esa-sceva/satcom-chunk-collection huggingface dataset:
However, specific information on how to determine blood conditions for test application and the differences in blood preservation techniques between anticoagulants is not provided. The evaluation of mechanically induced hemolysis may be affected by the donor species, sex, age, fasting, method of harvesting, anticoagulant properties, period of storage, biochemical state of the blood, and hemoglobin and hematocrit blood levels [8; 9]. Application of human blood is a potential strategy for overcoming interspecies differences during testing; however, securing the volume required is difficult, and regulatory challenges, such as Institutional Review Board (IRB) approval, may hamper product safety evaluation processes. Therefore, some studies have compared blood characteristics in vitro using various animal blood samples as alternatives to human blood [10; 11; 12; 13; 14; 15; 16]. The ASTM F1841-19 standard requires a hemolysis evaluation comparison by simultaneously performing tests between test and control groups using blood from the same animal within 48 h of collection [7]. In general hemolysis studies, the plasma-free hemoglobin (pfHb) concentration is used as an indirect evaluation indicator for hemolysis. However, for mechanically induced hemolysis, confirming test repeatability is limited in hemolysis evaluation using a single evaluation indicator because the initial state of the individual RBC can affect the hemolysis result significantly. Consequently, it is necessary to apply additional indicators to explain the hemolysis caused by various blood conditions by directly observing morphological changes such as structural damage to RBC membranes [17; 18; 19; 20; 21; 22; 23]. The structural organization of the cytoplasmic membrane contributes to mechanical integrity by responding quickly to external fluid shear stress throughout the circulatory life of RBCs. The degree of RBC membrane loss and the resulting increase in cell sphericity are used as indicators of severity for some human genetic anemia disorders [24; 25]. The aim of the present study was to evaluate the performance of a self-developed in vitro mock circulatory loop system model and to confirm the validity of the test method by introducing a morphological indicator that changes RBC shape from normal biconcave disc-shaped cells with a concave center to a spherical shape. Furthermore, the reliability of the in vitro mechanically induced hemolysis test (ISO 10993-4:2017) was assessed.
## 3 Results
### Microbiota Composition Determined by Cultivation  
By cultivation, the total number of bacteria in the cheese samples ranged between \(10^{7}\) and \(10^{9}\) CFUs per gram of the cheese, with the lowest number in cheese D and the highest in cheese B (Table 3).  
A total of 72 isolates were obtained in pure culture from all samples that were identified as distinct using MALDI-TOF mass spectrometry. Approximately half of the isolates exhibited identical 16S rRNA sequences, reducing the final number of unique isolates to 37 (Figure 4). The isolates were classified into four phyla--Firmicutes (15 isolates), Actinobacteria (7 isolates), Proteobacteria (14 isolates), and Pseudomonas (1 isolate). Mesophilic SCs of _L. lactis_ subsp. _lactis_, _L. lactis_ subsp. _cremoris_, _L. laudensis_, and _Leuconostoc_ were cultured from cheeses A, C, E, and F, but not from producers B and D. Other representatives of the phylum Firmicutes included various species of the genera _Staphylococcus_ and _Vagococcus_, along with one isolate of _Marinilactibacillus_ found in cheeses from all producers except F. _Brevibacterium_, _Glutamicibacter_, _Microoccus_, and _Corynebacterium_, all from the phylum Actinobacteria, were also successfully cultured. The phylum Proteobacteria was represented by various species of the genera _Vibrio_ and _Psychrobacter_. In contrast to the 16S rRNA gene amplicon sequencing results, species such as _Morganella_, _Shewanella_, _Enterobacter_, and _Citrobacter_ were additionally cultured from cheeses A and E. _Sphingomonas paucimobilis_ (the phylum Pseudomonas), not detected by amplicon sequencing, was cultured from cheese C. Not a single isolate of the phylum Bacteroid, which was abundant in cheese D, was captured by culture.  
\begin{table}
\begin{tabular}{c c} \hline
**Cheese** & **CFU/g** \\ \hline A & 1.3–2.3 \(\times\)\(10^{8}\) \\ B & 1.2–2.8 \(\times\)\(10^{9}\) \\ C & 2.9–4.0 \(\times\)\(10^{8}\) \\ D & 1.7–3.9 \(\times\)\(10^{7}\) \\ E & 6.9–8.4 \(\times\)\(10^{8}\) \\ F & 2.7 \(\times\)\(10^{8}\) \\ \hline \end{tabular}
\end{table}
Table 3: Total bacteria counts determined in the samples of smear-ripened cheese. CFU—colony forming unit.
## 2 Theory of the LRSC Algorithm
### Recognition of the Prediction Samples  
After the sparse solution was determined, recognition of the prediction samples was performed using a method similar to NN or NS. Specifically, it was realized by fitting the prediction samples based on the spectral data and the regression coefficient (sparse solution) that was determined:  
\[\overline{y}_{i}=X\delta_{i}(\overline{\beta}_{1}) \tag{10}\]As expressed in Equation (10), \(X\) denotes the spectral data of the training samples, \(\delta_{i}(\widetilde{\beta}_{1})\) represents the sparse regression coefficient solution, and \(\widetilde{y}_{i}\) denotes the prediction samples linearly estimated by one specific class of training samples. In addition, as the solution of the regression coefficient is sparse, it is possible to represent the prediction samples with training samples of the same class. The second-order norm of the residual between the estimated and actual prediction samples is calculated, and the attributes of the samples are judged according to the principle of the residual norm being the minimum, that is, the testing sample should belong to the same class label as the corresponding estimated sample giving the minimum residual norm, as shown in the following Equation (11):  
\[\underset{i}{\text{min}}r_{i}(y)=\left\|y-\overline{y}_{i}\right\|_{2} \tag{11}\]  
As mentioned above, the LRSC algorithm first uses a linear framework to represent all the training samples, and obtains the regression coefficient by determining the sparse solution. It then estimates the prediction samples according to the regression coefficient, and outputs a final result consisting of the class with a minimum residual norm between all the estimated and real prediction samples.  
The complete LRSC algorithm was calculated as follows:  
1. Input training spectral samples and training label and a predicted sample.
2. Calculate the regression coefficient \(\beta_{0}\) (sparse solution) of the predicted sample for the training samples.
3. Calculate estimated prediction sample \(\overline{y}_{i}\) based on each category of training data and regression coefficient \(\beta_{0}\).
4. Calculate the residuals of the estimated/predicted sample from each category _ri_.
5. Output the predicted label \(i\).
## 3 Data Treatment and Analysis
### Results  
The search in the systematic literature resulted in 386 records and six additional articles from referent lists. After removing 62 duplicates, 324 articles were screened at the title and abstract levels. There were 250 articles rejected since they did not meet the subject of research. For full text, 74 studies were accessed; however, 64 were excluded since they did not contain the BI location at least on one of the coordinate axes. The search process according to PRISMA is shown in Figure 2. The records of the remaining ten articles, with a pooled sample of 133 males and 51 females aged 11-25, were included for systematic review (Tables 1 and 2). However, there were not enough data for women and therefore it was not possible to relevantly compare differences in the stability and position of the BI within the FS and KS. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Articles** & **Participants** & **Capture System** & **Serve Types** & **Aim of the Study** & **Main Findings** \\ \hline \multirow{4}{*}{\
\begin{tabular}{} \end{tabular} } & 6 professional & & & The positions of the front foot, \\  & men, & 22 cameras & & determine whether & \\  & 18–24 y, & 250 Hz VICON & Flat serve & players serve & \\  & 183 \(\pm\) 6 cm & MX & & different court parts & \\  & 183 \(\pm\) 6 cm & & & from the same toss. & \\  & & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & 15 professional & & & The TP and RC of the kick serve occurred further to the right (18–21 and 30–35 cm, respectively) \\  & men, & 1400 Hz Basler & Flat serve & two-dimensional & from both serving sides. \\  & 25.3 \(\pm\) 4.6 y, & piA640 & Kick serve & kinematics of the & Compared to the second serves \\  & 81.7 \(\pm\) 8.1 kg & & & ball toss during & wide, the second serve to the T \\  & & & & different serve types. & had a TP and RC 11 and 18 cm \\  & & & & further to the right, but only from the device court. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the chosen studies for systematic review. Figure 2: The flow chart of the systematic approach to the retrieval articles relevant to the ball impact location within the tennis serve.
## 3 Probability of Chip Error in Coherent O-QPSK
### Half-Sine Pulse Shaping
#### 3.2.1 Case (a): \(\sqrt{I}<\sqrt{C}\)  
When \(\sqrt{I}\) is smaller than \(\sqrt{C}\), the demodulator never makes an error, since the received signal never crosses the other quadrants. Thus, \(P_{c}\) and \(P_{s}\) are equal to zero:  
\[P_{s}=0\] \[P_{c}=0\]
## 5 Evaluation of Results  
In order to evaluate the GANN modeling and prediction, residual values from the difference between the predicted and observed S4 and ROTI parameters are calculated. The mean and maximum residual values for the S4 and ROTI are given in Table 5. As it can be seen from Table 5, the highest average of S4 residuals is obtained for 2015, 2017, and 2020, in that order. It can be seen that the GANN prediction for the S4 in 2020 is higher than that in 2017 and 2015. This is due to less ionospheric perturbations and solar magnetic  
Figure 4: The predicted and observed ROTI during 24 h on 31 January, 31 March, and 31 July in 2015, 2017, and 2020.  
activity occurring in 2020, compared to 2015 and 2017. The maximum residual value on 31 July is equal to 0.04, which is less than the residuals on all other days, indicating that 31 July has the highest accuracy among the other days of each year. The highest value observed for residuals was measured on 31 March 2015, equal to 0.07, indicating that the prediction accuracy is lower on this day compared to the other days. Figure 5 depicts the residuals between the observed versus predicted values for the S4, along with the values of the vector of residuals.  
The maximum and average residual versus the observed and predicted ROTI is presented in Table 5 for 31 January, 31 March, and 31 July in 2015, 2017, and 2020. From Table 5, it can be seen that the ROTI prediction for 2020 compared to 2017 and 2015 has the lowest calculated residual value, which indicates the higher accuracy of the ROTI prediction for this year. The maximum residual value was calculated on 31 March 2015, which is equal to 0.19. Figure 6 represents the vector of residuals for the observed minus predicted ROTI.  
Figure 5: The residual vectors of observed minus predicted S4 values during 24 h on 31 January, 31 March, and 31 July in 2015, 2017, and 2020.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**2015**} & \multicolumn{3}{c}{**2017**} & \multicolumn{3}{c}{**2020**} \\ \hline \multirow{2}{*}{**Period of Time**} & **31** & **31** & **31** & **31** & **31** & **31** & **31** & **31** & **31** \\  & **January** & **March** & **July** & **January** & **March** & **July** & **January** & **March** & **July** \\ \hline Mean Absolute Residual S4 & 0.03 & 0.04 & 0.03 & 0.03 & 0.03 & 0.02 & 0.02 & 0.02 \\ \hline Max Absolute Residual S4 & 0.06 & 0.07 & 0.06 & 0.05 & 0.06 & 0.05 & 0.05 & 0.06 & 0.04 \\ \hline Mean Absolute Residual ROTI & 0.04 & 0.05 & 0.04 & 0.03 & 0.03 & 0.02 & 0.03 & 0.03 & 0.02 \\ \hline Max Absolute Residual ROTI & 0.18 & 0.19 & 0.16 & 0.15 & 0.14 & 0.13 & 0.16 & 0.16 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 5: The residual values of observed minus predicted S4 and ROTI during 24 h on 31 January, 31 March, and 31 July in 2015, 2017, and 2020.
## 3 Basic Principles of 3 MA
### Barkhausen Noise (BN) Method  
In order to induce the reorganization of the magnetic microstructure and thus to provide the Barkhausen Noise (BN) response of the specimen, the application of a variable magnetic field is necessary. This magnetic field is applied by the U-shaped yoke (see Figure 2, no. 3) with the magnetization coil (no. 4), as in case of the HA method, as described in Section 3.1. The applied magnetization amplitudes are sufficient to excite the inspected ferromagnetic materials up to their saturation levels. The magnetization frequency is adapted in order to avoid local eddy currents, thus not changing or distorting the magnetic hysteresis behavior in the specimen.  
During the reorganization of the ferromagnetic microstructure, Bloch wall displacements are generated, which occur by discreet jumps. These Bloch wall jumps result in sudden changes of magnetization, which causes magnetic flux variations. These variations can be detected with a receiver coil (no. 8) that is placed above the sample. After signal processing, which consists of several steps, like amplification and filtering, the 7 measuring parameters that are shown in Table 3 can be derived from the BN signal. The BN method uses analyzer frequencies mostly in the high kHz range. Therefore, only the near-surface area of the material can be inspected. A further disadvantage of the method is its sensitivity to electromagnetic interference, which is a result of the broadband signal analysis in conjunction with high amplification.
## 3 Methodology
### Feature Extraction
#### 3.3.3 Histogram of Oriented Gradients (HOG)  
HOG is a shape identifier that determines the layout and structure of a local character [37]. This HOG describes how the intensity gradient spreads across the region, which is used to determine the character. The HOG is often based on the gradient direction accumulating via the pixels of a small spatial region, such as a single cell. The HOG descriptor in each cell collects the gradient direction's local 1-D histogram. This process is performed by constructing the local histogram over a large spatial region, typically a block of cells, and using the data to normalized all of the block's cells. The detection window is placed over the overlapping grid in this case. Equation (6) represents the pixel's horizontal and vertical gradient:  
\[\begin{cases}G_{p}=I^{\prime}(p+1,q)-I^{\prime}(p-1,q)\\ G_{q}=I^{\prime}(p,q+1)-I^{\prime}(p,q-1)\end{cases} \tag{6}\]The magnitude and orientation of HOG gradients are expressed in Equations (7) and (8), respectively:  
\[G(p,q)=\sqrt{G_{p}^{2}+G_{q}^{2}} \tag{7}\]  
\[\theta(p,q)=\text{argtan}\bigg{(}\frac{G_{q}}{G_{p}}\bigg{)} \tag{8}\]  
The weights of the gradient magnitudes that are in the direction of interest are combined to generate the cell's histogram vector. Furthermore, the HOG's output vector is made up of normalized cells from many detection window elements and frames.
# Groundwater Storage Changes in China from Satellite Gravity: An Overview
## 2 Data and Methodology
### Data
#### 2.2.4. Precipitation Data and Drought Index  
Monthly gridded precipitation products from the China Meteorological Data Sharing Service System (CMDSSS) were used to obtain annual precipitation anomalies in our study regions, which are based on 2472 meteorological stations over China and the thin plate spline interpolation method. Additionally, a monthly dataset of Palmer Drought Severity Index (PDSI) on a 2.5\({}^{\circ}\) grid was used to quantify the dry or wet conditions in our study regions [77]. The PDSI data used in this study was downloaded from the University Corporation for Atmospheric Research (UCAR), U.S..
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 3 Convolutional Neural Network
### Convolutional Layer  
The convolutional layer scans the picture from top to bottom through a filter with a fixed size to obtain various local features as the input of the next layer. After ReLU function processing, the values smaller than 0 are exported as 0, while those larger than 0 are exported directly. This result is the feature map. Each point in the feature map can be regarded as a feature of the region in the original pattern and transmitted to the next layer. The convolutional layer of CNN concentrates on obtaining these local features of the pattern [14; 15].  
All the convolution operation outputs are then transformed by a nonlinear activation function. Equation (9) is an example of a nonlinear activation function [14]:  
\[\text{sigmoid}(x)\ =1/\left(1+e^{-x}\right) \tag{9}\]  
The convolution operation of the same convolutional layer and previous layer share the same weight, expressed as Equation (9), where l is the convolutional layer, W is the shared weight, M is the characteristic pattern differing from the previous layer, j is the characteristic pattern of one output, B is the bias in the layer, and (*) represents the convolution operation.  
\[X_{j}^{[l]}=\text{sigmoid}(\sum_{i\in M_{j}}X_{i}^{[l-1]}*W_{ij}^{[l]}+B^{[l]}) \tag{10}\]  
If the traditional deep learning network is used for recognizing an image, the original two-dimensional picture must be split into one-dimensional image. Each pixel is regarded as an eigenvalue and is put into the DNN architecture for analysis. Thus, these input pixels lose the original spatial arrangement information. The purpose of the convolutional layer of CNN is to maintain the spatial arrangement of the image and obtain a partial image as the input feature [14].
# Dynamic Test Measurements and Simulation on a Series Wound DC Motor
## 2 Modelling and Simulation of the SWDCM  
The nominal values of the tested and modelled SWDCM were as follows: type: DC/T4-48; power: 4 kW; torque: 14.7 Nm; RPM: 2800 1/min; voltage: 48 V; current: 104 A. The latest version of our model for SWDCMs is shown in Figure 1.  
Bases of the model are the differential equations below [11; 16]:  
\[U_{tap}-\big{(}R_{a}+R_{g}\big{)}\cdot I-\big{(}L_{a}(I)+L_{g}(I)\big{)}\cdot \frac{dI}{dt}-L_{ga}(I)\cdot\omega\cdot I=0 \tag{1}\]  
\[L_{ga}(I)\cdot I^{2}-M_{reth}(\omega)-M_{ell}(\omega)=J_{m}\cdot\frac{d\omega} {dt} \tag{2}\]  
In the equations, \(V_{accu}\) is the terminal voltage of the battery during the test, \(U_{brush}\) is the brush voltage, \(R_{r}\) and \(R_{s}\) are the electric resistances of the rotor and stator windings, while the resultant electric resistance of the wires, connecting the battery to the motor, is denoted by \(R_{wire}\). Characteristics \(L_{a}(I)L_{g}(I)L_{s}(I)\) and \(L_{r}(I)\) are the self-dynamic inductances of the stator and rotor windings, and \(L_{ga}(I)\) is their mutual-dynamic inductance [11; 16]. The characteristics above depend on the intensity of current flowing through the windings. The friction (bearing plus brush friction) and loading torques on the rotor of the motor are denoted by \(M_{load}(\omega)M_{ell}(\omega)M_{res}\) and \(M_{load}\) respectively, and \(J_{m}\) denotes the rotor's moment of inertia. The value of \(J_{r}\) sometimes can be found in the catalogue of the motor, while characteristics \(L_{s}(I)\), \(L_{r}(I)\), \(L_{sr}(I)\), and \(M_{res}\) have to be measured. In this research, both \(J_{r}\) and \(M_{res}\) were measured. The block diagram of our simulation program is presented in Figure 2. The program is based on differential Equations (1) and (2).  
Figure 1: The applied model for the simulation of SWDCMs [6; 11].  
Figure 2: The simulation program created in MATLAB/Simulink [11].  
Electric resistances \(R_{a}\)\(R_{g}\), \(R_{r}\), \(R_{wire}\), brush voltage \(U_{brush}\), and torques \(M_{load}\), and \(M_{res}\) are defined in the simulation program as constants, while dynamic inductances \(L_{s}(I)\), \(L_{r}(I)\), \(L_{sr}(I)\), and battery voltage \(U_{batt}(t)\) are given in the form of \"Lookup Tables\". In the following, dynamic test measurements on the SWDCM will be presented as well as the experimental procedure for the determination of dynamic parameters \(M_{res}\) and \(J_{r}\).
## 1 Introduction  
Gram-negative coccobacillus _Acinetobacter baumannii_ belongs to a group of ESKAPE pathogens. ESKAPE is the acronym for the group of bacteria that include _Enterococcus facicum_, _Staphylococcus aureus_, _Klebsiella pneumoniae_, _A. baumannii_, _Pseudomonas aeruginosa_, and _Enterobacter_ spp. Due to their ability to effectively _escape_ antibiotic treatments, these multidrug-resistant (MDR) pathogens are common causes of life-threatening infections affecting mainly immunocompromised and critically ill patients in intensive care units (ICUs) [1]. In recent years, the overall number of antibiotics that act on the ESKAPE pathogens decreased significantly [1; 2]. In 2017, the World Health Organization (WHO) published a list of 12 \"priority\" pathogens encompassing the ESKAPE group, for which new antibiotics are urgently needed. The WHO classifies carbapenem-resistant _A. baumannii_ as the number one critical pathogen. Major risk factors for the acquisition of _A. baumannii_ include antibiotic usage, especially \(\beta\)-lactams--the most commonly used drugs to treat infections caused by important pathogens which cause a variety of diseases in humans and animals. The second most common risk factor is mechanical ventilation, while other risks include surgical wound infections and invasive procedures such as central venous or urinary catheters [3]. It was demonstrated that approximately 1000,000 people globally are infected with _A. baumanniii_ every year, while the emergence of MDR strains is reported worldwide [4; 5; 6]. Antimicrobial treatment of MDR _A. baumanniii_ infections include colistin, sulbactam, and tigecycline, used in combination with other antibiotics [1; 7; 8]. A recently published global study, the Tigecycline Evaluation and Surveillance Trial (TEST), revealed that the percentage of MDR _A. baumanniii_ isolates was the highest among all analyzed Gram-negative bacteria, and it increased from 23% in 2004 to 63% in 2014 [4]. _A. baumanniii_ causes a range of infections, including ventilator-associated pneumonia, bacteremia, meningitis, urinary tract, wound, and bone infections [2; 9]. The risk of mortality is high and often reaches 40-50% in ICU [10; 11]. _A. baumanniii_ is a life-threatening problem not only because of multidrug resistance but also its ability to evade the host immune response and survive under harsh environmental conditions. In this review, we present various mechanisms that protect _A. baumannii_ against the innate host immune response and stresses caused by the outside host environment. We focus on (1) the ability of _A. baumanniii_ to survive long-term desiccation, (2) factors involved in maintaining proteome homeostasis in _A. baumannii_ cells, (3) the population heterogeneity in _A. baumanniii_ biofilms, and (4) the mechanism underlying _A. baumanniii_ antibiotic resistance.