esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Patients and Methods
### Feature Extraction  
The Radcloud platform (version V3.1.0, compatible for Windows) was employed for extracting CT images, producing 1409 quantitative imaging features in total. The extracted features were then grouped into three different categories: Group 1 (first-order statistics) comprised 126 descriptors that quantitatively characterize the voxel intensity distribution in the CT image, employing simple metrics frequently used in statistical analyses. Group 2 (features based on size and shape) included 14 3-D features reflecting the region of interest's size and shape. Group 3 (texture features) consisted of 525 textural features obtained from gray-level run-length and gray-level co-occurrence texture matrices, which enable differences in region heterogeneity to be quantified (Table 2). In terms of feature extraction, several methods were utilized to reduce dimensionality and select task-specific features for performance optimization. First, a variance threshold of 0.8 was applied for the purpose of eliminating redundant features, whereby features with variance eigenvalues < 0.8 were removed to eliminate redundant information. Second, the SelectKBest method was used; this single-variable feature selection technique allows the relationship between features and classification outcomes to be analyzed utilizing _p_-values. Features identified to have _p_-values < 0.05 were kept such that they could be analyzed further. The third step involved the application of the Least Absolute Shrinkage and Selection Operator (LASSO) technique. In the LASSO model, an L1 regularizer was utilized as the cost function. An error value of 5 was set for the cross-test, and the maximum number of iterations was defined as 1000. Figure 1: Examples of tumor segmentations on CT images. Subsequent to reducing the dimensionality and selecting task-specific features, the variance threshold technique was applied to select 469 features out of the total of 1409. This was followed by the selection of 26 features through the application of the SelectKBest technique. Lastly, the LASSO algorithm was employed, resulting in 9 optimal features being identified (Figures 2 and 3). We calculated the Radscore for each patient using the following formula. The Radscore was the comprehensive representation of the optimal features and was used to subsequently construct the model. The Radscore was calculated using the following formula:  
\[\text{Radscore}=\text{Intercept}+\text{radiomics\_feature\_1}\times\text{ coefficient\_1}\] \[+\ \ldots\text{radiomics\_feature\_i}\times\text{coefficient\_i}+ \ \ldots\text{radiomics\_feature\_k}\times\text{coefficient\_k}\]  
where Intercept represents the LASSO regression intercept, k denotes the overall number of features selected by the LASSO algorithm, is the LASSO coefficient of the i-th feature, and is the i-th radiomics feature.
## 2 Related Works
### Spectral Clustering Algorithm  
Spectral clustering uses the top eigenvectors of a matrix derived from the input data and transforms the clustering problem into a graph cut problem. The graph cutting approach clusters data points by attribute such that densely packed points are in same cluster, whereas the sparse are in different clusters. The minimum cut formula is written as:  
\[cut(x,y)=\sum_{i\in A,j\in B}W_{ij} \tag{1}\]where \(w_{ij}\) is the degree of a vertex from \(i\) to \(j\). There are two balanced cut approaches, Ratio Cut and Ncut; spectral clustering is a relaxation of the two. SC is an effective partitioning strategy that includes the normalized cut [27] and Ng-Jordan-Weiss (NJW) algorithms [28]. These algorithms are relaxations of the spectral partitioning problem; the SC algorithm with a Laplacian matrix can be described in Algorithm 1:  
```
Input: Dataset, number of clusters \(k\), parameter \(\sigma\) and number of iterations \(iter\) Output: the set of \(k\) clusters /* Note the symbols of \"/*\" and \"*/\" represent comments in this algorithm. */
1 Calculate the affinity matrix \(A\in\mathbb{R}^{n\times n}\) and define \(A_{ij}=exp(-\parallel s_{i}-s_{j}\parallel^{2}/2\sigma^{2})\) /* In which \(S_{i}\) and \(S_{j}\) are the original data points \(i\) and \(j\), repctively. */
2 If \(i\
eq j\) then the \(A_{ii}=0\)
3 The D is the diagonal degree matrix and computed with elements: \(d_{i}=\sum_{j=1}^{n}A_{ij}\) Given a graph G with \(n\) input vertices, the Laplacian matrix \(L(n\times n)=D-A\)
4 Find the \(k\) largest eigenvectors of the matrix \(L\) and \([x_{1}x_{2}\cdots x_{k}]\in\mathbb{R}^{(n\times k)}\)
5 Generate matrix \(y\) by renormalizing each \(x\) row, \(Y_{ij}=X_{ij}/(\sum_{j}X_{ij}^{2})^{1/2}\)
6 Minimize the distortion of each row \(Y\) to regard as the point in clustering term \(\mathbb{R}^{k}\) using any clustering algorithm, such as a distance-based clustering approach.
7 Finally, the original point \(s_{i}\) is assigned to cluster \(j\) when the row of \(y_{i}\) belongs to the cluster \(j\).
8 returnthe set of \(k\) clusters and cluster centre.
```  
**Algorithm 1**Spectral Clustering Algorithm
## 4 Methodology
### Data Acquisition
#### 4.2.4 Meteorological Parameters  
Air temperature, wind speed and total precipitation were extracted from the ERA5-CRP [55], shown in Figure 6. This project has a climate data store that was assembled using assimilation and advanced modelling to obtain the historical observations into a global consistent form. The air temperature is at a 2 m distance, and wind speed is at a 10 m distance from the surface of the Earth.
# Sink-Type-Dependent Data-Gathering Frameworks in Wireless Sensor Networks: A Comparative Study
## 2 Data-Gathering Frameworks Depending on Sink Types
### Ground Mobile Sink-Based Frameworks  
In this category, the frameworks assume a single or multiple ground robot that acts as a mobile sink. The robot can follow a trajectory, moving closer to the transmitting nodes, which results in a shorter transmission range and less energy consumption as data transmission is reduced. However, the delay is likely to increase. The terrain should be accessible for the robot to move freely.
## 3 Proposed Method
### Proposed Temperature-Based Mean-Shift Tracking
#### 3.3.2 Temperature-Based Gradient Ascent  
This section explains temperature-based gradient ascent. This part was not much different from the baseline. However, the gradient calculated based on the temperature histogram similarity weight \(W_{T}(x)\) was different compared with the baseline. The gradient ascent algorithm moves in the direction of the gradient maxima point dense region evaluated at each frame. As explained above, mean-shift  
Figure 6: Overview of the TIR-MS tracking.  
treats the feature space as a probability density function. So, we perform gradient ascent on the local estimated density until convergence for each video sequence.  
\[\Delta x=\frac{\sum W_{T}(x)(x-\bar{x}_{old})K(x-\bar{x}_{old})}{\sum W_{T}(x)K(x- \bar{x}_{old})} \tag{9}\]  
\[\bar{x}_{new}=\bar{x}_{old}+\Delta x \tag{10}\]  
To explain the variables in the above equations, \(x_{old}\) and \(x_{new}\) are center coordinate of ROI (x, y) of the prior frame and next frame, respectively. The parameter \(x\) is all of the pixels' coordinates of the present image. \(\Delta x\) is the gradient of the present ROI. \(W_{T}(x)\) and \(K(x)\) are temperature histogram similarity weight and kernel density function, respectively. In short, \(\bar{x}_{new}\) is calculated by summing \(\bar{x}_{old}\) and \(\Delta x\) each frame.
## 2 Materials and Methods
### Design of the Subject Study to Evaluate the Co-Simulation Model  
#### 2.3.1 Recording the Experimental Data  
For the evaluation of the co-simulation model, experimental data were collected in a subject study. Three healthy subjects participated in the study and gave written consent for participating in this study. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of the Department of Psychology of Helmut-Schmidt University Hamburg (HSU). By building the models based on physically existing technical systems, these systems can be used for collecting experimental data for the evaluation. Accordingly, the exoskeleton Lucy [1] and a cordless screwdriver (Festool cordless impact drill PDC 18/4, Festool GmbH, Wendlingen, am Neckar, Germany) including a power tool datalogger [44] was used for the subject study. The chosen use case was fixing a predrilled wooden component with a screw to an overhead plywood board. Here, one hand was used to position the predrilled wooden component (non-dominant hand), while the other hand uses the cordless screwdriver to screw (dominant hand). The use case is shown in Figure 7 and the study is described in detail by [PERSON] et al. [43].  
For the evaluation of the co-simulation model, the data of three subjects (age: 24 \(\pm\) 3.6 years, weight: 74.4 \(\pm\) 16.1 kg, height: 1.84 \(\pm\) 0.03 m, sex: male) were used. The subjects performed the use case with three different levels of exoskeleton support levels: no support, 50% support, and 100% support. For each support level, ten trials were performed. The movement of the subjects was recorded with the optical motion capture system Vicon (Vicon Bonita, Oxford Metrics Ltd., Oxford, UK), sampled at 100 Hz. The subjects wore a suit with attached markers on bony landmarks and required body parts according to the Plug-in-Gait model by Vicon. Although the Plug-in-Gait model is not a specialized marker model to be used for shoulder motions, as it is primarily used for gait analysis, due to its comprehensive marker attachment at the whole body, it was previously also used for analyzing reaching motions [45] or motions during playing tennis [46] and as comparison tool when analyzing shoulder motion [47]. The ground reaction forces of the subjects were measured using a force plate (AMTI, Advanced Mechanical technology Inc., Watertown, NY, USA), sampled at 100 Hz. In addition, surface electromyography (sEMG) was used to record muscle activations of selected muscles in the upper body while performing the activity for the subsequent evaluation of the co-simulation model. Since the exoskeleton, considered in the co-simulation model, is a support system for the shoulder area, the following muscles are used for evaluation: M. biceps brachii, M. deltoideus, M. latissimus and M. flexor carpi ulnaris. For placement of the sEMG electrodes, SENIAM recommendation was used [48]. These data are related to the maximum voluntary contraction (MVC) of the respective muscle of the test subject and can later be used for comparison with the biomechanical results of the co-simulation.
## 2 Utilization of MoT  
Most molybden present as low-grade ores is associated with other valuable metals, such as Pb, Ni, W, Cu and Re. Generally, molybdenum concentrates are obtained by selective flotation from low-grade ores and then produce MoO\({}_{3}\) or Mo metal through roasting-ammonia leaching process [5]. The floatation tailings resulting from this process still contain some valuable metals, such as Mo, Pb and Cu. Several techniques had been adopted to convert stacked MoT to reusable cleaner products, including reclamation of Mo and other metals, preparation of ceramics and building materials.  
[PERSON] et al. [5] proposed a method for the extraction of both Pb and Mo from MoT flotation associated with galena. [PERSON] et al. [6] conducted a test by means of magnetic separation to reclaim the iron mineral from a certain concentrating mill for MoT. [PERSON] et al. [7] proposed the appropriate conditions for recovering ultrafine molybden from MoT by oil agglomerate flotation. [PERSON] et al. [8] conducted an experimental research which may provide the theoretical and experimental basis for phlogopite flotation from MoT.  
[PERSON] et al. [9] prepared glass-ceramics by using MoT. The performance of glass-ceramics was superior to the other types of building decoration stone. The tests conducted by [PERSON] et al. [10] also confirm the feasibility of preparing glass-ceramics used for decoration from MoT. [PERSON] et al. [11] proposed a method to prepare foam ceramic using MoT and SiC. In addition, [PERSON] et al. [12] successfully prepared a new kind of water storage material using MoT as main raw material. This new technique was believed to be environmentally friendly, economical and promising for the full utilization of MoT.  
Among these techniques, one of the most effective ones is to use MoT in concrete productions, since the rapid developments of the building industry are still happening in many countries, especially in China. Tremendous amounts of concrete are being cast in building industry every day. As one of the solutions towards environment protection, the utilization of MoT in concrete products has been studied and encouraged. This paper reviews some of the studies on the use of MoT in concrete manufacturing, focusing on the effect of MoT on the properties of concrete.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The ECG Pipeline
#### 2.2.4 Local Peak Correction  
Stronger frequency filtering applied during the preprocessing step had a stronger impact on peak location, shifting R-peaks (and other peaks) in the ECG (Figure 6). To counteract the shifting-peaks effects, we implemented a novel local correction relative to the unfiltered signal. This correction iteratively searches for the largest peak \(\pm\) 0.01 s either side of the peak location on the processed ECG to check for a larger local peak within the raw unprocessed ECG, until no larger peak is found within the search limit. To distinguish this technique from \"peak detection\", it is referred to as \"peak correction\". This peak correction only has a small impact on peak location but preserves variations between peaks and allows for a more accurate comparison of specificity/sensitivity/positive predictive value measures (Appendix D). In instances where multiple indices could be labelled for a given peak, the closest peak to the preprocessed ECG was used (Appendix E).  
Figure 10: An age-based breakdown of Figure 8. A different age bracket is shown in each quadrant. The median is shown as a thick light-grey bar, and the IQR is shown as thin dark-grey bars. Each black dot represents one result.  
It was also observed that the first and last beats of an ECG were liable to be missed under certain methods. This was addressed by a one-second artificial extension of the signal at the start and the end. The first/last values of the preprocessed ECG were used as the constant values for the extensions at each end. While this only has a small impact for long recordings, it can be very impactful for shorter recordings.
# An Overview of Biopolymers for Drug Delivery Applications
## 4 Preparation Methods for Obtaining Biomaterials Used in DDSs
\begin{table}
\begin{tabular}{c c c c c} \hline
**Natural** & **Chemical Method** & **Physical Method** & **Drug** & **References** \\
**Biopolymer** & **Preparation** & **Preparation** & **D**arboxing** & **[143]** \\ \hline Chitosan & Ionic gelation & & Dacarbazine & [143] \\ \hline Chitosan & Freeze-drying & Methotrexate & [144] \\ \hline Chitosan & Freeze-drying & Curcumin & [145] \\ \hline Starch & Electrospinning & Chlorpheniramine maleate & [146] \\ \hline Starch & Reduction & & Hydroxycapmetothecin & [147] \\ \hline Starch & Dissolution & Doxorubicin & [66] \\ \hline Carboxymethyl cellulose & Freeze-drying & Doxorubicin & [148] \\ \hline Passion fruit peel cellulose & Hydrolysis & Tetracycline & [149] \\ \hline Cellulose & Solvent evaporation & Felodipine & [150] \\ \hline Albumin & Desolvation & Irinotecan & [151] \\ \hline Bovin serum albumin & \begin{tabular}{c} Desolvation and chemical \\ co-precipitation \\ \end{tabular} & Curcumin & [152] \\ \hline Albumin & Extrusion & Diazep & [153] \\ \hline Silk fibroin & Crosslinking & 5-fluorouracil & [154] \\ \hline Methacylated silk fibroin & Photocrosslinking & Mouse articular chondrocytes & [155] \\ \hline Silk fibroin & & \begin{tabular}{c} Supercritical CO\({}_{2}\) \\ \end{tabular} & Ibuprofen & [156] \\ \hline Collagen & Ionic gelation & & Doxorubicin & [157] \\ \hline Collagen & & Electrospinning & Ciprofloxacin & [131] \\ \hline Collagen & Desolvation & & Cardamom extract & [158] \\ \hline Gelatin & & \begin{tabular}{c} Supercritical CO\({}_{2}\) \\ \end{tabular} & 5-fluorouracil & [130] \\ \hline Gelatin & \
\begin{tabular}{c} Crosslinking by \\ desolvation \\ \end{tabular} &
\begin{tabular}{c} Economic nitrate \\ \end{tabular} & [159] \\ \hline \end{tabular}
\end{table} 
Table 2: Procedures for preparation and a list of medications included in the created biopolymer-based nanoparticles. Microemulsions are obtained via dispersion of the biopolymer in two immiscible liquids, with the use of emulsifiers or surfactants. The nanomaterials produced by this process typically have a diameter of 10-100 nm, are optically transparent, isotropic, thermodynamically stable, and have strong drug-solubilizing qualities [168; 169]. With a polar head and a non-polar tail, surfactants are amphiphilic substances that adjust at the interface between the water and oil phases. Thus, surfactants reduce the overall tension and promote miscibility. Microemulsions are predominantly used in lipophilic drug delivery (79.4%) through oil-in-water microemulsions and non-ionic surfactants (90%) used for oral or topical administration [169]. Microemulsions are versatile DDSs due to their different advantages (small particle size, capacity to incorporate hydrophilic and hydrophobic drugs, and easy emulsification preparation process) [170].
# Application of RFID to Soil-Erosion Research
## 3 Results
### Experiment 2  
#### 3.2.1 Survey of In-Situ Particles  
Using the grid to locate the reader from which to detect the particles, a wide range in the number of observations of the tagged particles was found. The minimum number of locations from which a particular particle was recorded was one, and the maximum was 33. The modal value was 15. The average distance from which particles could be detected was 62 mm.  
A method for locating RFID-tagged particles from multiple observations was presented by Reference [9] in which the \(X\) and \(Y\) coordinates of a particle are given by  
\[X=\frac{\max(x_{1}\dots x_{n})+\min(x_{1}\dots x_{n})}{2}, \tag{1}\]  
and  
\[Y=\frac{\max(y_{1}\dots y_{n})+\min(y_{1}\dots y_{n})}{2}, \tag{2}\]  
respectively, in which \(x_{1}\dots\ x_{n}\) and \(y_{1}\dots\ y_{n}\) are the coordinates of the reader from which the particular particle is recorded. Using this method, and assuming that there is no error in the actual locations of the particles with respect to the nominal array (but see Figure 5), the results are as shown in Figure 7. The minimum difference between the actual and computed locations of the particles is 6.3 mm, the average is 17.9 mm, and the maximum is 45.7 mm.  
Figure 6: Locations of tagged particles before and after experiment 1. Particles leaving the plot are shown located at the plot outlet. Distances on the axes are \(x\) and \(y\) coordinates from the location of the total station used to record the locations.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 2 System Architecture
### Testing and Evaluation  
The proposed system will be thoroughly tested and evaluated in a simulated environment before deployment in a real-world UAM ecosystem. The testing will focus on the following aspects:  
1. Link Performance: Link performance will be measured under various conditions to validate the link budget analysis and ensure that data transmission meets the requirements.
2. Handover Performance: Handover success rate will be evaluated to ensure seamless communication continuity during vehicle transitions.
## 1 Introduction
Figure 1: The map of the Great Plains with the National Land Cover Database (NLCD) 2006 as the backdrop, along with grassland and cropland flux towers used in this study as green and yellow points. Numbered flux tower labels refer to individual flux towers as designated in Table S1. Source: [21]. In addition to model development and map production, a secondary objective of this study was to perform a systematic comparative analysis of the grassland and cropland NEP results. Our hypothesis was that cropland ecosystems, being highly managed to optimize productivity of usually annual vegetation and often in a state of tilled (exposed) soil, vulnerable to respiration losses, would lean more towards the C sources. Conversely, grasslands with perennial vegetation, generally subject to little management and no tillage, would tend to perform as C equilibrium or sinks. Grassland and cropland make up approximately 85% of the total surface area of the U.S. Great Plains (48% grasslands or shrubs and 37% cultivated crops, pasture, or hay; Figure 1), signifying the importance of understanding C fluxes in these two ecosystem classes. This spatial and temporal synthesis of Ameriflux, Agriflux, and independent flux tower data was performed with attention to the stated goals of the North American Carbon Program (NACP) [22].
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 5 Validation  
The same data set were used to validate the performance of the above proposed two a priori models. Two solutions were determined by using cuboid model or empirical FFT model. The SLR and OBD are also used to validate the solutions' quality.  
Figure 6 illustrates the SLR residuals of these two solutions against the \(\varepsilon\) angle. In general, once the proposed models used as the priori models of ECOM1, the remarkable improvements can be obtained. For all of the two solutions with the a priori model, the \(\varepsilon\)-angle-dependent error was almost vanished, while the \(-17.0\) cm bias was reduced to \(-6.1\) cm and \(-4.0\) cm, respectively. Between the two solutions, almost same performance was obtained, though the SLR residuals of empirical FFT model became slightly scatter and has a little larger standard derivation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & \(D_{\mathbf{0,ap}}\) & \(D_{\mathbf{2c}}\) & \(D_{\mathbf{4c}}\) & \(B_{\mathbf{1c}}\) & \(B_{\mathbf{3c}}\) \\ \hline value & 3.05e-3 \(\times\)\(\beta^{2}\)-105 & 5.9e-3 \(\times\)\(\beta^{2}\)-8.62 & 0.96 & 1.6 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values for the empirical a priori SRP model expressed in Equation (14) (unit: nm/s\({}^{2}\), \(\beta\) in degrees).  
Figure 5: Variations of the model values for (**a**) and (**b**) \(D_{2c}\). Shadowed area for eclipse season.  
Figure 6: One-way SLR residuals of solutions using the a priori (**a**) cuboid model and (**b**) fast Fourier transformation (FFT) model for BeiDou-3 I2-S with respect to \(\varepsilon\) angle.  
Time series of daily OBDs for the two solutions are illustrated in Figure 7, and Table 5 lists the mean values in Along-track, Cross-track, and radial directions. For both solutions, almost identical OBDs are achieved, and show similar performance as that of ECOM1 solution. The radial OBDs show \(\beta\) angle dependent variations, and the worse OBDs have been identified in eclipse periods. For Galileo satellites with a priori model for ECOM1 ([PERSON] et al., 2016), the OBDs also have similar performance as that of solution determined with ECOM1 only.