esa-sceva/satcom-chunk-collection huggingface dataset:
# Vector Approximate Message Passing Based OFDM-IM Detection for Underwater Acoustic Communications
## 4 Numerical Simulations
### BER Performance with Different System Configuration  
Figure 6 studies the BER performance of detectors with different constellation modulation, i.e., BPSK, QPSK, 8 PSK, and 16 QAM. Combined with the setting \(\{n=4,k=2\}\), the data rates of these cases are \(\{1,1.5,2,2.5\}\) bit/s/Hz. In the four subfigures, we can see the IM-AMP has the worst performance. The incorporation of symbol sparsity improves the performance of IM-MMSE-AMP. The IM-MMSE-AMP has relatively good performance in the first three cases, but it diverges in the 16 QAM case. The proposed detector outperforms the AMP-based detectors in all considered scenarios and approximates most with the IM-ML along with IM-LLR. In the whole SNR range, the gaps among the three detectors, i.e., the proposed, the IM-LLR and the IM-ML, are within 1 dB. As the modulation order increases, the detection is harder due to the decreasing signal spatial distance. Additionally, in our proposed method, the constellation signals are equalized jointly. The better BER performance of our proposed VAMP-based detector proves better index detection results. The results show that the VAMP framework is more robust than the AMP ones when confronting diverse modulation methods.  
Figure 7 studies the performance of detectors with different combinations of SAPs and constellation points. These cases have the SE \(\{2,1.375,0.75,1.25\}\) bit/s/Hz. In Figure 7, the proposed detector achieves effective results, which are approximated with the ML and the LLR over the whole SNR range. When BER = \(10^{-3}\), the proposed detector is almost matched with the IM-ML, and the gain is more than 5 dB compared with IM-MMSE-AMP. The IM-AMP almost works worse due to the sensitivity to the SAPs as well as the errors. The IM-MMSE-AMP presents unstable performance especially in (8, 2, 2), it arrives at its error floor at SNR = 10 dB. OFDM-IM provides us the flexibility to design different systems with desirable SE, where the proposed detector can serve as a low-complexity and effective implementation.  
Figure 5: Channel error effects on BER comparisons of detectors when \(\xi^{2}=[0.01~{}0.1]\).  
Figure 6: BER comparisons of detectors with different modulation methods. (**a**) BPSK; (**b**) QPSK; (**c**) 8 PSK; (**d**) 16 QAM.  
Figure 7: BER comparisons of detectors with different subcarrier allocations. (**a**) (4, 3, 4); (**b**) (8, 3, 4); (**c**) (8, 2, 2); (**d**) (8, 4, 2).
## 3 Results and Discussion
### Monitoring of A Freeze-Drying Cycle for a Mannitol/Sucrose Solution
#### 3.1.3. Sublimation/Primary Drying  
The sublimation step started after the annealing step was completed by evacuating the freeze-drying chamber down to about 0.06 mbar, which then progressed for 23 h. Figure 4b shows that the sublimation rates were higher in cuvettes 1 and 5 (the slope is steeper 18-22 h into the program). This is in agreement with [PERSON] and [PERSON]'s observations [4]. At the beginning of the sublimation phase after 13.7 h, it can be seen from Figures 5b and 6b that the temperature at the surface of the cuvettes was rather uniform around \(-29\)\({}^{\circ}\)C regardless if a probe was present or not. In contrast to that, after 28.1 h, it can be observed in Figures 5c and 6c that there was a temperature difference of approximately 3.5 K at the surface in cuvettes 1 and 5 as compared with the cuvettes without probes. As mentioned before, care was taken not to measure the wires that connected the probes to the freeze-dryer. The thermal effect of the probe and the faster progress of primary drying can clearly be seen in Figure 7 as well. The leftmost cuvette (containing a Pt100 probe) was clearly ahead of the other two cuvettes also along the depth profile.
# Hair Removal Combining Saliency, Shape and Color
## 3 Experimental Results
### Quantitative Evaluation
#### 3.3.1 Quantitative Evaluation Based on Quality Measures
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Img** & **Met.** & **MSE** & **PSNR** & **MSE3** & **PSNR3** & **SSIM** & **MSSIM** & **VSNR** & **VIFP** & **UQI** & **NQM** & **WSNR** \\ \hline IMD006 & [PERSON] & 13.626 & 36.787 & 20.918 & 34.926 & 0.888 & 0.956 & 24.401 & 0.403 & 0.650 & **23.151** & 40.255 \\  & [PERSON] & **12.610** & **37.124** & **19.831** & **35.157** & **0.891** & **0.957** & **25.355** & **0.412** & **0.653** & 22.687 & **40.286** \\  & [PERSON] & 57.555 & 30.530 & 64.073 & 30.064 & 0.856 & 0.898 & 15.260 & 0.354 & 0.608 & 12.372 & 28.927 \\  & [PERSON] & 24.283 & 34.278 & 33.481 & 32.883 & 0.860 & 0.926 & 19.601 & 0.301 & 0.534 & 16.305 & 33.955 \\  & [PERSON] & 55.748 & 30.668 & 62.440 & 30.176 & 0.853 & 0.897 & 15.370 & 0.342 & 0.591 & 12.586 & 29.143 \\  & [PERSON] & 19.653 & 35.197 & 28.070 & 33.648 & 0.867 & 0.943 & 21.588 & 0.328 & 0.589 & 19.411 & 36.740 \\  & HR-SSC & 19.669 & 35.193 & 27.078 & 33.805 & 0.861 & 0.941 & 21.101 & 0.323 & 0.561 & 20.950 & 37.581 \\ \hline IMD010 & [PERSON] & 44.373 & 31.660 & 52.734 & 30.910 & 0.855 & **0.939** & 16.990 & 0.352 & 0.659 & **18.189** & **33.261** \\  & [PERSON] & 46.305 & 31.475 & 55.898 & 30.657 & **0.859** & 0.931 & 15.853 & **0.364** & **0.665** & 14.288 & 30.576 \\  & [PERSON] & 88.070 & 28.683 & 98.376 & 28.202 & 0.838 & 0.907 & 14.944 & 0.330 & 0.636 & 14.129 & 28.725 \\  & [PERSON] & 42.985 & 31.798 & 52.674 & 30.915 & 0.818 & 0.905 & **17.330** & 0.236 & 0.510 & 17.169 & 32.186 \\  & [PERSON] & 90.161 & 28.581 & 100.956 & 28.089 & 0.832 & 0.905 & 15.007 & 0.320 & 0.618 & 13.841 & 28.495 \\  & [PERSON] & **40.550** & **32.051** & **51.019** & **31.054** & 0.857 & 0.937 & 16.699 & 0.354 & 0.660 & 16.755 & 32.390 \\  & HR-SSC & 55.952 & 30.653 & 66.185 & 29.923 & 0.827 & 0.920 & 15.203 & 0.293 & 0.579 & 17.837 & 31.972 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quality evaluation of the results on the _H13 GAN-data_—best results are in bold. Figure 15: Resulting masks of methods Lee, Xie, and HR-SSC on _sH-data_. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Img** & **Met. ** & **MSE** & **PSNR** & **MSE3** & **PSNR3** & **SSIM** & **MSSIM** & **VSNR** & **VIFP** & **UQI** & **NQM** & **WSNR** \\ \hline IMD017 & [PERSON] & 18.625 & 35.430 & 24.645 & 34.213 & 0.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 3 Results
### Drought Characteristics of the YRB
#### 3.1.2 Spatial Variation Characteristics of SPEI  
Pixel by pixel analysis of spatial characteristics of SPEI trend changes and significance of YRB in annual and different seasons from 2001 to 2020 are shown in Figure 5. The results showed that the drought and humidity trends represented by the study area passed the significance test (\(p<0.05\)) with fewer pixels. In the annual SPEI trend, the northern part of the study area shows a humid trend, but there is almost no obvious humid area (Figure 5a,e), and the southwest shows a significant drought trend (6.71%, \(p<0.05\)), and its main vegetation types are grassland and forest. There is a significant (\(p<0.05\)) drying trend in the study area in spring, accounting for 95.78% of the total area of the study area (Figure 5f). There is a trend of humidification in most areas in summer (96.06%) (Figure 5g). Generally speaking, the spatial distribution characteristics of dryness and wetness in spring and summer show opposite trends. In autumn, 52.68% of the area showed a drying trend (Figure 5h), mainly distributed in the north and south of the Yellow River Basin. In spring, SPEI shows an obvious (\(p<0.05\)) drying trend, and 5.01% of the area shows a drying trend in summer, which is mainly distributed in the southeast of the study area, indicating that the dry and wet conditions in spring and summer have a great impact on the climate in YRB.
# Hybrid Simulation of Seismic Responses of a Typical Station with a Reinforced Concrete Column
## 6 Quasi-Static Test
### Experimental Observations  
In Figure 12, the crack pattern is observed and recorded on the four sides (Side A, Side B, Side C, and Side D). Side C was the tension side when the loading direction was positive, whilst Side D was the tension side when the loading direction was negative. Side A and Side B were the lateral sides, which were parallel to the loading direction.  
On Side A and Side B, cracks were horizontal before the loading displacement reached 40.0 mm. Such cracks were mostly tensile cracks. After the 40.0 mm loading stage, as loading proceeded, flexural cracks started to propagate, and the width of the flexural cracks increased. The propagation direction was oblique due to the effect of shear force. When the displacement reached 80.0 mm, on Side C, the width of largest horizontal crack reached 2.8 mm. When the displacement reached 100.0 mm, on Side C, the width of largest horizontal crack reached 4.6 mm. On Side D, the width of the largest horizontal crack reached 3.3 mm. When the displacement reached 140.0 mm, on Side C,  
Figure 11: Quasi-static loading procedure.  
Figure 12: Crack pattern of the specimen. Note: Side (**C**) was the tension side when the loading direction was positive, whilst Side (**D**) was the tension side when the loading direction was negative. Side (**A**) and Side (**B**) were the lateral sides, which were parallel to the loading direction.  
the width of largest horizontal crack reached 8.0 mm. On Side D, the width of largest horizontal crack reached 4.5 mm.
# A Predictive Reliability Model to Assess the Performance of Photovoltaic Systems
## 2 Materials and Methods
### Simulation of the PV System  
In this major step, the performance of the photovoltaic system was simulated considering the values of inputs and operational conditions of both regions. The simulation results in the corresponding values of the device factors and system responses. For this study, the specialized software called PVsyst (version 7) was used to simulate the work of the PV system with the following specifications:  
* System type: unlimited sheds;
* PV array of 308 PV modules (model _ET-P660_255 WWCO Maxim_);
* The inverter pack is composed of 44 inverters (model _CSI-1.5 KTL1P-GI-FL_).  
By default, this simulation software considers a redundant number of variables for input and calculates a redundant number of device factors and the three system responses. For this study, we considered the most discriminable variables and factors to eliminate the redundancy. An example is that the simulation software calculates \(Y_{a}\), \(APR\) and \(\eta_{arr}\) among other variables. From these three variables, we considered only the array yield \(Y_{a}\), as it indicates the values of \(APR\) and \(\eta_{arr}\).
# An Integrated Approach to Biomedical Term Identification Systems
## 4 An Architecture for Information Exploration
### Linking Identified Entities with Concepts in LOD  
The importance of exchanging information in the clinical domain comes from the new requirements of health care services. These organizations must continue to deliver their services effectively, efficiently and sustainably.  
The ability to exchange information and the possibility for it to be automatically interpreted and reused in different applications is known as semantic interoperability. This implies that the systems understand the information they are processing.  
The intelligence needed to understand the message is a complex task and it requires establishment of agreements between the different institutions regarding how to represent, describe and contextualize the information to be exchanged. This is made possible by the use of medical terminologies and ontologies that contain internationally standardized catalogs that unify the data used [35]. We have used and applied Linked Open Data technologies in order to take advantage of the knowledge available in the medical domain.  
The automatic recognition system developed shows the medical concepts detected together with their identifier code according to a predefined thesaurus. This code also includes a reference to a website where the user can obtain information about a specific concept.  
For example, the term '_colera_' is associated with the codes _C0008354_, _A00_ and _000303_ in UMLS, CIE-10 and Medline's encyclopedia respectively (Figure 5). Through their codes, we can access different websites and obtain relevant information from each one:  
* Medline: [[https://medlineplus.gov/spanish/ency/article/000303.htm](https://medlineplus.gov/spanish/ency/article/000303.htm)]([https://medlineplus.gov/spanish/ency/article/000303.htm](https://medlineplus.gov/spanish/ency/article/000303.htm)). In MedLine website we can find information related to symptoms, treatments, prevention, images, references to other articles, etc.
* CIE-10: [[http://eciemaps.msssi.gob.es/ecieMaps/browser/index_10_2008.htmlXsearch=A00](http://eciemaps.msssi.gob.es/ecieMaps/browser/index_10_2008.htmlXsearch=A00)]([http://eciemaps.msssi.gob.es/ecieMaps/browser/index_10_2008.htmlXsearch=A00](http://eciemaps.msssi.gob.es/ecieMaps/browser/index_10_2008.htmlXsearch=A00)). From the CIE-10 website we can browse by categories and subcategories.
* UMLS: [[http://linkedlifeddata.com/resource/umls-concept/C0008354](http://linkedlifeddata.com/resource/umls-concept/C0008354)]([http://linkedlifeddata.com/resource/umls-concept/C0008354](http://linkedlifeddata.com/resource/umls-concept/C0008354)) In this website we can find the description of the concept, the semantic type, the relationships with other concepts, related documents and alternative labels, etc. Another important feature of this website is the possibility of downloading the concept information in different formats (RDF, JSON, N3/Turtle and N-Triples).  
Figure 4: Name Entity Recognition example.  
Figure 5: Similar concepts detected for ‘_Colera_’ using LOD.
# Biofilm Lifestyle in Recurrent Urinary Tract Infections
## 2 Urinary Tract Infections (UTIs)
### Pathogenesis of UITIs  
The infection starts by the ability of some bacteria to cross the urethral sphincter muscle, which acts as a natural barrier against pathogens, followed by adherence to the urethral epithelium [19]. Adhesion is mediated by bacterial fimbiae and adhesins, which play a pivotal role in the initiation of urinary tract infections. Later, most pathogens would climb up the urethra by swimming to the urinary bladder [14; 22]. Colonization in the urinary bladder is attained by simultaneous expression of multiple virulence factors - mainly motility, adhesion and secretion of toxins like proteases, hemolysins, urease, colony-necrotizing factors, siderophores, and polysaccharide coatings. These toxins would reflect tissue necrosis and facilitate invasion, giving rise to a typicalystitis [23]. Common symptoms are bacteriuria accompanied by fever, dysuria, pyuria, itching, urinary frequency, urgency lower abdominal pain (LAP), suprapublic pain, burning sensation during urination, blisters and ulcers in the urogenital area [7; 36].  
Figure 1: The most frequent ureopathogens (adopted from [PERSON], et al., 2015). UTIs are caused by a wide range of Gram-negative and Gram-positive bacterial pathogens as well as fungi. Uropathogenic _E. coli_ (UPEC) is the most common causative agent for both uncomplicated (75%) and complicated (65%) UTIs. The most prevalent pathogens in uncomplicated UTIs, in order of prevalence, are _Klebsiella pneumoniae_ (6%), _Staphylococcus saprophtivicus_ (6%), _Enterococcus faecalis_ (5%), group B _Streptococcus_ (3%), _Proteus mirabilis_ (2%), _Pseudomonas aeruginosa_ (1%), _Staphylococcus aureus_ (1%), and _Candida_ spp. (1%). For complicated UTIs, the most prevalent pathogens are _Enterococcus_ spp. (11%), _K. pneumoniae_ (8%), _Candida_ spp. (7%), _S. aureus_ (3%), _P. mirabilis_ (2%), _P. aeruginosa_ (2%) and group B _Streptococcus_ (2%).
# Initial Estimation of Kinematic Structure of a Robotic Manipulator as an Input for Its Synthesis
## 2 Materials and Methods
### Type D  
_Joints on Bezier Curve while the Last Lies on Common Perpendicular to Achieve a Pose_  
This method extends type B estimation by adding a common perpendicular approach, used in type C, between the two last joints. This assures reaching the pose including orientation, as shown in Figure 7. The beginning steps are the same as in type C, but only from \(\textbf{J}_{1}\) to \(\textbf{J}_{n-1}\). The transformation of the last joint is determined using the following procedure.  
Figure 6: The schematic of the type C estimation; the base frame is black, the pose frame is red, and the end-effector frame is green and coincident with the pose.  
Again, a common perpendicular and the intersection points \(P\) and \(Q\) are found between the joint \(\mathbf{J}_{n-1}\) and the TCP pose \(\mathbf{J}_{n+1}\) using the already presented ways. \(\mathbf{J}_{n}\) is set equal to \(\mathbf{J}_{n-1}\):  
\[\mathbf{J}_{n}=\mathbf{J}_{n-1} \tag{40}\]  
Angle \(\theta_{n}\) between \(\mathrm{X}_{n-1}\) and \(\mathrm{X}_{n}\) axes is obtained:  
\[\theta_{n}=\tan^{-1}\left(\frac{||\vec{n}_{n-1}\times\vec{P}\underline{Q}||}{ \vec{n}_{n-1}\cdot\vec{P}\underline{Q}}\right) \tag{41}\]  
Right-hand rule check is performed:  
\[\theta_{n}=\begin{cases}-\theta_{n},&\textbf{if }\vec{\sigma}_{n-1}\cdot\vec{P} \underline{Q}<0\\ \theta_{n},&\textbf{otherwise}\end{cases} \tag{42}\]  
\(\mathbf{J}_{n}\) is rotated around \(\mathrm{Z}_{n-1}\) axis afterwards:  
\[\mathbf{J}_{n}=\mathbf{J}_{n}\mathrm{Rot}(z_{n-1},\theta_{n}) \tag{43}\]  
Translation of the \(\mathbf{J}_{n}\) along the \(\mathrm{Z}_{n}\) and \(\mathrm{X}_{n}\) is obtained by changing its translational vector \(\vec{t}_{n}\), and it is equal to the coordinates of point \(Q\):  
\[\vec{t}_{n}=Q \tag{44}\]  
Angle \(\alpha_{n}\) between \(\mathrm{Z}_{n}\) and \(\mathrm{Z}_{n+1}\) axes is calculated:  
\[\alpha_{n}=\tan^{-1}\left(\frac{||\vec{a}_{n}\times\vec{a}_{n+1}||}{\vec{a}_{ n}\cdot\vec{a}_{n+1}}\right) \tag{45}\]  
Right-hand rule check, if the dot product of \(\mathrm{Z}_{i+1}\) axis is in the positive direction of the \(\mathrm{Y}_{i}\) axis, the angle \(\alpha_{n}\) has to be multiplied by \(-1\):  
\[\alpha_{n}=\begin{cases}-\alpha_{n},&\textbf{if }\vec{\sigma_{n}}\cdot\vec{a}_{n +1}>0\\ \alpha_{n},&\textbf{otherwise}\end{cases} \tag{46}\]  
The final transformation matrix of the last joint \(\mathbf{J}_{n}\) is obtained:  
\[\mathbf{J}_{n}=\mathbf{J}_{n}\mathrm{Rot}(x_{n},\alpha_{n}) \tag{47}\]  
Figure 7: The schematic of the type D estimation; the base frame is black, the pose frame is red, the end-effector frame is green and coincident with the pose. Bézier spline is shown as a light blue curve.  
From this point, when all frames \(\mathbf{J}_{1.n}\) representing the joints are known, it is possible to derive the DH parameters between them.
# Numerical Study of Mixed Convection and Heat Transfer in Arc-Shaped Cavity with Inner Heat Sources
## 2 Physical Model and Numerical Method
### Basic Governing Equations  
The governing equations of flow field and temperature field in the coal shed are Navier-Stokes equations and energy equations based on the Boussineq hypothesis. In order to facilitate the comparison of working conditions with different Rayleigh numbers, the governing equation processed in this paper is dimensionless, and the dimensionless scale is as follows [25]:  
\[x,y\cong H;(T-T_{0})\cong(T_{h}-T_{c});\]  
\[u,\,v\cong u_{0};\rho\cong\mu u_{0}/H.\]  
The dimensionless governing equations are continuity, Navier-Stokes and energy equations, as follows:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}=0; \tag{1}\]  
\[\frac{\partial u}{\partial\tau}+u\frac{\partial u}{\partial x}+v\frac{ \partial u}{\partial y}=\frac{1}{\rm Re}\bigg{(}\frac{\partial^{2}u}{\partial x ^{2}}+\frac{\partial^{2}u}{\partial y^{2}}\bigg{)}-\frac{\partial p}{ \partial x}; \tag{2}\]  
\[\frac{\partial v}{\partial\tau}+u\frac{\partial v}{\partial x}+v\frac{ \partial v}{\partial y}=\frac{1}{\rm Re}\bigg{(}\frac{\partial^{2}v}{\partial x ^{2}}+\frac{\partial^{2}v}{\partial y^{2}}\bigg{)}-\frac{\partial p}{ \partial y}+\frac{Gr}{\rm Re^{2}}T; \tag{3}\]  
\[\frac{\partial T}{\partial\tau}+u\frac{\partial T}{\partial x}+v\frac{ \partial T}{\partial y}=\frac{1}{\rm PrRe}\bigg{(}\frac{\partial^{2}T}{ \partial x^{2}}+\frac{\partial^{2}T}{\partial y^{2}}\bigg{)} \tag{4}\]  
In fluid mechanics, Re, Ra, Pr, Nusselt number (local: Nu\({}^{\prime}\) and average: Nu) and aspect ratio (\(A\)) are important dimensionless parameters that control the intensity of mixed convection heat transfer. Here, the working fluid considered for this study is air (Pr = 0.71). Other definitions are as follows:  
\[{\rm Ra}=\frac{v}{\alpha}.\frac{g\beta L^{3}\Delta T}{v^{2}} \tag{5}\]  
\[{\rm Re}=\rho vH/\mu \tag{6}\]  
\[{\rm Nu}^{\prime}=-\partial T/\partial n \tag{7}\]  
Figure 1: Schematic of the arc-shaped cavity.  
\[\text{Nu}=\frac{1}{l}\int\limits_{l}\text{Nu}^{\prime}dl=\frac{1}{l}\int\limits_{l }\frac{\partial T}{\partial n}dl \tag{8}\]  
\[A=\frac{2H}{L} \tag{9}\]  
At the initial time, the working fluid is isothermal and motionless and satisfies \(T_{0}=0\). It is assumed that the arc wall isothermal cooling is \(T_{c}=-0.008\) and equal to the external temperature. The heat source wall is isothermal heating with \(T_{h}=0.9677\) ignoring the radiation effect. The bottom of the curved cavity is adiabatic, there is no slip or deformation on all walls and the fluid velocity on the wall is zero, as shown in Equation (9). The whole structure is subject to vertical downward gravity g = \(-9.8\) m/s\({}^{2}\).  
\[u=0,\,v=0,\,T_{0}=0\,(\tau=0) \tag{10}\]  
\[\mathbf{T_{h}}=0.9677,\,\,\mathbf{T_{c}}=-0.008,\,\,u=0,\,\,v=0,\partial T/\partial y=0 \tag{11}\]