esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Example of Scenario States Visualization
#### 3.2.2 Using the Algorithm to Construct Scenario States
For example, knowing that there is one area of each type, we have no doubt that a vertex with the control \(AT1\) represents the same object in both states even though the support element assigned to each vertex is different between states. However, we do not have such certainty for vertices with controls  
Figure 10: A Gantt diagram for the scenario from the second example. Activities marked as \(t\) in the row preceded by \(Ox\) denote involvement of the element \(x\) (\(x\) is the unique identifier of a task element given at its first appearance or at the beginning of a scenario) during the activity \(t\). Only elements that are active objects are included in the diagram. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Phase** & **Step** & **Result/Comment** \\ \hline
3 & \(i_{c}=i_{n}\) & \(i_{c}=((1,2),(2,2))\) \\ \hline
3 & \(r=Corr_{R}(t_{tra})\) & \(r=\) reaction rule \(r2\) \\ \hline
3 & \(result=Phase4(s_{c},m_{c},r,s_{0},m_{0},n_{x})\) & Phase 4 completed without error \\ \hline
3 & \(s_{c},m_{c},n_{x}=result\) & \(s_{c}=\)\(\left\{(0,3),(3,0),(4,1),(1,4),(2,2)\right\}\) \\  & \(n_{x}=5\) \\ \hline
3 & End—ok & \\ \hline
1 & \(W_{c},s,m_{s},i_{s}=result\) & \(W_{c}=\emptyset\) \\  & \(s=\)\(\left\{(0,3),(3,0),(4,1),(1,4),(2,2)\right\}\) \\  & \(i_{s}=((1,2),(2,2))\) \\ \hline
1 & \(d=d+1\) & \(d=3\) \\ \hline
1 & \(S_{r}=S_{r}\cup\left\{((d-1,s,m_{s},i_{s}))\right\}\) & \(S_{r}=S_{r}\cup\left\{\left((0,3),(3,0),(4,1),(1,4),(2,2)\right),\right\}\) \\ \hline
1 & End—ok & \\ \hline \hline \end{tabular}
\end{table} 
Table 18: _Cont._of the type \(B\). Unique identifiers point to unique objects between states, even if those objects have changed the controls representing them. Here is an example based on the elements of set \(S_{r}\) from Table 18 how to use a unique identifier mapping. For the state at time 1, the UI with the value of 3 points to the vertex with identifier 1. This means that it is the same task element that in the state at time 0 is represented by the vertex with identifier value of 3 and the same element that at time 2 is represented by the vertex with support 0.
# Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm
## 3 LSTM-CRF Model with the Integrity Algorithm
### Integrity Algorithm
#### POS Syntactic Rules  
As shown in Tables 2 and 3, certain POS syntactic rules are obtained using the context of attributes and attribute values via a corpus analysis. Rule\({}_{\text{id}}\) represents the encoding of rules; description represents the specific content of the rule; and output represents the recognized elements. where Ds is the dependency relation between word W\({}_{i}\) and W\({}_{j}\) (W\({}_{k}\)) and Ds contains {VOB, ATT, ADV, COO, CMP,... }, where W\({}_{j}\) (W\({}_{k}\)) is the first or last n words of W\({}_{i}\). Therefore, the dependency relation is based on the context of W\({}_{i}\). POS encompasses the part-of-speech information for W\({}_{i}\). Con (W\({}_{i}\)-W\({}_{j}\)) represents the connection from W\({}_{i}\) to W\({}_{j}\). The arrows represent the dependency relations. For example, W\({}_{i}\)\(\rightarrow\)Ds\(\rightarrow\)W\({}_{j}\) denotes that Wi depends on W\({}_{j}\) through a syntactic relation Ds, where W\({}_{i}\) is the father node of W. R1\({}_{i}\) indicates that an attribute (Attr) or attribute value (Val) is identified using only one dependency relation, and R2\({}_{i}\) indicates that there is more than one type of dependency relationship.  
Figure 3: Dependency analysis results.
# Thrust Augmentation of Micro-Resistojets by Steady Micro-Jet Blowing into Planar Micro-Nozzle
## 4 Results and Discussion
Figure 10: 3D representation of the water vapor flow into the micro-nozzle through contour plot of the Mach number in combination with streamline visualization: (**a**) test case N2_1 (baseline); (**b**) test case N2_5 (additional mass flow rate, jet 1 = ON, jet 2 = OFF); (**c**) test case N2_6 (additional mass flow rate, jet 1 = ON, jet 2 = ON). Regarding the boundary layer development at sidewalls, the momentum thickness along the width-wise direction at the exit section in the symmetry plane has been computed. As shown in Figure 12a, it reveals the existence of a \(c_{\mu}\) threshold limit equal to 0.1, above which the beneficial effect of secondary injection on the viscous layer at walls becomes relevant. When comparing the actuated cases to the baseline configuration (test case N2_1), \(\theta_{xy}\) in presence of secondary injection is higher at \(c_{\mu}<0.1\), with maximum momentum thickness of about 30 \(\upmu\)m exhibited by the test case N2_2 operating at \(c_{\mu}=0.00858\). On the other hand, at \(c_{\mu}>0.1\), \(\theta_{xy}\) significantly drops down up to about 5 \(\upmu\)m, retrieved by activating both micro-jets at the highest momentum co-efficient, i.e., the test case N2_6. Concerning the secondary injection in water-propelled VLM mode (Figure 12b), similar beneficial impact of the micro-jet blowing active control is figured out: \(\theta_{xy}\) decreases up to about \(-57\%\) at the highest \(c_{\mu}=0.168\) (test case H2O_3), with steeper negative slope than the momentum thickness reduction curve retrieved for the nitrogen gaseous secondary injection (red dash-dotted line in Figure 12a).
## 3 Results and Discussion
### Mechanical Properties of Asphalt Mixtures Modified by Diatomite and Crumb Rubber Particles
#### 3.1.2 Anti-Crack at Low Temperature  
Figure 7 shows the calculation results of \(R_{B}\), \(\varepsilon_{B}\), and \(S_{B}\) of eight groups of asphalt mixture. It can be seen from Figure 7 that the framework dense asphalt mixtures have higher \(R_{B}\), \(\varepsilon_{B}\), and \(S_{B}\) than the suspended dense asphalt mixture. From the perspective of \(\varepsilon_{B}\), it can be seen that the framework dense asphalt mixtures have better resistance to low temperature cracking than suspension dense asphalt mixtures. Comparing the test data of AC and DRAC or SMA and DRSMA, it can be seen that the addition of diatomite and rubber particles can increase the bending tensile failure strain of the asphalt mixture, reduce the bending stiffness modulus, and improve the bending tensile strength of the asphalt mixture. The addition of diatomite and rubber particles can improve the flexibility of asphalt mixture, furthermore, improve the low-temperature crack resistance of asphalt mixture. In addition, it can be seen that the performance improvement of asphalt mixtures by adding diatomite and rubber particles is still a little different from SBS modifier comparing with SAC (SBS modified asphalt mixtures with suspended dense gradation) and DRAC or SSMA (SBS modified asphalt mixtures with framework dense gradation) and DRSMA data. The diatomite and rubber particles are mainly used to strengthen the asphalt mixture by physical blending, which is weaker than SBS chemical modification. Moreover, as easily deformed particles, rubber particles will form a local weak area in the asphalt mixture, which will have a negative impact on the mechanical performance of asphalt mixtures enhanced by diatomite. Comparing the data of SDRSMA (SBS reinforced DRSMA), SDRAC (SBS reinforced DRAC), and other experimental groups, it can be seen that the diatomite and rubber particle compound modified asphalt mixtures used SBS modified asphalt as a binder has a better low-temperature cracking resistance, but it will also increase the economic cost to a certain extent.  
Comparing the test data of different gradations, it can be found that the maximum flexural strain of DRAC and SDRAC is increased by 23.83% and 10.58% compared with AC and SAC, respectively, while the maximum tensile strain of DRSMA and SDRSMA is increased by 12.55% and 5.09%, respectively, compared with SMA and SSMA. This shows that diatomite and rubber particles can improve the low-temperature performance of suspended dense asphalt mixtures better than the improvement of the low-temperature anti-cracking performance of framework dense asphalt mixtures, which is mainly attributable to the fact that the gradation of the dense-framed mixture SMA is highly dependent on the mineral material. The addition of rubber particles will affect the skeletal structure of the SMA during the fabricating process. Therefore, the rubber particles have a greater adverse effect on the mechanical properties of the SMA-graded asphalt mixture.
## 2 Automatic POD Diagnosis Method
### Single-Layer Convolutional Neural Network  
After the RDCA is established, an effective classifier is required to classify the encoded features. The general classifier uses the softmax classifier with a fully connected (FC)  
Figure 3: The weight residual structure of RDCA.  
layer [27]. However, this classifier is easily troubled by high dimensional data, especially when the output of RDCA is high dimensional.  
In this section, we propose a single-layer CNN, which consists of a convolutional layer, a pooling layer, a BN, and a FC layer. The single convolutional layer also enhances flexibility of auto-encoder, without considering the depth of the convolution filter in encoder. Followed by the pooling layer, we use the BN layer to speed up training. The output of the BN layer is set as \(\mathbf{z}_{c}\), and it is reshaped to a sequence to connect the FC layer.  
Then the feature vector \(\mathbf{z}_{c}\), an image containing abstract features, is fed to the softmax classifier. We set \(k\) as the total number of PQDs. The output of softmax classifier is obtained as  
\[P(y=j\mid\mathbf{z}_{c},\theta)=\frac{e^{\theta^{T}\mathbf{x}_{c}}}{\sum_{i=1 }^{k}e^{\theta^{T}\mathbf{z}_{c}}} \tag{12}\]  
where \(\theta\) and \(y\) denote the parameters of softmax classifier and output class, respectively. Normally, the category with the highest probability of PQDs can be specified as the final class. To further reduce the over-fitting under multiple combined disturbances, the dropout is applied to softmax classifier, which can randomly omit certain probability neural nodes [27].
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Parametric Study of Modified Antenna
#### 2.3.3 Changing DGS Length, L1  
The length of DGS affects the bandwidth of the antenna at 4.4 GHz, as shown in Figure 11. A smaller value of L1 slightly decreases the BW of the antenna and enhances the reflection coefficient of the antenna. A larger value of L1 enhances the bandwidth but greatly increases the reflection coefficient.  
Figure 11: Effects of DGS Length L1.  
Figure 10: Effects of Changing W2.  
2.3.4. Changing Parasitic Element Length, L2  
Increasing L2 results in a similar BW but also increases the reflection coefficient, as shown in Figure 12. Reducing L2 increases the BW but introduces a second resonant frequency at 8.8 GHz and increases the reflection coefficient.
## 4 Results and Discussion
### Sensitivity Index
The solvent mixture DEA/MDEA and DEA exhibited the least sensitivity to variability. MEA/MDEA, MPA/MDEA, MAPA, and AMP seemed to present the same behavior. In the region of \(\zeta=\) [\(-\)0.1, \(-\)0.2], DEA/MDEA continued to be the most promising mixture. DEEA/MAPA and MCA presented the same behavior. MEA/MDEA and MPA/MDEA exhibited the same profile. DEA, AMP, and MAPA seemed to be sensitive to variations. Solvents that had a high sensitivity were MEA and AMP/PZ. It is worth noting that the slopes of the DEA/MDEA lines in either of the favorable or unfavorable scenarios were close to zero for the entire range of the considered variability in Figures 3-5. DEA was also always close to DEA/MDEA but its controllability performance was slightly worse. On the other hand, MEA had a very steep slope and a much worse controllability performance. However, its slope became less steep when combined with MDEA. The concentration of MDEA was twice the concentration of MEA, MPA, or DEA; hence, this is an indication that MDEA could exhibit desirable controllability behavior.
# Extraction of Bioactive Compounds from _Ulva lactuca_
## 3 Results and Discussion
### Extraction of Phenolics and Carotenoids--Selection of Solvent
#### 3.3.5 Study of the Extracts' Antioxidant Activity over Time  
The evaluation of the antioxidant activity of the best _U. lactuca_ extract (E12) over time was studied at two levels. First, the extract's scavenging activity was examined versus time when in contact with free radicals. Second, the period during which the extract preserves its antioxidant activity when kept in storage was determined.  
The antioxidant activity of the E12 extract was measured exactly after 20 min, 30 min, 60 min, and 90 min of DPPH incubation to determine how rapidly the antioxidant compounds act. As shown in Figure 5A, the scavenging activity of the extract exhibited a linear decrease throughout the 90 min of study. The highest rate of DPPH decay occurred within the first 20 min of incubation, denoting a rapid reaction of the mixture of antioxidant species present in the extract with the free radicals. According to [PERSON] et al., 2012 [42], the expression of the results in terms of a kinetic approach not only considers the activity of the antioxidants but also provides information on how quickly they act, which is probably the result of the different kinetic behavior that the various antioxidants species present in the extract exhibit.  
Figure 5: Percent scavenging activity (% SCA) of _U. lactuca_ extract E12 versus time (min) (**A**) and antioxidant activity (IC50) of _U. lactuca_ extract E12 when stored at 4 \({}^{\circ}\)C in the dark for 15 days (**B**). Experimental data are mean values of three replications.  
Additionally, in order to test the effect of the storage time on the antioxidant activity, the optimum extract's IC50 was measured in frequent intervals for 15 days. When stored at 4 \({}^{\circ}\)C in the dark to minimize the degradation of carotenoids [39], only a slight decrease in the extract's antioxidant capacity was evident for the first 5 days of storage, followed by a drastic decrease thereon, as depicted in Figure 5B.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 8 Typical Use Cases of LoRaMesh  
IoT applications vary drastically, as do their requirements regarding power consumption, throughput, latency, coverage, cost, and QoS. In this section, we will only briefly highlight two examples of LPWAN IoT applications that could utilise the advantages of LoRaMesh vs. LoRaWAN.  
LPWAN technology assists the agriculture sector by providing improved monitoring and precise control, in sectors such as water management, irrigation management, livestock monitoring, and precision agriculture. A study in [36] identified reliability, QoS, and scalability as the top requirements in smart-agriculture IoT transceivers. Another area where LPWAN networks have become quite prevalent is in smart cities in sectors such as parking, waste management, lighting, infrastructure monitoring, and smart grids. The requirements for these applications overlap significantly with those of smart agriculture.  
LoRaMesh offers two main advantages above LoRaWAN to exceed in both power consumption, coverage, and QoS:  
* Extended coverage, through multi-hop networks. Devices further away from a gateway or with significant attenuation can receive network coverage. This not only improves coverage in extreme use-cases, but also reduces the number of GW's needed to cover an area, thereby reducing the cost of network deployment.
* Improved power consumption for nodes in extreme environments. Nodes located further from the GW do not need to default to a higher SF to reach a GW, thereby increasing ToA and power consumption. This is a significant advantage as nodes with attenuation are typically located in inhospitable environments. An increased battery lifetime can thereby reduce maintenance requirements.
## 2 Materials and Methods
### Fatty Acid Methyl Ester (FAME) Analysis and Biodiesel Quality Assessment  
#### 2.6.1 FAME Analysis  
FAMEs were extracted following a modified version of [PERSON]'s method ([PERSON] et al., 2013 [35]). Lyophilized biomass (30 mg) was disrupted using a Micromixer E-36 (Taitec, Saitama, Japan), Vortex-Genie (Scientific Industries, Bohemia, NY, USA), and a sonicator bath (Branson-5210, Branson Inc., Danbury, CT, USA) in the presence of glass beads and a 4:5 (\(v/v\)) mixture of chloroform (CHCl\({}_{3}\)) and MeOH. Phase separation was achieved by adding 2.5 mL of water containing 50 mM Tris and 1 M NaCl. Lipids were extracted through the repeated addition of CHCl\({}_{3}\) and sonication. The extracted lipids were then concentrated by evaporating CHCl\({}_{3}\) under reduced pressure using a rotary evaporator. The concentrated lipids were subsequently transesterified into FAMEs using 3 mL of MeOH containing 5% (\(v/v\)) sulfuric acid. The resulting hexane phase was filtered through a 0.2 \(\upmu\)m polyvinylidene fluoride syringe filter (Chromdisc, Hwaseong, Republic of Korea). The FAME components were analyzed by gas chromatography using an Agilent 7890 B system (Santa Clara, CA, USA) equipped with a mass selective detector (MSD, 5977 B, Agilent) and a DB-FFAP column (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m; Agilent). Additionally, compound identification was based on comparing the mass spectra with those in the Wiley/NBS library, with matches over 90% considered valid.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 4 Computational Results
### Test Problem P8 Results and Comparisons  
As stated previously, the eight facility test problem, P8, was randomly generated. See Table 4 for a summary of the results. The proposed GA obtained a TWD of 5667.17 for each of the 1-min runs (total of five runs). However, using the MILP presented earlier and the CPLEX solver, the optimal solution was obtained in 21.3 h of computation time (CP), which gives the minimum TWD of 5637.16 (bolded in Table 4). Although the proposed GA did not obtain the optimal solution, the TWD obtained is only 0.53% (less than 1%) above the optimal TWD. Note, % Improvement = 100 \(\times\) (Best TWD \(-\) TWD)/Best TWD (e.g., % Improvement = 100 \(\times\) (5637.16 \(-\) 5667.17)/5637.16 = \(-\)0.53%). Although attempts were made to improve the results for the proposed GA by running the test problems for 5 min, increasing the number of runs, etc., the same TWD of 5667.17 was obtained. It is well known that stochastic heuristics like GA and simulated annealing may obtain solutions in the vicinity of the local optima, without ever converging to the local optima. Therefore, a steepest descent heuristic, as in [PERSON] et al. (2018), can be used to ensure that the solutions obtained are local optimums. After running a simple tabu search heuristic on the final solutions obtained by the proposed GA, the optimal solutions were obtained after only a few iterations.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm
### Minimum-Energy-Based Imaging Algorithm
#### 3.2.4 Interpolation for Coarse Observation  
Since the coarse observations from Sat-1, which are processed by the existing algorithms [1; 2; 3], are used as the weighting coefficients on pixels in each range gate of the final imaging result, the nearest neighbor interpolation algorithm [24] is adopted to ensure that the coarse observations are consistent with the final image's sampling space. The interpolation principle can be expressed as  
\[\boldsymbol{\sigma}_{d}(\widetilde{x},\widetilde{y})=\boldsymbol{\sigma}_{d}^ {\prime}\biggl{(}\left[\frac{\widetilde{x}}{\Delta d_{x}/\Delta d_{x}^{\prime }}\right],\left[\frac{\widetilde{y}}{\Delta d_{y}/\Delta d_{y}^{\prime}} \right]\biggr{)} \tag{12}\]  
where \((\widetilde{x},\widetilde{y})\) is the 2D coordinates of pixels in the interpolated result, and \(\boldsymbol{\sigma}_{d}^{\prime}\) and \(\boldsymbol{\sigma}_{d}\) are the image amplitudes before and after the interpolation, respectively. \(\Delta d_{x}\) and \(\Delta d_{x}^{\prime}\) are the sampling space before and after interpolation along the X direction, respectively, and \(\Delta d_{y}\) and \(\Delta d_{y}^{\prime}\) are the sampling space before and after interpolation along Y direction, respectively. The diagram of interpolation is shown in Figure 8.  
Figure 7: Illustration of the extraction of \(\mathbf{S}_{i}\). (**a**) Diagram of the \(i\)th range gate, where each point represents a pixel. (**b**) Illustration of the RCM correction of the compressed echo of the \(i\)th range gate.
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
In the Key Generation (SKG_S_KEXMPTCP) module, considering host [PERSON] and host [PERSON] are communicating with each other with IP addresses IP\({}_{\text{[PERSON]}}\) and IP\({}_{\text{[PERSON]}}\) using MPTCP, here IP\({}_{\text{[PERSON]}}\) and IP\({}_{\text{[PERSON]}}\) will be used as public parameters by PKG to generate private keys for [PERSON] and [PERSON]. IBE uses PKG, a third-party authority, which provides the private keys to the communicating hosts based on their identity (i.e., email id, IP address, etc.). Here, the IP address and port of the communicating host will be used as a public parameter to generate the private keys for the sender and receiver. These private keys will be used in the MPC_S_KEXMTCP module to exchange the keys during the initial handshake by encrypting them using IBE. The communicating host will be authenticated by using the IP Address and port combination digitally signed by the host at the PKG. By using the IBE with MPTCP, the issue related to key distribution can be resolved, and the session key exchanged during the initial handshake can be secured by using SKEXMTCP. The main contributions of this paper are as follows:  
* MPTCP security threats are examined, and how threats can be used to launch session hijacking attacks is demonstrated. * The existing solutions to the various security threats of MPTCP are analyzed. * IBE is compared with the elliptic curve cryptosystem (ECC) in terms of performance and security. * SKEXMTCP using IBE is proposed and evaluated in terms of security. The structure of the paper is as follows: In Section 2, the background theory required to understand MPTCP and its vulnerabilities is discussed along with IBE. Section 3 covers the proposed SKEXMTCP. Section 4 analyzes the security of the proposed work and compares it with other solutions.