esa-sceva/satcom-chunk-collection huggingface dataset:
# Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
## 3 Emotional State Monitoring Methodology
### Data Annotation  
In order to train our supervised classifier, we annotated our data with the emotional state of the person at several moments of the day during their daily activities. In clinical psychology, mental assessment relies on retrospective reports, which normally are subject to recall bias. However, as noted by [PERSON] et al. [40], this approach limits the ability to precisely characterize, understand, and change the behavior in real-world conditions. In order to minimize the impact of those limits, we used ecological momentary assessment (EMA), in which very short questions are asked to the participant at different times during their daily life activities, as used in [41] to monitor teachers' emotional states and behaviors over time. This approach aims to study peoples' affective states in their daily lives by repeatedly gathering data in an individual's normal environment.  
Following previous works [20; 42; 43], we used the circumplex model of affect introduced by [PERSON] [13], which describes affective states as a bi-dimensional model guided by two dimensions, valence and arousal, as shown in Figure 3. This model is validated and represents the participant's emotional self-subjective evaluation. Following the guidelines from [44], we designed an app for the smartphone that used the measurements valence and arousal in a 5-point Likert scale, ranging from 0 to 4. Dimensional affective models, i.e., those which define affective states from different dimensions (valence and arousal), are more reliable than models based on discrete categories, i.e., asking directly affective categories, such as anger and fear, as stated in [45]. For this reason, we calculated the final affective state, which was obtained by interpolating the valence or pleasure and arousal levels as explained in [13]. The app's interface was kept simple to facilitate usage and to reduce attention theft, i.e., the shift of the user's attention only to the device rather than the real environment. In order to avoid generating extra anxiety due to a high load of questions [44], users were asked only five times per day about their valence and arousal levels. In addition, participants were free to input their levels at any time.  
Figure 2: Empatica E4 medical device. Front and back views.
# Investigation of Different Storage Systems for Solar-Driven Organic Rankine Cycle
## 2 Material and Methods  
### The Basic Examined Unit  
In this work, the basic examined unit is depicted in Figure 1. It was a solar-driven ORC with PTC and a storage tank. Three different storage systems were examined in this work, while the other parameters remained the same. The working fluid in the solar field was Therminol VP-1 [24], which can operate in the range of 12 to 400 \({}^{\circ}\)C without any issues. The ORC was a regenerative cycle and the main parameters of the basic unit are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Nominal electricity production & \(P_{d}\) & 10 kW \\ Turbine isentropic efficiency & \(\eta_{ls,T}\) & 85\% \\ Electromechanical efficiency in the generator & \(\eta_{mg}\) & 98\% \\ Pump isentropic efficiency & \(\eta_{ls,P}\) & 70\% \\ Motor-pump efficiency & \(\eta_{motor}\) & 80\% \\ Pinch point in the heat recovery system (HRS) & \(PP_{0}\) & 5 K \\ Default collecting area & \(A_{c}\) & 160 m\({}^{2}\) \\ Default storage tank volume & \(V\) & 10 m\({}^{3}\) \\ Default solar irradiation & \(G_{b}\) & 700 kW/m\({}^{2}\) \\ Default solar angle & \(\theta\) & 20\({}^{\circ}\) \\ Storage tank thermal loss coefficient & \(U_{T}\) & 0.5 W/m\({}^{2}\)K \\ Default ambient temperature & \(T_{amb}\) & 25 \({}^{\circ}\)C \\ Superheating degree in the turbine inlet & \(\Delta T_{sh}\) & 20 \({}^{\circ}\)C \\ Minimum temperature difference in the & \(\Delta T_{rec}\) & 10 \({}^{\circ}\)C \\ recuperator & \(T_{cont}\) & 40 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the solar-driven ORC unit.  
Figure 1: The examined system with parabolic trough collector (PTC), storage system and the organic Rankine cycle (ORC).  
The heat recovery system (HRS) was modeled by using the pinch point analysis and the minimum pinch point was set at 5 K. The recuperator was modeled in order to have a minimum temperature difference at 10 K, while there was superheating in the turbine inlet at 20 K. The isentropic efficiency of the turbine was 85% and the pump was 70%, while the electromechanical generator efficiency was 98% and the motor efficiency that moves the pump was 80%. Moreover, it is important to state that electricity production was set at 10 kW.  
The nominal selected solar beam irradiation was at 700 W/m\({}^{2}\) and the respective equivalent solar angle was 20\({}^{\circ}\). These were representative values for estimating the yearly system performance for the climate conditions of Athens (Greece) [25; 26]. The condenser temperature of the system was selected at 40 \({}^{\circ}\)C and the default ambient temperature at 25 \({}^{\circ}\)C, which are reasonable values. In the preliminary studies of the system, the solar collecting area was 160 m\({}^{2}\) and the storage tank volume was 10 m\({}^{3}\). These values were selected after some tests in the created program in order to provide a proper operation with a reasonable yearly operating capacity factor.
# Comparison of \({}^{210}\)Pb Age Models of Peat Cores Derived from the Arkhangelsk Region
## 2 Material and Methods
### Study of Physical and Radioactive Characteristics of Peat Samples
#### 2.2.4 Determining \({}^{137}\)Cs and \({}^{210}\)Pb Isotopes  
Radionuclide \({}^{137}\)Cs was detected via a CANNERRA Packard low background gamma spectrometer (USA) with coaxial semiconductor detector GX2018 using the Ge(Li) crystal and Genie-2000 program (Version 3.1). The resolution of the gamma spectrometer at the 1.33 MeV (\({}^{60}\)Co) line was 1.75 keV, and the relative efficiency was 22.4%.  
Detector calibration using energies and the assessment of registration efficiency were carried out using the standard solutions of the following radionuclides: \({}^{241}\)Am, \({}^{100}\)Cd, \({}^{88}\)Y, \({}^{137}\)Cs, and \({}^{152}\)Eu. The specific activity of \({}^{137}\)Cs was measured from the gamma line 661.66, with a quantum yield of 89.90% for \({}^{137}\)ZnBa radionuclide. The minimum measured radioactivity of \({}^{137}\)Cs at exposure t = 18,000 s for geometry of a flat container of a 0.1-L volume measure was 0.1 Bq [18].  
To determine \({}^{210}\)Pb, a peat sample was treated according to the methodology used in [19]. The obtained counting sample with radionuclides \({}^{210}\)Po and \({}^{210}\)Pb was measured 10 h after its receipt via the alpha-beta radiometer \"Abelia\". During this period, the daughter alpha and beta radionuclides (\({}^{212,214,216,218}\)Po, \({}^{210}\)Bi and \({}^{210}\)Bi) decayed. The range of measured specific activity according to this method extended from 10 to 2:\({}^{10}\)Bq\(\cdot\)kg\({}^{-1}\). Measurement uncertainty (\(p\) = 0.95) was estimated at each specific measurement and did not exceed 30% [19]. We noted that the average actual value for the studied sample was 10%.
## 3 An Improved Cuckoo Search Algorithm under Bottleneck-Degree-Based Search Guidance, TSCS-BD
### Tabu Search under Bottleneck-Degree Guidance
#### 3.3.3 Bottleneck-Degree Guidance for Tabu Search Neighborhood Structure  
In large-scale inter-cell scheduling, as the problem size increases, the solution space increases sharply, which leads to the decrease in the searching efficiency of the random neighborhood structure. The bottleneck machine plays a decisive role in the scheduling scheme. Therefore, targeted identification of bottleneck machines and construction of neighborhood structure can reduce the search scope of low-quality solution space and improve the overall search efficiency of the algorithm. The bottleneck degree can effectively identify the possibility that the node machine becomes a bottleneck. During the neighborhood construction stage, bottleneck machines are identified by calculating and sorting their bottleneck degree. Subsequently, candidate solutions are constructed by exchanging their positions within the sequential processing sequence.  
The specific steps are as follows:  
1. Selecting an individual to calculate the bottleneck degree with a \(P_{a}\) probability;
2. Sorting each machine according to the bottleneck degree, with the first three machines with the highest bottleneck degree regarded as bottleneck machines \(M_{b}\) in the current solution;
3. Randomly selecting one machine from \(M_{b}\), and constructing the corresponding candidate solution by exchanging the position of any two operations processed on the machine with constraints;
4. Repeating step (3) until enough candidate solutions are generated.  
In Figure 5, 1-1 represents the operation \(O_{11}\), the first operation of part 1. Assuming machine \(M_{3}\) is calculated to be a bottleneck machine, randomly selecting two operations processed on \(M_{3}\) and exchanging their positions, other operations remain unchanged. A feasible candidate solution has been obtained. Repeat this step until enough candidate solutions are generated, especially giving preference to those candidate solutions which reduce the maximum completion time compared with the initial solution.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameter** & **Parameter Meaning** \\ \hline \(w_{m}\) & Node load of node \(m\) \\ \(l_{st}\) & The shortest distance between nodes \(s\) and \(t\) \\ \(e_{st}\) & Network efficiency between nodes \(s\) and \(t\) \\ \(\delta_{st}\) & Number of shortest paths from node \(s\) to node \(t\) \\ \(\delta_{stm}\) & Number of shortest paths from node \(s\) to node \(t\) through node \(m\) \\ \(b_{m}\) & Betweenness centrality of node \(m\) \\ \(s_{m}\) & Bottleneck degree of node \(m\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of complex network features.
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 3 Approach
### Data Aggregation  
Data aggregation is essential for identifying all floors inside a building, as not all users are expected to visit all floors. In the data aggregation module, multiple recorded data are fused. Grouped by their GPS coordinates and combined with the building outline, extracted from OpenStreetMap [25], it is ensured that the data always correspond to the same building. More specifically, altitude information estimated from multiple users and labeled by their unique users identifier (UUID) are sorted by their time-stamp and fused together for the classification phase. Because the reference pressure for the altitude estimation is extracted by the same device as that used for estimating it, approximately at the same location for all the users, because of the novel approach for reference altitude extraction on the basis of the identification of the OITransition, there is no need to calibrate any sensor between different phones. In this paper we consider all existing entrances of a building to be at the same altitude. However, in the case of multiple entrances at different altitudes, the entrance altitude, as well as the longitude and latitude, can be extracted from [25], and then the OITransition can be used for the identification of the entrance location. Once the entrance location is identified, the difference between the global altitude of the entrance can be used for locally referring the floor height.
## 3 Communication Delay Issues with Combined TSC and TCR Controller
### TSC-TCR Controller Time Delay Issue  
Figure 5 displays the TSC's GPS-based shut control system. The controller input was gathered from the control center, as shown in Figure 5, and delays were created throughout the signal transmission, causing the production of such a controller output (alpha) to be delayed, reducing the system's power quality. Figure 5 depicts the GPS-enabled feedback control. It is also important to note that such delays affect both the transmission and distribution connections. As illustrated in Figure 5, the speed equivalent signal from each generator was routed through filters and an A/D adapter. The digitalized rate equivalent signals from the generators were then routed to a central control office, where they were synced using a GPS receiver. The synchronized signals were used to calculate the total W and time derivatives of TKED. The time derivative of the TKED signal was then given as data output to the other fuzzy controller input. In this case, the instructions could be transmitted and received by wireless or fiber optic methods.  
Figure 4: Control block for switching of TSC-TCR.
# Urban Flood Risk Assessment Based on a Combination of Subjective and Objective Multi-Weight Methods
## 2 Materials and Methods
### Overview of Study Area  
Liaocheng City is located in the western part of Shandong Province, with a geographical location of 35\({}^{\circ}\)47\({}^{\prime}\) N\(\sim\)37\({}^{\circ}\)02\({}^{\prime}\) N, 115\({}^{\circ}\)16\({}^{\prime}\) E\(\sim\)116\({}^{\circ}\)32\({}^{\prime}\) E. It is situated in the yellow flood plain of northwestern Shandong Province, with a gentle topography, high in the southwest and low in the northeast. There is a ground slope drop of 1/6500-1/7500 and a ground elevation of approximately 22.80-47.80 m, as shown in Figure 1.  
Liaocheng City undergoes significant seasonal changes and has monsoon climate characteristics, with a multi-year average precipitation of 555.9 mm. Of the annual precipitation, 60% is concentrated in the summer, when the city is prone to local flooding. The city's spatial and temporal distribution of precipitation is extremely uneven; from a temporal point of view, precipitation is mainly concentrated in the flood season, in July and August. From a spatial point of view, Dongchangfu District, Chiping District, and Gaotang County receive more precipitation, while Yanggu County receives less.  
Liaocheng City is one of the areas in Shandong Province that has suffered the most frequent and heaviest floods in history. Its floods have the following characteristics: periodicity, continuity, suddenness, and seasonality. Generally, inter-annual summer and autumn floods occur during the frequent-flood years and mega-flooding years, when there is a concentration of heavy rainfall with summer and autumn flooding characteristics. In recent years, Liaocheng City has been seriously affected by flooding. There have been five major rain and flooding disasters since 1990 in 2010, 2013, 2015, 2016, and 2018. On 9-10 August 2010, there was moderate to heavy rainfall in Liaocheng City, with an average precipitation of 145.4 mm in the city, causing serious urban flooding and waterlogging of farmland due to the high intensity of precipitation. In July-August 2013, heavy rainfall and torrential rains hit Liaocheng's territory, causing serious waterlogging in cities and farmland. A total of seven counties, except Xin County, were affected. In July-August2015, Liaocheng was hit by heavy rainfall and storms, and crops were severely affected. In mid-July 2016, under the influence of heavy rainfall over a large area in the Wei and Zhanghe River basins upstream of the Zhangwei River, the flooding of the Zhangwei River in Liaocheng was severe, resulting in the flooding of a large number of trees and crops on the river beaches. In 2018, heavy rainfall resulted in more severe waterlogging on urban roads and some agricultural land, as well as damage to some bridges and ditches. During 2010-2018, heavy rainfall and flooding disasters caused 9,614,800 mu of crops to be affected, 17,221 damaged and collapsed houses, and a population of 3,496,000 people to be affected, resulting in a direct economic loss of CNY 511.1 million [58].
## 3 SOC Balancing
### Balancing Principle  
The relationship between the total electrical energy \(W_{\text{totsc}}\) of supercapacitor \(C_{\text{sc}}\) and its terminal voltage \(U_{\text{sc}}\) can be expressed as:  
\[W_{\text{totsc}}=\frac{1}{2}C_{\text{sc}}U_{\text{sc}}^{2}. \tag{13}\]  
In reality, the usable energy \(W_{\text{sc}}\) of the supercapacitor is only a fraction of \(W_{\text{totsc}}\), and it depends on the maximum \(U_{\text{maxsc}}\) and minimum \(U_{\text{minsc}}\) allowable terminal voltage [8]:  
\[W_{\text{sc}}=\frac{1}{2}C_{\text{sc}}(U_{\text{maxsc}}^{2}-U_{\text{minsc}}^{2 }). \tag{14}\]  
Assuming the minimum allowable terminal voltage of the supercapacitor is half of its rated voltage, and the maximum allowable voltage is rated voltage, then the SOC can be expressed as:  
\[\begin{array}{c}S_{\text{soc}}=\frac{1}{2}C_{\text{sc}}U_{\text{sc}}^{2}- 1\zeta_{\text{sc}}(\frac{1}{2}U_{\text{sc}})^{2}\\ \frac{1}{2}C_{\text{sc}}U_{\text{sc}}^{2}-1\zeta_{\text{sc}}(\frac{1}{2}U_{ \text{sc}})^{2}\\ =\frac{4}{3}\left(\frac{U_{\text{sc}}}{U_{\text{sc}}}\right)^{2}-\frac{1}{3} \end{array} \tag{15}\]  
where \(U_{\text{sc}}\) represents the rated voltage of the supercapacitor. It is evident that the SOC of the ESM is positively correlated with its terminal voltage.  
According to Equation (4), the supercapacitor terminal voltage is directly proportional to the submodule capacitor voltage, with the scaling factor being the duty cycle \(d\) of the switch T\({}_{3}\). Substituting Equation (4) into Equation (15), the SOC can be rewritten as:  
\[S_{\text{soc}}=\frac{4}{3}\left(\frac{dU_{\text{sm}}}{U_{\text{sc}}}\right)^{ 2}-\frac{1}{3}. \tag{16}\]  
The SOC of the ESM is related to both the submodule capacitor voltage and the duty cycle of switch T\({}_{3}\). When implementing the centralized control strategy, the duty cycle of switch T\({}_{3}\) in each ESM is identical. Consequently, the SOC of each ESM in the arm becomes directly proportional to its corresponding submodule capacitor voltage. This fundamental relationship enables supercapacitor SOC balancing through submodule capacitor voltage control, forming the theoretical foundation for the balancing control strategy proposed in subsequent sections
###### Abstract  
This paper proposes a new spacecraft in-orbit launch design using a nonlinear configuration to utilize nonlinear dynamics for the enhancement of vibration isolation and launch performance. The in-orbit launch device has four springs, where the stroke directions of two springs are perpendicular to the launch direction so as to produce nonlinearity with negative stiffness for enhancing the launch velocity. The other two springs are designed to counterbalance the above negative stiffness when the launch outlet is shut down, leading to quasi-zero dynamic stiffness for vibration isolation enhancement. The dynamic equations of the in-orbit launch device for both the on- and off-launch are presented. Then the performance enhancement of both the vibration isolation and launch performance is thoroughly investigated via comparative study and parametric study. The resonance peak is reduced by 4.16 dB, the effective vibration isolation bandwidth is increased by 57%, and the launch speed is increased 1.64 times. This validates the performance improvement of the new launch device design and presents a useful guideline for application.  
launch device; nonlinear dynamics; vibration isolation; quasi-zero dynamic stiffness Article
# Characterization of Heat-Polymerized Monomer Formulations for Dental Infiltrated Ceramic Networks
## 3 Results and Discussion
### Degree of Conversion  
The degrees of conversion of the different monomer formulations cured at different temperatures using FTIR spectroscopy are shown in Figure 4. The conversion for BisGMA formulations ranged from 81.6 to 87.3%. The UDMA formulations ranged from 74.2 to 86.4%, with the exception being BisGMA:TEGDMA 2:8 and 1:9, which has significant amounts of TEGDMA (80-90 wt%) with a conversion of 65.9% and 22.8%, respectively. Pure TEGDMA is found to have low conversion [23]. The degree of conversion is analyzed in photo-polymerized resin as critically affecting mechanical properties. The BisGMA molecule has a stiff core, less degrees of freedom, and reduced mobility resulting in kinematically a low degree of conversion. Coupled with low viscosity TEGDMA, the network forms cross-links, which also reduces mobility as the reaction progresses. However, when the monomer formulations undergo heat-polymerization, there is no clear correlation with increasing TEGDMA seen in photo-polymerized monomer formulations. The difference is that heat-polymerization allows the continual required energy input to improve the mobility and reactivity. As a result, degree of conversion is independent of the amount of TEGDMA (as long as this is over 30 wt% when formulated with BisGMA). A study by [PERSON] et al. showed comparable monomer conversion of 81.9%DC for BisGMA:TEGDMA (5:5) and 81.7%DC for UDMA:TEGDMA (8:2) mixtures [47]. This study used comparable experimental settings as they heat-polymerized with 1 wt% BPO at 70 \({}^{\circ}\)C for 8 h.  
Figure 4: Degree of conversion of BisGMA:TEGDMA (**a**) and UDMA:TEGDMA (**b**) formulations.
## 5 MASSIVE Test-Bed  
### Test Bed for the Evaluation of the Performance  
In order to test the ability of the receiver to manage the signals coming from a number of uncoordinated transmitting terminals, an SDR-based test-bed was designed and integrated in the laboratory. The block diagram in Figure 10 represents the overall structure of the MASSIVE test-bed.  
The main components of the MASSIVE test-bed are:  
* MASSIVE terminal, a single-unit (1U) rack-mountable server which emulates one terminal,
* MASSIVE traffic generator (TGR), a single-unit rack-mountable server with the aim to emulate the aggregated traffic from a large population of terminals and with the possibility to add a software emulation of the propagation channel,
* MASSIVE gateway, a 2U rack-mountable server equipped with CPU-GPUs with the purpose of emulating the receiver part of the system,
* The SDR radio devices, one per server, which implement the RF front-end.  
Figure 11 shows the real configuration of the test-bed which was set up in the MASSIVE project and mounted in a rack. As shown in this figure, in addition to the components described above, it included a clock distribu  
Figure 9: Single block of the MSD, as reported previously in [2].