esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Assessment Results  
#### 4.3.1 Comparison with Landsat 7 ETM+ Data  
The RST-FLOOD results referring to 5 December 2013 were superimposed on the water affected areas shown in Figure 3c in order to assess their reliability. By comparing the areas detected as flooded by RST-FLOOD (orange pixels in Figure 10) and the change detection scheme, described in Section 3.2, on Landsat 7 ETM+ data (Figure 3c), a very good level of agreement can be observed (Figure 11).  
Across the scene, 191 and 357 pixels were flagged as flooded in the Landsat and RST-FLOOD map, respectively, with 120 common detections (Figure 11a). The remaining Landsat water affected pixels, not detected by RST-FLOOD, are around the lakes/ dams (35%) and along Basento and Bradano rivers (65%). Within the box (Figure 11b), \(\sim\)90% of total pixels detected by Landsat (i.e., 104) are also identified by RST-FLOOD that found a total of 293 pixels. Those remaining are all along the Basento and Agri rivers and were not detected by Landsat due the already-mentioned data gap acquisition.  
Figure 10: Flooded areas detected within the ROI by ALICE\({}_{\text{NDSI}}\)\(\geq\)3 on days between 4–8 December 2013.
# Non-Destructive Infrared Evaluation of Thermo-Physical Parameters in Bamboo Specimens
## 3 Inverse Problem Formulation
Even though we carefully choose the initial guesses as described above, when optimizing the three parameters at once, the results are not satisfactory. For any initial guess, the algorithm finds a convergence point that lies on a three-dimensional line as shown in Figure 4. Those optimal points are all equivalent and are in predefined boundaries, so they all could be a possible solution. However, they differ from the true parameter values. This undesirable behavior that does not allow the algorithm to be robust against initial conditions is a consequence of highly correlated parameters. Here, we deal with the problem by reducing the complexity of the inverse heat problem by optimizing over a two-dimensional space, where the parameter vector is now \(p=(k,\rho c_{p})\) while \(h\) is assumed constant. This action improves the algorithm robustness against initial conditions, reducing uncertainty in the estimation quantified through the asymptotical standard error \(\sigma_{p}\). Table 1a,b show the results of the curve-fitting scenario aforementioned. It is worth recalling that only two parameters \(k\) and \(\rho c_{p}\) are optimized, while \(h\) is assumed known. For this case, we try to fit a noisy temperature curve with parameters \(k=0.146\) W/m\(\cdot\)K, \(\rho c_{p}=3.5\times 10^{6}\) J/m\({}^{3}\cdot\)K, and \(h=10\) W/m\({}^{2}\cdot\)K. In a realistic scenario, the value of \(h\) would be difficult to find. That is why we assume different values for \(h\) keeping them in the natural convection range from 2 to 25 J/m\({}^{3}\cdot\)K, and then observe how these blindly chosen \(h\) values affect the estimation by using the relative error. Table 1a shows these results. \(p_{fit}^{-}\) is the average convergence value that results from initializing the LM algorithm with 50 initial guesses obtained as explained in previous paragraphs. As seen in the table, the standard deviation of these estimates is of order \(10^{-8}\) approximately, which shows that the algorithm responds well to initial conditions' variability. We see as well that when \(h\) is chosen mistakenly, the relative errors are very high; they can reach values up to 150%, which is unacceptable. Nonetheless, Table 1b shows that dividing \(k\) into \(\rho c_{p}\), the LM algorithm accurately retrieves thermal diffusivity (\(\alpha_{fit}\)) with an error up to 0.395% for any value of \(h\). As a consequence, estimating the thermal diffusivity of the material makes the LM algorithm less sensitive to the unawareness of \(h\).
# Improving the Design of MEMS INS-Aided PLLs for GNSS Carrier Phase Measurement under High Dynamics
## 4 Testing and Verification
### Description of the Test Method  
A spinning platform, which can have a high-speed circular movement, was used to carry out the dynamic test, shown in Figure 9. The speed of the servo motor could be set by control software. The GNSS antenna, the MEMS IMU STIM300, and the raw data recorder were fixed on the rail. The turning radius of the GNSS antenna was 1.5 m, and that of the IMU was 1.0 m. When the rail spun centering on the servo motor, the antenna and the IMU moved with large acceleration.  
Figure 10 shows the antenna movement trajectory and the IMU raw output. As seen in Figure 10a, the antenna had a circular motion with a radius of 1.5 m. The output of the IMU showed that the test platform started to accelerate at the 100 th second, and decelerate gradually when the velocity was large enough. The maximum acceleration of the IMU was 34.5 m/s\({}^{2}\), and the maximum angular velocity was 340\({}^{\circ}\)/s. Since the turning radius of the GNSS antenna was 1.5 times of that of the IMU, the maximum acceleration of the antenna was 51.75 m/s\({}^{2}\), and the maximum jerk of the antenna was larger than 300 m/s\({}^{3}\).  
Figure 8: Tracking errors of improved INS-aided PLL.  
Figure 9: A spinning platform used to carry out the dynamic test.
# A Novel Adaptive Gain of Optimal Sliding Mode Controller for Linear Time-Varying Systems
## 3 Illustrative Examples
### Example 5--Proposed Controller for Vibration Control with Random Step Wave Excitation (2)  
As a final simulation, the effect of the gain parameters are investigated by adopting the following parameters of \(t\in[0,400]\), \(M_{L\xi}(t)=160000,N_{L\xi}(t)=60000\), and \(\gamma=10\), those are different from those used in Example 4. Moreover, to reflect more practical applicability of the proposed control 2, the random step wave road excitation shown in Figure 13 is chosen in this simulation. Results of simulation are presented in Figures 14-17. From Figure 14a, it is seen that the gain value \(Q_{\Delta L}(t)\) is the positive value. The main control \(u^{*}(t)\) in Figure 14b obtains the maximum damping force of MR damper as 1000 N. This point shows that the disturbance (road excitation) is severe, and hence, needs more energy for effective vibration control. The state variables (displacement and velocity) at system and driver position are shown in Figures 15 and 16. It is clearly observed from that unwanted vibrations are well controlled at both positions. The power spectral density (PSD) diagram of the state variables is shown  
Figure 8: Control results under random bump road excitation: (a) gain function \(Q_{\Delta L}(t)\), (b) main control \(u^{*}(t)\).  
Figure 7: Random bump road excitation.  
in Figure 17. These results show that the initial vibration of the system and driver are decreased with large values after applying the controller. The results presented in this simulation also demonstrate that the proposed control 2 is very effective for vibration control of more realistic systems subjected to severe external disturbances. It is noted here that in this simulation of the proposed control 2 has been used with the new gain function matrix given in Equation (7), which is proposed in this work. From examples 4 and 5, it is known that the desired (or required) control performances can be achieved by changing the gain parameters associated with the gain function matrix.  
Figure 10: _Cont._  
Figure 9: Vibration control results at the seat position under random bump road excitation: (**a**) displacement, (**b**) velocity.  
Figure 11: Vibration control results at the driver position under random bump road excitation: (**a**) displacement, (**b**) velocity.  
Figure 10: State variable (displacement) at the seat position: (**a**) general view, (**b**) large view.  
Figure 12: Power spectral density of the proposed control system under random bump road excitation.  
Figure 13: Random step wave road excitation.  
Figure 14: Control results under random step wave road excitation: (**a**) gain function \(Q_{\Delta L}(t)\), (**b**) main control \(u^{*}(t)\).  
Figure 15: Vibration control results at the seat position under random step wave road excitation: (**a**) displacement, (**b**) velocity.
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
## 6 Conclusions  
The aim of this study was to present a method of optimizing virtual loudspeaker positions for spatial sound reproduction with head-tracked headphones. The rendering of spatial sound was implemented with the Spatial Decomposition Method in combination with nearest loudspeaker synthesis to analyze and reproduce measured room impulse responses in perceptual room acoustics studies. These studies have earlier used a static loudspeaker array to synthesize spatial impulse responses convolved with sound signals. However, a static reproduction array is not an optimal way of reproduction when concerning the arbitrary directions of early reflections. This is especially the case with headphones where the loudspeakers are virtualized with the help of HRTFs.  
The implementation was evaluated with a discriminative listening test, which consisted of one simple and one complex auditory scene. They were a small room with stereo loudspeaker audio and a concert hall with 24 channel orchestra music. The results implied that optimization of virtual loudspeaker positions is important in a small room. This is because misplaced early reflections cause image shifting and change perception of reverberance. However, this effect was not so large in the concert hall case. In any case, the number of virtual loudspeakers can be reduced to minimize the real-time computation required for HRTF processing, if the directions of direct sounds and early reflections are accurately reproduced. The study for the minimum number of virtual loudspeakers needed without deteriorating the sound quality is left for future work.
# Deep Validation of Spatial Temporal Features of Synthetic Mobility Models
## 3 Validation of Mobility Models  
In this section, we describe the first attempt to validate the movement steadiness of some benchmark mobility models. Such validation was previously only carried out only for the RWPMM. It is based on a number of mobility metrics in order to construct and deduce a precise assessment of each mobility imbalance. The motion test depicts rigorous features that have serious impacts on global network performances. The experimental settings are shown in Table 2. The analyzed validation is carried out based on the following parameters:  
* The four validated mobility metrics are the speed decay problem, spatial node distribution, density wave phenomenon, and average neighbor percentage.
* The six verified mobility models are distributed as follows: there are two entity models, the RWPMM and the MGMM, two correlated models, the RPGMM and the NCMM, and two human models: SLAW and SMOOTH.
* A spread out field of 1030 \(\times\) 1030 m is considered as a very prominent mobility pattern.
* One hundred twenty different scenarios are examined with trials of 20 iterations per model over 1000 s and an average speed of 5 m/s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mobility Models**} & \multirow{2}{*}{**Realistic**} & \multirow{2}{*}{**Scalable**} & \multicolumn{2}{c}{**Possibility of a**} & **Uses** \\  & & & **Mathematical Representation** & **Geographic Map** \\ \hline RWMM & N & Y & Y & N \\ MGMM & N & Y & N & N \\ RPGMM & N & Y & A & N \\ NMM & N & Y & N & N \\ SLAW & Y & N & A & Y \\ SMOOTH & N & Y & N & Y \\ \hline \hline \end{tabular}
\end{table}
Table 1: Qualitative features of mobility models; Y: Yes, N: No, A: Ambiguous.  
The aforementioned experimental parameters are combined to evaluate all possible scenarios with the aim of obtaining the most accurate and complete knowledge of mobility problems. Using MATLAB implementation tool, the simulation results are depicted and discussed in Figures 2-5 in the next sub-sections. These figures must endorse the movement strategy adopted by the analyzed models that were introduced in the previous section. Some important model features are described in Table 3, based on model conceptions. However, we will make sure that they are correct through these validations.
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 7 Discussions and Conclusions
### Main Conclusions  
We showed that for moderate spectral efficiencies (namely \(\sim 1\,\mathrm{bps}/\mathrm{Hz}\)), ACO-OFDM offers the best energy efficiency, i.e., it allows us to attain the maximum link range for a given transmit electrical power. For relatively high spectral efficiencies (namely larger than \(2\,\mathrm{bps}/\mathrm{Hz}\)), ACO will suffer from high PAPR due to the required too large constellation size. There, LACO-OFDM becomes the best choice, as it makes a good compromise between PAPR reduction (with respect to ACO) and lower required DC bias at the Tx (with respect to DCO). Indeed, a high signal PAPR can result either in a high clipping noise level at the Tx side or a constrained link range due to SiPM saturation at the Rx side. Nevertheless, relaxing the constraint of \(P_{\mathrm{Tx,e}}\) at the Tx, we showed that DCO-OFDM is the most flexible scheme, offering the largest LBI, thus allowing a somehow more robust link operation when \(P_{\mathrm{Tx,e}}\) cannot be adaptively adjusted to the changing channel attenuation.
## 4 Simulation Results
### Performance Analysis of CFO Estimation  
We use the mean relative error (MRE) to evaluate the CFO estimation performance of the proposed method, which is defined as follows:  
\[MRE\ =\ \frac{\frac{1}{N_{t}}\sum\limits_{n_{t}=1}^{N_{t}}\left|\Delta f _{est}^{(n_{t})}-\Delta f_{true}\right|}{\Delta f_{true}}\times 100\%, \tag{20}\]  
where \(\Delta f_{est}^{(n_{t})}\) is the CFO estimation obtained in the \(n_{t}\)-th correct detection, \(N_{t}\) is the total number of correct detections, and \(\Delta f_{true}\) is the true value of the CFO.  
Figure 2 shows the MRE curves of the proposed method obtained under different SIRs and \(E_{b}/N_{0}\)s. Similar to Figure 1, the MRE obtained under each SIR and \(E_{b}/N_{0}\) is the statistical result obtained with \(1\times 10^{5}\) repeated experiments.  
It can be seen from the curves marked with \"+\" in Figure 2a-c that the proposed method can achieve a CFO estimation error that is lower than 3% with the condition of  
Figure 2: MRE of CFO estimation under different SIRs and \(E_{b}/N_{0}\)s. (**a**) SIR = \(-10\) dB, (**b**) SIR = \(-6\) dB, (**c**) SIR = \(-2\) dB.  
\(E_{b}/N_{0}\) being as low as \(-5\) dB under different SIRs. In addition, when the SIR is fixed, the MRE can be further lowered as the \(E_{b}/N_{0}\) increases. As shown in the three figures of Figure 2, the CFO estimation error of the proposed method can reach a level that is lower than \(0.5\%\) when the \(E_{b}/N_{0}\) increases to \(11\) dB. These results indicate that the proposed method is effective for estimating CFO in situations with low SIR and low \(E_{b}/N_{0}\), and the estimation accuracy is high, which demonstrates the promising application prospects of the proposed method in interference environments.  
Similarly, a comparison of the CFO estimation performance among published methods and the proposed method is also made under the same conditions. The MREs obtained with the published methods are also shown in Figure 2 for the convenience of comparison. It should be noted that since the MRE is calculated under the condition of correct signal detection, there will be no MRE calculated when the missed detection probability is \(1\) because the algorithm detects no signals. Under such circumstances, there will also be no curves plotted in the figure. What is more, because the methods published in [12; 14] do not include CFO estimation, we combine them with the CFO estimation method published in [15] to conduct the simulation experiments, which is realized by using the method in [15] to estimate CFO on the basis of the signal detected using the methods in [12; 14].  
From comparisons made in the three figures of Figure 2, we can find that the proposed method possesses the lowest MRE among the five methods under different SIRs and \(E_{b}/N_{0}\)s. Moreover, for SIR = \(-10\) dB and \(-6\) dB, the MREs of the proposed method are much lower than those of the other four methods. Results shown in Figure 2 imply that the CFO estimation performance of the proposed method is superior to the published methods, and such superiority is more obvious in lower SIR and \(E_{b}/N_{0}\) situations.
## 3 Materials and Methods
### [PERSON]'s Algorithm  
[PERSON]'s algorithm (Algorithm 1) is widely used in the shortest path routing algorithm. It is based on a set of candidate neighbor nodes, and the calculation to identify the shortest path between a source \(s\) and a destination \(d\); in other words, it computes the shortest paths to all destinations [33].  
For the path calculation with [PERSON]'s algorithm, we need to calculate the cost between two nodes \(i,\;j\) and the path with the least cost to node \(j\), where \(j\;(j\
eq i)\). We can represent this as two parameters:  
\[dt_{i,j}=link\;cost\;between\;node\;i\;and\;node\;j \tag{11}\]  
\[Dt_{i,j}=cost\;of\;the\;minimum\;cost\;pathbetween\;node\;i\;and\;node\;j \tag{12}\]  
[PERSON]'s algorithm uses two lists to specify the minimum cost of nodes, one containing the computed nodes of the permanent list \(S\) and another for the nodes still missing from the provisional list \(S^{\prime}\). At each iteration, the algorithm computes the shortest path from a neighboring node \(k\) to node \(i\) with the lowest cost to node \(i\). The list \(S\) is incremented by adding the computed node. In contrast, the list \(S^{\prime}\) is decremented by removing the nodes that are added to S.  
Our work evaluated the shortest path using different QoS parameters (B, D, PL, Equations (8)-(10)). We use [PERSON]'s algorithm, modifying the minimum cost of the links with the metric we want to evaluate, and select the path that corresponds to the source and destination of the data flow.  
```
1:S = {i} //permanent list; start with source node i
2:S = N\(\backslash\){i} // tentative list (of the rest of the nodes)
3:Find k e S' such that \(D_{ik}=min_{m\in S}D_{im}\) // identify a neighboring node k not in the list S with the minimum cost path from i.
4:S = S U [K] //Add k to the permanent list S
5:S = S\(\backslash\){K} //Drop k from the tentative list S'
6:if s = //if S' is empty stop
7:end
8:For j e \(N_{k}\cap\)'S' do //consider the list of neighboring \(N_{k}\), of the intermediary k.
9:\(D_{ij}\)= min (\(D_{ij}\), \(D_{ik}+dt_{kj}\)) //check for improvement in the minimum cost path
10:return to step 3,
```  
**Algorithm 1** [PERSON]'s shortest path algorithm (centralized approach)
# Optimal Meshing Degree Performance Analysis in a mmWave FWA 5G Network Deployment
## 5 Experimental Simulation and Results
### Comparative Performance Analysis of Topologies in Terms of Data Rate  
Finally, Figure 12 illustrates the results of the data rate analysis across Topologies 1, 2, and 3 as shown in Table 1.  
The graph shows that the median data rate (i.e., the expected positive gain) is the highest in Topology 2, followed by Topologies 1 and 3. Specifically, the median data rate is around 15% higher in Topology 2 than in Topology1 and 3% higher in Topology 3 than in Topology 1. Thus, in terms of data rates, switching from Topology 1 to Topology 2 increases network performance, but switching from Topology 1 to Topology 3 decreases it by about 3%.  
The CVs of Topologies 1, 2, and 3 are 55%, 55%, and 41%, respectively. In other words, Topology 1 and Topology 2 are equally volatile in terms of data rate but switching from Topology 1 to Topology 3 decreases this risk by 14%. Therefore, Topology 2 is the best in terms of data rate and corresponding link budget since it has the highest expected gain and the lowest risk compared to the other topologies. Topology 3 is the second-best option since the reduction in the expected return (3%) is lower than the reduction in risk (6%) after switching from Topology 1.  
The simulation results show that there is clearly a trade-off between the objectives of low network latency and packet error rate on the one hand and high network throughput and high data rate on the other. Based on latency and PER, Topology 3 performs best, but Topology 2 delivers the better throughput and data rate, albeit with high volatility. More broadly, however, our results do not demonstrate a clear network performance advantage simply from increasing the density of links. This could be due to different reasons. Specifically, the increase in the number of the links might increase the probability of the occurrence of bottlenecks that reduce the overall network capacity due to a highest simultaneous transmission towards the POPs [52]. Other reasons include the limited load introduced during our tests (we expect some further improvements from denser deployments in saturated networks), or the nature of wireless mesh deployments. In any  
Figure 12: Data rate performance results.  
case, it is difficult to justify the additional investment required to realise such configurations given the limited expected returns seen here.
# Towards a Hybrid Security Framework for Phishing Awareness Education and Defense
## 11 URL Detection Model Architecture Specifications  
The selection of the model architecture is a crucial step in the design of an accurate deep-learning system for supporting the identification of malicious URLs. A well-designed architecture can have a significant impact on the model's ability to accurately identify threats while maintaining a low false positive rate. We developed six different models for Phishing URL detection that are based on various neural network architectures. We developed detection models based on Feedforward Neural Network (FNN), Bi-directional RNN (Bi-RNN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). Performance-related details on these architectures are discussed in Section 12.  
The efficiency of the developed neural network models is measured using standardized measurement matrices of confusion matrix, accuracy, precision, recall (sensitivity), and F1-score. They provide a comprehensive measure of the quality of the machine learning model.