esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis of Novel Chalcones as Acetylcholinesteraseerase Inhibitors
## 3 Results and Discussion
### Synthesis
#### 3.1.5 (E)-3-(2,3-dimethoxyphenyl)-1-(4-((2-hydroxybenzyl)amino)phenyl)prop-2-ene-1-one (A5)  
Yellow solid, very soluble in acetone, slightly soluble in ethanol and methanol, poorly soluble in water; mp: 186-188 \({}^{\circ}\)C; EIMS (\(m/z\)): 373.1245 [M \(-\) CH\({}_{3}\)]\({}^{-}\) (MW: 389.16); UV (\(\lambda_{\rm max}\) nm, EtOH): 359; \({}^{1}\)H-NMR (500 MHz, DMSO-\(d_{6}\)) 8 ppm: 3.77 (s, 3H, OMe), 3.83 (s, 3H, OMe), 4.3 (d, 2H, \(J\) = 5.5 Hz, CH\({}_{2}\)), 6.66 (d, 2H, \(J\) = 9 Hz, H3\({}^{\prime}\), H5\({}^{\prime}\)), 6.74 (t, 1H, \(J\) = 7.5 Hz, NH), 6.84 (d, 1H, H6\({}^{\prime\prime}\)), 7.05-7.16 (m, 5H, H3\({}^{\prime\prime}\), H4\({}^{\prime\prime}\), H5\({}^{\prime\prime}\)), 7.45 (dd, 1H, \(J\) = 2 Hz, \(J\) = 7 Hz, H6), 7.80 (d, \(J\) = 15.5 Hz, 1H, H\({}_{\alpha}\)), 7.87 (d, \(J\) = 15.5 Hz, 1H, H\({}_{\beta}\)), 7.92 (d, 2H, \(J\) = 8.5 Hz, H2\({}^{\prime}\), H6\({}^{\prime}\)), 9.61 (s, 1H, OH); \({}^{13}\)C-NMR (125 MHz, DMSO-\(d_{6}\)) 8 ppm: 55.79 (CH\({}_{2}\)), 56.05 (OCH\({}_{3}\)), 60.87 (OCH\({}_{3}\)), 111.2 (C3\({}^{\prime}\), C5\({}^{\prime}\)), 114.4 (C4), 118.8 (C3\({}^{\prime}\)), 119 (C6\({}^{\prime\prime}\)), 123.3 (C6), 124.2 (C\({}_{\alpha}\)), 124.7 (C5), 125.38 (C1\({}^{\prime\prime}\)), 127.8 (C1\({}^{\prime}\)), 128.3 (C4\({}^{\prime\prime}\)), 128.7 (C1), 130.9 (C2\({}^{\prime}\), C6\({}^{\prime}\)), 135.4 (C\({}_{\beta}\)), 147.98 (C2), 152.7 (C3), 153.1 (C4\({}^{\prime}\)), 155 (C2\({}^{\prime\prime}\)), 186 (C=O).
## 3 Results
### Neurite Outgrowth was Unaffected by NTP Treatment \(\leq\)50 mJ  
Planar primary cortical neuron cultures were subjected to a scratch assay and NTP treatment. Neurite infiltration into the gap area was assessed at three and seven days post plasma treatment (6 and 10 DIV, respectively). Neurons appeared to be more sensitive to plasma treatment as treatment with 90 and 140 mJ NTP resulted in widespread cell death (Figure 3C). These groups were excluded from further analysis. Although neurite regeneration significantly increased over time, no statistical differences were found with NTP treatment (Figure 3). However, the trend suggested that treatment with 20 mJ plasma increased neurite outgrowth compared to control groups at 6 DIV (Figure 3). Neurite outgrowth increased over time, as is evident by a reduced percentage gap distance at 10 DIV (7 days post plasma treatment) when compared to 6 DIV, with 10 DIV cultures exhibiting the same trends as those seen at 6 DIV (Figure 3D,F). The immunocytochemical analysis confirmed the neuronal phenotype of surviving cells and was indicative of neurite outgrowth and the health of the neuron-only cultures treated with 0 mJ-50 mJ of plasma at 6 DIV (3 days post-treatment) (Figure 3B).  
Figure 3: Plasma treatment of neuronal cultures alone did not affect neurite outgrowth into the gap area. (**A**) Phase/Contrast images showing neurite infiltration into the gap area at three days post-treatment (6 DIV) when treated with 0, 10, 20, and 50 mJ plasma. Scale: 250 μm. While a trend towards increased neurite outgrowth was observed with treatment of 20 mJ plasma, no statistical significance is seen due to plasma treatment. (**B**) Confocal reconstruction of plasma-treated cultures labeled positively for the neuronal marker, b-tubulin III (**green**) and nuclear counterstain, HOECHST (**blue**) three days post-treatment. Qualitative assessment of cultures indicates that neurons and extending neurites remained healthy three days post-treatment when treated with \(\leq\)50 mJ plasma. Scale: 150 μm. (**C**) Higher magnification phase-contrast images clearly showing the difference between cultures where plasma treatment reduced neurite outgrowth (90 mJ treatment) and cultures where plasma treatment increased neurite outgrowth (20 mJ treatment). Scale: 100 μm (**D**) Quantification of neurite outgrowth at three days post-treatment (6 DIV). No significant difference in neurite outgrowth was observed when compared to control, untreated groups. (**E**) Phase-contrast images of neuron-only cultures seven days post plasma treatment with 0, 10, 20, and 50 mJ plasma. Scale: 250 μm. (**F**) Quantification of neurite outgrowth at seven days post-treatment (10 DIV). No significant neurite outgrowth was observed when compared to untreated groups. (**D,F**) Bars represent SEM.
## 2 II-36, IL-37, and IL-38: A Complex Group of Pro- and Anti-Inflammatory Cytokines
### Maturation
#### 2.1.1 IL-36 and IL-36 Ra  
IL-36\(\alpha\), \(\beta\), \(\gamma\), and IL-36 Ra cytokines, similarly to IL-1\(\beta\), need to be processed to acquire their full agonist or antagonist activity. In its native form, IL-36 Ra has no antagonist ability; similarly, IL-36\(\alpha\), \(\beta\), and \(\gamma\) are 100-1000 times less active than their processed counterparts [10]. The A-X-D sequence rule has also been confirmed for IL-36\(\alpha\), \(\beta\), \(\gamma\), and IL-36 Ra by [PERSON] and colleagues in 2011 [10]. Recently, neutrophils proteases have been identified as the chief regulators of the processing of all the IL-36 family members, although with different specificity and affinity. Neutrophil elastase (NE) is the key enzyme required for enhancing IL-36 Ra activity, especially in the context of psoriatic skin inflammation [11]. IL-36\(\alpha\) seems to be activated by both NE and cathepsin-G, however, with differential patterns. Conversely, whereas cathepsin-G and proteinase-3 preferentially activate IL-36\(\beta\), IL-36\(\gamma\) can be processed by NE, proteinase-3, cathepsin-G [12], and cathepsin-S [13], the last being particularly important for enabling the IL-36\(\gamma\)-related inflammation in skin psoriasis [14]. Neutrophil extracellular traps (NETs), in addition to their antimicrobial role, can also serve as a platform for the activation of IL-1\(\alpha\) and IL-36 cytokines through NETs-associated cathepsin-G and NE [15]. Thus, neutrophils appear to be the principal cells responsible for IL-36 cytokines maturation via their secretory mechanisms. Therefore, they play an essential regulatory role on the IL-36-axis activity in skin- and joint-related inflammatory diseases [16].
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
## 2 HV-PLC Channel  
Figure 1 depicts an HV power transmission line connecting two power systems 1 and 2, along with the coupling equipment required to establish PLC communications between substations 1 and 2. Wave traps are essentially inductive devices intended to confine the high-frequency (HF) communication signals within the line-segment joining substations 1 and 2 and to prevent these signals from going into power systems 1 and 2. The wave traps must permit the free passage of the low-frequency high-power signal.  
The coupling capacitors, on the other hand, must prevent the high-power signal from entering the communication equipment and damaging it. Coupling capacitors must permit the passage of HF communication signals from the communication equipment into the HV power line.  
Due to power-system requirements, the conductors of long HV lines are rotated at certain intermediate points in order to exchange positions. These rotations are referred to as transpositions and have the purpose of balancing the line admittances and impedances at the power-transmission frequency. Nevertheless, for the communication HF signals, the transpositions constitute discontinuities that provoke reflections and losses.  
The performance of a PLC channel, either digital or analog, is determined by its frequency response and its noise features. The PLC bandwidth is assumed here to be from 30 kHz to 500 kHz [1]. In order to determine the frequency response of a PLC system, one must account for the wave propagation features of the line, the reflection effects and the coupling equipment.
## 3 Results  
### Land Cover Classification and Change Trajectories  
#### 3.1.1 Classification Maps  
The first output from the SVM classification is a land cover classification map for each of the four dates (Figure 4). These maps show where forest, grass, water, and wetland classes are located in PCNP. The 1993 and 2011 maps also contain cloud and cloud shadow classes. Figure 4 shows that, in all four maps, the four land cover classes are generally grouped together, but with transitional areas between them. Higher elevations and improved drainage promote closed forest in the northeast; seasonal inundations and nutrient-poor soils promote grasses and interspersed trees in the central and western low-lying areas; waterlogged soils promote wetlands in the east that border the grass, closed forest, and Atlantic Ocean to the south; water accumulates and discharges into the Caribbean Sea in the southwest and southeast. The most notable transitions are the more gradual transitions between grass and wetland, forest and wetland, and the quicker transition between grass and forest.  
The error matrices (Table 1) show where misclassification occurred in the four images. Table 1 also shows class error commission and omission percentages, and overall accuracy and kappa coefficients. The overall accuracy for the 1975, 1993, 2011 and 2019 classifications are 88.5%, 96.9%, 96.2% and 86.1%, respectively, and the kappa statistics are 0.8402, 0.9612, 0.9474 and 0.8289. While the classifications perform well overall, some confusion, especially in the grass and wetland classes, can be noted visually. The transitional areas between grass and wetland are most well defined in the 1975 land cover classification, with each subsequent map having less definition between classes. Another issue for the SVM classifier was differentiating between spectrally similar water and cloud shadow, especially in the 2019 land cover classification; nevertheless, grouping classes together through reclassification results in new classification maps where these issues are less pronounced.  
Figure 4: Support Vector Machine classification results based on Landsat imagery and other data for: (**a**) 1975; (**b**) 1993; (**c**) 2011; (**d**) 2019.
###### Abstract
Through these evaluations, we confirm that our proposed QoS control method is effective even assuming a real environment. It is also clear that it is better to perform two-hop expansion than one-hop case by setting the distance between transmitter and receiver in each hop appropriately. In addition, this paper contributes to theoretically clarifying the relevant optimum setting. The remainder of this paper is organized as follows. Section 2 introduces the related research in the field of this manuscript. In Section 3, the related descriptions of IEEE Std. 802.15.6 are explained. Section 4 shows our previously proposed error control method. The system model is described in Section 5. Computer simulated performance evaluations and theoretical analysis of the results are presented in Section 6. Section 7 concludes the paper.
## 2 System Model  
Before stating the research of the data-prefetching strategy, it is necessary to introduce the P2P-based VoD system. This system is an overlay network [10] built between the terminals and the integration content distribution network (CDN) [11]. It can automatically distribute the content and segment the traffic flow. This system can transmit the videos in CDN to P2P terminals (i.e., client node, CN) and share videos on the CN using specified strategies.  
As illustrated in Figure 1, this communication system [12] is divided into three layers: management layer, service layer, and terminal layer. The management layer is deployed on the backbone network, and mainly includes a P2P management subsystem (MS), which is responsible for managing all P2P regions. It collects the operation data of the network element in the service layer and displays the system status through web pages, which includes the peer online status, video distribution information, etc. In addition, it can provide the query function; each provincial or municipal network is divided into a P2P region. In each P2P region, a P2P super node (SN) and a P2P tracker subsystem (TS) are deployed to form the service layer. The SN is responsible for importing videos into the P2P system, splitting and managing the videos in the system, and providing video segments for other peers. The TS is mainly responsible for the management of CNs in a P2P region. Meanwhile, the TS continues the video directory management to support video positioning. The terminal layer consists of numerous CNs, which can be mobile phones [13], tablets, desktops, etc. The CN can download and play videos and provide video segments for other CNs in the same P2P region. However, this system cannot support inter-region data exchange among the CNs.  
When a CN goes online, it must first register with the TS. After the registration is successful, the servable video list of this region is sent to this CN by the TS. The users can select the video that interests them from the video list. After receiving the user's selection, the CN must determine the download order of the video segments according to the segment selection algorithm. Then, the CN requests the servable peers from the TS and sorts the servable nodes. After selecting the most suitable node as the content provider, the CN can download video segments in order. It is notably important to improve the user's viewing experience by determining a suitable order to download the video segments, which we will introduce in detail in the following chapters.  
Figure 1: Architecture of the peer-to-peer (P2P)-based video-on-demand (VoD) system.
# Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings
## Materials and Methods
### Conventional Methods  
#### 2.3.1 Mean Squared Error-Based Template Matching  
A basic template matching method for spike classification is mean square error (MSE)-based template matching [18]. By using the cluster center waveform of each PCA + K-means clustered data as the template waveforms, MSE between each test data signal and the template waveforms were obtained. Test signals were then assigned to the corresponding template cluster which resulted in the smallest MSE.
## 2 Materials and Methods
### Fundamental Operational Framework of Breast Ultrasound CAD  
The breast ultrasound CAD system is divided into two distinct modules: CADe (computer-aided detection), which delineates suspected lesion areas, and CADx (computer-aided diagnosis), which assesses the malignancy of these lesions (Figure 1). Initially, CADe is employed to identify and mark potential lesion regions. Subsequently, CADx takes over, characterizing and diagnosing the lesions based on BI-RADS criteria. The BI-RADS categories utilized in CADx serve a dual purpose: aiding the physician's diagnostic process and calculating lesion malignancy probabilities. Diagnosing a lesion's malignancy necessitates a pre-established automatic diagnostic model, which is developed using BI-RADS annotations derived from an extensive dataset of breast ultrasound images stored in the PACS (Picture Archiving and Communication System). This process begins with CADe marking and presenting suspected lesion areas within the ultrasound images, followed by the calculation and display of results in accordance with the BI-RADS lexicon.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Details** \\ \hline Total Number of Images & 5252 \\ \hline Time Frame & 2006–2012 \\ \hline Number of Benign Cases & 2745 \\ \hline Number of Malignant Cases & 2507 \\ \hline Mean Age of Patients (Benign Tumors) & 45 years \\ \hline Age Range of Patients (Benign Tumors) & 11–81 years \\ \hline Mean Age of Patients (Malignant Tumors) & 49 years \\ \hline Age Range of Patients (Malignant Tumors) & 24–86 years \\ \hline Ultrasound Imaging Device & Philips ATL iU22 \\ \hline Image Resolution & 1024 \(\times\) 768 pixels \\ \hline Spatial Resolution & 0.23 mm per pixel \\ \hline Frequency Range & 5–12 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset Description.
## 2 Method and Theory
### Overlap Efficiency
Once optimized, \(h\) is mainly dependent on the focus factors, allowing us to analyze the effect of the beam size on the output power, which is presented in the Results section. The simulation outputs are then directly compared to experimental data in the subsequent section.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
###### Abstract  
According to the data reported by the international and governmental agencies, the Russian Federation remains one of the world's major associated petroleum gas (APG) flaring nations. In the past decade, numerous studies have shown the applicability of satellite-based methods to estimate gas flaring. New satellite-based observations might offer an insight in region-, company-, and site-specific gas flaring patterns, as the reported data are often incomplete. We provide a detailed catalog of the upstream and downstream gas flares and an in-depth analysis at the country, region, company and site level of the satellite monitoring results of flaring in Russia from 2012 to 2020. Our analysis is based on the VIIRS Nightfire data and validated against high-resolution daytime satellite images and geographical and geological metadata published by the oil and gas companies and the Russian government. Gas flaring volumes in Russia are estimated to average at 23 billion cubic meters (BCM) annually (15% of global flaring), with 19 BCM (82% on national scale) corresponding to the oil upstream flaring, which has been subject to heavy government regulations since 2013. Despite initially dropping, observed flaring volumes have been on the climb since 2018. We are able to monitor seasonal variations, accidents in gas processing and to track the activities to reduce gas flaring. An effect of gas composition on the flare temperature is reported for oil and gas fields in Russia.  
Visible Infrared Imaging Radiometer Suite (VIIRS); Nightfire; gas flaring; associated petroleum gas; Russia Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], C. Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire  
30 April 2021 28 July 2021 5 August 2021  
Subject: 5 August 2021  
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## 5 Fabrication and Measurement  
In this article, a compact prototype with three passbands has been designed and fabricated to check the real-time performance. The filter was constructed using a step impedance resonator with metallic slots perturbation to the lower corner of the high impedance section to realise the first two passbands at 1.57 GHz for GPS and 3.5 GHz for WiMAX applications. The SIR was folded to reduce the circuit area, an essential requirement for communication system integration. The third passband at 4.23 GHz for satellite application is obtained using an L-shaped resonator coupled to the low impedance section of the SIR, resulting in a triband operating filter with good selectivity of multiple T.Z.s between the passbands. Eight T.Z.s were observed between the passbands at locations of 1.22/1.42/1.98/3.18/3.82/3.98/4.38/4.53 GHz, respectively. The suggested layout is fabricated on low tangent loss substrate material RO-4350 and tested on a ZNB-20 vector network analyzer. A good simulated and measured frequency plot of S\({}_{11}\) and S\({}_{21}\) is shown in Figure 17 was obtained. The proposed filter has low signal attenuation of less than 1.2 dB and high signal reflection of better than 25 dB for the three passbands. The fractional bandwidths achieved are 2.54%, 4.2%, and 1.65% at 1.57/3.57/4.23 GHz, respectively, with rejection levels in the stopband greater than 15 dB. The area occupied by the filter on a substrate or in a circuit is 0.31 \(\lambda_{\mathrm{eff}}\)\(\times\) 0.24 \(\lambda_{\mathrm{eff}}\) (0.086 \(\lambda_{\mathrm{g}}\)\({}^{2}\)), where \(\lambda_{\mathrm{eff}}\) is the effective dielectric  
Figure 16: The magnitude of the electric field intensity (**a**) E due to the frequency ratio K. (**b**) E due to the L-shaped resonator at 4.23 GHz.  
Figure 15: Nature of the current distribution of the triband filter (**a**) surface current density due to the frequency ratio K. (**b**) surface current density due to L-shaped resonator at 4.23 GHz.  
constant of the material calculated at the lowest frequency. Table 2 listed the comparison of this work with other triband filters in the literature in terms of size, FBW, IL, R.L., and selectivity.  
After evaluating the above features, it is concluded that the prototype has good merits in a compact size, FBW, IL, R.L., and high sharp selectivity.
# Shear Strength of Trapezoidal Corrugated Steel Webs for Horizontally Curved Girder Bridges
## 2 Elastic Global and Local Shear Buckling Stress of CSWs
### Elastic Global Shear Buckling Stress of CSWs
#### 2.2.2 Calculation of Coefficient \(k_{g}\)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(h^{2}\)/(\(Rd\))} & \multicolumn{10}{c}{\(D_{\text{s}}\)/\(D_{\text{y}}\)} \\ \cline{2-13}  & **0.0005** & **0.001** & **0.0015** & **0.002** & **0.0025** & **0.003** & **0.0035** & **0.004** & **0.0045** & **0.005** & **0.006** & **0.007** \\ \hline
0 & 9.3520 & 11.1647 & 12.4023 & 13.3594 & 14.1630 & 14.8562 & 15.4733 & 16.0290 & 16.5382 & 17.0068 & 17.8556 & 18.6097 \\
5 & 9.3521 & 11.1650 & 12.4028 & 13.3602 & 14.1640 & 14.8574 & 15.4748 & 16.0308 & 16.5402 & 17.0091 & 17.8586 & 18.6132 \\
10 & 9.3525 & 11.1659 & 12.4044 & 13.3624 & 14.1669 & 14.861 & 15.4793 & 16.0360 & 16.5463 & 17.0161 & 17.8567 & 18.6237 \\
15 & 9.3532 & 11.1675 & 12.4071 & 13.3661 & 14.1718 & 14.8672 & 15.4867 & 16.0447 & 16.5564 & 17.0276 & 17.8817 & 18.6411 \\
20 & 9.3541 & 11.1697 & 12.4109 & 13.3713 & 14.1786 & 14.8758 & 15.4971 & 16.0570 & 16.5706 & 17.0437 & 17.9019 & 18.6653 \\
25 & 9.3554 & 11.1725 & 12.4157 & 13.3780 & 14.1874 & 14.8868 & 15.5103 & 16.0726 & 16.5887 & 17.0643 & 17.9276 & 18.6963 \\
30 & 9.3568 & 11.1760 & 12.4216 & 13.3861 & 14.1990 & 14.8998 & 15.5256 & 16.0907 & 16.6095 & 17.0887 & 17.9575 & 18.7314 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Global shear buckling coefficient \(k_{g,f}\) for various values of \(h^{2}\)/(\(Rd\)) and \(D_{\text{s}}\)/\(D_{\text{y}}\) when \(|\)/\(t\) = 5, \(\gamma\) = 0.4 and \(\beta\) = 1.8\(\alpha\) for the four-edge fixed support. The first row of data for \(h^{2}/(Rd)=0\) in Tables 4-6 represents the global shear buckling coefficient of CSWs for straight girders. Through fitting of the first-row data in Tables 4-6, for CSWs with 0.0005 \(\leq\alpha\leq 0.007\), the global shear buckling coefficients \(k_{g,s}\), \(k_{g,f}\), \(k_{g,fs}\) for straight girders can be estimated respectively by Equations (19) and (20). For a four-edge simple support:  
\[k_{g,s}=36.8\alpha^{0.2648} \tag{19}\]  
For a four-edge fixed support, or for two edges constrained by flanges fixed and the other two edges simply supported:  
\[k_{g,f}=k_{g,fs}=67.7\alpha^{0.2608} \tag{20}\]  
For trapezoidal CSWs that are commonly used in actual bridges, the rigidity ratio \(\alpha\) can be expressed as Equation (21). \[\alpha=\frac{D_{x}}{D_{y}}=\frac{q^{2}t^{2}}{2s(3a+c)d^{2}} \tag{21}\]  
It is worth mentioning that the formulas for the global shear buckling stress \(\tau_{g}^{e}\) (Equation (18)) and the global shear buckling coefficient \(k_{g}\) (Equations (19) and (20)) proposed in this paper are slightly different from these proposed by previous researchers [8]. This is due to the fact that while previous researchers derived the formula for the global shear buckling stress \(\tau_{g}^{e}\) the complete CSW was treated as an orthotropic plate with its length much larger than its width, and using a simplified deflection function, which is different from the function used in this paper.