_{m}) /c\big{]} \tag{5}\] \[\exp\{i\pi K_{r}\big{[}t-2r_{p,q}(t_{m})/c\big{]}^{2}\}\exp\{i2\pi f _{c}\big{[}t-2r_{p,q}(t_{m})/c\big{]}\}\exp\{il_{p}\varphi_{p}(t_{m})\}\]  
where the target azimuth position is \(x-(q-1.5)L_{a}\) with respect to the \(q\)th Rx antenna, and \(r_{p,q}(t_{m})\) is the round trip target range history, which can be expressed as:  
Figure 1:0  
For a certain receiving antenna, the superimposed echo signals radiated by both Tx1 and Tx2 can be received. Under this circumstance, the orthogonal properties of OAM cannot be utilized, so that it is hard to distinguish each echo signal of the different OAM modes.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### Selection of FSSs Based on Its Classification and Performance
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Factor** & **Impact/Outcomes** \\ \hline Type of array element & Slot element & Passband \\ \hline Patch element & Stopband \\ \hline Large Loop element & High BW \\ \hline Hexagonal Loop & Wide band \\ \hline Loaded 3 or 4 Legged element & Narrow band \\ \hline Mendered/Convoluted element & Unit cell size reduction but manufacturing limitation \\  & & may occur \\ \hline Fractals patch & Reduces the electrical length, Inter-element spacing \\  & (IES) \& so resonant frequency \\ \hline Self similar feature of fractal & Multiband response but drawback of early \\  & & appearance of grating lobes \\ \hline Dissimilar fractal geometric patterns in single FSS & Double/Multiband response \\ \hline Tunable FSS (Active element based) & Tunability in polarization and resonant frequency \\ \hline Active element in AFSS & Deviation in operating frequency, power \\  & & management issues, high insertion losses and \\  & & erroneous response \\ \hline Array profile & Reduce the IES of gangbuster & Large BW and delay in onset of grating lobes \\ \hline Reduce the Unit cell (small electrical dimensions) & High angular stability \\ \hline Increase in periodicity & BW and resonant frequency reduces, higher \\  & & distortion and early onset of grating lobes \\ \hline Electrically subwavelength array separation at & Elimination of unwanted interference Nulls \\  & & \\ \hline Decrease of IES & Stable resonant frequency, delayed onset of grating \\  & & lobes \\ \hline IES variation & BW change \\ \hline Circumference of element change & Effect on electrical length \& so resonant frequency \\ \hline Structure profile & Cascaded surfaces & Faster roll-off \\ \hline Multilayer FSS (stacking structure) & Increase in BW \\ \hline AFA configuration & Higher selectivity and BW \\ \hline
3D FSS & Complex structures but faster roll-off, stable response (ln- and out-band) \\ \hline Air gap separation & BW variation \\ \hline Dielectric profile & No dielectric substrate & Unstable BW of transmission curves \\ \hline Dielectric Profile \& IES & Defines the ripples (Flatness of response) \\ \hline Low dielectric constant & Constant BW with AOI \\ \hline High dielectric constant & More angular stability \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the key factors linking the structure geometries, and performance characteristics.
# Signal Quality Assessment for Transdermal Optical Wireless Communications under Pointing Errors +
## 2 System Model
Of note, the value of \(\alpha(\lambda)\) depends on the optical properties of the skin and can be obtained numerically from experimental results from [10; 11]. Based on these values and, by employing the trust region method (the interested reader can find more information about this method in [23; 24; 25]), we can extract a novel closed-form expression for the skin attenuation coefficient in the region of \([400,1800\;\mathrm{nm}]\) as  
\[\alpha(\lambda)=\sum_{i=1}^{8}a_{i}\exp\left(-\left(\frac{\lambda-b_{i}}{c_{ i}}\right)^{2}\right), \tag{13}\]  
where the values of \(a_{i}\), \(b_{i}\) and \(c_{i}\), with \(i=1,2,\cdots,8\), are provided in Table 1, and \(\lambda\) is given in nm. Note that the accuracy of this expression has been evaluated higher than 99.7% by utilizing the coefficient of determination (R-squared). R-squared is the square of the correlation between the response values and the predicted response values and can be expressed as the ratio of the sum of squares of the regression and the total sum of squares. The noise component can be analyzed as  
\[n=n_{b}+n_{DC}+n_{t}, \tag{14}\]  
where \(n_{b}\), and \(n_{DC}\) stand for the background and dark current shot noises, respectively, which can be modeled as zero-mean Gaussian processes with variances  
\[\sigma_{b}^{2}=2|e|RBP_{b}, \tag{15}\]  
and  
\[\sigma_{DC}^{2}=2|e|BI_{DC}, \tag{16}\]  
with \(B\), \(P_{b}\) and \(I_{DC}\), respectively, being the communication bandwidth, the background optical power, and the intensity of the dark current. Finally, \(n_{t}\) stands for the thermal noise, which can be also modeled as a zero-mean Gaussian process with variance \(\sigma_{t}^{2}\). Note that, since \(n_{b}\), \(n_{DC}\), and \(n_{t}\) are zero-mean Gaussian processes, \(n\) also follows zero-mean Gaussian distribution with variance [15]  
\[\sigma^{2}=\sigma_{b}^{2}+\sigma_{DC}^{2}+\sigma_{t}^{2}. \tag{17}\]
## 3 Results and Discussion
### Dimensional Stability and Thermal Stability  
Five WPC materials with the best mechanical properties (WF/PE-9, WF/PP-9, WF/PVC-9, WF/ABS-8, WF/HDPE-9) were selected to compare their dimensional stability and thermal stability. As shown in Figure 5a,b, the hydrostability of all WPCs was better than that of MDF and HDF [23] where the WF/ABS-8 showed the lowest hydrostability in terms of water absorption (6.36%) and thickness swelling (6.17%), which was also lower than in the same type of WPC (water absorption after 24 h of 42.2%) [19]. On the one hand, the space for absorbing water was reduced due to the lower porosity, on the other hand, the added plastic in the viscous state [24] flowed through and covered the surface of the fiber and reduced the hydroxyl groups which could be combined with water molecules. In Figure 5c, the hydrophobicity of WPC was significantly higher than that of poplar in which the WF/PP-9 possessed the worst hydrophilicity; the finding was consistent with the surface lightness test (Figure 5d). During the hot-moulding process, part of the lignin in the wood flour would infiltrate to the surface after it reached the glass transition temperature [25; 26]. As the temperature cooled down, the lignin that had been infiltrated to the surface would be solidified, which improved the hydrophobicity of the surface because of the hydrophobicity of lignin. Therefore, this explains why the surface lightness and hydrophilicity of all WPCs were lower than those of poplar.  
Figure 5: (**a**) Water absorption of WPC, MDF and HDF. (**b**) Thickness swelling of WPC, MDF and HDF. (**c**) Contact angle of WPC and poplar at 0 s, 5 s and 10 s. (**d**) Lightness contrast of WPC and poplar.  
The thermal stability of poplar and WPC were investigated. It was found that the mass loss (340 to 600 \({}^{\circ}\)C) of WPC was lower than that of poplar (Figure 6a). Figure 6b shows that the temperature of the maximum degradation rate of all WPCs was lower than that of poplar, where the highest temperature was achieved by WF/ABS-8. This finding could be attributed to the low decomposition temperature of ABS (250 \({}^{\circ}\)C) [27], and the decomposition temperature of other plastics is lower than that of ABS. Overall, WF/ABS-8 had better thermal stability than other WPCs in thermal reaction.
# A New Resource Allocation Protocol for the Backhaul of Underwater Cellular Wireless Networks
## 4 Simulation and Performance Analysis Results
### Simulation Results and Performance Analysis
#### 4.2.1 Scenario 1
This is because a UBS returns some of its backhaul resource to the UBSC more frequently when \(\rho_{LIM}\) is high, and a UBS will have a high probability to be under-utilized when \(N_{MN}\) is small.
## 4 Results  
### NPP Spatial Distribution and Change  
This work adopted a new method to estimate regional NPP by using MODIS atmospheric and land parameters. This method improved the APAR and NPP calculation accuracy by using remote sensing parameters instead of coefficient transfer. The NPP from 2001 to 2011 was simulated by the remote sensing-process coupling model (GLOPEM-CEVSA). The average annual NPP distribution for 2001-2011 was plotted (Figure 3). According to the results of model inversion, we can find that the total annual NPP in the study area ranged from 0 to 1040.3 gC/m\({}^{2}\)a, the annual total NPP was nearly 0.5 PgC a\({}^{-1}\), and the mean NPP was 534.47 g C/m\({}^{2}\)a in 11 years. NPP values from 2001 to 2011 were used to calculate the average NPP of forest (412.29 gC/m\({}^{2}\)a), evergreen coniferous forest (663.51 gC/m\({}^{2}\)a), evergreen broad-leaved forest (830.4 gC/m\({}^{2}\)a), deciduous needle-leaf forest (525.81 gC/m\({}^{2}\)a), deciduous broadleaved forest (726.74 gC/m\({}^{2}\)a), shrub (554.83 gC/m\({}^{2}\)a), cropland (347.93 gC/m\({}^{2}\)a), grassland (362.40 gC/m\({}^{2}\)a), urban land (203.32 gC/m\({}^{2}\)a), and bare land (110.68 gC/m\({}^{2}\)a).  
The overall regional changes indicated a high NPP in the east and the north and a low NPP in the west and the south. The spatial distribution of simulated NPP was corresponded to land cover type. The Daba Mountains and Wuling Mountains in the northeastern area in the central part of Chongqing are covered by extensive temperate forests, thereby resulting in a higher NPP that averaged more than 800 gC/m\({}^{2}\)a mostly in the Three Gorges reservoir area. This area has undergone extensive human disturbance, whereas the mountains are less disturbed as well as having a naturally more dense vegetation coverage. The western region and most of the main city were mainly low-value areas. The NPP between 100 and 200 gC/m\({}^{2}\)a is mainly along the coast of Yangtze River and urban built-up zone. Meanwhile, the NPP within the range of 300-700 gC/m\({}^{2}\)a was distributed in the central and western region in Chongqing. This is related to the distribution of karst topography in Chongqing.  
The NPP value appeared to fluctuate from the year 2001 to 2011 in the whole area (Figure 4), the range of NPP is 465.60-646.11 gC/m\({}^{2}\)a, with an average of 534.55 gC/m\({}^{2}\)a. In 2003 and 2008, the values show a significant peak, with annual NPP average of 646.11 gC/m\({}^{2}\)a and 555.98 gC/m\({}^{2}\)a. Especially the NPP value of 2003 is the highest among the 11 years. The values of NPP is relatively low for the years 2001, 2006 and 2009, the mean values of NPP are 465.60 gC/m\({}^{2}\)a, 504.45 gC/m\({}^{2}\)a and 517.47 gC/m\({}^{2}\)a, respectively. The total variation range of the NPP value is 3.83 \(\times\) 10\({}^{13}\)-5.32 \(\times\) 10\({}^{13}\) gC/a, the average is 4.4 \(\times\) 10\({}^{13}\) gC/a, and the fluctuation range is 0.59 \(\times\) 10\({}^{13}\) gC/a, accounting for 13.4% of the total.  
Figure 3: Spatial distribution of mean NPP from the year 2001 to 2011.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 2 Materials and Methods
### Power Module
#### 2.2.3 Mechanical Submodule  
This submodule obtains energy from vibrations. The energy is generated through a low-cost cantilever-type vibration sensor based on a piezoelectric film (PVDF). In the preliminary tests, three piezoelectric films were tested. One of them (MSP1006) generated a very low voltage in the working tests due to it does not have a mass at one extreme, so, it was discarded before check it in the real scenario. Between the other two, MSP1007 and MSP6914 (both with the mass at one extreme), the first one reach and maintain a voltage of 50 mV during four days; the second one had an increased progression during four days and, finally, it reached a voltage of 1.023 V. This is the main reason why the 1005939-1 MSP6914 was chosen. Moreover, it has a physical size of 17.80 mm \(\times\) 7.00 mm \(\times\) 9.40 mm, which fits in our tag design. This piezoelectric is frequently used to monitor continuous or impulsive vibration in cars or infrastructures.  
The sensor is then connected to a step-up DC/DC regulator with a smaller range of voltages than the chip in the solar module, as this type of technique generates far less energy. In addition, the converter is needed to obtain a constant charging output, as in the solar energy technique. The chosen device, the ADP5091, includes a Maximum Power Point Tracking (MPPT) control feature that keeps the input voltage ripple within a fixed range to maintain the power conversion stable. Another notable characteristic is that it has two different outputs; one that we have dedicated to the regulator submoduleto power the processing module directly (called SYS), and the other to charge the supercapacitor (called BAT), both with the same output value, 4.2 V.