esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analysis for 3D Modeling of Consolidation-Induced Solute Transport Using OpenFOAM
## 2 Theoretical Model
### Numerical Implementation  
Based on the flexibility of OpenFOAM v8, in this study, two new solvers were developed specifically for simulating consolidation and solute transport to meet the demands of complex coupled studies. Figure 1 illustrates the main steps and logical progression of this solver, which can be broadly divided into three phases: pre-processing, simulation, and post-processing.  
The pre-processing phase involves constructing the geometric model, generating the mesh, and setting boundary conditions. After defining relevant parameters, the spatial and temporal domains are discretized, creating a computational domain ready for simulation.  
During the simulation phase, the consolidation solver first calculates the distributions of stress, displacement, and pore water pressure by simultaneously solving the storage equation and the displacement equation based on [PERSON]'s consolidation theory. The output of the consolidation solver, including temporal and spatial variations in pore pressure and soil deformation, is then passed to the solute transport model. This one-way coupling ensures that the effects of consolidation, such as changes in permeability and pore structure, are accurately incorporated into the transport analysis. The solute transport solver subsequently calculates the migration of contaminants considering advection, dispersion, and adsorption processes.  
Finally, in the post-processing phase, visualizations of pressure, displacement, and concentration fields, as well as trend curves, are generated to provide a comprehensive assessment of the effects of soil properties on solute transport. These insights can inform further analysis and optimization, particularly in fields such as environmental engineering and soil mechanics.  
The source codes for the two solvers are provided in a database that is available to the public.  
Figure 1: Numerical simulation procedure for the present integrated model.
## 4 Experimental Verification and Analysis  
In the second section, we propose and establish a rigid transformation mathematical model from image coordinate system to the robot coordinate system at any height h through the standard small hole imaging model in the first section. Next, we mainly verify the correctness of the rigid transformation model proposed in Section 2 through experiments.  
First, in the standard pinhole camera model established in Section 1, the matrix m in Equation (4) is called the camera's internal parameter matrix, which determines how points  
Figure 5: Coordinate system transformation.  
in the real world are projected to the camera. Through the Zhang Zhengyou calibration method, we obtain the internal parameter matrix \(M\) of the camera.  
Second, by eliminating the barrel distortion of the camera lens, the four-point calibration method is used to calibrate the four-point coordinates of the calibration plate at different heights in the machine coordinate system and the image coordinate system. The \(\overset{R}{C}M\) matrix parameters at different heights \(H\) are calculated.  
Finally, we made a broken line diagram of \(\overset{R}{C}M\) matrix parameters and calibration height \(H\) in the three-dimensional coordinate system, which clearly showed the distribution of \(\overset{R}{C}M\) matrix parameters at different heights. There was an obvious linear relationship between \(\overset{R}{C}M\) matrix parameters and calibration height, and it further fitted the linear relationship between \(\overset{R}{C}M\) matrix parameters and calibration height \(H\). Through the experimental results, the random error in the calibration system is analyzed, the linear relationship between the calibration height and the camera pixel density is studied, and the systematic error in the experimental process is deeply studied. Through verification, the positioning error of the proposed arbitrary height rigid transformation model in practical application is less than 0.08%. The specific experiment and analysis process are as follows.
## 4 Results and Discussions  
### Validation Using Detector Emulator  
Figure 6 illustrates the configuration for validation using a detector emulator. The CAEN NDT6800D [18] was used to generate the simulated pulse signals shown in Figure 7.  
In this experiment, we validated linearity and response time performance. To validate linearity, the pulse signals generated by the NDT6800D with a range of 1 to 10\({}^{6}\) cps and a constant or random signal frequency were inputted to the FPGA evaluation board. The random signal frequency was based on a Poisson distribution. The result of linearity validation is shown in Figure 8. We confirmed that FPGA outputs are within \(\pm\)5% of the span. The result satisfies the requirement mentioned in Section 3.1, indicating that the proposed algorithm demonstrates good linearity performance.  
To validate response time, we assumed the trip set point is 10\({}^{5}\) cps in this experiment, referred to as the traditional PWR [13], and set the input signal by varying the count rate from \(-\)10% to +10% of the set point. The result of the response time is shown in Figure 9. The FPGA outputs reached the set point after approximately 5 ms, within the required performance response time mentioned in Section 3.1. This indicates that the proposed algorithm demonstrates rapid response performance.  
Figure 6: Experimental configuration with detector emulator.  
Figure 7: NDT6800D output pulse signal.
## 3 Proposed Model and its Phases
### Member Association Phase (MAP)  
In the association phase, all selected cluster heads will broadcast hello messages to form clusters by utilizing the features of the carrier sense multiple access (CSMA) medium access control (MAC) protocol [42]. The other nodes remain active to respond to the transmitted hello messages. After receiving a hello message from the cluster head, a non-cluster head node will be selected as a cluster member node. It is also possible that a node receives multiple hello messages from multiple cluster heads. In this case, the signal strength is the node's primary parameter to associate itself with the cluster head. i.e., a node associates itself with a cluster head with the most robust signal strength. The detailed MAP process is shown in Figure 7.  
\[Selec_{criteria}=\frac{RSS1}{Dis_{1 nRangeCHs}} \tag{18}\]where \(RSSI\) is the received signal strength indication and \(Dis_{InRangeCHs}\) is the distance between a non-cluster head node and the selected cluster head. Non-cluster heads select a cluster head with the maximum value of \(Selec_{criteria}\) and send the association requests (As-Reqs) to the respective CHs using the CSMA MAC protocol. Simultaneously, cluster heads remain in a waiting state for association requests from non-cluster heads to form a cluster. After receiving all requests, the cluster head broadcasts a planned TDMA schedule to all member nodes. This TDMA schedule message from the nodes works as a confirmation report of their membership status in that cluster. These member nodes will only transmit their information when they reach their assigned periods. This period will be the active mode of the node. For the remaining duration, nodes deactivate their transmitter. This mode is called the sleep mode of the nodes.  
Figure 7: Flow control of the proposed cluster head selection and cluster formation techniques.
## 2 Materials and Methods
### Methodology
#### 2.4.1 Description of SDSM  
The SDSM is a coupled Multiple Linear Regression (MLR) and Stochastic Weather Generator (SWG) model developed by [39; 40; 41; 42; 43; 44]. In this method, statistical and empirical relations between NCEP's predictors and observed predictands are generated by MLR throughout predictors screening and SDSM calibration processes [45; 46; 47; 48; 49]. In this study, the projected climate scenario (Table 1) was determined in the calibration phase through predictor screening. Let us recall that to screen the predictor variables, it is necessary to individually correlate them with the predictands, i.e., precipitation and temperature. Considering this aspect, a conditional sub-model was selected for precipitation to account for wet-day occurrences, whereas an unconditional process was adopted for independent temperature. As a result, predictor variables with a \(p\) range of (\(0\leq p\leq 0.05\)) were chosen as the best fit correlated predictors for a single predictand. Moreover, using available observed daily data from 1978 to 2000, the model was calibrated and validated for \(T_{max}\) and \(T_{min}\). However, for precipitation downscaling, the NCEP predictors were used for model calibration and GCM predictors for model validation using data of the same period 1990-2001 (details can be found in Section 3.1).  
Furthermore, the weather generator process was conducted to produce synthetic daily data by using calibrated output file (*PAR-extension) and observed NCEP selected predictors variables. It allows the verification of calibrated model using observed data and generated artificial time series for the present status of climate [50]. Lastly, the scenario generation was performed following a procedure analogous to the weather generation process. The only difference between weather generation and scenario generation processes is the H3A2a_1961-2099 file that was selected in the GCM directory for scenario generation (instead of the NCEP_1961-2001 file for weather generation). Scenario generation gives the projected climate predicted variables supplied by the global climate models.  
Figure 3: Schematic methodological framework.
## 5 Conclusions  
* COTS synthetic sapphire bearings have been investigated as axial bearings for a magnetic cardan suspension embedded in a passive pointing mechanism based on lunar gravity for space qualification.
* Several JB samples were tested on an HW, simulating the 1 kg estimated mass of the CCR package of the NGLR experiment, coupled with AISI 314 and AA 6081 spindles. Different load profiles have been imposed; the INFN LaRA payload for the NASA Perseverance mission, and its version with a second order manual notching.  
Figure 21: Lower JB after notching test.  
* Tests showed that along the x and y directions no damages were produced on the sapphires. On the z direction both sapphires were badly destroyed at nominal loads. Along the same axis with dynamic load notching, the upper JB showed no damage on the surface at all.
* From the analysis, three main issues can address the failure, generated from exceeding the ultimate tensile stress in the material: 1. Poor mechanical quality of the endstone housing, resulting in bending moment load; 2. High stress level in the endstone due to its thickness (<1 mm); 3. Stress amplification due to resonances of the ASA truss and cap, requiring redesigning and/or a more accurate notching analysis;  
The evidence that, in some conditions, the JBs reported no damage, encourages further investigation of this solution, which is really appealing from the cost point of view. The purpose of this work is to qualify COTS JB parts as a whole for space missions. The experiment proposed can be considered as a technological demonstrator. In order to perform a quantitative analysis of the JB, an engineering model must be designed and manufactured in a way that will allow the determination of the residual starting torque in the align by gravity process of the NGLR package after the random test.  
All authors contributed equally to the different stages of the work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Authors are grateful to [PERSON] for technical assistance in 3D printing.  
The authors declare no conflict of interest.
# Research on Rapid Recognition of Moving Small Targets by Robotic Arms Based on Attention Mechanisms
## 4 Experimental Results and Discussion
### Comparative Analysis of Experimental Indicators
The results of the training under the improved model are presented in Figure 11.  
Beyond the metrics traditionally utilized for assessing model performance, the training velocity of the models is also an essential factor to consider. Hence, subsequent to employing a pre-trained model and determining the optimal parameters across varying loss functions, this study incorporates FPS (frames per second) as a criterion to evaluate the computational speed of the model. FPS is calculated on a computer by employing the 'time0' function to capture the timestamps before and after processing a specified quantity of images, followed by dividing this duration by the total number of images processed. Figure 12 shows the recognition effect for fruits that move quickly. It can be seen that for fruits that moved quickly, even if there was blurring, the model was able to accurately recognize them. Thus, the model has good robustness.
###### Abstract  
This paper investigates the fault-tolerant tracking control problem of high-speed trains (HSTs) subject to unknown model parameters with unavailable uncertainties, unmeasurable additional disturbance, and unpredictable actuator faults constrained by actuator saturation. An adaptive passive fault-tolerant tracking control strategy based on variable-gain proportion-integral-derivative (PID)-type sliding mode surface is proposed to handle the problem. Unknown model parameters, gains of the PID-type sliding mode surface, and upper bounds of the lumped system uncertainty which includes additional disturbance, modeling uncertainties, and uncertainties resulting from actuator faults, are estimated online by adaptive technology. The input saturation (actuator output saturation) constraint is handled by introducing an auxiliary signal. The proposed controller can compensate for the effects of the lumped uncertainty and the actuator faults effectively. Moreover, the controller is model-independent, which means it requires no prior knowledge of model parameters and upper bounds of the lumped uncertainty, and does not depend upon fault detection and diagnosis module. The asymptotic stability of the closed-loop train system is demonstrated by Lyapunov theory. Good fault-tolerant tracking capacity, effective anti-actuator saturation ability, and strong robustness of the proposed controller are verified via numerical simulation.  
Keywords:fault-tolerant tracking control; model-independent; adaptive control; sliding mode surface; actuator saturation; high-speed trains +
Footnote †: journal: Applied
# Multi-Path Data Distribution Mechanism Based on RPL for Energy Consumption and Time Delay
## 3 Adaptive Multipath Traffic Loading Based on RPL, AMTL-RPL  
This section present an adaptive multipath traffic loading distribution method based on RPL. We realize the entire network's energy balance and reduce the end-to-end delay by evaluating the energy consumption and cache utilization of bottleneck nodes in the network.
# Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
###### Abstract  
With Generative AI (GenAI) entering medicine, understanding its decision-making under uncertainty is important. It is well known that human subjective risk appetite influences medical decisions. This study investigated whether the risk appetite of GenAI can be evaluated and if established human risk assessment tools are applicable for this purpose in a medical context. Five GenAI systems (ChatGPT 4.5, Gemini 2.0, Qwen 2.5 MAX, DeepSeek-V3, and Perplexity) were evaluated using Rheumatoid Arthritis (RA) clinical scenarios. We employed two methods adapted from human risk assessment: the General Risk Propensity Scale (GRiPS) and the Time Trade-Off (TTO) technique. Queries involving RA cases with varying prognoses and hypothetical treatment choices were posed repeatedly to assess risk profiles and response consistency. All GenAIs consistently identified the same RA cases for the best and worst prognoses. However, the two risk assessment methodologies yielded varied results. The adapted GRiPS showed significant differences in general risk propensity among GenAIs (ChatGPT being the least risk-averse and Qwen/DeepSeek the most), though these differences diminished in specific prognostic contexts. Conversely, the TTO method indicated a strong general risk aversion (unwillingness to trade lifespan for pain relief) across systems yet revealed Perplexity as significantly more risk-tolerant than Gemini. The variability in risk profiles obtained using the GRiPS versus the TTO for the same AI systems raises questions about tool applicability. This discrepancy suggests that these human-centric instruments may not adequately or consistently capture the nuances of risk processing in Artificial Intelligence. The findings imply that current tools might be insufficient, highlighting the need for methodologies specifically tailored for evaluating AI decision-making under medical uncertainty.  
machine learning; personalized medicine; natural language processing (NLP); risk aversion; rheumatoid arthritis +
Footnote †: journal: Applied sciences
## 3 Results and Discussions
### Numerical Results  
The results of the numerical modeling compared to the experimental results are shown in Figure 10. The comparison in Figure 9 illustrates the accuracy of the Finite Element Model (FEM) against the experimental (EXP) data for different impact velocities (v = 0.40 m/s, 0.52 m/s, and 0.65 m/s). The FEM results matched closely the experimental data, demonstrating the model's ability to predict the dynamic response accurately. The modeling of the dynamic impact of the steel specimens managed to capture the impact and the resulting displacement with satisfactory accuracy. It is important to note that no fitting parameter was used in this comparison. Instead, the FEM was calibrated using initial experimental results to estimate the friction coefficient of the system. Once calibrated, the FEM was applied to predict the impact responses shown in Figure 9 without further adjustments, ensuring an unbiased comparison. It should be noted also that the numerical modeling served the rule of calibrating the setup by estimating the friction of the railing and motion system. This was achieved by devoting the results of the five specimens with IDs 2,3,5,6, and 7 as per Table 5, for the calibration of the results. Based on this calibration procedure, the friction coefficient of the built setup was estimated to be 0.37.  
The numerical modeling results, compared with the experimental data, are presented in Figure 10. The Finite Element Model (FEM) accurately predicted the dynamic response for different impact velocities (0.40 m/s, 0.52 m/s, and 0.65 m/s). The FEM was calibrated using experimental data from specimens 2, 3, 5, 6, and 7 (Table 5) to estimate the friction coefficient, which was determined to be 0.37. The calibrated FEM provided reliable predictions without any additional fitting parameters. Once calibrated, the FEM was applied to predict the impact responses shown in Figure 9.  
Sensitivity analysis of the FEM revealed deviations of up to \(\pm\)8%, primarily due to variations in material properties, boundary conditions, and the estimated friction coefficient. Friction accounted for most of the discrepancies between the experimental and numerical results, while the effect of air resistance was negligible at the tested low velocities. However, a sensitivity analysis of energy loss from air resistance would be necessary for higher velocity operations. Additionally, noise in the load data during experiments contributed to some misalignment between the physical and numerical models.  
Figure 9: Force–mid-span displacement response of the dynamic impact and quasi-static testing.
# Concept Drift Adaptation with Incremental-Decremental SVM
## 3 Background: The Adaptive Window Model for Drift Detection and the Incremental-Decremental SVM
### Concept Drift with Adaptive Window
For all samples \(x_{i}\) inside the sliding window, we constructed the error \(\epsilon_{i}\) using several simulated Bernoulli distributions. Afterwards, we computed the average error difference for each window split. A reference classifier with 85% accuracy, with no drift, is simulated with a Bernoulli distribution of \(p=0.15\). We simulated 20 such distributions. For drift simulation, we created 20 mixed distributions by concatenating the first 700 samples from a Bernoulli distribution with \(p=0.15\), with the last 300 samples from another Bernoulli distribution with \(p=0.4\). Thus, we simulated a sudden drop in the classifier's accuracy from 85% to 60%. Then, we obtained the test margins \(\epsilon_{cut}\) and \(\epsilon_{cut\_adjusted}\) from Equations (2) and (4) by successive splits of \(W_{0}\) and \(W_{1}\) for the shifting window, imposing a limit of at least 41 samples (for statistical relevance). In Figure 1, it can be seen that the two margins, \(\epsilon_{cut}\) and \(\epsilon_{cut\_adjusted}\), are somewhat similar. However, the adjusted threshold (\(\epsilon_{cut\_adjusted}\)) is more resilient to false positives on smaller partitions, and more conservative on larger ones. Figure 1: Simulated thresholds for 20 random Bernoulli distributions with no concept drift (consistent distributions, green) and with 20 drift mixed Bernoulli distributions (given in black). The drift point is at sample 700, where the probability parameter changes from 0.15 to 0.4. Drift is detected at the sample where the difference of the splits’ averages intersects the computed threshold \(e\).
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 4 Mathematical Analysis
### Probability Analysis
#### 4.1.2 Probability Equation for TNFD Communications  
The probability equations for the two types of TNFD communications are derived in this section, and these are described as follows,  
Source-Based TNFD Communications  
* The conditional probability that PT has data packet for PR is \(\frac{m(n-1)\lambda}{\lambda_{tot}}\).
* The probability that the corresponding PR does not have data for corresponding PT in time \(T_{1}\) is \(\left(e^{-\lambda T_{1}}\right)\).
* The nodes (ST) that are hidden to the PR have minimum one data packet for the PT in time \(T_{2}\) (where, \(T_{2}=T_{RTS}+T_{CTS-AI}+T_{NF}+2T_{S}+T_{WAIT}\)) is \(\left\{1-\left(e^{-(am(n-2)\lambda T_{2})}\right)\right\}\). Hence, the probability for the STNFD communications is, \[P_{STNFD}=\frac{m(n-1)\lambda}{\lambda_{tot}}\Big{(}e^{-\lambda T_{1}}\Big{)} \Big{\{}1-\left(e^{-(am(n-2)\lambda T_{2})}\right)\Big{\}}\] (3)  
Destination Based TNFD Communications  
* The conditional probability that the PT has data for PR is \(\frac{m(n-1)\lambda}{\lambda_{tot}}\).
* The probability that the corresponding PR does not have data for the PT in time \(T_{1}\) is \(\left(e^{-\lambda T_{1}}\right)\).
* The probability that the PR (it acts as ST also) has minimum one data packet in time \(T_{1}\) for any other node that is hidden from PT and is in the range of PR is \(\left\{1-\left(e^{-(m(n-2)\lambda T_{1})}\right)\right\}\alpha\). Thus, the probability for the DTNFD communication is, \[P_{DTNFD}=\frac{m(n-1)\lambda}{\lambda_{tot}}\Big{(}e^{-\lambda T_{1}}\Big{)} \Big{\{}1-\left(e^{-(m(n-2)\lambda T_{1})}\right)\Big{\}}\alpha\] (4) Therefore, the total probability for TNFD communication is, \[P_{TNFD}=\frac{m(n-1)\lambda}{\lambda_{tot}}\Big{(}e^{-\lambda\ T_{1}}\Big{)} \Big{\{}1-\left(e^{-(am(n-2)\lambda T_{2})}\right)\Big{\}}+\frac{m(n-1) \lambda}{\lambda_{tot}}\Big{(}e^{-\lambda\ T_{1}}\Big{)}\Big{\{}1-\left(e^{-( m(n-2)\lambda T_{1})}\right)\Big{\}}\alpha\] (5)