esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Problems Associated with Difficult Soil Conditions
### Injecting Atomized Cement Slurry  
A new type of construction machine is introduced, where cement slurry is accompanied by compressed air at the injection nozzle and atomized cement slurry is injected to mitigate the entrained rotation phenomenon, as shown in Figure 10[16]. The injected air can function as a ball bearing to increase the fluidity of the soil and binder mixture. Figure 11 shows the stabilized soil strength distribution within the column obtained in field tests to compare the effect of atomized cement slurry injection (CI-CMC technique) and ordinary machines. The 28-day strength of the column stabilized by the ordinary machine shows a comparatively small strength on the order of 1000 to 1700 kPa, while that stabilized by the CI-CMC technique shows a large strength on the order of 3100 to 3600 kPa. The figure also shows that the strength variation of the CI-CMC technique is smaller than that of the ordinary machine. As a result, atomized cement slurry injection can function to keep the fluidity of fresh stabilized soil high, increase the strength, and decrease the strength variation.  
Atomized cement slurry can disturb the soil structure and increase the fluidity of the soil and cement mixture, which can contribute to the decrease in the penetration resistance and required mixing energy and, in turn, the mixing machine can penetrate and mix a hard soil layer. Figure 12 shows the required auger torque to compare the CI-CMC and ordinary techniques [16]. In the case of the ordinary mixing machine, the mixing machine is stopped at the hard layer at a depth of approximately \(-8\) m; however, the CI-CMC machine can penetrate through the hard layer. According to the case history, the CI-CMC technique can penetrate approximately 2.5 m into the mudstone layer with SPT \(N\)-values of approximately 25 to 50 (\(q_{\text{u}}\) = 4 to 5 MPa).  
Figure 9: Effect of dispersant on increases in strength of the soil and binder mixture [14]. (**a**) Glycolic acid dispersant; (**b**) polycarboxylic acid dispersant.
## 2 Materials and Methods
### Statistical Analysis Strategy  
First, sensitivities of ecosystem-scale seasonal WUE to phenological factors were examined by Spearman partial correlation analysis for each valid pixel, after controlling for environmental factors (including temperature, precipitation, solar radiation and CO\({}_{2}\) concentration). Meanwhile, the variation in WUE in response to phenological and environmental factors at the FFT level was quantitatively identified by a panel (data) analysis [46] using a linear random-effects generalized least squares (GSL) regression model [47] across all pixels in each FFT. The partial correlation analysis and panel analysis were implemented in MATLAB R2013a (The Mathworks, Inc., Natick, MA, USA) and STATA 14 (StataCorp LP, College Station, TX, USA), respectively.  
Then, response patterns of WUE to phenology were identified according to relationships of WUE and its components (GPP and ET) with the phenological factor (SOS or EOS). Here, six response patterns of WUE to phenology were defined by the partial correlation coefficients of R\({}_{\text{WUE}}\) (WUE vs. phenology), R\({}_{\text{GPP}}\) (GPP vs. phenology) and R\({}_{\text{ET}}\) (ET vs. phenology), including R\({}_{\text{WUE}}\) \(<\) 0, R\({}_{\text{GPP}}\)\(<\) 0 and R\({}_{\text{ET}}\)\(<\) 0 (Pattern I), R\({}_{\text{WUE}}\)\(<\) 0, R\({}_{\text{GPP}}\)\(<\) 0 and R\({}_{\text{ET}}\)\(>\) 0 (Pattern II), R\({}_{\text{WUE}}\)\(<\) 0, R\({}_{\text{GPP}}\)\(>\) 0 and R\({}_{\text{ET}}\)\(>\) 0  
Figure 2: Comparison between the tower-based and satellite-based (**a**) start (SOS) and (**b**) end (EOS) of growing season at the FLUXNET sites and the corresponding MODIS 3 \(\times\) 3 pixels. Solid and dash lines denote significant and no significant linear regression, respectively, between tower-based and satellite-based phenological factors. DOY denotes day of year. The details regarding the FLUXNET sites are provided in Table S1.  
(Pattern III), R\({}_{\rm WUE}>\) 0, R\({}_{\rm GPP}<\) 0 and R\({}_{\rm ET}<\) 0 (Pattern IV), R\({}_{\rm WUE}>\) 0, R\({}_{\rm GPP}>\) 0 and R\({}_{\rm ET}<\) 0 (Pattern V) and R\({}_{\rm WUE}>\) 0, R\({}_{\rm GPP}>\) 0 and R\({}_{\rm ET}>\) 0 (Pattern VI).
## 3 Results  
### Physicochemical Characterization of \(\beta\)-CD-ALG with or without ICA  
\(\beta\)-CD-ALG was synthesized by reacting aminated ALG with carboxylated \(\beta\)-CD, and synthesis of \(\beta\)-CD-ALG was analyzed with proton NMR. In Figure 1 for the proton NMR spectrum of \(\beta\)-CD-ALG, proton peaks at 4.93 ppm were assigned to -CH of \(\beta\)-CD and ALG. Multiple peaks that ranged from 3.15 to 4.12 ppm signified protons of \(\beta\)-CD and ALG. In addition, methylene proton peaks from aminated ALG were detected at 2.76 ppm.  
Hydrodynamic particle sizes and polydispersity indices (PDI) of \(\beta\)-CD-ALG with or without ICA (monitored by dynamic light scattering) were as follows: \(436.80\pm 75.95\) nm and \(-30.47\pm 0.8\) mV for \(\beta\)-CD-ALG, \(439.30\pm 77.45\) nm and \(-16.77\pm 3.6\) mV for ICA (1 \(\upmu\)M)/\(\beta\)-CD-ALG, \(485.00\pm 128.50\) nm and \(-15.37\pm 3.8\) mV for ICA (5 \(\upmu\)M)/\(\beta\)-CD-ALG, and \(467.10\pm 67.00\) nm and \(-9.23\pm 2.0\) mV for ICA (10 \(\upmu\)M)/\(\beta\)-CD-ALG, respectively. Figure 2 exhibits the \(\beta\)-CD-conjugated alginate (\(\beta\)-CD-ALG) and ICA/\(\beta\)-CD-ALG measured using ATR-FTIR spectra. The FT-IR spectra of \(\beta\)-CD-ALG, \(\beta\)-CD demonstrated major special peaks at 3300-3400 cm\({}^{-1}\), 2,854 cm\({}^{-1}\), 1,153 cm\({}^{-1}\), and 1,029 cm\({}^{-1}\). These peaks correspond to the stretching of O-H, CH\({}_{2}\), and C-C and the bending vibration of O-H, respectively. After inclusion of ICA on \(\beta\)-CD-ALG, the ATR-FTIR spectra showed characteristic absorptions for carbonyl (1652 cm\({}^{-1}\)) and aromatic (1598 cm\({}^{-1}\), 1509 cm\({}^{-1}\), and 1073 cm\({}^{-1}\)) moieties in ICA. The estimated ICA loading amount (efficiency) was \(22.14\pm 3.37\) \(\upmu\)g (\(36.89\pm 5.62\%\)) for ICA (1 \(\upmu\)M)/\(\beta\)-CD-ALG, \(85.67\pm 3.29\) \(\upmu\)g (\(28.56\pm 1.10\%\)) for ICA (5 \(\upmu\)M)/\(\beta\)-CD-ALG, and \(195.09\pm 5.39\)\(\upmu\)g (\(32.52\pm 0.90\%\)) for ICA (10 \(\upmu\)M)/\(\beta\)-CD-ALG.  
The morphologies of \(\beta\)-CD-ALG with or without ICA are depicted by the TEM images in Figure 3. All samples were nano-sized by spherical type.  
Figure 1: (**a**) \(\beta\)-cyclodextrin-niginate (\(\beta\)-CD-ALG) structure. (**b**) The proton nuclear magnetic resonance (\({}^{1}\)H-NMR) spectrum of \(\beta\)-CD-ALG.  
Figure 3: TEM images of (**a**) \(\beta\)-CD-ALG, (**b**) ICA (1 \(\upmu\)M)/\(\beta\)-CD-ALG, (**c**) ICA (5 \(\upmu\)M)/\(\beta\)-CD-ALG, and (**d**) ICA (10 \(\upmu\)M)/\(\beta\)-CD-ALG. Scale bar: 2 \(\upmu\)m.
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 4 Methodology of Algebraic Point Projection
### Algebraic Point Projection for a NURBS Curve
#### 4.1.2 Second Order Algebraic Point Projection in Physical Space  
For test points near the curve, the first-order algebraic point projection method described in Section 4.1.1 performs well; however, as shown through the example in Figure 8, when points are farther away, the projection is not accurate. The example is of a quadratic Bezier curve with test points on a horizontal line. The failure of first-order algebraic point projection is because the distance function derived in Equation (14) is essentially a first order approximation using Taylor expansion. To improve accuracy and make algebraic point projection possible for points farther from the curve, we present next a second-order algebraic point projection algorithm in the following. While the second-order algorithm is also approximate, in practice, it is sufficient for enriched immersed boundary analysis since only quadrature points near the immersed boundary needs to be projected on to the surface.  
The second-order approximation of resultant \(\Gamma(\mathbf{x}_{f})\) at footpoint \(\mathbf{x}_{f}\) can be written as:  
\[\Gamma(\mathbf{x}_{f})=\Gamma(\mathbf{x})-\frac{\partial\Gamma(\mathbf{x})}{ \partial\mathbf{n}}d_{quad}+\frac{\partial^{2}\Gamma(\mathbf{x})}{\partial \mathbf{n}^{2}}d_{quad}^{2} \tag{25}\]  
Since the resultant at footpoint is zero, i.e., \(\Gamma(\mathbf{x}_{f})=0\), Equation (25) can be rearranged as:  
\[d_{quad}=\frac{1}{\mathbf{n}\cdot\mathbf{H}\cdot\mathbf{n}}(\
abla\Gamma \cdot\mathbf{n}\pm\sqrt{(\
abla\Gamma\cdot\mathbf{n})^{2}-2\Gamma\mathbf{n} \cdot\mathbf{H}\cdot\mathbf{n}}) \tag{26}\]  
where \(d_{quad}\) is the distance estimate between the test point and footpoint based on the second-order approximation. \(\
abla\Gamma\) and \(\mathbf{H}\) are the gradient and Hessian of algebraic level sets, which are calculated as described in Section 4.1.1. For test points near the curve, a good approximation of normal vector \(\mathbf{n}\) could be \(\mathbf{n}=\frac{\
abla\Gamma}{\|\
abla\Gamma\|}\) or \(\mathbf{n}=\frac{\
abla d}{\|\
abla d\|}\).  
Algebraic point projection in two-dimensional physical space is validated in Figure 9, where the second-order point projection result is compared against first-order point projection as well as Newton-Raphson iterations for various test distances. Both algebraic point projection methods yield the reference solution obtained using the Newton-Raphson method in the limit when the query point is on the curve/surface, and when test distance is small (\(\bar{d}=0.1\)). The second-order point projection, however, converges to the solution when distances are larger (\(\bar{d}=0.3\)), where the first-order point projection fails.  
Figure 8: First-order algebraic point projection fails to reach footpoints on the curve for test points not close to quadratic Bézier curve.
## 6 Proposed Interaction Diagram for Plugging
### Testing of the Diameter Criterion & Interaction Diagram  
DFLTD contained 23 tests where CPT data were available [Appendices A and B]. These load tests were initially excluded from the analysis because they lacked the SPT data required for computing the capacity using the FHWA, USACE, Revised Lambda, and API design methods. Therefore, the author used these 23 cases to test the diameter criterion (Figure 4) and the interaction diagrams (Figure 6). A similar methodology and voting routine were used and APILE was again utilized. Four CPT design methods were employed namely: (i) the Norwegian Geotechnical Institute (NGI) method [52; 53]; (ii) the Imperial College Pile (ICP) method [54]; (iii) the Fugro method [55]; and (iv) the University of Western Australia (UWA) method [56]. It is essential to acknowledge that, while APILE employs FUGRO-04 and UWA-05, updated methods (FUGRO-10, and UWA-13) have since been developed. Some design methods employ plugging assumptions inherent to their formulation. Nevertheless, APILE calculations were carried out assuming the three aforementioned plugging conditions [Appendix D]. The authors decided to use the pre-programmed design methods in APILE to: (1) help ensure that the results are easily adopted in practice, (2) comply with the current practice; and (3) avoid claims of possible computational errors by the authors.  
The capacities corresponding to the three plugging conditions and their statistics for the four CPT methods are presented in Figure 7. On average, the performance statistics in terms of average \(Q_{c}\)/\(Q_{m}\) and its standard deviation (Target 1 and 0) is somewhat better than the classic methods presented earlier (Figure 3), but scatter between measured and calculated capacities persists.  
The relationship between L/D and diameter for all CPT tests is presented in Figure 8, with the presumed plugging condition obtained via voting identified using the symbols **U** for unplugged, **P** for plugged. The diameter criterion and the two previously identified interaction diagrams are also superimposed on the data. One difference is that the CPT methods can in fact identify internal plugged cases and two piles were identified as internally plugged. These two cases are shown in the figures, designated by the letter **I**, but excluded when computing accuracy. The accuracy of the diameter criterion was found to be 60% while the accuracy of the two interaction diagrams was found to be 60.9% and 56.5%. These results suggest that the diameter criterion offers results that are as good as the interaction diagrams. More importantly, it suggests that the accuracy of the diameter criterion is in the order of 70 \(\pm\) 10%.
# Toward SDM-Based Submarine Optical Networks: A Review of Their Evolution and Upcoming Trends
## 5 Internal Architectures of Submarine Cable Systems and BUs
From the past to the future era of submarine terminal equipment (SLTE/CLS), there is a lot of progress on transponders and power management of the cable systems as demonstrated in Figure 31. The first generation of CLS, up until 2010, achieved data rates of up to 40 G and baud rates of ~11 Gbaud. It used QPSK modulation formats and implemented technologies such as coherent detection and PMD compensation. The second generation (2012-2015), when compared to the features of the first generation, added higher data rates up to 200 G, baud rates of 28-35 Gbaud, enhanced 16 QAM modulation formats, SD FEC, and CD pre-dispersion. The third generation (2016-2019) achieved data rates up to 400 G, modulation formats up to 32 QAM, modulation of 2D, 4D, Nyquist shaping, and improved FEC. Figure 31: Evolution of submarine line terminating equipment. Figure 30: Branch types implemented by ROADMs–eBUs. Finally, in the fourth generation (2020+), the data rates increased up to 800 G, the baud rates up to 95 Gbaud, new modulation formats up to 64 QAM, and features such as const. shaping (PCS), improved FEC and nonlinear comp added to the newest generation of this evolution. Although the commissioning process (the final testing before going commercial) is always performed by the cable provider, recently emerged devices for the dry plant, led to the open cables design model [19]. In this model, the cable system is deployed by one provider and several others can utilize it with their own SLTE.
## 3 Data and Methodology
### Methodology
#### 3.2.1 Parameters of the Neural Network  
The network architecture implemented in the H2O package needs to be configured by setting different parameters, that will affect the behavior of the neural network and influence the final results. The most important parameters are: number of layers, neurons per hidden layer, L1 (\(\lambda\)), \(\rho\), \(\epsilon\), activation and distribution functions and end metric. These are the parameters that the GA will optimize.  
The parameter \(\lambda\) controls the regularization of the model by inserting penalties in the model creation process in order to adjust the predictions as much as possible with actual values and the penalization is defined by the following equation:  
\[\lambda\sum_{i=0}^{n}\left|w_{i}\right|. \tag{1}\]  
In Equation (1), \(n\) is the number of weights received by the neurons and \(w_{i}\) represents the weight for the neuron \(i\).  
The parameter \(\rho\) allows us to manage the update of different weights of synapses and is used to maintain some consistency between the different updates of previous weights.  
The parameter \(\epsilon\) prevents the deep learning algorithm from being stuck in local optimums or to skip a global optimum, and can assume values between 0 and 1.  
The activation function can assume three values: tanh (hyperbolic tangent), ramp function, maxout.  
Seven different possibilities are considered for the distribution function: Gaussian, Poisson, Laplace, Tweedie, Huber, Gamma and Quantile.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(w\) & **\#Rows** & **\#Columns** & **File Size (In MB)** \\ \hline
24 & 20,742 & 48 & 6 \\
48 & 20,741 & 72 & 9 \\
72 & 20,740 & 96 & 11.9 \\
96 & 20,739 & 120 & 14.9 \\
120 & 20,738 & 144 & 17.9 \\
144 & 20,737 & 168 & 20.9 \\
168 & 20,736 & 192 & 23.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset information depending on the value of \(w\).  
The end metric defines the specific measure that is used to stop early the training phase of the deep learning algorithm. There are seven different possibilities: mean squared error (MSE), Deviance (the difference between an expected value and an observed value), root mean squared error (RMSE), mean absolute error (MAE), root mean squared log error (RMSLE), the mean per class error and lift top group. The last metric is a measure of the relative performance.  
The possible values for each parameter are shown in Table 2.  
As we described before, the activation function, distribution function and end metric are categorical parameters, so each value corresponds to a specific category of the parameter.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 7 Measurement Result and Discussion
### Two Tags Test  
The measurement setup for the two tags test is shown in Figure 16. A photo of the lab setup is shown in Figure 17. Two tags and five antennas are used in the system. A power splitter is used on the tag side to provide the FMCW signal to both tags. In this case, the power of the FMCW signal provided to tags is 3 dB smaller than the single tag test. Note that the power splitter is not a necessary component in this system. It is used only due to the shortage of amplifiers. There should be an amplifier attached to each tag.  
The baseband signal for both tags is spectrum spread by using a different signature code. Different lengths of code and different symbol rates have been tested. In this case, only BPSK signals are transmitted. A 5 Gbps baseband signal has been transmitted with an 8-bit signature code so that the highest data rate that has been transmitted is 625 Mbps  
Figure 16: A measurement setup for the two tags test.  
Figure 17: A photo of the measurement setup in the lab.  
Figure 15: EVM VS. symbol rate with different modulation order signals of single tag measurement.  
with both tags. The constellation diagrams of the received raw signal and the demodulated signals for two tags are shown in Figure 18. Before the demodulation, the received signal is a mixture of two signals from two tags. The constellation of it is cloudy. After applying the correlation with two signature codes, two signals from different tags are separated. Their constellations then become clear. By using different length signature codes, the EVM varies accordingly. The EVM of two signals are similar. Figure 19 shows the EVM decreases when chip length increases.  
Due to the limited measurement distance in the lab and the slow sweep time of the signal synthesiser, the offset frequency at the receiver side is very small. To further test the system performance with larger offset frequencies, offset frequencies of 50 kHz and 49 kHz are added from the tag side by AWG. As Figure 20 shows, EVM and frequency estimation error decrease with chip length increase. Comparing Figures 19 and 20, when using the same symbol rate signal in the system, higher offset frequencies cause higher EVM. For the 1 Gbuad signal used in the system, the EVM increase by around 10% when offset frequencies exist. If an FMCW with 1 GHz bandwidth and 1 ms duration time is used in this system, the frequency estimation error shown in Figure 20 will cause a 60 mm to 500 mm range estimation error with different chip lengths. This is due to an immature frequency estimation solution, and it can be further improved with more advanced processing methods.  
Figure 19: EVM VS. different chip length and different symbol rate of two-tag measurement.  
Figure 18: Constellation diagrams of (**a**) received signal from two tags with 625 Mbps BPSK of each tag, (**b**) demodulated signal from the first tag, and (**c**) demodulated signal from the second tag, where 8 bits signature code are used.
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
###### Abstract
The traditional anti-jamming algorithm will have a long non-convergence time each time the signal is received, and a lot of information will be lost in the non-convergence stage [23], which seriously affects the actual performance of the navigation receiver. Therefore, to solve this problem has become an important research topic in the literature on T/R time-sharing receiver technology. However, there is no research on this problem which can be directly applied to the navigation receiver. Observing the characteristics of the interference signal, the signal is stable compared to navigation signals that change randomly in different periods [24]. Therefore, the idea of this paper is to generate preliminary information according to interference characteristics to solve the problem of data loss caused by filter re-convergence. This paper presents a robust time-domain anti-jamming algorithm based on T/R time-sharing technology. The prior information is generated using the persistence and stability of interference, which provides the initial data for the anti-jamming filter. Section 2 describes the mathematical model of T/R time-sharing and the time-domain anti-jamming filter. Section 3 analyzes the influence of time-domain anti-interference on T/R time-sharing performance. Section 4 introduces the robust anti-jamming algorithm based on T/R time-sharing technology and analyzes its performance. Section 5 verifies the robust anti-jamming algorithm in different interference and signal scenarios. Finally, the algorithm is summarized.
## 2 Method
### Spatial Information Refinement Module  
[PERSON] et al. [37] proposed dilated convolution, which makes the convolution layer keep the size of the feature map unchanged while increasing the receptive field. In this paper, a Spatial Information Refinement Module (SIR) is proposed where multi-level dilated convolution is used to extract feature maps from different degrees of receptive fields. The receptive field in the extracted features increases for multi-scale semantic information, and this information from low-level feature extraction of implementation is used to refine spatial information, while the features of the high-level feature space are employed to compensate for lack of location information. The SIR structure is as shown in Figure 1, which consists of three parts: spatial information refinement submodule I (SIR-SubI), submodule II (SIR-SubII), and submodule III (SIR-SubII), corresponding to the three branches of the SIR Structure in Figure 1 from top to bottom. The input features are generated by these three submodules, respectively, to generate dilated convolution feature maps of different scales. Then, they are mapped to the specified high-level feature map to obtain the multi-scale semantic information feature map, so that the high-level features can obtain richer semantic information. The first submodule consists of three 3 \(\times\) 3 convolutions with dilation coefficients 1, 2 and 3, respectively. The second submodule consists of two 5 \(\times\) 5 convolutions with dilation coefficients 1 and 2, respectively. The third sub-module is composed of an 8 \(\times\) 8 convolution, and its expansion coefficient is 1. After the output of the three sub-modules, the activation function ReLU and batch normalization are used, respectively, to accelerate the convergence speed of the network. Through these three spatial information refinement sub-modules, low-level features can enable high-level feature maps to obtain multi-scale semantic information and receptive fields, making up for the loss of semantic features caused by the reduction in feature map size caused by multiple convolutions.