esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Transmission error (TE) and backlash are important parameters used to evaluate the transmission accuracy of cycloid-pin drives. Existing calculation methods are mostly based on two-dimensional tooth profile models, and these methods ignore the influence of some abnormal meshing phenomena caused by profile modifications (PMs), manufacturing errors (MEs), and assembly errors (AEs), such as the instantaneous mesh-apart of tooth pairs and the eccentric load on the tooth surface. To fill this gap, a novel approach to accurately calculating the TE and backlash of a cycloid-pin gear pair based on the error tooth surfaces is proposed, and its feasibility and effectiveness are validated by comparison with the theoretical analyses and the results from the literature. Based on this, the effects of the PMs, MEs, and AEs on the transmission accuracy are studied, which will be helpful in optimizing the tooth profile design of a cycloid gear and the tolerance allocation during the installation of a gear pair. The proposed method is also expected to provide accurate error excitation data for the dynamic analysis of cycloid-pin drives.  
transmission error; backlash; cycloid-pin drives; profile modifications; manufacturing errors; assembly errors +
Footnote †: journal: Applied Sciences
# GPR Data Interpretation Approaches in Archaeological Prospection
## 4 Multi-Disciplinary Approach
### Data Fusion  
Data fusion in archaeological prospection is an alternative approach for combining quantitative information acquired from different sensors employed in geophysical methods and remote sensing techniques. This is performed by mainly applying statistical methods and transforms to project the different information of each sensor in the same space. The main challenge of this approach is to find effective methods in transforming the various data into combinable representations. An early attempt shows improvements in visualizing the depth, location, and geometry of archaeological targets measured by GPR, magnetic and dipole-dipole geoelectric methods, where the integration of normalized data were performed into a shared space [30]. Other methods, commonly applied in image analysis and computer vision, are explored in Reference [31] to describe more complex targets measured by GPR, magnetic gradiometry, electrical resistivity, magnetic susceptibility, soil conductivity, and aerial thermography. The results revealed the limitations and benefits of each method while showing the potential to improve data interpretation. Similar studies, where integration methods are compared, are conducted using synthetic data [32], real data measured by multiple geophysical methods from different sites [33], or both synthetic and real data [34]. Other attempts involved developing a methodology that integrates GPR data with multispectral satellite images to enhance the interpretability of detected archaeological targets in the near-surface layers [35; 36]. The developed methodology is based on a regression model aiming to correlate GPR and ground spectroradiometer data using the vegetation indices that are subsequently projected on high resolution image of the area of interest. The proposed methodology is tested using data collected from a Neolithic tell in eastern Hungary. Linear regression [35] and Bayesian Neural Networks (BNNs) [36] are examined with the former showing more potential. Further, the potential of 3D data fusion following a multi-disciplinary approach is discussed in Reference [37].
## 5 Experiments and Results
### Transferability Analysis
These maps show the probability of each pixel belonging to one of the two classes of destroyed or no-damage. Precise damage assessment results are visible for different regions. In order to enhance the classification results, we utilized the provided building mask to refine the final classification output. The refined binary damage assessment map is visualized in the last column. Each building is colorized red or green to show the destroyed or no-damage classes, respectively. Moreover, building borders are similarly colorized and overlaid to show the ground truth information for each building. Some features were identified in the Bam images that could negatively affect the predictions, including (1) yard walls of properties have guided the localization algorithm to think of yards as building regions and incorrectly assign building pixels to them, (2) dense and tall trees on some part of the study area, intensified varying illumination angles, have made difficult conditions for identifying the correct class for some pixels, and (3) using white materials in furnishing yards have mistaken the algorithm to think of them as buildings. However, the results demonstrated the applicability and robustness of the method over the unseen dataset of the Bam earthquake.
###### Abstract  
Partial discharges (PDs) are one of the leading causes of catastrophic power transformer failures. To prevent such failures, online PD monitoring systems are increasingly being implemented. In this paper, to the best of the authors' knowledge, a case study analysis of short-term PD monitoring is presented for the first time using a combination of acoustic emission and ultra-high-frequency methods. Studies have shown that this approach, supported by selected statistical methods for analyzing the convergence (such as the confusion matrix and agreement metrics) of acoustic and electromagnetic pulse detection, improves the reliability of PD detection. Furthermore, it was shown that short-term PD monitoring enables the identification of time windows during which discharges occur periodically and the determination of the transformer phase containing the PD source. This, in turn, facilitates the application of the time difference of arrival (TDoA) technique for the precise localization of transformer insulation defects.  
Keywords:partial discharge; power transformer; online monitoring; UHF; acoustic emission
## 2 Mathematical Setup
### Basics of Thermal Wave Interference
#### 2.3.1 One-Layered Coatings on a Substrate
Analogous to the first case, one obtains the following expression corresponding to the surface temperature fraction: \[\sum_{n=0}^{\infty}b_{n} =T_{0}A_{0}R_{1}\exp\left(-2\sigma_{1}L_{1}\right)\sum_{n=1}^{ \infty}(1+R_{0}^{n}R_{1}^{n}\exp\left(-2n\sigma_{1}L_{1}\right))\] \[=T_{0}A_{0}R_{1}\exp\left(-2\sigma_{1}L_{1}\right)\sum_{n=0}^{ \infty}R_{0}^{n}R_{1}^{n}\exp\left(-2n\sigma_{1}L_{1}\right)\] Figure 5:0 (10) Figure 5:1 Figure 5:2 where the geometric series formula is used, since \(|R_{0}R_{1}\exp\left(-2\sigma_{1}L_{1}\right)|<1\) holds true. Figure 5:3 Figure 5:4 Figure 5:5 Figure 5:6  
This provides the following expression for the time-dependent temperature at the surface:  
Figure 5:7  
Note that the phase shift in this formula, given by \(\frac{\pi}{4}\), is excluded and has the meaning of normalizing the temperature by subtracting an \"infinitely\" thick layer, cf. Equation (6). The wave vector \(\sigma_{1}=(1+i)\sqrt{\frac{\omega}{2\alpha_{1}}}=(1+i)\frac{1}{\mu_{1}}\) is complex and so is the temperature amplitude. It can be split into its real and imaginary parts providing an expression in polar coordinates. To this end, one sets  
Figure 5:8  
and calculate  
Figure 5:9 Figure 4:0  
and  
Figure 4:1 Figure 4:2  
Define \(x\coloneqq\frac{-2L_{1}}{\mu_{1}}\). Then the amplitude \(A_{\tilde{T}}\) is obtained using  
Figure 4:3 Waves that are reflected first at the interface between \(M_{1}\) and \(M_{2}\). Figure 4:4 Figure 4:5  
as well as the phase angle \(\varphi_{\hat{T}}\)  
\[ \begin{split}\varphi_{\hat{T}}&=\tan^{-1}\Bigg{[} \frac{\text{Im}\big{[}\hat{T}(x=0)\big{]}}{\text{Re}\big{[}\hat{T}(x=0)\big{]} }\Bigg{]}\\ &=\tan^{-1}\Bigg{[}\frac{[1+R_{0}][R_{1}\exp{(x)}\sin{(x)}]}{1+(1- R_{0})R_{1}\exp{(x)}\cos{(x)}-R_{1}^{2}R_{0}\exp{(2x)}}\Bigg{]}\end{split}  \tag{14}\]  
which provides an expression for the complex temperature amplitude at the surface in polar coordinates, i.e.,  
\[\hat{T}(x=0)=A_{\hat{T}}\exp{(i\varphi_{\hat{T}})}.\]  
The advantage is that the amplitude, as well as the phase, are measurable real valued expressions.
## 4 Hammer Impacting Energy Transfer Efficiency of Offshore SPT
### Rod Length Correction of Offslope SPT  
Effective hammer impacting energy is used to represent the rod length correction in the land SPT (effective hammer impacting energy is the work conducted by the probe penetrating into the soil to overcome the resistance of the soil under the action of one hammering):  
\[\alpha=\frac{E_{(l)}}{E_{(2)}} \tag{6}\]  
Figure 8: Relationship between hammer impacting energy and test depth.  
Figure 7: Hammer impacting energy transfer efficiency of SPT.  
where \(E_{(l)}\) is the effective hammer impacting energy of SPT with different rod lengths (J); \(E_{(2)}\) is the effective hammer impacting energy of the SPT at the rod length of 2 m (J); and \(\alpha\) is the rod length correction coefficient. During the offshore SPT, the influence of the water depth on the hammer impacting energy attenuation in the probe cannot be ignored. Therefore, it is necessary to introduce a water depth correction coefficient to ensure the accuracy of the exploration data. In this paper, the average hammer impacting energy at the top test points under different water depths is used as the correction base point, and the ratio of its hammer impacting energy to that of the probe rod at the water-soil contact surface is defined as the water depth correction coefficient:  
\[\alpha_{\text{w}}=\frac{E_{1}}{E_{2}} \tag{7}\]  
where \(E_{1}\) is the average value of hammer impacting energy of the probe rod at the water-soil contact surface (J); \(E_{2}\) is the average value of hammer impacting energy at the top test points (J); and \(\alpha_{\text{w}}\) is the correction coefficient of water depth. The effect of exploration depth on hammer impacting energy distribution is described, and a survey depth correction coefficient for the offshore SPT is proposed:  
\[\alpha_{\text{s}}=\frac{E_{l}}{E_{3}} \tag{8}\]  
where \(E_{l}\) is the effective hammer impacting energy of SPT with different survey depths (J); \(E_{3}\) is the effective hammer impacting energy of the SPT at the survey depth of 2 m (J); and \(\alpha_{\text{s}}\) is the correction coefficient of survey depth. The correction method of blow counts in offshore SPT can be expressed as:  
\[N_{1}=\alpha_{\text{w}}\alpha_{\text{s}}N \tag{9}\]  
where \(N_{1}\) is the blow counts after correction and \(N\) is the measured blow counts in the field.
# Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
## 3 Results and Discussion  
### Material Characterization  
In order to confirm whether melamine and sodium alginate were successfully linked, we measured and compared the infrared spectra of sodium alginate, Alg-Mel hybrid and Pb\({}^{2+}\)-loaded Alg-Mel. As can be seen in Figure 2, the broad adsorption peak at 3371 cm\({}^{-1}\) belongs to the \(\rm{v_{O-H}}\) vibration. The weak adsorption peak at 2919 cm\({}^{-1}\) was attributed to the aliphatic C-H stretching vibration. The adsorption peaks at 1591 and 1329 cm\({}^{-1}\) belong to C=O and C-O, respectively [10; 11]. When compared with the infrared spectrum of raw sodium alginate, there is a new adsorption peak of N-H stretching vibration at 3217 cm\({}^{-1}\) in the spectra of Alg-Mel and Pb\({}^{2+}\)-loaded Alg-Mel, which confirms that melamine was successfully linked with alginate. Moreover, after loading Pb\({}^{2+}\), the COO\({}^{-}\) (carboxylate) peak of the Alg-Mel shifted from 1329 to 1294 cm\({}^{-1}\), which might be caused by the coordination of the carboxylate groups with the Pb\({}^{2+}\)[12; 13].  
Furthermore, the morphology of Alg-Mel was also characterized by SEM. As shown in Figure 3, the Alg-Mel is Oval-shaped and owns an uneven surface. On the other hand, the elemental analysis indicates that the mass percentage of carbon, nitrogen and hydrogen of Alg-Mel is 37.71%, 14.76% and 3.93%, respectively. It indirectly indicates that the Alg-Mel is rich in oxygen, and these oxygen-containing groups such as -OH, -COO\({}^{-}\) are beneficial in increasing the adsorption capacity of target metal ions.  
Figure 3: (**a**) Digital photo of Alg-Mel; (**b**) SEM image of Alg-Mel at low magnification; (**c**) SEM image of Alg-Mel at high magnification.  
Figure 2: Infrared spectra of (**a**) raw sodium alginate, (**b**) Alg-Mel and (**c**) Alg-Mel loaded Pb\({}^{2+}\).
# Improvement of Fast Simplified Successive-Cancellation Decoder for Polar Codes
## 1 Introduction
The main contribution of this study is as follows. 1. This approach realizes the lowest latency compared to the existing methods without error-correction performance loss. 2. The approach is highly adaptable to different rates and different channel conditions. 3. Our method facilitates further study of latency reduction for polar decoding. Future advances in recognition-based methods and check-based methods can be integrated together to implement a faster SC decoder. We begin this paper by reviewing the SSC and Fast-SSC decoding algorithms in Section 2. We then present our improved decoding algorithm (Fast-ISSC) and analyze its latency and decoding performance in Section 3. In Section 4, we present simulation results obtained using the proposed algorithm. Finally, conclusions are presented in Section 5. Figure 1: Relationships between the various SC decoding latency reduction methods.
## 3 Methods
### HVSR Modeling  
The average HVSR curves were computed by Geopsy software ([[http://www.geopsy.org](http://www.geopsy.org)]([http://www.geopsy.org](http://www.geopsy.org)), accessed on 15 September 2022) [34] following standards from the SESAME project manual [35]. Table 6 shows the input parameters for the HVSR calculations separately for St3 site records and for Typ2 site records. The input parameters were chosen so that the number of windows and samples in each window met the requirements from the SESAME guideline for the expected fundamental frequency.  
At the first stage, a set of 25-s long time windows was selected by the STA/LTA \"antitrigger\" algorithm, based on [35; 36; 37], so that the STA/LTA values were between the specified maximum value to avoid transients and minimum values, to avoid anomalously low amplitudes. No preliminary filtering was applied. No overlapping was applied since the total duration of the noise record was enough.  
HVSR computations were conducted as follows for each time window with the following averaging of the resulting curves:  
\[HVSR=\frac{\sqrt{\left(\left|H_{1}\right|^{2}+\left|H_{2}\right|^{2}\right)/2}} {\left|V\right|} \tag{1}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value (for St3 Records)** & **Value (for Typ2 Records)** \\ \hline Record duration & \(\sim\)6 h & \(\sim\)8 h \\ \hline Window length & 25 s. & 25 s. \\ \hline Filtering & no & no \\ \hline STA & 1 s. & 1 s. \\ \hline LTA & 30 s. & 30 s. \\ \hline Min STA/LTA & 0.2 & 0.2 \\ \hline Max STA/LTA & 2.5 (Oct); 2.5 (Nov); 3.9 (Dec) & 1.83 (Oct); 1.35 (Nov); 1.24 \\  & (Dec) \\ \hline Number of windows & 24 (Oct); 29 (Nov); 26 (Dec) & 27 (Oct); 27 (Nov); 28 (Dec) \\ \hline Horizontal components & squared average & squared average \\ \hline Taper window type & Tukey, width 5\% & Tukey, width 5\% \\ \hline Smoothing & rectangular window, width 1 Hz & rectangular window, width 1 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 6: The input parameters for the HVSR modeling.  
where \(H_{1}\) and \(H_{2}\) are Fourier spectra amplitudes of the two horizontal components of seismic noise, while \(V\) is the Fourier spectrum amplitude of the vertical component of seismic noise.  
Since the raw spectra contain a lot of frequency samples which show a lot of very narrow oscillations and spikes, while computing the HVSR, the very small values reached by raw spectra lead to unusual values of the ratio. Therefore, before computing the HVSR, the Fourier spectra amplitude of the three components were smoothed by a rectangular window of 1 Hz width.
## 3 Results and Discussion
### Properties of Slag Cements (SCs) manufactured by 40% Slag
#### 3.3.2 Setting Time  
The initial and final setting times of the manufactured slag cements is presented in Table 11. Initial setting times of cements with 40% slag are lower than the corresponding ones with 20% and in some cases lay marginally within the limits of EN 197-1 [17] for 32.5 typical strength cements. The higher initial setting time was measured for the mixture with slag 3-1a, in agreement to the results obtained in case of CPCs with 20% slag. Overall, the relatively fast onset setting of the slag cement mixtures is mainly correlated to their calcium aluminates content of the used slags as described above. However, the obtained results cannot totally be/gr explained by the mineralogical composition of the slags. For example, for the water-cooled slags 4-1b and 3-2b, although the use of 20% in cement resulted in increase of the initial setting time compared to OPC, increasing slag content to 40%, resulted in decreased initial setting time. Further, unexpected fast onset of setting was also measured for cement with slag 3-1b, in which the ettringite was detected. These results are most probably attributed to the \"false set\" phenomenon, which is usually experienced in the case of partially hydrated cements deriving from improper storage conditions. It is noted that the used clinker was stored for a period of almost one and a half year in plant's warehouse for the elaboration of all the lab scale experiments.  
Figure 11: Normalized compressive strength of cements manufactured with air-cooled slags in relation to their slag content (**a**) after 28 days of curing and (**b**) after 90 days of curing.  
Figure 12: Normalized compressive strength of cements manufactured with water-cooled slags in relation to their slag content (**a**) after 28 days of curing and (**b**) after 90 days of curing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slag** & **Initial Setting Time (min)** & **Final Setting Time (min)** \\ \hline – & 120 & 170 \\ \(\rightarrow\) & 135 & 185 \\
3-1a & 125 & 185 \\
3-2a & 90 & 170 \\
4-2a & 75 & 145 \\
5a & 75 & 125 \\
3-1b & 75 & 155 \\
3-2b & 80 & 140 \\
4-1b\({}^{*}\) & 90 & 170 \\
4-2b & 85 & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Initial and final setting time of manufactured SCs (40% slag).
# Review and Development Trend of Digital Hydraulic Technology
## 2 Review of Digital Hydraulic Technology  
### Definition of Digital Hydraulic Technology  
The definition of digital hydraulic technology has not been unified yet. At present, all the mainstream digital hydraulic definitions can reflect the characteristics of digital hydraulic partly, but they all have an inappropriate coverage, which leads the definition of digital hydraulic technology to be ambiguous. Among them, the definition proposed by [PERSON] points out two characteristics of digital hydraulic technology; namely, discrete and active control. The former is the inherent property of digital hydraulic technology, because the signal controlling the digital hydraulic components is a discrete digital signal. The latter gives the essential characteristics of digital hydraulic technology only partly, because active control is not equal to intelligent control. For example, the proportion-integration-differentiation control (PID control), which is common in hydraulic systems, can realize the active control of system's output. But it cannot be called intelligent control for the reason that it cannot perform intelligent behaviors related to human intelligence, such as judgment, reasoning, perception, communication, etc. Therefore, [PERSON]'s definition does not completely reflect the essential characteristics of digital hydraulics. Another mainstream definition of digital hydraulic was put forward by [PERSON], which mainly highlights the discrete characteristics of digital hydraulic technology. It has two aspects, control signal discretization and fluid flow discretization, but it ignores the essential characteristic of digital hydraulics--the intelligent control. For example, traditional proportional control and simple switch control also have discrete characteristics, but they will never be considered digital hydraulic technology. Therefore, [PERSON]'s definition is not perfect either.  
On that basis, this paper gives the definition of digital hydraulic technology based on the viewpoints of researchers all over the world. We define the digital hydraulic technology as a system which controls a discrete fluid with a modulated, discrete, digital signal directly to realize active and intelligent control of the system output. The hydraulic components with such technical characteristics can be defined as digital hydraulic components. The system composed by digital hydraulic components can also be defined as digital hydraulic system. What is more, the essential feature of digital hydraulic technology is intelligent control; technology which can only realize on/off control cannot be classified as digital hydraulic technology.
# A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain
## 2 Proposed Method  
We surveyed several places to determine the typical terrains that need to be photographed and established an image dataset accordingly. The dataset is weakly annotated, which means it is only annotated according to terrain type, rather than having more complex and explicit strong annotations according to physical properties. We introduce the process of the vision-based two-stage framework which is used to estimate the physical properties. In the first stage, a ResNet-based semantic segmentation network named TerrainNet is designed to infer different terrains of the image densely. In the second stage, from the perspective of bionics, we design, extract and analyze image features, and finally realize the mapping from terrain types to physical properties using a decision-making mechanism.
## 5 Discussion
### The Diversified Factors Affecting the Evolution Patterns of Tidal Flats  
The sedimentary evolution of the aquaculture and non-aquaculture regions of each sandbank is heterogeneous due to their different locations, hydrodynamic conditions and distribution of laver cultivation. Hydrodynamics plays a significant role in affecting sediment transport and appears seasonal variations [46]. Impacted by the monsoon climate, rough sea conditions in the sea north of Jianggang port caused by cold waves in autumn and winter play a leading role in the partial sediment transition [56], while typhoon waves in summer affect southern part of radial sandbanks mainly. Taken the Jianggang port as the boundary point, current velocity of north tidal is faster than the south. Moreover, the different distributions of aquaculture regions in each sandbank also influence evolution patterns. The Dongsha and Niluoheng sandbanks show an atypical erosion-and-deposition cycle, because in both sandbanks the aquaculture regions are distributed in the seaside margins. The billow caused by cold waves brings sediment out from the aquaculture regions during the growing period (from winter to spring), leading to sedimentary erosion but not siltation in the aquaculture regions. The aquaculture region of the Liangyuesha sandbank is relatively evenly distributed in the periphery and the sediment supply from peripheral sandbanks is one of the sources of inner sandbank sediment [57].  
Thus the deposition and erosion keeps balance generally and a typical sedimentary evolution pattern was observed. The Tiaozini, Gaoni and Jiangjiasha sandbanks and the Zhugensha sandbank have excellent stability and are located in the sedimentary convergent center leading to a typical sediment evolution arising from layer cultivation.  
It is worth noting that the rhythmic results of this study are only derived from satellite images. Evolution patterns of Jiangsu coast will be analyzed based on space/aerial/ground remote sensing technologies in further study. Combined with Synthetic Aperture Radar (SAR), terrestrial laser scanning and drones, we will acquire field observation data and optimize the accuracy of DEM to derive more regular results.