esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Registration Strategy for Thermal-Visible Images in Substations
## 2 Proposed Methodology
### Second Registration
#### 2.2.3 Feature Matching and Transformation  
After obtaining a description of the feature points, it is necessary to establish the correct correspondence between these features.  
BBF performs feature matching. The algorithm extracts the feature vector of the high-dimensional space, and provides a correct probability estimation to the approximate matching, arranges all approximate matching in a sorted manner, and selects the optimal matching as the initial matching result. Furthermore, the algorithm establishes an absolute time constraint for the search process. When all the matching results in the priority queue have been inspected, or the match time is over the time threshold, the algorithm designates the presently acquired  
Figure 4: The process of constructing the Gaussian pyramid via ADU-PIIFD.  
optimal result as the approximate nearest neighbor. After completing the matching verification on one side, the image alignment is rechecked by the mechanism to review the matching results, and the same results of the two matchings are screened out. However, there will still be false matches in the optimal bilateral matching results selected by the above work, and the geometrical constraint [46] is used to filter the results again. The local domain structure around the control point is detected, and the nearest neighbor of the control point under the Euclidean distance is used as its neighborhood. A penalty mechanism based on the distance consistency of the matching pair is introduced to measure whether all the assumed matching pairs satisfy the geometric constraints used to maintain the local neighborhood structure to eliminate incorrect matchings. Finally, image pairs usually have almost the same viewing angle, but when the camera is close to the target object, there can still be significant differences in the viewing angles. This leads to projection distortion when mapping objects from three-dimensional space onto a two-dimensional image. Therefore, projective transformation is used to calculate the coordinate transformation model to address the issues caused by differences in viewing angles.  
The above is the whole process of thermal-visible-image registration.
## 7 Conclusions  
In this paper, we have proposed the use of Restricted Boltzmann Machine (RBM) vectors for the tasks of speaker tracking and speaker clustering in TV broadcast shows. RBM is applied for learning a fixed dimensional vector representation of a speaker which is referred to as an RBM vector. First, a Universal RBM model is trained with a large amount of available background data. Then an adapted RBM model is trained per test speaker. The visible to hidden weight matrices along with the bias vectors of these adapted models are concatenated to generate RBM supervectors. The RBM supervectors are further subjected to a PCA whitening with dimensionality reduction to extract the desired RBM vectors. These RBM vectors are used in the tasks of speaker clustering and speaker tracking. For speaker clustering experiments, two linkage algorithms for an AHC approach are explored with RBM vectors scored using cosine and PLDA. Using cosine scoring, the performance of the proposed system is better for both the linkage algorithms as compared to i-vector based clustering. Overall, the single linkage algorithm with 2000 dimensional RBM vectors is the best choice for our experiments, using both cosine and PLDA scoring. For speaker tracking experiments, we performed speaker segmentation followed by a speaker identification. We proposed the use of RBM vectors for the speaker identification stage. In general, the proposed system is more effective in low MST regions. The experimental results have shown that, in terms of EER, the proposed system outperforms the baseline i-vectors system using both cosine and PLDA scoring methods. We conclude that the RBM vectors can be successfully used as a speaker representation in speaker clustering and speaker tracking tasks.  
Figure 7: EER variation comparison for the proposed RBM vectors with 800 dimensional i-vectors. Different lengths of RBM vectors are evaluated.  
**Author Contributions:** Conceptualization, U.K., P.S. and J.H.; Funding acquisition, J.H.; Investigation, U.K.; Methodology, U.K., P.S. and J.H.; Project administration, J.H.; Resources, J.H.; Software, U.K. and P.S.; Supervision, J.H.; Writing--original draft, U.K.; Writing-- review & editing, P.S. and J.H..  
**Funding:** This work has been developed in the framework of DeepVoice Project (TEC2015-69266-P), funded by Spanish Ministry.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Prediction of Intensive Care Unit Length of Stay in the MIMIC-IV Dataset
## 2 Methods
### Feature Engineering  
First, we consider the diagnoses in the MIMIC-IV dataset that are coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD) [41]. Approximately 55% of diagnoses are coded in ICD-9 and approximately 45% are coded in ICD-10. In order to capture diagnoses as a feature, we need to convert the ICD codes into a single version. For this purpose, we used the R package 'touch' (tools of utilization and cost in healthcare), which maps all ICD-9 codes to version 10 in four different cases: conversion of an ICD-9 code to (1) an ICD-10 code; (2) multiple ICD-10 codes; (3) a combination of ICD-10 codes; and (4) a non-ICD-10 code [42]. The first and last cases give unambiguous results, covering 73.89% and 2.00% of all hospital admissions with diagnoses coded in ICD-9, respectively, while the second and third cases (with 23.26% and only 0.67% coverage) require further processing. For example, in case two, ICD-9 = 5715 is converted into ICD-10 K740, K7460, and K7469, while case three maps the ICD-9 = 8603 to S271 XXA+S21309A. In the latter case, we split the resulting combination of ICD-10 codes into multiple ones, similarly to in the second case. We then reduced the number of characters in the ICD-10 codes to maintain the same character length and searched for the least common factor by keeping the last letters. Finally, we verify that all ICD-10 codes are identical and replace the ICD-9 code with the truncated ICD code. If there is no match to the ICD-10 code, the ICD code is set to non-existent. The technical details of the conversion of the ICD codes are presented in the (Supplementary Materials). Once the ICD-9 to ICD-10 mapping is complete, we group the diagnoses into 22 chapters according to the categories specified by the WHO ([[https://icd.who.int/browse10/2019/en](https://icd.who.int/browse10/2019/en)]([https://icd.who.int/browse10/2019/en](https://icd.who.int/browse10/2019/en)), accessed on 3 June 2023), and represent each ICD code by its corresponding group.  
Vital signs and laboratory parameters were measured several times during each ICU stay. We represent these temporal parameters by their mean value over all measurements within the first 24 h of the ICU stay. In cases where no measurements were taken during this period, the parameters are declared as not available (NA). This single representation (mean) serves to reduce the complexity of the model, although it can also affect the model performance due to the lack of detailed information. Furthermore, we dichotomized ICU stays into short and long, defining a threshold of 4 days for the classification problem. Here, we classified LOS at the third quartile into short and long, as 75.2% of the data comprised LOSs of less than 4 days. For the regression, we left the LOS as a floating point number, rounding it to one decimal place.
## 3 Satellite-to-Ground QKD Simulator
### Atmospheric Link Modeling  
#### 3.2.1 Geometrical Loss  
Geometrical loss is a primary factor affecting the link budget in space-based quantum communication, and it increases quadratically with distance. For a satellite orbiting in LEO/MEO, the distance between the satellite and the ground depends on the satellite's elevation angle and can be computed using Equation (1).  
\[d(\theta)=R_{e}(\sqrt{\left(\frac{H+R_{e}}{R_{e}}\right)^{2}-\mathit{cos}^{2} \theta-\mathit{sin}\theta}), \tag{1}\]  
Figure 2: Flow chart of the satellite-to-ground MATLAB 2020 software calculator.  
where \(H\) is the satellite's altitude above the Earth's surface, \(R_{e}\) is the Earth's radius, and \(\theta\) is the elevation angle. The geometrical loss factor can then be calculated using (2) according to [53]:  
\[A_{geo}=\left(\frac{\lambda}{4\pi\;d(\theta)}\right)^{2}\times\left(\frac{\pi \;D_{r}}{\lambda}\right)^{2}\times\left(\frac{8}{\theta_{B}^{2}}\right), \tag{2}\]  
where \(D_{r}\) is the receiving aperture diameter and \(\theta_{B}\) is the half-width divergence angle of the transmitted beam, which assumes a Gaussian beam profile.
# Amharic _Adhoc_ Information Retrieval System Based on Morphological Features
## 5 Experiment
### Corpora Preparation  
#### 5.1.1 Test Collection  
Test collection plays a crucial role for designing, developing, and maintaining IR systems. However, test collection for Amharic _adhoc_ IR did not exist. Thus, we created the first Amharic IR test collection named 2 AIRTC and share it to the IR research community [50]. The test collection was created based on the framework used in TREC and Cranfield project.  
The Amharic test collection contains representative documents and topics. The test collection consists of 12,583 documents, 240 topics and the corresponding relevance judgments. Documents were collected from Web, news agencies, and individuals. The topic set was created by considering both current issues and the topics that were likely to be treated in our initial corpus. They cover diverse issues and include short and medium topics in addition to collocation. The document relevance judgment was made manually by assessors using a precise guideline. We ran the title fields of the topics on our initial corpus using Lemur toolkit and Web using Google search engine to acquire a first retrieved document list of a maximum of 50 documents per topic. We fused both retrieved document list (Lemur LM and Google retrieval results) on our initial collection and Web documents for the complementary documents). Each document from the fused list was then judgedfor relevance. Furthermore, exhaustive relevance judgments were performed on some topics on the document collection to acquire more relevant documents. A document is marked as relevant based on the narrative information in the topic; thus, it should not simply contain words from a query but rather fulfill the information need. The summary of the test collection is presented in Table 6 and more details are available in [50].
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 1 Introduction
Even for a few points, the brute force method, i.e., trying all cases for each pair of consecutive points, becomes rapidly infeasible due to the exponential growth of possible test cases. Recent solutions to the multipoint problem are based on a Non-Linear Programming approach (NLP) [12; 13] or Mixed-Integer Non-Linear Programming (MINLP) [12]. The most effective technique is actually the iDPP method based on the Iterative Dynamic Programming Principle [12] and is suitable for parallel computing architectures on CPUs as well as on GPUs [14]. A special case of the MPMDP requires finding an obstacle-free path through three points, which is a problem with name and importance: 3 PDP--_Three-Point Dubins Problem_. Despite being a subcase of the solved MPMDP case, the 3 PDP has received special attention in the last decade because of its application as a subroutine in a high-level planner for the Dubins Traveling Salesman Problem (DTSP) [15; 16; 17]. This subroutine is used to insert a new point in an existing path; therefore, the initial, the new middle, and the final point are specified, as well as the initial and final angles. Thus, the 3 PDP reduces to the one-dimensional problem of finding the angle corresponding to the central point, so that the curvature constraint is satisfied, and the minimum length is preserved. To solve this non-smooth minimization problem, several methods can be employed [18; 19; 20]. The crucial point of interest in this special instance of the MPMDP is the computational time: since it is called repeatedly as a subroutine of the DTSP, the solutions based on brute force, Dynamic Programming, or NLP/MINLP are considered too inefficient because of the considerable overhead. Therefore, a solution to this problem has been sought, as detailed in the next section, aiming at computational efficiency.
# \(H_{2}/H_{\infty}\) Output Tracking Control with a Performance Compensator for Aeroengines
## 3 Nominal Controller Design
\begin{array}[] {c}Q^{1/2}\\ -R^{-1/2}B^{T}P\end{array} \right]\), and the Inequality (20) is re-expressed as \(\tilde{A}P+P\tilde{A}^{T}+\tilde{C}^{T}\tilde{C}<0\). Meanwhile, applying the Schur Complement Lemma into Equation (14) yields  
\[trace(\tilde{B}_{w}^{T}P\tilde{B}_{w})=trace(B_{w}^{T}PB_{w})<\gamma_{2}^{2} \tag{21}\]  
According to Lemma 1, we have \(J_{2}<\gamma_{2}\). Therefore, according to (a) and (b), the state feedback controller \(u=[K_{1},K_{2}]\,x=-R^{-1}B^{T}Y^{-1}x\) is a solution for the \(H_{2}/H_{\infty}\) Tracking Problem  
**Remark 1**. : _LMIs (13) and (14) provide a controller derived from the GARE expressed in Equation (18) for the tracking problem. When \(M=0\), the solution of the GARE expressed in Equation (18) is equivalent to the following multi-bjective optimization problem (MOP):_  
\[ \begin{array}{c}\underline{\gamma_{2}}\!=\!\!\min_{\gamma_{\infty},Y}\,\, \gamma_{2}\\ s.t.\left\{\begin{array}{ccc}\left[\begin{array}{cc}YA^{T}+AY-BR^{-1}B^{T }+\gamma_{\infty}^{2}B_{w}B_{w}^{T}&Y\\ Y&-Q^{-1}\end{array}\right]<0\\ \left[\begin{array}{cc}\gamma_{2}^{2}&B_{w}^{T}\\ B_{w}&Y\end{array}\right]>0\end{array}\right.\end{array}  \tag{22}\]  
Different from the GARE approach, this LMI approach can be combined with other LMI constraints, such as input saturation [25].
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
## 2 Three Types of Fiber Mode
### Linearly Polarized Modes  
Linearly polarized (LP) modes are another group of fiber mode base. Their polarization states are also linearly polarized, but the polarized directions are the same, invariant with the spatial angle. However, the polarized amplitude at each point changes periodically with the spatial angle. If considering the radial field distribution simultaneously, the intensity patterns of LP modes are \(2l\) lobes, where \(l\) is the azimuthal order, as mentioned for CV modes. LP modes are not the eigenmodes in fiber unless the four degenerated modes are strictly degenerated. However, on the end facet of fiber, the field can still be expressed in LP mode bases because there is no restriction of cylindrical waveguide outside the fiber. Any \(l\)th order electric field in fiber can also be decomposed into four LP mode bases with different complex amplitudes. Figure 2c,d show the propagating properties and time average intensity patterns of two typical LP modes \(LP_{11}^{even}\) and \(LP_{11}^{odd}\), where \(\hat{x}\) denotes the linear polarization along the \(x\)-axis and \(LP_{1,m}^{even}\) and \(LP_{1,m}^{odd}\) represent the two LP modes with complementary intensity patterns. Here \(\hat{x}\) can be replaced by other linearly polarized symbols when the observed coordinates change.
###### Abstract  
Background: This study aims to evaluate the antibacterial effect of _Lespedeza cuneata_ extract on _Porphyomonas gingivalis_ (_P. gingivalis_), a principal bacterium in periodontal disease, and its impact on human gingival fibroblasts (HGFs). Methods: Dried _Lespedeza cuneata_ was extracted using 70% ethanol, concentrated, and freeze-dried to obtain the _Lespedeza cuneata_ extract in powder form. The antibacterial effect, indicated by the inhibition of _P. gingivalis_ growth, was assessed by administering concentrations of 1, 3, 5, 10, 20, 30, and 40 mg/mL. After 24 h of anaerobic incubation, colony-forming units per milliliter (CFU/mL) were measured. Cytotoxicity on HGF cells was evaluated after treatment with WST-1 solution followed by incubation at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) for 2 h. Cell morphology and proliferation were assessed using the Sulforhodamine B (SRB) assay. Results: The antibacterial effect of _Lespedeza cuneata_ extract was concentration-dependent, with 99.98% inhibition observed at 5 mg/mL, 99.99% at 10 mg/mL, and no detectable CFUs were observed at 40 mg/mL under the tested conditions. Evaluating the change in growth rate of HGF cells showed a decrease in cell viability as the concentration increased, and the application of _Lespedeza cuneata_ extract at 10 mg/mL was found to be a safe and effective concentration with a half-maximal inhibitory concentration (IC50). Conclusion: Based on the antibacterial effect, cytotoxicity, and safety profile of _Lespedeza cuneata_ extract, it holds potential as a natural extract material for the prevention, improvement, or treatment of periodontal disease. Additionally, validation of the practical approach will be necessary via a clinical applicability evaluation.  
Lespedeza cuneate, Porphyomonas gingivalis; human gingival fibroblast; antibacterial effect; cell viability
## 3 Results  
### Phytochemical Analysis  
Table 1 lists the chemical components of the _R. chalepensis_ ethanolic extract, as well as their % content, elution sequence, and retention period. The chemical analysis revealed a total of thirty-four components (96.4%) in the extract. The LC/MS chromatogram in Figure 1 illustrates this. Notably, the extract contains high concentrations of quercetin (9.5%), 2,2-Dimethyl-3-methylidenebicyclo [2.2.1] heptane (8.1%), and myricetol (8.0%). Conversely, the least prevalent component in the extract was phellandrene (0.1%).  
The identified compounds were meticulously categorized based on their chemical structure, providing a comprehensive understanding of the _R. chalepensis_ ethanolic extract. Monoterpenes emerged as the most abundant, comprising 48.5% of the extract. Flavonoids (23.7%) and phenols (11.2%) were the most prevalent classes, while fatty acids represented the minor portion of the extract, making up just 2.4% (Table 2).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**No.** & **Compound** & **Retention Time (min)** & **Percentage (\%)** \\ \hline
1 & Phellandrene & 1.5 & 0.1 \\
2 & 2,2-Dimethyl-3-methylidenebicyclo [2.2.1] & 2.3 & 8.1 \\
3 & D-Limonene & 3.4 & 4.9 \\
4 & Alpha-Pinene & 5 & 6.1 \\
5 & Methyl heptyl ketone & 6.1 & 2.2 \\
6 & 2-Borranone & 7.9 & 6.4 \\
7 & Piperitone & 8.3 & 1 \\
8 & P-Menth-4(8)-en-3-one & 9.1 & 0.3 \\
9 & Eucalyptusptol & 9.9 & 2.2 \\
10 & 2-Borranol & 10.3 & 1.5 \\
11 & 2-Isoborneol & 10.9 & 3 \\
12 & 3,7-Dimethylocta-1,6-dien-3-ol & 11.1 & 6.9 \\
13 & \(\gamma\)-Terpinene & 12.3 & 5.9 \\
14 & Methyl nonyl ketone & 13.2 & 2.1 \\
15 & 4-Hydroxycinamic acid & 13.9 & 3.8 \\
16 & 4-Hydroxy-3-methoxybenzoic acid & 14.3 & 0.3 \\
17 & 3,4,5-Thrihydroxybenzoic acid & 15.1 & 4.1 \\
18 & Methyl undecyl ketone & 16.1 & 0.9 \\
19 & 3,4-Dihydroxycinamic acid & 16.6 & 1.8 \\
20 & Methyl decyl ketone & 17.1 & 1.1 \\
21 & 4-Hydroxy-3-methoxycinamic acid & 18.2 & 0.9 \\
22 & Linalocal acetate, & 19.4 & 2.1 \\
23 & 3,7-Dimethylocta-1,6-dien-3-yl acetate & 23 & Alpha-Humulene \\
24 & Unknown & 21.9 & 0.2 \\
25 & (\(-\))-\(\beta\)-Eudesmol & 23 & 0.8 \\
26 & Elemane, Elemol alcohol & 23.3 & 1.9 \\
27 & Viridifloral & 23.6 & 1.4 \\
28 & Octadecanoic acid & 24.3 & 2.4 \\
29 & Quercetin & 25.9 & 9.5 \\
30 & 3’-Methylquercetin & 26.4 & 1 \\
31 & Myricetol & 27.7 & 8 \\
32 & 5-Caffeoylquinic acid & 28.4 & 0.3 \\
33 & Unknown & 29.6 & 3.4 \\
34 & Rutin & 31.1 & 5.2 \\ \hline \hline \multicolumn{3}{l}{Total identified compounds} \\ \hline
96.4\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical profile of _R. chalepensis_ ethanolic extract.
## 3 Results and Discussion
### E-Nose Application to Discriminate Quality from Green Coffee Beans  
The volatile compounds of green coffee beans were also evaluated with E-nose devices to discriminate samples based on their quality. The results showed that the PCA was able to identify green coffee beans with different quality categories (Figure 2). The 62.9% of the total variance of the data was explained by PC1, while PC2 explained 21.7%. This is an interesting result since the combined sensors of the electronic devices used were able to discriminate Arabica samples based on their sensory quality. This result could prove useful for companies given that these devices enable a quick and efficient preliminary classification of the quality of the coffee beans before they are subjected to a roasting process. This would allow a quality classification of green coffee to be conducted at the first phases of coffee processing.  
Next, a PLS-DA was performed using a leave-one-out cross-validation. The confusion matrix of the model indicates that the sum of the diagonal elements gave a hit rate of 100%. Eleven samples from each class were used to construct the model allowing green coffee beans with different sensorial quality to be discriminated. The confusion matrix of the PLS-DA model for green coffee beans shows that the sum of the diagonal elements gave a hit rate of 100% for high and low quality, while giving 93.8% for medium quality. The medium-quality group presented aromas from the other samples whose classification was not 100%. The rest of the samples were successfully predicted with an accuracy of 100%. Therefore, the model established that it could predict a correct discrimination of the green coffee beans of different quality.