esa-sceva/satcom-chunk-collection huggingface dataset:
# Promising Novel Biomarkers in Cardiovascular Diseases
## 1 Introduction
### SuPAR  
Soluble urokinase plasminogen activator receptor (suPAR) is a membrane-bound protein, which is expressed on vascular endothelium and immunologically active cells. It serves as an inflammatory marker and is also useful for risk stratification concerning development of CVD [41; 42; 43]. Elevated suPAR levels reflect subclinical inflammation and are observed in individuals with increased alcohol consumption or smokers [44; 45; 46].  
SuPAR and C-reactive protein (CRP), both indicators of inflammation and valid biomarkers for CVD processes, do not reflect the same pathophysiologic entities. SuPARhas been shown to be a sign of atherosclerosis and endothelial dysfunction; CRP, on the other hand, is associated with body mass index and waist circumference [42; 45].  
Increased suPAR levels are associated with microalbuminuria in individuals with increased risk of diabetes mellitus type 2 [47]. [PERSON] et al. observed a predictive value of suPAR for early detection of diabetes mellitus type 2 in a cohort of 1933 subjects [48]. Furthermore, suPAR was shown to be of prognostic value regarding the outcome of different types of cancer [49; 50]. It additionally seems to play a role in the pathogenesis of chronic kidney disease, particularly focal segmental glomerulosclerosis (FSGS) [51].  
In an attempt to implement new prognostic biomarkers in routine clinical practice, [PERSON] et al. found no survival benefit when determining suPAR levels in the emergency department [52]. In patients with sepsis, systemic inflammatory response syndrome, or bacteriaemia, suPAR levels were shown to be of prognostic value in estimating disease severity and outcome. However, its diagnostic value is low, as it resembles an unspecific marker of inflammation [46; 53]. Yet, its prognostic value seems to be superior to other inflammatory biomarkers, such as procalcitonin (PCT), likely due to minimal circadian changes in suPAR levels [54; 55].  
SuPAR has been shown to be a strong and independent marker of mortality in patients with chronic HF or type 2 diabetes and can also improve the accuracy of risk stratification when combined with traditional measures, such as evaluation of cardiovascular risk factors, coronary artery calcifications, or high-sensitivity CRP [56; 57]. Figure 1 provides an overview of different pathological pathways addressed by novel biomarkers.  
Figure 1: Illustration of novel biomarkers and pathophysiological mechanisms in different organ systems (remodelling, inflammation, calcification, diabetes and renal dysfunction; arrows = strong relationship, dotted arrow = partial relationship).
# A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation +
## 2 Overview of Locally Accessible Cloud System (LACS)
### Locally Accessible Cloud System (LACS)  
As part of our disaster response measures, we have created a LACS [16]. LACS enables people in a disaster area to communicate locally after a major disaster if the network is cut. LACS consists of a small server, a Wi-Fi access point, and a battery. LACS is able to provide cloud-based services at the local level, regardless of the situation. LACS offers a variety of service features such as information broadcasting, bulletin board, a social network service, instant messaging services, e.g., video conferencing, file sharing, and so on. Additionally, LACS can be used as a center for disseminating information in an area where the network is disconnected. In addition to using the LACS's coordination function in disaster response systems, LACS can also be applied to other daily services like e-health, and e-education. The LACS prototype and its basic features are shown in Figures 1 and 2.
# Bridge Damping Extraction Method from Vehicle-Bridge Interaction System Using Double-Beam Model
## 2 Formulation of the Analytical Theory
### Simplification of VBI System  
In the VBI problem, the vehicle body and bridge are generally considered to be connected by a vehicle suspension system. The vehicle model is simplified to a spring mass model, as shown in Figure 1, such as the 1/2 car or 1/4 car model [15; 16; 17; 18; 19]. In this paper, the 1/4 car model, which ignores the suspension mass in (c), was used. Since the purpose of this study did not require an accurate VBI system response calculation, model (c) was convenient and effective for explaining the effect of axle coupling on the damping of a VBI system.  
Only uniform traffic flow acting on the bridge was considered in this study. Uniform traffic flow refers to traffic flow in which each vehicle has the same dynamic parameters (e.g., the distance between vehicles is the same and the traffic flows at the same speed). In this case, the vehicle suspension system is modeled as uniformly distributed springs and dampers, and the vehicle body traveling across the bridge is modeled as a Euler beam with zero flexural stiffness and a zero damping coefficient, effectively reducing a VBI system into a double-beam system, as shown in Figure 2. The upper girder boundary conditions are free-free, while the lower girder boundary conditions depend on the bridge. The upper beam mass, connection layer stiffness, and double-beam system damping coefficient can be replaced by the equivalent traffic flow parameters, which are defined as follows:  
\[m^{\prime}=nm_{v}/L,\;k^{\prime}=nk_{v}/L,\;c^{\prime}=nc_{v}/L \tag{1}\]  
where \(m_{v}\) is the average mass of vehicles on the bridge; \(k_{v}\) and \(c_{v}\) are the average vehicle stiffness and the average vehicle damping factor, respectively; \(n\) is the number of vehicles on the bridge; and \(L\) is the bridge length.  
Figure 1: Simplified plane models of the vehicle: (a) 1/2 car model; (b) 1/4 car model; (c) 1/4 car model without suspension mass.
###### Abstract
To achieve this, we design a transformer module to capture global contextual information, which enhances the correlation between feature information by using the transformer's unlimited effective receptive fields. Unlike the existing transformer models, our Polarformer incorporates the CNN-based module and the transformer-based module under the deep neural network after the LPTM. For the local visual features, the CNN-based module extracts the local visual features with CNNs to obtain more discriminative features, and then we combine the feature pyramid network (FPN) [12] to improve their spatial resolution, which handles multi-scale feature information. Figure 1: The region enclosed by the green dotted circle is the optic disc (OD); the central bright zone enclosed by the blue dotted circle is the optic cup (OC); and the region between them is the neuroretinal rim. (**a**) An example of a healthy eye. (**b**) An example of a glaucoma-suspicious eye. For the global contextual information, our transformer-based module introduces global contextual information that leverages unlimited effective receptive fields to enhance useful features and suppress useless feature responses, thereby, distilling spatial relationships. In summary, we propose a hybrid CNN-transformer and polar transformation network to solve the problem mentioned above. Our Polarformer aims to extract discriminative and semantic features for robust OD and OC segmentation, thus, developing a more accurate segmentation model. The remainder of this paper is organized as follows: We present related works on the segmentation of the OD and OC in Section 2. Section 3 gives the specific details of our model. In Section 4, we give our experimental results, compare them with other methods and discuss our approach. In Section 5, we conclude our work.
## 2 Materials and Methods
### Western Blot  
WB was used to detect the protein expression of the proliferation-related PI3K-AKT signaling pathway and migration-related WNT/\(\beta\)-catenin signaling pathway. Briefly, proteins (20 \(\upmu\)g) were separated by SDS-PAGE (sodium dodecyl sulfatepolyacrylamideamide gel electrophoresis, P0012A) and then transferred to nitrocellulose membranes. Rabbit anti-PI3K antibody (1:1000 3011S), rabbit anti-AKT antibody (1:1000 4685S), rabbit anti-CyclinD1 antibody (1:1000 55506), rabbit anti-ERK antibody (1:1000 A16686), rabbit anti-mTOR antibody (1:1000 2983), and rabbit anti-STAT3 antibody (1:1000 4904), were purchased from Cell Signaling, Danvers, MA, USA and used for incubation membranes. Rabbit anti-WNT3A antibody (1:1000 A0642), rabbit anti-\(\beta\)-catenin antibody (1:1000 A19657) and rabbit anti-ACTB antibody (1:1000 AC062) were purchased from Abclonal, Wuhan, China and also used for incubation membranes. Horseradish peroxidase-coupled anti-rabbit IgG secondary antibody (1:10,000 A5014) (AB Clone, Wuhan, China) was then incubated for 40 min. Chemiluminescence kit (Thermo Fisher Scientific Searle Scientific Company, USA A3855) and chemiluminescence gel imaging system (Tanon, Shanghai, China) were used to detect and quantify protein. Finally, the ratio of fluorescence intensity of each objective band to the 0 s control group was calculated.  
To further validate the effect of low-dose ROS produced by NTP treatment on the phosphorylation of protein, we supplemented the WB experiments with p-AKT (Beyotime, China, 1:1000, AF5740) and p-\(\beta\)-catenin (Wuhan, China, 1:1000, AP0979). According to the needs of the experiment, they were divided into four groups: control group (no NTP treatment and NAC pretreatment), NTP treatment group (NTP treatment for 15 s), NAC group (NAC pretreatment only) and NAC pretreatment group (NAC pretreatment before NTP treatment for 15 s).
# Highly Integrated Wideband Transmit/Receive Module for X-Band SAR Applications
## 2 TRM Architecture
The LTCC substrate is AuSn soldered on the molybdenum carrier, and the Kovar\({}^{\circledR}\) ring is also AuSn soldered on the top of the substrate. The Kovar\({}^{\circledR}\) cover is laser-welded on the Kovar\({}^{\circledR}\) ring. RF blindmate connectors are directly soldered on the substrate, while DC interconnections are gold pads placed on the bottom of the substrate, which may be accessed through two windows on the molybdenum carrier and contacted using solderless interposers. The LTCC multilayer substrate, based on a high-frequency, low-dielectric loss tape system manufactured by FERRO (A6M-E), features embedded RF structures inside its inner layers, such as RF transitions, RF couplers, and RF combiners. The design of the RF passive devices and interconnects will be analyzed in detail in Section 3.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Case Design and Performance Metrics
#### 5.1.1 Case 1: Whole Day  
In Case 1, the traffic information is obtained from the actual VDs that were built on the 42 KM milepost on National Freeway No. 1 on 31 July 2008. This study used the real traffic information to simulate the vehicle movement and MS communication behaviors. The length of a three-lane highway is 10 km was considered in experimental environments. In this case, 10 cells were sequentially distributed on the 10-km length highway from 0 km to 10 km, and a 1 km length road segment could be covered by a cell. Therefore, 11 handover points were distributed on the 0 km length highway. Furthermore, 11 _Data Collection Points_ (DCPs) were assumed to be located at these 11 handover points. When a car passed a DCP, the passing time was recorded. It means that the coverage area of \(\text{Cell}_{1}\) is the area between the first \(\text{DCP}_{1}\) and the second \(\text{DCP}_{2}\). In this case, this study obtained the traffic information for the whole day and analyzed the CBP during traffic peak hours and traffic off-peak hours.  
Figure 17: The architecture of simulation [28].
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 4 Analysis and Discussion
### Analysis B
#### 4.2.1 Direct Influence Factor Group  
[PERSON] et al. [43] suggested a method to evaluate abandoned mine subsidence risk using only the depth to a gangway, which can be collected easily from existing materials. Many other studies also placed much importance on the depth to a gangway or goaf as a major factor inducing subsidence [30; 44; 45; 46; 47].  
Accordingly, it can be stated that the depth to a goaf is a vital factor to the occurrence of subsidence and that the magnitude of subsidence is greatly affected by goaf height and the horizontal distance to a goaf. Thus, the depth to goaf was classified into 11 groups by using the depth range to allocate the points between 10 and 100, and this was adjusted by goaf height and distance (Table 3). Finally, the subsidence risk for direct influence factors was calculated by Equation (1).  
\[F_{D}=F_{a}\cdot F_{b}\cdot F_{c} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multicolumn{2}{c}{**Accuracy**} \\  & \multicolumn{2}{c}{**(Number of Data)**} \\ \cline{2-3}  & **Coal Mines** & **Other Type Mines** \\ \hline \multirow{2}{*}{Training data} & 97\% & 97\% \\ \cline{2-3}  & (106) & (101) \\ \hline \multirow{2}{*}{Validation data} & 83\% & 75\% \\ \cline{2-3}  & (12) & (8) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of analysis A by the data type.
# Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks
###### Abstract  
It has been widely speculated that the performance of the next generation based wireless network should meet a transmission speed on the order of 1000 times more than the current cellular communication systems. The frequency bands above 6 GHz have received significant attention lately as a prospective band for next generation 5G systems. The propagation characteristics for 5G networks need to be fully understood for the 5G system design. This paper presents the channel propagation characteristics for a 5G system in line of sight (LOS) and non-LOS (NLOS) scenarios. The diffraction loss (DL) and frequency drop (FD) are investigated based on collected measurement data. Indoor measurement results obtained using a high-resolution channel sounder equipped with directional horn antennas at 3.5 GHz and 28 GHz as a comparative study of the two bands below and above 6 GHz. The parameters for path loss using different path loss models of single and multi-frequencies have been estimated. The excess delay, root mean square (RMS) delay spread and the power delay profile of received paths are analyzed. The results of the path loss models show that the path loss exponent (PLE) in this indoor environment is less than the free space path loss exponent for LOS scenario at both frequencies. Moreover, the PLE is not frequency dependent. The 3 GPP path loss models for single and multi-frequency in LOS scenarios have good performance in terms of PLE that is as reliable as the physically-based models. Based on the proposed models, the diffraction loss at 28 GHz is approximately twice the diffraction loss at 3.5 GHz. The findings of the power delay profile and RMS delay spread indicate that these parameters are comparable for frequency bands below and above 6 GHz.  
5G; smart city; IoT; channel propagation; 3.5 GHz; 28 GHz; delay spread; path loss Article
## 2 Materials and Methods
### Backpropagation Neural Network Predictive Model
#### 2.6.3 Evaluation Indices for Network Performance  
The coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE) served as evaluation indices for the BPNN's predictive performance. R\({}^{2}\) indicates the degree of fitting of the predictive model: the closer its value is to 1, the more accurate the predicted results. RMSE and MAE evaluate the error of the predictive model. The closer their values are to 0, the smaller the predictive error. The expressions for R\({}^{2}\), RMSE, and MAE are as follows [30,31]:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\bigl{(}Y_{ei}-Y_{pi}\bigr{)}^{2}}{\sum_{i= 1}^{n}\bigl{(}Y_{ei}-\overline{Y_{e}}\bigr{)}^{2}}, \tag{10}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\cdot\sum_{i=1}^{n}\bigl{(}Y_{ei}-Y_{pi}\bigr{)} ^{2}}, \tag{11}\]  
\[\text{MAE}=\frac{1}{n}\cdot\sum_{i=1}^{n}\bigl{|}Y_{ei}-Y_{pi}\bigr{|}, \tag{12}\]  
where \(Y_{ei}\) is the experimental value for the \(i\)th sample, and \(i=1\), \(2\), \(\ldots\), \(n\), where \(n\) is the total number of samples. \(Y_{pi}\) is the predicted value for the \(i\)th sample, and \(\overline{Y_{e}}\) is the average of the experimental values for all samples.
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 7 System Performance
### Effect of Deformation on Coils  
A pair of axially aligned printed coils with varying curvature of RX coil was used to simulate the deformation of the coil in the wearable device. The maximum output power and DC to DC efficiency against the curvature of the deformed RX coil are shown in Figure 12 with a coil separation distance of 5 mm. The maximum output power is reduced by 33% and the efficiency reduces by 20% as the coil was deformed to vary its curvature from 8 m\({}^{-1}\) to 16 m\({}^{-1}\). This range of curvature matches the diameters of human legs for 90.1% people in UK [27].  
Figure 11: DC to DC efficiency at optimal output current against separation distance between different types of paired coils. The same trends of the effect of separation on DC to DC efficiency are shown on all types of coils.  
Figure 12: Maximum output power and DC to DC efficiency of WPT employed flexible coils with varying curvature of receiver coil at 5 mm center separation distance from transmitter coil. The maximum output power drops 33% as a result of the deformation of the receiver coil.  
When the separation distance between the TX and deformed RX coil was increased, the DC to DC efficiency reduces as shown in Figure 13. It shows that the separation distance of center aligned printed coil pairs is the major influencing factor for the DC to DC efficiency of WPT systems utilizing flexible coils. In an application of a device worn on the body the separation distance between the TX and RX coils will typically be in the range between 0 and 20 mm; however, the range of anticipated deformation of the coil is in the range 0 to 16 m\({}^{-1}\). Using these range values and the data presented in Figures 11 and 13 the separation distance can be seen to cause a DC to DC efficiency drop of 27.7% when the curvature is kept constant, whereas varying the curvature from 0 to 16 m\({}^{-1}\) while keeping the separation distance constant only causes the DC to DC efficiency to fall by 8.8%.
## 4 Case Study
### Stage A: Efficiency Evaluation by B-DEA Model
As decision makers began to prioritize gains over losses \((0.5<\Theta\leq 0.65)\), the rankings of WWS-9, WWS-6, WWS-4, and WWS-5 improved, while WWS-10, WWS-1, and WWS-3 deteriorated. When the importance degree of gains against losses is moderate \((0.65<\Theta\leq 0.85)\), the change in rank mainly occurs at the bottom of the chart between WWS-17, WWS-18, and WWS-19. When decision makers are mostly concerned with gains \((0.85<\Theta<1)\), the efficiency rankings are fraught with volatility. The most notable phenomenon is the severe ranking drop of WWS-13 from 2 nd to 9 th and the rise of WWS-8 from 7 th to 3 rd. In the middle of the rankings, the drop in WWS-4's ranking led to an increase in WWS-2 and WWS-3. At the bottom of the rankings, WWS-17 and WWS-18 swap positions.
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
###### Abstract  
No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality immediately without prior training or learning. Experimental results on four popular datasets show that the proposed metric outperforms SSIM and well-matched to state-of-the-art NR IQA models. Furthermore, we apply NSSIM with known IQA approaches to blurred image restoration and demonstrate that NSSIM is statistically superior to peak signal-to-noise ratio (PSNR), SSIM and consistent with the state-of-the-art NR IQA models.  
no-reference; re-blur; structural similarity; image quality assessment +
Footnote †: journal: Applied
###### Abstract  
Inter-annual and seasonal variations of energy, vapor water, and carbon fluxes and associated climate variables in a middle taiga pine (_Pinus sylvestris_) forest on sandy soils and in a northern taiga larch (_Larix gmelinii_) forest on permafrost in central Siberia were studied from eddy covariance measurements acquired during the growing seasons of 1998-2000 and 2004-2008, respectively. Both the pure Scots pine of 215-year-old and pure Gmelin larch of 105-year-old forests naturally regenerated after forest fires, differed by their tree stand characteristics, and grew in extremely contrasting environments with distinctive climatic and soil conditions. Net radiation was greater in the pine forest due to higher values in the summer months and a longer growing season. Sensible heat flux was the larger term in the radiation balance in both forests. The Bowen ratio stayed between 1 and 2 during the growing season and was as high as 8-10 in dry spring in both forests. In the dry summers, latent heat explained 70%-80% of the daily net ecosystem CO\({}_{2}\) exchange (NEE) variation in both forests. The average NEE was significantly smaller in the larch ecosystem at \(-4\) umol m\({}^{-2}\)s\({}^{-1}\) compared to \(-7\) umol m\({}^{-2}\)s\({}^{-1}\) in the pine forest. NEP for the growing season was 83 in the larch forest on continuous permafrost and 228 g C m\({}^{-2}\) in the pine forest on warm sandy soils. Water use efficiency was 5.8 mg CO\({}_{2}\) g\({}^{-1}\)H\({}_{2}\)O in the larch forest and 11 mg CO\({}_{2}\) g\({}^{-1}\)H\({}_{2}\)O in the pine forest and appeared to be consistent with that in boreal forests. As a result of the forest structure change from Gmelin larch to Scots pine due to the permafrost retreat in a warming climate, the boreal forest C-sink may be expected to increase. Thus, potential feedback to the climate system in these \"hot spots\" of forest-forming replacement species may promote C-uptake from the atmosphere. However, as many studies suggest, in the pace of transition from permafrost to non-permafrost, C-sink would turn into C-source in hot spots of permafrost retreat.  
energy; water vapor and carbon flux; water use efficiency; energy; heat and water balance; permafrost; Scots pine; Gmelin larch; interior Siberia 2023  
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