esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In the present research, an environmental friendly adsorbent, Iron-Z-A, has been practically applied to adsorb as well as desorb phosphate from an aqueous solution by successful incorporation of iron into the skeleton of Z-A in the form of Fe\({}^{3+}\), named as Iron-Z-A. The adsorbed phosphate's amount is found to be 156.45 mg by each gram of Fe, whereas each gram of Al contributed to 16.19 mg of phosphate adsorption. The derivatives of phosphate at different pH of an aqueous solution behaved as a ligand and interacted with OH radical of iron on zeolite surface, making the chemisorption process of adsorption-desorption of phosphate a reversible one. The adsorption and desorption of phosphate are by the 'ligand exchange principle' exhibited by Fe in the zeolite frame. [PERSON]'s model of adsorption isotherm, and pseudo-second-order kinetics showed best fitting of the adsorption data.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/22/4897/sl](http://www.mdpi.com/2076-3417/9/22/4897/sl)]([http://www.mdpi.com/2076-3417/9/22/4897/sl](http://www.mdpi.com/2076-3417/9/22/4897/sl)). Table S1: EDS data, Figure S2: Experiment to observe the phosphate adsorption capacity of zeolite-A and iron-zeolite-A from an aqueous solution at pH \(\leq\) 5, Figure S3: Optimization of pH for adsorption of phosphate from an aqueous solution using iron-zeolite-A, Figure S4: Optimization of pH for desorption of phosphate from an aqueous solution using iron-zeolite-A, Figure S5: Adsorption of phosphate from an aqueous solution using different dosage of the iron-zeolite-A adsorbent at pH \(\leq\) 5, Figure S6: Determining reaction order by log plotting.  
**Author Contributions:** Conceptualization by K.S.K. and M.S.; methodology by K.S.K. and M.S.; software support by A.A.; validation by K.S.K. and M.S.; formal analysis by M.S. and S.K.; investigation by M.S. and S.K.; resources by K.S.K.; data curation by M.S. and A.A.; writing--original draft preparation by M.S.; writing--review and editing by M.S. and K.S.K.; visualization by M.S. and S.K.; supervision by K.S.K.; project administration by K.S.K. and S.K.; funding acquisition by K.S.K.  
**Funding:** This project was funded by \"Korea Institute of Civil engineering and building Technology\" (KICT), Rep. of Korea, grant number 20190151-001. The APC was funded by the same project # 20190151-001.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: The above figure explains the interacting mechanism behind adsorption and desorption of phosphate on the surface of iron incorporated Iron-Z-A.
## 3 Manipulation of Micro Actuator  
The fabrication of micro-actuators/robots by TPP has attracted attention due to its specific strength in that a variety of materials can be printed and integrated within a 3D micro actuator/robot for the functionalization of the remote control of the actuator. Though high-resolution, complex 3D structure fabrication is possible, a polymeric structure that is fabricated using only photocurable resins intrinsically lacks mechanical strength, electrical conductivity, and magnetic and optical properties. Hence, new approaches are needed to achieve efficient actuation and remote manipulation. Various methods have been developed for efficient actuation and control, and they can be classified into two categories: material-based and structural-based treatments. A material-based treatment is a modification of the photocurable resin itself; on the contrary, a structural-based treatment is a method of imparting function to a fabricated structure. For example, mixing nanomaterials into a photocurable resin for functionalization is a material-based treatment [46], and surface coatings on the outer surface of a fabricated structure via diverse deposition methods such as chemical vapor deposition (CVD), electron beam (e-beam) deposition, physical vapor deposition (PVD), and sputtering are structural-based treatments [50].  
For reciprocal motions, it is difficult to avoid zero net movements. This could be solved by non-reciprocal movements such as bionic scallop and corkscrew motions [51]. Moreover, rotational motion could be converted into a translational motion using a helical structure that mimics the swimming of bacterium by rotating the bacterial flagella [52]. Considering the environment in which a micro-actuator/robot operates where viscosity is dominant, it is more efficient to move an actuator using different and diverse methods such as chemical, magnetic, optical, and acoustic force rather than a load for efficient non-contact/non-invasive movement and manipulation to a targeted location [53].
# An Efficient Local Search Heuristic for Earth Observation Satellite Integrated Scheduling
###### Abstract  
Earth observation satellites (EOSs) are taking a large number of pictures with increasing resolution which produce massive image data. Satellite data transmission becomes the bottleneck part in the process of EOS resource management. In this paper, we study the earth observation satellite integrated scheduling problem (EOSIS) where the imaging activities and download activities are considered integratively. We propose an integer linear programming model to formulate the problem. Due to the NP-hardness of the problem, we propose an efficient local search heuristic (ELSH) to solve problems of large size. ELSH uses a dedicated local search method to guarantee algorithm performance and efficient constraint handling mechanisms to guarantee algorithm efficiency. Numerical experimental results show that the algorithm demonstrates excellent performance on a set of benchmark instances. The ELSH achieves optimal results for all small-size instances (with 50 targets, two satellites, and three ground stations), and is very robust for large instances with up to 2000 targets. Moreover, the proposed ELSH easily dominates the state-of-the-art algorithm.  
1
## 3 Methods
### Data Collection  
The research will follow a structured methodology to identify, collect, and map relevant studies on the use of _LLM_s in medicine--it will include peer-reviewed articles, research reports, theses, conference proceedings, and corporate documents that discuss the use of _LLM_s in the medical context, with particular attention to describing the architectural models employed, application areas, temporal trends, and international collaborations.  
The study incorporates a comprehensive search across scientific databases (PubMed, Scopus, IEEE Xplore, and Google Scholar) to capture peer-reviewed articles, research  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Developer** & **Purpose** & **Features** \\ \hline BioGPT [23] & Microsoft Research & Biomedical NLP & Trained on biomedical literature for text generation and clinical question answering \\ \hline GatorTron [24] & University of Florida & Clinical data analysis & Trained on millions of electronic medical notes to improve clinical language understanding \\ \hline MedPaLM [25] & Google Health & Diagnosis and medical Q& Combines PaLM with medical data to answer clinical questions \\ \hline PubMedBERT [26] & — & Biomedical NLP & BERT version optimized for PubMed articles, useful for text classification and information extraction \\ \hline Galactica [27] & Meta AI & Scientific research & Trained on scientific texts, including medical publications \\ \hline ClinicalBERT [28] & — & Clinical language understanding & Adaptation of BERT to electronic health records (EHR) \\ \hline BioBERT [29] & — & Biology and medical applications & Trained on PubMed and PMC to enhance NLP accuracy in healthcare \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main _LLM_s in the medical domain with purposes and key features  
Figure 2: Processing flow of this systematic review.  
reports, theses, conference proceedings, and corporate documents discussing the use of _LLM_s in medicine. The search strategy is designed to maximize coverage and relevance by using a combination of keywords related to large language models, artificial intelligence, and healthcare applications. Inclusion and exclusion criteria are defined to focus on works that explicitly mention the architecture, application, and impact of _LLM_s in the medical domain. To minimize bias, the following steps were implemented: (i) Search Strategy Optimization: using broad search terms and synonyms to capture diverse terminologies associated with _LLM_s; (ii) Reviewer Consensus: Multiple reviewers independently screened titles and abstracts to ensure consistent application of inclusion criteria, and discrepancies were resolved through consensus or consultation with a third reviewer; (iii) Data Validation: Cross-verification of selected studies to eliminate duplicates and ensure relevance.
# Progressive Collapse Analysis of SRC Frame-RC Core Tube Hybrid Structure
## 3 Finite Element Model  
### Constitutive Model of Materials  
In general, bilinear stiffness degeneration behavior is employed to simulate the cumulative damage of structure [36; 37]. In this paper, stiffness degeneration of materials are defined in the finite element (FE) model to simulate the cumulative damage of structure. Constitutive model curves of steel and concrete are shown in Figure 7a,b [6], respectively. In Figure 7a, the stress-strain curve exhibits two stages, including elastic and hardening stages. \(E_{\mathrm{s}}\) is modulus of elasticity, \(f_{\mathrm{y}}\) is yield strength, and \(E_{\mathrm{p}}\) is hardening modulus. In Figure 7b, the constitutive model of concrete is based on the Kent-Park model [38], both unconfined and confined concrete fibers in this constitutive model consider tensile strength with linear degeneration. \(f_{\mathrm{c}}\), \(E_{\mathrm{t}}\),\(f_{\mathrm{t}}\), and \(E_{\mathrm{u}}\) are ultimate compressive strength, rigidity after cracking, ultimate tensile strength, unloading rigidity. The calculating formula is expressed as below:  
\[\sigma=\left(\begin{array}{cc}f_{\mathrm{c}}\Big{[}\frac{2\varepsilon}{0.00 2}-\big{(}\frac{\varepsilon}{0.002}\big{)}^{2}\Big{]}&\varepsilon\leq 0.002\\ f_{\mathrm{c}}[1-Z(\varepsilon-0.002)]&0.002\leq\varepsilon\leq\varepsilon_{20 }\\ 0.2f_{\mathrm{c}}&\varepsilon\geq\varepsilon_{20}\end{array}\right. \tag{1}\]  
\[Z=\frac{0.5}{\varepsilon_{50u}+\varepsilon_{50u}-0.002} \tag{2}\]  
Figure 6: Test results: (**a**) The hysteretic curve and (**b**) lateral displacement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Vibration Modes**} & \multicolumn{2}{c}{**Initial State**} & **Failure State** \\ \cline{2-3}  & **Test Value/Hz** & **Test Value/Hz** \\ \hline
1 & 5.94 & 3.26 \\
2 & 18.98 & 6.22 \\
3 & 25.13 & 15.96 \\
4 & 37.93 & 31.82 \\
5 & 50.03 & 42.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test value of frequencies.  
\[\varepsilon_{50\text{u}}=\frac{3+0.002f_{c}}{0.002f_{c}-1000} \tag{3}\]  
\[\varepsilon_{50\text{h}}=\frac{3}{4}\rho_{\text{s}}\sqrt{\frac{B}{s_{\text{h}}}} \tag{4}\]  
where, \(\varepsilon\), \(Z\), \(\varepsilon_{50\text{u}}\), \(\varepsilon_{50\text{h}}\), \(\rho_{\text{s}}\), \(B\) and \(S_{\text{h}}\) are the concrete strain, the slope of the descending branch of the stain-stress curve, the stain at \(0.5f_{c}\), the strain increase of confined concrete over unconfined concrete at \(0.5f_{c}\), the ratio of stirrup, width of concrete core area and the spacing of stirrup, respectively.
## 3 System Performance
### Throughput  
Throughput represents the total number of bits (in bits/s) forwarded from wireless LAN layer to higher layers in all WLAN nodes of the network (Figure 9). In our case, we represented this information for the global network, using one patient and one sensor node. The average throughput for the network simulated is 40,450 bps. This throughput decreases when we focus on one patient, in this case being approximately 3220 bps. Finally for one sensor node the average throughput is 546 bps.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Medium/Large Satellite Constellations
#### 3.1.4 Hj-1c  
Being China's first civilian radar satellite, HJ-1C is part of the HJ-1 (Environment-1) constellation implemented by the National Committee for Disaster Reduction and State Environmental Protection Administration (NDRCC/SEPA) [79]. HJ-1A and HJ-1B are optical satellites with a hyperspectral camera and an infrared camera, respectively; they were launched together in 2018 and placed into a circular Sun-synchronous orbit with a 650 km altitude, 4-day repeating (recursive) ground tracks, and an angular position difference of 180\({}^{\circ}\). On the other hand, HJ-1C has a launch mass of 890 kg, nearly twice as heavy as the other optical satellites (470 kg), and was launched into a different Sun-synchronous circular orbit with a 500 km altitude and 31-day repeating cycles. HJ-1C has a dawn-dusk orbit, meaning that its LTDN (Local Time on Descending Node) is 06:00 h, which is also different from 10:45 h of the other satellite. The three satellites are heterogeneous, as the \"2 + 1 constellation\" notation suggests, but share common mission goals of environmental protection and disaster monitoring. Examples of disaster monitoring include flood, drought, typhoons, earthquakes, grassland or forest fires, and oil spills [80]. The flexibility of HJ-1A to vary its orbit-repeating period between 4 and 31 days suggests synchronized interoperability with HJ-1C, which has a 31-day repeat cycle. The S-band SAR was developed with the assistance of Russia, jointly from NPO Mashinostroyenia (rocket design bureau) and Vega Radio Engineering Corporation. The radar powered by two 40 Ah batteries experienced limited functionality due to antenna damage prior to decommissioning [81]. The impacts of such unit malfunctions would be confined in a mission utilizing swarms of small SAR satellites where a unit may be commissioned or decommissioned flexibly.  
Figure 13: 3D point cloud representing parameters estimated from SAR tomographic inversion on a. Stack of 50 TerraSAR-X images over Barcelona, Spain: (**left**) Ground height. (**right**) Average deformation rate. Source: ETH Zurich.
## 4 Results and Discussion
### SVM Testing  
SVM algorithm with the settings explained above is evaluated on the prepared test data set. The confusion matrix of obtained results is shown in Figure 16.  
It is evident from the Figure 16 that all kernels of SVM misclassify the pure red chili sample as oil adulterated. However, if we remove dimensionality reduction, i.e., PCA, and use all 75 wavelengths of interest (500-700 nm), as detailed in Section 4.2, the accuracy of the algorithm increases to 100%. The prediction accuracies of SVM with reduced dimensionality and with all 75 featured wavelengths are shown in Table 3.  
Figure 16: Confusion matrix of Linear, Quadratic, Cubic and Guassian kernel functions for SVM classifier. (**a**) Linear kernel function; (**b**) SVM with 2 nd Degree Polynomial Kernel; (**c**) SVM with 3 rd Degree Polynomial Kernel; (**d**) SVM with rbf Kernel (\(gamma=3.4,C=1\)).  
In our previous work [32], we proposed a method of detection of powder adulterants in red chili, but color adulterations were not considered. We discussed above how and why these adulterations failed our developed model and proposed another model that classifies pure chili from the color adulterated. Thus, by assembling both classifiers, multiclass SVM for color adulteration detection followed by anomaly detection for powder adulteration can be used for a complete analysis of a sample.
# A Review of Thermal Error Modeling Methods for Machine Tools
## 2 Thermal Error Modeling Method for Machine Tools
### Support Vector Machine (SVM)  
SVM is a classifier developed from the generalized portrait algorithm in pattern recognition. Its early work was published in 1963 by the former Soviet scholars [PERSON] and [PERSON] [75]. Based on the principle of structural risk minimization, the upper limit of functional error is minimized by SVM. It has strong nonlinear function fitting ability [76], which makes it solve the practical problems better [77; 78; 79; 80; 81; 82]. Although SVM had faster convergence speed and higher prediction accuracy, it is still a new attempt to apply the support vector machine regression modeling theory for the thermal error modeling of machine tools.  
In 2013, [PERSON] et al. [78] established the spindle thermal error models based on SVM and multiple regression respectively for the leaderway-V450 machining center. By analyzing the accuracy of the cross-quarter experimental data, it was verified that the SVM model used for thermal error compensation not only had high accuracy, but also good robustness. Besides, it was found out that when there were less data for modeling, the prediction accuracy and robustness of multiple regression model were poor. However, the SVM model still had higher prediction accuracy even when the working conditions changed [79]. In 2017, [PERSON] et al. [81] combined the grid search method to optimize the parameters (the penalty parameters and kernel parameters) of the SVM thermal error model to improve the performance of the thermal error model. After applying the improved model on the \(X\) and \(Z\) axes of the CNC platform, the \(X\)-axis positioning error and the \(Z\)-axis positioning error were reduced 89.55% and 85.67%, respectively. The compensation performance was better than that of the BP neural network. Similarly, in the research of [PERSON] et al. [80] in 2020, the PSO algorithm was used to optimize the parameters of the SVM, and the prediction model PSO-SVM was proposed to predict thermal error. Compared with the traditional SVM model, the new model had higher prediction accuracy. More importantly, it could avoid falling into local optimization and reduce the training time effectively. Furthermore, in the research of [PERSON] et al. [82], based on least squares support vector machine (LSSVM, a mature algorithm improved on the SVM), a more effective model (GA-LSSVM), which was combined with genetic algorithm (GA) was proposed. Compared with the LSSVM model and the traditional BP model, the GA-LSSVM model had the following superiority: (1) the absolute residual and mean square error were the smallest; (2) the model determination coefficient R\({}_{2}\) was the largest; and (3) performance was the best. Based on the synthetic temperature information (STI), the whale algorithm was used to optimize the thermal error model of support vector regression by [PERSON] et al. [83] in 2021. Compared with the S-GA-SVR thermal error model optimized by genetic algorithm, the RMSE of thermal error in the Z direction of the spindle was reduced 46.6%.
## 3 Background
### Anomaly Detection with Autoencoders  
The main idea behind autoencoder based anomaly detection is to measure how much the reconstructed data deviates from the original data. An autoencoder has an unsupervised learning objective whose primary task is to copy the input to the output [39]. Therefore, an autoencoder is trained to reconstruct data by minimizing this objective function, or loss function. For anomaly detection, reconstruction error is used as the anomaly score. Data points which generate high reconstruction errors can be categorized as anomalous data points based on a threshold value. When autoencoders are used for anomaly detection, they are trained using semi-supervised learning. In this scenario, only normal data instances are used in training process. The training dataset should be cleaned from  
Figure 1: Illustration of an autoencoder.  
anomalous data points and outliers as much as possible for a successful model generation. Therefore, for the anomaly detection model to be successful, it is critical to design a pre-processing phase that can filter out or correct such noise from the data when possible. This can be achieved using a variety of unsupervised domain-specific methods, such as statistical and distance-based methods. For example, in the context of time series data, autoregressive models, in the context of spatial data, distance- and density-based models can be used for noise removal. Techniques such as Principle Component Analysis (PCA) and Singular Value Decomposition (SVD) methods can also be used in order to improve the representation quality of data and remove discordant data points [1; 3]. After the training process, the autoencoder will generally reconstruct normal data with very small reconstruction error. As the autoencoder has not encountered with the abnormal data during the training, it will fail to reconstruct them and generate high reconstruction errors which can be used as anomaly score.  
There are some practical issues in using autoencoders with contaminated training data (dataset with normal and anomalous data points). Since anomalies are treated as normal data points during the training phase, there will be inevitably more errors in the model compared to training with only normal data points. If we try to overcome these errors by tuning the network with more layers and neurons, we may face the problem of overfitting which is the significant problem in the case of deep neural networks. A sufficiently complex deep autoencoder may even learn how to represent each anomaly with sufficient training by generating low reconstruction errors which would be a problem in anomaly detection [3].
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 7 Proposed 1D CNN Regression for Cutter Wear Prediction
### Mean Squared Error (MSE)  
The mean squared error (MSE) function was used as a loss layer in the convolutional neural network to compute the loss or error between the actual and predicted output. The _MSE_ was proposed for the regression task. Equation (6) presents the _MSE_ loss function [54].  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i}) \tag{6}\]  
where \(n\) refers to the size of the test dataset, \(y_{i}\) is the actual output, and \(\hat{y}_{i}\) is the predicted output.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 4 Privacy-Based Refinement
### Application to Oblivious Mechanisms  
We conclude the discussion on privacy-based refinement by showing the usefulness of our strong \(\sqsubseteq^{\text{prv}}\) order in the case of oblivious mechanisms.  
**Theorem 5**.: _Let \(f:\mathcal{X}\to\mathcal{Y}\) be any query and \(A,B\) be two mechanisms on \(\mathcal{Y}\). If \(A\sqsubseteq^{\text{prv}}B\), then \(A\circ f\sqsubseteq^{\text{prv}}B\circ f\)._  
This means that replacing \(A\) by \(B\) is the context of an oblivious mechanism is always safe, regardless of the query (and its sensitivity) and regardless of the metric by which the privacy of the composed mechanism is evaluated.  
Assume, for instance that we care about standard differential privacy, and we have properly constructed \(A\) such that \(A\circ f\) satisfies \(\varepsilon\)-differential privacy for some \(\varepsilon\). If we know that \(A\sqsubseteq^{\text{prv}}B\) (several such cases are discussed in Section 6), we can replace \(A\) by \(B\) without even knowing what \(f\) does. The mechanism \(B\circ f\) is guaranteed to also satisfy \(\varepsilon\)-differential privacy.  
Note also that the above theorem fails for the weaker order \(\sqsubseteq^{\text{prv}}_{d}\). Establishing \(A\sqsubseteq^{\text{prv}}_{d_{\mathcal{Y}}}B\) for some metric \(d_{\mathcal{Y}}\colon\mathbb{M}\mathcal{Y}\) gives no guarantees that \(A\circ f\sqsubseteq^{\text{prv}}_{d_{\mathcal{X}}}B\circ f\) for some other metric of interest \(d_{\mathcal{X}}\colon\mathbb{M}\mathcal{X}\). It is possible that replacing \(A\) by \(B\) in that case is not safe (one would need to re-examine the behavior of \(B\), and possibly reconfigure it to the sensitivity of \(f\)).  
Table 3 summarizes the relations between the various orderings.