esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Oktracxin A Analysis  
The OTA analysis followed our previous work [53; 54]. The ultrasound-assisted extraction was used to extract OTA from culture sample with 10 mL of methanol aqueous solution (7:3, \(v/v\)) for 30 min. This procedure was repeated twice with 5 mL of solution each time. Extracts were filtered through a Whatman glass microfiber filter (Sigma Aldrich) to remove the hyphae and spores. Subsequently, the resultant extract was filtered through 0.22 \(\upmu\)m nylon syringe filters (Lanyi, Beijing, China) before high-performance liquid chromatography (HPLC) analysis. The liquid chromatography (LC) system consisted of a fluorescence detector (RF-20 Axs) and a pump (LC-20 AT) (Shimadzu Scientific Instruments, Kyoto, Japan) with a 5 \(\upmu\)m Prodigy ODS3, 100 A, 250 \(\times\) 4.6 mm analytical column (Phenomenex, Torrance, CA, USA). Separation was carried out by using isocratic elution with isometric mobile phase A (composed by a water and glacial acetic acid (99:1, \(v/v\)) solution) and mobile phase B (composed of acetonitrile and glacial acetic acid (99: 1, \(v/v\)) solution), at a rate of 1.0 mL/min and 20 \(\upmu\)L injection. Detection of OTA was performed using 333 nm and 460 nm as wavelength settings for excitation and emission, respectively. Quantification of OTA was carried out by measuring its peak area according to a five-point calibration curve between 3.2 and 4000 \(\upmu\)g/L, which was constructed by five serial dilutions of the OTA standard solution. The squared correlation coefficient (r\({}^{2}\)) was 1.
# Experimental Investigation of Clearance Influences on Cage Motion and Wear in Ball Bearings
###### Abstract  
Clearances of cages in ball bearings, including pocket and guiding clearances, play a vital role in the stability and reliability of bearings. In this paper, experiments on the cage motion and wear were carried out to investigate the influence of clearances in ball bearings. Firstly, the cages with a series of pocket and guiding clearances were specially designed and tested for prescribed operating conditions on a bearing test rig in which the cage motions were measured, and corresponding wear was also observed. Then, the normalized trajectory, waveform, and spectra of cage motion were constructed and compared to illustrate the effects of clearances on the cage motion and then to establish the relationship between cage motion and wear. Results reveal that the cage motion and wear are both significantly affected by its clearances. The increment of cage guiding clearance makes the whirl trajectories of the cage regular and the motion frequency of cage motion significantly change. However, the increment of cage pocket clearance make the whirl trajectories change from well-defined patterns to complicated ones, and the frequency of cage motion apparently changes. Additionally, the bearing wear is closely related to the cage motion. If the inner ring frequency is of domination for the cage motion, the cage guiding surface will wear seriously. While cage motion is dominated by two times cage frequency in spectrum domain, the cage pocket will wear more seriously.  
cage motion; cage wear; guiding clearance; pocket clearance; characteristic frequency +
Footnote †: journal: Medical Engineering and Automation  
1
Footnote 1: School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian116034, China; [EMAIL_ADDRESS]  
2 College of Locomotive and Rolling Stock Engineering, Dalian Jiaotong University, Dalian 116028, China; [EMAIL_ADDRESS]  
3 School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China; [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (W.S.)
# An Immersive Virtual Reality Simulator for Echocardiography Examination
###### Abstract  
Echocardiography is a medical examination that uses ultrasound to assess and diagnose the structure and function of the cardiac. Through the use of ultrasound waves, this examination allows medical professionals to create visualizations of the cardiac muscle, enabling them to diagnose and monitor conditions such as cardiac diseases, abnormalities, and functional disorders. An echocardiogram plays a crucial role in the early detection and diagnosis of various cardiac issues such as hypertension, myocardial infarction, valvular cardiac disease, and myocardial hypertrophy. It significantly contributes to determining treatment and management strategies. To achieve accurate disease diagnosis and develop appropriate treatment plans through echocardiography, it is essential to have a thorough understanding of proper probe usage, the precise acquisition of echocardiographic images, and the ability to interpret various echocardiographic examinations such as two-dimensional, M-mode, Doppler, etc. To enhance the skills required for echocardiography, medical educational institutions conduct theoretical classes, practical sessions using patient models, and clinical practice sessions with actual patients. However, issues such as inadequate practical adaptation due to theory-centric education, limitations in practical opportunities due to insufficient practice equipment, ethical or safety concerns arising during clinical practice, and a lack of educators leading to insufficient feedback, are currently being encountered. Hence, there is a need for new educational methods that can address the existing challenges in echocardiography education. In this paper, as part of these efforts, we propose a virtual reality-based immersive simulator for practical echocardiography training. The proposed echocardiography simulator allows users to explore a virtual echocardiography examination space by wearing a head-mounted display (HMD). This simulator consists of 3D virtual space models, interactive models manipulated by interaction devices, and 3D patient models containing normal or abnormal anatomical cardiac models. Using interactive devices such as HMD controllers and haptic devices, users can manipulate 3D models related to echocardiography within the simulator and interact with 3D patient models containing normal or abnormal anatomical cardiac models, allowing for the practice of echocardiography examinations. Ultimately, a performance evaluation of the developed immersive virtual reality simulator and usability validation targeting medical university students were conducted. The evaluation and validation results confirmed the potential efficacy of the proposed echocardiography VR simulator.  
echocardiography; virtual reality; simulator; interaction; immersion; haptic; head-mounted display; clinical practice +
Footnote †: journal: Medical
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 3 Proposed Scheme  
The proposed scheme utilized the concept of geospatial object-based image analysis (GEOBIA) [23] in land cover classification. Figure 3 shows the workflow of proposed scheme. The GEOBIA approach for land cover classification consists of three steps, including feature extraction, image segmentation, and object-based classification. The input data are LiDAR data from different wavelengths, and the preprocessing feature includes extraction, such as geometrical and spectral features. After selecting the training dataset, the object-based classification process applies segmentation and the SVM classifier, and the output data are the classification results from different combinations, including three combinations based on different features (Cases 1 and 2) and different data sources (Case 3). All the combinations used object-based classification to detect land cover types, and the results of all combinations were used to compare the performance of different features.  
Figure 3: The workflow of land covers classification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Object (Pixel)** & **Building** & **Tree** & **Grass** & **Road** & **Soil** & **Sum** \\ \hline Training & 30 (4140) & 30 (4630) & 30 (5136) & 30 (4761) & 20 (1697) & 140 (24,906) \\ Test & 30 (3212) & 30 (3337) & 30 (4166) & 30 (2967) & 20 (1284) & 140 (19,252) \\ Total & 60 (7352) & 60 (7967) & 60 (9302) & 60 (7728) & 40 (2981) & 280 (44,158) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of objects and pixels for training and test area.  
Figure 2: Distribution of training areas (pink block) and test areas (green block), the red line is the boundary between water and land.
# Development of Communication Protocols between BIM Elements and 3D Concrete Printing
## 5 Analysis of Results
### In Situ Maximum Printing Speed Test in Terms of Defined Points Spacings
#### 5.4.2 Results of the Maximum Speed Test  
Figure 10 shows the maximum experimental speeds obtained from the measurements made with the robotic arm with a constant theoretical speed of 1 m/s and with different spacings between points. The boxplot shows each of the maximum speeds for different spacings between points, along with Table 2 which complements the boxplot (quartiles, median, lower and upper limits, and atypical values).  
In summary, Figure 10 and Table 2 show the speed distribution for the different measurements made. The points spacing of 12.5 mm and 50 mm present a positive asymmetry since the highest concentrations of their data are in the area below the median. It can be seen that for the 12.5 mm spacing, all the data are contained in the diagram, without presenting outliers. For the 50 mm spacing, data are presented above the upper limit, with one single outlier only. For speeds with a spacing of 25 mm, a negative asymmetry occurs, where most of the data are concentrated in the area above the median, and two outliers above the upper limit.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 4 Discussion
### Effect of Carbon Source Inducers on Cellulase and Xylanase Production  
Cellulases and xylanas are members of glycoside hydrolases, which are well-known for their side activities (not investigated here), for instance, endoglucanas are capable of degrading hemicellulose at a low level and some xylanases exhibit cellulolytic activity. This has been attributed to their modular structure comprising a noncatalytic carbohydrate binding module (CBM) and the catalytic domain [62]. Studies have shown that the array of enzymes produced by microbes is dependent on the type of substrate on which they are cultured [63]. Nevertheless, the result of this study agrees with an earlier report on inducing effect of saccharides for hemi(cellulases) production by a _Streptomyces_ sp. [29]. The mechanisms governing extracellular enzyme production in response to carbon sources are said to be influenced by low-molecular-weight fragments of substrates produced by a small quantity of constitutively formed enzymes [64]. Various reports show that cellulase and xylanase induction seems to be species-specific in different bacterial strains. For instance, studies have reported that cellulase production by _Bacillus_ VITRKHB was optimal in fermentation media supplemented with xylose as the carbon source; on the other hand, the best carbon source for cellulase production by _B. subtilis_ and _B. pumilus_ EWBCM1 was lactose and galactose, respectively [48; 65; 66]. Furthermore, xylose was the preferred carbon for xylanase production by _Arthobacter_ sp. MTCC 5214 [67], while [PERSON] et al. [68] reported dextrose as the best inducer of xylanase production by _Streptomyces_ _rameus_. The _Streptomyces_ strain in this study was able to produce cellulase-free xylanase as well as xylanase with low cellulase activity, depending on the carbon source (Table 1). Whereas the extracellular enzyme produced by the bacterial strain in the absence of carbon source (control medium) had low activity of xylanase, cellulase activity was not detected, which suggest that xylanase and cellulase production by _S albidoflavus_ strain SAMRC-UFH5 was inducible [7]. In addition, the highest xylanase activity recorded in wheat straw signifies the versatility of this strain for xylanase production using low cost carbon source.
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 4 The Proposed Algorithm
### Proposed CP Tensor Decomposition with Weighted Bias
After obtaining the estimated component matrices: \(\hat{\mathbf{A}}\), \(\hat{\mathbf{B}}\) and \(\hat{\mathbf{C}}\), we turn to the channel parameters estimation by using the correlation-based scheme adopted by [16].
## 2 Materials and Methods
### The Mechanical Design and Working Principle of MyFlex-\(\delta\)
#### 2.1.1 Mechanical Design of MyFlex-\(\delta\)  
MyFlex-\(\delta\) can be divided into subgroups (Figure 1a). The foot subgroup consists of the three elastic elements (upper blade, middle blade and lower blade) joined together at the metatarsal level by two bolts. The spring holder is also part of the foot subgroup. The tendon subgroup consists mainly of the link part. In detail, the link consists of two hingeparts, one upper and one lower. The third subgroup is the ankle subgroup, consisting mainly of the ankle frame, spherical ankle joint and tube connector. The spherical ankle joint is composed of the spherical component and its seats, which are made of two parts (one upper seat and one lower seat). The seats are fixed to the ankle frame by means of screws. The three components of the spherical ankle joint are made of carbon fiber composite material. The foot subgroup and the tendon subgroup are connected via the spring holder and the lower link. The connection between the spring holder and the lower link is made through a hinge joint, while the connection between the spring holder and the middle blade is made through bolts. The upper link is connected to the tube connector in the same way the lower link is connected to the spring holder, i.e., by means of a hinge joint. Finally, the ankle subgroup is connected to the foot subgroup by two screws.  
The spherical ankle joint combined with the elastic elements, and in particular with the middle blade, allows the foot not only to rotate in the sagittal plane but also in the other two (transverse and frontal). Its fork shape (Figure 1c) allows the middle blade to also have torsional deformations in the frontal and transverse planes, allowing the inversion/eversion and abduction/adduction rotations of the foot.
## 4 Numerical Examples
### Rectangular Plate Involving a Center Crack  
Consider a rectangular plate involving a center crack as shown in Figure 17. The material properties are \(E=1.0\times 10^{6}\) Pa and \(v=0.3\). The width and length are \(2b=1\)m and \(2c=2\)m, respectively. The crack length is \(2a\) and the plate thickness is \(h\). Divisions of 2728 and 7338 triangular elements are employed for the calculation of SIFs of the center crack plate. The displacement and moment boundary conditions are also illustrated in Figure 17. The numerical results obtained by TrSDTPE, ffSDTPE, and FiSDTPE for different \(a/h\) values are reported in Table 7, along with the reference solutions by [PERSON] et al. [68] and [PERSON] et al. [69] based on FSDT for comparison. In [PERSON] et al. [68], a cracked plate is analyzed by employing the mesh-free reproducing kernel approximation formulated by Mindlin-Reissner plate theory, and the moment intensity factor is evaluated by the \(J\)-integral with the aid of nodal integration. In [PERSON] et al. [69], the crack problem is solved by the dual boundary element method based on Reissner plate formulation, and the stress resultant intensity factor is calculated by employing the \(J\)-integral techniques. The SIFs in Table 7 are normalized by \(F_{1}=\frac{h^{2}K_{1}}{6M\sqrt{\pi a}}\). It is observed that the present elements show a high solution accuracy for the calculation of the SIFs.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline _a/h_ & **0.8 (0.2/0.25)** & **1.0 (0.25/0.25)** & **4.0 (0.2/0.05)** & **5.0 (0.25/0.05)** \\ \hline TrSDTPE (2728) & 0.8577 & 0.8981 & 0.7235 & 0.7602 \\ TrSDTPE (7338) & 0.8589 & 0.9005 & 0.7243 & 0.7622 \\ FfSDTPE (7338) & 0.8548 & 0.8968 & 0.7224 & 0.7610 \\ FiSDTPE (7338) & 0.8627 & 0.9036 & 0.7266 & 0.7633 \\ [PERSON] [68] & 0.8683 & 0.9096 & 0.7287 & 0.7663 \\ [PERSON] [69] & 0.8694 & 0.9094 & 0.7347 & 0.7702 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Normalized SIFs \(F_{1}\) for the center cracked plate.  
Figure 16: Typical meshes for the circular plate.
# Models to Identify Small Brain White Matter Hyperintensity Lesions
## 4 Results
### Comparison of Proposed Segmentation Models
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Author/Year** & **Lesion Type** & **MR Modality** & **Dataset** & **Methods** & **DSC** \\ \hline [PERSON] et al. [104], 2018 & WMH & T1-w and FLAIR & MICCAI 2017 WMH & U-Net & 0.8 \\ \hline [PERSON] et al. [35], 2018 & WMH & T1-w and FLAIR & WMH (private) & CNN (uResNet) & 0.7 \\ \hline [PERSON] et al. [105], 2019 & WMH & T1-w and FLAIR & MICCAI 2017 WMH & SC U-Net & 0.78 \\ \hline \multirow{3}{*}{[PERSON] et al. [34], 2020} & \multirow{3}{*}{Stroke} & T1, T2, FLAIR & \multirow{3}{*}{ISLES 2015 (SISS)(SPES)} & \multirow{3}{*}{U-Net} & 0.59 \\ \cline{3-5}  & & DWI, CBF, CBV & & & 0.84 \\ \cline{3-5}  & & TTP and Tmax & & & \\ \hline [PERSON] et al. [106], 2020 & WMH, Ischemic & \multirow{3}{*}{T1-w and FLAIR} & MICCAI 2017 WMH (train), & \multirow{3}{*}{M2 DCNN} & 0.84 \\ \cline{3-5}  & stroke & & ISLES 2015 (test) & & \\ \hline [PERSON] et al. [107], 2020 & WMH & T1, FLAIR & MICCAI 2017 WMH & ResNet+ SVM & 0.8 \\ \hline [PERSON] et al. [108], 2020 & Stroke & DWI & Acute Infarct (Asan Medical dataset) & \multirow{3}{*}{U-Net+ SE (squeeze} & 0.85 \\ \cline{3-5}  & WMH & FLAIR & MICCAI 2017 WMH & & 0.77 \\ \hline [PERSON] et al. [109], 2020 & WMH & T1, FLAIR & MICCAI 2017 WMH & U-Net+ CRF+ Spatial & 0.78 \\ \hline [PERSON] et al. [16], 2021 & WMH & T1-w and FLAIR & MICCAI 2017 WMH & \begin{tabular}{c} U-Net+ highlighting \\ foregrounds (HF) \\ \end{tabular} & 0.81 \\ \hline [PERSON] et al. [110], 2021 & Stroke & \begin{tabular}{c} T1-w, T2-w, DWI \\ and FLAIR \\ \end{tabular} & ISLES 2015 (SISS) & \
\begin{tabular}{c} Multi-level RoI \\ aligned CNN \\ \end{tabular} & 0.77 \\ \hline \multirow{3}{*}{[PERSON], et al. [111], 2021} & Stroke & DWI & Private &
\begin{tabular}{c} Faster R-CNN \\ YOLO v3 \\ \end{tabular} & 0.89 \\ \cline{3-5}  & & SSD & \\ \hline \end{tabular}
\end{table} 
Table 13: Comparative analysis between the proposed method and approaches in the literature. Figure 31: ROC curve comparison for detection and classification of the lesions using the criteria by Experts (AUC = 0.976), Detectron2 (AUC = 0.929), and YOLOv8 (AUC = 0.524). The curve illustrates the trade-off between true positive rate (sensitivity) and false positive rate, highlighting the good performance of Detectron2, comparable to experts, while YOLOv8 shows limited discriminatory power for lesion classification. The dashed diagonal line represents a random classifier (AUC = 0.5).
## 4 Experimental Results
### AE Characteristics
#### 4.3.1 Evolution of AE Energy  
The overall evolution of the AE energy and accumulated AE energy for different kinds of sandwiched coal-rock specimens is presented in Figure 6. Detailed descriptions of the AE energy and accumulated AE energy at different load times are as follows.  
During stage I, the AE energy of the sandwiched coal-rock specimens was maintained at relatively small values with little fluctuation (see Figure 6). It was primarily released due to the closure effect of the initial pores and cracks. With the increase of loading time, the AE energy was accumulated slightly at a very slow rate.  
The output of AE energy at stage II was mainly attributed to the generation and expanding of micro-cracks in the sandwiched coal-rock specimens. It was activated gradually along with the linear growth of the bearing capacity. Correspondingly, the accumulated AE energy also presented a gentle smooth rising tendency.  
The AE behavior of sandwiched coal-rock specimens became more intense at stage III. Massive AE energies were released at this stage. It resulted in the fast growth of accumulated AE energy. The exacerbation of internal damage and the acceleration of the micro-cracks' expansion provides an explanation for this phenomenon. It is worth noting that the sudden growth of AE energy was observed along with stress drops in Figure 6a,d. As soon as the appearance of the maximum bearing stress, peak AE energy was recorded. As presented in Figure 6, the maximum values of AE energy for C-R, R-C, R-C-R, and R-C-R-C-R were approximately 35.45 \(\times\) 10\({}^{4}\) mV\(\cdot\)mS, 41.68 \(\times\) 10\({}^{4}\) mV\(\cdot\)mS,  
Figure 10: Distribution of AE events of the R-C-R-C-R sandwiched coal-rock specimen during the loading process. The letters shown in the figure correspond to the purple markers of Figure 7d. (**a**) 0 s; (**b**) 300 s; (**c**) 600 s; (**d**) 700 s; (**e**) 800 s; (**f**) 900 s; (**g**) 1000 s; (**h**) 1100 s; (i) 1200 s; (j) 1270 s.  
\(25.31\times 10^{4}\) mV\(\cdot\)mS, and 36.74 \(\times\) 10\({}^{4}\) mV\(\cdot\)mS, respectively. The fastest growth rate of accumulated AE energy at this point was also captured for different kinds of sandwiched coal-rock specimens.  
Due to the exacerbation of internal damage and the generation of macro-cracks, the release of AE energy for the sandwiched coal-rock specimens was still active during stage IV. However, it tended to be chaotic as a whole. As shown Figure 6, the increasing tendency of accumulated AE energy was continuously monitored during stage IV.
## 3 Data Source and Methods
### Methods
#### 3.2.2 Match Filter  
The match filter algorithm identifies and estimates the methane content by matching preset methane spectral characteristics with similar features found in actual observational data. The core concept of this algorithm is that each input spectral radiance value can be represented as the background radiance of an area without methane enhancement, and as the radiative disturbance caused by changes in methane concentration [32]. The specific calculation is as follows:  
\[\alpha=\frac{\left(x-\mu\right)^{\mathrm{T}}\beta^{-1}t}{t^{\mathrm{T}}\beta^{ -1}t} \tag{2}\]  
In the formula, \(\alpha\) represents the column methane concentration in milligrams per square meter (mg\(\cdot\)m\({}^{-2}\)); \(x\) is the observed radiance value; \(\mu\) is the mean background radiance; \(\beta\) is the covariance of the radiance values within the dataset, and the target spectrum that determines the spectral features to which the match filter is sensitive; T denotes the transpose of a matrix, which is a matrix operation; t is the radiative disturbance in the target spectrum, and this disturbance is dependent on the methane absorption spectrum \(s\) and the mean background radiance \(\mu\). The calculation is as follows:  
\[t_{s}(\mu)=\mu\times s \tag{3}\]  
In the equation, the unit of the methane absorption spectrum \(s\) is obtained through atmospheric radiative transfer simulations in MODTRAN5.2.1 (MODerate resolution atmospheric TRANmission) [37], and \(t_{s}(\mu)\) denotes the background radiance value when the absorption spectrum is \(s\). This allows for the estimation of methane concentration. \(\times\) represents multiplication in mathematics. The absorption characteristics of methane in the shortwave infrared range within the atmosphere can be simulated using MODTRAN's atmospheric radiative transfer model. The match filter algorithm has been successfully applied for the retrieval of methane point source column concentrations from both airborne and satellite-based hyperspectral data [38].