esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Dicyandiamide-Imprinted Polymers with Computer-Aided Design
## 2 Results and Discussion  
### Selection of the Appropriate Calculated Method  
DFT calculations have been carried out in order to study the DCD imprinted polymers. There are a lot of DFT methods, but different methods should be applied in different research systems. The choice of calculated methods has a great influence on the accuracy of the calculated results. Herein, we simulated the structural parameters of DCD using the B3 LYP, PBE1 PBE, wB97 XD, M062X, and LC-wPBE methods with the 6-31G(d,p) basis set. These calculation methods have been used in the previous researches on the MIPs. For example, [PERSON] et al. designed a new computational model capable of understanding the nature of interaction between the template and the functional monomers by employing the B3 LYP method [22]. [PERSON] et al. used the B3 LYP method to design the MIPs for voltammetric sensing of homovanillic acid [23]. [PERSON] et al. investigated the molecularly imprinted nanospheres for selective recognition of \(\alpha\)-tocopherol succinate by using the B3 LYP method [24]. [PERSON] et al. used the quantum chemical method PBE1 PBE to investigate the interaction between melamine and acrylamide in the MIPs [25]. Otherwise, wB97 XD [26; 27], M062X [28; 29], and LC-wPBE [30] methods have also been used for simulating the MIPs.  
Herein, the appropriate calculated method was confirmed through comparing the calculated results and the experimental data [31]. As shown in Table 1, the computational structural parameters and the experimental data are highly similar, which indicates that the DFT is effective and reliable in the optimization of the geometry structures. Among these methods, the M062X/6-31G(d,p) method should be the most appropriate calculated method, because the structural parameters calculated on the basis of the M062X method are the closest values to the experimental data. For example, the practical bond length of N9-C8 is 1.22 A, and the value given by the M062X method is 1.18 A. In contrast, the values given by the wB97 XD, PBE1 PBE, and LC-wPBE methods are all 1.16 A, and the one from the B3 LYP method is 1.17 A. Moreover, the practical bond length of N1-C2 is 1.34 A. The value given by the M062X method is 1.28 A, while the values given by other methods are all 1.27 A. As a result, the M062X/6-31G(d,p) method was selected as the appropriate calculated method.
# Multilevel Coding for the Full-Duplex Decode-Compress-Forward Relay Channel
###### Abstract  
The Decode-Compress-Forward (DCF) is a generalization of Decode-Forward (DF) and Compress-Forward (CF). This paper investigates conditions under which DCF offers gains over DF and CF, addresses the problem of coded modulation for DCF, and evaluates the performance of DCF coded modulation implemented via low-density parity-check (LDPC) codes and polar codes. We begin by revisiting the achievable rate of DCF in discrete memoryless channels under backward decoding. We then study coded modulation for the decode-compress-forward via multi-level coding. We show that the proposed multilevel coding approaches the known achievable rates of DCF. The proposed multilevel coding is implemented (and its performance verified) via a combination of standard DVB-S2 LDPC codes, and polar codes whose design follows the method of Blasco-Serrano.  
decode-and-forward; compress-and-forward; multilevel coding +
Footnote †: journal:
## 2 Mathematical Modelling
### Model Formulation
#### 2.2.2 Description of Model Parameters  
Based on the above assumptions, if \(L\) is the set of stations and \(K\) is the set of vehicle sizes, for a certain period of time, according to the reservation data, all the vehicle tasks generated by the previous scheduling are sorted by departure time to form the set of departure tasks \(M=\left\{M_{k}|k=(1,2,3)\right\}=\left\{V_{1},V_{2},\cdots,V_{i},V_{j},\cdots\right\}\), where \(M_{k}\) represents the set of departure tasks with the required vehicle size \(k\), \(k=(1,2,3)\) represents small vehicles, medium vehicles, and large vehicles respectively, and \(V_{i}\) and \(V_{j}\) represent the vehicle tasks with the number \(i,j\). For task \(V_{i}\), there are \(V_{i}=\left\{l_{i^{\prime}}^{o},l_{i^{\prime}}^{o},t_{i}^{o},k_{i}\right\}\), where \(l_{i}^{o}\) and \(l_{i}^{d}\) represent the departure station and the final station of the task \(V_{i}\), \(t_{i}^{o}\) represents the departure time of the starting station of the training task, and \(k_{i}\) represents the required vehicle size of the task \(V_{i}\). After identifying the departure demand during the period, optimizing the matching and connection between the trips can determine the best vehicle scheduling plan.  
The symbols and descriptions of the parameters used in the scheduling model are detailed in Glossary.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Probability-Based Resilience Approach  
The probability-based resilience estimation is a traditional method that has been used historically to help infrastructures predict event occurrences based on historical data. In [61], the authors introduced a probabilistic method for estimating the total infrastructure resilience. They mathematically described it in Equation (17) concerning predefined performance standards \(A\), given a seismic event of size \(i\):  
\[R=P(A|i)=P(r_{0}<r^{*}and;t_{1}<t^{*}) \tag{17}\]  
where \(r_{0}\) = initial infrastructure function loss; \(r^{*}\) = a determined standard of robustness representing the \"highest tolerable loss\" in infrastructure function after a disturbance event; \(t_{1}\) = time to complete recovery; and \(t^{*}\) = the \"highest tolerable disruption time\", i.e., the maximum acceptable duration for infrastructure function to return to pre-earthquake levels. They defined resilience \(R\) as \"the probability that the system of interest will meet predefined performance standards in a seismic event of magnitude i\" (Equation (17)). Figure 17 illustrates the use of each element and how they are estimated. The authors focused on assessing resilience concerning the changes in the infrastructure function product. Figure 16: Conceptual illustration of MCEER’s seismic resilience loss measurement [37]. _Limitations:_ The probability-based approach heavily relies on data to estimate the likelihood of an event occurring and its potential impact on infrastructure resilience. However, the availability and reliability of such data pose significant challenges. For instance, the historical data used for probability calculations may not accurately represent the current state of the infrastructure or the potential risks it faces. Additionally, the data may not be complete or may be biased towards certain types of events, leading to inaccurate predictions. Therefore, it is crucial to ensure that the data used in probability-based approaches are up-to-date, accurate, and comprehensive to obtain reliable estimates of infrastructure resilience. This paper presents a summary of all the methods and metrics used for determining the resilience of Smart ICIs, as shown in Table 3. The classification used in this table is based on the modeling strategies presented in [20], with the addition of four approaches: indicator-based, quantification-based, seismic-based, and probability-based approaches. The table indicates the model and threat type associated with each method. For instance, the LIOH approach in the indicator-based method uses the level-based model to detect resilience and can be applied to all types of threats.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Long-Range Communications
#### 4.4.2 Internet-Based Communication in Telemedicine  
Internet-based communication in telemedicine has previously been presented in [29], comprising a teleconsultation system and an online medical information system. The network was used to connect medical specialists, devices, and data within various sites using the internet. The use of low bandwidth communication employed by the system was, however, noted to limit the high-volume transmission of medical data.  
The authors of [81] developed a prototype internet-based telemedicine system that establishes an audio-visual connection as well as a data path from the client's home to the care-giver through a web browser interface. The developed data path was able to provide other diagnosis functions as well, such as the use of an electrocardiogram, blood pressure, respiration, body temperature, and stethoscope.  
In [110], a wireless IP network that provided voice communication as its basic service was reviewed. The internet-based system converts analogue voice signals into digital data, compresses and segments the data into packets, adds an IP address, and then enables distribution over the wireless IP network. Because the system is internet-based, normal PCs and other devices can easily be connected through the use of TCP/IP addresses. The system provides multimedia communication services such as data, audio, video, and imaging for telemedicine applications.  
Although 3G and 4G communications are able to support video conferencing in telemedicine, medical image transmission has been shown to create nodal delay when using the internet [80]. To mitigate this effect, the authors designed a parabolic dish antenna that supports fast communication using the internet for telemedicine communication.
# A New Soil Conditioner for Highly Permeable Sandy Gravel Stratum in EPBs
## 3 Results and Discussion  
### Apparent Viscosity Test  
#### 3.1.1 Difference between Traditional Slurry and B/A Modified Slurry  
Viscosity tests of traditional bentonite slurry at different concentrations, including 10 wt%, 13 wt%, 15 wt%, and 18 wt% were performed. As shown in Figure 2(a), the viscosity is closely related to time regardless of which slurry concentration is used. More specifically, the viscosity of traditional slurry increases first and then remains stable over time. For example, the viscosity of traditional slurry at a concentration of 15 wt% sharply increases from 24 mPa\(\cdot\)s to 41 mPa\(\cdot\)s within 0-10 h. Subsequently, its viscosity is stable at approximately 43 mPa\(\cdot\)s after 10 h. This is because the main component of bentonite is clay mineral (i.e., montmorillonite), which has a multilayer crystal structure. When water was added into bentonite, the water molecules easily entered the smectite layers and thus lead to the lattice expanding. As hydration time increases, the water molecules were transformed into a hexagonal ring structure through hydrogen bonding, and thus the interaction force montmorillonite crystal was enhanced [23]. In addition, it can be seen from Figure 2(b) that the fluidity of slurry is great when its viscosity is less than 35 mPa\(\cdot\)s, while the fluidity deteriorates as its viscosity increases to larger than 40 mPa\(\cdot\)s, and then the slurry exhibits a slow flow state when the viscosity exceeds 50 mPa\(\cdot\)s.  
According to the construction experience [24], proper pumpability of bentonite slurry is necessary to ensure smooth tunneling. Since bentonite slurry at 13 wt% concentration exhibited proper viscosity and sufficient hydration degree, the concentration was used to prepare the B/A modified slurry sample. Figure 2(c) is the schematic diagram of the mixture of 13 wt% bentonite slurry and 20 wt% ammonium chloride solution. Compared with the traditional slurry (Figure 2(b)), the viscosity of bentonite slurry rapidly increases after adding ammonium chloride solution and the B/A modified slurry exhibits a nonflowing state. Therefore, the ammonium chloride solution can be used as a viscosity modifier for traditional slurry.  
Figure 2(d) illustrated that the ammonium ions can exchange with calcium, magnesium and sodium in bentonite and are not easily occluded by other ions, which is caused by ammonium ions that have a small hydration radius, low hydration energy, similar ionic radius with a six-membered ring radius in a clay crystal lattice [25]. Therefore, ammonium chloride solution can act as a viscosity modifier of traditional slurry. The apparent viscosity of traditional slurry increased and the fluid loss of sand gravel samples reduced after adding solution containing ammonium ions. Subsequently, the solution added with excess ammonium ions would lead to aggregation of montmorillonite particles and then the aggregates were surrounded by water molecules to form high consistency and viscosity copolymer slurry.  
Figure 2: Permeability test device.
## 2 Materials and Methods
### Framework of a River Health Warning System and Tool Development
#### 2.4.1 Conceptual Framework of a River Health Early Warning System  
To better manage and conserve river systems, a river health early warning system with the ability to analyze and interpret results in real time is required. A system has been designed, through which the current and future conditions of a river system can be characterized and the possible warning scenarios and position can be located. The conceptual framework of this system is illustrated in Figure 2. It begins with characterizing river health features and identifying their indicators, continues with data collection and pre-processing, and then envelopment analysis. Lastly, an early warning tool which is integrated with the three-phase approach, geographic information system (GIS), and decision support system (DSS) techniques, has been developed to address the possible risks and dominant causal factors and to identify sites in need of attention the most. The general processes are shown as follows:  
* Concept definition and indicator identification: The system starts with clustering river types, characterizing health features and identifying a list of potential indicators of rivers or watersheds. To assist in setting river health guidelines and in stratifying indicators and field sites, rivers in a watershed are divided into many types or sub-types or sub-catchments with GIS. In turn, each sub-catchment is divided into a series of reaches or rivers, which are each assigned a value for stream order, rainfall, altitude, and slope.
* Data collection and pre-processing: River health data are collected via multiple monitoring or sampling sites that have been selected to cover an extensive range of levels at the reach, sub-catchment, and catchment scales. Monitoring and sampling data are digitized and pre-processed to store in the relevant database. In addition, the geographic map should be previously digitalized sand uploaded.
* Envelopment analysis: After indicators, variables, and predictors have been identified as the parameters of the three-phase approach, data are enveloped and input. Then, short- or long-term trends in river health are analyzed and predicted when normal or extreme threats occur.
* GIS-based early warning and DSS tool design: To efficiently improve prediction and make precise early warning decisions, a GIS-based early warning and DSS tool, the river health prediction and early warning system (RHP-EWS), has been developed, into which the components and processes described above have been integrated. With this tool, multiple-source processed and early warning information could be disseminated to decision-makers or other end-users while efficient timely responses on river health to early warning information could be issued and implemented.
## 3 Materials and Methods
### Preparation of 2-substituted-N-(5-((4-sulfamoylphenyl)azo)thiazol-2-yl)acetamide Derivatives **7**
_2-((4-Antipyrinylthiazol-2-yl)imino)-5-(4-nitrobenzylidene)thiazolidin-4-one_ (**10b**). Orange solid, yield = 69%, m.p. = 294-295 \({}^{\circ}\)C. IR (\(\overline{v}\)/cm\({}^{-1}\)): 3192 (N-H), 1718 (C=O). 1H-NMR (DMSO-\(d_{6}\)): 8 2.64 (s, 3H, CH\({}_{3}\)), 3.31 (s, 3H, CH\({}_{3}\)), 7.31-7.60 (m, 6H, Ar-H and thiazole-H5), 7.78 (s, 1H, olefinic CH=C), 7.82 (d, \(J\) = 8.80 Hz, 2H, Ar-H), 8.30 (d, \(J\) = 8.40 Hz, 2H, Ar-H), 12.56 (s, 1H, NH). MS \(m/z\) (%): 518 (M\({}^{\rm+}\), 47.4%). 13C-NMR (DMSO-\(d_{6}\)): 8 12.58, 34.19, 108.62, 116.45, 121.09, 123.27 (2C), 124.81 (2C), 125.48, 129.16 (2C), 130.34 (2C), 135.27, 139.96, 142.18, 147.29, 151.06, 156.41, 160.20, 161.38, 165.29, 168.40. MS (EI): \(m/z\) (%) = 518 (molecular ion, 65.7%). Analysis for C\({}_{24}\)H\({}_{18}\)N\({}_{6}\)O\({}_{4}\)S\({}_{2}\) (518): Calcd. : C, 55.59; H, 3.50; N, 16.21%. Found: C, 55.77; H, 3.42; N, 16.09%. _2-((4-Antipyrinylthiazol-2-yl)imino)-5-(4-bromobenzylidene)thiazolidin-4-one_ (**10c**). Yellow solid, yield = 83%, m.p. = 275-276 \({}^{\circ}\)C. IR (\(\overline{v}\)/cm\({}^{-1}\)): 3132 (N-H), 1702 (C=O). 1H-NMR (DMSO-\(d_{6}\)): 8 2.62 (s, 3H, CH\({}_{3}\)), 3.32 (s, 3H, CH\({}_{3}\)), 7.28 (s, 1H, thiazole-H5), 7.36-7.78 (m, 10H, Ar-H and olefinic CH=C), 12.48 (s, 1H, NH). 1\({}^{\rm 3C}\)-NMR (DMSO-\(d_{6}\)): 8 12.67, 34.49, 108.57, 114.72, 120.30, 122.75, 124.92 (2C), 125.16, 128.38 (2C), 129.64 (2C), 131.06 (2C), 133.29, 143.04, 148.38, 151.36, 155.91, 160.47, 161.84, 164.85, 167.76. MS (EI): \(m/z\) (%) = 551 (molecular ion, Br-79, 73.4%) and 553 (M\({}^{\rm+}\), Br-81, 75.6%). Analysis for C\({}_{24}\)H\({}_{18}\)BrN\({}_{5}\)O\({}_{2}\)S\({}_{2}\) (552): Calcd. : C, 52.18; H, 3.28; N, 12.68%. Found: C, 52.39; H, 3.21; N, 12.81%. _3.9. Synthesis of 2-amino-1-(4-antipyrinylthiazol-2-yl)-3-cyano-4,5-dihydro-5-oxo-1H-pyrrole_ (**12**)  
To a suspension of chloroacetamido derivative **1** (2 mmol, 0.72 g) and malononitrile (2 mmol, 0.13 g) in 20 mL ethanol, five drops of Et\({}_{3}\)N were added, then the reaction mixture was heated under reflux for 2 h;(C\(\equiv\)N), 1705 (C=O). 1H-NMR (DMSO-\(d_{6}\)): 8 2.65 (s, 3H, CH\({}_{3}\)), 3.32 (s, 3H, CH\({}_{3}\)), 4.36 (s, 2H, CH\({}_{2}\)), 7.26 (s, 1H, thiazole-H5), 7.34-7.58 (m, 5H, Ar-H), 12.47 ppm (s, 2H, NH\({}_{2}\)). 13C-NMR (DMSO-\(d_{6}\)): 8 11.69, 29.26, 34.41, 64.21, 102.79, 107.82, 113.76, 123.48 (2C), 126.14, 128.25 (2C), 134.23, 141.61, 150.97, 152.19, 155.47, 159.36, 164.73. Analysis for C\({}_{19}\)H\({}_{16}\)N\({}_{6}\)O\({}_{2}\)S (392): Calcd. : C, 58.15; H, 4.11; N, 21.42%. Found: C, 58.10; H, 4.15; N, 21.33%.
## 3 Results
(#) Represents differences between Coospo Armband vs. Coospo and Garmin Straps. Such outliers can exaggerate the appearance of disagreement between devices and should be interpreted with caution. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Stage** & **CS (bpm)** & **GS (bpm)** & **CA (bpm)** & **CS-GS** & **GS-CA** & **CS-CA** \\  & **Mean (CV)** & **Mean (CV)** & **Mean (CV)** & **ICC (Low, Up)** & **ICC (Low, Up)** & **ICC (Low, Up)** \\ \hline Effort 1 & 115 (10) & 115 (10) & 114 (9) & 1.00 (0.90, 1.10) & 0.10 (0.04, 0.15) & 0.11 (0.06, 0.16) \\ Effort 2 & 116 (10) & 116 (10) & 116 (11) & 0.86 (0.77, 0.96) & 0.17 (0.11, 0.23) & 0.10 (0.06, 0.15) \\ Effort 3 & 116 (11) & 116 (10) & 115 (10) & 0.91 (0.82, 1.00) & 0.59 (0.57, 0.61) & 0.50 (0.47, 0.52) \\ Effort 4 & 117 (11) & 117 (11) & 117 (11) & 0.90 (0.99, 0.81) & 0.10 (0.05, 0.14) & 0.15 (0.09, 0.21) \\ Effort 5 & 118 (12) & 118 (12) & 117 (12) & 1.00 (0.90, 1.10) & 0.47 (0.45, 0.50) & 0.45 (0.42, 0.48) \\ Effort 6 & 117 (12) & 117 (12) & 116 (12) & 0.89 (0.98, 0.79) & 0.47 (0.44, 0.49) & 0.32 (0.29, 0.36) \\ Effort 7 & 119 (12) & 119 (11) & 118 (12) & 0.97 (0.87, 1.07) & 0.46 (0.43, 0.48) & 0.39 (0.36, 0.47) \\ \hline Recovery 1 & 117 (12) & 117 (12) & 119 (11) & 0.76 (0.68, 0.85) & 0.69 (0.67, 0.70) & 0.58 (0.56, 0.60) \\ Recovery 2 & 118 (12) & 117 (12) & 120 (12) & 0.72 (0.64, 0.80) & 0.77 (0.7
## 3 Results and Discussion
### Orthogonal Experiment and Range Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**A (\(w_{1}\)) (mm)**} & \multirow{2}{*}{**B (\(h_{1}\)) (mm)**} & \multirow{2}{*}{**C (\(h_{2}\)) (mm)**} & \multirow{2}{*}{**D (\(h_{3}\)) (mm)**} & \multirow{2}{*}{**Mass Uniformity Coefficient \(\varphi_{m}\)**} & \multirow{2}{*}{**Pressure Drop**} \\  & & & & & & \\ \hline
1 & 6 & 10 & 6 & 6 & 0.131 & 25.473 \\
2 & 6 & 14 & 8 & 8 & 0.104 & 20.651 \\
3 & 6 & 18 & 10 & 10 & 0.108 & 18.151 \\
4 & 8 & 10 & 8 & 10 & 0.180 & 18.866 \\
5 & 8 & 14 & 10 & 6 & 0.153 & 17.048 \\
6 & 8 & 18 & 6 & 8 & 0.156 & 16.758 \\
7 & 10 & 10 & 10 & 8 & 0.149 & 14.778 \\
8 & 10 & 14 & 6 & 10 & 0.176 & 20.048 \\
9 & 10 & 18 & 8 & 6 & 0.162 & 16.309 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of orthogonal experiment. of the channel for a balance of the two evaluation criterions was a narrow channel but a long distance of \(h_{2}\) to gain a better comprehensive performance of the cooling plate.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 1 Introduction  
Natural hazards not only pose a significant threat to human lives and property but also to the overall ecosystem in which we live. In the past, people relied on the assumption that only certain types of natural hazards could occur in their area. This assumption was mainly based on the local and regional characteristics of the territory. However, today, we are witnessing that this is no longer the case. An example is the tornado that struck the Czech Republic in 2021 [1].  
In recent years, we have seen multiple devastating crisis events, or disasters, caused by natural forces. Numerous examples can be cited, with some of the most destructive disasters occurring within the European Union and its neighbouring countries.  
The 2023 earthquake on the Turkey-Syria border claimed more than 56,000 lives, according to the Centre for Disaster Philanthropy. Infrastructure in the affected area was destroyed, with more than 230,000 buildings damaged or completely destroyed [2].  
The impact of climate change and natural factors will lead to more frequent crisis events in the future, with an expected increase in their scale. In central Europe, the mostcommon type of crisis event is flooding [3]. Devastating floods are occurring increasingly often across the European Union. An example is the August 2023 floods in Slovenia. A combination of several factors led to catastrophic flooding in the country, with a calculated return period of 250 years. Key contributing factors included weather anomalies such as above-average rainfall in the affected area, autumn-like weather patterns, high soil moisture, and an increased frequency of storms. Additionally, the affected region had already been impacted by minor landslides and fallen vegetation, which had not been cleared from forests before the flood. As a result, the disaster caused damages amounting to nearly 10 billion euros, affected more than 12,000 homes, and resulted in three fatalities [4].  
Another example of severe flooding includes the 2024 floods in Germany [5], the Czech Republic [6], and the Spanish city of Valencia, where more than 190,000 people were affected. Additionally, 3200 km of roads and engineering networks were damaged, and more than 200 lives were lost [7].  
Beyond flooding, wildfires also pose a major threat in Europe. In recent years, large wildfires have particularly affected Greece and Portugal [8]. Other natural hazards include snow avalanches and storms, landslides, droughts, hurricanes, thunderstorms, and extreme heatwaves.  
These crisis events demonstrate that their frequency is increasing and their consequences are becoming more severe. To ensure the protection of the population, early warning systems (EWSs) play a crucial role. Given these circumstances, it is essential for such systems to be as comprehensive as possible.