esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Case-Study
#### 2.4.2 Remote Sensing Data  
Data Acquisition and Preprocessing  
The Sentinel-2 Multispectral Imager is a European Space Agency (ESA) satellite for Earth observation that provides imagery with 13 spectral bands from the near-infrared (NIR) to short-wave infrared (SWIR). The radiometric images are provided with a range of spatial resolutions of 10 m, 20 m and 60 m (Table 1). Each Sentinel-2A or -2B satellite revisits the same area every 10 days (5 days with the twin satellites together).  
Images containing the study vineyard blocks (Section 2.4.3) were selected and processed via the Google Earth Engine (GEE) platform that enables large-scale processing of Sentinel-2 L2A (Sentinel-2A and Sentinel-2B) products. The time period considered for the study was from 13 th May to 20 th August 2019, which is the most relevant period to monitor vine growth vegetation in this region [36]. Following [PERSON] et al. [37], decision trees and Bayesian models for cloud detection were applied to the Sentinel-2 images via GEE Javascript API to leave them in ready-to-use formats. Considering the revisit period of the Sentinel-2 (A/B) satellites, 25 images should have been potentially available over each block for the chosen time period. However, after cloud detection, the number of available images for each vineyard was 11 on average, with a range from 7 to 16 images depending on the location of the various vineyards. By using the GEE cloud-based platform to select Sentinel-2 images and pre-process them, the output was a file containing the spectral band values for each date (available images) and for each vineyard block.  
There were 12 spectral bands (among the 13 available from Sentinel-2 satellites) used in the model (Table 1). Spectral band 10 at 1380 nm was not used as it corresponds to a high atmospheric absorption band dedicated to visible and sub-visible cirrus cloud detection [37]. To avoid mixed pixels, a 10 m inner buffer was imposed at the border of each block to ensure only pure vine pixels were considered. Pure pixels within each block were then averaged for each spectral band at each individual date, i.e., regardless of vineyard block size, each block had one value for each spectral band at a given date.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 3 Waterborne and Foodborne Protozoan Infections
### Pathogenic Protozans in Drinking Water  
According to a systematic review of waterborne parasitic infections occurring worldwide from 2017 to 2020, drinking water was the source of infection in about 18 outbreaks and protozoans isolated from patients were _Cryptosporidium_ spp., _Giardia_ spp., _B. hominis_, _C. cayetanensis_, _E. histolytica_, _D. fragilis_, and _T. gondii_[80].  
A comprehensive review found that drinking water was involved in 17.06% of 416 outbreaks occurring from 2017 to 2022, while the remainder was associated with recreational water and swimming pools [81]. Reports regarded mainly developed countries with efficient national surveillance systems and adoption of sensitive detection methods, while outbreaks in developing countries with poor hygiene levels and inadequate water treatment standards were likely underestimated. Not publishing reports in peer-reviewed journals contributes to underestimation [80; 81].  
In a study on the occurrence of pathogenic protozoans in drinking water in African countries, _Cryptosporidium_ spp. was detected in 4-100% samples of tap water and sachet water in Egypt, Ethiopia, Ghana, Uganda, Zambia, and Zimbabwe. _G. intestinalis_ was detected in tap water in Egypt, Ethiopia, South Africa, and Sudan, with prevalence ranging between 1% and 38%. _C. cayetanensis_ was detected with a prevalence of 5% in tap water in Ghana [14].
## 3 Sample Preparation for Carotenoids Determination  
Carotenoid isolation includes several essential steps: sample pretreatment (which involves drying, grinding, and storing to stabilize carotenoids), extraction, clean-up (using solid-phase extraction (SPE) or liquid-liquid extraction (LLE) to remove any interfering compounds), chemical modification (such as saponification and derivatization), and pre-concentration (in some instances).  
The compact structure of plant cell walls and the association of carotenoids with various molecules, such as proteins and fatty acids, require preliminary steps to mitigate the influence of the matrix before extraction. These steps aim to break down barriers, facilitate solvent penetration into the tissue, and improve mass transfer. The type of pre-treatment used before extraction is directly related to the cells' processing characteristics. Several cell disruption methods are available, including physical methods (e.g., cryogenic grinding, bead milling, cooking, high-pressure homogenization, ultrasonication, etc.), chemical methods (acids and surfactants), biological methods (germination), and enzymatic lysis. Enhancing the effectiveness of cell wall damage can increase carotenoid yields by up to tenfold [54]. In the case of samples rich in water, it may be advisable to freeze-dry prior to extraction, while matrices rich in starch should be heat-treated or enzymatically hydrolyzed to enhance the extraction process of carotenoids. In turn, matrices rich in proteins require protein precipitation with acids, while samples rich in fat or pigment require often saponification before further analysis. Though sample preparation varies significantly between different laboratories, it should be of great importance to standardize sample preparation procedures to compare obtained data. Additionally, despite the variety of methods, several limitations need to be addressed, including energy consumption, toxicity, and the stability of metabolites observed during the extraction of carotenoids [55].
## 3 Controller Design Based on Legendre Polynomial FAT
### Adaptive Sliding Mode PID Controller Design  
#### 3.2.1 Sliding Mode Manifold Design  
The sliding mode control has been widely used in nonlinear systems, due to its robustness to modeling uncertainty and external disturbances [16; 25; 26]. However, for the sliding mode control, the conventional linear sliding mode manifold can be used to eliminate the steady-state error and guarantee the asymptotic stability of system errors, but the tracking error cannot converge to zero in finite time, so it cannot solve the motion lag caused by the joint lag. To ensure the quick convergence of the joint errors and reduce the motion lag caused by the joint lag, this paper designed a PID sliding mode manifold with the integral and differential of tracking error, shown in Equation (13).  
\[s\,=k_{p}e(t)+k_{i}\int e(t)dt+k_{d}\dot{e}(t) \tag{13a}\] \[e(t)=\eta_{d}-\eta \tag{13b}\]  
where, \(k_{\rm p}\), \(k_{\rm i}\), and \(k_{d}\) are the positive constant, and \(k_{\rm d}\in\)[0,1]. \(\eta_{\rm d}\) and \(\eta\) are the joint vectors of the desired position and actual position, respectively.  
Based on the PID sliding manifold of Equation (13) and sliding mode control theory, the sliding mode control law of the manipulator will be designed as Equation (14).  
\[U=k_{d}{}^{-1}\Big{[}k_{p}\dot{e}+k_{i}\dot{e}+k_{d}\ddot{x}_{d}+k_{d}\dot{H} +hs+\rho sign(s)\Big{]}+u_{c} \tag{14}\]  
where, \(h\) is the positive constant, \(\ddot{x}_{d}=\ddot{\eta}_{d}\), and \(u_{\rm c}\) is the robust term of sliding mode control. In Equation (14), it is difficult to select a reasonable value for the error-boundary \(\rho\), too conservative values would cause high frequency chattering of the system. To this end, the author obtains the error-boundary control law through the stability analysis, and adjusts the value of \(\rho\) online, as shown in Equation (20).  
Next, the first-order derivation of the sliding manifold (13a) with respect to time is used for the stability analysis. Then, substituting Equations (4) and (14) into (13a), we can get Equation (15).  
\[\dot{s} =k_{p}\dot{e}+k_{i}e+k_{d}\ddot{x}_{d}-k_{d}U+k_{d}H \tag{15}\] \[=k_{d}(H-\hat{H})-[hs+\rho sign(s)]-k_{d}u_{c}\]  
Substituting Equation (14) into Equation (15) and transforming it into an error state space equation, as shown in Equation (16).  
\[\ddot{e}+(k_{p}/k_{d})\dot{e}+(k_{i}/k_{d})e=(H-\hat{H})-k_{d}{}^{-1}[hs+\hat{ \rho}sign(s)]-u_{c} \tag{16}\]  
Converting Equation (16) to a matrix form:  
\[\dot{E}=AE+Bw \tag{17a}\] \[E=[e\dot{e}]^{T}A=\left[\begin{array}{cc}0&1\\ -k_{p}/k_{d}&-k_{i}/k_{d}\end{array}\right]^{T}B=\left[\begin{array}{c}0\\ 1\end{array}\right]\] (17b) \[w=(H-\hat{H})-k_{d}{}^{-1}[hs+\hat{\rho}sign(s)]-u_{c} \tag{17c}\]
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
###### Abstract  
The conversion of unstructured clinical data into structured formats, such as Fast Healthcare Interoperability Resources (FHIR), is a critical challenge in healthcare informatics. This study explores the potential of large language models (LLMs) to automate this conversion process, aiming to enhance data interoperability and improve healthcare outcomes. The effectiveness of various LLMs in converting clinical reports into FHIR bundles was evaluated using different prompting techniques, including iterative correction and example-based prompting. The findings demonstrate the critical role of prompt engineering, with the two-step approach shown to significantly improve accuracy and completeness. While few-shot learning enhanced performance, it also introduced a risk of overreliance on examples. The performance of the LLMs is assessed based on the precision, hallucination rate, and resource mapping accuracy across mammography and dermatological reports from two clinics, providing insights into effective strategies for reliable FHIR data conversion and highlighting the importance of tailored prompting strategies.  
large language models; Fast Healthcare Interoperability Resources; unstructured clinical data; healthcare informatics; natural language processing; prompt engineering Article
## 4 Experiments
### Evaluation Task and Metrics
#### 4.3.2 Performance Measurements  
We adopted the evaluation metrics commonly used in the field of knowledge graph completion, i.e., MR, MRR, Hits@N.  
Mean Rank (MR): It averages the ranking of all triplets in the test set. MR reflects the overall ranking of the correct triplets in the test set, and the smaller the value, the better the performance of the model. Here, \(i\in\{1,2,\ldots,|S|\}\) is a set of ranking results and \(rank_{i}\) is the ranking of the correct triplet in this group order. The MR is calculated using Equation (8).  
\[MR=\frac{1}{|S|}\sum_{i=1}^{|S|}rank_{i} \tag{8}\]  
Mean Reciprocal Rank (MRR): It is an international entity and relation completion standard. MRR is the reciprocal of the ranking of the position where the correct target is located. The larger the value, the better the accuracy of the model prediction, and the better the effect of the model. The MRR is shown in Equation (9).  
\[MRR=\frac{1}{|S|}\sum_{i=1}^{|S|}\frac{1}{rank_{i}} \tag{9}\]  
Hits@N: It evaluates the proportion of triads in the top N of the test set, which reflects the accuracy of the model prediction. A larger value of Hits@N indicates a higher accuracy of the model prediction. Here, we take N to be equal to 1,3, and 10. The calculation of Hits@N is shown in Equation (10).  
\[Hits@N=\frac{1}{|S|}\sum_{i=1}^{|S|}\parallel(rank_{i}\leq N) \tag{10}\]  
where \(\parallel(\cdot)\) is the indicator function. It is defined from Equation (11).  
\[\parallel(rank_{i}\leq n)=\begin{cases}1,\ \frac{\text{\tiny{$\exists$}}}{ \text{\tiny{$\exists$}}}rank_{i}\leq n\\ 0,\ \frac{\text{\tiny{$\exists$}}}{\text{\tiny{$\exists$}}}rank_{i}>n\end{cases} \tag{11}\]
## 5 Small Borehole Array Lateral Logging Forward Modeling
### The Impact of Anisotropy  
Existing studies have extensively explored the response of lateral logging resistivity in anisotropic formations, but the comprehensive response characteristics of micro-borehole array lateral logging tools under anisotropic conditions, as well as the effects of the formation thickness and anisotropy coefficient on logging results, have not been sufficiently  
Figure 10: Resistivity curves in multi-layer scenarios.  
Figure 9: Resistivity curves for different formation thicknesses.  
investigated. This research, through detailed model simulations, further enriches the theoretical understanding of logging responses under anisotropic formation conditions, providing new insights for the interpretation of logging data in complex formation environments [24]. The simulation parameters used in the model included the following: horizontal formation resistivity, \(\mathrm{Rt}=10\)\(\mathrm{\Omega}\cdot\mathrm{m}\); mud resistivity, \(\mathrm{Rm}=0.1\)\(\mathrm{\Omega}\cdot\mathrm{m}\); borehole diameter, \(\mathrm{d}=8\) inches; surrounding rock resistivity, \(\mathrm{Rs}=2.5\)\(\mathrm{\Omega}\cdot\mathrm{m}\). Figure 11 shows the trends of resistivity at four investigation depths as a function of formation thickness under different anisotropy coefficients. Analysis of the graph revealed the following observations: firstly, the resistivities from RLA1 to RLA4 displayed negative correlation; secondly, as the anisotropic coefficient, \(\lambda\), increased, the measured resistivity values rose, showing a trend of rapid growth, followed by stabilization; finally, the sensitivity to formation anisotropy differed among the various modes, and although the impact of anisotropy was eventually reflected in all modes, the critical thickness of formation with an effect increased in succession [25].  
Figure 12 depicts the dynamic trends of resistivity with changes in the anisotropy coefficient under four different investigation modes, with a constant layer thickness [26]. Analysis indicated that in anisotropic formation environments, the resistivity data from RLA1 to RLA4 showed a negative relationship; secondly, within the range where the formation thickness was less than 0.2 m, the observed resistivity changes were minimally affected by the anisotropy coefficient; finally, once the formation thickness exceeded 0.4 m, the resistivity's response to different anisotropy coefficients became pronounced, indicating that the resistivity signal can effectively reflect changes in the anisotropic characteristics of the formation [27].  
Figure 11: Changes in resistivity curves with formation thickness in anisotropic formations for fixed anisotropy coefficients.
## 4 Conclusions  
This work presents the reaction performance of Al/MoO\({}_{3}\) nanothermite prepared by the electrospray strategy with Al nanoparticles and MoO\({}_{3}\) nanostrips. We compared it with the physically mixed counterpart. The morphology of the electrosprayed Al/MoO\({}_{3}\) nanothermite particles resembled a bird nest, showing good integrity compared to the loose physical mixture. DSC-TG thermal analyses showed that there were two main exothermic processes for electrosprayed Al/MoO\({}_{3}\) particles. The Kissinger method and Coats-Redfern integral method were applied to estimate the activation energy and the thermodynamic reaction mechanism, respectively. Furthermore, the pressurization behavior was measured. Both the maximum pressure and the pressurization rate of the electrosprayed sample were higher than those of the mechanically mixed one. Moreover, the electrosprayed sample was more sensitive to flame and impact stimuli than the mechanically mixed one, which will benefit its application in primer or percussion caps. In addition, the combustion performance of the sample was evaluated. Under confined conditions, the reaction rate of the electrosprayed sample was so fast that fierce deflagration was achieved. On the contrary, the mean combustion rate of the mechanically mixed sample was much slower and gentler. Even under unconfined conditions, the mean combustion velocity of the electrosprayed sample was a hundred times as fast as that of the mechanically mixed sample. In conclusion, the electrospray integration of Al/MoO\({}_{3}\) nanothermite will be a good strategy to acquire both a high reaction rate and moderate sensitivity. And the resulting products will be superior candidates for green primer and civil excavation applications.  
Conceptualization, methodology, validation, formal analysis, investigation, writing--original draft preparation, resources, Y.L; data curation, writing--review and editing, visualization, H.R. and Q.X.; funding acquisition, Y.L. and Q.X. All authors have read and agreed to the published version of the manuscript. This work was supported by the Excellent Discipline Cultivation Project by JHUN (grant no. 2023 XKZ042), and the Doctoral Research Start-up Fund Project (grant no. PBSKL-2022-QD-03).  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.
## 5 Experiments and Assumptions on Abnormal Behavior Detection and Noise Removal Performance
### Parameters Used in Ensemble Model for Attack Detection and Noise Reduction  
This section describes the ensemble model used to perform the proposed methodology's noise reduction and attack detection. In this experiment, a voting-based ensemble model was constructed using the six models (Decision Tree, Random Forest, SVM, Naive-Bayes, K-NN, Logistic Regression) described in Section 2.2. The constructed model was  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Type** & **Quantity** \\ \hline Total Data & 225,711 \\ \hline Normal data & 97,686 (43.28\%) \\ \hline Attack data & 128,025 (56.72\%) \\ \hline Filtered attack data & 25,605 \\ \hline Features & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Overview.  
used for both network attack detection and noise reduction. The following is a description of the parameters used in each internal model.
## 4 Results and Discussion
### Splitting Tensile Strength  
Figure 10 shows the splitting tensile strength results of concrete mixtures incorporating various rubber sizes (R1, R5 and R10) and their dosages (10%, 20% and 30%) at 14, 28 and 56 days. All the results showed a CV of less than 1.3%. It was observed that the splitting tensile strength increased with testing stages with various dosages of rubber and their sizes. For example, approximately 13% and 27% increases in tensile strength were observed for the mixture incorporating 10% of the R5 rubber at 28 and 56 days, respectively, compared to 14 days due to the continuous hydration process [44].  
Figure 10: _Cont._  
For the mixture incorporating 10% of rubber, an approximately 13% increase in tensile strength was observed for the mixture with R5 rubber compared to that of the similar mixture with the R1 rubber size. This increase in tensile strength from the R1 rubber size to the R5 rubber size was mainly due to the lower stiffness and the larger pore volume of the smaller rubber particles (R1), leading to a decrease in the mechanical strength of the mixture incorporating the R1 rubber aggregates [35].  
On the other hand, a reduction in tensile strength was observed for the mixture incorporating the R10 rubber size compared to that of the R1 and R5 rubber sizes. For example, for the mixture incorporating 20% of rubber, an approximately 10% decrease in tensile strength was observed for the R10 rubber size compared to that of the R1 rubber size. A similar decreasing trend was observed between R5 and R10. This reduction in tensile strength for the R10 rubber size compared to the R1 rubber size was mainly due to the softer nature of the rubber particles. Larger rubber particles produce more tensile stresses in the cement paste vicinity. These tensile stresses produce premature cracking in the concrete, leading to a decrease in the loading carrying capacity [40].  
Figure 11 shows the effect of rubber dosages on the 28-day tensile strength of the concrete mixtures. It was observed that the tensile strength at 28 days decreased with the higher dosages of rubber (Figure 11). For example, the mixture incorporating the R5 rubber showed approximately 11% and 23% decreases in the 28-day splitting tensile strength for  
Figure 10: Splitting tensile strength results for specimens incorporating various dosages of rubber: (a) R1, (b) R5, (c) R10.  
10% and 20% dosages, respectively, compared to that of the similar mixtures without rubber. Similarly, a higher reduction in tensile strength was observed for the mixture incorporating 30% of rubber.
## 4 Results
### Spectral
The spectrum associated with the greatest amount of Fe in this case does not appear to have the greatest 900 nm absorption depth, as compared to the Al\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) spectra, and is seemingly free from indicative kaolin group absorptions located at 2206 nm [40; 47] and 2705 nm [40] and 2761 nm [40] which are present in the Al\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) spectra. The Fe spectrum in this case, and because of the lack of other mineral absorptions, is probably a relatively pure Fe sample. The CaO and MgO (and the sample with the highest LOI) samples are consistent with carbonates. Absorption features associated with carbonates are observed at approximately 2300 nm [41; 48], 2500 nm [41], 3500 nm, 4000 nm, 4600 nm and 6400-6500 nm [7]. Calcium dominated carbonates, such as calcite, have absorptions at longer wavelengths in the 2300 nm and 2500 nm spectral regions as opposed to those carbonates, such as siderite or magnesite, where Fe or Mg replaces the Ca, and the absorption features shift to shorter wavelengths [48; 49; 50]. As noted, the Al\({}_{2}\)O\({}_{3}\) spectrum display several absorptions commonly associated with kaolinite but also contain jarosite as defined by a distinct absorption at 2260 nm [51]. The sample associated with the greatest TiO\({}_{2}\) has weak kaolin group absorptions at 2160 nm and 2200 nm and around 2700 nm. The spectrum relating to the highest valued SiO\({}_{2}\) is devoid of discernible absorption features in the Visible/Near Infrared/Shortwave Infrared (VNIR/SWIR) but is distinguishable as a quartz sample by the notable peaks located at approximately 8500 nm, 9000 nm, 12,500 nm and 12,800 nm [42; 43]. Lastly, the spectrum associated with the highest K\({}_{2}\)O value is almost free of any discernible absorption features with the exception being kaolin group absorptions around 2700 nm. In this case the SWIR absorptions around 2200 nm that are also associated with the kaolin group are not discernible.
# Multi-Exposure Image Fusion Based on Weighted Average Adaptive Factor and Local Detail Enhancement
## 3 Proposed Methods  
### Proposed Frame  
The fusion framework of this paper is shown in Figure 1, which includes two steps: (1) For the input LDR multi-exposure image sequence, use the well-exposure evaluation function and chrominance evaluation function proposed in this paper to determine the exposure weight map and the chrominance weight map; after normalization and fusion, a comprehensive weight map is obtained. Secondly, the initial fusion pyramid is obtained by fusing the Laplacian pyramid of the input image and the Gaussian pyramid of the comprehensive weight map; and (2) Use the K-means algorithm to divide the input image into a low-exposure image set (LEI) and an over-exposure image set (OEI) according to the image brightness histogram, and then apply the fast local Laplace filter (FLLF) to enhance the pixels of the brighter region of the LEI and the darker region of the OEI.