esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Conclusion and Performance Summary  
The GA-based satellite constellation optimization results confirm that the combination of VLEO (300 km) and LPWAN technologies generally provides superior performance across the evaluated metrics.  
**1. Service Quality Analysis (Revisit Time and Data Transmission Volume)**  
* **Average Revisit Time:** The VLEO (300 km)-LoRa configuration achieved the shortest average revisit time, demonstrating effective minimization of revisit intervals.
* **Maximum Revisit Time:** The LEO-NB-IoT configuration exhibited the shortest maximum revisit time, with VLEO (300 km)-LoRa and Mioty configurations following closely.
* **Data Transmission Volume:** The highest data transmission volume was recorded in the VLEO (300 km)-NB-IoT configuration, primarily due to NB-IoT's higher bit rate.  
**2. Economic Efficiency Analysis (Satellite Count and GA Value)**  
* **Satellite Count:** The VLEO (300 km)-LoRa constellation required the fewest satellites and achieved the lowest GA value, indicating a highly efficient configuration.
* **VLEO (200 km) Comparison:** Although the VLEO (200 km)-NB-IoT configuration deployed one fewer satellite, its performance was slightly inferior, and any potential savings in launch cost are likely offset by the higher commercial demand for the 300 km orbit.  
**3. Interaction Between Orbit and Communication Technologies**  
Figure 21: Total number of satellites and economic efficiency analysis.  
* The VLEO (300 km) orbit consistently demonstrated better performance across all LPWAN technologies.
* LoRa maintained an optimal balance between revisit time and data transmission, while NB-IoT achieved higher data transmission but required more satellites.
* Mioty exhibited longer revisit times compared to LoRa and NB-IoT across all configurations.  
Overall, these results indicate that the GA effectively optimizes satellite constellations to achieve a balanced trade-off between service quality and economic efficiency. The VLEO (300 km) setup, in particular, emerges as the most promising configuration, especially when paired with LoRa and NB-IoT.
## 3 Results and Discussions
### Effects of Single Zernike Mode Aberration
For a Zernike mode which has less effect on thickness even with large amplitude, the corrector may consume a large amount of modulation to get a poor correction performance, which may even lead to a large correction error. Hence, it would be more efficient to develop a suitable strategy to filter out the Zernike modes to be corrected for different illumination beams. For TPBB, Zernike modes with higher azimuthal order should be considered with higher weights.
## 2 Literature Review
### Quadcopter Path Planning Assistance System  
Traverse planning ensures smooth quadcopter system operation, and motion planning makes the quadcopter intelligent. This analysis focuses on quadcopter dynamics, studying motion while considering force or torque, recalling Figure 2. Continuous first and second derivatives of angular displacement and acceleration are necessary for smooth system variation over time.  
If the position is known in the Cartesian coordinate system, then solving the inverse kinematics provides two sets of orientation values. The DC motor of the quadcopter must therefore produce this particular angular displacement. In order to control the motor, the only option is to utilize the quadcopter scheme (Table 2). To ensure smooth variation in theta during a particular cycle time, the controller has to feed a smooth curve passing through \(\theta_{1}^{s}\), \(\theta_{1}^{1}\), \(\theta_{1}^{2}\), \(\theta_{2}^{3}\), and \(\theta_{1}^{G}\). Similarly, it will have to find out another smooth curve for \(\theta_{2}^{s}\), \(\theta_{2}^{1}\), \(\theta_{2}^{2}\), \(\theta_{2}^{3}\), and \(\theta_{2}^{G}\), the smooth variation of \(\theta_{2}\).  
Consider the case polynomial trajectory plan,  
\[\left\{\begin{aligned} & t=t_{i}=0,\\ & t=t_{f};\\ &\theta=\dot{\theta}_{i}\\ &\theta=\theta_{f}\\ &\dot{\theta}=0\end{aligned}\right. \tag{46}\]  
so that  
\[\theta(t)=C_{0}+C_{1}t+C_{2}t^{2}+C_{3}t^{3} \tag{47}\]  
where \(C_{0},C_{1},C_{2},\text{ and }C_{3}\) are coefficients. Differentiating \(\theta(t)\) with respect to time gives the angular velocity:  
\[\dot{\theta}(t)=C_{1}+2C_{2}t+3C_{3}t^{2} \tag{48}\]  
Applying the initial conditions to angular displacement and the velocity equation \(\left\{\begin{aligned} & C_{0}=\theta_{i}\\ & C_{1}=0\end{aligned}\right.\) gives:  
\[\left\{\begin{aligned} & C_{0}+C_{1}t+C_{2}t^{2}+C_{3}t^{3}= \theta_{f}\\ & C_{1}+2C_{2}t+3C_{3}t^{2}=0\end{aligned}\right. \tag{49}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Cartesian Scheme** & **Quadcopter System Scheme** \\ \hline \((X_{s},Y_{s})\) & \((\theta_{1}^{s},\ \theta_{2}^{s})\) \\ \hline \((X_{1},Y_{1})\) & \((\theta_{1}^{1},\ \theta_{2}^{1})\) \\ \hline \((X_{2},Y_{2})\) & \((\theta_{1}^{2},\ \theta_{2}^{2})\) \\ \hline \((X_{3},Y_{3})\) & \((\theta_{1}^{3},\ \theta_{2}^{3})\) \\ \hline \((X_{G},Y_{G})\) & \((\theta_{1}^{G},\ \theta_{2}^{G})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters assumed for the quadcopter system.  
Substituting the Equation (30) into Equation (33), we obtain  
\[\theta(t)=\theta_{i}+3\frac{\left(\theta_{f}-\theta_{i}\right)}{t_{f}^{2}}t^{2}-2 \frac{\left(\theta_{f}-\theta_{i}\right)}{t_{f}^{3}}t^{3} \tag{50}\]  
Theta is then actually a function of time \(t\), and can be plotted as a trajectory line.
## 8 Implementation and Performance Evaluation
### Comparative Analysis with Other Protocols
#### 8.3.1 Security Comparison  
All compared protocols aim to provide mutual authentication and key agreement. Table 3 summarizes key security features:  
The security properties used in Table 3 are defined as follows:  
* F. Secrecy--Forward Secrecy: Compromise of long-term keys does not compromise past session keys.
* KCI-Resist.--Key Compromise Impersonation Resistance: The protocol resists impersonation attacks even when a user's private key is compromised.
* PFS--Perfect Forward Secrecy: session keys remain secure even if long-term keys are later compromised.
* Mutual Auth.--Mutual Authentication: both entities verify each other's identity during the protocol.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Protocol** & **Auth.** & **F. Secrecy** & **KCI-Resist.** & **K-Confirm** \\ \hline GN-AK (orig.) & Yes & Yes & Partial & Optional \\ Enhanced GN-AK & Yes & Yes & Yes & Optional \\ HMQV & Yes & No & Partial & No \\ ECKAS-DH1 & Yes & Yes & No & No \\ NAXOS & Yes & Yes & Yes & No \\ \hline \hline \end{tabular}
\end{table}
Table 3: Security Features Comparison.
## 2 Materials and Methods
### Site Description and Field Observations  
The Mer Bleue bog (Figure 8) is \(\sim\)2800 ha in area and is located \(\sim\)15 km east of Ottawa, ON, Canada. The site has a pronounced microform hummock-hollow topography, with \(\sim\)2/3 of the surface area occupied by hummocks and the remaining \(\sim\)1/3 by hollows [24; 26]. The groundcover is mainly Ericaceous shrubs, underlain by _Sphagnum fuscum_ and _Sphagnum capillifolium_ on hummocks and _Sphagnum magellanicum_ and _Sphagnum angustiolium_ on hollows [39; 40; 41]. Daily d\({}_{\rm WT}\) was measured during 1998-2004 in a well below the average hummock surface using a custom float and counterweight system attached to a potentiometer [24; 40; 42]. Daily near-surface \(\theta\) was measured during 1999-2004 at 0.1 m below the hummock surface and at 0.03 m below the hollow surface using water content probes (model CS615, Campbell Scientific, Logan, UT, USA); their calibration and normalization are described in [24; 40; 42]. Hourly Is\({}_{\rm wr}\), T\({}_{\rm air}\), RH, and v\({}_{\rm wr}\) were continuously measured at 2 m above the canopy during 1998-2004 [24; 42; 43]. Hourly ecosystem GPP observations with daily aggregations for model-data comparisons were made available during 2000-2004, obtained by partitioning from measured eddy covariance (EC) NEE [24; 42]. There were some GPP observations in 1999, but they were much fewer than those in each of the years 2000-2004, hence were not used in model-data comparisons.  
Figure 8: A map of the Mer Bleue study area (**above**) with the microtopography of the Mer Bleue bog study site (**below**).
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 4 Experiment Parameters and Tests  
There are three parameter sets and four spacing scenarios for a total of 12 experiments. The Standard parameter set is the basic setup that generally produced favorable resultsover the course of development. The Double-Delay parameter set tests the effects of a larger overall delay on retransmissions. To this end, it uses values for the various delay parameters (such as \(r_{D,min}\) and \(r_{R,range}\)) that are twice as large as those in the Standard parameter set. Note that both the minimum values and the random ranges are doubled in the Double-Delay set. The Double-Random parameter set was used to test the effects of larger random ranges on the retransmission delay, while having the same minimum values as the Standard set. Thus, the minimum delay values are unchanged compared to the Standard set while the random range components are all doubled. There were a total of 20 nodes in the simulation. The Standard, Close, and Far spacing scenarios placed the nodes 30 m, 10 m, and 60 m apart, respectively. The Far-to-Close spacing scenario starts the nodes at 60 m apart and moves them to be 10 m apart by the end of the simulation. The first SM is sent at 20 s so the system has some time to stabilize, with a new SM occurring every 5 s thereafter for a total of 20 safety events. Table 2 lists the parameter values used for the experimental simulations. The parameter abbreviations are as follows:  
* \(p_{PRTX}\): Minimum reception probability to be eligible as a PRTX.
* \(t_{D}\): Delay time per unit distance for distance-based SM delays; see Equation (1).
* \(r_{D,min}\): Random component minimum for distance-based SM delays; see Equation (1).
* \(r_{D,range}\): Random component range for distance-based SM delays; see Equation (1).
* \(r_{R,min}\): Random component minimum for non-distance-based SM delays; see Equation (2).
* \(r_{R,range}\): Random component range for non-distance-based SM delays; see Equation (2).
* \(r_{S,range}\): Random component range for small extra SM delays; see Equations (1) and (2).
* \(t_{K}\): Transmission keep-out time; only one SM transmission may occur within this amount of time.
* \(LD_{n}\): Path loss exponent for log-distance loss model.
* \(LD_{d}\): Reference distance for log-distance loss model.
* \(LD_{l}\): Reference loss for log-distance loss model.
* \(Nak_{d1}\): [PERSON] loss model distance 1.
* \(Nak_{d2}\): [PERSON] loss model distance 2.
* \(Nak_{m0}\): [PERSON] loss model \(m\) value for distances from 0 to \(Nak_{d1}\).
* \(Nak_{m1}\): [PERSON] loss model \(m\) value for distances from \(Nak_{d1}\) to \(Nak_{d2}\).
* \(Nak_{m2}\): [PERSON] loss model \(m\) value for distances from \(Nak_{d2}\) and beyond.
# Facial Expression Recognition Based on Auxiliary Models
## 3 The Proposed Method  
### Data Pre-Processing  
Images of most face databases include not only faces, but also much background information; therefore, removing background is an important step in face expression recognition preprocessing. Although there are many face databases online, the face regions in most of them are not cut out and cannot be directly used in face expression recognition experiments. If the uncropped image is directly used as the input image, it will not only bring a huge amount of calculation, but also affect the final expression recognition results. Therefore, whether the human face can be correctly detected has a great impact on the expression recognition accuracy. To improve the accuracy of face detection, this paper introduces the method of eye positioning to improve the result of face detection. Eyes are the most prominent facial feature; once their position in the face is relatively fixed, the entire useful face will be obtained easily. Because there is a certain distance between the eyes and the size of the face, many face detection algorithms have a strong dependence on the location of the eye, taking the location of the eye as an important step in the recognition process. Other prominent facial features, such as mouth, nose and eyebrows, can be easily obtained from fixed geometric relations after positioning eyes, so the accurate positioning of human eyes is helpful for the face positioning. The algorithm to obtain the cropped face region image is shown in Figure 1, and experimental results are shown in Figure 2.  
Figure 1: Algorithm to crop face region. An important pre-processing method is used in this paper. Eye orientation is the first step, then other important components are located based on the eye orientation, and finally the picture size scale of face area is set to 1:1.
## 3 Computational Model
### Governing Equations  
Steady-state three dimensional CFD computations were performed for the considered geometry shown in Figure 2. Differential equations governing the fluid flow are given as [28; 29].  
\[\Delta\left(\rho u\right) =0 \tag{1}\] \[\Delta\left(\rho u\;u\right) =-\frac{\partial\rho}{\partial x}+\Delta\left(\mu\;grad\;u\right) +\left[-\frac{\partial\overline{\rho u^{\prime}\;v^{\prime}}}{\partial x}- \frac{\partial\overline{\rho u^{\prime}\;v^{\prime}}}{\partial y}-\frac{ \partial\overline{\rho u^{\prime}\;w^{\prime}}}{\partial z}\right]\] (2) \[\Delta\left(\rho u\;v\right) =-\frac{\partial\rho}{\partial y}+\Delta\left(\mu\;grad\;v\right) +\left[-\frac{\partial\overline{\rho\;u^{\prime}\;v^{\prime}}}{\partial x}- \frac{\partial\overline{\rho\;v^{\prime}\;1^{\prime}}}{\partial y}-\frac{ \partial\overline{\rho\;v^{\prime}\;w^{\prime}}}{\partial z}\right]\] (3) \[\Delta\left(\rho u\;w\right) =-\frac{\partial\rho}{\partial z}+\Delta\left(\mu\;grad\;w\right) +\left[-\frac{\partial\overline{\rho u^{\prime}\;w^{\prime}}}{\partial x}- \frac{\partial\overline{\rho v^{\prime}\;w^{\prime}}}{\partial y}-\frac{ \partial\overline{\rho\;w^{\prime}\;2}}{\partial z}\right] \tag{4}\]  
The energy equation can be found as follows to consider the heat transfer, as in the current study:  
\[\Delta\left(\rho lu\right)=\Delta\left(k\;grad\;T\right)+\Phi+\left[-\frac{ \partial\overline{\rho\;u^{\prime}\;l^{\prime}}}{\partial x}-\frac{ \partial\overline{\rho\;v^{\prime}\;l^{\prime}}}{\partial y}-\frac{\partial \overline{\rho\;w^{\prime}\;l^{\prime}}}{\partial z}\right] \tag{5}\]  
Figure 2: CFD model for the inverted U-tube with dimensions.  
I is the total energy, \(\rm I=h-\frac{P}{\rho}+\frac{(u^{2}+v^{2})}{2}\), while \(\Phi\) is the function of dissipation. h is taken to be the sensible enthalpy where \(\rm h=\sum\limits_{i}Y_{i}h_{i}\) and \(\rm h_{i}=\int_{T_{ref}}^{T}C_{pi}dT\). \(\rm T_{ref}\) is the reference temperature and it is equal to 298.2 K.
_Algorithms_**2013**, \(6\), 726-746; doi:10.3390/a6040726  
_algorithms_  
ISSN **1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**An Efficient Local Search for the Feedback Vertex Set Problem**  
**[PERSON] \({}^{1,2}\), [PERSON] \({}^{1,2,\ast}\), [PERSON] \({}^{1,2}\) and [PERSON] \({}^{1,2}\)**  
\({}^{1}\) Key Laboratory of Pattern Recognition and Intelligent Information Processing, Shiling, Institutions of Higher Education of Sichuan Province, Chengdu 610106, China; E-Mails: [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (X.Z.); [EMAIL_ADDRESS] (Z.S.)  
\({}^{2}\) School of Information Science and Technology, Chengdu University, Chengdu 610106, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-028-84616090.  
_Received: 24 June 2013; in revised form: 7 August 2013 / Accepted: 28 October 2013 / Published: 1 November 2013_  
**Abstract:** Inspired by many deadlock detection applications, the feedback vertex set is defined as a set of vertices in an undirected graph, whose removal would result in a graph without cycle. The Feedback Vertex Set Problem, known to be NP-complete, is to search for a feedback vertex set with the minimal cardinality to benefit the deadlock recovery. To address the issue, this paper presents NewkLS_FVS(LS, local search; FVS, feedback vertex set), a variable depth-based local search algorithm with a randomized scheme to optimize the efficiency and performance. Experimental simulations are conducted to compare the algorithm with recent metaheuristics, and the computational results show that the proposed algorithm can outperform the other state-of-art algorithms and generate satisfactory solutions for most DIMACSbenchmarks.  
**Keywords:** feedback vertex set; local search; variable depth search; heuristic
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 2 Materials and Methods
### Quantification of Carbonyls in Vaping Emissions of E-Liquids  
Derivatization of vaping emissions with 2,4-DNPH was used to estimate the levels of seven target carbonyls (formaldehyde, acetaldehyde, acrolein, acetone, propionaldehyde, butyraldehyde, and benzaldehyde). The 2,4-DNPH derivatization method used was modified from a previously published paper [40]. Vaping emissions (total 25 puffs) from each e-liquid were collected using the same cold trap method and vaping topography described in Section 2.3. The vaping samples were reacted with 1 mL of 6.5 mM 2,4-DNPH solution (acidified with HCl in acetonitrile) for 90 min at 50 \({}^{\circ}\)C. Afterwards, each vaping emission sample was aliquoted into three amber vials and stored at 4 \({}^{\circ}\)C before LC/ESI-QTOFMS analysis. A carbonyl-2,4-DNPH mixture standard (certified reference material in acetonitrile) containing the target carbonyls was used to quantify the levels of interested carbonyls in emission samples.  
An Agilent Technologies 6545 LC/Q-TOFMS system equipped with an ESI source was utilized for analysis. The diluted carbonyl-2,4-DNPH standard mixture contained 150 ng/uL formaldehyde-2,4-DNPH, 100 ng/uL acetaldehyde-2,4-DNPH, and 50 ng/uL acrolein-, acetone-, propionaldehyde-, butyraldehyde-, and benzaldehyde-2,4-DNPH. The sample mixtures were separated by a Poroshell 120 EC-C18 column (3 \(\times\) 100 mm, 2.7 \(\upmu\)m). The mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile. The mobile phase gradient was 35% B-35% B from 0 min to 5 min, 35% B-70% B from 5 min to 12 min, and 70% B-70% B from 12 min to 14 min. The post-run time was 5 min. The sample injection volume was 20 uL, and the instrument was operated in the negative ion mode with a 0.5 mL min\({}^{-1}\) flow rate.
## 2 Results
### _srrA_, _marA_, _lapD_ and _pldA_ Are Indispensable in the _lapC190 Mutant Background  
To further understand the mechanism of suppression by various genes whose increased expression mitigates the Ts phenotype of _lapC190_ bacteria, a systematic series of bacteriophage-mediated transduction was executed. This was to ascertain if there are any genetic interactions and if any of such genes are essential for the growth and viability of _lapC190_ bacteria. We focused on those genes, whose overexpression also restores LpxC levels. Thus, null alleles of such candidates were introduced in _lapC190_ bacteria. In parallel, converse reverse transductions were also carried out by attempting to bring in a _lapC190_ mutation in deletion derivatives of genes whose overexpression results in the restoration of growth. Such transductions revealed that the presence of SrrA, MarA, LapD and PldA are absolutely required for the viability of _lapC190_ bacteria even at 30 \({}^{\circ}\)C but not in the wild-type parental strain (Table 2). Thus, no viable transductional _AstrA lapC190_ and _lapD lapC190_ combinations could be obtained. However, when the wild-type copy of the _srrA_ or _lapD_ gene was provided on a plasmid, viable chromosomal _AstrA lapC190_ and _lapD lapC190_ mutational combinations were obtained at the same frequency when  
Figure 6: Restoration of LPS amounts by overexpression of _marA_, _ycej_, _srrA_ and _dksA_ to different extents in _lap190_ bacteria. Immunoblots of whole cell lysates obtained from isogenic strains grown at 30 \({}^{\circ}\)C, following a temperature shift for 2 h at 43 \({}^{\circ}\)C. Expression of the specific gene was induced by the addition of IPTG. An equivalent portion of whole cell lysates were applied and samples were resolved on a 15.5% SDS-Tricine gel and LPS was transferred by Western blotting. Amounts of LPS were revealed using a LPS-specific WN1 222-5 monoclonal antibody. For immunoblotting, LpxC-specific antibodies were used and the relevant genotype is indicated. An equivalent amount of total proteins was resolved by an SDS-PAGE prior to immunoblotting using chemiluminescence detection kit.  
the wild-type parental strain was used as a recipient to bring in either _srrA_ or _lapD_ null mutations (Tables 2 and 3). In contrast, the _AdksA lapC190_ mutational combination was found to be viable up to 37 \({}^{\circ}\)C, although the colony size was heterogeneous. A deletion of either the _marA_ gene or the _pldA_ gene could be introduced in _lapC190_ bacteria, albeit at a highly reduced frequency, and the majority of such transductants were found to be not viable (Table 2). These genetic interactions allow us to conclude that SrrA, MarA, LapD and PldA are essential for the viability of strains lacking the chromosome of the DNA sequence encoding the periplasmic domain of LapC.
## 6 Experiment and Result Analysis
### Evaluation Metrics and Experimental Parameter Settings  
#### 6.2.1 Model Evaluation Metrics  
ACC (Accuracy) is a metric that measures the precision of the model in identifying targets. The ROC [38] curve reflects the relationship between sensitivity and specificity. The ROC curve is obtained by calculating the True Positive Rate (TPR) and False Positive Rate (FPR), as shown in Equations (22) and (23). AUC (Area Under the Curve) quantifies the area under the ROC curve, shown in Equation (24), providing a comprehensive measure of the model's classification performance across different thresholds and effectively mitigating the subjective impact of threshold selection. Therefore, this study employs AUC and ACC as primary evaluation metrics to validate the performance of the predictive network models. These model evaluation metrics are defined as follows.  
\[FPR=\frac{N_{FP}}{N_{FP}+N_{TN}} \tag{22}\]  
\[TPR=\frac{N_{TP}}{N_{TP}+N_{FN}} \tag{23}\]  
\[Accuracy=\frac{N_{TP}+N_{TN}}{N_{TP}+N_{TN}+N_{FP}+N_{FN}} \tag{24}\]  
In the three equations provided above, \(N_{TP}\) represents the count of true positive samples, \(N_{FN}\) represents the count of false negative samples. \(N_{FP}\) stands for the count of false positive samples, while \(N_{TN}\) indicates the count of true negative samples.