esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods
## 2 Results and Discussion
### Characterisation of the \"Lead\" Compounds
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multicolumn{1}{c}{**No.**} & \multicolumn{1}{c}{**ID Code**} & \multicolumn{1}{c}{**Primary Sites of Metabolism**} & \multicolumn{1}{c}{**Secondary Sites of Metabolism**} & \multicolumn{1}{c}{**Tertiary Sites of Metabolism**} & \multicolumn{1}{c}{**Quaternary Sites of Metabolism**} \\ \hline \(1\) & 1 Aa7 & N-dealkylation & Amine hydroxylation & Aliphatic hydroxylation & O-dealkylation \\ \(2\) & 1 Aa8 & N-dealkylation & Amine hydroxylation & Aliphatic hydroxylation & O-dealkylation \\ \(3\) & 2 Ba2 & N-dealkylation & N-oxidation & N-dealkylation & Aliphatic hydroxylation \\ \(4\) & 2 Ba5 & N-dealkylation & N-dealkylation & N-oxidation & Aliphatic hydroxylation \\ \(5\) & 2 Ba6 & N-dealkylation & None & N-dealkylation & N-oxidation \\ \(6\) & 3 Aa3 & N-dealkylation & Amine hydroxylation & Aromatic hydroxylation & Aliphatic hydroxylation \\ \(7\) & 3 Aa5 & N-dealkylation & Amine hydroxylation & Aliphatic hydroxylation \\ \(8\) & 4 Da11 & N-dealkylation & None & Amine hydroxylation & Aliphatic hydroxylation \\ \(9\) & 4 DRe6 & N-dealkylation & None & Amine hydroxylation & Aliphatic hydroxylation \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Compound metabolism assessed using SmartCyp [131] and SOMP [132]. The bioactivity of the nine selected compounds was characterised using the following parameters: G protein-coupled receptor ligand, ion channel modulator, kinase inhibitor, nuclear receptor ligand, protease inhibitor, and enzyme inhibitor (Table 6). In addition, the most probable molecular targets and their identification data were determined using the SWISSTarget predictor (Table 7) [133]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **ID Code** & **GPCR Ligand** & **Ion Channel Modulator** & **Kinase Inhibitor** & **Nuclear Receptor ligand** & **Protease Inhibitor** & **Enzyme Inhibitor** \\ \hline
1 & 1 Aa7 & \(-0.42\) & \(0.17\) & \(-1.01\) & \(-0.86\) & \(-0.2\) & \(0.09\) \\
2 & 1 Aa8 & \(-0.41\) & \(0.13\) & \(-1\) & \(-0.84\) & \(-0.21\) & \(0.09\) \\
3 & 2 Ba2 & \(-0.11\) & \(0.18\) & \(-1.01\) & \(-0.9\) & \(-0.28\) & \(0.19\) \\
4 & 2 Ba5 & \(-0.02\) & \(0.11\) & \(-0.89\) & \(-0.55\) & \(-0.15\) & \(0.14\) \\
5 & 2 Ba6 & \(-0.02\) & \(0.15\) & \(-0.96\) & \(-0.68\) & \(-0.24\) & \(0.11\) \\
6 & 3 Aa3 & \(-0.1\) & \(0.23\)* & \(-0.38\) & \(-0.94\) & \(0.27\)* & \(0.7\)** \\
7 & 3 Aa5 & \(-0.21\) & \(0\) & \(-0.28\) & \(-0.67\) & \(0.33\)* & \(0.43\)* \\
8 & 4 Da11 & \(-0.69\) & \(-0.26\) & \(-1.36\) & \(-0.93\) & \(-0.44\) & \(0.27\)* \\
9 & 4 Db6 & \(0.12\) & \(0.83\)** & \(-0.65\) & \(-1.06\) & \(0.67\)** & \(0.87\)** \\ \hline \hline \end{tabular}
* * values above 0.2. ** values above 0.5.  
\end{table} 
Table 6: Bioactivity assessed using Molinspiration [134].
# A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow
## 4 Results  
We now illustrate the findings that we obtain from the analysis of the collected network traces. We first show which network protocols that each service employs for streaming and signalling (e.g., user's commands), and analyze in detail the different approaches used for audio and video transmission. We then provide quantitative figures for the volume of traffic the services generate at different video quality levels and study the impact of mobile network scenarios. Finally, we study the contacted servers in terms of autonomous systems (ASs) and RTT distance, and discuss how the infrastructures are organized.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
## 4 Distributed Global Function Model Finding for Wireless Sensor Networks Data
### Description of DGFMF-WSND  
Firstly, local function model is solved by FF-GEP on each grid node. Then, global function model is obtained by GMG-UNLS. In order to achieve DGFMF-WSND, firstly, _WSDL_ document which describes FF-GEP is defined. On this basis, server program of DGFMF-WSND is prepared and various _XML_ documents and _properties_ files of the grid service are released. Finally, _Gar_ package is compiled by _ant_ tool, and the service is deployed in the _Tomcat_ container. The users can access the service by writing the client program.  
A whole algorithm based on grid service includes client and server. DGFMF-WSND is described respectively from client and server. The description of whole algorithm is listed as follows.  
```
Input: GEPGSH, popSize; maxGen; maxFitness; \(P_{s}\), \(P_{m}\), \(P_{t}\), \(P_{r}\); Output: Global Function; Begin {
1. ReceivePara (T, GEP Paras, i, GEPGSH); // Parameters are received from \(i\)th client according to GEPGSH.
2. InitPop (Pop S); //Initializing population of FF-GEP.
3. LocalFunction (i) = FF-GEP (popSize; maxGen; maxFitness; \(P_{s}\), \(P_{m}\), \(P_{t}\), \(P_{r}\)); Client:
4. T = Read (SampleData);
5. int gridcodes = SelectGridCodes ();
6. for (int i = 0; i < gridcodes; i++) {
7. TransPara (T, GEP Paras, i, GEPGSH); //Transmitting parameters to server.
8. TransService (LocalFunction [i]);}
9. GlobalFunction = GMG-UNLS (LocalFunction);
10. return Global Function;}
```  
**Algorithm 4. DGFMF-WSND**  
For the distributed algorithm, time-consumption of the algorithm is an important index which must be considered in the design and implementation. From Algorithm 3, we know that execution time of the DGFMF-WSND algorithm includes time of FF-GEP algorithm on each grid node, time of transmission parameters and GMG-UNLS algorithm. In a LAN environment, the time of transmission parameters can be ignored.  
Let total time of the DGFMF-WSND algorithm be \(t_{total}\), time of FF-GEP on each grid node be \(t_{FF-\text{\emph{GEP}}}\), time of data transmission be \(t_{transParas}\), time of GMG-UNLS algorithm be \(t_{GMG-UNLS}\). Then Equation (10) is shown as following:  
\[t_{total}=t_{FF-GEP}+t_{transParas}+t_{GMG-UNLS} \tag{10}\]  
Time of DGFMF-WSND can be very convenient to take on calculation and evaluation by Equation (10).
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 6 Conclusions  
This study proposed an unsupervised method called LTS-SEI, which is the first work to address the long time span SEI problem. Domain adaptation was introduced into SEI to learn stable RFFs that are less affected by time changes. On the one hand, LTS-SEI implements adversarial learning at the shallow layer and aligns features at the deep layer of the model, which enables it to learn domain-invariant RFFs. On the other hand, LTS-SEI enhances intraclass consistency of deep features, which effectively increases the accuracy of SEI. The idea proved feasible in solving the long time span SEI problem. We demonstrated the effectiveness of LTS-SEI from different perspectives using real received satellite navigation signals. When the existing methods suffer difficulty in identifying long time span signals, LTS-SEI can still recognize specific emitter signals with time intervals approaching 2 years. This indicates that LTS-SEI has practical ability to predict the identity of specific emitters. However, the following three topics on long time span SEI need further research:  
1. In this study, we used 13-m and 40-m large-aperture antennas to receive space signals, which enhanced the quality of the signals. However, new methods need to be studied when addressing low SNR-specific emitter signals with long time spans.
2. The experimental data in this study are of high-orbit satellite signals, with the sampling rate of the acquisition equipment being 250 MHz. Sufficient data samples ensure effective training of deep models. However, we must develop small samples for long time span SEI methods when the number of training samples is small.
3. In practice, the model's predictive ability must be based on real training data without labels. Therefore, we must design new unsupervised learning algorithms to further enhance the practicality of long time span SEI.  
Conceptualization, P.L.; methodology, P.L. and L.G.; software, P.L. and H.Z.; validation, P.L.; formal analysis, P.L., P.S. and Z.C.; investigation, P.L. and L.G.; resources, L.G., H.Z. and X.L.; data curation, H.Z.; writing--original draft preparation, P.L.; writing--review and editing, P.L., L.G., P.S. and Z.C.; visualization, P.L.; supervision, L.G. and X.L.; project administration, L.G. and H.Z.; funding acquisition, L.G. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technical Support Talent Plan of Chinese Academy of Science (Grant No. E317 YR17), the Project for Guangxi Science and Technology Base and Talents (Grant No. GK AD22035957), the National Natural Science Foundation of China (Grant No. 12273045), the Western Talent Introduction Project of Chinese Academy of Sciences (Grant No. E016 YR1R) and High Level Talent Project of Shaan xi Province (Grant No. E039 SB1K).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 12: Identification result on small training samples.
# Numerical Analysis of the Beam-Column Resistance Compared to Methods by European Standards
## 3 Verification According to European Standards  
A comparison of ultimate resistance of the beam-column using design methods according to Eurocode 3 and Eurocode 9 with the numerical analysis is presented in this section. As was mentioned earlier in this paper, the comparison was performed for beam-columns with IPE and RHS cross-sections for five load cases. The flexural buckling resistance was investigated for both axes for both cross-sections. Moreover, in the case of members with IPE cross-section, which are prone to torsional deformations, the lateral-torsional buckling resistances were compared. This comparison was performed for members with two types of boundary conditions, both for warping restrained and unrestrained end cross-sections. The reason is that these types of constraints can produce different results and, therefore, they can provide another perspective from the viewpoint of this analysis. To provide better understanding of this chapter, the organization of the following sections is here briefly discussed. The analysis for members with IPE cross-section according to different loading conditions is presented first, followed by analysis for members with an RHS cross-section. The interaction curves according to design approaches and numerical analysis are presented in the following sections, together with a short discussion of the results. The design of beam-columns according to Eurocode 3 have to satisfy multiple design equations; two equations providing buckling resistance according to clause 6.3.3 of this standard, and also equations determining cross-sectional resistance according to the appropriate class of cross-section specified in Section 6.2 in Eurocode 3. Therefore, the lowest resistance from these equations is considered in the interaction curves below. The buckling resistance design equations according to this standard are as follows:  
\[\frac{N_{Ed}}{\frac{\chi_{y}N_{\text{Rk}}}{\gamma_{\text{MI}}}}+k_{yy}\frac{ M_{y,Ed}+\Delta M_{y,Ed}}{\chi_{LT}\frac{M_{y,\text{Rk}}}{\gamma_{\text{MI}}}}+k_{ yz}\frac{M_{z,Ed}+\Delta M_{z,Ed}}{\frac{M_{z,\text{Rk}}}{\gamma_{\text{MI}}}}\leq 1, \tag{1}\]
# Performance Analysis of Mesh Based Enterprise Network Using RIP, EIGRP and OSPF Routing Protocols +
## 7 Conclusions  
In a wireless communication network, identify the optimal path from the sensor node to the destination is more difficult. The routing protocols help to find an optimal path between source and destination nodes and minimize these difficulties. The optimal path selection depends upon several factors. This research discusses and analyzes different parametric aspects of routing protocols. RIP, EIGRP and OSPF routing protocols were analyzed and evaluated via an extensive simulation process using carefully selected parameters to acquire the features of their routing algorithms. The measured metrics are voice, HTTP, and video traffic transmitted and received, as well as average end-to-end latency and average point-to-point throughput. The protocol RIP has shown the most significant uncertainty, whereas OSPF has demonstrated the lowest latency. Furthermore, the OSPF protocols attain a better throughput than any other protocols evaluated in this test scenario. From the above result, we see that the OSPF routing protocol is more suitable for multi-hop wireless sensor networks.  
In future research, we will work on simulations with much more realistic topologies and increased optimization accuracy to enhance and show the efficacy of routing protocols in terms of wireless sensor network performance.  
Conceptualization, M.H.K.; software, M.H.K. and M.G.M.; writing--original draft preparation, M.H.K.; writing--review and editing, M.A.K., M.S.I. and M.M. supervision, M.S.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Human Ground Control Performance in Unmanned Aerial Systems
## 3 The Ground Control Station Prototype
### The Ground Control Station Interface  
In this sub-section, we explain how we took into account the previous requirements to design the remote-control interface of the persistent surveillance system. Analyzing the user interface in Figure 1, we immediately distinguish two areas: on the left we have a sidebar that allows the operator to interact with the application; at the top there are buttons to create or set the mission, information on the targets, and acquisition of the mission log;while at the bottom we see the possibility of connecting to drones with LEDs indicating the connection, to start and end the mission; most of the screen is intended for the map.  
Once the connection is made, the LED highlights the new connection status on the map, the targets are added to the mission and the drones at their starting point are indicated. In addition, at the bottom for each drone a card is created with the data related to the drone:  
* drone identification,
* indication of next target,
* coordinates of the drone,
* battery status,
* buttons to send the drone back to the take-off,
* button to view the artificial horizon related to the drone.  
Once given the start mission command, one will see the drones move towards the second target the algorithm set. In the event the drone's battery status is 30%, a notification is displayed in the upper right corner (Figure 2). If it is 10%, the drone returns to the take-off automatically and a notification is shown in the upper right corner (Figure 3). Finally, when the battery is near 0% the drone lands automatically and the interface shows the landing position.  
In Figure 4 we see how the interface provides the operator with the view of the artificial horizon associated with any aircraft selected in the swarm, to facilitate the display of all the information at the same time, if this component is opened in the same position, all other information regarding the drone is displayed.  
Figure 1: The GCS interface before establishing the connection.  
Figure 3: The GCS interface during the mission execution in case the drone battery is \(\leq\)10%.  
Figure 2: The GCS interface during the mission execution in case the drone battery is \(\leq\)30%.
# Responses of Water Use Efficiency to Drought in Southwest China
## 3 Results  
### Spatial and Temporal Variations of Drought in Southwest China  
The total land area affected by drought in Southwest China showed large interannual variations from 2000 to 2017 (Figure 2a). Overall, the drought area accounted for nearly half of the total land area in 2006. During the period 2010-2013, the drought area exceeded 55% of the entire region for four consecutive years. After 2013, the drought area substantially decreased due to the alleviation of drought. The drought area for the four broad vegetation types showed generally similar interannual variations (Figure 2b). The severe drought and extreme drought occurred in the region in 2006 and 2010 were also reported in the literature [8; 27]. We used the proportion of the drought area in Southwest China to measure the spatial extent of drought, and selected 2006 and 2010 as two drought years for further analysis.  
\begin{table}
\begin{tabular}{c c} \hline
**scPDSI** & **Wet or Dry Conditions** \\ \hline \(\geq\)4 & Extreme humid \\ 3–4 & Severe humid \\ 2–3 & Moderate humid \\ 1–2 & Slight humid \\ \(-\)1–1 & Normal \\ \(-\)2—1 & Slight drought \\ \(-\)3—2 & Moderate drought \\ \(-\)4—3 & Severe drought \\ \(\leq\)–4 & Extreme drought \\ \hline \end{tabular}
\end{table}
Table 2: The wet or dry conditions based on scPDSI.  
The spatial distribution, the frequency of drought (scPDSI \(<-1\)), and the trend of scPDSI from 2000 to 2017 are shown in Figure 3. On average, most parts of Southwest China were characterized by \"normal\" or slight drought categories (Figure 3a). The frequency of drought was relatively high in Southwestern Yunnan and central Sichuan (Figure 3b). During the 18-year period, drought occurred more than 10 times for many areas in Southwest China. The Sichuan Basin and the junction of Sichuan and Yunnan were relatively humid with low drought frequency (0-5 times). Figure 3c showed the scPDSI trend from 2000 to 2017 calculated with the M-K method. The scPDSI in the Sichuan Basin showed a significant increasing trend from 2000 to 2017, indicating that this region exhibited a wetting trend. By contrast, the scPDSI showed a significant decreasing trend in Northwest Yunnan and Southwest Sichuan, indicating a drying trend in this region.
## 1 Introduction
### Layers
#### 1.1.3 Technologies Layer  
The technologies layer covers all the technological aspects of smart grids [9; 10; 11; 12] related to energy technologies and information and communications technology (ICT), among which, but not exclusively, smart grid networks, distributed energy resources (DER) such as photovoltaic (PV) systems, wind turbines, micro-combined heat and power (\(\upmu\)CHP) [13; 14], energy storage systems [15], home energy management systems (HEMSs), DSM [16], demand shifting [17], demand and supply forecasting algorithms, electric vehicles (EVs), EV charging stations, stationary fuel cells, and hydrogen fuel cell electric vehicles (FCEVs) [18].
## 3 Results and Discussions
### The 28-Day Compressive Strength  
Figure 5 shows the typical normalized 28-day compressive strength of UHPC cured under different durations of heat curing. The vertical axis of the figure indicates a ratio of 28-day compressive strength (\((f^{\prime}_{c})_{H}\)) of UHPC under heat curing condition relative to that one under standard curing condition (\((f^{\prime}_{c})_{S}\)) of the counterpart specimen without fly ash. The ratio of \((f^{\prime}_{c})_{H}/(f^{\prime}_{c})_{S}\) slightly increased as the heat curing age increased up to 2 days, beyond which it was insignificantly affected by the heat curing age, although higher compressive strength (\(f^{\prime}_{c}\)) was obtained at a heat curing age of 7 days for UHPC specimens with the fly ash content \(R_{f}\geq\) 50% (Figure 5a). At the same heat curing age, higher values of \((f^{\prime}_{c})_{H}/(f^{\prime}_{c})_{S}\) were observed for UHPC mixtures with more contents of fly ash. For specimens prepared to investigate the heat curing ages, \((f^{\prime}_{c})_{H}/(f^{\prime}_{c})_{S}\) ranged between 1.14 and 1.47 for UHPC with \(R_{f}=\) 70% and 1.0 and 1.29 for UHPC with \(R_{f}=\) 20%. This implies that the preference of the addition of fly ash for enhancing the compressive strength of UHPC requires the early heat curing procedure which can be recommended as at least 2 days under 90 \({}^{\circ}\)C from the present tests. The compressive strength enhancement of UHPC with fly ash due to heat curing was also affected by _W_/_B_ (Figure 5b). At the  
same heat curing ages, higher values of \((f_{c}^{\prime})_{H}/(f_{c}^{\prime})_{S}\) were obtained for UHPC specimens with lower _W/B_. For example, \((f_{c}^{\prime})_{H}/(f_{c}^{\prime})_{S}\) ranged between 1.09 and 1.50 for UHPC with _W/B_ = 0.12, and 1.03 and 1.34 for UHPC with _W/B_ = 0.18.  
As curing time of heat curing increased, \((f_{c}^{\prime})_{H}/(f_{c}^{\prime})_{S}\) increased, including sharply with 70% FA content (in Figure 5).
## 1 Introduction  
Incidence and severity of adverse events (AEs) after endoscopic retrograde cholangiocarcinoma-giopancreatography (ERCP) are undoubtfully different from those that may follow other therapeutic endoscopy procedures. Post-ERCP pancreatitis (PEP) has long been described as the most frequent post-procedural AE, resulting in relatively high morbidity and mortality rates. These are followed by increased lengths of hospital stay, thus triggering a heavy burden on healthcare-associated costs [1; 2; 3]. Nevertheless, evaluation and quantification of such burden has been hard to achieve due to the ongoing controversy regarding definition of the AE itself, its follow-up protocols, and its severity assessment.  
Currently, PEP is defined as a new or worsened abdominal pain combined with more than 3 times the normal value of amylase or lipase concentrations at more than 24 h after ERCP and a requirement of hospital admission or prolongation of a planned admission [4]. The proposed definition of PEP has been introduced by [PERSON] et al. [5] and it has since been used in most trials and published papers. Based on this definition, the reported overall incidence of PEP ranges between 3% and 10%, from most of the systematic reviews and meta-analyses [2; 3; 6]. The majority of PEP patients develop a mild case, with an overall mortality rate of under 1% [1; 2].  
Despite its relatively high incidence, there has yet been no specifically designed tool for the evaluation of PEP severity nor of its impact on the length of hospital stay and inpatient mortality rates. Although not specifically designed for PEP, the revised Atlanta Classification of Acute Pancreatitis Consensus [7] managed to stratify pancreatitis severity based on the presence and duration of organ failure rather than duration of hospitalization and may provide an alternative for assessing PEP severity and mortality.  
Consequently, given such a potential gap in the workup of ERCP patients, our aim was to evaluate whether such parameters as the length of stay, incidence of organic dysfunction, or mortality could be predicted by standardized indicators like the systemic inflammatory response syndrome (SIRS) or the quick Sequential Organ Failure Assessment (qSOFA) score. Investigating SIRS follows several systemic parameters associated with inflammation including the heart and respiratory rates, body temperature, and white blood cells. On the other hand, qSOFA assessment consists of evaluating three components respiratory rate, change in mental status, and systolic blood pressure, which have been associated with a high risk for organ dysfunction.