esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Speed up Single-Frequency PPP Convergence with Quad-Constellation GNSS and GIM
## 5 Conclusions  
A significant challenge for the single-frequency PPP to achieve high-accuracy position solutions is its long convergence time of up to a few hours. In this study, an approach is proposed to speed up the single-frequency PPP convergence by combing the quad-constellation GNSS and GIM data. In this approach, the ionospheric delay and code hardware delay are estimated together as a single unknown parameter in an un-combined PPP observation model. The slant ionospheric delay and code hardware delay acquired from the GIM and multi-GNSS DCB products are introduced to the observation model as pseudo-observables with a time varying weight.  
Datasets collected at twelve MGEX stations on seven consecutive days are exploited to evaluate the proposed single-frequency PPP approach. Different agencies' GIM products and multi-GNSS DCB products are compared and evaluated using GNSS measurements. Results indicate that the CODE GIM products and the DLR DCB products exhibit higher accuracy. A total of 332 datasets are utilized to assess the convergence performance of our proposed approach. Numerical results indicate that the quad-constellation GNSS plus GIM PPP reduces the convergence time by 56%, 47%, and 41% in the east, north, and up directions over the GPS-only PPP, respectively. With the inclusion of the GIM data, the single-frequency PPP can considerably reduce the positioning errors in the early stage of the filter processing. By using a time varying weight scheme, the final positioning accuracy is not affected by the GIM data quality. As a result, the horizontal position accuracy of several centimeters can be achieved using the single-frequency measurements collected by geodetic-type receivers.  
The financial supports from National Key Research and Development Program of China (No. 2016 YFB0501803) and National Natural Science Foundation of China (No. 41674039) are greatly appreciated. Contribution of data from IGS is also appreciated.  
[PERSON] conceived of and designed the experiments. [PERSON] performed the experiments and analyzed the data. [PERSON] wrote the paper. [PERSON] and [PERSON] reviewed and revised the paper.  
The authors declare no conflict of interest.
# Research on Shear Behavior of Sand-Structure Interface Based on Monotonic and Cyclic Tests
## 6 Conclusions  
In this study, a series of tests were conducted on the monotonic and cyclic shear of the sand-steel interface. The mechanical properties of the shear behavior of the interface between the sand and structure are analyzed. The conclusions are as follows:  
1. In the monotonic shear test, when the interface is relatively smooth, the shear stress-shear displacement curves of loose sand and dense sand both show strain hardening characteristics. When the interface is rough, there will be strain softening for dense sand. The initial shear stiffness of the sand-steel interface increases with the increase in normal stress, interface roughness, or the relative density of sand.
2. Both loose sand and dense sand generally show volume shrinkage during the cycle. The initial loading process is similar to the case of static loading. In the later stages  
Figure 13: Final unloading stiffness of different stress level amplitudes. (**a**) \(D_{r}=0.3\). (**b**) \(D_{r}=0.9\).  
of dynamic loading, the shear shrinkage per cycle is relatively small and continues to develop. Additionally, as the normal stress increases, the accumulated normal deformation during dynamic loading decreases.
3. When the stress level amplitude reaches 90%, residual deformation will occur during the cyclic shearing process. For loose sand in particular, the maximum deformation exceeds \(u_{sr}/u_{sy}\) of 12%. However, when the stress level amplitude is less than or equal to 60%, there is almost no residual shear deformation.
4. The unloading stiffness of the interface between the sand and the structure is always greater than the initial loading stiffness. Additionally, with the increase in the number of cycles, the loading stiffness continues to increase, and it may eventually approach the unloading stiffness. In addition, the increase in normal stress and interface roughness can improve the loading and unloading stiffness.  
Conceptualization, P.Z. and K.F.; methodology, P.Z. and K.F.; formal analysis, P.Z. and K.F.; investigation, P.Z. and S.D.; resources, P.Z. and K.F.; data curation, P.Z. and S.D.; writing--original draft preparation, P.Z. and K.F.; writing--review and editing, P.Z. and K.F.; funding acquisition, K.F. 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 (Grant Number 51778557).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors are grateful for the technical support provided by the School of Building Science and Engineering, Yangzhou University, and also want to thank Applied Sciences Editorial Office for their patient editing and support during the writing of this manuscript.  
The authors declare no conflict of interest.
## 5 Results and Findings
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model** & **Group-1** & **Group-2** & **Group-3** & **Filter** & **Training** & **Validation** & **Test** & **Space** & **Time (s)** \\
**Name** & **Filter Num** & **Filter Num** & **Filter Num** & **Size** & & & & & \\ \hline model 16 & 16 & 32 & 64 & (3,3) & A: 0.795 & A: 0.847 & A: 0.836 & 30,500 & 0.0005 \\  & & & & L: 0.38 & L: 0.29 & L: 0.3 & & & \\ \hline model 17 & 16 & 32 & 64 & (5.5) & A: 0.809 & A: 0.847 & A: 0.829 & 66,340 & 0.0008 \\  & & & & L: 0.34 & L: 0.27 & L: 0.28 & & & \\ \hline model 23 & 16 & 64 & 32 & (5.5) & A: 0.80 & A: 0.86 & A: 0.85 & 66,340 & 0.0008 \\  & & & & L: 0.37 & L: 0.27 & L: 0.27 & & & \\ \hline model 26 & 16 & 64 & 64 & (5.5) & A: 0.818 & A: 0.85 & A: 0.84 & 82,340 & 0.0013 \\  & & & & L: 0.34 & L: 0.26 & L: 0.27 & & & \\ \hline model 44 & 32 & 32 & 64 & (5.5) & A: 0.84 & A: 0.86 & A: 0.848 & 74,340 & 0.0008 \\  & & & & L: 0.31 & L: 0.25 & L: 0.26 & & & \\ \hline model 50 & 32 & 64 & 32 & (5.5) & A: 0.81 & A: 0.85 & A: 0.829 & 74,340 & 0.0010 \\  & & & & L: 0.36 & L: 0.26 & L: 0.28 & & & \\ \hline model 52 & 32 & 64 & 64 & (3.3) & A: 0.82 & A: 0.82 & A: 0.79 & 39,140 & 0.0007 \\  & & & & L: 0.32 & L: 0.31 & L: 0.34 & & & \\ \hline model 62 & 64 & 16 & 64 & (5.5) & A: 0.80 & A: 0.84 & A: 0.836 & 82,340 & 0.0014 \\  & & & & L: 0.35 & L: 0.27 & L: 0.28 & & & \\ \hline model 68 & 64 & 32 & 32 & (5.5) & A: 0.81 & A: 0.84 & A: 0.836 & 74,340 & 0.0010 \\  & & & & L: 0.35 & L: 0.28 & L: 0.29 & & & \\ \hline model 70 & 64 & 32 & 64 & (3,3) & A: 0.83 & A: 0.836 & A: 0.837 & 39,140 & 0.0007 \\  & & & & & L: 0.34 & L: 0.32 & L: 0.31 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Top 10 models performance comparison for the Accuracy (A), Loss (L), Space (in bytes), and Time (in seconds). Further analysis was performed based on three parameters such as accuracy, light weightness, and speed to identify the best model in real time. The scenario is shown in Figure 6 as a ternary diagram. Min-max scaling was performed on the accuracy, space, and time at first. All values are rounded up to two decimal places. Lightweightness and speed were calculated by subtracting scaled space and time from 1, respectively. Considering the Ternary diagrams, it is clear that model 17 outperforms all other models based on three parameters. In the ternary graph, the value which is closer to the center of the triangle is considered to be the best one. Model 17 lays close to the center of the ternary diagrams while comparing with other models which reveals it as the most suitable model for the food calorie estimation. Figure 5: Line graph for top 10 models performance based on accuracy and time. Figure 6: Ternary diagrams for top 10 models performance based on accuracy, lightweightness, and speed.
## 2 Materials and Methods  
### System Architecture  
The proposed CDSS architecture is modular and layered, designed to handle the end-to-end flow from data acquisition to clinical decision support in a privacy-preserving, scalable, and low-latency manner. Figure 1 depicts the overall system, which we summarize as follows. The system ingests data from multiple hospital sources, processes it through successive layers including local ML/LLM analysis and federated model updates, and ultimately provides decision outputs to clinicians via a user interface. Table 1 summarizes the architectural modules and their respective roles. This overview helps contextualize the function of each layer before we examine the system architecture and evaluation. Implementation specifics--covering the underlying Technology Stack--are provided in Supplementary Section S1.  
Figure 1 highlights how low-latency ingestion feeds the LLM and FL layers before recommendations surface in the User Interface (UI).  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Component** & **Function** \\ \hline Data Ingestion Layer & Collect and preprocess data with clinically acceptable latency from diverse sources (EHRs, clinical notes, IoT devices, etc.) \\ \hline FL & Train AI models on decentralized data at each site to preserve privacy; aggregate model updates into a global model \\ \hline LLM Layer & Interpret unstructured text (e.g., clinical notes) and perform language tasks like question-answering or summarization \\ \hline Big Data Management & Store and manage large volumes of structured and unstructured data; enable efficient retrieval and parallel processing \\ \hline DSE & Combine observations from data (structured and unstructured) to generate clinical recommendations (e.g., triage level, diagnostic suggestions) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key System Components and Their Functions in the Proposed CDSS Architecture.  
Figure 1: High-level data flow from ingestion through federated training to clinician UI.
## 3 Models
### The LCPSO Algorithm  
#### 3.4.1 Adding an Anti-Collision Behavior to PSO: CPSO  
The objective here is to determine a potential field \(P_{i}(t)\), inspired by PSO potential (6), that meets Definition 1. To do this, Ref. [20] introduced a variation of PSO, called CPSO, which was demonstrated experimentally with interesting results [20; 37]. To derive the equations of CPSO, we define the following unbounded repulsive potential [39]:  
\[P^{r}\big{(}d_{i,j}(t)\big{)}=\log\big{(}d_{i,j}(t)\big{)}. \tag{8}\]  
This potential verifies that \(P^{r}(d_{i,j}(t))\to-\infty\) when \(d_{i,j}(t)\to 0\). If we sum the attractive potential (7) and the repulsive one (8), we obtain a potential that meets Definition 1.  
Those models of potentials are not unique. The state of the art provides good examples of possible potential functions for flocking algorithms [38; 39]. However, for the analysis at equilibrium, we need the attractive potential defined in (7) for two reasons. First, we keep the links with the original PSO algorithm. Second, this particular model is necessary for some theorems, Theorem 4 in particular, which is important to determine the characteristics of our swarm formation.  
The repulsive potential is then added to the PSO equation:  
\[\mathbf{v}_{i}(t+\Delta t)= c_{0}\mathbf{v}_{i}(t)+c_{1}\alpha_{i1}(t)\Big{(}\mathbf{p}_{i}^{b }(t)-\mathbf{p}_{i}(t)\Big{)} \tag{9}\] \[+c_{2}\alpha_{i2}(t)\Big{(}\mathbf{p}^{s}(t)-\mathbf{p}_{i}(t) \Big{)}-c_{3}\sum_{j=1,j\
eq i}^{N}\
abla_{\mathbf{p}_{i}}P^{r}\big{(}d_{i,j}( t)\big{)},\]  
where \(c_{3}\) is the constant repulsive weight between trackers \(i\) and \(j\).
## 5 Quality Attribute Outcomes
### Data Integrity, Trustworthiness, Transparency, and Accountability
#### 5.2.1 Data Immutability and Trustworthiness  
In blockchain systems, the immutability of data builds a foundation of trust, as it assures users that recorded information is permanent and cannot be tampered with. For instance, in the SmartWalk project described by [PERSON] et al. (2017), a blockchain is used to log physical activity data for older adults. This application enables healthcare providers to rely on the authenticity of the data when creating personalized health interventions, as the blockchain ensures data integrity.
## 4 Results and Discussion  
We evaluated the proposed methodology based on the system's accuracy in correctly identifying the RS sentences. We extracted patterns as well as salient terms from a published hypertension guideline annotated by a physician [33]. The guideline consists of 78 recommendation sentences out of 278 sentences. The guideline sentences were annotated as Condition-Action (CA), Condition Consequences (CC), Action (A), and Not Applicable (NA). However, we considered CA, CC, and A tagged as recommendation sentences while NA tagged sentences as NRS. For method evaluation, we used 70% of sentences for pattern extraction and 30% for the testing. Furthermore, we evaluated the extracted patterns on Rhinosinusitis [37] and chapter 4 of asthma guideline [38] to check the generalization and accuracy of the extracted patterns. The details of datasets are given in Table 8. The evaluation detail of each method is described in the following subsections.
## 4 Results
### Training Configuration  
During the training phase, a loss function was established to update the parameters within the mode. This loss function encompassed mean squared error (\(MSE\)) loss, L1 weight regularization, and L2 weight regularization. By minimizing the loss function, the model's parameters were updated using the backpropagation algorithm and an optimizer, gradually refining the model and enhancing its predictive performance. The loss function is defined as follows:  
\[Loss=MSE+\frac{{{\lambda_{1}}}}{{n}}{\left\|{{w}}\right\|_{1}}+\frac{{{\lambda_ {2}}}}{{n}}{\left\|{{w}}\right\|_{2}} \tag{27}\]  
The \(MSE\) loss measures the average squared difference between the model's predicted values and the true values, serving as an indicator of the model's fitting ability and predictive accuracy. The L1 and L2 weight regularization terms are employed to control the complexity of the model and prevent overfitting. \({{\lambda_{1}}}\) and \({{\lambda_{2}}}\) are regularization parameters, while \(w\) represents the weight coefficients, helping to balance the importance of different components within the loss function.  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}}{\left({{y_{i}}-{\hat{y_{i}}}}\right)^{2}} \tag{28}\]  
where \(y_{i}\) represents the actual photovoltaic power data, \({\hat{y_{i}}}\) denotes the predicted photovoltaic power data, and \(n\) represents the size of the dataset.  
The objective of L1 regularization included in the loss function is to achieve a sparse model and prevent overfitting through the utilization of the deep model. Moreover, L2 regularization in the loss function serves to prevent the occurrence of excessively large parameter values within the model, thereby averting the dominance of a single feature over the predictive performance of the model. L1 regularization and L2 regularization can be defined as follows:  
\[{\left\|{{w}}\right\|_{1}}={\sum_{i=1}^{n}}{\left\|{{w_{i}}}\right\|},\ {\left\|{{w}} \right\|_{2}}={\sum_{i=1}^{n}}{\sqrt{{{W_{i}^{2}}}}} \tag{29}\]  
Then, the loss function can be rewritten as follows:  
\[Loss=\frac{1}{n}{\left({{\sum_{i=1}^{n}}{\left({{y_{i}}-{\hat{y_{i}}}}\right) ^{2}}}+{{\lambda_{1}}}{\sum_{i=1}^{n}}{\left|{{W_{i}}}\right|}+{{\lambda_{2}} }{\sum_{i=1}^{n}}{\sqrt{{{W_{i}^{2}}}}}}\right) \tag{30}\]  
In the proposed model, the [PERSON] optimization algorithm [36], which adaptively adjusts the learning rate, was utilized to optimize the model's parameters.
## 2 Analytical Two-Link Model and Equations of Motion
### Analytical Two-Link Model
The restoring moment varies within a narrow range. However, when the absolute value of the sum of the attack angle and the angle of the first link is greater than \(\theta_{c}\), i.e., the shoulder contacts the head, the rotational stiffness of the first link becomes \(k_{12}\), which is much greater than \(k_{11}\). In this condition, the restoring moment changes from the maximum value in the non-contact case, and the rate of change with respect to the angle will increase.
## 3 Proposed Method  
The solution to the GNSS positioning error estimation problem depends on the analysis and processing of historical data. Currently, the common methods of GNSS positioning error estimation tend to ignore the relationships among different types of data, resulting in unsatisfactory estimation accuracy. With the rapid development of artificial intelligence methods, the excellent ability of artificial intelligence methods in data processing and feature learning has been proved. Therefore, after exploring and experimenting with various artificial intelligence methods, a multi-head attention gating unit (MAGU) that combines the MHA and gating operation was designed, and then the MAGU and MLP models were integrated to address the problem of GNSS positioning error estimation. The MHA is beneficial to improving the feature extraction ability of the model. The gating operation can enhance the model's dynamic information selection capability.  
The traditional MLP model is a feedforward neural network model. It consists of multiple neural layers. Each MLP neuron is connected to all neurons of the previous layer, and receives input from the previous layer. Weighting calculations and nonlinear transformations are then performed on the input and, finally, the result is passed to the next layer. The explored model represents an enhancement of the traditional MLP model, incorporating a gating operation and a MHA, and updating the network structure and parameters.  
According to Figure 2, the overall process of the designed scheme is to first extract relevant parameters from GNSS data and adopt them as feature variables. Next, the positioning error is obtained through the positioning solution, and the positioning error is selected as the target variable. Then, the feature variables and target variables are input  
Figure 1: Schematic diagram of positioning error estimation.  
into the improved MLP model for multiple iterations of training. Finally, the estimated value of the positioning error is output.
## 2 Experimental Setup  
### Fiber Manufacturing  
A brief overview of the process for 3D printed fibers is shown in Figure 1. It can be divided into five steps, and are described in short. (1) First, a photocurable resin was fabricated by mixing 2-phenoxyethanol (POE), 2-hydroxyethyl methacrylate (HEMA), diphenyl(2,4,6-trimethylbenzoyl) phosphine oxide (DPO), tetra(ethylglycol) diacrylate (TEGDA), and hydroquinone (Hyd), (all purchased from Sigma-Aldrich, Australia), which is a photoresin provided for use with digital light processing (DLP) 3D printer. Then, high purity silica glass nanoparticles (purchased from Evonik, Australia) were dispersed in the resin and sued in printing. (2) The optical fiber cladding preform was designed by Inventor CAD (the targeted cladding diameter was \(\phi\) = 125 \(\upmu\)m) and then fabricated using a DLP 3D printer (Asiga Freeform Pro 2 75 UV; x,y pixel resolution \(\sim\)75 \(\upmu\)m, Asiga, Sydney, Australia). (3) To make the preform core, germanium oxide (GeO\({}_{2}\)) powder was added into the previously mixed resin. This increases the refractive index of the mix. The resin was subsequently injected into the cladding preform. (4) With high temperature heating, the preform undergoes thermal debinding above 300 \({}^{\circ}\)C to remove the polymer resin, leaving only a silica structure. (5) Finally, the preform is inserted into a supporting silica tube and placed inside a draw tower and drawn into fiber (L\(\sim\)2-3 km, cladding diameter \(\sim\)125 \(\upmu\)m) at a temperature T\(\sim\)1900 \({}^{\circ}\)C. More details of the fabrication process can be found in References [6; 7; 17].
## 2 Theory
### Photomechanical Efficiency Figure of Merit  
Young's modulus and the linear photomechanical response constant can be used to determine the efficiency of transferring mechanical energy to the material from the absorbed light energy. In the configuration where the sample length is constrained to be constant, the photomechanical Figure of Merit (FOM) is given by [16; 22; 23]  
\[FOM=\frac{(\kappa_{\sigma}^{(1)})^{2}}{E}, \tag{9}\]  
where \(\kappa_{\sigma}\) is the photomechanical stress response and \(E\) is Young's modulus. The \(FOM\) can be used to compare materials. The temperature-dependent figure of merit due to the heating mechanism can be determined using the temperature-dependence of Young's modulus and the heating model of the photomechanical response. As will be described later, the temperature-dependent Young's modulus for dye-doped polymers obeys the empirically determined equation [23]  
\[E=\frac{E_{0}}{(1+(\frac{T}{T_{c}})^{n^{\prime}})}, \tag{10}\]  
where \(E_{0}\), \(T_{c}\), and \(n^{\prime}\) are determined from a fit of the data to Equation (10).  
The temperature-dependent figure of merit due to the heating mechanism using Equations (8) and (10) is given by [23]  
\[FOM(T)=\frac{(\kappa^{(1)}(  
The figure of merit can be determined either from a direct measurement of \(\kappa^{(1)}(T)\) or \(E(T)\). Alternatively, measurements of \(E(T)\) and \(\sigma(T)\) at a few temperatures can be used to obtain the parameters \(E_{0}\), \(T_{c}\), \(T_{0}\), \(\sigma_{0}\), \(n\), \(n^{\prime}\) from fits of the data to Equations (6) and (10). These parameters, along with knowledge of the heat capacity \(c\), the dimensions and density of the sample, and the response time \(t_{1}\) of the photomechanical effect for the specific sample, can then be used to evaluate the temperature-dependent figure of merit given by Equation (11). This analytical expression provides the figure of merit for a broad range of temperatures and can be used in material models of the response.