esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods  
### Site Directed Mutagenesis  
Site directed mutagenesis was used to introduce selected mutations in the _cry1 Ab_ gene. Mutagenic PCR reactions were performed by using 200 ng of pHT315-Cry1 Ab plasmid [25], 50 pmol of mutagenic primer and 20 \(\upmu\)L of Phusion High-Fidelity PCR 2X Master Mix (ThermoFisher Scientific, Waltham, MA, USA). Thermocycling conditions were: one incubation at 98 \({}^{\circ}\)C for 1 min; 30 cycles of 98 \({}^{\circ}\)C for 10 s, 55 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 5 min; and one incubation at 72 \({}^{\circ}\)C for 5 min. The PCR reaction was purified and methylated parental DNA was digested with 1 U of Dpnl (New England Biolabs, Ipswich, MA, USA) for 2 h at 37 \({}^{\circ}\)C. The reaction was then electrotransformed in _Escherichia coli_ DH5\(\alpha\) and grown on an LB medium containing 100 \(\upmu\)p/mL of ampicillin for 16 h at 37 \({}^{\circ}\)C. Plasmids were extracted and prepared for Sanger sequencing. Double mutants of Cry1 Ab toxin were generated using the single mutants as the template for a second PCR mutagenic reaction. Primers used for mutagenesis were: S39C, 5\({}^{\prime}\)-CCC CAA TCG ATA TTT GCT TGT CGC TAA CGC-3\({}^{\prime}\); S41C, 5\({}^{\prime}\)-CGA TAT TTC CTT GTG CCT AAC GCA ATT TCTT G-3\({}^{\prime}\); S71C, 5\({}^{\prime}\)-GAA TTT TTG GTC CCT GTC AAT GGG ACG CAT TTC-3\({}^{\prime}\); W73C, 5\({}^{\prime}\)-GGT CCC TCT CAA TGC GAC GCA TTT CTT GTA C-3\({}^{\prime}\); I97C, 5\({}^{\prime}\)-GGA ACC AAG CCT GTT CTA GAT TAG AAG G-3\({}^{\prime}\); T239C, 5\({}^{\prime}\)-GAG AAT TAA CAC TAT GTG TAT TAG ATA TCG-3\({}^{\prime}\).
## 3 Methodology
### Parameterize Excitation Coefficient Database  
The CF excitation coefficient is a function of the non-dimensional amplitude (_A/D_) and the normalized oscillation frequency (\(f_{\rm osc}\)) of the cylinder (\(\hat{f}_{\rm CF}=\frac{f_{\rm osc}D}{U}\)). The excitation coefficient database in empirical programs is often represented by a set of parameters.  
The default set of excitation parameters in VIVANA are defined as a function of response amplitude ratio and excitation coefficient for a given non-dimensional frequency as shown in Figure 1. Four values are used to define one excitation coefficient curve at each non-dimensional frequency.  
The curve in Figure 1 is explained as follows:  
* Point A presents the \(CF\) excitation coefficient value where the amplitude ratio (\(A\)/\(D\)) is zero, i.e., \(C\epsilon_{CF,(A/D)=0}\). Here \(D\) is the corresponding cylinder diameter.
* Point B outlines the maximum \(CF\) excitation coefficient \(C\epsilon_{CF}=max\) to the corresponding amplitude ratio \((A/D)_{C\epsilon_{CF}=max}\).
* Point C states the \(A\)/\(D\) values where \(C\epsilon_{CF}\) is zero.  
The default excitation database in VIVANA contains curves for 26 different non-dimensional frequencies as shown in Figure 2. These 26 curves are simplified to 3 representative non-dimensional frequencies (\(f_{CF}\)) (vertical lines) as shown in Figure 2 and the corresponding values are presented in Table 1. This curve is represented by 4 excitation parameters. This curve is systematically changed by using 2 modification factors of amplitude ratio and excitation coefficient. The lower and upper ranges of non-dimensional frequency ranges are the same as the default set of parameters and they remain constant. The values between specified points will be linearly interpolated.  
Figure 1: Three-point excitation coefficient curve for CF response.  
Figure 2: Parameterization of the excitation coefficient curves.  
There may exist several frequencies that can be potentially excited along the length \(L_{e,i}\) of the structure. The length of the occupied excitation zones of each frequency is defined by the corresponding non-dimensional frequency range (\(f_{CF}=0.12-0.31\) is used as the default range). In order to define the priority among all possibly acting frequencies, an excitation parameter based on energy considerations has been defined:  
\[E_{i}=\int_{L_{e,i}}U_{N}^{3}(s)D^{2}(s)(A/D)_{Ce=0}ds \tag{6}\]  
The integrals for the excitation parameters are computed over the excitation zone for each frequency. The parameter \((A/D)_{Ce=0}\) is the non-dimensional amplitude where the excitation coefficient \(Ce\) changes from a positive to a negative value. For a riser with a constant diameter subjected to the uniform flow, the dominating response frequency is around non-dimensional frequency 0.172, where \((A/D)_{Ce=0}\) has the highest value.
## 3 Experimental Results
### Experimental Environment  
Our proposed model is designed to reliably detect pedestrians under challenging conditions such as low light and adverse weather. It employs YOLOv4 for object detection and is implemented on an NVIDIA RTX 3060 (Santa Clara, CA, USA) and an Intel Core i7-12700 CPU (Santa Clara, CA, USA). YOLOv4 [15] is compatible with a range of deep learning frameworks, including TensorFlow and PyTorch, and offers versatility and scalability in various environments. This is particularly relevant in domains such as autonomous driving and CCTV monitoring, where real-time pedestrian detection is of paramount importance. The selection of YOLOv4 as our detection model was driven by the necessity to detect pedestrians of varying shapes in complex environments. Furthermore, there is a potential for extending its capabilities to embedded systems in the future. For the test evaluation, we used the KITTI Open Dataset [29], which consists of 1467 RGB camera images and LiDAR PCD acquired at the same time and location. Of these images, 1200 (approximately 80%) were employed for model training, with the remaining 267 being used for testing. All images were resized to 1242 pixels \(\times\) 375 pixels, ensuring consistency between the RGB and depth maps. Moreover, the dataset was restricted to pedestrian-class instances.  
The proposed object detection model, trained on various environments of autonomous driving scenarios, was evaluated using AP as a performance metric. AP evaluates the model performance by calculating the area under the precision-recall curve. Precision is defined as \(\text{TP}/(\text{TP}+\text{FP})\), representing the proportion of correctly detected instances among all detected instances, while recall is defined as \(\text{TP}/(\text{TP}+\text{FN})\), representing the proportion of correctly detected instances among those that need to be detected. Here, TP, FP, and FN stand for True Positive, False Positive, and False Negative, respectively [30].  
Figure 4: Flowchart for non-maximum suppression (NMS).
# SDN Anomalous Traffic Detection Based on Temporal Convolutional Network
## 4 Experiments and Analysis
### Evaluation Metrics  
In the experiments, _Accuracy_, _Precision_, _Recall_ and _F1-score_ are used to evaluate the performance of the model. Accuracy indicates the proportion of the number of samples correctly classified by the model to the total number of samples, which can measure the overall classification performance of the model; Precision indicates the proportion of the samples predicted by the model to be anomalous traffic that are actually anomalous traffic, reflecting the accuracy of the model in predicting anomalous traffic; Recall indicates the proportion of the samples that are actually anomalous traffic that are correctly predicted by the model to be anomalous traffic, reflecting the model's ability in detecting anomalous traffic. The _F1-score_ is the reconciled average of the precision rate and the recall rate, which integrates the precision rate and the recall rate of the model and can reflect the performance of the model more comprehensively. The formulas for these evaluation metrics are as follows:  
\[Accuracy=\frac{TP+TN}{TP+TN+FN+FP}\times 100\% \tag{10}\]  
\[Precision=\frac{TP}{TP+FP}\times 100\% \tag{11}\]  
\[Recall=\frac{TP}{TP+FN}\times 100\% \tag{12}\]  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{13}\]  
where _TP_ is the number of true examples, _TN_ is the number of true negative examples, _FP_ is the number of false positive examples, and _FN_ is the number of false negative examples.
## 3 Results and Discussion
### Content of Phenolic Compounds in Residues of the Fermentation Process  
After the fermentation process, between 250 and 350 g of residue per kg of grape used was generated. This significant amount of residue is important from a nutraceutical point of view due to its high content of phenolic compounds [25; 39]. The content of phenolic compounds in a mixture of the residues obtained using the method in this work was quantified, and an average of 2771 mg EAG/100 g on a dry basis was determined. In order to save time, the experiment design was performed using a commercial microwave to determine the influence of time and power on the extraction of phenolic compounds from grape residues. [PERSON] et al. [22] used extraction times between 15 and 90 min and reported slightly increased phenolic compound contents. In the present work, much shorter times were used to prevent large increases in temperature from degrading the compounds present. The design was enacted with times between 10 and 30 s and powers from 100 to 300 W on 3 g of residues macerated in 15 mL of 70% acidified ethanol. The results obtained are presented in Table 3.  
The highest recovery percentage was 65% when extraction was performed at 300 W for 30 s. The time factor was significant (\(p<0.05\)); that is, in 30 s, a greater quantity of phenols was recovered than in 10 s of extraction, regardless of the microwave power used (Figure 5). [PERSON] et al. [39] reported that phenolic compounds can be extracted from grape seeds using microwaves in 4.6 min, obtaining compounds that are normally determined in a 24 h maceration, and they reported that time is a significant factor in phenol extraction, while potency is not a significant factor.  
Figure 5: Pareto diagram, where A: time, B: power, AB: time–power interaction (the x-axis represents Fisher’s F value, and the red dotted line is the error limit allowed when \(\alpha=0.05\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Exp.** & **Power (W)** & **Time (s)** & **\% Recovery *** \\ \hline
1 & 100 & 10 & 39 \\
2 & 100 & 10 & 42 \\
3 & 300 & 10 & 51 \\
4 & 300 & 10 & 50.8 \\
5 & 100 & 30 & 58.8 \\
6 & 100 & 30 & 54.8 \\
7 & 300 & 30 & 65 \\
8 & 300 & 30 & 53 \\
9 & 200 & 20 & 54 \\
10 & 200 & 20 & 43 \\ \hline \hline \end{tabular}  
* Percentage recovery of phenolic compounds.  
\end{table}
Table 3: Recovery of phenolic compounds by short microwave extraction.
## 3 Methodology
### Multi-Sensor IF Estimation Algorithm  
In this section, we develop a multi-sensor IF estimation algorithm by extending an existing mono-sensor IF estimation algorithm [27]. The proposed algorithm first estimates the IF of the strongest component, removes it from the given signal and this process is repeated till the IF of all the components has been estimated. To estimate the IF of the strongest component, the method first searches for the strongest energy TF point and then tracks the IF by exploiting the slow variation in IF curves. The details of the algorithm are given in the following subsections.
## 3 Results and Discussion
### Formulation Development  
The compositions of the SDs with GEF and other excipients in detail were summarized in Table 1. The co-precipitation method was successful to prepare the SDs of GEF using TPGS and SLS as a surfactant and HP \(\beta\)-CD as a complexing agent. The formulations were developed with varying concentrations of the surfactants and HP \(\beta\)-CD. The concentration of GEF was kept constant. For each group, we have developed three formulations. Based on the value of percentage yield (%) and cumulative drug release (%), the optimal one from each group (such as F1, F2 and F3) was selected for further investigation. The formulation development for optimization purposes has been supplied as Supplementary Material (Table S1 and Figure S1). For the optimal formulations, the relative yield (%) of GEF SDs was less (61.75 \(\pm\) 4.86%) in the case of F2 (SLS as surfactant) as compared to F1 (86.54 \(\pm\) 5.52%), where TPGS was used as a surfactant in the present work. Therefore, the complexation of GEF SDs with HP \(\beta\)-CD was completed only for the F1 composition (where TPGS was used as a surfactant). The yield (%) of F3 (GEF-TPGS with HP \(\beta\)-CD SDs) was found to be 82.55 \(\pm\) 3.95%. The yield of F3 was slightly less as compared to F1, which was expected due to the increase in total mass of this formulation.  
The percentage yield values indicated that the type of surfactant has an important role in the formation of SDs of lipophilic molecules, which greatly depends on the inherent property of the surfactants and the nature of the interaction between SDs and surfactants [36]. The higher yield of GEF SDs with TPGS in the present investigation might be attributed to the non-ionic nature of TPGS with the hydrophilic-lipophilic balance (HLB) value of 13.2 and low critical micelle concentration (CMC, 0.02%, \(w/w\)) [37]. The amphiphilic nature of the hydrophilic polar head and lipophilic alkyl end of TPGS makes it an ideal biomaterial to develop different drug delivery systems, including the SDs, which would be able to improve the solubility, drug release pattern, absorption and bioavailability of the drug [38].  
In contrast, the SLS is an anionic surfactant with high HLB (\(\approx\)40.0) and CMC (0.24%, \(w/w\)) [36; 39], resulting in lower yield of GEF SDs. The electrostatic interaction due to the pKa values (pKa\({}_{1}\) 5.4 and pKa\({}_{2}\) 7.2) of dibasic molecule (GEF) and ionization of SLS might be responsible for this phenomenon. Moreover, the hydrophobic interactions (i.e., \(\log p\) value 4.85, polar surface area, conformational flexibility and HLB, etc.) might be the reasons. Therefore, the surfactants (TPGS and SLS) used as stabilizers to prepare SDs of GEF in the present investigation critically affected the physicochemical properties and stability of GEF, which was also reported previously [40]. Further studies to elucidate the exact mechanism for the higher yield of GEF SDs using TPGS as a stabilizer are presently underway.
## 4 Simulation and Performance Analysis
#### Performance Analysis Under Vehicle Density  
Herein, we examine the effect of vehicle density on the effectiveness of the proposed approach. In this simulation, geometrical localization for GSP outage is used with EGR.  
According to the result shown in Figure 14, the EGR has the minimum end-to-end delay when compared to other contemporary routing approaches. Due to its distinctive forwarding vehicle selection procedure, the end-to-end delay of the both EGR and SLD-GEDIR is not affected much by increasing vehicle density. However, EGR takes less time for the transmission of the packets from a source vehicle to the destination vehicle because it uses residual link lifetime, which reduces the number of unnecessary attempts of sending a  
The results shown in Figure 15 reveal that, with 100 vehicles in the forward transmission region, the throughput of EGR is almost the same as in the contemporary routing approaches. However, as the number of vehicle increases, the throughput of the contemporary routing approaches J-GEDIR and P-GEDIR declines more quickly than in both EGR and SLD-GEDIR. The rate of decrement in SLD-GEDIR is more compared to EGR. This is due the fact that EGR picks forwarding vehicle with the highest reliable link between itsself and the sender, reducing the amount of packet loss during transmission. The proposed EGR gives an average 10% higher throughput compared to SLD-GEDIR, wherein, for EGR, it is about 25% to 30% higher than that of J-GEDIR and P-GEDIR, respectively. Hence, EGR outperforms contemporary routing approaches in terms of throughput.  
Figure 14: Effect of vehicle density on end-to-end delay.  
Figure 13: Effect of Velocity on one-hop link disconnection.  
The simulation results for one-hop link disconnection with increasing vehicles density are shown in Figure 16. It is evident that EGR has fewer one-hop link disconnections than the routing approaches that are considered to be contemporary routing approaches. It is also interesting to note that, in contrast to contemporary routing approaches, the link disconnections of both EGR and SLD-GEDIR do not change much with increasing vehicle density but rather slightly decrease. This is because both routing approaches only ever chooses one forwarding vehicle, but in other two contemporary routing approaches, J-GEDIR and P-GEDIR, the number of forwarding vehicles rises as vehicle density rises. For the proposed EGR, the average rate of one-hop link disconnection is about 3%, whereas it is about 5% for SLD-GEDIR, and it ranges from 20% to 30% for the rest of the J-GEDIR and P-GEDIR contemporary routing approaches. As a result, it can be seen that the proposed EGR exhibits less link disconnection than most contemporary routing approaches.  
Figure 16: Effect of vehicle density on one-hop link disconnection.  
Figure 15: Effect of vehicle density on throughput.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 4 Proposed Methodology
### Collection of Input Data  
Mobility management entity maintains the records of entry. Additionally, exit times of each user entity (UE) are maintained at Base Station (BS). At each cell, UE's dwell time \(T_{i}\) is computed.  
\[T_{i}=ExitTime_{cell-m}-EntryTime_{cell-m} \tag{1}\]  
The format of the input data is given in Table 1 which contains the information of all visited base stations (BSs) at a given time interval.  
Assume the mobility pattern as \(X_{m}=\{x_{1},x_{2}\ldots x_{m}\}\) recorded for a user entity (UE). The movement of a UE is denoted by \(x_{i}\) during a time interval \(t_{i}\) during which a call is made by UE. The mobility pattern \(X_{m}\) is defined as the cell number and the time duration spent in that particular cell.  
\(X_{i}\) represents a combination of \(u_{i},t_{i}\). Here, \(u_{i}\) represents the cell number and \(t_{i}\) represents the duration of time spent.  
For example, a mobility pattern is recorded for m = 3: i.e., transition from cell 1 to cell 3. The time spent is 10, 20, and 12 min in each cell, respectively. The mobility pattern may be written as:  
\[\begin{array}{l}X_{3}=\{x_{1},x_{2},x_{3}\}=\{(u_{1},t_{1}),(u_{2},t_{2}),(u _{3},t_{3})\}\\ =\{(1,10),(2,20),(3,12)\}\end{array} \tag{2}\]
# Designing Parallel Adaptive Laplacian Smoothing for Improving Tetrahedral Mesh Quality on the GPU
## 3 Parallel Adaptive Laplacian Mesh Smoothing on the GPU
### Step 2: Determining Boundary Nodes  
There are two types of nodes in a tetrahedral mesh: boundary nodes and internal nodes. Only the internal nodes are involved in the smoothing iteration, and the boundary nodes are used as mesh constraints to fix their coordinates. Therefore, it is necessary to determine which nodes are boundary nodes before iterating. The core idea of determining boundary nodes is straightforward. The face in a tetrahedral mesh shared by two tetrahedrones at the same time is an internal face, and all three nodes on the internal face are internal nodes, while the face that has only been used once is the boundary face, and the nodes that make up the boundary face are boundary nodes. The specific procedure to determine boundary nodes is as follows.  
1. Invoke all threads in the thread grid to traverse each tetrahedron in the mesh, and the node indices of four faces in each tetrahedron are stored from small to large in memory;
2. Sort all the faces in the array from small to large in parallel;
3. Compare each face in the array with its adjacent face in parallel to choose the boundary face that has been used only once and mark all three nodes of the boundary face as internal nodes saved in the CUDA kernel.  
Through the above operations, we add a new attribute value for all nodes in memory to facilitate the subsequent iterative calculation.
## 3 Results  
### Evaluation Methods  
Precision rate, Recall rate, and mean Average Precision \((mAP)\) indicators are dedicated to evaluating the model's performance. If the predicted result is true and the ground truth result is also true, the test result is regarded as a true positive (\(TP\)). If the predicted result is true and the ground truth result is false, the test result is regarded as a false positive (\(FP\)). If the predicted result is false but the ground truth result is true, the test result is regarded as a false negative (\(FN\)).  
Equation (5) defines the Precision rate. Precision describes the amount of the positives predicted by the detector as true positives. Equation (6) defines the Recall rate. Recall describes the amount of true positive examples recalled by the detector from the perspective of true results. When calculating Precision and Recall separately by gradually lowering the IOU thresholds that consider detections to be true. With Recall on the horizontal axis and Precision on the vertical axis formed precision-recall (P-R) curve; Equation (7) defines the \(mAP\) index. It describes the area enclosed by the P-R curve and the coordinate axis and characterizes the model's combined performance in terms of Precision and Recall. The experiments are implemented on an Nvidia RTX-2070 Super GPU.  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[mAP=\int_{0}^{0}P(R)dR \tag{7}\]
## 5 Conclusions  
In this work, an IRS-assisted D2D underlay cellular network is investigated and structured by the MDP model in the RL framework. The network's spectral efficiency is maximized by jointly optimizing the transmit power, the resource reuse indicators, and the IRS reflection coefficients. An RL-based solution architecture is proposed to solve the constructed optimization problem. First, the Q-learning-based solution scheme is utilized. Then, to make a scalable solution, a deep Q-learning solution scheme with experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem with the consideration of the continuous-valued state and action spaces. Simulation outcomes clearly demonstrate that the proposed RL-based solution schemes achieve a significant SE performance gain compared to the traditional underlay D2D network without RL under different parameter settings. This research can be continued to explore complex cases, such as multi-antenna base stations, multiple IRSs, and the multi-agent framework of the MDP model.  
Conceptualization, A.S.; methodology, A.S.; software, A.S.; validation, A.S.; formal analysis, A.S.; investigation, A.S.; writing--original draft preparation, A.S.; writing--review and editing, A.S. and X.F.; visualization, A.S.; supervision, X.F.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.