esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of High Numerical Aperture Cylindrical Surface with Iterative Stitching Algorithm
## 5 Conclusions  
An iterative stitching algorithm was proposed in this study for data processing of interferometric measurements of cylindrical surfaces. The technique was demonstrated with an example by determining the surface topography of a cylindrical lens with high NA. By constantly calculating the deviation of the measured overlapping region and then adjusting the position of each sub-aperture, the obtained experimental results had fewer residual errors than when using the traditional method. Possible registered miss-arrangements during measurement were corrected in the data processing. The advantages of this new method enable achieving higher reasonability and reliability than the traditional techniques. The calculation results quickly converge, but the improvement in accuracy is not obvious with increasing numbers of iterations. In addition, due to the high accuracy of the experimental device, a higher number of iterations was not required.  
Writing--Original Draft Preparation and Software: D.C.; Formal Analysis, Investigation and Methodology: J.P.; Project Administration and Supervision: Y.Y. and A.A.; Supervision: A.A.; Editing & Review and editing: Y.Y. and S.V.  
Figure 10: (**a**) Data comparison of eight groups of the peaks-and-valleys (PV) value of residual error; (**b**) the root mean square (RMS) value of the residual error.  
Figure 9: The interferogram of translation error \(Err_{i}^{k}\).  
**Funding:** The authors are grateful for support from the following research grants: (1) the National Natural Science Foundation of China (NSFC) (No. 51775326); (2) National Science and Technology Major Project (No.2016 YFF0101905); (3) Natural Science Foundation of China (NSFC) (No. 61605126).  
**Acknowledgments:** [PERSON] thanks the 'Strategic Partners Scholarship Fund' program of Shanghai University for the support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion  
The present study demonstrated that baseline evaluation of the cardiac and vascular parameters might improve the evaluation of the prognoses of women suffering from major gynecological cancers (uterine corpus, cervical, and ovarian). In particular, the endothelial function (brachial artery FMD \(\leq\) 5.8%) and left ventricle contractility ( LVEF \(\leq\) 57%) demonstrated that they were independently related to the mortality of patients with gynecological cancers. The prognoses of patients suffering with cancer is worse despite the improvements in therapy and advances in early diagnosis [1; 2; 3]. The rare prevalence of gynecological cancers and the difficulties in early diagnosis are responsible for the poor survival rate of patients [18]. The identification of early signs of poor prognoses in cancer patients--and gynecological ones in particular--is a fundamental step when evaluating such individuals in order to better stratify their risk of adverse events beyond the evolution of the cancer itself. Echocardiography and vascular ultrasound examinations may represent good tools to be included in the general evaluation of patients with cancer before chemo- or radiotherapy and surgery in order to better evaluate their baseline cardiovascular risk and intensify treatments for preventing adverse events. The prognostic values of the LVEF in cancer patients who underwent chemotherapy were established [19; 20]. All forms of chemotherapy are known to promote cardiac dysfunction in a reversible or irreversible manner [14]. Even patients without significant cardiovascular risk factors may experience cardiac toxicity from chemo- or radiotherapy upon a long-term follow-up [21; 22; 23]. The mechanisms by which heart can be deeply affected \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Univariate Analysis**} & \multicolumn{3}{c}{**Multivariate Analysis**} \\ \hline
**Parameter** & \(\beta\pm\)**SE** & _p_**-Value** & \(\beta\pm\)**SE** & **Wald** & _p_**-Value** \\ \hline LVEF & \(-0.228\pm\) 0.068 & 0.0009 & \(-0.225\pm\) 0.089 & 6.379 & 0.0115 \\ FMD & \(-0.213\pm\) 0.073 & 0.0037 & \(-0.249\pm\) 0.106 & 5.497 & 0.0190 \\ Left E/A ratio & \(1.123\pm\) 0.299 & 0.0002 & 0.714 \(\pm\) 0.353 & 4.084 & 0.0433 \\ TAPSE & \(-0.061\pm\) 0.063 & 0.3302 & & & \\ Age & \(0.044\pm\) 0.018 & 0.0163 & & & \\ Peripheral artery diseases & \(1.703\pm\) 0.779 & 0.0288 & & & \\ Chemotherapy & \(-1.212\pm\) 0.627 & 0.0532 & & & \\ Surgical intervention & \(1.088\pm\) 0.560 & 0.0522 & & & \\ \hline \hline \end{tabular}  
* **Abbreviations:** FMD: flow-mediated vasodilitation; LVEF: left ventricle ejection fraction; SE: standard error.  
\end{table} 
Table 4: Univariate and multivariate Cox regression analysis with independent all-cause mortality predictors.
## 3 Methods
### Automated Geometric Correction Processing Method
#### 3.2.1 Step 1: Reference Images Selection  
There were three criteria for selecting a reference image. Firstly, we selected the MODIS images with the same spatial coverage as the BNU-1 image as well as up to 5 h of different acquisition times. Secondly, the MODIS images covering the BNU-1 image and its surrounding 10 km area were chosen (one BNU-1 image corresponds to multiple MODIS images). Thirdly, the SIFT algorithm was adopted to extract the CPs from each image pair, where the BNU-1 image and the MODIS image are the target image and the reference image, respectively. The MODIS image with the most CPs was used as a reference image for the geometric correction. If more than one MODIS image has the highest number of CPs, the one whose acquisition time is closer to the BNU-1 image's acquisition time is preferred as the reference image. The reference image used for geometric correction of the BNU-1 image is referred to as the corresponding MODIS image hereinafter.
# Signal-to-Noise Ratio Analysis of Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy
## 3 Correlation Analysis Between Interferogram and Reconstructed Spectrum Signal-to-Noise Ratio  
From the basic principle of FTS, we know that there is a Fourier transform relationship between the interferogram and the reconstructed spectrum. According to the characteristics of Fourier transform, for a signal, its energy is conserved before and after Fourier transform, and the two follow [PERSON]'s theorem. Therefore, according to [PERSON]'s theorem [25; 26], for FTS, the interferogram, reconstructed spectrum, and noise as a special signal, its energy is also conserved during the Fourier transform process. Therefore, this characteristic can be used to analyze the SNR of BPS-FTS. Assuming that the number of sampling points of the interferogram is K, the interferogram energy at each OPD is \(I(x_{it})\), the noise signal energy is \(n(x_{if})\), the energy of each spectral segment of the reconstructed spectrum signal is \(B(v_{if})\), and the noise is \(n(v_{if})\), then the energy relationship after discretization is as follows:  
\[\sum_{it=1}^{K}|I(x_{it})|^{2}\approx\frac{1}{K}\sum_{if=1}^{K}\Big{|}B(v_{if}) \Big{|}^{2} \tag{21}\]  
\[\sum_{1}^{K}\Big{|}n(x_{if})\Big{|}^{2}\approx\frac{1}{K}\sum_{1}^{K}\Big{|}n(v_ {if})\Big{|}^{2} \tag{22}\]  
In the interference domain, we can use the average energy of the interferogram as a measure of the interferogram energy, expressed as \(I_{ave}\), and use the average energy of the noise signal at this time as a measure of the noise energy of the interferogram, expressed as \(n_{I_{ave}}\). In the spectral domain, we use \(B_{ave}\) to express the average energy of the reconstructed spectrum signal as a measure of the spectrum signal energy and use \(n_{B_{ave}}\) to express the average energy of the noise signal at this time as a measure of the noise energy of the reconstructed spectrum signal. Then, Equation (21) can be expressed as follows:  
\[K\cdot I_{ave}^{2}\approx\frac{1}{K}\cdot K\cdot B_{ave}^{2} \tag{23}\]  
Similarly, for noise, Equation (22) can also be rewritten as follows by averaging the energy:  
\[K\cdot n_{I_{ave}}^{2}\approx\frac{1}{K}\cdot K\cdot n_{B_{ave}}^{2} \tag{24}\]  
That is,  
\[n_{I_{ave}}\approx\sqrt{\frac{1}{K}}\cdot n_{B_{ave}} \tag{25}\]  
For FTS, the interferogram is transformed into a spectrum through Fourier transform. The number of points in the interferogram and the spectrum are the same, as is the noise. However, for the spectrum, it is usually limited to a certain frequency range, that is, the spectrum is usually a bandpass signal. At this time, if the noise is not considered, the reconstructed spectrum includes both the useful signal and the zero value. When the noise is considered, the zero position is replaced by the noise signal. Among them, the number of useful signal points is usually called the number of spectral bands V (usually V \(<\) K), that is, the energy of the interferogram is concentrated in the spectrum corresponding to V spectral bands.
# Microbial Depolymerization of Epoxy Resins: A Novel Approach to a Complex Challenge
## 2 Materials and Methods
### Experimental Set-Up  
#### 2.2.1 Standard Soil (Microcosms)  
To study the influence of microbes on epoxy in a more representative environment than the normal microbial cultures, soil microcosms were prepared. The selected microbe for this experiment was the fungus _Gandderma adsersum_ (Empa 645), obtained from the collection of the Cellulose and Wood Materials Laboratory, Empa, Switzerland. This fungus was isolated from forest soil. In addition, during this experiment, the natural microbiome of the selected soil was also tested, since one set of microcosms was not autoclaved. The preculture of _G. adsperum_ was cultivated in MA media (malt 12 g/L and agar 20 g/L). For the preparation of a fungal suspension, the mycelium was removed from the surface of a 2-week-old pre-culture with tweezers and a scalpel and placed in 10 mL of sterile physiological water.  
The humidity rate of the selected soil (Frux ED 73, Einheits Erde, Germany) was measured in triplicates by a simple drying procedure. The obtained value was used to prepare 2 sets of microcosms of 500 g of soil with 2 different water-holding capacities (whc)--60% (addition of 45.8 mL of tap water) and 90% (addition of 134.5 mL of tap water). Scheme 1 illustrates the conditions tested during this experiment. Microcosms were prepared by filling 1 L glass jars with 500 g of soil and the above-mentioned amount of water to test the influence of different whc on the interaction between microbes present in the microcosms and epoxy. In each set, the same parameters were tested; sterilization of soil by autoclaving (sterile soil) and soil not autoclaved (unsterile soil). For both conditions (sterile and unsterile soil), 10 mL of sterile water (soil) and of 10 mL of _G. adspersum_ suspension (fungus) were added. Plates were removed for analysis after 1, 3 and 6 months. After the analysis, a piece of each plate, previously incubated for 3 and 6 months, was placed again in the microcosms and the analysis was repeated after 3 additional months on the same plates (6 months* and 9 months*). Three jars were prepared for each condition for a total of 48 microcosms. In order to have 4 replicates for each condition, 4 epoxy samples (2 for 10 for 60 mm) were placed in each microcosm. Sterilization of the epoxy samples was performed with the UV source of the microbiological sterile bench (standard procedure with a mixture of UVA and UVB rays). Two types of controls consisted of untreated samples (untreated) and UV-treated samples (untreated UV) to evaluate the influence of UV sterilization on the properties of the epoxy. Controls samples were stored in a desicator during the incubation of the test samples in the microcosms.  
To study the influence of the tested conditions on the properties of the material and to detect the potential depolymerization of the epoxy, the following analyses were performed: weight loss, hardness and colorimetric measurements, flexure tests, wettability and chemical changes, through contact angle and FTIR analysis.
# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 2 Materials and Methods
### Materials  
The formation of the database consists of images collected from different sources (sites distinct from the melanoma images). During the formation of the base, there was no access to information on the means of capture. Therefore, they are bases with different types of resolution, lighting, etc., so some images with difficult identification were discarded.  
The proposed technique was evaluated in four different public datasets images, PH2 [4], ISBI 2016 challenge [5], ISBI 2017 challenge [30], and DermIS datasets [31]. An outline of the publicly available dermatological datasets is summarized in Table 1.  
The PH2 database means the Pedro Hispano Hospital that deals with dermatology by dermoscopic images in a Tubingen mole analyzer system. In this system, the image is enlarged twenty times with a resolution of 768 \(\times\) 560 pixels at eight-bit (red-green-blue) colors. The two hundred images for different cases of skin die such as the first eightieth is the common mole and the other eightieth is a non-standard mole and the remaining number is for the malignant of skin cancer. The database consists of a medical explanatory note for every image depending on the medical segmentation of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Databases** & **\# of Images** & **Melanoma Images** & **Non-Melanoma Images** & **Ref.** \\ \hline PH2 & 200 & 40 & 160 & [4] \\ ISBI 2016 challenge & 900 & 273 & 627 & [5] \\ ISBI 2017 challenge & 2000 & 374 & 1626 & [30] \\ DermIS & 300 & 300 & 0 & [31] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Outline of the publicly available datasets.  
Figure 3: Flowchart for artificial bee colony-based melanoma detection.  
tested region and including the ground truth images. The manual partition of the lesions area and dermoscopic norm (ground truth) were implemented using a proficient dermatologist [4].  
From the ISBI 2016 and ISBI 2017 databases, the utmost complex images (i.e., the dermoscopy images with bubbles, hair, and several colors) were utilized in their original dimensions. These challenge databases comprise eight-bit RGB dermoscopy images with various image dimensions, from 540 \(\times\) 722 to 4499 \(\times\) 6748 pixels. Also, both the databases provided the original images paired with the lesion segmentation boundaries (ground truth), which were noted by professional dermatologists.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 4 Performance Evaluation
### Experimental Results
#### 4.2.2 Performance of Different Methods
Overall, our proposed fused positioning method statistically performs the best.
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods
### Experimental Procedure  
The experiment was conducted in November 2020. Before the formal test, subjects were provided with experimental training on the experimental process, with guidance on filling out the subjective questionnaire, test exercises, and explanation of the consent form. During the formal experiment, the subjects completed four experimental tests under each noise exposure condition (Figure 3). (1) Acoustic and Thermal Adaption (40 min): The experimenter played the specified sound file and began to record the sound signal in the left ear of the subject until the end of the experiment. At the same time, the experimenter measured the environmental parameters in the working area of the subjects. For the rest of the experiment, the participants wore EEG and ECG measuring equipment. (2) Cognitive Test 1 (25 min): Under the guidance of the experimenter, the subjects completed the PVT test, 2-BACK test, and Stroop test, in turn, with a 5 min rest after each test. (3) MATB Test (75 min): Before carrying out the MATB test, the subjects first carried out a resting test for about 5 min. After that, the subjects completed calibration of the eye movement equipment under the guidance of the principal investigator. Finally, the subjects completed the MATB tasks under three mental workload levels according to the preset task load order. At the end of each MATB test, the subjects completed the NASA-TLX scale, and then took a 5 min rest. (4) The focus of this study was MATB task and vigilance performance. Therefore, the data from Cognitive Test 2 (50 min) is not analyzed in this manuscript.
## 3 Results
### The Effect of the Sample Distance from the DCSBD Plasma Layer on Plasma Etching  
Diffuse coplanar surface barrier discharge in a configuration with a concavely curved electrode arrangement is capable of generation of visually almost uniform diffuse plasma layer of some 0.3-mm effective thickness. In this work, the polymer foil was treated at a distance in the range 0.2-0.6 mm from the alumina ceramics.  
It was already reported that DCSBD plasma consists of optically two distinctive regions: (i) diffuse plasma located directly above strip electrodes and (ii) the gentle streamer filaments located above the space between the electrodes [21]. It was shown that the decreasing gap between the conductive sample and the ceramics with the plasma layer led to the change of generated plasma--extinction of filamentary plasma was observed.  
Figure 8 summarizes the results of 1 s plasma etching in pure H\({}_{2}\) plasma at distances 0.2-0.6 mm. Original SEM images appear in Figure 9. As observed, there is an optimal distance for plasma etching of polymer mesh foil. If the distance is larger than 0.5 mm, the surface of the sample is disrupted by the streamers which are generated perpendicularly to its surface. This results in inhomogeneous etching and uncovering the metal wire only at a small area, as seen in Figure 9d, for the distance of 0.6 mm, together with the rough transition zone between polymeric and metal area. If there was a small gap between the sample and the plasma layer, the streamers could not be creating, and plasma was generated preferably in the diffuse mode. On the other hand, a small gap between the sample and the ceramics with the electrode system led to an increase of thermal heating of the polymer. As seen from SEM micrograph in Figure 9a, thermal heating may cause a cracking of the polymer in the transition zone around a free metal wire. A roughening of the wire and sputtering of the thin silver coating from the wire was observed.  
Figure 7: Exposed metal wires after etching in (**a**) ambient air plasma, (**b**) nitrogen plasma, (**c**) H\({}_{2}\)/N\({}_{2}\) plasma (50%) and (**d**,**e**) hydrogen plasma. Samples (**a–d**): treatment time 3 s (12 \(\times\) 0.25 s for sample (**d**)); sample (**e**): treatment time 1 s in 100% hydrogen plasma.
## 2 Results and Discussion
### Matrix Effects
The MS signal was suppressed for BPA, EE, and PRDN with a matrix effect generally ranging from moderate to severe in all the sample matrices. The matrix effect for 4 OP and 4 NP ranged from soft signal suppression or enhancement to severe signal suppression (>\(-\)50% ME) in treated and surface water. Severe MS signal suppression of 4 OP and 4 NP was observed for all raw wastewater sample matrices with a smaller variation in magnitude of %ME between sites. The MS signal at SRM (359\(\rightarrow\)329) measured for PRNL was less prone to signal suppression than the signal at SRM (358\(\rightarrow\)327) measured for PRDN. For both PRDN and PRNL, the magnitude of the signal suppression correlated with the expected complexity of the matrix in the raw, treated wastewater, upstream, and downstream surface water. The range of %ME for E1, E2, and DES was larger in the raw wastewater than other sample matrices attributed to the highly variable nature of the raw wastewater matrix at the different WWTPs. The response of DES at SRM (267\(\rightarrow\)252) more commonly observed MS signal enhancement than other target analytes even in the raw wastewater, and there was evidence of the magnitude of %ME for a target analyte varying with the site location and SRM of each analyte. Baseline shifts in the chromatograms at and near the retention time of the DES were observed with some sample injections. DES was not detected in the surface or wastewater samples. The largest average MS signal suppression was observed for 4 OP in raw wastewater (99 \(\pm\) 1.1%) at SRM 205 \(>\) 106. All other matrices had lower %ME with higher variability in %ME for the same transition of 4 OP. Generally, soft to moderate matrix suppression was observed for natural estrogens in surface water at WWTPs located in larger population cities (WWTP1 and 2). Compounds with high retention times such as 4 OP and 4 NP commonly observed more severe matrix suppression in raw wastewater at all sites (average %ME was \(-\)100%). The average %ME for all WWTPs decreased (more MS signal suppression) with the matrix complexity (\(-\)19 \(\pm\) 35%, \(-\)29 \(\pm\) 29%, \(-\)31 \(\pm\) 25% and \(-\)42 \(\pm\) 39% for upstream surface water, downstream surface water, treated, and raw wastewater, respectively).
# Review of sEMG for Robot Control: Techniques and Applications
## Abbreviations  
\begin{tabular}{l l}
**Abbreviation** & **Complete Spelling** \\ AMP & Amplitude \\ ANN & Artificial Neural Network \\ AR & Autoregressive Coefficient \\ BPNN & Back-Propagation Neural Network \\ CC & Cepstrum Coefficient \\ ConvEMG & Convolutional Electromyography \\ CRNN & Convolutional Recurrent Neural Network \\ CviT & Convolutional Vision Transformer \\ DT & Decision Tree \\ ELM & Extreme Learning Machine \\ EMD & Empirical Mode Decomposition \\ ESECW & Empirical Mode Filtering and Self-Enhancement Algorithm with Classical Wavelet \\ fApEn & Fuzzy Approximate Entropy \\ FD & Frequency Domain \\ FFT & Fast Fourier Transform \\ GRU & Gate Recurrent Unit \\ HIST & Histogram \\ IAV & Integral of Absolute Value \\ IEMG & Integrated Electromyography \\ KNN & K-Nearest Neighbor \\ LDA & Linear Discriminant Analysis \\ LDB & Local Discriminant Basis \\ LOG & Log Detector \\ LSTM & Long Short-Term Memory \\ MAV & Mean Absolute Value \\ MDF & Median Frequency \\ mDWT & Marginal Discrete Wavelet Transform \\ MEMD & Multivariate Empirical Mode Decomposition \\ MF & Mean Frequency \\ MFR & Mean Firing Rate \\ MLP & Multi-Layer Perceptron \\ MNF & Mean Frequency \\ MPF & Mean Power Frequency \\ NN & Neural Network \\ OFNDA & Orthogonal Fuzzy Neighborhood Discriminant Analysis \\ OMA & Online Moving Average \\ PCA & Principal Component Analysis \\ RF & Random Forest \\ RMS & Root Mean Square \\ RNN & Recurrent Neural Network \\ SampEn & Sample Entropy \\ SD & Standard Deviation \\ SSC & Slope Sign Change \\ SSD & Sum of Squares Difference \\ SSI & Simple Square Integral \\ STFT & Short-Time Fourier Transform \\ SUKF & Scale Unscented Kalman Filter \\ SVM & Support Vector Machine \\ SVR & Support Vector Regression \\ TD & Time Domain \\ VAR/VR & Variance \\ WL & Waveform Length \\ WPT & Wavelet Packet Transform \\ ZCR & Zero Cross Rate \\ ZC & Zero Cross \\ \end{tabular}