esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Power Production  
Two challenges are faced when calculating the power production from OPV panels incorporated in an arched greenhouse. The first challenge concerns the arched section geometry since each point of a section of the roof has a different inclination angle. Consequently, the energy production of the entire panel surface will be determined by the cell with the worst inclination depending on the internal inter-connection. This challenge was treated with the choice of narrow OPV panels (\(0.15\times 0.82\) m) arranged along the main greenhouse axis in such a way that each panel traversed only 0.15 m across the cross-section. In this way the inclination angles of the beginning and the end differ only by \(1.54^{\circ}\) allowing us to consider for each panel an average inclination angle. The OPV electrical characteristics are V\({}_{\text{oc}}\) = 6 V, I\({}_{\text{sc}}\) = 1 A, V\({}_{\text{mp}}\) = 4.3 V, I\({}_{\text{mp}}\) = 0.9 A.  
The second challenge concerns the choice of inverter due to the low V\({}_{\text{mp}}\) and power which prevents them to collaborate with the majority of existing inverters who have a voltage threshold in the order of 250 V or higher. This challenge was treated with the choice of a mini inverter with power threshold 3 W and minimum MPP voltage 28 V, considering appropriate panel interconnection. This way, the yearly power production from the OPV in the greenhouse roof per greenhouse meter was calculated for the whole section and is presented in Table 11 for the three examined cases. For the calculation, the overall temperature coefficient was considered 0.02%/\({}^{\circ}\)C [12]. In the same table, the primary energy and CO\({}_{2}\) saving offered by the PV are presented considering that electrical power is replaced.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PAR normal transmittance, \(\tau_{\text{n}}\) [-]** & 30\% & 45\% & 60\% \\ \hline
**Power generation [kWh m\({}^{-1}\) y\({}^{-1}\)]** & 323 & 242 & 158 \\
**Primary electrical energy saving [kWh m\({}^{-1}\) y\({}^{-1}\)]** & 937 & 702 & 458 \\
**CO\({}_{2}\) reduction [kg m\({}^{-1}\) y\({}^{-1}\)]** & 319 & 239 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Yearly power production per greenhouse length meter.  
Nevertheless, the design of a PV system incorporated in an arched greenhouse in terms of its environmental footprint depends on many factors, including the type of the crop, the greenhouse operating strategy, and the size of the greenhouse, which affect the yearly power demand profile and the available roof area.
## 3 Results and Discussion  
### Characterization of Aroma Compounds in RZT Identified by SBSE-GC-MS  
As displayed in Table 1, a total of 112 volatile compounds from six representative and typical RZT samples were detected and identified by SBSE-GC-MS (Figure 2a). According to the differences in chemical structure, these volatiles can be further divided into12 categories, including aldehydes, alkenes, esters, alcohols, oxygen heterocycles, nitrogen heterocycles, aromatics, phenols, alkanes, organic acids, and amines. Figure 2b illustrates the distribution of quantities and contents for a variety of volatile compounds in RZT. RZT had the greatest number of esters (22 types), followed by alkanes (21 types), alcohols (18 types), ketones (13 types), and so on. In addition, the primary volatile categories of RZT were esters (2035.25 \(\upmu\)g/kg), alcohols (1799.02 \(\upmu\)g/kg), alkanes (991.88 \(\upmu\)g/kg) and ketones (691.96 \(\upmu\)g/kg), which together accounted for 74.91% of the total volatile contents. In comparison, phenols (94.73 \(\upmu\)g/kg) and amines (155.96 \(\upmu\)g/kg) were present at the lowest concentration. As Figure 2c illustrates, twenty volatiles with high abundance (>100 \(\upmu\)g/kg) were identified in RZT samples. Notably, (Z)-3-hexanoic acid hexenyl ester (433.71 \(\upmu\)g/kg) and geraniol (406.46 \(\upmu\)g/kg) were the most abundant. The former has an apple- and pear-like scent and was previously found in high abundance in XinYang MaqJian green tea [16]. It was discovered that (Z)-3-hexanoic acid hexenyl ester could increase the expression of the essential enzyme genes in the synthesis pathway, _CsADH1_, _CsADH3_, and _CsLOX3_, to strengthen the tea plant's tolerance to the cold [17]. Geraniol, a colorless, oily terpene with a rose-like fragrance, is reported to be the most prevalent volatile in pan-fired green teas [12]. Linalool (292.88 \(\upmu\)g/kg) was the third most abundant in RZT, sharing the same precursor (geranyl pyrophosphate, GPP) with geraniol [18]. It exhibits a distinct floral and sweet aroma, differing from that of geraniol, and is also abundant in various premium green tea products [5; 19; 20]. According to earlier research, cold stress can also cause a rise in the amounts of geraniol and linalool [21]. Although the exact process by which geraniol and linalool are inducted in tea is still unknown, it is hypothesized that the rise in these volatiles is connected to an alteration in the tea tree's metabolic pathway at low temperatures. Figure 2: Result of volatile compounds in RZT identified by SBSE-GC-MS. (**a**) A representative total ion flow diagram of the RZT sample. (**b**) Volatile profiles of RZT samples. The left part of the figure shows the content of each volatile category, and the right part shows the number of volatiles in each category. (**c**) Details of volatile compounds with high contents (>100 \(\upmu\)g/kg).
# 3D DNA Nanostructures: The Nanoscale Architect
## 2 3D DNA Nanostructure Design
### Scalability  
In general, there has been a trend towards scaling up production and size of 3D DNA nanostructures across all techniques. One strategy has been to pursue improvements to scaffold strand size and customization. In order to create larger, more diverse DNA origami as single, one-pot syntheses, either longer scaffolds or many orthogonal scaffolds are required, and avenues such as re-engineering bacteria and enzymatic toolkits to those ends [35; 36; 37] have been investigated. Doing so can suppress cost, relieve design limitations to improve the practicality of 3D DNA nanostructures across broader size scales, and also ensure immunocompatibility of DNA nanostructures for in vivo applications. For more information on this topic we suggest readers consult an excellent previous review [38].  
On the other hand, rather than facing the challenges associated with longer scaffold strands, other novel techniques instead rely on some form of hierarchical assembly. The [PERSON] group demonstrated gigadalton-scale DNA nanostructures by utilizing self-assembly techniques upon an already assembled DNA origami component to create large, polygonal DNA nanostructures [39]. Each component was a V-shaped structure (_V-brick_) that could join to others by attachment sites on their flat sides. A tripod structure of three V-bricks together translated to the angle of the vertex it would represent in a 3D polygonal shape, such that polygonal structures would self-assemble towards an energetically favorable configuration matching the number of vertices necessary to form a closed shape. The [PERSON] and [PERSON] groups have proposed using _Meta-DNA_[40; 41] as another route towards scaling up the size of DNA nanostructures. Meta-DNA are 3D DNA origami nanostructures that have been constructed to resemble both the shape and Watson-Crick base pairing rules of the standard nucleotides of DNA. These Meta-DNA nucleotides hierarchically assemble into double helix superstructures resembling DNA which can then be used to emulate DNA origami designs but at a magnified scale.  
In comparison to depending on specific hybridization reactions to assemble multicomponent structures, which can be restrictive and difficult to reverse, alternative methods have also been demonstrated to have the propensity to assemble large, complex 3D DNA nanostructures, such as by using base stacking or shape-complementary binding [42]. As already mentioned, extended methods of other standard techniques, such as for wireframe DNA origami [32; 33] or DNA bricks [16], have also looked to making their own methods suitable for larger and more mechanically rigid designs. Assemblies utilizing crystallographic regularity of DNA origami nanostructures can also provide large templates for the assembly of guest molecules [43; 44].
## 4 Results and Discussion
### Sensitivity Analysis  
As mentioned earlier, the proposed heat transfer model uses a combination of Woschni heat transfer correlation during combustion and the proposed heat transfer during scavenging calculated, using Equation (7) and Equation (16), respectively. Two constants are used to adjust the correlation coefficients, one to adjust the combustion (coefficient \(a\) in the Woschni equation) and the other for the proposed heat transfer coefficient (coefficient \(b\)). A  
Figure 6: Results comparing Woschni and proposed heat transfer model for **(A)**: Heat transfer coefficient-1D model (**B**): In-cylinder temperature evolution during scavenging, **(C)**: In-cylinder trapped mass, **(D)**: logarithm pressure vs. Volume evolution.  
sensitivity analysis is performed by varying the two coefficients to assess the impact on the trapped mass at the end of the scavenging process. Furthermore, in this analysis, the engine settings are maintained as in Section 4.2.  
In this analysis, 35 points are studied, with values for coefficient \(b\) ranging from 2-6 and coefficient \(a\) ranging from 40-100. Figure 7A provides the information on the effect on trapped mass in absolute terms, while Figure 7B, depicts the information on the differences in trapped mass with variations in percentage in coefficients \(a\) and \(b\) from the mean values. For this calculation, Equation (21) is used:  
\[c_{i}=\left(\frac{v_{i}}{\overline{v}}-1\right)100, \tag{21}\]  
where \(v_{i}\) is the respective value of coefficient \(a\), \(b\) or trapped mass and \(\overline{v}\) is the mean value for coefficient \(a\) is 4, while the mean value for coefficient \(b\) is 70 and the mean value of trapped mass is approximately 0.455 g. From Figure 7, it can be observed that the effect of coefficient \(b\) has a higher impact on trapped mass than coefficient \(a\).  
For instance, from Figure 7A, considering a horizontal line at coefficient \(a\) of 60 (blue dotted line) and observing the trapped mass at coefficient \(b\) of 4 yields a value of 0.46 g, while for the same value of coefficient \(a\) and a coefficient \(b\) of 6, it can be seen from Figure 7A that the trapped mass has changed to 0.49 g. This change in trapped mass is about 6.5%, as observed in Figure 7B. On the other hand, from Figure 7A, for a constant value of coefficient \(b\) of 3 (red dotted line) and changing the coefficient \(a\) from 70 to 90 results in a trapped mass of 0.44 and 0.45 g, respectively. In other words, a change of 2% in trapped mass can be observed from Figure 7B. This analysis shows that the results for trapped mass are affected by the chosen value of coefficient \(b\), so special care has to be taken when calibrating the model correlation.
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 7 Performance Analysis
### Time Cost of Update Algorithm  
In the update algorithm, this paper is compared with the schemes of [PERSON] [24], [PERSON] [25], and [PERSON] [20]. Since these schemes are added and deleted with this algorithm, they are all shown in the following figure.  
As can be seen from Figure 10, the solution update of [PERSON] et al. [20] took more time. In this scheme, for each keyword that needs to be updated, the file corresponding to it needs to be re-encrypted once. That is, the data owner does not care about the previous encrypted file set; she/he re-encrypts the new file set, and then sends it directly into the database. The reason for the time consumed is that some calculations used in the encryption process, such as pseudo-random function F, bilinear pair e, etc., will be more time-consuming than hashing operations. In addition, the update scheme of [PERSON] et al.'s [20] scheme does not take into account whether the newly added file has been added before, which will lead to repeated searches, or whether the new file is deleted and the old version is added, resulting in the deleted file being obtained by the data user.  
In [PERSON] et al.'s [25] scheme, the previous addition of files was also not taken into account in the update. Although the time is shorter, with the increase in the number of files, the time is the fastest growing, even exceeding the original time-consuming Chen2 scheme.  
Among the schemes proposed by [PERSON] et al. [24], the time is second only to that proposed in this paper. However, the problem is the same as that in the previous two schemes; the existing files and their status are not considered. This not only adds unnecessary storage space but also affects subsequent search results. Finally, the scheme in this paper is superior to the other three schemes both in terms of rationality and time.
## 7 Parameter Optimization of Terminal Sliding Film Controller Based on JLSCOA
### Fitness Function
From Figure 4, it can be seen that under the control of the terminal sliding mode controller optimized by JLSCOA, the ship can stabilize at the desired position [5 m, 5 m, 10\({}^{\circ}\)] from the initial position [0 m, 0 m, 0\({}^{\circ}\)] within a limited time. The positioning process is stable, and the response time is quick. Figure 5 shows that the sliding mode control force output is continuous and bounded, with both the magnitude and process of the control force being within a reasonable range. Figure 6 indicates that the ship's velocity changes smoothly from zero to a larger value and then back to zero, demonstrating that the entire positioning process is relatively stable. This suggests that the system control performance is ideal and meets the accuracy requirements of most practical engineering applications, highlighting the effectiveness of the terminal sliding mode controller optimized by JLSCOA. A comparative simulation analysis is performed between the terminal sliding mode controller with given parameters and the terminal sliding mode controller optimized using the JLSCOA algorithm. The parameters of the given terminal sliding mode controller are \(\lambda=0.1,k_{2}=1,\xi_{2}=1.1,\delta=0.3\), and \(a=0.5,k=10\). Figures 7-9 show the position variations of the ship in three degrees of freedom. From the figures, it can be seen that the ship equipped with the terminal sliding mode controller  
Figure 5: Ship velocity variation curve. Figure 6: Ship control force curve. optimized by the JLSCOA algorithm can complete the positioning process more quickly, with an average position change time of approximately 300 s faster than that of the given parameter set. Figures 10-12 depict the velocity variations in three degrees of freedom. The results indicate that the given parameter set exhibits significant velocity oscillations and smaller control forces, while the JLSCOA optimized set shows larger control forces with almost no velocity oscillations. Figure 8: Comparison of lateral position variations. Figure 7: Comparison of longitudinal position variations. Figure 9: Comparison of pitch angle variations. Figure 11: Comparison of lateral velocity variations. Figure 12: Comparison of pitch rate variations. Figure 4:0 Comparison of longitudinal velocity variations.
## Appendix A Transforming Signal to Concentration  
Assuming the gradient echo sequence, \(C_{tissue}\) in a given time step \(t\) is derived from the observed signal \(S(t)\) using the transformation  
\[C_{tissue}(t)\ =\ \frac{R_{1}(t)-1/T_{10}}{r_{1}} \tag{1}\]  
where \(T_{10}\) is the longitudinal relaxation time before arrival of the tracer agent bolus, \(r_{1}\) is the compartment-specific longitudinal relaxivity constant and  
\[R_{1}(t)\ =\ -\frac{1}{TR}\ln\left[\frac{1-a(t)b}{1-a(t)b\cos\alpha}\right] \tag{2}\]  
with  
\[a(t)\ =\ \frac{S(t)}{S_{0}} \tag{3}\]  
\[b\ =\ \frac{\left(1-e^{-R_{10}TR}\right)}{1-\cos\alpha\ e^{-R_{10}TR}}. \tag{4}\]  
In the equations above, \(\alpha\) and \(TR\) are the MRI sequence parameters of flip angle and repetition time, \(S_{0}\) is the signal baseline, i.e., before arrival of the CA bolus, and \(R_{10}\) is the relaxation constant equal to 1/T\({}_{10}\). Note that in the case of the tissue signal, the conversion function is valid only under the assumption of equal longitudinal relaxivities in the EEV and IV compartments.
## 2 Materials and Methods
### Instantaneous Frequency and Phase  
The analytic signal is constructed by combining the original data and its Hilbert transform to calculate the instantaneous phase [37]. The complex signal \(z_{x}(t)\) can be constructed as follows:  
\[z_{x}(t)=x(t)+iH[x(t)] \tag{7}\]  
where \(x(t)\) is the original real signal, and \(H[x(t)]\) is the Hilbert transform of the real signal \(x(t)\) defined as follows:  
\[H[x(t)]=\frac{1}{\pi}\ p.v.\ \int_{-\infty}^{\infty}\frac{x(\tau)}{t-\tau}\ d\tau \tag{8}\]  
where \(p.v.\) denotes Cauchy's principal value. The instantaneous phase of the signal can be calculated from the complex analytic signal as follows:  
\[\varphi_{x}(t)=argz_{x}(t) \tag{9}\]  
The instantaneous frequency can then be calculated from the instantaneous phase as follows:  
\[f_{x}(t)=\frac{1}{2\pi}\ \frac{d}{dt}\ \varphi_{x}^{uu}(t) \tag{10}\]  
where \(\varphi_{x}^{uu}(t)\) is the unwrapped instantaneous phase.  
To estimate the instantaneous phase at the edge of the sliding data segment of 500 ms and to avoid edge effects, 64 ms segments were trimmed on both ends. The remaining 372 ms data segment was then introduced into the AR model to generate AR coefficients. The AR model order was fixed to 30 to iteratively forecast the 128 ms in the future [21]. The predicted 128 ms window was then used to calculate the analytic signal based on the Hilbert transform. As suggested by [PERSON] [23], there is atrade-off between the length of the predicted segment and the efficacy of the filter. This study used a 128 th order two-pass FIR band pass filter. A higher filter order delays the signal longer, whereas a low filter order <100 does not remove frequencies outside the passband effectively. For the LMS-based AR model, the same, and twice (256 ms) prediction lengths were compared with the AR model.
# Energy Efficient Secure Communication Model against Cooperative Eavesdropper
## 2 System Model
### Decoding Information by Using Jamming Signal Estimate
Thus, J can counteract against the active cooperative eavesdropper network by adjusting \(\beta\) value while considering the SINR value from (8), (13), (6) and (14). The following steps are required to find the optimum \(\beta\) value. 1. Find the approximate SNR value for the system model and it is denoted as \(\gamma_{sys}\). Follow further steps to determine optimum \(\beta\), based on \(\gamma_{sys}\). 2. Set the maximum acceptable bit error rate (BER) performance level of E1 at \(\gamma_{sys}\) under the non cooperative (NC) scenario and the SINR is given in (6) and under the NC scenario. \(T_{J}\) does not impact the BER of E1. In cooperative eavesdropper scenario, for both E1 and E2, their BER performance for a given \(\gamma_{sys}\) is dependent on SINR, \(T_{J}\), the number of receiving antenna and IBDFE, the BER performance of E1. 3. Then, the BER performances of E2 and E1 can be degraded by optimizing the \(\beta\) value and by increasing \(T_{J}\). The equation for \(\varepsilon_{X,J}\) at E2 is given as  
\[\varepsilon_{X,J}=f_{MSE}\bigg{\{}\frac{p_{J}}{\sigma_{N,J}^{2}+(\varepsilon_ {X,J}/\beta_{E2})(1+\varepsilon_{H,JE2})}\bigg{\}}\ \ ;\ \ \ P_{J}=\frac{\sigma_{X,J}^{2}}{T_{J}}. \tag{15}\]  
Figure 1: Cooperative eavesdroppers in the system model. In this model, the jamming signal does not interfere with legitimate users. In this (15), in general, \(\varepsilon_{X,I}\) increases with the increase in \(\beta_{E2}\) but in contrast \(1/\beta_{E2}\) decreases the \(\varepsilon_{X,J}\) value. Therefore, for a given \(\gamma_{sys}\), the optimum \(\beta_{E2}\) is the maximum \(\beta_{E2}\) value at which the value of \((\varepsilon_{X,I}/\beta_{E2})\) is maximum. In general, the rate of increase in \(\varepsilon_{X,I}\) will gradually decrease after certain \(\beta_{E2}\) value. In the following Section 3, through Monte Carlo simulation, we estimate the approximate \(\beta\) value.
# TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4
## 4 System Model Assumptions and Proposed Mechanism
### TARS Mechanism
#### 4.4.2 Counter Selection  
According to the standard [33], once an RB is assigned to a vehicle, a selection counter is randomly set up between five and fifteen. The counter is decremented by one after each packet transmission. For congested scenarios, the selection of a counter value with a uniform random selection probability has limitations. For instance, a vehicle in a congested network and in a scarce network has equal probability of selecting a higher counter value and vice versa. We argue that the counter selection should be adaptive and based on the network congestion. This ensures that vehicles in a congested network autoselect lower counter values to allow fair opportunities for transmission for neighbor vehicles.  
To incorporate adaptiveness in counter selection, the proposed approach divides the selection counter window into two, i.e., [5 to 10] and [10 to 15]. Subsequently, Equation (9) is used to find the probability of setting the counter between the two selection windows.  
\[S_{c}=1-[R_{s}^{-1}*\beta]\quad,\quad\beta\to 35\% \tag{9}\]  
where \(S_{c}\) is the selection counter, \(R_{s}^{-1}\) is the inverse of channel load, and \(\beta\) is a threshold after which our selection counter is used. As a design choice, \(\beta\) is set to 35%. The result of Equation (9) is the probability for setting the counter between [5 to 10], and (\(1-S_{c}\)) is the  
Figure 9: RB selection.  
probability for setting the counter between [10 to 15]. For \(R_{s}^{-1}\) less than \(\beta\), our approach follows the standard, i.e., the counter is set between [5 to 15] and for \(R_{s}^{-1}\) greater than or equal to \(\beta\), and when the channel is busier, Equation (9) is used, i.e.,  
\[\begin{cases}R_{s}^{-1}<\beta,&S_{c}(Std)\\ R_{s}^{-1}\geq\beta,&S_{c}\end{cases} \tag{10}\]  
When the channel is busy, the proposed approach sets the counter to a smaller value. The significance of defining two ranges for the counter and adaptive counter value selection is bound to provide fairness in channel access in congested scenarios. Figure 9(a),b shows the effect of using \(S_{c}\) for counter selection.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 2 Materials and Methods
### The Three-Channel Filter for FDI
#### 2.2.3 The Filter Design
Brief Description  
The first two parts present the calculation formula for analyzing the channel filter's parameters, which are mainly affected by the weight of the ratio of cut-off frequency to rise time. Therefore, the channel filter's parameters are chosen mainly through analyzing the changes in the ratio. Taking the second-order channel filter as example, the large ratio causes large parameters of the second-order channel filter; this means that the FDI system using the large parameters will have a lower false alarm rate and false isolation rate but a longer delay time. To detect soft fault, it is reasonable for the second-order channel filter to have large parameters.