esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of MHS-SPME-Arrow Method
Three spiked concentration levels in refined rapeseed oil were also studied by the standard addition method (SA). The results in Table 2 showed that the recoveries of pyrazines analyzed were in the range of 90% to 115% in all cases, indicating that the method was reliable and accurate within the concentration range of the model experiments. The stable isotope dilution analysis (SIDA) has proven to be very precise in the model experiments, even at a very low extraction yield [39]. The main reason is that SIDA uses the most suitable internal standard: Stable isotopes of the analytes, which can fully be recovered for the losses. This study compared the quantitative results of SIDA and MHS-SPME of flavor-enhanced rapeseed oil, using 2-methyl-pyrazine labeled \({}^{2}\)H\({}_{6}\) as the stable isotope internal standard. The concentration of 2-methyl-pyrazine was 1.21 \(\pm\) 0.13 \(\upmu\)g/g for SIDA and 1.43 \(\pm\) 0.11 \(\upmu\)g/g for MHS-SPME-arrow, respectively. There was no difference (\(p>\)0.05) by statistical analysis. Thus, the data showed that the MHS-SPME-arrow could be applied to the analysis of these pyrazines in oil samples. By this conclusion, the method has been set up after condition optimization and SIDA method verification. An efficient and effective MHS-SPME-arrow-GCMS analytical protocol is established and can be used to quantify the flavor compounds in oils.
# Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention
## 4 Interference-Aware Schedulability Analysis for Fixed Priorities  
In this section, first, we present a well-known fixed-priority schedulability test for constrained-deadline task models. Traditional models overlook the interference arising from the concurrent execution of different tasks across multiple cores. Then, we contribute to the field in this sense by including interference in the schedulability test.
## 5 Conclusions  
This paper proposes a new practical trajectory tracking controller for the 3 WID3 WIS mobile robot. The strategy combines trajectory planning and a robot trajectory tracking controller. In terms of path planning, an A* algorithm with a non-point mass model is applied to enable the 3 WID3 WIS mobile robot to quickly pass through narrow and constrained areas. A practical controller based on dynamic model predictive control (DMPC) is introduced for trajectory tracking of the 3 WID3 WIS mobile robot. By incorporating dynamic effects into the MPC to describe control quantities and dynamically adjusting the prediction horizon, the computational complexity is reduced. The controller discussed here offers a more practical way to optimize the control process and improve tracking performance across different applications. Additionally, lateral and heading angle errors are reduced, allowing the robot to perform tracking tasks more accurately.  
Experiments show that DMPC demonstrates superior performance in trajectory tracking, with higher efficiency compared to traditional MPC. The experimental results show that DMPC improves the lateral trajectory tracking accuracy by 62.94% and the heading angle tracking accuracy by 34.81% compared to MPC. The feasibility and effectiveness of the method are validated through trajectory tracking tests on the 3 WID3 WIS mobile robot, and its potential for real-time applications is explored. In future work, we will attempt to optimize the prediction horizon further with information from other sensors and integrate reinforcement learning with MPC to improve the tracking accuracy of the robot further.  
Conceptualization, C.X., X.Z., R.C. and B.L.; software, X.Z., R.C. and W.H.; validation, C.X., Y.L., W.H. and R.C.; writing--original draft, X.Z. and Y.L.; writing--review and editing, C.X., R.C., B.L. and Y.L.; supervision, C.X., W.H., F.Y. and B.L.; funding acquisition, C.X., F.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the \"Smart Construction\" Young Science and Technology Talent Joint Project of Hubei Province (Grant No. 2023 DJC006), the Hubei University of Technology Research Fund (Grant No. BSQD2020009), the National College Student Innovation and Entrepreneurship Training Program (Grant No. 202210500002).  
The datasets presented in this article are not readily available because of the confidentiality of the areas covered by the project. Requests to access the datasets should be directed to [EMAIL_ADDRESS].  
Figure 12: The heading error of a complex curve trajectory using DMPC and MPC.  
Conflicts of Interest: Authors [PERSON] and [PERSON] were employed by CCCC Second Harbor Engineering Company Ltd. and CCCC Wuhan Harbor Engineering Design & Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 System Model
In the case of continuous phase shift at IRS, the phase term of \(\theta_{i}^{(1)}\) and \(\theta_{i}^{(2)}\) are completely compensated by IRS, and the corresponding distribution of random variable \(B=A\) is shown in Figure 3. In the case of discrete phase shift, the uncompensated phase term \(e^{i(\theta_{i}^{(1)}+\theta_{i}^{(1)}-\psi_{i})}\) follows uniform distribution in \((0,\pi)\) when \(b=1\), (0, \(\pi/2\), \(\pi\), \(3\pi/2\)) when \(b=2\), and (0, \(\pi/4\), \(\pi/2\), \(3\pi/4\), \(\pi\), \(5\pi/4\), \(3\pi/2\), \(7\pi/4\)) when \(b=3\). This discrete phase shifter affects the mean and variance of the random variable \(A\). The Probability Density Function (PDF) for the random variable \(A\) is expressed as [33],  
\[f_{A}(x)=\frac{x^{\alpha}}{\beta^{\alpha+1}\Gamma(\alpha+1)}e^{-(x/\beta)}dx \tag{6}\]  
where \(\alpha=\frac{K_{1}^{2}}{K_{2}}-1\) and \(\beta=\frac{K_{2}}{K_{1}}\). The parameters \(K_{1}\) and \(K_{2}\) are given by \(K_{1}=E[A]\) and \(K_{2}=4 Var[A]\), respectively. The mean and variance of the random variable \(A\) are expressed as [34],  
\[E[A]=N\frac{\pi}{4}; \tag{7}\]  
\[Var[A]=N\bigg{(}1-\frac{\pi^{2}}{16}\bigg{)} \tag{8}\]  
By substituting the (7) and (8), the parameters \(\alpha\) and \(\beta\) are determined as  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]0  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]1The third and fourth terms in (5) are the additive white Gaussian noise (AWGN) \(w_{n},n=1,2\) with variance \(N_{0}\) and SI \(SI_{n},n=1,2\) with variance \(\sigma_{w}^{2}\). Similarly, the received signal at the source node \(S_{2}\) is expressed as,  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]2  
In the case of the continuous phase shifter, (5) and (11) can be rewritten in terms of \(A\) as,  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]3  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]4  
In the case of the discrete phase shifter, (5) and (11) can be rewritten in terms of \(B\) as,  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]5  
\[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]6  
The PDF of the ideal continuous and discrete phase shifter of 1-bit, 2-bits, and 3-bits are plotted using a histogram and it is shown in \[y_{IRS}=\sqrt{P_{s}}[h_{i}^{(1)}x_{1}+h_{i}^{(2)}x_{2}]e^{-i\psi_{i}} \tag{2}\]7 From Figure 3, it is clear that increasing the discrete phase shifter bit level shifts the mean and variance towards the ideal continuous phase shifter.
## 2 Airport Operations Reference Scenario
### Reference Taxi Timelines for Inbound and Outbound Flights  
In order to define more precisely the criteria against which the taxi-route optimality is judged, as well as all the constraints affecting the taxi route assignment problem, we shall break down the timeline of the reference taxi process for both inbound and outbound flights, EXIT and EXOT, respectively.  
Considering inbound flights, after touchdown, the aircraft has to complete the landing and free the runway. This part of the process is constrained by international regulation to occur as soon as possible and cannot be included in any route planning process. As a consequence, it may be only estimated by the XRP and is denoted as the Estimated Runway Exit Time (ERET). Then, the aircraft enters the airport taxiway system. This phase is obviously subject to route planning and optimization. The time necessary to complete this phase is thus determined by the XRP and shall be a target for the relevant ATCo, denoted as the Target Taxiways Engagement Time (TXET). At last, the aircraft exits the airport's taxiway system and shall enter the apron. The time necessary to complete this phase is estimated by a purposely developed apron model [10] and is denoted as Estimated Apron in Time (EAIT). The following relationship is established by the EXIT breakdown (Figure 1), relating the ELDT provided by AMAN to the EIBT, which is of interest to multiple stakeholders and shall be delivered by the XRP to the CDM.  
(1)  
Considering outbound flights, off-block is estimated to not occur prior to TOBT by the aircraft operator and handling agents. The ATCo, upon suggestion by the XRP, has the possibility to delay the off-block, specifying its own target time for starting the off-block process (TSAT). Then, upon completion of the push-back (if applicable) the aircraft shall exit the apron. Again, the time necessary to complete this phase is estimated by a purposely developed apron model [10] and is denoted as Estimated Apron-Out Time (EAOT). Then, the aircraft enters the airport's taxiway system. This phase is obviously subject to route planning and optimization. The time necessary to complete this phase is thus determined by the XRP and shall be a target for the ATCo, denoted as TXET, as in the arrival phase. At last, the aircraft arrives at the Runway Holding Position (RHP), where the TWR is in charge of delivering the line-up and take-off clearances to the aircraft. It is clear that the XRP has no control on this phase, as well, whose duration is only estimated and denoted as Estimated Runway Clearance Time (ERCT). The following relationship is established by the EXOT breakdown (Figure 2). It relates the TSAT, delivered by the XRP to TWR, to the TXET optimized by the XRP in order both to meet the TTOT provided by DMAN and to comply with the TOBT delivered by the CDM.  
\[\mathrm{TSAT}=\mathrm{TTOT}-\mathrm{TXET}-\mathrm{EAOT}-\mathrm{ERCT}\ ;\textit{ subject to:}\ \mathrm{TSAT}\geq\mathrm{TOBT} \tag{2}\]
# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 3 Results and Discussions
### Tests on the MEW2012 Leaf Dataset  
#### 3.4.1 Introduction to MEW2012 Leaf Dataset  
The proposed method was evaluated on the MEW2012 leaf dataset [36], which is a more complex dataset consisting of 153 species of trees and shrubs from Central Europe. Each category contains between 50 and 99 images, resulting in a total of 9745 leaf images. Figure 13a-c, respectively, depict a portion of the original sample images from the MEW2012 leaf dataset, the input images obtained through direct compression processing, and the input images acquired via MEQ standardization processing. The analysis of this dataset reveals that directly compressing the original sample results in more severe leaf deformation and adversely affects the recognition efficacy of the CNN model. Furthermore, due to the high similarity in shape and color among certain leaf samples, accurately identifying these tree species poses a significant challenge.
# Exploring ISAC: Information-Theoretic Insights
## 4 Sensing at the Rx (Rx-ISAC) with Sensing Distortion
### Capacity-Distortion Tradeoffs  
We start with the model without state information at the Tx. In this case, the optimal estimator at the Rx is a symbol-by-symbol estimator based on the observed sequence of outputs and the decoded codeword, and the capacity-distortion tradeoff was characterized in [21]. Figure 6: ISAC with Rx sensing  
**Theorem 4**. : _When the Tx has no knowledge about the state sequence \(S^{n}\), the capacity-distortion function is given by_  
\[\mathcal{C}_{\text{No-CS}}:\ =\ \max_{P_{X}\in\mathcal{P}_{\text{D}}}I(X;Y), \tag{26}\]  
_where_  
\[\mathcal{P}_{\text{D}}=\bigg{\{}P_{X}\,\bigg{|}\,\sum_{X\in\mathcal{X}}P_{X}(x )E[d(S,\hat{s}^{*}(X,Y))]\leq\text{D}\bigg{\}}, \tag{27}\]  
_and \(\hat{s}^{*}(\cdot,\cdot)\) is the optimal estimator introduced in (51) with the feedback output \(Z\) replaced by the decoder output \(Y\)._  
This capacity-distortion tradeoff for the setup where the Tx is not informed about the state sequence was also extended to a multi-access setup with multiple transmitters, see [88] and to a two-hop setup [90]. In the latter work, it is shown that a decode-(indirectly)-compress-and-forward strategy achieves the capacity-distortion function. Consider next the scenarios where the Tx does learn the state sequence \(S^{n}\) either causally or strictly causally, see [61; 89]. In these cases, the Tx wishes to assist the Rx in the sensing task by conveying information about the state sequence to the receiver in the same spirit as it sends data. In other words, the Tx will compress the observed state sequence and send the compression information to the Rx, which then reconstructs the compressed version of the state. The Rx finally applies an optimal symbol-by-symbol estimator to this compressed sequence as well as to the decoded input codewords and the observed channel outputs. In the case in which the Tx observes the state sequence only causally or even strictly causally, it has to employ a block Markov coding scheme, where in each block, it sends compression information about the state sequence from the previous block. Transmission of this compression information and of the data is performed using an optimal data communication scheme. Specifically, in the setup with strictly causal state information, a standard channel code is used that ignores the state information completely. For the setup with causal state information, the Tx has to resort to Shannon strategies, which have been shown to achieve capacity in these setups. Note that under Shannon strategies the channel inputs are generated symbolwise from an auxiliary codeword and the state sequence. The Rx thus does not have access to the channel inputs even when it decodes the codewords correctly. Nevertheless, it can be shown that the symbolwise estimator based on the decoded codewords and the observed channel output sequence achieves the optimal Rx sensing performance. **Theorem 5** (Theorem 2, [61]).
## 3 Results and Discussion  
### Visual Quality  
Fresh-cut 'Granny Smith' apples were visually inspected for changes in colour and surface appearance during storage. After 9 days of storage, the non-treated control group received the lowest scores, with 81-100% of the apple slice surface showing dryness and colour change (Figure 1A). The AEW-treated fresh-cut apples showed better visual surface appearance with 0-20% change in appearance and dryness. While AIEW-treated fresh-cut apples had scores of \(\approx\)3 with 41-60% showing changes in appearance and dryness. According to the visual surface appearance score, AEW-treated samples had the highest scores throughout the storage duration compared to the control that became brown (Figure 1A).  
As shown in Figure 1B, untreated samples promoted browning with a high degree of dryness on the surface of apple slice, while AIEW fresh-cut apple slices demonstrated slight dryness on the surface (Day 9). On the other hand, AEW-treated fresh-cut apples demonstrated surface colour retention when compared to the AIEW and control (Figure 1B). The observed browning in control samples could be associated with chilling injuring due to the low storage temperature [13].  
Figure 1: **(A)** Visual scoring for colour changes and surface appearance (dryness) of treated and untreated samples during storage at 2 \({}^{\circ}\)C for 9 days and (**B**) picture of minimally processed ‘Granny Smith’ apples treated with AEW = acidic electrolyzed water (200 mg L\({}^{-1}\)); AIEW = alkaline electrolyzed water (200 mg L\({}^{-1}\)); and CO = control (untreated) after 9 days at 2 \({}^{\circ}\)C. Colour graphics only available online.  
Browning in apples is primarily due to enzymatic oxidation involving the polyphenol oxidase (PPO) enzyme and phenolic compounds [5; 6; 13]. When apple tissue is damaged or exposed to air, PPO catalyses the oxidation of phenolic compounds to quinones, which then polymerizes into brown pigments [5; 6]. PPO activity is generally higher at neutral to slightly acidic pH levels. Therefore, by increasing and or reducing the pH of the electrolyzed water, this can create conditions less favourable for PPO activity, thereby slowing down or inhibiting browning [13]. Alkaline electrolyzed water can inhibit this process due to the presence of hydroxyl ions (OH\({}^{-}\)) and its high pH which can neutralize or disrupt the function of PPO. In contrast, acidic electrolyzed water, with its low pH, can also prevent browning by reducing the pH environment due to the presence of hypochlorous acid (HOCI), which can inhibit PPO enzyme activity and decrease the stability of quinones [13]. The results, therefore, suggest that both AEW and AIEW treatment could minimize chilling-induced browning at low storage temperature.
## 2 Magnetic Mechanism and Control System for a Wireless Operation
### Interactions between Magnets in the Multi-Link Magnet Device  
The magnetic forces between the multiple magnets in the four containers can facilitate and determine the natural finger movements (flexion and extension). Note that up to three disk magnets (3 mm thickness) can be installed in the fabricated containers. Although the fabricated multi-links maintain the finger positions, the magnets installed in the container can generate attractive or repulsive forces that disrupts natural flexion and extension. Therefore, we investigated interactions between magnets by conducting magnetic simulations and observing the field distributions, as shown in Figure 4. In the simulations, we examined the distribution and field strength according to changes in distance between the containers, as shown in Figure 4a.  
Figure 4: (**a**) Magnetic field distribution according to the magnet number installed in containers and (**b**) magnetic force measurement according to magnet numbers for the equivalent condition of (**a**).  
Here, the containers contained (3, 3), (3, 2), and (3, 1) magnets, and the maximum separation distance was 30 mm, which was approached in 5 mm intervals. The results revealed that each paired magnet produces decoupled field distributions in accordance with the magnetic pole directions. When the two magnet sets in the fabricated device are closest to each other for finger flexion, their separation is approximately 8 to 10 mm. The simulation results indicate a 10 mm separation distance between two magnet sets, with the (3, 3), (3, 2), and (3, 1) containers producing field strengths of 195.79, 178.42, and 150.34 kA/m, respectively. The decoupled fields generated a repulsive force between magnet sets, as shown in Figure 4b. Note that excessive repulsion interferes with the natural grasping motion of the hand. For grasping, \(\beta\) should be minimized, but this angle is maintained by the repulsive force. The experiment revealed that the decoupled field distributions at 10 mm separation generate repulsive forces of 3.144, 2.25, and 1.04 N, respectively. If the repulsive force exceeds 2 N, the patient cannot generate a natural gripping motion because of the magnet repulsive force. Therefore, one, three, one, and three magnets are installed in four different containers.
# Comparative Study on Vibration Characteristics of Biaxial Carbon/Glass Hybrid Wind Turbine Blades
## 5 Analysis of Blade Vibration Characteristics
### Modal Analysis  
In the undamped and prestressed state, the root of the blade is constrained and the three blade modes have the same vibration mode. The first six modal frequencies of the blade are shown in Table 8, and the first six modal modes of the blade are shown in Figure 16:  
Figure 16: Blade First-Six Order Modal Map. (**a**) First-order swing. (**b**) Second-order swingand oscillation. (**c**) Third-order swing+oscillation. (**d**) Fourth-order swing+oscillation+torsion. (**e**) Fifth-order swing+swing+twist. (**f**) Sixth-order swing+swing+twist.  
From the table, it can be seen that compared to A1, the first- to sixth-order modal frequencies of A2 differ by 0.3945 Hz, 1.4220 Hz, 2.4558 Hz, 2.8573 Hz, 3.3610 Hz, and 4.3270 Hz, respectively. The vibration frequencies of each order vary greatly, indicating that the carbon-glass ratio between 2:6 and 4:4 has a significant impact on the blade modal frequencies. Compared to A2, A3 has a frequency difference of 0.1426 Hz, 0.5132 Hz, 0.8978 Hz, 1.0940 Hz, 1.3790 Hz, and 1.6960 Hz in the first- to sixth-order modes. It can be seen that the carbon to glass ratio between 4:4 and 6:2 has a relatively small impact on the modal frequency of the blade as the carbon content continues to increase.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 7 Case Study: Modelling Chemical Reactor/Operator Using GDABM
### Agent Reactor  
The _agent reactor_ has production lines \(\textit{ProductionLine}_{1}\) and \(\textit{ProductionLine}_{2}\).  
The agent reactor is connected to the three valves (\(v_{1}\), \(v_{2}\), and \(v_{3}\)). Valves \(v_{1}\) and \(v_{2}\) are the input valves used to load products to the reactor. Valve \(v_{3}\) is used to unload the products. The reactor is equipped with a level sensor that reads the current \(\textit{volume}\) of the product inside the reactor in real-time. During a chemical reaction, the reactor enters in a state lock then it will remain unlock once the reaction is done.  
The agent reactor has one attribute (\(V_{max}\)) that represents the maximum capacity of the reactor in addition to seven variables that are described as follows:  
Figure 8: Generic Dynamic Agent-Based Risk Model (GDABM).  
Figure 9: Chemical Operator/reactor.  
_Volume V_: It has the current volume of the product in the reactor,  
_Gas Concentration (GC)_: It has the concentration of the gas in the reactor's environment,  
_Release Rate RR_: It is the rate in which the gas is released from the reactor.  
\(4\)._._ _Input_\({}_{1}\)_: It is the valve used to load the products from_ ProductionLine_\({}_{1}\) _when_ \(v_{1}\) _is open._  
\(5\)._ _Input_\({}_{2}\)_: It is the valve used to load the products from_ ProductionLine_\({}_{2}\) _when_ \(v_{2}\) _is open._  
\(6\)._ _Output_\({}_{0}\)_: It is the valve used to unload products to_ ProductionLine_\({}_{3}\) _when_ \(v_{3}\) _is open._  
\(7\)._ _State 5: It describe the state of the reactor that can be_ **Locked L** _or_ **Unlocked U**_._  
_The reactor's nominal mode has two activities transform products, and wait. Transform products is the activity that transforms two quantities of consumed elements,_ \(P_{1}\) _and_ \(P_{2}\)_, to produce_ \(P_{3}\) _(produced element), as shown in Figure_ 10_. The_ \(transform-product\) _activity is only enabled when the reactor has products ready and its state is Locked. In the nominal mode, whenever the state of the reactor is Unlocked, Wait activity is triggered._