esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights on Mechanical Stimulation of Mesenchymal Stem Cells for Cartilage Regeneration
## 4 Practical Applications
Flexibility of the neo-formed cartilage and differentiation of the stem cells were achieved through stimulation by hydrostatic pressure, in order to achieve boundaryless tissue consistency between the formed necocartilage and the damaged host cartilage in the temporomandibular joint (TMJ). TMJ offers an interesting environment for evaluating the integration of a cartilage construct within the damaged surface and to compare the behavioral differences of fibrocartilage in comparison to hyal cartilage of the knee joint. In TMJ, the control of applied loads is difficult because of the impossibility to immobilize this joint, so the adaptation and responses of the construct to biophysical stimuli are more consistent and are extremely suitable to be studied as a model for cartilage regeneration approaches [37]. The hydrostatic pressure device, in this experiment, applied compression ranging from 90 to 150 kPa, revealing that proliferation and chondrogenic markers of the BMSC/PRF constructs were highest during the first days and gradually decreased at 6 days, suggesting that BMSCs could have limited chondrogenic capacity in relation to decreased growth factor release from the PRF. The authors concluded that pressure is an indispensable stimulus in order to promote cell proliferation, tissue regeneration, and repair mechanisms and to obtain a physiologic hierarchical and polar arrangement of the neoformed tissue.
## 3 Results and Discussion
### Characterization of RT18 HC@AC Filler
#### 3.1.2. Thermal Properties of AC-RT18 HC Filler
The results revealed enthalpies of 260.0 J/g (\(\Delta\)Hm) and \(-212.9\) J/g (\(\Delta\)Hs) for pure RT18 HC and enthalpies of 117.3 J/g (\(\Delta\)Hm) and \(-115.2\) J/g (\(\Delta\)Hs) for the encapsulated RT18 HC. The theoretical melting enthalpy of the composite material was calculated using the following equation [56]:  
\[\Delta H_{m}^{theoretical}=\Delta H_{m}^{PCM}(1-\varphi\%) \tag{2}\]  
Figure 4: DSC curves of 3 RT18 HC@AC composite in comparison with the corresponding curves of pure RT18 HC. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Initial** & **Weighting** & **Mass Loss on TG** & \
\begin{tabular}{c} **Encapsulation** \\ **According to TG** \\ **Curves** \\ **(\% wt.)** \\ \end{tabular} &
\begin{tabular}{c} **Encapsulation** \\ **According to TG** \\ **(\% wt.)** \\ \end{tabular} \\ \hline
0.2 RT18 HC@AC & 20 & 28.24 & 19.93 & 24.90 \\
0.5 RT18 HC@AC & 50 & 38.50 & 22.48 & 29.00 \\
0.8 RT18 HC@AC & 80 & 65.30 & 26.47 & 36.00 \\
1 RT18 HC@AC & 100 & 97.63 & 28.24 & 35.80 \\
2 RT18 HC@AC & 200 & 114.60 & 34.46 & 52.60 \\
3 RT18 HC@AC & 300 & 260.00 & 54.73 & 120.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RT18 HC loading on AC according to weighting and thermogravimetric results. where \(\Delta H_{m}^{theoretical}\) and \(\Delta H_{m}^{PCM}\) are the theoretical melting enthalpy of the 3 RT18 HC@AC composite and the measured melting enthalpy of RT18 HC, respectively, and \(\varphi\)% is the weight fraction of the sample. Using the above equation, the theoretical melting and freezing enthalpies of 3 RT18 HC@AC were found to be \(117.78\) J/g and \(96.44\) J/g, respectively, for a \(54.7\)% RT18 HC content, according to the thermogravimetric analysis. These values seem consistent with the values measured using DSC (\(117.3\) and \(-115.2\) J/g, respectively).
# An Islanding Signal-Based Smooth Transition Control in AC/DC Hybrid Micro-Grids
## 2 Hybrid Micro-Grid Modeling  
Regarding a real plant in a southern county of Jiangsu Province, China, an AC/DC hybrid micro-grid model has been built, as shown in Figure 1, where PV represents photovoltaic panel, DC represents the direct current, AC represents the alternating current, PCC represents the point of common coupling, and ILC represents the interlink converter.  
In grid-connected mode, the utility grid provides the voltage and frequency references for AC sub-network. Renewable sources in the AC or DC sub-network work in MPPT(Maximum Power Point Tracking) mode to generate maximum power. The energy storage component absorbs power to charge, then shuts off when it is fully charged. The DC sub-network is connected to the AC bus through an ILC, and operates as an active load of AC sub-network. In a grid-connected operation, the AC sub-network, DC sub-network, and utility grid can realize the free flow of power in the entire network through an ILC and PCC. Power fluctuations caused by output changes and load changes can be balanced.  
When fault happens in the utility grid, the PCC switch receives a signal and disconnects the micro-grid; then the hybrid micro-grid starts to work under a mixed islanding mode. DGs in an AC sub-network work under P-V, Q-f droop control to provide voltage and frequency reference for the AC system. DGs in a DC micro-grid work under P-V droop control to provide voltage reference for the DC system. The offset of AC and DC voltage represents the load condition in sub-networks. By measuring the offset of AC and DC bus voltage, and putting it into the control, free power exchange can be achieved between the AC and DC sub-network. In this way, a droop control in the entire network can be realized [22], as shown in Figure 2. Herein, ESS indicates energy storage system and DG indicates distributed generator.  
The droop lines of AC and DC sources can be gathered in the same axis, as shown in Figure 3. The ILC serves as the bridge between the AC and DC sub-networks, and plays an important role in the power exchange on both sides. Expected load sharing in the entire micro-grid can be achieved by controlling AC unit voltage to be equal to DC unit voltage. Because the voltage value corresponds to the consumed power, the outer power loop can be modified to the voltage loop. The ILC control strategy is shown in Figure 4, where PWM means pulse-width modulation.  
Figure 1: Hybrid micro-grid structure of a small plant.  
Figure 4: Control strategy of the ILC.  
Figure 3: Scheme of entire network droop control.  
Figure 2: Scheme of entire network droop control.  
In the control loop, the \(d\)-axis takes AC and DC bus voltage under control, and the \(q\)-axis keeps the reactive power equal to 0.
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 2 Organizational Models for MAS
### Modeling Dimensions
#### 2.1.3 Social Systems and the Normative Dimension  
While the functional, structural and interactive dimensions can be justified by analysing the modeling of systems in general, they are not sufficient to classify all modeling concepts appearing in organizational models.  
According to [22], three basic types of systems and corresponding models can be identified: _Deterministic_, i.e., systems and models in which neither the parts nor the whole are purposeful; _animated_, i.e., systems and models in which the whole is purposeful but the parts are not; and _social_, i.e., systems and models in which both the whole and the parts are purposeful. A fourth type is also considered, _ecological_, i.e., systems and models in which the parts are purposeful but the whole is not. Given this classification, we can say that traditional software systems are deterministic, autonomous agents are animated, and agent organizations are social. Bigger and more encompassing MAS aggregating agents and agent organizations form ecological systems.  
Being deterministic, traditional software systems tend to have an architecture in which the functional behavior, the internal structure and the structural behavior are foreseen in detail. Their components are not conceived as purposeful entities with autonomous behaviors. On the contrary, they are designed to obey rigidly what is fixed in the architectural specification of the system.  
Regarding agent organizations characterized as social systems, the idea of agents obeying rigidly the prescriptions of functional, structural and interactive specifications is not realistic. Agents are conceived as self interested components, especially in open MAS. Therefore, neither the functional, structural and interactive specifications can be very detailed to the point of precisely determining the minutiae of the joint structuring and behaving of the agents, nor one can assume benevolence from the agents with respect to the organizational goals.  
In this context our analysis is that the specification of _norms_ (permissions, prohibitions, obligations, etc.), as occurs in human organizations design, are also expected to show up in organizational models. They will work as a complementary mechanism helping to couple more flexibly the agents to the organization. On the one hand, norms provide explicit means to capture interdependencies among the functional, structural and interactive aspects (e.g., agent playing a given role in the internal structure is obliged to behave functionally or structurally in a given way). On the other hand, norms can be used to explicitly regulate sanctions or penalties to deviant behavior.  
Accordingly we define a fourth and last category for the analysis of organizational modeling concepts:  
* The _normative dimension_, characterized by modeling concepts to further restrict, regulate and interrelate elements from the other modeling dimensions, given the expected autonomous behavior of the agents.
## 4 Case Example: Australian Public-Private Partnership (PPP) Schools
### Data Analysis in t = 9 and Discussion  
Tables 1, 3 and 4 detail the data source for the reliability analysis in t = 9. Based on the established framework, \(A(t\ =\ 9)\) and \(P(t\ =\ 9)\) was computed. As a result, in t = 9, \(\mu_{A,9}\ =\ 4.896\), \(\sigma_{A,9}\ =\ 0.014\), correspondingly, \(\mu_{P,9}\ =\ 5.75\), \(\sigma_{P,9}\ =\ 0.28\). The reliability index \(\theta_{t}\) is an indicator of \(P_{f,t}\), which means higher \(\theta_{t}\) represents lower \(P_{f,t}\), implying preferable service performance social PPP project. For case study schools, the reliability index \(\theta\) at t = 9 years was 3.058, thus \(P_{f,t\ =\ 9}\ =\ 1-\varnothing(3.058)\ =\ 0.1\%\), representing the probability of failure is 0.1% when a PPP school had been operating for 9 years. Given the \(l_{1}\) is 10%, this result indicates excellent performance for the PPP school project. In other words, these is enough resilience between end-user's satisfaction and actual servicer-delivered performance, which can protect the government from major unexpected needs or changes during the operations. Figure 5 shows the distribution of \(A(t)\) and \(P(t)\) at 9 years (t = 9), i.e., \(F_{AoDS,9}\) and \(F_{PSQ,9}\).  
This result is supported by the latest industrial report [9], one finding of which is that 82% of end-users expressed a strong appreciation of the quality of services provided by the facility management operator in a PPP facility, and 95% of them stated that their PPP project delivered on the service promised by the relevant state government and delivery agency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Categories** & \(Score_{A}\) & \(Score_{B}\) & \(Score_{C}\) & _Mean_ & _S.D._ \\ \hline Teaching and Non-teaching Space & 4.66 & 4.64 & 5.03 & 4.77 & 0.21 \\ Operation Activity & 5.5 & 6.5 & 6.5 & 6.16 & 0.58 \\ Maintenance Activity & 6 & 7 & 6 & 6.33 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Interview result for end-user’s perceived service quality (t = 9).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scale** & **Descriptor** & **Questionnaire** & **Payment Regime** \\ \hline
7 & is an outstanding/exemplar space or activity & Extremely satisfied & \(\backslash\) \\ \hline
6 & is among best space or activity in & & \\  & comparable schools & Very satisfied & \(\backslash\) \\ \hline
5 & is fit for purpose & Satisfied & Full payment \\ \hline
4 & might not be fit for purpose & Unsure & Full abatement \\ \hline
3 & space or activity not fit for purpose with & & \\  & minor issues & Dissatisfied & No availability \\ \hline
2 & space or activity comfortably falling short & & \\  & of being fit for purpose with major issues & Very dissatisfied & \(\backslash\) \\ \hline
1 & space or activity greatly falls short of being & & \\  & fit for purpose & Extremely dissatisfied & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scale descriptions.
## 4 Proposed Experimental Approach
### Experimental Setup  
Several deep learning models were trained, tested, and evaluated during the experiments for efficiency observations. Inception-v3 [19] is a sophisticated and widely used method that outperforms the majority of inception networks. DarkNet-53 is a deep learning algorithm with high detection accuracy that is gaining prevalence in utilization for state-of-the-art object detection challenges [20]. InI-WRN-16-8-square-3 is a wide residual network backbone application of the InI module that includes square G-filters for CNN structure abstraction [21; 22]. The InI-PyramidNet-mix-5 CNN technique is the result of applying the InI module to the pyramidal residual network backbone [21; 23; 24], and it includes mixed G-filters for structural modeling of CNNs. Additionally, GoogleNet [25] and YOLO [26] were tested and evaluated for wind turbine blade defect detection.  
Individual model performance was used to train the selected deep learning models. All the models were trained and evaluated five times, and the average achievement was described and detailed to ensure that the results were accurate. Structural cracks, Fatigue damage, Leading Edge erosion, and Delamination are the most common anomaly types of wind turbine blade defects detected during experiments.
## 3 Enhanced Fault Current Limiting Circuit
### Basic Topology  
The EFCLC is proposed on the basis of analyzing the fault current limitation characteristics (Figure 13). During the normal state, the series-connected IGBTs, namely, \(T_{1}\) and \(T_{2}\), are opened. When a fault is detected, \(T_{1}\) and \(T_{2}\) are closed by a switching-off signal, and the energy dissipation resistance \(R_{FCL}\) and FCL reactor \(L_{FCL}\) are inserted in the fault circuit. \(L_{FCL}\) will be bypassed via bidirectional parallel-connected thyristors \(T_{3}\) and \(T_{4}\). The surge arrestor is equipped to protect circuit components from surge current and voltage. In addition, the thyristors can function as a voltage stabilizer due to the surge voltage across \(L_{FCL}\). Considering zero voltage switching and loss reduction, the IGBTs are protected by a snubber circuit, which consists of snubber capacitor, snubber resistance, and diode [34].
# Automatic Essay Evaluation Technologies in Chinese Writing--A Systematic Literature Review
## 3 Results
### Feature Extraction and Selection (RQ2)
#### 3.2.3 Spelling Errors  
In contrast to English, Chinese characters and words have unique rules and characteristics regarding construction and usage, often requiring contextual understanding. Chinese spelling errors can be categorized into two types: \"cuozi\" (wrong character) and \"biezi\" (confusing character). \"Cuozi\" refers to the characters that do not appear in the Chinese character library due to stroke or radical errors during hand-writing. Identifying \"cuozi\" in Chinese compositions relies on techniques such as optical character recognition and evaluation, which have not been extensively investigated in existing ACEE research. \"Biezi\" refers to incorrectly using characters due to similar pronunciations or shapes. Early studies relied on manual recognition and annotation [18; 19]. Researchers have recently established corpora or confusion sets of \"biezi\" to automatically recognize and annotate them using pattern matching or language models. [PERSON], [PERSON] et al. [43] constructed a \"biezi\" recognition corpus, including customized corpora from elementary textbooks, idiomatic expressions with high \"biezi\" occurrence rates, and normal corpora from newspaper texts. They also developed detection rules based on the corpus to identify \"biezi\" in the text. [PERSON], [PERSON] et al. [44] established a table of easily confused characters and used WFSA to detect \"biezi\" in compositions using an n-gram model while creating a table of commonly misspelled characters for correction. [PERSON], [PERSON] et al. [45] labeled the positions of \"biezi\" and provided correction suggestions by combining the Soft-Masked BERT (BidirectionalEncoder Representations from Transformers) [46] model with tables of phonetically and visually similar characters.
## 3 Experiment and Result
### Microburst Observations  
The microburst has a strong divergent structure, which causes a large loss of speed over 15 m/s (30 kt) [1]. The microburst is first introduced in view of dangerous and highly regarded atmospheric disturbances. A typical microburst event was observed on 28 April, 15:53-16:02 (UTC). The radial speed is shown in Figure 4a. Despite the influence of thunderstorm weather, the detection range of the lidar can still reach 5 km. There was a sudden change in radial speed at 2 km southwest of the lidar, where significant divergence velocity pairs were found along the radial direction. Radial speeds at the azimuth angle of 188\({}^{\circ}\) to 196\({}^{\circ}\) are shown in Figure 4b. The forward speed reaches 14 m/s and changes rapidly to a negative speed of \(-8\) m/s. The difference between the two opposing velocities exceeds 22 m/s within 2 km. The two retrieved wind components are shown in Figure 4c,d. The horizontal wind vector shows an obvious divergence structure. If the aircraft lands from the south glide path, the airspeed will suddenly change from a headwind to a downwind. Such a severe loss of wind speed would seriously threaten aircraft safety.  
Figure 5 shows the windshear center distribution on the runway due to the microburst. 3 MF, 2 MF, and 1 MF in the X axis refer to the three monitor subareas in the landing sides, while 3 MD, 2 MD, and 1 MD refer to the monitor subareas in the departure sides. The  
Figure 4: Microburst results detected by the CDWL on 28 April, 15:54:12-15:54:42 (UTC). **(a,b)** radial speed, **(c)** horizontal speed. The pink rectangle means the area most affected by microburst. The detail structure of horizontal speed in pink rectangle in **(c)** is shown in **(d)**. The sign of radial speed is defined as positive when the movement is toward the lidar, and vice versa.  
purple, blue, and red color points represent windshear ramps in 35L/17R, 35R/17L, and 01L/19R, respectively. Windshear ramps larger than 7 m/s are counted, and the number of these ramps reflects the intensity of the wind shear in the monitoring area. A threshold of 500 is adopted for the generation of the windshear message, which is relative to the size of the subarea and spatial resolution. The threshold is chosen through mutual verification with the crew report. The glide paths of the three runways were affected by the microburst, especially for 35R/17L and 01L/19R. These glide paths have many windshear ramps, leading to windshear alert messages. The windshear ramp number of 01L/19R reaches 2.4 \(\times\) 10\({}^{6}\), and the maximum windshear exceeds 25 m/s. Three aircrafts encountered windshear when they landed at 35L, as reported by crews.
Accurate spectral line parameters of the targeted molecule (such as pressure broadening coefficient and line strength) are critical for TDLAS spectrometers in the context of a precise amount of fraction monitoring of trace species with a low signal-to-noise ratio [13; 14; 15]. For example, fixed wavelength TDLAS has been normally used in shock tube experiments, and the spectral line parameters (pressure broadening or cross-section) are essential for species quantification. The pressure-broadening coefficient of NO has been investigated by many researchers, including both theoretical and experimental aspects [16; 17; 18; 19; 20]. In 1982, [PERSON] attempted to measure the pressure-broadening coefficient for R-branch lines of NO near 5.4 \(\upmu\)m using a computerized tunable diode laser spectrometer [17]. Further, [PERSON] et al. measured the lineshape parameters of NO \(A\to X\)(0,0) transitions broadened by Ar, He and N\({}_{2}\) using Laser Induced Fluorescence (LIF) at room temperature [21]. In another study, [PERSON] et al. utilized a rapid tuning intracavity-doubled continuous-wave (CW) dye laser to resolve absorption lineshapes in the NO \(A\gets X\) (0,0) band at 226 nm and obtain the room-temperature broadening coefficient for argon as 0.503 cm\({}^{-1}\) atm\({}^{-1}\) and for nitrogen as 0.583 cm\({}^{-1}\) atm\({}^{-1}\), respectively [22]. So, an accurate and consistent spectroscopic database is needed for quantitative species measurements. For the exact quantification of NO, it is important to carefully measure the spectral line parameters [11; 22]. The current study focuses on the measurement of NO (R11.5) \(\Omega\)3/2 and \(\Omega\)1/2 transition lines in the 1-0 band. Six pressure broadening coefficients for NO--CO\({}_{2}\), -O\({}_{2}\), -N\({}_{2}\), -He, -H\({}_{2}\) and -Ar--are obtained based on the same experimental set-up and the data evaluation methodology.
# GNSS-Assisted Low-Cost Vision-Based Observation System for Deformation Monitoring
## 2 General Concept and Approach
### Design Concept of the VOS
The resulting error can be determined via expression (8):  
Figure 7:0  
By expression (8), the following error distribution was obtained (see Figure 10). The obtained errors will be used for the comparison analysis in Section 3. Figure 7:1 The resulting error graph. Figure 7:2 The graph of the defocusing error. With the camera resolution, it is possible to calculate the ground coverage to find the ranges of displacements that a single measuring chain can measure. The relationship between the camera geometric parameters and the ground coverage is presented in Figure 7:3 The size of the ground coverage can be calculated by (9):  
Figure 7:4  
where \(d_{x,y}\) is the half-size of the CMOS matrix in pixels, \(S\) is the distance in mm, \(L_{x,y}\) is the physical size of the CMOS matrix in mm, \(m,n\) is the size of the CMOS matrix in pixels, and \(f\) is the focal distance. If one supposes that the VOS sensors were aligned during the installation, then every single chain has a ground coverage from \(\pm\)1000 mm up to \(\pm\)3000 mm (Figure 12). However, these figures determine the total size of the field of view. Actually, the range of the detectable displacements is restricted by the QR target size. Expression (9) is also used for QR target size calculation. If we want the QR target to take at least 200 \(\times\) 200 px in the image, then the necessary size of the QR target can be retrieved in Figure 7:5  
Figure 7:6 The relationship between camera parameters and size of ground coverage. Figure 7:7 The relationship between camera parameters and size of ground coverage. Therefore, the single chain needs a QR target size from 60 mm to 140 mm for distances 15-35 m. These values determine the size of the possible detectable displacements for different distances.