esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Regression Algorithms  
Since the impacts of influence factors were complicated, nonlinear algorithms (including ML and DL algorithms) were adopted to establish the correlation between ICMV and ISMV when the influence factors were introduced. Figure 10 illustrates the correlation relationships between actual ISMV and the ISMV predicted by different nonlinear algorithms in the test sets (i.e., 20% of the total analysis data, the other 80% were used for training). For comparison, the regression results of multiple linear (MPL) algorithm are also plotted in Figure 10.  
From Figure 10, nonlinear algorithms had more satisfactory R\({}^{2}\) values and RMSE and showed better generalization performance compared to MPL algorithm. ANN showed the best performance among the nonlinear algorithms whereas the performance of RNN was the worst owing to the significant overfitting problem. In practice, the training time of RNN model was set as little as possible (only 500 times here) to avoid overfitting, but the effect was still far poorer than expected. Further, the performance of the RNN in the training set worsened as the training time was lessened, demonstrating that the RNN was inapplicable in the regression between ICMV and ISMV. Though the R\({}^{2}\) values of LGBM was better than that of RF, its predicted values were smaller than real values led to the RMSE of LGBM was relatively high compared to RMSE of RF.  
Figure 9: Correlation of ICMV and ISMV at different moisture content zones, (**a**) CMV vs. \(E_{\text{LWD}}\), (**b**) CMV vs. compaction degree, (**c**) CCV vs. \(E_{\text{LWD}}\), (**d**) CCV vs. compaction degree, (**e**) \(E_{\text{vib}}\) vs. \(E_{\text{LWD}}\), (**f**) \(E_{\text{vib}}\) vs. compaction degree.  
In order to predict the ISMV by ICMV more precisely, monadic linear regression was employed based on the predicted results of different algorithms. It means that the nonlinear algorithms or MPL are the primary learner whereas the monadic linear algorithm is the secondary learner. The predicted results are shown in Figure 11. From Figure 11, the predicted results were very close to actual values in terms of both numbers and trends after monadic linear regression. It demonstrates that ensemble learning is a simple and effective method to improve the accuracy of the regression between ICMV and ISMV [39]. Although the ensemble method was used, the predicted results of MLP were relatively disappointing compared to another algorithms. It indicates that, when the influence factors are introduced, the correlation relationships between ICMV and ISMV have not been characterized adequately if only the linear algorithms are used. In view of prediction accuracy, the combination of RF and linear algorithm is the most recommended regression method in IC application.  
Figure 10: Correlation between actual and predicted ISMV of different algorithms, (**a,b**) actual \(E_{\text{LWD}}\) vs. predicted \(E_{\text{LWD}}\), (**c,d**) actual compaction degree vs. predicted compaction degree.
# Bistatic Forward ISAR with DVB-T Transmitter of Opportunity
## 6 Discussion  
An analytical and geometrical approach was applied to describe the BFISAR scenario. Dynamic position vectors of the object's mass-center and scattering point from the object space were derived. Based on the mathematical description of the DVB-T waveform, a model of the BFISAR signal reflected from a 3-D object was created and analyzed. BFISAR signal synthesis was interpreted as a spatial transformation of the 3-D object's image function into a 2-D signal function with a projective phase operator. Based on the Taylor  
Figure 5: BFISAR complex image after azimuth compression and shifting: (**a**) real and (**b**) imaginary parts.  
Figure 6: DVB-T BFISAR complex image: (**a**) amplitude image and (**b**) phase image.  
expansion of the BFISAR signal phase, linear and higher-order phase terms were defined and physically interpreted. The linear phase terms have a Fourier structure and define an object motion of first order, i.e., radial displacement. The higher-order terms define an object's motion of higher order, i.e., tangential and radial complex maneuvering. In other words, the BFISAR signal formation is a 2-D Fourier transformation of the 3-D image function with a higher-order phase correction.  
The image reconstruction is an inverse operation, i.e., an inverse spatial transformation of the 2-D signal function into a 2-D image function with an inverse projective phase operator with the same structure. Thus, the image reconstruction can be interpreted as an inverse 2-D Fourier transformation of the higher-order phase-compensated BFISAR signal.  
The analytical description of the BFISAR image allows the derivation of the image reconstruction algorithm, including higher-order phase compensation of the BFISAR signal, range compression by the inverse Fourier transform in the range direction, and azimuth compression by the inverse Fourier transform in the azimuth direction of the range-compressed signal. The complex image preserves only phases defined by distances to scattering points at the moment of imaging. The higher-order terms' coefficients are calculated iteratively using an image quality-evaluating cost function.
# Improved Adaptive Successive Cancellation List Decoding of Polar Codes
If there is an error decoding result after CRC, re-decoding starts by increasing \(L\) until the decoding passes the CRC or \(L\) equals to \(L_{\text{max}}\). Although the algorithm reduces the computation complexity without loss of performance, when a CRC fails, it has to re-decode from the first bit, which greatly increases the decoding delay caused by error bits. Recently, [PERSON] et al. proposed a segmented adaptive SCL decoding algorithm (SCAD-SCL) based on AD-SCL in [21]. This algorithm divides codewords into two segments by adding CRC in the middle of the codewords and uses the AD-SCL method in decoding. Hence, when decoding errors from the second part occur, there is no need to re-decode from the first bit. Compared with AD-SCL, SCAD-SCL reduces computation complexity. However, both AD-SCL and SCAD-SCL discard the previous decoding results and directly re-decode by increasing \(L\) when CRC fails, which wastes the useful information from the previous decoding. In addition, the experiment analysis shows that the performance of the second part is almost independent of size \(L\), when there is an error in the first part of the decoding. Based on the above analysis, SCAD-SCL proposed in [21] still needs to be improved further. In this paper, a simplified adaptive SCL segmentation algorithm (SAD-SCL) based on CRC-aided is proposed. Before the re-decoding of updating value \(L\) each time, SAD-SCL uses the existing log likelihood ratio (LLR) information to locate the range of burst error bits. This proposed method is to locate a starting bit in the re-decoding. Then, the re-decoding starts at the locating bit with the smallest index in this range. The SAD-SCL algorithm further avoids redundant computation in the decoding process with negligible loss of performance and has a lower computational complexity than SCAD-SCL.
# Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
## 3 Results
### Study Areas and Data
#### 3.1.2 Experimental Data  
The experimental data include sentinel-1 and GaoFen-3. Sentinel-1 data in the experiment are dual-polarization GRD products (VV and VH polarized images) in the IW mode with the C-band. The final data used in the experiment were obtained after orbit correction, thermal noise removal, radiometric calibration, speckle filtering, and terrain correction [41]. All this preprocessing was achieved with SNAP software. Then, Python code was used for feature extraction, superpixel segmentation, samples, training GCN, and accuracy evaluation. GaoFen-3 is a C-band SAR imaging satellite launched by the Chinese government. The data used in the experiment were in full-polarization mode with a resolution of 1 m. The whole study area was labeled through a pre-classification operation and by manual correction, meaning each superpixel has its ground label (water or non-water). This was used to evaluate the accuracy of various methods and to validate the efficacy of the proposed method.
# Marine-Originated Materials and Their Potential Use in Biomedicine
## 5 Mollusk-Originated Components and Their Biomedical Applications  
Mollusks such as cuttlefishes, octopuses, squids, oysters, and mussels are great sources of aragonite and, consequently, of hydroxyapatite [17]. Hydroxyapatite is a widely used bioceramic due to its ideal properties. Actually, it is a calcium phosphate (Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)) (Figure 2) that is used in environmental and biomedical applications.  
Hydroxyapatite artificial implants are very popular in applications of hard tissue replacements. This material allows the acceleration of bone growth assisting the implant to be incorporated [87]. Corals and mollusks can provide hydroxyapatite, and its microstructure is similar to the inorganic mineralized structure of natural bones [88]. Thus, various studies have focused on the possible use of marine-derived hydroxyapatite obtained from cuttlefish bone from _Sepia officinalis_ via hydrothermal transformation because it can help make procedures low-cost and eco-friendly. Also, the porosity and bioactivity of this material allows its use in scaffold development [17].  
Hydroxyapatite also has a close analogy to the inorganic portion of human teeth. The enamel, which is the external layer of a human tooth, consists of ~97% inorganic compounds, and the dentine consists of ~70% inorganic material, with hydroxyapatite being the main part of this inorganic phase. Thus, it is used as a biomaterial in dentistry for implants [89]. Table 4 gathers some biomedical applications of marine-originated hydroxyapatite.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mollusk-Originated Component** & **Source** & **Application** & **Ref.** \\ \hline hydroxyapatite & cuttlefish bone from _Sepia officinalis_ & biomedicine and environment (tissue engineering, bone repair) & [17] \\ \hline hydroxyapatite & mollusks & biomedicine and environment (tissue engineering, bone repair): artificial implants & [87] \\ \hline hydroxyapatite & mollusks and corals & biomedicine and environment (tissue engineering, bone repair) & [88] \\ \hline hydroxyapatite & Green mussel shells (_Perna canaliculus_) and _Evechinus chloroticus_ shells & biomedicine and environment (denistry) & [89] \\ \hline \end{tabular}
\end{table}
Table 4: Biomedical applications of mollusk-originated hydroxyapatite.  
Figure 2: Schematical representation of hydroxyapatite.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 5 Future Work
### Mobility Support  
We note that RPL has been designed for static nodes within the networks while the majority of the LLN applications require mobility support within the routing protocols in order to improve the performance of RPL for nodes mobility [71; 72; 73]. A particular scenario is the unavailability of a node's preferred parent node that drastically decreases the transmission loss and delay due to switching and resuming communication with the preferred parent. As highlighted in Table 3, the ME-RPL [44] and GTM-RPL [38] address  
\begin{table}
\begin{tabular}{p{56.9 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Category** & **Working Principle** & **Issues** **Addressed** & **Advantages** & **Disadvantages** \\ \hline SSCF [56] & \begin{tabular}{l} Sink coordination is used \\ to exchange \\ network-defined \\ parameters such as \\ network size and sink \\ mobility. \\ \end{tabular} & \begin{tabular}{l} Lack of \\ sink-to-sink \\ coordination. \\ \end{tabular} & \begin{tabular}{l} Achieved higher PDR of \\ 22\% and 26\% in random \\ and grid topologies. \\ \end{tabular} & \begin{tabular}{l} Latency of RPL and SSCF \\ is high. \\ \end{tabular} \\ \hline Downward path construction & \begin{tabular}{l} The downward routes \\ allow ancestors of old and \\ new routes to send \\ no-path DAO messages \\ upon receiving updated \\ DAO messages. \\ \end{tabular} & \begin{tabular}{l} Issues with a \\ rank of \\ detached \\ mobile \\ nodes. \\ \end{tabular} & \
\begin{tabular}{l} Improves PDR from \\ 20–80\%, and also reduces \\ overall overhead without \\ using location information. \\ \end{tabular} &
\begin{tabular}{l} Packet losses may occur if \\ the mobile node is no \\ longer reachable to the old \\ parent; we note that the \\ cancellation of the old \\ route is still not done by \\ the new DAO. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of Miscellaneous Solutions.  
the lack of identification of mobile nodes, however as addressed by the KP-RPL [54] and MP-RPL [67] proposals, these protocols do not address the issue of lack of positioning information of mobile nodes. Therefore it is important to efficiently address the mobile node's positioning information in coordination with identifying the mobile nodes. In addition, as highlighted in Tables 2 and 3, there are certain RPL extensions, e.g., Enhanced hand-off mechanism [65], that incorporate the nodes' mobility and deals with reduced handoff delays. Hence, it is important to propose a comprehensive framework that would use such RPL extensions in combination in order to collect positioning information while identifying the parent mobile nodes, which reduces the hand-off delays and enhance throughput during nodes mobility. Following, the basic RPL with these enhancements could be applied to support a wide range of real-time and non-real-time (e.g., simple data/file transfer) applications with necessary modifications and enhancements.
# Blind Phase Search with Angular Quantization Noise Mitigation for Efficient Carrier Phase Recovery
## 5 Conclusions  
The inherent discrete phase search nature of the conventional BPS (C-BPS) algorithm is shown to introduce quantization noise in its phase noise estimator which limits its phase noise tolerance and implementation complexity. A new CPR scheme, F-BPS, is proposed to mitigate the angular quantization noise of the C-BPS by using a LPF operation performed on the C-BPS phase noise estimator. The performance of the F-BPS scheme is evaluated both in simulations and experimentally for 16 QAM and 64 QAM signals to corroborate a significant phase noise tolerance increase. Results also show the possibility of drastically reducing the complexity implementation of the proposed CPR scheme as only a small number of test phases are required to achieve the same phase noise tolerance as for the C-BPS case.  
**Acknowledgments:** This work was supported by EU project ICONE, gr. #608099, GRIFFON, gr. #324391, Vetenskapsradet PHASE, gr. #2016-04510 and Swedish SRA ICT-TNG program. The equipment was funded by [PERSON] foundation.  
**Author Contributions:** [PERSON], [PERSON], and [PERSON], proposed the concept. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], initiated the study. [PERSON] and [PERSON], carried out the simulations. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conducted the experimental analysis. [PERSON], [PERSON], and [PERSON] guided the studies. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] participated in writing the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 3 System Architecture
### Structure Design of the Border Node  
The border node comprehends an MCU, a wireless communication module, an optical fiber port, and an Ethernet port. Border nodes manage the connection between the router nodes and act as an endpoint for the data from router and leaf nodes. It can also coordinate how the mesh network must behave, including more nodes and defining routing paths. Finally, it can also act as a gateway for internet access, allowing information flow to a central server. Figure 8 shows a photo of the border node presenting the interface of the monitored data.
# Dynamic Stability Finite Difference Time Domain Analysis of Landfill Based on Hypergravity Test
## 4 Numerical Model Verification  
To verify the accuracy and reliability of the numerical model, the dynamic response of the bottom acceleration sensor A1 and pore pressure gauge P5 was obtained using numerical modeling and compared to the centrifuge test data, as shown in Figures 5 and 6. Figure 5 compares the time history curves of the bottom acceleration under different seismic conditions. The time history curve of the bottom acceleration obtained using FDM nonlinear analysis is basically consistent with the peak acceleration value of the centrifuge test results. Figure 6 compares the bottom pore pressure response of the model under different conditions. The FDM nonlinear analysis results are consistent with the centrifuge test results, which can well simulate the variation law of excess pore pressure within the landfill under different water levels and seismic intensities. At low water levels and small earthquakes, the main manifestation is the accumulation of excess pore pressure. At high water levels and large earthquakes, there is a significant negative excess pore pressure, which the tensile stress of the slope under seismic action may cause.  
As shown in Figure 7, the deformation pattern of the FDM nonlinear analysis landfill model is consistent with the centrifuge test results. The double-sided slope MSW landfill exhibits an asymmetric deformation pattern after the earthquake: the deformation of the landfill is mainly on one side, including a large area including the top of the landfill, which moves towards that side, while the middle and lower areas of the landfill on the other side move in the opposite direction.  
Figure 5: Comparison of acceleration time history of A1: (**a**) \(R_{w}=0.25\), \(k_{h}=0.13\), and (**b**) \(R_{w}=0.50\), \(k_{h}=0.46\).  
Figure 6: Comparison of the pore pressure response of different positions: (**a**) P5:\(R_{w}=0.25\), \(k_{h}=0.13\) (b) P2:\(R_{w}=0.50\), and \(k_{h}=0.13\).
## 3 Results
### Data Quality Capabilities Comparison  
The coverage of DQ domains across the packages varies considerably (Figures 2 and 3); a detailed overview is provided in Tables A1-A7 of the Appendix. While some domains are covered by almost all packages, such as crude missingness or range and value violations, other domains receive little coverage of package built-in functionalities, such as qualified missingness or contradictions (Figure 3). Only one of the packages contains functionalities to directly target repeated measurements. Packages that are based on rule checking (category 3 in Section 3.3) incorporate on average more indicators than packages focusing on descriptive data overviews and data exploration (category 2 in Section 3.3). Figure 3 indicates that the packages cover at most eight of the ten domains of the framework, while almost half cover at least six domains.  
Figure 2: Number of packages with relevant output to detect issues in distinct data quality domains (Section 2.2.1) with respect to the total 27 packages included in this review.
## 1 Introduction
They highlighted that the primary function of the fence is to obstruct the backward leakage flow and to diminish the circumferential movement of the TLV. Furthermore, the fence mitigates the dual-leakage tip clearance flow generated due to the interaction between the TLV and the blade, consequently reducing the intensity of the separation vortex in the tip clearance. In the field of counter-rotating axial flow fans, [PERSON] and [PERSON] et al. [19; 20; 21] qualitatively analyzed the motion characteristics of different tip clearance leakage vortex structures. They only made a rough estimate of the leakage vortex trajectory and did not conduct an in-depth discussion on the backflow. In summary, researchers currently have a certain understanding of the flow characteristics of tip leakage flow in axial flow fans. However, in the field of mining contra-rotating axial flow fans, the tip clearance flow field of the impeller is more complex, and there is no relevant literature that has quantitatively analyzed the motion trajectory of the tip leakage vortex. Moreover, the blocking effect of backflow on the impeller flow passage during the interaction between the tip leakage flow and the mainstream is not yet clear, and the relationship between energy loss in the region near the tip clearance and the flow field is still unclear. This study investigates the flow characteristics and loss mechanisms of the tip leakage flow in mining contra-rotating axial flow fans under various flow rates. Firstly, numerical simulations of the internal flow within the fan were conducted, and the accuracy of the simulation results was verified through experiments. Secondly, the impact of different flow rates on the distribution characteristics of the tip leakage flow was discussed. Then, the Q-criterion vortex identification method was employed to conduct a detailed analysis of the tip leakage vortex trajectory. Subsequently, the blockage effect in the tip backflow region was analyzed, and the influence of backflow on the main flow was quantified. Finally, the distribution characteristics of high-loss regions within the leakage flow were studied, and the loss mechanism of the tip leakage flow was explored.
# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## 5 Further Research Needs and Challenges in Optimization of Ship Energy Systems
### Synthesis Optimization
#### 5.1.3 SDO Optimization Including Synthesis Optimization of Working Fluids  
As indicated in Table 1, the Organic Rankine Cycle (ORC) is often the subject of optimization. In these works, the working fluid is predetermined or several available working fluids are investigated, and the combination of fluid and system with the best performance is finally selected.  
A different and very interesting approach is followed in [94]: The fluid is synthesized during the optimization procedure by computer-aided molecular design using several molecular groups (e.g., -CH3, -CH2, =CH2, =CH-, etc.). The aim of the optimization is to determine the optimal combination of the molecular groups and thermodynamic variables that maximize the power output generated by the ORC for specified heat source and heat sink. In this particular application, the synthesis of the system is fixed. The complete SDO optimization can be further developed to include molecular synthesis of the working fluid(s).
# An Improved Skewness Balancing Filtering Algorithm Based on Thin Plate Spline Interpolation
## 1 Introduction
To boost this method's adaptability to complicated environments, this paper proposes an improved skewness balancing filtering algorithm based on thin plate spline (TPS) interpolation. The proposed algorithm filters the nonground points in an iterative manner. A reference surface that reflects the fluctuation of the terrain is generated using the TPS interpolation method. Accordingly, the elevation difference from each point to the surface can be calculated. By applying the skewness balancing principle to these elevation differences, nonground points can be removed automatically. The proposed algorithm was tested using the datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The experimental results show that this presented method can adapt to complex environments and achieve a higher filtering accuracy than the traditional skewness balancing algorithm. Owing to the simple principle and high automation, the proposed method has the potential to contribute to the postprocessing of airborne LiDAR point clouds.