esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
###### Abstract  
X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI's GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta's LLAMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.  
XPS; APXPS; large language models; curve fitting; materials science; synchrotron Article
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 3 Results and Discussion
### Antifungal Efficacy of Standard Fungicide and Biogenic Iron Oxide Nanoparticles Against
Interestingly, the phytosynthesized IONPs revealed a higher antifungal efficiency against the tested strain compared to the commercial fungicide of metalaxyl + mancozeb at low concentrations of 50 and 100 ppm, indicating their possible application of these nanoparticles as alternative to the chemically synthesized fungicides and avoiding their harmful impact on the environment [69]. Furthermore, the utilization of the biogenic IONPs presents a potential solution to address the issue of fungal resistance resulting from the excessive application of chemical fungicides [70; 71]. A prior investigation has provided evidence that the first stage in which the biogenic IONPs exhibit antifungal effectiveness is attributed to their disruption of the cell envelope, which plays a crucial function in fungal cells. The presence of antimicrobial interaction might lead to  
of ascorbic acid ranged from 37.56% to 86.12%, respectively, for concentrations of 50 to 250 ug/mL, and the results of this investigation were consistent with the findings of a prior research [73]. Moreover, the detected IC\({}_{50}\) value of the biogenic IONPs was found to be 122.57 ug/mL, whereas the standard ascorbic acid revealed IC\({}_{50}\) of 108.21 ug/mL.
# Experimental Studies on the Spraying Pattern of a Swirl Nozzle for Coal Dust Control
## 5 Conclusions  
The experimental and numerical studies about the mechanism and effectiveness of a swirl nozzle have been described for dust control and the nozzle was successfully used in underground coal mines. Further investigations may be performed on the interactions between traditional nozzle structure and this swirl nozzle structure in the chamber, and eddy simulations could be conducted. However, the current study can provide the following conclusions:  
1. For this swirl nozzle, when the spray pressure is low, the particles' SMD decreases rapidly with the increase of the spray pressure. When the pressure reaches 5 MPa, the particles' SMD decreases slowly; even if the spray pressure continues to increase, the particles' SMD reduces very slowly. According to this character, a normal pressure pump can be used in practice instead of an expensive high-pressure pump for cost reduction.
2. Along the axial distance from the nozzle, the fog particles' SMD increase rapidly and then decrease rapidly; after 100 cm, it gradually becomes stable. The processes of increase and decrease are relatively short. The particles' SMD was the largest in the center of the fog field and decreases gradually along the radial direction.
3. The axial speed of particles increases with the increase of spray pressure, however, the increase rate tends to be slow when the pressure is above 5 MPa. The speed of particles decreases with the increase of aperture under the same spray pressure. The speed of the fog particles decreases gradually with the increase of axial distance in the fog field, and the rate of decrease gradually slows down.
4. Increasing of spray pressure and decreasing of nozzle aperture will make the fog particles' size distribution more concentrated and uniform. The fog particles' size distribution is also more uniform where it is further away from the nozzle axially and radially.  
[PERSON], [PERSON] and [PERSON] conceived the whole procedure of this study; [PERSON] performed, analyzed the mechanical tests and wrote writing-original draft preparation; [PERSON] and [PERSON] made revisions of the manuscript; [PERSON] carried out project management and investigation.  
This research was funded by the Joint Funds of the National Natural Science Foundation of China (No. U1261107) and the Fundamental Research Funds of the Natural Science of Shanxi Province (No. 2012011018-1).  
The authors declare no conflict of interest.
## 3 Experimental Phenomena and Their Analysis
### Analysis of Experimental Results and Discussion
#### 3.4.2 Stiffness Degradation Curves  
The development of cracks in the specimen during loading, the entry of longitudinal bars and bolts into the elastoplastic working section, and the slip of the bond between reinforced concrete cause damage to the structural components. When damage accumulates to a certain value, residual deformation occurs in the structural components, there is plastic damage that cannot be elastically restored, and their stiffness begins to deteriorate. The degradation in the seismic performance of a structural component can be expressed in terms of the cut-line stiffness \(K_{i}\)[34]. Due to the presence of the corbel, there was a constraint on the rotation, making the stiffness different when the forces were applied in the upper and lower directions, so the calculation was carried out in separate directions.  
\[K_{i}=\frac{|F_{i}|}{|X_{i}|} \tag{1}\]  
where \(F_{i}\)--the load value at the \(i\)th positive or negative peak point; \(X_{i}\)--the displacement value at the \(i\)th positive or negative peak point.  
The stiffness degradation curves of the specimens are shown in Figure 17. The stiffness degradation curves of the two groups of specimens show a similar trend. The initial stiffness of the specimens is larger, and the stiffness degradation rate is slow, indicating that these joints have a strong resistance to lateral movement. As the displacement loading proceeded, the specimens developed cracks and the original cracks continued to develop. The deformation of the bolts at the joints resulted in the enlargement of the cracks at the joints, and the stiffness of the specimens gradually degraded. The degradation rate was faster at the beginning and slowed down at the end, and the curve tends to flatten out. The rate of stiffness degradation of specimen J2 in each displacement stage is lower than that of specimen J1, and the stiffness in each displacement stage is greater than that of specimen J1, indicating that increasing the strength of the bolts had a greater effect on the joints' stiffness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Condition** & \(\Delta L_{1}\) **(mm)** & **L (mm)** & \(\theta\Big{(}\frac{\Delta L_{1}}{L}\Big{)}\) \\ \hline Specim cracking & 6.4 & 550 & 1/86 \\ Specim yielding & 12.9 & 550 & 1/43 \\ Specim damage & 23.3 & 550 & 1/24 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Displacement angle of specimen J2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Condition** & \(\Delta L_{1}\) **(mm)** & **L (mm)** & \(\theta\Big{(}\frac{\Delta L_{1}}{L}\Big{)}\) \\ \hline Specim cracking & 4.4 & 550 & 1/125 \\ Specim yielding & 10.2 & 550 & 1/54 \\ Specim damage & 19.1 & 550 & 1/42 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Displacement angle of specimen J1.  
The residual stiffnesses of specimens J1 and J2 are 1.16 and 0.5, respectively, indicating that the new joints still had a certain load-bearing capacity when they were damaged.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.16 The Indicator to Measure Renewable Energies  
This indicator promotes local renewable energy production or the availability of renewable energy sources in the region. It has been promoted by SBTool\({}^{\text{PT}}\) Urban, SNTool, BREEAM C, and LEED ND, while addressed by Levels, ISO 37120, and SDGs. Therefore, this indicator is recognized as a prioritized environmental concern. According to SBTool\({}^{\text{PT}}\) Urban Guide, this indicator can be assessed by calculating the ratio of the renewable energy produced locally per the total estimated energy consumption for the project. To calculate this indicator through the CIM model, the energy data and the estimated energy consumption must be added to the model, and then the calculation can be performed by Dynamo routine. In their study, [PERSON] and [PERSON] [31] analyzed similar parameters, proposing a CIM platform to evaluate renewable energy production and building energy consumption in an urban area in Aldingen, Germany.
## Appendix A Numerical Simulation of Data Set Using Simulation Example  
To mimic the variability in the delay values due to various physiological conditions over a period of time, we choose the constant value of delay to be 0.088 s and considered small stochasticity around the constant value of the delay for healthy individuals and small stochasticity around the gradual increase in delay values (from 0.088 s to 0.11 s) for PPD individuals. These stochastic variabilities follow Gaussian distribution and are generated such that the signal-to-noise ratio is 30 dB. To simulate instability due to gain, we chose a gradual increment in gain with a 0 to 20% increase in controller gains (\(k_{p}\) from 15 to 18, \(k_{i}\) from 4 to 4.8 Hz and \(k_{d}\) from 0.5 to 0.6 s) with the small stochasticity around a constant 0.088 s delay. Further, we introduced system and measurement noise (with zero mean and 0.01 variance) in the simulation example. Next, we ran the simulation with a step input for each value of the delay and gains. From the step response data, we estimated poles using MPM. We followed the same procedure for all values of delay and gains to obtain a real part of estimated poles over a period of time for each of the individuals. Figure 3 shows an example of a simulated data set of the real part of estimated poles for healthy and PPD individuals over time.
## 3 Results  
### Changes in Soil and Waste Cement Characteristics  
The physical and chemical characteristics of the soil used in the test are listed in Table 1. Before the experiment, the pH was 5.5, and the soil Cd concentration was 3.16 mg\(\cdot\)kg\({}^{-1}\), which was significantly higher than the maximum permissible limit (0.6 mg\(\cdot\)kg\({}^{-1}\)). Its organic matter content and ion exchange capacity (CEC) were 9.965 g\(\cdot\)kg\({}^{-1}\) and 5.79 cmol\(\cdot\)kg\({}^{-1}\), respectively.  
The soil pH, CEC, and SOM changes after incubation with three rates of OPC, FAC, and ZEC are presented in Figure 3. Application of 1.0%, 2.0%, and 3.0% of OPC, FAC, and ZEC significantly (\(p<0.05\)) increased soil pH. Compared with control soil, the pH of OPCincreased by 1.7, 2.3, and 2.5 units at 1.0%, 2.0%, and 3.0% application rates, respectively. After FAC application at 1.0%, 2.0%, and 3.0%, the soil pH increased by 1.4, 2.5, and 2.7 units, respectively. Additionally, applying ZEC at 1.0%, 2.0%, and 3.0% resulted in a significant increase in the soil pH by 1.0, 1.9, and 2.3 units, respectively, compared to the control. Adding OPC, FAC, and ZEC prominently increased soil CEC under all application rates. A shift in soil CEC from 5.79 cmol\(\cdot\)kg\({}^{-1}\) (control) to a maximum of 8.9 cmol\(\cdot\)kg\({}^{-1}\) was observed in 3% OPC treatment, followed by 8.82 cmol\(\cdot\)kg\({}^{-1}\) and 8.36 cmol\(\cdot\)kg\({}^{-1}\) in 3% FAC and ZEC treatments, respectively. On the contrary, all treatments had no significant impact on the SOM content, compared to control, (\(p>0.05\)).  
The XRD patterns of OPC, FAC, and ZEC are illustrated in Figure 1. The main mineral phases of OPC, FAC, and ZEC were calcium carbonate and silicon compounds (silica and dicalcium silicate). The presence of CaCO\({}_{3}\) (2\(\theta\) of 23\({}^{\circ}\), 29\({}^{\circ}\), 35\({}^{\circ}\), 39\({}^{\circ}\), 43\({}^{\circ}\), 47\({}^{\circ}\), and 48\({}^{\circ}\)) was observed in all samples. Either SiO\({}_{2}\) or Ca\({}_{2}\)SiO\({}_{4}\) were present in OPC, FAC, and ZEC.  
SEM images revealed the morphological and structural characteristics of the samples (Figure 2). OPC, FAC, and ZEC exhibited a rough surface with obvious pore structure, thus having a higher potential to promote the adsorption of heavy metals.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 4 Multi-Object Detection for Inland Ships
### Transfer Learning Training Strategy  
Transfer learning [33] is a machine learning technique that facilitates the transfer of knowledge from a source domain to a target domain, thereby improving the learning performance in the target domain. The quality of data annotation during training significantly impacts the training effectiveness. Feature transfer enhances the model's generalization performance, even when dealing with significantly large target datasets. In various studies related to transfer learning, DANN [34] introduces adversarial network concepts into the field, optimizing feature mapping parameters. In conventional training, a large amount of labeled data is typically required, and the manual data labeling process is labor intensive and costly. Due to the scarcity of ship image datasets with well-annotated information, ship detection training datasets often rely on ship SAR data, which are also limited in quantity.  
There are two common approaches for applying transfer learning. The first approach is fine-tuning, which involves modifying a pre-trained network obtained from another source for the specific learning task. Usually, the weights of the network are initialized with pre-trained weights instead of random initialization. The second approach is using a fixed feature extractor, where the pre-trained network is used as a feature extractor for new tasks. Typically, the earlier layers of the network are frozen, and only the last fully connected layer is trained. Subsequently, the pre-trained network acts as a feature extractor, and the later layers are unfrozen to better learn the feature extraction parameters for the task at hand. The strategy employed in this paper involves both using a fixed feature extractor and adopting the fine-tuning approach.  
This study chooses the UA-DETRAC high-speed dataset due to the similarity of target vehicle characteristics with ships. The high-speed dataset presents varying sizes of objects based on distance, which aligns with our target network. In the initial 30 epochs, all feature extraction layers (i.e., all network layers except the last Head layer) are frozen, and only the parameters in the Head prediction part are updated. This allows the network to first update the parameters of the ship detection module without affecting the previously trained feature extraction layers. The UA-DETRAC dataset is used to train YOLOv5s and obtain a pre-trained weight model. In the subsequent 100 epochs, all feature extraction layers are unfrozen to enable the YOLOv5s network to update all weight parameters. The transfer learning strategy employed in this paper allows for training a ship detection model with satisfactory accuracy and robustness, despite the limited availability of training samples in the target dataset.
# Development of a Simplified Human Body Model for Movement Simulations
## 2 Materials and Methods
### Movement Experiment Performed with the IMU System  
In order to simulate the motion of the developed model in Adams software, it was necessary to assign displacements (drives) in each kinematic pair. To obtain appropriate motion data, it was decided to conduct an experiment with the aid of a volunteer wearing a set of inertial sensors from an STT iSen IMU system v2020 (Figure 3a). This system can measure human body movements without complicated equipment or any kind of interference with the human body. The system details are provided in [24; 25], and one of its case studies for gait analysis is presented in [26]. Using straps and according to the manufacturer's recommendations, the wireless IMU sensors are placed on the volunteer's specific body spots (Figure 3b), directly on the skin or clothes. Each inertial sensor provides global orientations with regard to its own housing, which corresponds to the adequate body segment's orientation. Sensors main features include: accuracy of Pitch/roll \(<\) 0.5\({}^{\circ}\) (RMS) and Heading \(<\) 2.0\({}^{\circ}\) (RMS), 3-axis gyroscope: \(\pm\)2000\({}^{\circ}\)/s, 3-axis accelerometer: \(\pm\)16 g, 3-axis magnetometer: \(\pm\)1300 uT, maximum sampling rate: 400 Hz, dimensions: 56 \(\times\) 38 \(\times\) 18 mm, weight: 46 g. Measurements from each IMU sensor are time-stamped for data synchronization, but temporal synchronization in multi-sensor IMU systems [27; 28] needs to be taken into consideration when dealing with possible inaccuracies. In addition, when it comes to biomechanical analysis, the software of the iSen system v2020 supplies skeleton rotation. Similar to the developed model, the ISB recommendation standards are generally used for the IMU system definition of biomechanical joint angles, with details provided by the manufacturer [24]. However, because the sensors are not perfectly placed along human bones, a particular process must be carried out to compute sensor-to-bone calibration (biomechanical calibration)--the patient/sportsman is asked to stay in a given position (such as the T-stance). Due to this, the system enables movements in each joint, in the form of three biomechanical angles, to be determined. These were collected during the experiment described below and used in subsequent simulation studies.  
The performed experiment used the \"Full body with hands\" measurement protocol with 16 sensors and T-stance as the initial calibration (Figure 4a). The movement performed in the experiment involved lifting an object with a bent spine (a light rectangular box placed within reach) from the ground using both hands (Figure 4b). Therefore, the additional goal of measuring, modeling, and analyzing this type of movement is also realized.
## 2 Numerical Setup
### Mesh Generation  
The reliability of the simulation and accuracy of the numerical results rely on the quality of the mesh. In our work, we used a multi-block structured mesh for the whole computational domain, including the impeller and the extended inflow and outflow sections. Figure 3 shows an enlarged view of the structured mesh around the blade. It is seen that the orthogonal mesh is produced on the surfaces of the blade as well as the leading and trailing edges. The minimum orthogonal quality is 0.43, and the maximum orthogonal skewness is 0.57; the maximum aspect ratio is 0.59. The mesh has undergone sufficient quality inspection to meet the computational requirements.
# Optimizing CNT Loading in Antimicrobial Composites for Urinary Tract Application
## 1 Introduction
Despite the proven antimicrobial activity of CNT composites [18; 21], their application as a coating for urinary tract devices is still unexplored and supplementary investigations are required to improve their antimicrobial properties. To the best of our knowledge, only two studies have been devoted to the development of antimicrobial CNT composites for application in urinary tract devices in controlled hydrodynamic conditions [23; 24]. In these studies, chemically and texturally modified MWCNT/PDMS composites efficiently modulated _E. coli_ adhesion under conditions that mimic the urinary tract environment. However, there is a lack of knowledge about the efficacy of these composites in the prevention of biofilm formation under this specific context (hydrodynamic conditions, biological fluid, and temperature). Hence, the present study aimed to optimize MWCNT loading incorporated into a PDMS matrix and evaluate the performance of produced MWCNT/PDMS composites in preventing _E. coli_ biofilm formation. Additionally, ball-milled CNT/PDMS composites were prepared as a strategy to evaluate the influence of CNTs dispersion pattern on the antimicrobial activity of the final composites.