esa-sceva/satcom-chunk-collection huggingface dataset:
# T-Splines for Isogeometric Analysis of the Large Deformation of Elastoplastic Kirchhoff-Love Shells
## 2 T-Splines
### Bezier Extraction  
The Bezier extraction operator is used to convert T-spline elements into Bezier elements, and performs canonical treatment of T-junctions, which are similar to 'hanging nodes' and cannot be allowed in the mesh of the traditional finite element method. Figure 5a shows a cubic T-mesh, where the elements with red borders cannot achieve internal C\({}^{\infty}\) continuity due to the T-junctions (indicated by red circles). Through the segmentation of all the elements influenced by T-junctions, Bezier extraction delimits areas in which all element T-spline basis functions are C\({}^{\infty}\). Therefore, these delimited polygons are called Bezier elements, as shown in Figure 5b. In addition, from the perspective of programming, the majority of classical finite element procedures, including parts of shell kinematics and nonlinear constitutive laws, could be easily reused in isogeometric analysis with the Bezier extraction method. To simplify the implementation of the visualization pipeline, only the Bezier mesh rather than T-mesh is plotted with the obtained results in this paper. The details of Bezier extraction are presented in [47,48].  
Figure 4: Unstructured cubic T-mesh (**a**) and T-spline surfaces (**b**).  
Figure 3: Structured cubic T-mesh (**a**) and T-spline surfaces (**b**).  
The mathematical basis of Bezier extraction is that over each element \(e\) of Bezier mesh, the localized B-spline basis function \(N_{a}^{e}\) in Equation (1) can be expressed by coefficients \(C_{a,b}^{e}\) and the same-degree Bernstein basis function \(B_{b}(\xi)\), which is defined on the parameter field \(\Omega\in[-1,1]^{2}\). The linear transformation over element \(e\) can be shown as  
\[N_{a}^{e}=\sum_{b=1}^{(p+1)^{2}}C_{a,b}^{e}B_{b}(\xi),\xi\in\Omega, \tag{2}\]  
where \(p\) denotes the degree of the basis functions; for cubic T-spline surfaces, \(p=3\). In matrix-vector form, Equation (2) is written as  
\[\mathbf{N}^{e}=\mathbf{C}^{e}\mathbf{B}. \tag{3}\]  
Thus, the blending function vector \(\mathbf{R}^{e}\) can be expressed as  
\[\mathbf{R}^{e}=\frac{\mathbf{w}^{e}\mathbf{C}^{e}\mathbf{B}}{\langle\mathbf{W }_{\mathbf{b}},\mathbf{B}\rangle},\mathbf{W}_{\mathbf{b}}=(\mathbf{C}^{e})^{T }\cdot\mathbf{W}^{e}, \tag{4}\]  
where \(\mathbf{W}^{e}=diag(\mathbf{w}^{e})\); \(\mathbf{w}^{e}=\{w_{a}^{e}\}_{a=1}^{n_{e}}\); and the operator \(\langle\cdot,\cdot\rangle\) is defined as the dot product of vectors. The geometric map in the physical space can be expressed as  
\[\mathbf{x}^{e}=(\mathbf{P}^{e})^{T}\mathbf{R}^{e}. \tag{5}\]  
Naturally, we can acquire the displacement field:  
\[\mathbf{u}^{e}=(\mathbf{U}^{e})^{T}\mathbf{R}^{e}, \tag{6}\]  
where \(\mathbf{U}^{e}\) is the displacement vector of the control points for the element \(e\). The computation of large deformation KL shells needs to evaluate the first and second derivatives of T-spline basis functions based on Bezier extraction. The derivatives of Equation (4) are presented in Appendix B.
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 3 Methodology
### Random Forest Classifier  
Random forest (RF) is an ensemble of machine learning algorithms consisting of large number of decision tree classifiers where each tree is constructed using a different subset of the training set. In its architecture, the RF classifier fits a number of decision tree classifiers on various subsamples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting [51]. When compared to other classical machine learning algorithms, the random forest classifier has demonstrated its ability to yield high quality classification mapping with fast computation time [52; 53]. The tree depth and minimum sample size are the two most important parameters in the RF classifier.  
Three different scenarios were tested using the RF classifier. First, the RF classifier was modeled using SAR data only (one for PC values and another for the WT coefficients). Second, NDVI optical data was tested in another RF classifier. Finally, we combined SAR and optical data in a third RF classifier.  
Using SAR data only, the RF classifier was applied for the PC variables and the WT coefficients separately in two different classifiers denoted later by PC-RF and WT-RF respectively. First, each RF classifier (PC-RF and WT-RF) was trained using all variables of each method (328 values for the PC-RF and 256 coefficients for the WT-RF). This step gives the importance of each variable which is a number between 0 and 1 evaluating the contribution of the variable in the classification. The importance of the variables was then analyzed and a threshold value of the importance was determined to select the significant variables only. This step allows reducing the number of variables for each classifier while retaining good accuracy. Then, both RF classifiers were regenerated using only important variables for each method. In all tested RF classifiers, the number of trees was set to 500. This number is fixed by increasing the number of trees until the accuracy of the classification converges. The tree depth was kept at its default value which is the square root of the number of variables available. In order to train and validate the model on two independent datasets, we randomly split the database into 50% for training and 50% for validation [23; 54].  
Using optical data only, the 17 PC values obtained from the NDVI data are implemented in a random forest classifier later denoted by NDVI-RF. As done for SAR data, important variables were then retained based on a threshold value of a variable's importance, and the RF classifier was reestablished using these variables only.  
Finally, a RF classifier was modeled using both SAR and optical data. From the two SAR classifiers (PC-RF and WT-RF) the PC-RF was chosen since its results show slightly better performance. Therefore, the important variables obtained in the PC-RF classifier were combined with the important variables obtained in the NDVI-RF in a new RF classifier.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## 8 Mutual Information in the Fixed and Adaptive Codebooks  
How do we find the change in mutual information provided by the adaptive codebook and the fixed codebook? The adaptive codebook relies on long-term prediction to model the vocal tract excitation due to the speaker pitch. As such, an approach similar to that used for the short-term or linear predictor is possible. The fixed codebook contribution in terms of bits/sample is less direct. We could attempt to estimate the codebook complexity, or we could simply use the number of bits/sample used to transmit the fixed codebook excitation. We elaborate on each of these in the following subsections.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 5 Comparison of The Three Types of Sensor Networks
### Mobility/Geographic-Coverage  
_Ranking:_ VSN \(>\) CSN \(>\) SSN  
_Reasons:_  
The mobility of the carrier enables a sensor node to cover sufficient large geographic areas within a short period of time. Higher spatial resolution of the sensed data can be achieved and fewer number of sensor nodes are required compared with systems using stationary carriers.  
_Explanation:_  
The sensor nodes carried by the public transportations in VSN systems are with the highest mobility among the three types of sensor networks. Following is the sensor nodes carried by the public users in CSN systems because the users travel much slower than the vehicles and the users spend most of time indoor or inside cars [114]. The stationary sensor nodes in SSN systems are with the lowest or zero mobility. Intuitively, the geographic coverage of a sensor node is proportional to the mobility of the carrier.
# Multi-Feature Guided Low-Light Image Enhancement
## 4 Experiment and Result Analysis
### Result Analysis
#### 4.2.1 Comparison with Classic Methods  
We compare the proposed method with the current mainstream traditional image enhancement methods and also state-of-the-art methods. In order to better compare the visual quality, considering the influence of noise, as some methods do not consider image denoising, we first use the BM3D method to denoise the image, and then input the image into the method that does not take denoising into account. Table 1 shows the quantitative comparison results of the classic image processing methods and the model we proposed on the LOL dataset. Our experimental results are marked in italics. Data can prove that our proposed method is significantly better than other traditional image denoising methods in the table, in terms of quality evaluation.  
Figure 3 shows an example of the results of specific low-light images generated in each method. Through the comparison, it can be clearly seen that the image enhanced by our proposed model has a better effect.  
In addition to the evaluation of image quality, the speed of image processing is also an important indicator to test the level of the model. In order to verify this standard, we used 20 images with a resolution of 1024 \(\times\) 768 for time testing. Under the premise of image quality assurance, the average time of our proposed method to process an image of the above size was 0.42 s. The time taken by each method to process an image of the same size is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PSNR** & **SSIM** \\ \hline HE & 16.52 & 0.62 \\ MSR & 13.01 & 0.43 \\ SRIE & 11.54 & 0.47 \\ Retinex-Net & 16.35 & 0.51 \\ LIME & 16.28 & 0.53 \\ MF & 15.35 & 0.60 \\ NPE & 13.25 & 0.45 \\ OURS & _18.89_ & _0.75_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison table of results of each method.  
Figure 3: Comparison of image processing results of each model. (**a**) Low-light input image; (**b**–**h**) the results obtained by the current traditional method; (**i**) the results of the model proposed in this paper; (**j**) the real image under normal illumination.  
After comparison, it can be found that our model is more efficient than traditional image enhancement methods on the basis of ensuring quality.
# The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis
## 4 Results and Discussion
### The Evolution of Healthcare Delivery
These technologies not only improve treatment adherence but also \"assist in the self-monitoring of disease signs and symptoms\" (E2), which is vital in the management of chronic conditions. * Patient empowerment, education, and decision making: The concept of \"empowerment\" is crucial in the era of I5.0. \"Holding each individual accountable for their own physical and mental health\" (E1) is a step toward autonomy and informed decision making. Patient education is paramount, and access to specialized information is necessary for active and conscious participation. However, it is imperative to have \"the accompaniment of a specialized professional\" (E5) to ensure the correct interpretation of information. * Challenges and opportunities in implementation: Transitioning to a proactive, patient-centered health model presents challenges. \"But we can't always control what patients will understand on the other side\" (E4), highlighting the variability in patient comprehension of information. On the other hand, I5.0 offers a significant opportunity to improve the healthcare system's efficiency and reduce costs, \"allowing for an improvement in quality of life and at the same time substantially reducing the costs incurred by medical assistance\" (E9). * Toward an integrated healthcare system: Developing an integrated and personalized healthcare system is a goal of I5.0. It is increasingly \"perceptible a desire and beginning of the realization of a greater focus on preventive medicine\" (E8). However, this trend is still in its infancy and somewhat \"disorganized,\" often driven by technological \"hypes\" such as wearable monitoring technologies rather than by comprehensive health policies. To solidify this shift, \"healthcare services need to be organized in a way that promotes more personalized care, greater interaction with healthcare professionals, and access to quality health information\" (E8). The establishment of comprehensive health policies and societal changes are necessary to contribute to these paradigm shifts. \"The integration of I5.0 principles empower patients with tools, information, and all the support to make their own decisions\" (E9), shaping a future where the patient's role is more significant than ever. This evolution is seen as essential for the advancement of healthcare in the future, with a consensus among professionals that \"it is one of the most promising aspects of the application in health\" (E10). To realize the full potential of this promise, however, \"there is a need for the development of health policies and societal changes that also contribute to these paradigm changes\" (E8).
## 2 The Relaxed Flexible Flow Shop Problem Model with Sequence-Dependent Setup Times (RFFSP) Methods  
The relaxed flexible flow shop problem (RFFSP) with sequence-dependent setup times consists of a set \(N=\{1,\,2,\,\ldots,\,n\}\) of \(n\) jobs that are processed by a set \(M=\{1,\,2,\,\ldots,\,m\}\) of \(m\) stages in serial. Each \(k\)-th stage, \(k\in M\), has a set \(M_{k}=\{m_{1},\,m_{2},\,\ldots,\,m_{k}\}\) of \(m_{k}\) identical parallel machines, where each \(j\)-th job, \(j\in N\), in \(k\)-th stage, \(k\in M\), can be processed in any \(i\)-th machine, \(i\in m_{k}\). Each job requires \(m\) operations to be completed. One operation \(o_{ijk}\) of the \(j\)-th job is processed in the \(k\)-th stage using the \(i\)-th machine, taken from the set \(M_{k}\) of parallel machines, where each machine can only process one job at a time. Also, an operation being processed cannot be interrupted until it is finished. Each of the \(n\) jobs is first processed in stage 1, then in stage 2, and so on, indicating that a precedence order to process the job's operations in each stage exists. The processing time of the \(j\)-th job in the \(k\)-th stage is defined as \(p_{jk}\). Furthermore, each machine needs one setup time, that is, a time to be ready after processing one operation and starting the next. If the \(l\)-th and the \(j\)-th jobs are sequentially processed in the \(k\)-th stage by the \(i\)-th machine, this machine's setup time is defined as \(ST_{jlk}\). If the machines in one stage are identical, the setup time is defined as \(ST_{jlk}\). In particular, if \(j\) is the first job assigned to the \(i\)-th machine in the \(k\)-th stage, its initial setup time is \(ST_{0 jk}\), and if \(l\) is its last job assigned, its setup time is \(ST_{10k}\).  
One example of an RFFSP is described in [22] for a labeling company. The jobs are processed in seven stages (printing, labeling, lamination, die-cut, inject, and scan EPC). Each stage has several identical machines to execute its operations, and the job must be processed in sequence, starting with the printing stage. When a machine finishes processing a job, it needs to be cleaned, adjusted, and revised to verify if it has enough supplies to perform the next job.
## 5 Conclusions and Future Work  
This paper identifies the significance of improving the stability of a low-cost GPS receiver. An improved low-cost receiver has a potential to provide location information as precisely and accurately as high-cost receivers. Thus, the spatial coverage could be increased by deploying a number of low-cost receivers. This leads to a better determination of location. The applications of a low-cost GPS receiver with improved stability are not only limited to positioning but the receiver could also be used for weather-related studies.  
Figure 8: Modified Allan deviation for the \(Refsys_{ubx}\) values. The red curve is for before the application of the correction and the blue curve is for after the correction has been applied.  
To achieve this, the paper proposed a machine-learning algorithm to predict the time differences between a low-cost GPS receiver and a higher-end receiver. The predicted differences were then used to correct the errors associated with the low-cost receiver. This then improved the stability of the receiver. Results show that for an averaging time of 1 day, the stability is improved by almost \(10^{3}\) times.  
Moreover, the proposed methodology can extend its application in remote calibration of atomic clocks. Traditionally, the atomic clocks are transported to the calibration center, but with the proposed methodology, the clock can be calibrated remotely via GPS satellites. This saves both cost and time for the companies that need the calibration services.  
In future work, the Ublox data, after being corrected, will be used in deriving location information and an improvement in the accuracy of the navigation will be shown. Also, the prediction of the differences will be done for different time steps in the future (say 6 h advance in time) and the effects of it will be studied.  
Conceptualization, S.M.; methodology, S.S. and S.M.; software, S.S.; validation, S.M., Y.S.M. and Y.C.T.; formal analysis, S.M. and Y.S.M.; investigation, S.M., S.S. and Y.C.T.; resources, Y.S.M.; writing--original draft preparation, S.M. and S.S.; writing--review and editing, S.M., Y.S.M. and Y.C.T.; visualization, S.M. and Y.S.M.; supervision, S.M. and Y.S.M.; project administration, S.M.; funding acquisition, S.M. and Y.S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Agency for Science, Technology and Research (A\"STAR) under Project No. 222D800033 [Career Development Fund], and the National Research Foundation, Singapore and A\"STAR under its Quantum Engineering Programme (NRF2021-QEP2-01-P03).  
The data collected for this research involve the time and frequency data from the cesium clocks of NMC, which are not allowed to be shared publicly.  
The authors declare no conflicts of interest.
###### Abstract
The use of multi-constellation GNSS augmented with precise atmospheric corrections could reduce the PPP-RTK convergence time to several seconds or even make it instantaneous [12]. However, current research mainly focuses on the static scenario, such as in the case of [23; 24], who evaluated the performance of commercial PPP-RTK services in static scenarios. Although the positioning accuracy of PPP-RTK has also been evaluated in kinematic scenarios for vehicles [25] and inland waterway vessels [26], those studies only used GPS and Galileo without analyzing the improvements in terms of convergence time and positioning accuracy with the addition of BDS. Indeed, BDS can significantly increase the number of available satellites and improve geometric configurations, which is particularly noticeable in the Asia-Pacific region [27]. Other researchers have analyzed the PPP-RTK positioning performance of GPS/Galileo/BDS [28; 29]; however, the benefits derived from the precise atmospheric correction products still need further refinement. Additionally, RTK is still the method commonly used for vehicle navigation [30], and there is a lack of real experimental analysis on whether PPP-RTK can replace RTK for high-precision positioning in kinematic scenarios. In summary, the goal of this research was to implement a high-precision GPS/Galileo/BDS PPP-RTK method based on undifferenced GNSS observations with a fast convergence of a few seconds as well as centimeter-level positioning accuracy to support kinematic vehicle navigation, similar to RTK but without requiring dense ground reference station networks [31]. In the manuscript, PPP Float represents floating precision point positioning. PPP-AR represents OSB correction based on PPP Float, and PPP-RTK refers to atmospheric constraints based on PPP-AR. Performance with and without atmospheric augmentation products was evaluated. In the following section, the mathematical model of PPP-RTK we used is described in detail. This is followed by the analysis of the accuracy and convergence time for a set of static positioning and vehicle kinematic positioning experiments in the urban area of Wuhan. Finally, the conclusions are drawn.
## 1 Introduction
established a thermodynamic model of the working process in a water-injected twin-screw water vapor compressor and validated its accuracy with the help of the experimental recorded \(p\)-\(V\) indicator diagrams [21]. [PERSON] et al. developed a water-injected twin-screw compressor applied in a mechanical vapor compression desalination system, and the effect of important parameters on the compressor performance was experimentally investigated [22]. [PERSON] et al. developed an oil-free water-lubricated twin-screw air compressor and experimentally investigated the compressor's performance under the influence of rotating speed, discharge pressure, water injection flowrate, and water injection mode in order to search for optimal operating parameters [23]. [PERSON] et al. established a thermodynamic model of the working process of their developed twin-screw air compressor with water injection used for PEM fuel cell systems and carried out experiments to verify the model [24]. [PERSON] et al. built an experimental platform to monitor the temperature and relative humidity of the air upon suction and exhaust of a compressor under a constant pressure and water injection rate and at variable compressor operating speeds in order to develop effective humidification and cooling schemes for fuel cell stacks as well as cooling for compressors [25]. [PERSON] et al. studied the lubrication characteristics of a water-lubricated bearing in a single-screw compressor, verifying the reasonability of the proposed model and simulation results with the help of an axial thrust bearing test rig developed by the authors [26]. To sum up, dry twin-screw air compressors are suitable for application in fuel cell systems, and cooling jackets often need to be equipped to carry away the heat generated during operation. However, the relevant research is mainly focused on liquid-injected screw air compressors, and there is currently a lack of specific research on the effectiveness of cooling jackets. Therefore, to fill this gap, a dry screw air compressor used to provide clean air for fuel cell systems was selected as the research object, and an experimental platform was built in this study in order to investigate the effect of a cooling jacket on the performance of the dry twin-screw air compressor under different rotating speeds and discharge pressures. Considering the strong correlation between water and PEMFCs, its suitability for the cooling jacket of dry screw compressors is incomparable to other liquids. Therefore, this study only adopted water as the working medium for the cooling jacket.