esa-sceva/satcom-chunk-collection huggingface dataset:
# Albatross-Like Utilization of Wind Gradient for Unpowered Flight of Fixed-Wing Aircraft
## 5 Simulation Results and Discussion
### Discussion of Criterion for Unpowered Climbing
#### 5.1.2 Wing Loading  
According to Equation (22), a large wing loading produces a small wing loading fraction \(\Pi_{\text{S}}\). Therefore, a larger wing loading is preferred for effortless climbing. This may seem incredible considering that traditional aeronautics textbooks suppose that a small wing loading and high aspect ratio wing are favorable to climbing, with the former inducing a low speed, which reduces the drag and power consumption. However, the important consideration here is the energy harvested from the  
Figure 7: Plot of \(\Pi_{A}\) as a function of \(C_{L}\) for an albatross, showing the optimal value of \(C_{L}\).  
wind gradient for unpowered flight, rather than power conservation in powered flight. The speed increases due to the larger wing loading, which results in the aircraft crossing more wind layers and collecting more energy during a given period. This is intuitive from an energy viewpoint.  
A large aircraft generally has a larger wing load compared to a small one. [PERSON] (1966) proposed a model for estimating the wing loading based on a cubic scaling law. He described the relationship by defining the following upper and lower boundaries:  
\[\frac{m}{s}\leq 18.67(9.8m)^{\frac{1}{3}}-1.01\qquad Upper\quad boundary\quad\text{and} \tag{53}\]  
and  
\[\frac{m}{s}\geq 9.78(9.8m)^{\frac{1}{3}}-1.01\qquad Lower\quad boundary \tag{54}\]  
[PERSON] (1966) validated this estimate using an aircraft database and the Great Flight diagram. Many other models have been proposed for different types of aerial vehicles [PERSON] (1966). Although a large wing loading is favorable for climbing, the applicable loading is limited by the structural strength, cruising speed, and mission configuration.
## 4 Results and Discussions  
### Network Structure  
The three functional connectivity networks are shown in Figure 3 with the empirical probability distributions of the weights in semi-logarithmic scale. The three matrices are very different form each other: the SYNC network is sparser, with only a small number of entries with high synchronization values. This aspect is also emphasized by the probability distribution which shows a right long-tailed trend, with few outliers between 0.2 and 1. This finding highlights a fist important difference between the connectivity matrices, i.e., most regions exhibit a very intermittent synchronization behavior, although on average the pairs of ROIs are moderately statistically correlated in time and frequency (median value of Pearson matrix \(\sim\)0.3 and median value of coherence matrix \(\sim\)0.25). The same results were found for all the sessions of the subjects as reported in Figures SI-1 and SI-2. Additionally, the Wilcoxon rank sum test was used to compare the distributions of each couple of metrics, i.e., SYNC-Pearson, SYNC-coherence, Pearson-coherence within each session. For each comparison we found \(p<0.0001\) confirming that the connectivity matrices are statistically different at 5% significance level.
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 5 Architecture Description and Evaluation  
### The Proposed Architecture  
The proposed architecture of this work is showcased in Figure 5. It addresses lightweight authentication and key establishment for NB-IoT IoT devices. It is of note that the architecture is generic for both a Radio Access Network (RAN) and an authentication protocol. In this sense, the RAN employed in its implementation could be LTE in the case of 4G or New Radio (NR) for the case of 5G systems. Likewise, for the case of the authentication protocol, the proposed architecture may be implemented for the best suited EAP lower layer. The IoT device is the device that needs to perform the authentication and key establishment process. It contains the needed hardware and software to perform a connection to the Core through an NB-IoT RAN code and include at least one NB-IoT-compatible radio modem that is capable of communicating over the chosen radio band configured by the Mobile Operator. Hence, any resource-constrained IoT device that is capable of implementing the authentication binary code will be compatible with this proposal. More details on this are presented in Section 6.1. The RAN represents the end of the constrained radio link and is composed by base stations, compatible with either LTE (eNodeB) or NR (gNB), depending on the specific implementation. The Core is the element that manages the IoT device's serving network and its connection to data networks, in this case, the Internet. The IoT controller is the EAP authenticator that steers the process; it implements the lightweight authentication protocol employed by the end-device. In addition, the IoT controller communicates through an AAA protocol with the AAA server.  
First, when powered on, the IoT device will perform the RAN attachment against a base station. Once attached to the base station, it will connect to the serving network provided by the Core. Next, in turn,  
Figure 4: Diagram of a precise agriculture use case.  
the Core will activate the required mechanism that provides the functionality of sending packets to the external data network, i.e., the Internet. The IoT device will then send the user plane IP/IPv6 packets addressed to the IoT controller. These packets carry an authentication protocol data unit (PDU), which can be instantiated in either PANA or LO-CoAP-EAP. Finally, the IoT device will establish a security association with the IoT controller through the EAP. As a result of this process, the IoT device will be considered as authenticated and derive crypto key materials.
## 4 Experimental Procedures
### Quantification of Infectious HCV Virions and HCV RNA  
Viral titers were determined by limiting dilution analysis of culture supernatants, as described before [82,88]. Briefly, medium from each sample in the siRNA transfection/JFH-1 infection experiments was collected and stored at -20 \({}^{\circ}\)C. Cell supernatants were serially diluted 10-fold in complete DMEM. Naive Huh 7.5 cells were grown on cover slips in 24-well plates and infected with the transfection sample medium for 2h at 37 \({}^{\circ}\)C and then supplemented with fresh complete DMEM. The level of HCV infection was determined 48 hours post-infection by immunofluorescence staining for HCV NS5A.  
For immunofluorescence assay, cells were extensively washed with PBS, fixed with 4% paraformaldehyde, and permeabilized with 0.2% Triton X-100. Fixed cells were blocked with 1% bovine serum albumin and 1% normal goat serum in PBS. HCV NS5A protein was detected in cells by incubation with an NS5A-specific monoclonal antibody and visualized with the secondary goat anti-mouse IgG conjugated with Alexa Fluor 594 fluorescein (Invitrogen, 1:1000 dilutions). Cover slips were mounted onto slides with DAPI (Vector labs), and the HCV NS5A were visualized by fluorescence microscopy (Nikon E400). The viral titer is expressed as focus-forming units per milliliter of supernatant (ffu/mL), as determined by the average number of NS5A-positive foci detected by immunofluorescence for NS5A.  
For HCV RNA quantification, total cellular RNA was harvested and purified using Trizol Reagent (Invitrogen), reverse transcribed to cDNA using SuperScript III; Invitrogen. Real-time PCR was performed using Universal Probe Library (as described above) and the JFH-1 5 UTR specific primers 5'-CATGGGCGTTAGTATGAGGTCg-3' and 5'-GGTTCCGAGACCACTATG-3'.
###### Abstract  
Nowadays, the accurate identification of natural frequencies and damping ratios play an important role in smart civil engineering, since they can be used for seismic design, vibration control, and condition assessment, among others. To achieve it in practical way, it is required to instrument the structure and apply techniques which are able to deal with noise-corrupted and non-linear signals, as they are common features in real-life civil structures. In this article, a two-step strategy is proposed for performing accurate modal parameters identification in an automated manner. In the first step, it is obtained and decomposed the measured signals using the natural excitation technique and the synchrosqueezed wavelet transform, respectively. Then, the second step estimates the modal parameters by solving an optimization problem employing a genetic algorithm-based approach, where the micropopulation concept is used to improve the speed convergence as well as the accuracy of the estimated values. The accuracy and effectiveness of the proposal are tested using both the simulated response of a benchmark structure and the measurements of a real eight-story building. The obtained results show that the proposed strategy can estimate the modal parameters accurately, indicating than the proposal can be considered as an alternative to perform the abovementioned task.  
smart civil structures; modal parameter estimation; synchrosqueezed wavelet transform; optimization problem; genetic algorithms; micropopulation +
Footnote †: journal: Applied Sciences
## 3 Test Results and Analysis
### Relative Dynamic Modulus of Elasticity After Freeze-Thaw Cycles  
The relative dynamic elastic modulus of the specimens was calculated by carrying out acoustic tests using a wave velocity meter to determine the relative dynamic elastic modulus of the specimens after different freezing and thawing cycles. The formula is as follows:  
\[p_{i}=\frac{f_{i}^{2}}{f_{0}^{2}}\times 100\% \tag{2}\]  
where \(P_{i}\) is the relative dynamic elastic modulus of the specimen after n freeze-thaw cycles, \(\%;f_{1}\) is the transverse fundamental frequency of the specimen after n freeze-thaw cycles, Hz; and \(f_{0}\) is the transverse fundamental frequency of the specimen before the freeze-thaw cycles, Hz.  
The relative dynamic elastic modulus of the specimen under different freeze-thaw cycles is shown in Figure 5. With the increase in the number of freeze-thaw cycles, the relative dynamic elastic modulus of the specimen decreases. This is because the small cracks in the specimen's interior and surface continue to increase and connect throughout the process of the freezing and thawing cycles. The small cracks become larger, the specimen's interior and surface show a brittle phenomenon, and the specimen breaks, causing the relative dynamic elastic modulus of the specimen to continuously decrease. It can be clearly seen from the figure that the relative dynamic elastic modulus of groups B-5, C-5, and D-5 is significantly smaller than that of group A-5, which proves that the addition of rubber can significantly improve the frost resistance of concrete. The relative dynamic modulus of elasticity of groups C-5 and D-5 is not much different but is slightly better than that of the B-5 group. Therefore, the frost resistance of concrete can be better improved when the rubber particles are smaller.  
Figure 4: Variation in mass loss rate with the freeze–thaw cycle.  
Figure 5: Variation in relative dynamic elastic modulus with the number of freeze–thaw cycles.
## 5 Simulation Results and Analysis
### Simulation Result
Figure 9 compares the impact of different algorithms on average AoI in a scenario with 50 vehicles. It can be observed that as message size increases, average AoI also tends to increase. This is because the larger size of messages requires higher transmission rates, necessitating greater bandwidth and higher SINR. Among the three algorithms depicted above, MPDQN generally achieves the lowest average AoI, followed by the GA algorithm, highlighting the effectiveness of MPDQN. Figure 10 compares the influence of different algorithms on average energy consumption in the same scenario as Figure 9. Here, energy consumption is averaged over the number of vehicles depicted in Figure 8. Consistent with the previous findings, MPDQN outperforms GA and random algorithms, ensuring lower energy consumption during vehicle communication in the scenario. Figure 8: Average energy consumption with \(N_{v}\) under different algorithms.
## 4 Simulation and Analysis  
To validate the accuracy of models and the effectiveness of the algorithms in this study, three simulation experiments are performed in this section, and simulation results are analyzed according to several evaluation indexes. Firstly, all initial parameters used in simulations are given in the following Section. Then, for situations of three different typical maneuvers performed by the target, classical discrete PID control method [28], AFC method, and the method proposed in this study are respectively used to solve the aforementioned UCAV attack guidance model, and the guidance effects are compared and analyzed.
## 2 Figure of Performance for Analysis
### Gdop  
The geometry arrangement of satellites, as well as satellite visibility, is a factor that affects navigation performance. Assuming position estimation and estimated clock bias error as \(\Delta\mathbf{x}=\overset{\star}{\mathbf{x}}-\mathbf{x}\), \(\Delta b=\hat{b}-b\), and the measurement errors are zero-mean, uncorrelated, and identically distributed, the covariance is calculated as follows:  
\[Cov\bigg{[}\begin{array}{c}\Delta\mathbf{x}\\ \Delta b\end{array}\bigg{]}=Cov\bigg{[}\begin{array}{c}\overset{\star}{ \mathbf{x}}\\ \hat{b}\end{array}\bigg{]}=\sigma^{2}\big{(}\mathbf{G}^{\rm{T}}\mathbf{G} \big{)}^{-1}=\sigma^{2}\mathbf{H} \tag{4}\]  
where \(\mathbf{G}\) is the geometry matrix, \(\mathbf{x}=(x,y,z)^{\rm{T}}\) is the nominal vector of the user's ECEF position estimation, \(b\) is the range equivalent of clock bias, and \(\sigma\) is the total UERE composed of the satellite clock and ephemeris error, atmospheric error, receiver noise, and multipath expressed in units of distance. The diagonal components of the H-matrix are used to indicate the variance of the \((x,y,z)\) components of the position error and the clock bias.  
The geometric dilution of precision (GDOP) and root-mean-squared error of the three-dimensional position are expressed in (9) and (10), respectively.  
\[\rm{GDOP}=\sqrt{H_{11}+H_{22}+H_{33}+H_{44}} \tag{5}\]  
Figure 2: Sequential search acquisition schema.  
RMS(3D position and clock bias estimation error) = \(\sigma\)-GDOP (6)  
In general, GDOP is about two to three for open ground areas. Since the GDOP computed as above is affected by the number of visible satellites, visibility more than minimum number of satellites contributes to the improvement of the GDOP performance. Thus, the increase of satellite visibility using side lobe signals will eventually influence navigation performance.
## 2 Materials and Methods
### The 3 PG-MT-LSTM Hybrid Modelling Approach
#### 2.2.3 Loss Function  
In this study, the loss function used in the MT-LSTM model consists of mean squared error (\(MSE\)) loss function and biomass compatibility rules. MSE loss function is defined as the average of squared differences between the actual and the predicted value (Equation (7)). In the MT-LSTM model, stem (\(y_{stem}\)), root (\(y_{root}\)), leaf biomass (\(y_{leaf}\)), aboveground biomass (\(y_{aboveground}\)), and total biomass (\(y_{total}\)) were all estimated independently. The addition of biomass correlation ensures the compatibility of the model, which is important for accurate biomass estimation. The rule is based on the principle of mass balance, which states that the total biomass in the system must always be consistent with the sum of the components. The following equations (Equations (8) and (9)) are the biomass correlation rules added to the loss function:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(y_{i\_true}-y_{i\_prediction}\right)^{2} \tag{7}\]  
\[y_{total}=y_{aboveground}+y_{root} \tag{8}\]  
\[y_{aboveground}=y_{stem}+y_{leaf} \tag{9}\]  
In Equation (7), \(n\) is the number of samples, while \(y_{i\_true}\) and \(y_{i\_prediction}\) are the observed value of the \(i\)th sample and its corresponding predicted value, respectively. \(y_{total}\). \(y_{aboveground}\), \(y_{root}\), \(y_{stem}\), and \(y_{leaf}\) are the predicted values of the total, aboveground, root, stem (with bark and branch), and leaf biomass of the forest stand, respectively.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### RSSI Based Solutions
#### 4.3.4 Enhanced Hand-Off Mechanism  
Authors [65] proposed an Enhanced Hand-off based stable route selection. DIS and DIO messages in RPL are used as a beacon message. In this mechanism, the mobile IoT node transmits the frame width, a variable is used for measuring the link quality from the number of packets needed in a defined time period, a DIO message is received as a unicast acknowledgment packet encompassing average received signal strength (ARSSI) level which helps in filtering asymmetric links. Mobile nodes continue with data transmission by detecting the justifiable quality. Neighbourhood Discovery (ND) is initiated if the ARSSI link is degraded and data transmissions are resumed once the ARSSI level is suitable. For examining the best parent node, mobile nodes do a comparison of ARSSI present in the DIO messages of individual nodes with the predefined threshold level. In case ARSSI is less than the threshold, the mobile nodes continue broadcasting DIS messages at defined intervals. Alternatively, if ARSSI is above the threshold, the process of network discovery stops and mobile nodes connect to the best route with an updated parent node for performing data communications. The simulation results show that with an increase in traffic burst interval of 2 s, 85% PDR is achieved by this mechanism. Furthermore, the increase in traffic impacts the parent node discovery so for data traffic interval of 2 s, the hand-off and route formation delay rise to 60 ms. Similarly, the traffic overhead is estimated by the control messages sent for selecting the best path; for traffic intervals of 2 s, an overhead of 18% is obtained.
# Analysis of Scintillation Effects on Free Space Optical Communication Links in South Africa
## 7 Average Bit Error Rate (BER) Analysis
### Binary Phase Shift Keying (BPSK) FSOC Links
Figure 7: BER of BPSK FSOC links vs. average SNR (dB) over weak and moderate to strong atmospheric turbulence channels for various cities in South Africa. Equations (1)-(3), (15), (35), (38), (40), (41), (46) and (76) are employed for plotting the BER results of the FSOC links deployed in all the cities in Figure 7b-d, while Figure 7a is computed using Equations (1)-(3), (15), (30), (31), (35), (46) and (79). Figure 7a-d also show that the presence of finite microscale and macroscale eddies result in poorer BER performances when compared with instances when those eddies have sizes of zero and infinity in the Kolmogorov model with infinitely large inertial range. In order to obtain a BER of \(10^{-2}\) over a link distance of 2 km for FSOC links in all the cities investigated, receive SNRs of \(\sim\)84 and \(\sim\)81 dB are required based on the finite inner and finite outer scale model in Figures 6a and 7a, respectively. The BPSK FSOC links investigated in Figure 7a-d generally outperform the OOK FSOC links in Figure 6a-d.
# Natural Virtual Reality User Interface to Define Assembly Sequences for Digital Human Models
## 2 Materials and Methods
### Technical System
#### 2.2.3 Grip Type Recognition System  
IPS IMMA has a preconfigured database with nine different grip types: tip pinch, chuck grip, lateral pinch, pistol grip, prismatic four-finger pinch, spherical grip, cylindrical power grip, diagonal power grip and parallel extension (Figure 5). Engineers use the grip types in IPS IMMA and attach a selected grip from the database to the object which is used for the ergonomic assessment. To achieve the same results with the WIMP interface and the VATS, the VATS needs to detect the grip types of the database. If the system were not able to distinguish between the grip types and use the finger angles to define how the DHM grabs the object, it would be too complicated, almost impossible, for engineers to reproduce the same simulation results. Engineers would then have to define each finger angle independently. This makes it necessary to detect the nine different grip types from IPS IMMA.  
The backend of the grip type recognition system is SciKit Learn [29]. It provides several machine-learning algorithms and can easily be integrated into other software. As we already know the categories (grip types), we implemented a supervised classification algorithm. To identify the appropriate algorithm, we recorded between 120 and 400 datasets for each grip type.  
Figure 4: Visualized distance measurements between the hand and the virtual object.  
Figure 5: The implemented grip types of the grip type recognition system.  
Two expert users used the leap motion to grab the objects with the nine different grip types. Figure 6 presents the objects. One dataset contains the following values:  
* There were 14 joint angles: angles of one hand: two for the thumb, three for index, middle, ring, and pinky;
* Four finger distances: distances between thumb to index finger; index to the middle finger, middle finger to ring finger and ring finger to pinky finger;
* Four thumb distances between the thumb and the index, middle, ring and pinky finger;
* Two values for the orientation of the hand;
* Six binary values: information if a finger or the palm is in contact with the surface.  
We split the data into 70% training data and 30% testing data and compared the correct prediction between the random forest classifier (0.98), nearest neighbors' classifier (0.93), as well as the support vector classification (0.96). These results show that the random forest classifier performs best for this application. The grip type recognition system analyzes the features and sends back the highest-ranked grip types. Users intend to define a specific grip type. If the system recognizes the wrong grip type, the manipulation in VR still works. Still, to be able to conduct the ergonomic simulation, the user then has to perform the task again with the correct grip type. The recognized grip type is used as the input for the DHM IMMA for ergonomic simulations.
## 5 Results and Discussions  
The results of theoretical analyses and simulation studies are presented with tables and time response plots to evaluate the performance of the proposed control strategies involving LQR, PSO, and fractional-order controllers implemented to cope with the adverse dynamics of the pitch maneuvers under various fault conditions, namely, various degrees of losses in the actuator effectiveness due to hydraulic fluid leakages or insufficient lubrication. As a further comparison, the pole placement controller is applied under the same conditions. The results produced by the components of the proposed control strategy and that of others are presented under individual subsections to exhibit the achievements.