esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Study
### Parametric Analysis
#### 3.5.3 Influence of the Pile Foundation Length  
Figure 16 illustrates the lateral bearing capacity characteristics of the GPF for different lengths, specifically 0.8 m, 0.9 m, and 1.0 m. In the case of the GPF with a length of 0.8 m, the characteristic value of lateral load-bearing capacity is 0.31 kN. When the length of the GPF is increased to 0.9 m, the corresponding characteristic value of lateral load-bearing capacity increases to 0.4 kN. Furthermore, at a length of 1.0 m, the GPF exhibits a further increase in the characteristic value of lateral load-bearing capacity, reaching 0.71 kN. The observed trend suggests that the lateral load-bearing capacity of the GPF grows proportionally with its length. This implies a correlation between the lateral load-bearing capacity and the depth of embedment into the soil. As the embedment depth increases, the lateral load-bearing performance improves. However, it is important to note that this variation is not uniform.  
The analysis of parameters shows that Young's modulus significantly influences the GPF, along with the depth of its embedment into the foundation. Therefore, when undertaking engineering projects, it is crucial to choose construction materials that match project requirements and determine the ideal depth for embedding the GPF. This ensures that its lateral load-bearing capacity can be effectively maximized.  
Figure 16: Effect of length of the pile.  
Figure 15: Influence of pile Young’s modulus.
## 2 Controller Design
### Nonlinear Controller for Current Dynamics  
The current tracking errors are defined as  
\[\begin{split} e_{d}=& e_{d_{d}}-e_{d}\\ e_{q}=& e_{i_{d}}-e_{q},\end{split} \tag{2}\]  
where \(e_{d_{d}}\) is the desired direct current and is set to 0 for the FOC and \(e_{q_{d}}\) is the desired quadrature current that is yet to be defined. The current tracking error dynamics are  
\[\begin{split}\dot{e}_{d}=&\dot{e}_{d_{d}}-\frac{1} {L}(v_{d}-Ri_{d}+N_{r}L\omega i_{q})\\ \dot{e}_{q}=&\dot{e}_{q_{d}}-\frac{1}{L}(v_{q}-Ri_{ q}-N_{r}L\omega i_{d}-K_{m}\omega)\end{split} \tag{3}\]  
**Theorem 1**.: _Suppose the current tracking error dynamics (3). If the electrical controller (4) is applied to the current tracking error dynamics (3)_  
\[\begin{split} v_{d}=& Lk_{d}e_{d}+L\rho_{d}\text{ sgn}(e_{d})+Ri_{d}-N_{r}L\omega i_{q}+L\dot{e}_{d_{d}}\\ v_{q}=& Lk_{q}e_{q}+L\rho_{q}\text{sgn}(e_{q})+Ri_{ q}+N_{r}L\omega i_{d}+K_{m}\omega+L\dot{e}_{q_{d}}\end{split} \tag{4}\]  
_where \(k_{d}\), \(k_{q}\), and \(\rho_{d}\), \(\rho_{q}\) are control gains and positive constants, respectively, then the current tracking errors \(e_{d_{d}}\) and \(e_{q_{d}}\) converge to zeros in finite time._  
Proof.: Lyapunov candidate function \(V_{e}\) is defined as  
\[V_{e}=\frac{1}{2}e_{d}^{2}+\frac{1}{2}e_{q}^{2}. \tag{5}\]  
The derivative of \(V_{e}\) is  
\[\dot{V}_{e}=e_{d}(\dot{e}_{d_{d}}-\frac{1}{L}(v_{d}-Ri_{d}+N_{r}L\omega i_{q} ))+e_{q}(\dot{e}_{q_{d}}-\frac{1}{L}(v_{q}-Ri_{q}-N_{r}L\omega i_{d}-K_{m} \omega)). \tag{6}\]  
Using the electrical controller (4), \(\dot{V}_{e}\) becomes  
\[\dot{V}_{e}=-k_{d}e_{d}^{2}-k_{q}e_{q}^{2}-\rho_{d}\text{sgn}(e_{d})-\rho_{q} \text{sgn}(e_{q}). \tag{7}\]  
Thus, the current tracking errors \(e_{d_{d}}\) and \(e_{q_{d}}\) converge to zero in finite time.  
After the current tracking errors \(e_{d_{d}}\) and \(e_{q_{d}}\) converge to zeros in finite time, the PM stepper motor model (8) becomes a second-order model as  
\[\begin{split}\dot{\theta}=&\omega\\ \dot{\omega}=&\frac{1}{\eta}(-B\omega+K_{m}i_{q_{d}} +d).\end{split} \tag{8}\]
# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 4 Results and Discussion
detected assignments are actual ships about 97% of the time. The main reason for false detection was the ghost phenomena. Even though the ghost effect was reduced by the azimuth low-pass filtering, some targets still had a higher brightness value. One of the false detections was due to the barge. The one non-detected ship had a lower value in the NL-means-filtered images, unlike the other ships, because the brightness value of the ship target had a lower value. Kompsat-5 SAR imagery has severe ghost phenomena and side-lobe effects. Due to these effects, it is very hard to identify ship targets. To improve the ship-detection rate, we applied the proposed method to the Kompsat-5 SAR image. Almost all the ships were detected by the proposed method, although some of the ghost objects were detected as ship objects. This means that the proposed method was successfully applied to ship detection from the Kompsat-5 SAR image.
# Responses of Water Use Efficiency to Drought in Southwest China
## 3 Results
### Responses of WUE to Drought in Southwest China

Table 3: Percentages of grid cells with \(\Delta\)WUE \(>\) 0 and \(\Delta\)WUE \(<\) 0 in 2006 and 2010 for different vegetation types and all vegetated areas in Southwest China. Figure 9: Responses of monthly WUE to drought: (**a**) seasonal variations of WUE and scPDSI in the selected drought region (Figure 8a) in 2006; (**b**) seasonal variations of WUE and scPDSI in the selected drought region (Figure 8b) in 2010; (**c**) the relationship between monthly WUE and monthly scPDSI in 2006; (**d**) the relationship between monthly WUE and monthly scPDSI in 2010. The responses of ecosystem WUE to water status at the monthly timescale may be affected by different plant growth periods. Therefore, we also examined how annual WUE changed with scPDSI (Figure 11). The relationship between WUE and scPDSI at the annual timescale also varied among vegetation types. For forests, shrublands, and croplands, annual WUE showed a pattern of increasing first and then decreasing with the decrease of scPDSI. For grasslands, annual WUE decreased first and then increased with the decrease of scPDSI. In addition, forests showed relatively strong adaptability to drought as annual WUE was relatively stable under moderate drought; the WUE of shrublands was sensitive to drought, and it reached the peak value when scPDSI was near 0; the WUE of grasslands and croplands were also sensitive to wet or dry conditions; and the valley/peak value of WUE was reached when scPDSI was \(-0.5\) to 0. The patterns of annual WUE with changing scPDSI depended on the responses of annual GPP and ET to scPDSI (Figure 11). GPP and ET had different patterns with the changes in scPDSI. For example, with the decrease of scPDSI, GPP showed the pattern of first increasing and then decreasing, while ET continued to slowly decrease. For all the four broad vegetation types, the variations of annual GPP with scPDSI were generally similar to those of annual WUE. Therefore, the variations in annual WUE with changing wet or dry conditions were mainly caused by the variations in annual GPP. Figure 10: Responses of WUE to drought at the monthly timescale in areas affected by severe drought for different vegetation types: (**a**) forests; (**b**) shrublands; (**c**) grasslands; (**d**) croplands.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 4 Proposed Autonomous Scheduling Scheme for Convergecast
Figure 1: An example of the Escalator schedule: (**a**) network topology of the example; (**b**) Escalator’s generated schedule for the example  
The operation of Escalator consists of four parts: timeslot allocation, sliding slotframe, channel offset selection and baseline slotframe size determination. Firstly, the timeslot allocation mechanism allocates two dedicated timeslots to each node for the receive and transmit operations, respectively. Secondly, the sliding slotframe mechanism shifts its allocated timeslots for pipelined transmission between parent and child nodes. Thirdly, the channel offset selection mechanism minimizes timeslot usage by parallelizing concurrent transmissions of packets. Finally, the baseline slotframe size determination mechanism finds the optimal baseline slotframe size for minimizing the conflicts between the convergecast and baseline slotframes. The detailed operation of each mechanism is described in the next section.
## 4 Dynamic Modeling of the Two-Stage WPT System  
From (16), the output voltage variation \(\Delta v_{1}\) and the inductor current variation \(\Delta i_{\text{L}}\) in one switching period are derived as:  
Figure 6: CC&OV-PT control to suppress low-frequency oscillations.  
\[\Delta v_{1}=\frac{T_{0}^{2}}{2 LC_{1}}\Big{(}-V_{\rm i}D_{v}^{2}+2V_{\rm i}D_{v}-V_ {1}\Big{)}+\frac{T_{0}}{C_{1}}i_{L}(nT)\ -\ \frac{8m(R_{2}+R_{\rm Lx})V_{1}}{\omega_{1}C\big{[}R_{1}(R_{2}+R_{\rm Lx})+ \omega_{1}^{2}M^{2}R_{1}(R_{2}+R_{\rm Lx})+\omega_{1}^{2}M^{2}\big{]}} \tag{23}\]  
\[\Delta i_{L}=\frac{(V_{\rm i}D_{v}-V_{1})T_{0}}{L} \tag{24}\]  
According to (23), the discrete iterative model of the front-stage Buck converter of the WPT system is obtained, namely,  
\[X_{n+1}=\left\{\begin{array}{c}AX_{n}+B^{\rm H}\ CX_{n}\leq V_{\rm ref}\\ AX_{n}+B^{\rm L}\ CX_{n}>V_{\rm ref}\end{array}\right. \tag{25}\]  
where:  
\[\begin{array}{c}X_{n+1}=\left[\begin{array}{c}v_{1}(nT+T)\\ i_{\rm L}(nT+T)\end{array}\right]\text{, }X_{n}=\left[\begin{array}{c}v_{1}(nT)\\ i_{\rm L}(nT)\end{array}\right]\text{, }\\ A=\left[1-\frac{T_{0}^{2}}{2 LC_{1}}-\frac{8m(R_{2}+R_{\rm Lx})\pi^{2}}{\omega_ {1}C[R_{1}(R_{2}+R_{\rm Lx})+\omega_{1}^{2}M^{2}]}\quad\frac{T_{0}}{C_{1}} \right]\\ B^{\rm H}=\left[\begin{array}{c}\frac{T_{0}^{2}V_{1}}{2 LC_{1}}(-2D_{\rm H} ^{2}+5D_{\rm H})\\ \frac{T_{0}V_{1}D_{\rm H}}{L}\end{array}\right]\text{, }B^{\rm L}=\left[\begin{array}{c} \frac{T_{0}^{2}V_{1}}{2 LC_{1}}(-2D_{\rm H}^{2}+5D_{\rm L})\\ \frac{T_{0}V_{1}D_{\rm L}}{L}\end{array}\right]\text{, }C=\big{[}1\quad a\big{]}\end{array}\]  
With regards to the output voltage of the WPT system, we have [29],  
\[v_{0}(nT)=av_{1}(nT-t_{\rm b}) \tag{26}\]  
where \(a\) is the amplitude ratio of the output to input voltages, and \(t_{\rm b}\) is the delay time of the output voltage lagging the input voltage. The output voltage variation in a one switching cycle can be expressed as:  
\[\Delta v_{0}(nT)=v_{0}(nT+T)-v_{0}(nT)=a\Delta v_{1}(nT-t_{\rm b}) \tag{27}\]  
Therefore, the discrete iterative model of the two-stage WPT system controlled by CC&OV-PT can be expressed by:  
\[Y_{n+1}=AY_{n}+B \tag{28}\]  
where:  
\[Y_{n+1}=\left[\begin{array}{c}v_{0}(nT+T)\\ i_{\rm L}(nT+T-t_{\rm b})\end{array}\right]\text{, }Y_{n}=\left[\begin{array}{c}v_{0}(nT)\\ i_{\rm L}(nT-t_{\rm b})\end{array}\right]\]
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 2 Related Work
### Privacy-Preserving for Edge-Assisted Visual SLAM  
The transmission data in edge-assisted visual SLAM contain privacy-sensitive information. Input images may include personal details such as faces, license plates, and location data, which, if exposed, could lead to unforeseen consequences [5]. Moreover, the output 3D point clouds have been shown to be invertible, allowing the reconstruction of original scenes [6; 7], thereby compromising user privacy.  
[PERSON] et al. [14] proposed a privacy-preserving method for visual SLAM that lifts 3D points into 3D lines during computation. This approach assumes that privacy can be preserved simply by transforming points into lines during processing. However, the 3D line cloud is discretized into a 3D point cloud during relocalization. Thus, the map generated by SLAM still has the risk of being attacked and leaking privacy [6; 7].  
Despite the growing adoption of edge-assisted visual SLAM, no existing privacy-preserving approach addresses both input data and generated outputs. Given that these data serve as potential vectors for privacy breaches, effective protection mechanisms are essential. This paper proposes a dual-component approach for privacy preservation in edge-assisted visual SLAM. By combining image inpainting for sensitive areas with point cloud encryption, the proposed method aims to safeguard user privacy while maintaining the accuracy of pose estimation.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 2 Landslide-Related Data Inventory in Hong Kong
### Geological Map  
For years, the GEO has been undertaking extensive geological studies for Hong Kong. The geological data are well-documented to facilitate the planning and land use management of the city. Among others, fifteen 1:20,000-scale geological maps accompanied by six geological memoirs covering the entire territory of Hong Kong were prepared in the 1980s with reference to a large amount of available borehole data, tunnel logs, cut slope exposures, and geophysical survey records. As such, these geological maps are more detailed than the regional geological maps of other national surveys [7]. These maps have been further updated since 2003 to incorporate new geological information and the latest knowledge advancements. The updated maps were fully digitized and implemented on a GIS platform such that geological enquiries and geological data modelling could be carried out efficiently. An extract of the geological map is shown in Figure 2d.
## 2 Materials and Methods
### RP-HPLC Analysis of Catechins and Caffeine (CAF)  
The catechin profiles and CAF content of the samples were determined by RP-HPLC/PDAD following the method of [PERSON] et al. [23], which has already been validated in our previous research focusing on green tea [21] and tea extracts [20]. Since the aromatic structural similarity of the tea catechins made the separation difficult, a Waters Spherisorb ODS2 column (100 \(\times\) 4.6 mm i.d.) from Waters (Milford MA, USA) was used to separate the individual catechins with high resolution, particularly between EC and EGCG, due to its hydrophobicity, carbon number (12%), and small particle size (3 \(\mu\)m). The mobile phase A was composed of acetonitrile/ethyl acetate/0.1% orthophosphoric acid/water (4.25:14:44.75:50, _v/v/v/v_), and the mobile phase B was acetonitrile/water (1:1, _v/v_). Baseline separation was achieved with a gradient elution as follows: 100% A for 10 min, followed by a linear gradient between phase A and phase B at an increasing rate of 2% per min of phase B until 20% of B was reached. This composition was maintained until the end of the run at a flow rate of 0.75 mL/min. The injection volume was 5 \(\mu\)L and the total run time was around 40 min. The column was maintained at 35 \({}^{\circ}\)C and coupled to an Agilent Technologies (Palo Alto, CA, USA) Liquid Chromatograph series 1200 equipped with a PDAD fixed at 280 nm. The chromatograms were recorded according to the retention time (RT), and the quantitative analysis was achieved by the external standard method using the ChemStation Chromatography Software from Agilent Technologies. The sample concentration was limited to the range of linearity in order to avoid peak tailing and RT shifting, which may occur when the sample amount approaches the column sample load capacity. Peak identity was assigned based on the RT following comparison with the authentic standards and/or by spiking the sample with the same standards. The presence of individual catechins was also confirmed by superimposing the spectrum of the peak with the corresponding authentic standard spectrum. The average of the triplicate measurements was used to calculate the catechin and CAF contents, and was expressed as the milligrams per gram of the sample on a dried extract basis. The total ECDs, total esterified catechins, and total non-esterified catechins were obtained by summation as follows: EC + EGC + EGCG + ECG, EGCG + ECG + GCG, and C + EC + EGC + GC, respectively.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 2 Summary of the BDS B1 Signal
### Brief Introduction of the B1I and B1C Signals  
The basic characteristics of the B1I and B1C signals are given in Table 1.  
The B1I signal has been a major civilian signal of the BeiDou regional system and will be retained in the BeiDou global system for legacy users. The B1I signal adopts the classical BPSK modulation with a chip rate \(f_{code}\) of 2.046 MHz, usually written as BPSK(2). The B1I ranging code has 2046 chips and is a balanced Gold code truncated with the last chip. The center frequency of the B1I signal is  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Signal** & **Component** & **Center** & **Modulation** & **Phase\({}^{o}\)** & **Power** & **Subcarrier/MHz** & **Pseudo-Random** \\  & & **Frequency/MHz** & **Modulation** & **Phase\({}^{o}\)** & **Proportion** & **Subcarrier/MHz** & **Code/Mcps** \\ \hline BII & \(-\) & 1561.098 & BPSK(2) & 0 & 1/2 & \(-\) & 2.046 \\ \hline \multirow{2}{*}{BIC} & pilot & 1575.42 & QMBOC \({}^{1}\) & 90/0 & 3/8 & 1.023/6.138 & 1.023 \\  & data & 1575.42 & BOC(1,1) & 0 & 1/8 & 1.023 & 1.023 \\ \hline \hline \end{tabular} \({}^{1}\) Quadrature Multiplexed BOC.  
\end{table}
Table 1: B1I and BIC Signal Structure.  
1561.098 MHz [20; 21]. The details of the B1I signal can be found in the official documentation [6]. The PSD of B1I with BPSK(2) modulation can be expressed as follows [22]:  
\[G_{B1I}(f)=f_{code}\frac{\sin^{2}(\frac{\pi f}{f_{code}})}{\left(\pi f\right)^{2}} \tag{1}\]  
The new B1C signal, modulated on the carrier with a center frequency of 1575.42 MHz, will be broadcast in the BeiDou global system. The B1C signal incorporates the innovative data-plus-pilot signal structure, and contains 25% power in the data component and 75% in the pilot component. Both signal components are found to repeat after 10,230 chips and spread at the code chip rate \(f_{code}\) of 1.023 Mcps. While the data component is generated with the BOC(1,1) modulation with a 1.023 Mcps square wave subcarrier frequency, the pilot component modulation, called multiplexed BOC (MBOC) [7] has a spectrum produced by a BOC(1,1) component and a BOC(6,1) component with a 6.138 Mcps square wave subcarrier frequency. Time-multiplexed BOC (TMBOC) is used in the new-generation BeiDou-3 experimental satellites, and is produced by replacing four of the 33 spreading symbols with BOC(6,1) modulation while retaining BOC(1,1) modulation for all other spreading symbols [5; 14]. However, in the future BeiDou global system, QMBOC with BOC(1,1) and BOC(6,1) modulated on the orthogonal phase of the carrier will be adopted [23]. The PSD of B1C, with 1/11 of the total power in BOC(6,1) and the remaining 10/11 of the total power in BOC(1,1), can be expressed as follows [22]:  
\[\begin{array}{ll}G_{B1C}(f)&=\frac{10}{11}G_{BOC(1,1)}(f)+\frac{1}{11}G_{BOC (6,1)}(f)\\ &=\frac{f_{code}}{11\pi^{2}f^{2}}\sin^{2}(\frac{\pi f}{f_{code}})\bigg{[}10\tan ^{2}\!\bigg{(}\frac{\pi f}{2f_{code}}\bigg{)}+\tan^{2}\!\bigg{(}\frac{\pi f}{1 2f_{code}}\bigg{)}\bigg{]}\end{array} \tag{2}\]
## 2 Performance Assessment
### Signal-to-Noise Ratio Simulations  
In this section, the interval of values is determined in which the instrument system constant \(K_{s}\) must be found to satisfy the initial specification of pesticide cloud measurement at a distance of 500 m. For  
Figure 1: MPE for an individual pulse _vs._ pulse repetition frequency (PRF).  
this purpose, SNR simulations were conducted for three wavelengths: 905 nm, 1064 nm and 1.5 \(\upmu\)m. Emission at 355 nm was not considered since the doses are additive in the UV-A region and consequently the safety level falls drastically for prolonged exposures. Another drawback of UV-A radiation is that it requires special optical material, since optical glass is not transparent and molecular backscatter in the ultraviolet range is very high [17]. Emission at 523 nm was also disregarded as it has MPE values similar to at 905 nm, but at this latter wavelength solar radiation (background noise) and atmospheric extinction are lower.
## 3 Results  
To evaluate the methodology presented in the previous section, we applied all the possible sequences (70 in this case) to the system to constitute the dataset. It has then been divided into two groups: train and test. We performed that selection randomly. In what follows, we only use the training dataset to build the ROMs and the testing dataset to validate them. Whether we applied the regression with the DMD or the MLP, the presented results have been produced with the same methodology, sketched in Figure 12. Section 3.1 presents the results we obtained with the MLP regression and Section 3.2 the results we obtained when using the DMD.  
To avoid the impact of very small solutions in the calculation of relative errors, the absolute error in L2-norm is considered. Sometimes, we will give a percentage to discuss the error. It is defined as the maximum error divided by the maximum displacement at each state.
_Applied_  
_sciences_  
Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults  
**[PERSON] 1,2,3, [PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 4 and [PERSON] 1,2**  
Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China; [EMAIL_ADDRESS] (C.Y.); [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (Z.D.)  
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China  
The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan 430081, China  
Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, China; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]; Tel.: +86-27-6886-2857; Fax: +86-27-6886-2212  
Academic Editors: [PERSON] and [PERSON]  
Received: 7 January 2017; Accepted: 17 April 2017; Published: 19 April 2017  
**Abstract:** To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.  
**Keywords:** blind source separation; regenerated phase-shifted sinusoid-assisted EMD; fault diagnosis