esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To achieve this purpose, for the TA-MCF proposed herein, a multicore structure was used to take advantage of the mode-coupling mechanism in manipulating the light transmission. Meanwhile, only one ring of down-doped cylindrical inclusions was employed in the fiber cladding to form an all solid simplified microstructure cladding, which can also be seen as a low-index trench around the multicore region. It is shown that the dispersion of the fundamental supermode can be effectively managed with the help of this trench. In addition, a broader single-supermode operation region and a flatter-top optical intensity distribution were achieved through the introduction of the low-index trench. After the investigation of the structural parameters' influences on the dispersion of the fundamental supermode, two kinds of TA-MCFs with single-supermode transmission and nearly zero flattened dispersion were designed. For the seven-core TA-MCF, flattened dispersion of \(-0.46\) to \(1.35\,\mathrm{ps/(nm\cdot km)}\) was obtained from \(1.50\) to \(2.04\,\mathrm{\SIUnitSymbolMicro m}\), as well as bending loss as low as \(0.085\) dB/km at 1550 nm with 35-mm bending radius. More significantly, the flattened dispersion TA-MCFs proposed herein have all the advantages of a quasi-single material, with an all solid scheme and simplified structure.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 5 Conclusions  
Based on remote sensing data products, this paper studies the spatiotemporal variation of WUE and its driving factors in different ecological functional areas in the North China Plain from 2001 to 2017. The spatial distribution of WUE and GPP in the North China Plain is similar, with the multiyear average of WUE at 0.74 g C m\({}^{-2}\) y\({}^{-1}\). The variation trend of WUE is mainly affected by the variation trend of GPP, and its area accounts for about 44.38% of the total area of the study area. The change trend of WUE mainly showed a mild decrease and a mild increase, accounting for 73.22% of the total area of the study area; the area with medium-low fluctuation of WUE accounted for the largest proportion, accounting for 59.90% of the total area of the study area. In addition, the multiyear average value of WUE in the ecological functional area is Qin Ling Mountains deciduous forests > Central China loess plateau mixed forests > Mongolian-Manchurian grassland > Ordos Plateau steppe > Changjiang Plain evergreen forests > Huang He Plain mixed forests > Bohai Sea saline meadow, from high to low. The influence of precipitation on WUE is higher than that of temperature. The area of WUE that increased with the increase of precipitation accounted for 23.74% of the area of the North China Plain, and was mainly distributed in the QLMDF, CJPEF and HHPMF ecological functional areas. The above analysis of WUE changes and their driving factors in the ecosystem functional areas of the North China Plain further allows us to understand the WUE changes and their driving factors in different ecosystem functional areas and provides a reference and theoretical basis for better protection and restoration of the carbon and water cycles in ecosystem functional areas.  
Conceptualization, methodology, writing, Y.H.; software, W.C.; investigation, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) (grant number 41930650) and the National Key Research and Development Program of China (grant number 2016 YFB0501404).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 2 Research Methodology  
In this research study, we have conducted a systematic literature review (SLR) with the purpose of analyzing the challenges, limitations, and recent trends in the domain of STSS in a targeted way. Furthermore, we have embraced the philosophy of SLR from references [13; 14; 15] to successfully complete the depth of knowledge for this study. However, the study is further broken down into three phases. Phase (i): describes the planning of this study with the purpose to identify the research questions, describe the rationale of the work, and verify and validate the review of the protocol. Phase (ii): outlines how the information was combined, which studies were chosen, how the data were extracted, and how the inclusion criteria were determined. Phase (iii): covers the writing and validation of a systematic literature review. Moreover, the phases of the systematic review process are also illustrated in Figure 3.
# Human-Robot Collaboration: An Augmented Reality Toolkit for Bi-Directional Interaction
## 5 Augmented Reality Features
### Robot State  
Once the robot hologram has been properly positioned in the AR environment, the virtual joint positions must be updated in real time to trace the movements of the corresponding real twin. To this aim, a script fetching data from ROS by subscribing to the topic /joint_states has been developed. This topic provides the exchange of JointState messages [32], containing information about the position, \(q\), velocity, \(\dot{q}\), and torque, \(\tau\), of the joints. Based on the acquired data, the script updates the robot hologram for each frame through Unity's _Update_() trigger.  
Consider the teaching-by-showing technique for robot programming: while the operator guides the cobot manually along the desired motion path, the data read by joint position transducers are stored and can be played back [31]. As this action could be performed also by an operator without technical knowledge, the teaching-by-showing technique is widespread in industrial field. During the training for the task, it should be useful to inform the operator about the status of the robot joints in terms of the distance for their physical limits (\(q_{min}\) and \(q_{max}\)). In detail, a sphere per virtual joint is displayed in AR environment and aligned to the relative robot joint. As can be seen in Figure 5, the \(i\)-th sphere (with \(i=1,\ldots,7\)) is characterized by variable colors to inform the operator about the joint status:  
* green, if the \(i\)-th joint position is away from the limit, i.e., \[q_{min_{i}}+10^{\circ}<q_{i}<q_{max_{i}}-10^{\circ};\] (4)
* yellow, if the \(i\)-th joint position approaches the limit, i.e., \[q_{min_{i}}+5^{\circ}<q_{i}\leq q_{min_{i}}+10^{\circ}\quad\text{OR}\quad q_ {max_{i}}-10^{\circ}\leq q_{i}<q_{max_{i}}-5^{\circ};\] (5)
* red, if the \(i\)-th joint position is very close or equal to the limit \[q_{min_{i}}\leq q_{i}\leq q_{min_{i}}+5^{\circ}\quad\text{OR}\quad q_{max_{i }}-5^{\circ}\leq q_{i}\leq q_{max_{i}}.\] (6)  
In other words, the color switches from green to yellow if the joint position is less than 10 degrees far from the physical limit, whilst from yellow to red when it is less than 5 degrees from it.  
\begin{table}
\begin{tabular}{l c c} \hline  & **Average** & **Standard Deviation** \\ \hline \(x\) axis [mm] & \(-\)1.094 & 0.475 \\ \(y\) axis [mm] & \(-\)4.279 & 0.949 \\ \(z\) axis [mm] & 2.910 & 2.555 \\ \(x\) axis [deg] & 0.434 & 0.468 \\ \(y\) axis [deg] & \(-\)0.381 & 0.403 \\ \(z\) axis [deg] & \(-\)0.805 & 0.326 \\ \hline \end{tabular}
\end{table}
Table 2: Average and standard deviation error on 25 calibration executions.  
The joints spheres can be activated or deactivated through the _Show/Hide Joint Spheres_ button.
# Evaluation of pH and Optical Properties of Dual Ringe HEDP Irrigating Solution
## 2 Materials and Methods
### pH Measurements  
The pH measurements were conducted using the SevenDirect SD20 pH meter (Metller Toledo, Greifensee, Switzerland). The methodology adhered to the manufacturer's guidelines and standard practices for reliable pH determinations in solutions. Prior to sample analysis, pH electrodes were calibrated with buffer solutions. Following calibration, the pH electrode was carefully immersed into 5 mL of each sample solution, and sufficient stabilization time was allowed. The pH readings were recorded, and the electrode was rinsed with distilled water between measurements to prevent cross-contamination. Calibration with buffer solutions and periodic checks ensured the accuracy of the pH readings. Measurements were performed five times for each solution.
# An Indoor Collaborative Coefficient-Triangle APIT Localization Algorithm
## 4 Simulation and Analysis of Results
### Node Localization Coverage Rate of the Proposed Localization Algorithm  
The node localization coverage rate is another important indicator used to assess the performance of a positioning system.  
Figure 6 shows the curves of node localization coverage rates with the increase in the proportion of anchor nodes in the CCAL, RSSI, IAPIT and APIT algorithms.  
As shown in Figure 6, when the proportion of anchor nodes increased, the node localization coverage rates of the four localization algorithms increased. When the proportion of anchor nodes was certain, the node localization coverage rate of CCAL was the highest, basically remaining at 100%. This is because the CCAL algorithm turns the positioned unknown nodes into anchor nodes, and uses the maximum likelihood method and legality testing, thereby greatly reducing the number of nodes that cannot be positioned when the proportion of anchor nodes is small.  
Figure 7 shows the curves of node localization coverage rates with the increase in the communication radius of the unknown node, while the proportion of anchor nodes remains 20%.  
As shown in Figure 7, the node localization coverage rate of RSSI, APIT and IAPIT clearly increased when the communication radius of the unknown node increased. This is because there are less than three anchor nodes surrounding many unknown nodes when the communication radius is smaller. On the one hand, many nodes cannot be positioned in these three algorithms. On the other hand, the localization coverage rates of the CCAL algorithm change slowly. This is due to the fact that the CCAL algorithm uses the maximum likelihood method to estimate the coordinates of nodes that cannot be positioned, thereby ensuring the node localization coverage rate.  
Figure 6: Curves of localization coverage rates with the proportion of anchor nodes varying.
## 4 Results and Discussions
### Simulation Results  
The following test cases are considered in this section:  
* Test-I: Balanced to unbalanced grid,
* Test-II: Voltage sag,
* Test-III: Phase step change, and
* Test-IV: DC offset.  
Figure 1 shows the comparative simulation for Test-I. In this test, the negative and zero sequence voltage had a sudden step change of \(+0.1\) p.u. and \(+0.05\) p.u., while the positive sequence voltage had a step change of \(-0.2\) p.u. The frequency estimated by the adaptive observer-based techniques converged in 2-cycles, while ANF took 2.5 cycles with significantly higher overshoot. In addition, the positive, negative, and zero sequence amplitudes are estimated within \(\approx\)0.5 cycle by the adaptive observer-based techniques, while it is more than \(\approx\)1 cycle for ANF.  
Voltage sag of \(-0.5\) p.u. in balanced grid is considered in Test-II. Simulation results are given in Figure 2. As shown in Figure 2c, SAO and GNAO converged within 2 cycles, and GAO took 3 cycles, while, for ANF, it is almost 5 cycles. This shows that adaptive observer-based techniques are suitable for fast and accurate estimation of grid frequency in the presence of large voltage sag. In case of positive sequence amplitude estimation, half cycle convergence can be observed for the adaptive observer-based techniques, while it is \(\approx\)1 cycle for the ANF-FLL.  
A sudden jump of \(-45^{\circ}\) phase angle in a balanced grid is considered in Test-III. Simulation results as shown in Figure 3 demonstrate that all the techniques are heavily affected by the sudden change in phase angle. However, adaptive observer-based techniques converged very fast within roughly 45 milliseconds, while ANF took more than 60 milliseconds-nds. In addition, the peak overshoot for ANF was also \(\approx\)+1.5 Hz higher than the adaptive observer-based techniques. This demonstrates the advantage of the adaptive observer-based techniques for challenging scenarios, such as sudden large change in phase angle.  
Due to current transformer saturation, signal conditioning problem, etc., DC offset can often corrupt the measured voltages. This is considered in Test-IV. The considered DC offset values are \(+\)0.05 p.u., \(+\)0.1 p.u., and \(-\)0.05 p.u. in phase a, b, and c, respectively. Results in Figure 3(b) show that ANF is very sensitive to DC offset. The frequency estimation ripple is between 48.2 Hz and 50.8 Hz for ANF. Among the adaptive observer-based techniques, GAO is the most sensitive with estimation ripple between 49 Hz and 50.78 Hz. GNAO and SAO have similar estimation ripple and the amplitude is lower than ANF and GAO.  
By considering the test results as shown in Figures 1-4, it can be claimed that adaptive observer-based techniques have performed better than ANF in almost every situations. As such ANF will not be considered for experimental validation test in Section 4.2. Among adaptive observer-based techniques, GAO under performed compared to GNAO and SAO. It is to be noted here that all the adaptive observer-based techniques have similar dynamic performance.  
Figure 1: Numerical simulation results for Test-I.
## 3 Results and Discussion
### Concrete Compressive Strength and Axial Force
When viewed for a single compressive strength of concrete (fck) (Figure 6b-d), the growth in shear capacity with increasing axial force follows a clear polynomial pattern. This general behaviour contrasts with the linear pattern observed in Figure 5 for all concrete grades, suggesting that for higher values of v, the incremental gain in shear capacity tends to diminish and approach a plateau. In Figure 6a, a consistent increase in shear capacity is observed as v increases for all fck values, though the impact is more pronounced for higher-strength concretes. Concretes with lower fck, such as 20 MPa, exhibit more limited growth in shear capacity compared to stronger concretes, such as 50 MPa, demonstrating that higher-quality concrete amplifies the effect of increased axial load. Figure 6: Normalised axial forces curves \(\times\) Maximum shear for different compressive concrete strength (**a**) Global Trends for all concrete grades, (**b**) Magnified view for fck = 50 MPa, (**c**) Magnified view for fck = 35 MPa, (**d**) Magnified view for fck = 20 MPa. Figure 6b provides a detailed view of the shear capacity behaviour for high-strength concrete (fck = 50 MPa) under different normalised axial loads. The curve follows a polynomial pattern, where shear capacity increases rapidly for higher values of v. This indicates that stronger concretes significantly benefit from increased v, achieving higher levels of shear capacity, particularly at v = 0.8. Figure 6c, which examines concrete with moderate strength (fck = 35 MPa), shows a similar trend but lower absolute shear capacity values than fck = 50 MPa. The increase in shear capacity with rising v is consistent but less pronounced, indicating that intermediate-strength concrete is less sensitive to axial forces than high-strength concrete. In contrast, Figure 6d depicts the behaviour of low-strength concrete (fck = 20 MPa). In this case, the curve is noticeably flatter, indicating that the impact of increasing v on shear capacity is limited. This demonstrates that, for low-strength concretes, the increment in axial load does not result in significant gains in shear capacity, reflecting a reduced contribution of this type of concrete to the overall structural stability of the wall. When analysing the relationship between v and shear capacity across different fck values, it is evident that higher fck concretes exhibit a more pronounced increase in shear capacity as axial load increases. For fck = 50 MPa, the rise in v has a significant impact, leading to elevated shear capacities. In contrast, for fck = 20 MPa, the increase is much more limited, with lower sensitivity to variations in axial load.
# Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification
###### Abstract  
In this paper, we present a novel optical microfluidic cytometry scheme for label-free detection of cells that is based on the self-mixing interferometry (SMI) technique. This device enables simple, fast and accurate detection of the individual cell characteristics and efficient cell type classification. We also propose a novel parameter to classify the cell or particle size. Artificial polystyrene beads and human living cells were measured using this system, and the SMI signal properties were statistically evaluated. The capability of the proposed cytometer for cell type discrimination and size classification has been validated by the measurement results. Our study can provide a very simple technique for cell enumeration and classification without any extra devices such as high-speed camera, photomultiplier and spectrometer. Moreover, the fluorescence staining operation which is necessary in traditional flow cytometry methods is not required either in our system.  
self-mixing interferometry; microfluidic chip; hydrodynamic focusing; flow cytometry; label-free +
Footnote †: journal: Applied Sciences
## 1 Introduction
Several studies using the RJMCMC method on the Weibull distribution have been carried out. First, [PERSON] et al. [52] used the RJMCMC method to implement a Bayesian variable selection for the Weibull regression model for breast cancer survival cases. The study conducted by [PERSON] and [PERSON] [53] also used the RJMCMC method as covariate selection for the Weibull distribution in two datasets, namely Stanford heart transplant and lung cancer survival data. [PERSON] et al. [54] used the RJMCMC method to search for the best configuration of functions for Lidar waveforms. In their library of modeling functions, there are generalized Gaussian, Weibull, Nakagami, and Burr distributions. With their analysis, the Lidar waveform is a combination of these distributions. In our research, we have used the RJMCMC method on the Weibull distribution but from a different perspective, namely identifying multimodal data by determining the number of components, then determining the membership of each mixture component and determining the estimation results. This paper is organized as follows. Section 2 introduces the basic formulation for the Bayesian mixture model and hierarchical model in general. Section 3 describes the location-scale distributions and NG-RJMCMC algorithm. Section 4 describes the transformation from the Weibull distribution to the location-scale distribution, determining the prior distributions, and explaining the move types in the RJMCMC method. Section 5 contains the simulation study. Section 6 provides misspecification cases as the opposite of a simulation study to strengthen the proposed method. Section 7 provides analysis results for applications of enzyme, acidity, and galaxy datasets, as well as our data, namely dengue hemorrhagic fever (DBD) in eastern Surabaya, East Java, Indonesia. The conclusions are given in Section 8.
# Coupling Denoising to Detection for SAR Imagery
## 3 Results
### Implementation Details  
We implemented our unsupervised denoising model following self-Poisson Gaussian [38], however, adopted Gamma noise modeling as in Speckle2 Void [37] to characterize the SAR speckle. Our implementation for detection framework was based on the MMDetection tool box [39] which is developed in PyTorch [40]. Stochastic gradient descent (SGD) Optimizer [41; 42] with momentum of 0.9 was used for optimization. We trained a total of 24 epochs, with an initial learning rate of 0.0025, momentum of 0.9, and weight decay of 0.0001. We experimented with ResNet-50-FPN and ResNet-101-FPN backbones [43; 44]. All evaluations were carried out on a TITAN Xp GPUs with 12G memory.
# Topology Optimisation under Uncertainties with Neural Networks
## 2 Topology Optimisation under Uncertainties
### Stochastic Model Formulation
#### 2.2.1 Adjoint Equation  
For the risk-aware version of Equation (3) with respect to the CVaR parameter \(\beta\), we define the cost \(J^{\varepsilon}_{\beta}(\varphi):\mathbb{R}^{d}\to\mathbb{R}\) by  
\[J^{\varepsilon}_{\beta}(\varphi)=\text{CVaR}_{\beta}\Big{[}\int_{\Gamma_{g}}g \cdot u\,\mathrm{d}\mu\Big{]}+\gamma E^{\varepsilon}(\varphi),\quad\gamma>0. \tag{7}\]In the special case \(\beta=0\), the CVaR is nothing else than the mean, i.e.,  
\[J_{0}^{e}(\varphi)=\mathbb{E}\Big{[}\int_{\Gamma_{g}}g\cdot u\,\mathrm{d}\mu \Big{]}+\gamma E^{e}(\varphi).\]  
This results in the _stochastic minimisation problem_ analogous to Equation (4) given by  
minimise  
\[J_{\beta}^{e}(\varphi)\]  
over  
\[\varphi\in H^{1}(D)\]  
(8)  
s.t. Equation (2) holds a.s., \(0\leq\varphi(x)\leq 1\) for all \(x\in D\) and \(\int_{D}\varphi\,\mathrm{d}\mu=m|D|\).  
Following [2], the CVaR can be approximated in terms of the plus function. The solution of Equation (8) can hence be rewritten as  
\[\min_{\varphi\in H^{1}(D)}J_{\beta}^{e}(\varphi)=\min_{\varphi\in H^{1}(D),t \geq 0}\Big{(}t+\frac{1}{1-\beta}\mathbb{E}\Big{[}\Big{(}\int_{\Gamma_{g}}g \cdot u\,\mathrm{d}\mu\Big{)}_{+}\Big{]}+\gamma E^{e}(\varphi)\Big{)}. \tag{9}\]  
An obvious approach to solve this optimisation problem is a Monte Carlo simulation, i.e., for each iteration step \(n\in\mathbb{N}\) with evaluation of \(u_{n}\), state Equation (1) have to be solved for different parameter realisations. The associated adjoint problem to Equation (9) reads  
\[\int_{D}\sigma(\varphi,p)\mathcal{E}(v_{p})\,\mathrm{d}\mu=\begin{cases}0,& \text{if }\int_{\Gamma_{g}}gu\,\mathrm{d}\mu-t\leq 0\\ \int_{\Gamma_{g}}(1-\beta)^{-1}gu\,\mathrm{d}\mu,&\text{else}\end{cases}\quad \text{a.s.} \tag{10}\]  
Consequently, the solution of Equation (10) is given by  
\[p=\begin{cases}0,&\text{if }\int_{\Gamma_{g}}gu\,\mathrm{d}\mu-t\leq 0\\ (1-\beta)^{-1}u,&\text{else}\end{cases}\quad\text{a.s.}\]
# Intelligent Stuck Pipe Type Recognition Using Digital Twins and Knowledge Graph Model
## Materials and Methods
### Recognition System of Stuck Pipe Type Knowledge Map Based on Digital Twin
#### 2.3.2 Recognition System of Stuck Pipe Type Based on Knowledge Graph  
The intelligent recognition system of stuck pipe type was established by constructing a question-answering system, and the technical principle is shown in Figure 8. The physical model in the previous section, the SAX analysis model of the data trend, and the statistical query of some information such as the log section of the case well were used to describe the incident characteristics of the case well, which then were automatically converted into the query commands of the knowledge graph through the software. After that, based on these characteristics, we used the software to provide some questions, which were made to locate the corresponding concepts and entities in the knowledge graph. In other words, we can search for whether there are relevant stuck pipe types in the knowledge graph, thus achieving the identification of stuck pipe types.  
Figure 7: Knowledge graph of stuck pipe types based on digital twins.  
For example, if the degree of stuck pipe at the pulling out exceeds 30%, the back reaming torque and the back reaming hookload gradually increase, the reaming torque and the reaming hookload gradually decrease, and the standpipe pressure increases during the circulation process. The above features can be input into the graph for identification to achieve a matched stuck pipe type, that is cutting-bed stuck pipe.