esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] [18] evaluated the ability and effectiveness of Fourier transform infrared spectroscopy (FTIR) and e-nose-GC combined with chemometric techniques to discriminate nine powdered rhizomes of Zingiberaceae in Malaysia. [PERSON] et al. [19] applied proton nuclear magnetic resonance (1H-NMR) to analyze the metabolites of three species of _Curcuma_, and the results showed that their primary and secondary metabolites could significantly distinguish the three species of _Curcuma_. Therefore, spectroscopy has certain feasibility and effectiveness in species identification and classification applications. However, both methods have their advantages and disadvantages; GC-MS has the advantages of high-throughput analysis of a large number of metabolites and can be used for quantitative evaluation, but it has drawbacks such as complex operation, strong intra-class variability, and difficulty in separation and clustering. Spectroscopy has advantages such as speed, simplicity, non-destructive testing, and strong repeatability, but cannot be associated with individual chemical substances [20]. The combination of these two technologies can make up for the shortcomings and combine the advantages, to provide a more comprehensive identification and classification of differential metabolites between different species. Most of the research on the metabolites of Zingiberaceae plants used a single technology. A few studies, such as [PERSON] et al. [21], used GC-MS and near-infrared (NIR) spectra to compare and identify the metabolites of _Amomum tsaoko_ and _Amomum paratsaoko_. However, there has been no comprehensive comparative analysis of various common Zingiberaceae spices by combining GC-MS with spectroscopy. Therefore, the main purpose of this study was to conduct a comprehensive quantitative and qualitative evaluation of thirteen common Zingiberaceae spices in the Chinese market based on GC-MS and spectroscopy. The specific aims were to: (1) compare the differential metabolites of thirteen Zingiberaceae spices using non-targeted GC-MS metabolomics and quantitatively evaluate their key metabolites; (2) qualitative evaluation of thirteen Zingiberaceae spices using near-infrared (NIR) and mid-infrared (MIR) combined spectroscopy, rapid identification, and classification of different Zingiberaceae spices. Thisstudy provides a powerful tool for distinguishing different Zingiberaceae spices and helps to reduce the phenomenon of adulteration and mixing of Zingiberaceae spices.
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 1 Introduction  
The increasing digitization of academic content and the rise in institutional repositories have profoundly reshaped how scholarly work is created, stored, and accessed. A significant portion of this content consists of student-authored theses and dissertations--collectively referred to as the gray literature. While these works often contain valuable empirical research, they are frequently overlooked in scholarly ecosystems due to inadequate metadata practices that hinder their discoverability, classification, and reuse [1, 2, 3, 4].  
A key challenge lies in the limitations of existing metadata standards. General-purpose schemas such as Dublin Core [5, 6, 7, 8] and MARC [9, 10] have been widely adopted in academic repositories, yet they offer only basic descriptive capabilities and fail to capture the structural and methodological complexity of empirical research, particularly in the social sciences. On the other hand, more specialized models--most notably the DataDocumentation Initiative (DDI) [11]--provide rich metadata structures suitable for quantitative datasets but remain too complex and domain-specific for practical use in thesis documentation by students or non-specialist staff.  
This paper addresses the gap between simplicity and descriptive depth by introducing a novel, hybrid metadata framework specifically designed to support the documentation of empirical content in student theses. Our approach integrates selected elements from DDI into the familiar structure of Dublin Core, creating a lightweight but expressive model that can accommodate quantitative, qualitative, and mixed-methods research. This positions our framework as a practical solution that balances usability and metadata richness--two qualities often found at odds in existing standards.  
The motivation for this work stems from the pressing need to make student-authored empirical research more visible, reusable, and systematically integrated into scholarly infrastructures. By equipping repositories with a metadata system that captures essential research characteristics without overwhelming users, we aim to bridge the gap between professional data documentation practices and everyday academic workflows. Furthermore, our framework contributes to ongoing efforts to elevate the status of the gray literature as a discoverable and credible source of scholarly knowledge.  
To assess the validity and utility of the proposed model, we applied it to a representative sample of sociology theses. This empirical investigation enabled us to refine the metadata fields based on real-world documentation patterns and led to the development of a functional web-based prototype. The system supports guided metadata entry, modular form generation, and repository-ready output formats--all without requiring technical expertise.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 2 Background
### Measuring Oxygen in Packaging
#### 2.2.1 Colorimetric Indicators  
The oxidation of a dye to create a differently colored substance is a relatively simple method. The dyes can be present as a gel or liquid with which the examined bottle is filled. More practical can be the solid versions that are glued to the inner wall of the packaging [59]. There are multiple variations on this type, but the most well-known kind employs a methylene blue dye in a solid matrix that starts from a reduced, colorless leuco-form and can be easily oxidized to a blue conjugated structure [60]. The presence of glucose in the indicator can reverse this reaction by reducing it back to its colorless state [60; 61; 62]. The reverse reaction is slow, so the incorporation of a TiO\({}_{2}\)-photocatalyst may cause the dye to reset only after a light-trigger. Controlling the reverse reaction is preferred to avoid interference with ongoing experiments [61]. Other light-sensitive systems with similar reaction kinetics comprise of riboflavin as a fluorescent dye and ethylene-diaminetetraacetic acid (EDTA) as photo-reducing agent [63].  
The sacrificial reductants in each of these indicators are only present in a limited quantity, so the amount of measurements that can be executed are limited as well. However, it is possible to create fully reversible colorimetric oxygen indicators, based on hemoglobin as present in human blood [64]. The iron-functionalized proteins reversibly bind oxygen for transport in the bloodstream, thereby granting it its typical red color. However, such proteins are unstable in storage and degrade within days. As an alternative, a synthetic cobalt-complex has been found that can reversibly bind oxygen for several months [64]. These visual indicators are easy and cheap, but often difficult to quantify and exhibit only limited sensitivity [59; 62]. They are therefore not employed very often as an analysis method, but can be used in intelligent packaging inks as an indicator for the consumer [65]. Recently, another method, called Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been getting more attention as well [66; 67]. A laser beam at narrow wavelengths around 760 nm and attenuated to a specific packaging may decrease in intensity throughout a package after light absorption by oxygen. Lambert-Beer's law can then provide a quantified oxygen concentration.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 4 Analysis and Discussion of Simulations and Experiments
### Static Analysis of a Refueling Pipe
Because of the bias loading characteristic of the refueling pipe, an air-bearing linear guideway is used in the experiment to keep the pipe moving in a straight line to test the axial stiffness, and the low friction coefficient of the air-bearing linear guideway does not generate additional resistance, which will not affect the accuracy of the experiment. Figure 16: Comparisons of **(a)** axial deformation and **(b)** maximum stress between results of ABAQUS and theory by changing the number of finite elements. Red dashed line represents the theoretical result; the blue line connecting blue points represents the ABAQUS result. Figure 17: The converged results of simulation: **(a)** axial deformation and **(b)** maximum stress. By hanging weight underneath the pipe (as in the simulation), the elongation \(\lambda_{\mathrm{e}}\) can be tested, and the axial stiffness of the refueling pipe \(K_{\mathrm{e}}\): \(K_{\mathrm{e}}=0.2579\)\(\mathrm{N/mm}\). Comparing the experimental and theoretical results, the relative error of the axial stiffness is less than 4.4%, which validates the accuracy of the theoretical formula (Equation (20)). Thus, the theoretical formulas can guide the design of an elliptical-helical pipe with bias loading.
# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 5 Experimental Evaluation
### Experimental Results
#### 5.3.1 Effect of Mixed Data Composition
Figure 12 demonstrates that the best results are achieved when using only the sanitized data. The mixed data produces intermediate results that get worse with the increase of the original data proportion, rendering the mixed data closer to the original one. One notable effect is that the group from which the original data is preserved affects the results in the same direction. More precisely if the original data from the protected group is preserved, all the metrics in the protected group will become closer to their original unfair values. The same trend is observed in the privileged group. These effects are particularly observable on the Positive and the True Positive Rates. Figure 11: BER and miss prediction rates of GB (trained to predict the sensitive attribute on Adult). From left to right: BER, miss prediction rate on the original and sanitized part of the mixed data, miss prediction rate per groups. The standard deviation for all computed results is below 0.05. Figure 12: (Left to right) False Positive rates, predicted Positive Rates and True Positive Rates obtained in each group with GB when predicting the decision attribute. We can relate these observations to our previous hypothesis about the prediction (of \(S\) or Y) on the mixed data that can be considered as two separate operations. The invariance to \(p\%\) of the predicted positive rate in the privileged group can be explained by the fact that the sanitization process did not modify the amount of positive decision in the privileged group, but rather enhance the protected one (_Baseline S_.). Another interesting aspect is that the false positive rates in the protected group remained unchanged when using \(San\)\(y\), while it varied with the proportion \(p\%\) in the privileged data. In fact, as the false positive rate in the protected group did not change with the sanitization (_Baseline O_. and _Baseline S_. ), we can expect the metric in the protected group not to be affected by the amount of sanitized data. With the original decision, the metric varies significantly in both groups. This can be explained by the fact that the transformation of the data is made without having the correct decision, reflecting, in consequence, the disagreement between the profile and the associated decision. Finally, in Figure 13, we observe that the decision prediction accuracy is above 85% for any chosen proportion.
## 2 Correlation Model of Monitoring Data  
### Correlation Analysis of Monitoring Data  
The monitoring structural responses from different locations and different types of sensors are in the correlated relationship. The Pearson correlation coefficient is an index to measure the linear correlation degree of structural responses.  
The structural response matrix of \(m\) measuring points under the load in \(n\) time steps is denoted as **X**:  
\[\textbf{X}=\left[\begin{array}{cccc}\textbf{x}_{1}&\textbf{x}_{2}&...& \textbf{x}_{m}\end{array}\right]=\left[\begin{array}{cccc}x_{11}&x_{12}& \cdots&x_{1m}\\ x_{21}&x_{22}&\cdots&x_{2m}\\ \vdots&\vdots&\vdots&\vdots\\ x_{n1}&x_{n2}&\cdots&x_{mm}\end{array}\right] \tag{1}\]  
\[\textbf{x}_{i}=\left[\begin{array}{cccc}x_{i1}&x_{i2}&\cdots&x_{in}\end{array} \right]^{T} \tag{2}\]  
In the Equations (1) and (2), \(\textbf{x}_{i}\) represents the measurements of the measuring point \(i\) under the load of \(n\) time steps. When the fault sensor works in normal, the measurement of the measuring point where the fault sensor located is denoted as **y**. The Pearson product-moment correlation coefficient [9] of the measurement from the arbitrary measuring point and the measuring point where the faulty sensor located is denoted as \(\rho_{i}\)  
\[\rho_{i}=\frac{\text{cov}(\textbf{x}_{i},\textbf{y})}{\sigma_{\textbf{x}_{i}} \sigma_{\textbf{y}}}=\frac{E(\textbf{x}_{i}-\mu_{\textbf{x}_{i}})(\textbf{y} -\mu_{\textbf{y}})}{\sigma_{\textbf{x}_{i}}\sigma_{\textbf{y}}} \tag{3}\]  
\[\sigma_{\textbf{x}_{i}}=\frac{1}{n-1}\sum_{k=1}^{n}\bigl{(}\textbf{x}_{i}(k)- \mu_{\textbf{x}_{i}}\bigr{)}^{2} \tag{4}\]  
\[\sigma_{\textbf{y}}=\frac{1}{n-1}\sum_{k=1}^{n}\Bigl{(}\textbf{y}(k)-\mu_{ \textbf{y}}\Bigr{)}^{2} \tag{5}\]  
\[\text{cov}(\textbf{x}_{i},\textbf{y})=\frac{1}{n-1}\sum_{k=1}^{n}\bigl{(} \textbf{x}_{i}(k)-\mu_{\textbf{x}_{i}}\bigr{)}\Bigl{(}\textbf{y}(k)-\mu_{ \textbf{y}}\Bigr{)} \tag{6}\]In Equation (3), \(\sigma_{\mathbf{x}_{i}}\) and \(\sigma_{\mathbf{y}}\) represent the response variance of the measurement point \(i\) and the fault sensor, respectively; \(\text{cov}(\mathbf{x}_{i},\mathbf{y})\) is covariance, \(\mathbf{x}_{i}(k)\) and \(\mathbf{y}(k)\) represent the measurement of point \(i\) and the fault sensor in the \(k^{\text{th}}\) time step, respectively; \(\mu_{\mathbf{x}_{i}}\) and \(\mu_{\mathbf{y}}\) are the mean value of the measurements \(\mathbf{x}_{i}\) and \(\mathbf{y}\), respectively.
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 1 Introduction
We describe our methodology to process, assemble, and merge the different datasets. We discuss the precision of the Landsat-8 and SAR sensor products conclude on the quality of ice motion products generated from both sensors and provide insights about future large-scale mapping of ice sheet motion with satellites.
# Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
## 2 Materials and Methods
### Ball-on-Disk Wear Testing  
The test was made with the High Temperature Tribometer equipment (CSM Instruments, Buchs, Switzerland), Figure 1. The 29 mm circular samples were cut in a thickness of 6 mm, and then followed by standard metallographic preparation to obtain a mirror finish.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Si** & **Fe** & **Cu** & **Mn** & **Mg** & **Zn** & **Ni** & **Cr** & **Pb** & **Ti** & **Sr** \\ \hline
8.734 & 0.895 & 2.757 & 0.183 & 0.252 & 0.993 & 0.121 & 0.025 & 0.063 & 0.059 & 0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition provided by supplier of A332 alloy (wt.%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Castings Samples**} & \multirow{2}{*}{**Alloy A332**} & \multicolumn{3}{c}{**Refiners**} & \multirow{2}{*}{**Modifier Sr**} \\ \cline{3-4} \cline{6-7}  & & & **Ti** & & **B** \\ \hline As-cast & 99.98 & 0 & 0 & 0 & 0.02 \\ Al5 Ti2B & 92.98 & 5 & 2 & 0.02 \\ Al5 Ti1B & 93.98 & 5 & 1 & 0.02 \\ \hline \end{tabular}
\end{table}
Table 2: Alloy compositions prepared as a function of grain refiner (wt.%).  
The tests were performed without lubrication, at room temperature. The parameters of the ball-on-disk testing are shown in Table 3. The friction coefficient (\(\mu\)) between the Al\({}_{2}\)O\({}_{3}\) spheres and A332 samples was measured for each cycle and this value was plotted. The characterization of the worn tracks was performed through SEM images and non-contact optical profilometer (OP).  
The volume loss of the wear track was obtained using OP, assuming no significant wear of the sphere used in the test, due its high hardness in relation to the surface tested. The optical profilometer obtained images of the worn tracks after the ball-on-disk testing. The volume loss was obtained from the 3D view of the complete worn tracks in all the samples from the equipment software calculation, using the surrounding tooth surface as the reference plane. The volume of a hole was measured and used as a value of volumetric loss. The relative wear resistance (\(\Psi_{\text{h}}\)) was calculated from Equation (2) [22], i.e., lower values represents lower wear resistance.  
\[\Psi_{\text{h}}=\frac{\text{W}_{\text{hE}}}{\text{W}_{\text{hPV}}} \tag{2}\]  
where W\({}_{\text{hPV}}\) is volumetric loss standard (mm\({}^{3}\)), and W\({}_{\text{hE}}\) is volumetric loss of reference sample (mm\({}^{3}\)).  
Furthermore, the line profiles were extracted from the surface to show a cross-sectional view of the wear track. The equipment used was an optical profilometer 3D CCI Lite from [PERSON], with an optical lens of 10\(\times\).
## 3 Proposed Architecture: CS-SR  
The proposed architecture of cloud service selection and recommendation system CS-SR is shown in Figure 1. The CS-SR has three main objectives: First, it involves a decision-making system (D.M.S.) that takes the request of the user for services, processes the request, and returns the appropriate service to the user. Secondly, our proposed method prevents unnecessary comparison of the services and ranks them. Thirdly, our approach reduces the overall execution time taken to find the best services and also evaluates the energy consumption used in finding optimal service according to customer requirements.  
The working of the proposed architecture CS-SR is as follows: the user will send the request for the required services to the decision-making system [D.M.S.]. The request of the user consists of two parameters: QoS attributes range and priority for QoS attributes. The user requirement helps the decision-making system (D.M.S.) to understand the user requirements expected from the offered services. The D.M.S. will filter out those services that fit into the range from the service list [1 to N]. The service list is made available to the D.M.S. by the cloud service providers [1 to S]. The services offered by the provider also consist of the QoS parameter value and the feedback received from the experience customers. The D.M.S. in CS-SR architecture helps in reducing the total execution time by removing services that do not fit in the range taken from the user. The Cs-SR architecture also increases the overall accuracy of the service selection and ranking because it considers using both quantitative and qualitative QoS attributes for selecting and ranking of service.  
The decision-making system (D.M.S.) helps in ranking the service and provide the optimal service to the user, as shown in Figure 2. The D.M.S. system will act as the central coordinating point by maintaining the request of all the users and concurrently finding out the service for each customer. The D.M.S. gets activated as soon as the user request the service. The user requirements are taken through, and the QoS attributes are taken in range (min, max), and the user priority is related to the QoS attributes. The D.M.S. system is comprised of four phases, i.e., (1) filtration, (2) evaluation, (3) integration and ranking, (4) selection and recommendation. Figure 2 shows the internal working of CS-SR, and the detailed discussion of each stage in CS-SR discussed below.
# U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution
## 2 Proposed Method
### Semi-Automatic GTs  
Medical semantic segmentation aims at pixel classification. When generating a bone model, missing parts of borderlines are not admitted. This is the main reason why generating such a model by hand takes time. By contrast, various CNN-based methods, such as the U-net, have been proposed to estimate semantic segmentation levels automatically, but these methods learn based on benchmark datasets [26; 27], so each patient's GT data are essential for obtaining their unique bone models. This is why U-net-based methods need more handcrafted GT data than using conventional image processing. The proposed method overcomes this problem using rough handwritten lines as input. The lines show the borderlines of a bone in a CT image but are often not closed, which is essential for GT data. The proposed method refers to this as a combinatorial problem and generates closed regions like a single pen stroke. Moreover, the bone of interest is sometimes divided into multiple parts, so the proposed method uses DBSCAN to extract multiple parts in an image by clustering in advance. DBSCAN can gather points of high density and is effective for points that contain clusters of similar density. Therefore, multiple parts are clustered accurately because points consisting of handwritten lines are distributed continuously and in an unbiased way.  
Figure 1: Components of the proposed method.  
Let each image \(I_{t},t=1,\dots,T\) consist of colored pixels \(p_{i},i=1,\dots\) from handwritten lines. The following algorithm generates the GT of \(I_{t}\). Based on the generated GT images, the proposed method adopts a U-net, as shown in Figure 1, for segmentation.  
**O-0:**: \(C=\{p_{i};i=1\dots\}\),
**O-1:**: Divide \(C\) into \(M\) parts using DBSCAN, \(C=C_{1}\cup\dots\cup C_{M}\),
**O-2:**: \(m\gets 1\),
**O-3:**: while \(m<=M\)  
min. \(\sum_{i\in C_{m}}\sum_{j\in C_{m}}d_{ij}x_{ij}\), \(d_{ij}=|p_{i}-p_{j}|\),  
s.t. \(\sum_{j=1}^{|c_{m}|}x_{ij}\) (\(\forall_{i}\in C_{m}\)),  
\(\sum_{i=1}^{|c_{m}|}x_{ij}\) (\(\forall_{j}\in C_{m}\)),  
\(\sum_{i\in S}\sum_{j\in C_{m}\smallsetminus S}x_{ij}\geq 1\) (\(\forall S\in C_{m},S\
eq\emptyset\)),  
\(x_{ij}\in\{0,1\}\) (\(\forall i,j\in C_{m}\)),  
Draw a line between \(p_{i}\) and \(p_{j}\) when \(x_{ij}=1\),  
Fill in the boundary,  
\(m\gets m+1\).
## 1 Introduction
### Contributions  
This paper focuses on evaluating the cross-correlation fFunction (CCF) and cross-correlation function with Hilbert transform (CCFHT) for TPE in PIR sensor-based vehicle speed measurement. The specific contributions of this work are as follows:  
* A detailed mathematical evaluation of conventional and enhanced cross-correlation methods for TPE.
* An investigation into sensor response divergence and its effects on estimation accuracy.
* A numerical analysis of system parameters--including bandwidth, signal-to-noise ratio (SNR), and sensor time constants--to assess their influence on time period estimation performance, complemented by limited experimental validation to compare the overall performance of CCF and CCFHT.  
By integrating theoretical modeling, simulation, and real-world validation, this study provides practical insights into the performance and limitations of low-cost PIR-based vehicle speed sensing, laying the foundation for future ITS applications that require robust and lightweight estimation techniques.