esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Examples
### Thermal Effect in the Fracture  
To further demonstrate the capability of the proposed PD model to simulate the ductile fracture involving thermal effects, a series of simulations of the crack extension of an asymmetric notched plate under different loadings is considered. The geometry and loading conditions of the specimen are shown in Figure 9. Two loading cases are given below:  
1. The velocity of \(v=20\) mm/s is applied to both ends of the specimen while the temperature is set to be constant.
2. The specimen is fixed at both ends while a temperature change \(\dot{T}=-20,000\)\({}^{\circ}\)C/s is applied.  
Figure 8: Contour plots of von Mises stress obtained by (**a**) the bond stretch criterion at \(t=113.1\) μs and (**b**) the proposed criterion at \(t=69.6\) μs.  
Figure 9: The geometry of the specimen with two different loading conditions.  
Moreover, the material properties are given in Table 3, and the transient heat conduction is ignored in case ii. The horizon \(\delta\) is set to be 0.6 mm, and \(l\) is set to be 0.2 mm. The time increment is chosen as \(\Delta t=0.01\) us.  
The crack initiation times of the specimen under the two loading cases are 2.4 ms and 7.4 ms, respectively. Figure 10 shows the contour plots of von Mises stress result in the two cases at crack initiation time. It can be seen that there is a huge difference in stress distribution. In case i, the high stress level is mainly concentrated in the center of the specimen. However, the stress in the specimen is more uniform under the thermal effect. This leads to a difference in the further crack propagation.  
Figure 11 shows the crack propagation process of two cases, including contour plots of damage \(D\) and fracture level \(d\). For case i, the crack initiates at the two notches and gradually expands along the oblique direction of the sample with the loading of uniaxial tensile displacement. For case ii, the crack also appears at both notches. However, the cracks extend obliquely and then laterally to the other side of the notch. In addition, the damage distribution is also different in the two cases. In case i, the damage is mainly distributed within the area of the line connecting the two notches. In case ii, the damage distribution is similar to case i at the initial fracture moment. With the crack propagation, most of the areas of the specimen are damaged, which leads to a different crack form with respect to case i. These results demonstrate that the proposed PD model has the ability to simulate the ductile fracture, and the temperature effect cannot be neglected when the fracture analyses are performed on the structure.  
Figure 10: Contour plots of von Mises stress obtained by loading (**a**) case i and (**b**) case ii at crack initiation time.
## 4 Methodology
### Proposed Framework
```
Input : Source data: TwiBot-20 dataset with \(\;\;Nu_{t}^{twi},\;C_{t}^{twi},t_{i}^{twi},E^{twi},\;i=1,2,3,...,N\;\;\) Target data: unlabeled data of different hashtags with \(\;\;Nu_{j}^{un},\;C_{j}^{un},\;E^{un},\;j=1,2,3,...,J\) (unlabelled data size) Output : Node labels for the unlabeled data \(\;\;l_{j}^{un}\) Train Phase: 1.\(\;\;model\leftarrow\;\) Initialize parameters \((\mathbf{W},b)\)
2.Forepoch inepochs: 3.Fori inN: 4.\(\;\;l_{i}^{twi}\gets model\big{(}Nu_{t}^{twi},\;C_{t}^{twi},E^{twi}\big{)}\;\) using Equations (1) and (9)-(11)
5.EndFor 6.\(\;\;loss\gets BCE(\;\;l^{twi},\;L^{twi})\;\) using Equation (12)
7.Calculate the gradients(\(\
abla loss_{\mathbf{W}},\;\
abla loss_{b}\))
8.Update the parameters through back propagation \(\;\;\mathbf{W}\leftarrow\mathbf{W}-\;\
abla loss_{\mathbf{W}}\)
9.\(\;\;b\gets b-\
abla loss_{b}\)
10.EndFor 12.Save\(\;\;model\) Prediction Phase: 13.Forj inJ: 14.\(\;l_{j}^{un}\gets model(Nu_{j}^{un},\;C_{j}^{un},\;E^{un})\)
15.EndFor 16.Train a random forest model on \(\;\;l_{j}^{un},Nu_{j}^{un},\;C_{j}^{un},\;forj=1,2,3,...,J\)
17.Record performance
```  
**Algorithm 1** Bot-MGAT with TL \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Activation function & Leaky ReLU \\ Learning rate & 0.001 \\ Optimizer & Adam \\ Epoch & 100 \\ Dropout & 0.5 \\ Weight decay & 0.00003 \\ Message passing layers & 2 \\ Folds & 10 \\ Embedding size & 128 \\ \(\beta\) & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bot-MGAT parameters.
## 2 Materials and Methods
### Determination of Volatile Compounds  
Volatile compounds in pesto sauces were analyzed using headspace solid-phase microextraction (HS-SPME) coupled with GC-MS (QP201 OSE, Shimadzu, Tokyo, Japan). First, the pesto sample (5 g) in a 20 mL headspace vial with a PTFE septum was incubated at 30 \({}^{\circ}\)C for 120 min to reach headspace equilibrium. Then, a DVB/CAR/PDMS fiber (Supelco, Darmstadt, Germany) was exposed to headspace for 30 min for the extraction of volatile compounds. After extraction, compounds were desorbed for 3 min in a GC column (SLB-5 MS, 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). The injection port operated in splitless mode, with split flow activated after 2 min at 250 \({}^{\circ}\)C. Helium was used as the carrier gas (1.2 mL/min). The MS operated in electron impact mode (70 eV), with an ion source temperature of 200 \({}^{\circ}\)C. The column temperature program was 40 \({}^{\circ}\)C (5 min), increasing to 160 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min (held for 10 min), then rising to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min. Volatile compounds were identified by comparing mass spectra and retention indices (RIs) with spectral libraries ([PERSON] 229, FFNSC13) and the literature [20]. RIs were calculated using an alkane standard (C8-C17) according to Equation (2), which is as follows:  
\[RI_{X}=(RI_{Z}+100)\times[(RT_{x}-RT_{Z})/(RT_{z+1}-RT_{z})] \tag{2}\]  
where \(RI_{X}\) and \(RT_{x}\) are the index and retention time of the evaluated compound, respectively. \(RI_{z}\) and \(RT_{z}\) refer to the index and retention time of the standard hydrocarbon whose retention time precedes that of the evaluated compound, respectively. \(RT_{z+1}\) indicates the retention time of the standard hydrocarbon following the evaluated compound.
## 2 Experimental Setup and Data Acquisition
### Voice Recording Process  
The human study protocol (A09-M46-11A) was approved by the Institutional Review Board at McGill University. The experiment was conducted in a voice recording studio. Participants were 31 native Canadian English speakers aged from 18-40 years. All participants were females and had no history of voice disorders or laryngeal discomfort during the experiment, as self-reported subjectively. The protocol for each participant began with a training session followed by a formal recording session. The duration of the entire session for each participant was approximately 30 min. During the training session, the participants were instructed by an SLP to practice producing five vowels in the three target voice types (modal, breathy and pressed). The five vowels were [a:] in \"father', [ae] in \"cat\", [e] in \"bed\", [i:] in \"heat\" and [u:] in \"food\". Each vowel is characterized by one unique vocal tract shape [32]. In this experiment, only glottal articulators (i.e., intrinsic laryngeal muscles) were expected to vary across different voice qualities. The vocal tract shape was presumed to remain constant for the same vowel. The influence  
Figure 1: The physical prototype and schematic design of the NSA.  
Figure 2: The transfer function between the LDV and the NSA signals for two sensors.  
of pitch and loudness on voice type discrimination was not considered and was left outside the scope of this study. The SLP provided exemplars of different voice types and kinesthetic tasks of laryngeal muscles to aid accurate replication of modal, breathy and pressed voice types. The participants did not proceed to formal recording until the SLP confirmed they could reliably produce all three voice types. During the formal recording session, the participants were required to pronounce stable and sustained vowels for at least two seconds. The Linear Pulse Code Modulation (LPCM) encoding mode with a 44.1-kHz sampling rate in the SONY voice recorder was used. The participants were required to produce the three different types with short breaks between each vowel. During each utterance, the participants were required to maintain the pitch and loudness at their greatest effort. Repetition of the corresponding utterance was required until the target voice type was met, as judged by the SLP.
# The Significance of Calcium in Photosynthesis
## 4 Ca\({}^{2+}\) Involved in Regulating Photosynthetic Enzyme Activity of Carbon Assimilation  
The Calvin cycle is the main pathway of carbon assimilation, and it occurs on the stroma of the chloroplast. Sedoheptulose-1,7-bisphosphatase (SBPase) and fructose-l,6-bisphosphatase (FBPase) are the two key enzymes in the Calvin cycle [28; 29], and their activities are regulated by Ca\({}^{2+}\)[30]. The two types of FBPase are cytoplasm FBPase and chloroplast FBPase [84]. The decrease in activity of chloroplast FBPase and SBPase can reduce the chloroplast content and inhibit plant growth, and the absence of these two enzymes in higher plants may damage photosynthesis [7]. Even though Ca\({}^{2+}\) can regulate carbon assimilation by mediating these two enzymes, high concentrations of exogenous Ca\({}^{2+}\) can inhibit carbon assimilation [85]. This phenomenon may be related to different experimental conditions, but the related mechanism remains to be further studied. Transketolase (TKL) is another key enzyme of the Calvin cycle, which occurs in the chloroplast [86]. It is involved in the regeneration of various substances in the Calvin cycle, such as erythrose4-phosphate (E4P) and xylulose5-phosphate (XSP) [86]. TKL was found to be phosphorylated in the chloroplast extract and was speculated to be related to the Ca\({}^{2+}\)-dependent pathway [31]. Additionally, CP12, a nuclear-encoded chloroplast protein with high Ca\({}^{2+}\) affinity [34], can regulate the Calvin cycle by mediating the formation of the PRK/GAPDH/CP12 complex, which consists of phosphoribulokinase (PRK), CP12, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) [87]. Simultaneously, this complex can be affected by the ratio of NADP(H)/NAD(H) [32] and thioredoxin (TRX) [33].
## 3 Thermal Control System Scheme and Unit Design
### Second-Stage Thermal Control System inside the Rack
#### 3.3.2 Design of Thermal Control Terminal  
The thermal control terminal is the key unit of the thermal control system in the payload rack; its main function is to receive and distribute the liquid cooling resources from the TCB according to the requirements of payload inside the rack. It is also used to monitor the health status and key parameters of the thermal control system. Three different types of thermal control terminals are designed: (a) liquid-cooled thermal control terminal; (b) gas-liquid thermal control terminal and (c) secondary fluid loop terminal. All the thermal control terminals are designed as standard drawers with dimensions of 456 mm \(\times\) 560 mm \(\times\) 173 mm.  
Figure 8 shows the working principle of the liquid-cooled thermal control terminal. The thermal control terminal connects to the TCB through quick disconnect, the fluid pipe  
Figure 7: Principle of second-stage thermal control system in different racks: (**a**) primary liquid-cooled rack; (**b**) gas–liquid mixed cooled rack; (**c**) secondary circuit liquid-cooled rack.  
is divided into 2-3 branches after the fluid medium enters the rack, a linear flow control valve is installed in each branch and the valve can be controlled by instruction to adjust the flow resistance of the branch. All branches are equipped with flow sensors to monitor and control the flow rate of working fluid; the inlet and outlet of the thermal control terminal are equipped with liquid temperature and pressure sensors to measure fluid parameters. Each branch of the thermal control terminal is provided with a working fluid inlet and outlet for connecting the cold plate of the load.  
The design principle of gas-liquid mixed thermal control terminal is extended on the basis of liquid-cooled thermal control terminal. As shown is Figure 9, a gas-liquid heat exchanger is connected in series in the first branch to collect heat from the air loop. An axial flow fan is arranged inside the unit to provide circulating power for the air loop, which can provide maximum of 204 cm\({}^{3}\)/h air flow. The air loop provides eight parallel air supply interfaces; temperature sensors and air flow regulators are installed at the entrance of each branch.  
Figure 8: Design principle of liquid-cooled thermal control terminal.  
Figure 9: Design principle of gas–liquid mixed thermal control terminal.  
As shown in Figure 10, the design principle of secondary circuit thermal control terminal is basically consistent with that of TCB drive unit. A small pump assembly is arranged inside the device, and the pump can provide liquid flow rate in the range of 0-250 L/h and can provide a maximum of 80 kPa hydraulic head. A small liquid-liquid heat exchanger is arranged inside the device to provide the heat exchange interface between the secondary fluid loop of the rack and the TCB.
## 1 Introduction
This method can achieve high-precision gas metering and gas leakage monitoring, but the method is carried out in an ideal experimental environment without considering the actual gas network pressure fluctuations and noise. In addition, the retrofit of the solution is costly and requires the disassembly of the gas meter. In general, intelligent management schemes for existing diaphragm gas meter have complicated transformation steps and high costs, and are not suitable for the existing large number of conventional membrane gas meters. In view of the above problems, this paper proposes an indirect gas measurement method by analyzing the working principle of the automatic gas meter and the pressure value of the gas outlet. The method can realize the digital transformation of the gas consumption data with high reliability, low cost and easy implementation without requiring any modification or disassembly of the meter. The rest of this paper is organized as follows. In Section 2, some related principles about diaphragm gas meter are presented. Section 3 introduces the gas consumption measurement method proposed in the paper and the associated key technologies. Section 4 analyzes the rationality of this method. Section 5 illustrates the experimental process and results. Conclusions are presented in Section 6.
## 4 Discussion
### Number of User Interactions and Time on Usage Scenario  
With the real prototype, the participants required the least amount of time to complete the tasks. Novices required significantly more time to complete the tripping process. Thisdifference can be explained by the limited experience of the novices in controlling onshore drilling rigs. We attribute the differences between the VR drilling experts and real drilling experts groups to altered feedback modalities in the VR environment. To perform the tripping process, the participants had to operate joysticks and rotary controls. An important type of feedback when operating joysticks is the restoring force; the user experiences resistance when moving the joystick, and can better control the deflection [54; 55].  
Detent torque is an important feedback for the operation of rotary controls [56]. A user must generate a defined torque to move from one switching position to the next. These feedback components are missing in VR, which makes the operation of the control elements more difficult.  
The fact that the prototype was more difficult to operate in VR than in real-world settings is also indicated by the number of interactions required. The drilling experts required fewer interactions with the real prototype than for VR for all test scenarios. In VR, experts had to correct the set values more often; however, these differences were not statistically significant.
# Harnessing Agri-Food Waste as a Source of Biopolymers for Agriculture
## 3 Sustainable Strategies and Future Challenges for AFW Disposal  
The sustainability of agricultural supply chains has become a priority concern in recent years, driven by growing awareness of environmental issues, resource scarcity, and the need to feed a growing world population [133].  
The challenge at hand involves managing waste generated throughout the supply chain, from farm to fork. Innovative waste management models are emerging as important tools to address this issue and improve the sustainability of food supply chains [31]. AFW recovery holds great potential for economic, social, and environmental benefits. Many countries around the world have implemented food and waste recovery strategies. The reuse of AFW encompasses the conversion into useful products, such as biofuels and chemicals [134]. To achieve this goal, various methods have been exploited to add value to AFW, including pyrolysis, anaerobic digestion, decomposition, chemical and thermal treatment, solid catalysis, extraction, and fermentation [31; 135].  
Biorefineries are an innovative approach to waste management in the agricultural food supply chain, aiming for the complete utilization of all the components of AFW. This goal represents an environmentally friendly solution, as it encompasses advanced biochemical and biotechnological processes to convert organic waste streams into high-value products such as biofuels, biochemicals, and bioplastics. By utilizing by-products that would otherwise be discarded, biorefineries not only reduce waste, but also contribute to the development of a more sustainable and resource-efficient supply chain. Governments play a critical role in facilitating the transition to innovative waste management models in agri-food supply chains. By implementing supportive policies, regulations, and financial incentives, policy makers can encourage investment in sustainable waste management infrastructure, drive research and development of waste reduction technologies, and create a favourable environment for innovation and collaboration [136].  
The development toward the sustainability of food supply chains depends on the adoption of innovative waste management models that prioritize resource efficiency, minimize waste generation, and create value from waste streams. Converting agricultural waste into potential valuable products and closing the loop aligns with the principles of the circular economy in agriculture and requires collaboration among all actors in the food supply chain.  
Adopting a circular economy approach, harnessing technological advances, promoting biorefineries and by-product recovery, fostering collaborative partnerships, engaging con sumers, and providing policy support and incentives is crucial to build resilient, sustainable, and environmentally friendly agri-food supply chains.
## 3 Controllers Design
### The first group of simulations for the planar AAAP system.
The simulation results with additional disturbances are presented in Figure 10. As shown in Figure 10a-c, the first three links are stabilized at the desired angle and the PL rotates with a small stabilizing speed at \(t=7.01\) s. The first three links are stabilized at the desired angle and the PL rotates at a small stabilizing speed at \(t=7.01\) s. As a result of satisfying the control objective in the first stage, the initial system is treated as a planar Pendubot from \(t=7.01\) s to \(t=50\) s. Finally, the last link stabilizes at the desired angle, \(q_{4d}=-0.5462\) rad, and the endpoint reaches the target position at \(t=50\) s (as shown in Figure 10d). From the simulation results, the control technique is still effective. Figure 10: The planar AAAP system’s simulation results in case C (First group). (a) Joint angle; (b) joint angular velocity; (c) joint torque; (d) endpoint coordinates. Figure 9: The planar AAAP system’s simulation results in case B (First group). (a) Joint angle; (b) joint angular velocity; (c) joint torque; (d) endpoint coordinates. The second group of simulations for the planar AAAP system  
For the second group of simulations of the planar AAAP system, we chose the same structural parameters as in [37], which are shown in Figure 7:0  
The chosen initial states are as follows:  
Figure 7:1  
The parameters in (21) are \(e_{1}=e_{2}=0.0001\). When we give a target position (\(x_{d}=1.0,y_{d}=1.5\)), the target angles for all links determined by Algorithm 1 are  
Figure 7:2  
**Case A: Zero Initial Velocity**  
The controllers (16) have parameters \(P_{i}=1.03\) and \(D_{i}=1.79\) for the simulation. The chosen initial velocities of all links are zero. The simulation results for every link with a zero beginning velocity are shown in Figure 7:3 As shown in Figure 11a-c, the last link rotates at a steady speed at \(t=6.57\) s while the first three links remain steady at their target angles. From \(t=6.57\) s to \(t=50\) s, the initial system is thought to be the planar Pendubot since the first stage's control objectives have been met. Eventually, the last link is stabilized at the desired angle, \(q_{4d}=-0.3991\) rad, and the endpoint reaches the target position at \(t=50\) s (as shown in Figure 11d). The simulation results indicate that the control method is still effective for the planar AAAP system. \begin{table}
\begin{tabular}{c c c c c} \hline
**Link \(i\)** & \(m_{i}\) **(kg)** & \(L_{i}\) **(m)** & \(I_{i}\) **(m)** & \(I_{i}\) **(kg \(\cdot\) m\({}^{2}\))** \\ \hline \(i=1\) & 0.5 & 0.6 & 0.25 & 0.0104 \\ \(i=2\) & 0.6 & 0.6 & 0.30 & 0.0180 \\ \(i=3\) & 0.6 & 0.6 & 0.30 & 0.0180 \\ \(i=4\) & 0.8 & 0.8 & 0.40 & 0.0427 \\ \hline \end{tabular}
\end{table} 
Table 4: Structural parameters for the planar AAAP system (second group). Figure 11: The planar AAAP system’s simulation results in case A (second group). (**a**) Joint angle; (**b**) joint angular velocity; (**c**) joint torque; (**d**) endpoint coordinates.
## 4 Empirical Estimation Strategy  
### Unit Root Test for Stationarity  
Despite the fact that the autoregressive distributed lag (ARDL) model requires no pre-testing for inspection of variables stationarity through the unit root test. The Augmented Dickey-Fuller (1979) [64] unit root test and [PERSON] (1988) [65] unit root test with trend and intercept was used to determine that none of the variables considered were integrated to order two. This is because the ARDL bounds testing approach is invalidated in cases where I(2) variables are used. Therefore, the unit root test was performed using Equation (3):  
\[\Delta Z_{t}=\alpha_{0}+\beta_{0}T+\beta_{1}Z_{t-1}+\sum\limits_{i=1}^{m} \alpha_{1}\Delta Z_{t-1}+\mu_{t} \tag{4}\]  
where Z indicates the variables being tested for the unit root, T represents a linear trend, \(\Delta\) indicates the first difference, t shows the time, \(\mu_{t}\) is the error term, and m represents achieving white noise residuals.
## 2 Related Work
### E-Health Applications
Although there are many e-Health applications available today, this work focuses on developing a hybrid application that focuses on fall and movement detection and on medical status monitoring in a controlled environment, based on a WSN, to detect falls, tachycardia and bradycardia for the elderly population. To achieve this, this research focused on the use of an accelerometer and a heart rate sensor, connected to a previously developed mobile monitoring node to collect data and send it to the WSN Infrastructure, only when abnormal readings are detected.