esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Sensor Placement for Modal Testing Using Machine Learning
## 2 A Review of Traditional Sensor Placement Techniques for Modal Testing
### Effective Independence Method (EIM)
The higher the effective independence score of a candidate sensor location, the more important that location is for calculating the independence of the mode shapes. Therefore, sensor locations with the lowest values are eliminated, and the effective independence score is then recalculated from the subset of candidate locations. The lowest ranking row of \(\mathbf{EI}\) and the corresponding row in \(\mathbf{\Phi}_{s}\) are eliminated, and the new \(\mathbf{\Phi}_{s}\) is then input into Equation (2). Where the values of \(\mathbf{EI}\) are equal, either sensor location could be removed from the set of sensors without impacting the linear independence of the target modes. The process is repeated until the desired number of sensors is reached. The sum of the column vector \(\mathbf{EI}\) must always be equal to the number of target modes and, as a result, \(\mathbf{EI}\) must be recomputed whenever a node is removed as irrelevant. As such, it is optimal to remove only one node per iteration. Additionally, it is impossible to have fewer sensors than target modes. The process is complete when the desired number of sensors is reached or when all remaining sensor locations have similar effective independence values [20]. The determinant of the Fisher information matrix \(A_{0}\) is used to measure how much information is covered by a given sensor set:  
\[A_{0}=\mathbf{\Phi}_{s}^{T}\mathbf{\Phi}_{s} \tag{10}\]
## 3 Experimental Results  
We developed a program using LabVIEW 2020 (National Instruments, Austin, TX, USA) to realize the proposed TMPR, for the identification of the MNS workspace, and for the 3D reconstruction of a vascular map. We validated the results by performing an in vitro experiment of an MNS mock-up and a vascular jig and an in vivo experiment in which an MMR was remotely operated in the SFA of a mini pig.
## Materials and Methods  
### Materials and Membrane Preparation  
Two different grades of highly porous PTFE film (i.e., hydrophobic and hydrophilic grades, Advantec MFS, Inc., Tokyo, Japan) were employed as the substrates for preparing PFAEMs. The specifications of the porous PTFE substrates are listed in Table 1, showing the almost identical pore dimensions. \(N\),\(N^{\prime}\)-Dimethylaminoethyl methacrylate (DMAEMA) and divinylbenzene (DVB) were chosen as the main monomer and cross-linker, respectively. \(p\)-Xylylene dichloride (XDC) was used for both the quaternization and cross-linking of the polymer, and benzophenone (BP) was selected as the initiator for the photo-polymerization. The reaction scheme of DMAEMA-DVB copolymer, which is cross-linked and quaternized by XDC, is suggested in Figure 1. As shown in the scheme, the synthesized ionomer has a double cross-linking structure by DVB and XDC, realizing a high cross-linking degree and ion-exchange capacity, at the same time. For the membrane preparation, first, monomer mixtures consisting of 79-98 wt% DMAEMA, 1-20 wt% DVB, and 1 wt% BP were prepared. Porous PTFE substrate was then dipped in the monomer solution for several hours, followed by a photo-induced polymerization in a lab-made ultraviolet (UV) chamber (high pressure, lamp power = 1 kW) for 10 min. For the successive quaternization and cross-linking, the PTFE substrate filled with poly(DMAEMA-DVB) was immersed in 0.05 M XDC-EtOH solution at 50 \({}^{\circ}\)C for 12 h and then sequentially treated with 0.5 M NH\({}_{4}\)Cl, 0.5 M HCl, and distilled water. The fabricated PFAEMs were then immersed in 0.5 M NaCl or 1.0 M KOH before evaluation. All the reagents were supplied by Sigma-Aldrich (St. Louis, MO, USA) and utilized as received. We also chose Neosepta(r) AMX (Astom Corp., Tokyo, Japan) as the reference membrane to compare with the PFAEMs fabricated in this work.  
Figure 1: Reaction scheme of the anion-exchangeable polymer, with quaternary ammonium groups and two different kinds of cross-linking sites.
# An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs
## 3 Methodologies  
### A New Paradigm for Safety-Critical Software Development  
There are typically two modes of safety-critical software development in fields such as nuclear energy and the automotive industry. The first mode is manual coding, which involves the following development processes: conceptual design, requirement analysis, high-level design, detailed design, software coding, software testing, and verification. The second mode is model-based development, with SCADE software (16.4) being a representative example. According to the traditional V-model, the main development processes in this mode include conceptual design, requirement analysis, model design, software testing, and verification. Due to SCADE's ability to auto-generate code, the software coding phase is reduced in the development process.  
Based on the principles of code generation from large language models, the starting point for code generation is software requirements. As long as there are clear prompts, a large language model can generate corresponding software code. Therefore, the safety software development method based on large language models presents a new mode, with the main development processes including conceptual design, requirement analysis, software testing, and verification. As shown in Figure 1, the development flow and stages are significantly reduced in this new mode, leading to a significant improvement in safety-critical software development efficiency.  
\begin{table}
\begin{tabular}{c c} \hline
**Reference** & **Prompt Examples** & **Feature** \\ \hline [PERSON] et al., 2023 [36] & Prompt1: Write a self-contained function block in 61131-3 structured text that implements a counter. & function descriptions and code language constraints \\ \hline [PERSON] et al., 2023 [36] & Prompt2: Write a self-contained function block in IEC 61131-3 structured text to compute the mean and standard deviation for an input array of 100 integers. & function descriptions and code language constraints \\ \hline \end{tabular} Based on the analysis in Table 1, the prompt methods that include software function descriptions and code language constraints can be referred to as “Prompt-simple”. On the other hand, the method that combines software function descriptions, code language constraints, detailed step descriptions, and functional examples can be referred to as “Prompt-specific”.  
\end{table}
Table 1: _Cont._In the new safety-critical software development paradigm, the main task for developers is to articulate accurate requirements and enable LLMs to better understand them.
## 2 Data and Method
### Satellite Data  
The GF-4 satellite is a geosynchronous orbit optical satellite with an orbital height of 36,000 km and a resolution of 50 m, which fills the gap of high-resolution geosynchronous orbit optical remote-sensing satellites in China. GF-4 was successfully launched at the XiChang Satellite Launch Center on 29 December 2015, and sent back the first image on 5 January 2016 [34]. This is China's first high-resolution geosynchronous orbit remote-sensing satellite, equipped with six bands and a visible light staring camera (PMS) with a resolution of 50 m/mid-wave infrared 400 m and a width of more than 400 km (Table 1) [35]. The GF-4 satellite provides fast, reliable and stable optical remote-sensing data for disaster detection, coastal zone management, meteorology and other applications, opening up a new field of high-resolution Earth observation in geosynchronous orbit [36]. In this study, 20 PMS images from GF-4 were applied to analyze Chla concentration and distribution trend in Bohai sea. The detailed information about the PMS images including the bands, resolution, width and revisit time of the PMS sensor is shown in the Table 1.
## 2 Mathematical Model of the Plant
\[I_{pi}=I_{p_{i}^{\prime}}+K_{i}^{2}\;I_{mi}, \tag{12}\]  
\(I_{p_{i}^{\prime}},\;\;\text{and}\;K_{i}\) represent the \(i^{th}\) hinge's belt-pulley system's moment of inertia, and ratio of teeth, respectively, \(I_{mi}\) represents the \(i^{th}\) motor's moment of inertia, and \(C_{i}\) represents the viscous friction coefficients of the \(i^{th}\) hinge,  
\[C_{pi}=C_{p_{i}^{\prime}}+K_{i}^{2}\;C_{mi}, \tag{13}\]  
\(C_{mi},\;\;C_{p_{i}^{\prime}}\) represent the viscous friction coefficients of the \(i^{th}\) motor and the \(i^{th}\) hinge's belt-pulley system, respectively, \(t_{mi}\) represents the control torque of the \(i^{th}\) motor, and \(d_{i}\) represents the disturbance torque to the \(i^{th}\) link. Linearizing the model described in Equation (3) [1] around the operating point \(\theta_{1}\;\;=\theta_{2}\;\;=\theta_{3}=0\), results in the following:\[\left[ \begin{array}{ccc}J_{1}+I_{p1}&l_{1}\ M_{2}-I_{p1}&l_{1}\ M_{3}\\ l_{1}\ M_{2}-I_{p1}&l_{2}+l_{p1}+l_{p2}&l_{2}\ M_{3}-I_{p2}\\ l_{1}\ M_{3}&l_{2}\ M_{3}-I_{p2}&J_{3}+l_{p2}\end{array} \right]\left[ \begin{array} []{c}\ddot{\theta}_{1}\\ \dot{\theta}_{2}\\ \ddot{\theta}_{3}\end{array} \right] \tag{14}\] \[+\left[ \begin{array}{ccc}C_{1}+C_{2}+C_{p1}&-C_{2}-C_{p1}&0\\ -C_{2}-C_{p1}&C_{p1}+C_{p2}+C_{2}+C_{3}&-C_{3}-C_{p2}\\ 0&-C_{3}-C_{p2}&C_{3}+C_{p2}\end{array} \right]\left[ \begin{array}{c}\dot{ \theta}_{1}\\ \dot{\theta}_{2}\\ \dot{\theta}_{3}\end{array} \right]\] \[+\left[ \begin{array}{ccc}-M_{1}g&0&0\\ 0&-M_{2}g&0\\ 0&0&-M_{3}g\end{array} \right]\left[ \begin{array}{c}\theta_{1}\\ \theta_{2}\\ \theta_{3}\end{array} \right]+\left[ \begin{array}{ccc}K_{1}&0\\ -K_{1}&K_{2}\\ 0&-K_{2}\end{array} \right]\left[ \begin{array}{c}t_{m1}\\ t_{m2}\end{array} \right]\] \[=\left[ \begin{array}{ccc}1&-1&0\\ 0&1&-1\\ 0&0&1\end{array} \right]\left[ \begin{array}{c}d_{1}\\ d_{2}\\ d_{3}\end{array} \right]\]  
Arranging the model in state-space representation, given that the system has two inputs (\(u_{1}\), \(u_{2}\)) and three outputs (\(\theta_{1}\), \(\theta_{2}\), \(\theta_{3}\)), and substituting the nominal values of the parameters given in Table 1[1], yields  
\[\dot{\mathbf{x}}(t)=\left[ \begin{array}{cccccc}0&0&0&1&0&0\\ 0&0&0&0&1&0\\ 0&0&0&0&0&1\\ 12.54&-8.26&-0.39&-0.043&2.75&-0.36\\ -4.38&36.95&-3&0.086&-9.57&2.29\\ -6.82&-22.94&11.93&-0.034&6.82&-2.86\end{array} \right]\mathbf{x}(t)+\left[ \begin{array}{cccc}0&0\\ 0&0\\ -50&6.12\\ -124.2&48.62\end{array} \right]\mathbf{u}(t) \tag{15}\] \[\mathbf{y}(t)=\left[ \begin{array}{cccccc}1&0&0&0&0&0&0\\ 0&1&0&0&0&0\\ 0&1&0&0&0&0\end{array} \right]\mathbf{x}(t)+\left[ \begin{array}{cccc}0&0\\ 0&0\\ 0&0\end{array} \right]\mathbf{u}(t)\]  
where the state vector \(\mathbf{x}\) is \(\left[\theta_{1}\,\theta_{2}\,\theta_{3}\,\dot{\theta}_{1}\,\dot{\theta}_{2}\, \dot{\theta}_{3}\right]^{\mathsf{T}}\), the control input vector \(\mathbf{u}\) is \(\left[u_{1}\quad u_{2}\right]^{\mathsf{T}}\), and the output vector \(\mathbf{y}\) is \(\left[\theta_{1}\,\theta_{2}\,\theta_{3}\right]^{\mathsf{T}}\). Then, for this MIMO system, \(n=6\) states and \(m=2\) inputs.
## 6 Experiments and Results
### Standalone AtomGID Experiments  
To evaluate the performance of AtomGID, the team first developed an experimental protocol using the trilateration algorithm and the smart home infrastructure at our lab. The experiments were conducted in the kitchen where four RFID antennas enable the 14.12 cm in localization precision [31]. The gestures were made with four basic directions. The length of each atomic direction varied from 30 to 60 cm. The idle gesture was also added to the dictionary. Figure 8 shows the 13 gestures that were selected for the first experimental phase. This phase was a scripted scenario in the smart home using the real RFID system. The participants were recruited to perform each gesture ten times. Lines were traced on the kitchen counter and the participants were asked to follow them. Our lab is not equipped with technology enabling us to gather a ground truth dataset; therefore, we assumed that the guidelines were approximately followed (from visual observations). It resulted in a recognition accuracy of 77%. It may seem low, but the majority of the errors are due to segmentation and the insertion of an idle time between directions. Moreover, RFID tracking is more difficult than RFID localization, which might result in a lower accuracy in the trilateration itself.
## 3 Results
### Simulation Analysis of Earthquake Damage of Oil and Gas Pipeline Structure
#### 3.1.2 Stress and Strain Analysis of Pipeline Structure
The combined effect of multi-directional seismic waves resulted in a more intricate maximum strain pattern for the pipe bridge structure. When subjected to bidirectional seismic waves, both ends of the pipe and bottom of the bridge tower experienced smaller maximum stresses compared to those induced by unidirectional seismic waves since joint earthquake action weakens the overall strain within the pipe bridge system. However, under three-way ground motion, strains intensified at both ends of the pipeline, with an increase 1.2 times greater than that observed under unidirectional ground motion. In summary, when considering the fluid-structure coupling effect of oil and gas pipelines, the maximum stress and strain occur at the inlet and outlet ends under earthquake loading. The pipeline is susceptible to buckling and failure at both ends. Under unidirectional seismic waves, longitudinal waves induce a greater strain response compared to vertical waves. Transverse seismic waves have the most significant influence on the stress-strain behavior of the structural system, with the maximum strain observed at the bottom of the bridge towers. The combined action of bidirectional seismic waves reduces stress and strain at critical points along the pipeline, while three-directional seismic wave combinations exacerbate failure and damage in oil and gas pipelines.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 4 Discussion
### Uncertainties Analysis
Moreover, satellite pixel scale and the footprint size of ground observation may be another factor that influenced the accuracy when validating the GPP estimates. The spatial representativeness of the observed GPP data related to the height of the eddy covariance instruments. In this study, the heights of the eddy covariance instruments range from 2.85 m to 4.50 m, the footprint size of carbon flux is mainly smaller than 200 m, and the spatial representative of ground observation is more close to the 30 m scale, which is the reason why the accuracy of downscaled GPP is higher. The large number of carbon flux station in the maize cropland, small footprint size of ground observed GPP data, and mixed pixel phenomenon are the main reasons of GPP underestimation at 1 km scale compared with ground observed data.
# The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins
## 1 Introduction  
As a result of the consumption of non-renewable energy and the quest for energy independence and low-carbon development, the primary focus of energy sources in the 21 st century is progressively shifting towards sustainable energy. Sustainable energy encompasses renewable energy, nuclear energy, and natural gas, all of which can be utilized continuously and efficiently over an extended period without causing harm to the environment. In comparison to natural gas and nuclear energy, renewable energy offers greater security, lower long-term costs, and superior environmental performance. Consequently, renewable energy has emerged as the foremost direction for sustainable energy development.  
With the increasing adoption of renewable energy and ongoing reforms in the power market, the implementation of AI (Artificial Intelligence) Grid technology has emerged as a prominent trend in power system development [1]. However, the inherent instability and uncertainty associated with renewable energy sources pose significant challenges to power load forecasting, resulting in operational inefficiencies and safety risks within the power system.  
Digital twin technology has the capability to integrate historical load data, weather data, renewable energy data, and other parameters to establish prediction models in the field of renewable energy power load forecasting. These models are used to forecast the future power generation situation within a specific time range. Furthermore, digital twin technology utilizes simulation techniques to enable online monitoring and analysisof critical nodes in the power system, facilitating real-time optimization and control [2]. Specifically, in the domains of wind energy and photovoltaic (PV) power generation, digital twin technology can utilize weather data such as wind speed, wind direction, and temperature to construct collaborative power generation prediction models that incorporate multiple factors. The application of statistical models aims to improve the operational efficiency and reliability of the power system.  
In the context of the AI Grid, digital twin technology plays a crucial role by seamlessly integrating physical systems with virtual models [3]. This integration creates a highly realistic simulation environment enabling accurate monitoring, optimal control, and decision support for power system operations. For example, in the field of power equipment operation and maintenance management, digital twin technology facilitates online monitoring and status diagnosis of equipment, enabling timely detection of faults and abnormalities, and providing appropriate solutions [4; 5; 6; 7]. Additionally, digital twin technology enables comprehensive data analysis and modeling of the power system, empowering participants in the power market with more accurate and reliable decision support.
## 6 Conclusions  
Data recorded during the test performed using HYPROP equipment mimic the behavior observed in real disks and provide an indication of the delayed response with depth during evaporation and infiltration. This continuous dynamic process was simulated considering a coupled thermohydraulic response. The effect of volume changes during the cycles was discarded from the analysis because the material did not show any deformation during the wetting and drying cycles. The environmental conditions were translated into numerical boundary conditions considering drying modeled only by the transport of water vapor after equilibrium with the room atmosphere, while water in the liquid phase was added upon wetting. Model calibration was performed to achieve the final suction at the end of each drying, adjusting the wetting rate.  
The drying rate depends only on the relative humidity of the room and is controlled uniquely by the equations solved by the program, while the wetting rate is controlled by the user by defining the time interval to which water in the liquid phase is added. Further work must be conducted to include the porous tip of the sensors, as the thermal and hydraulic properties of this material may also contribute to the delay registered.  
The simulation was able to reproduce the overall behavior observed in four complete drying-wetting cycles but the drying rate simulated was faster than the observed one. The numerical curves describing suction and the amount of vapor and temperature are identical, confirming that vapor generation and its equilibrium control the hydraulic response of the material. Vapor generation and diffusion depend on temperature; therefore, the correct characterization of the thermal properties of the material is of paramount importance when dealing with evaporation and related non-steady equilibrium problems.  
Conceptualization, E.P. and C.J.; Data curation, E.P. and C.J.; Formal analysis, R.C. and C.J.; Funding acquisition, C.J.; Investigation, E.P. and R.C.; Methodology, E.P., R.C. and C.J.; Resources, C.J.; Software, E.P. and R.C.; Supervision, C.J.; Validation, R.C. and C.J.; Writing--original draft, E.P. and R.C.; Writing--review and editing, R.C. and C.J. All authors have read and agreed to the published version of the manuscript.  
Funding: E.P. and C.J. acknowledge the financial support on the experimental equipment of the perspective program BioGeoCivil (grant 11344) and CEAMaS (Civil Engineering Applications for Marine Sediments), supported by the European Regional Development Funding through INTERREG IV B. R.C. acknowledges financial support from the Portuguese Foundation for Science and Technology, FCT I.P., refs. SFRH/BSA/114289/2016 and PTCDT/ECM-GEO/0728/2014.  
Informed Consent Statement: Not applicable.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Modeling Principles
### General Dynamic Vibration Absorbers (DVA) Modeling Approach
#### 2.1.1 Passive Tuned Mass Damper (TMD) Control  
A TMD device produces control actions as a result of the relative motion of its mass against the structural mass such that the control force term, \(u(t)\) in Equation (2) is calculated at each time step by:  
\[u(t)=k_{p}x_{r}(t)+c_{p}\dot{x}_{r}(t) \tag{4}\]  
In this equation, \(\dot{x}_{r}(t)\) and \(x_{r}(t)\) are respectively the relative velocity and displacement between the \(n\)th and \((n-1)\)th DOFs and \(c_{p}\) and \(k_{p}\), are the passive damping and stiffness coefficients respectively. To this date, most of the tuning of the mechanical parameters \(c_{p}\) and \(k_{p}\) of a TMD device is achieved via closed-form expressions derived from the minimisation of the rms acceleration response of a single degree of freedom (SDOF) subjected to white noise or harmonic excitation. While this approach is broadly accepted, representing civil engineering structures with an equivalent SDOF system can lead to significant errors in the estimation of their dynamic response. The problem amplifies when one considers the probabilistic nature of the knowledge of the system's properties and the fact that the estimated properties can vary with time (e.g., amplitude dependence, fluid-structure interaction, etc.). Moreover, obtaining TMD mechanical parameters through the use of harmonic or flat spectrum inputs may not always yield optimum values [18]. In this paper, because the motion of long period structures is generally governed by the first modal response, the stiffness coefficient of the auxiliary device is selected just as the mass damper is tuned to the fundamental frequency of the structure, whereas the damping coefficient is obtained using the expressions found in [19,20], and validated and adjusted when necessary through numerical optimisation based on [21,22] for the case of the more complex, wind-excited multi-degree of freedom (MDOF) structural system.