esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mechanical and Morphology Analysis of the Curaua/Composites  
Tensile and flexural tests were carried out to investigate the influence of the filler content and the fiber treatments on the mechanical properties. The results of the mechanical analysis are shown in Table 2. According to Table 2, the most peculiar difference between  
Figure 3: TGA/DTG curves of the untreated and treated fibers. the compared composites is the distinctly higher strength of all PLA-containing materials. The pure polymers' strength values and elastic moduli agree with the literature [43]. Focusing now on the results of the flexural modulus, a good correlation between fiber content and rigidity was found. As expected, a higher filler amount increased flexural modulus for both matrix materials. Also, regarding flexural modulus, it is observed that both untreated and treated fibers were able to raise the flexural modulus compared to pure matrices. In other words, significantly different elastic modules were found, as shown by the statistical analysis. Note that the composites with untreated fibers decreased elastic modulus (compared to the other PLA composites with 20% filler content). The fiber treatments mentioned in this study had a low impact on the elastic moduli of the composites. However, these treatments improved the adhesion between the matrix and the fibers. This conclusion was based on the observation that differences in the mechanical properties of composites are more related to the percentage of filler content than to changes in elastic moduli. The explanation for this statement is that although the elastic moduli have not shown great variations with fiber treatments, other analyses suggest the removal of amorphous components, which implies better interaction at the fiber-matrix interface, as mentioned in our previous study [26]. Therefore, the improvement in adhesion was inferred from the analysis of other tests and properties, which showed that the treated fibers integrated more effectively into the matrix than the untreated fibers. This better integration can result in more efficient load transfer between the fiber and the matrix, reflected in other mechanical properties in addition to the elastic modulus. The strong results are discussed separately for each matrix material to improve this text's readability. The tensile strength of the unfilled PLA matrix to be 58.2 \(\pm\) 2.1 MPa is shown in Table 2. Although there was a decrease in the tensile and flexural strength of some groups of PLA composites reinforced with curaua, it is reported that according to the Dunnett's test, there was a significant difference between CWM 10 wt% and CWM 20 wt% with the PLA matrix (tensile strength) and CWM 20 wt% compared to the PLA matrix (flexural strength). The literature points out that this increase in the resistance properties may be related to the applied chemical treatment that removed the amorphous components
# Explainable AI in Education: Techniques and Qualitative Assessment
## 5 Discussion
A few features, including the number of completed assessments (assessment_count) and the submission delay (delay), are evident in Figure 6 and had a crucial role in the prediction model. ANNs integrate these features in a more complex way, capturing interactions between multiple elements at once, whereas DT depends on binary splits to make decisions about these aspects. For example, instead of only assessing if a student was late in submitting assignments, an ANN may investigate how this delay interacts with the overall number of assessments completed or the specific module the student is enrolled in, as indicated by features like code_module_BBB or sum_click. Based on the analysis, it can be concluded that the ANN model delivers more consistent and accurate predictions compared to the DT model. Additionally, the stability scores indicate \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Instance** & **True Class** & **Original Prediction** & **Fidelity (\%)** & **Stability (\%)** \\ \hline
1529 & 1 & 0 & 100 & 100 \\
684 & 0 & 0 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fidelity and stability scores for selected instances in a decision tree model. that, although the ANN model with LIME explanations is effective, the ANN model with SHAP explanations exhibits higher stability, resulting in more consistent predictions. Finally, the findings from the OULAD dataset might be affected by its specific features, like the demographics, culture, and educational background of the Open University students. As a result, the outcomes are likely more relevant to online or distance learning settings and may not fully apply to the traditional classroom environment with different teaching methods. Additionally, the geographic and demographic diversity represented by the dataset enhances its applicability to other online education platforms globally. However, researchers should be mindful of potential limitations when applying the results to settings with different education systems or cultures.
## 2 Methodology  
### Variational Mode Decomposition  
Introduced in 2014, variational mode decomposition (VMD) is a non-recursive signal decomposition method [42]. VMD operates by seeking a set of modes and their respective center frequencies from the input signal. Through Hilbert transform, the original signal is decomposed into K modal components of limited bandwidth, denoted as \(\upsilon_{k}(t)\). Each IMF possesses a center frequency represented as \(\omega_{t}\). The modal decomposition is subject to two constraints: modal superposition and equivalence to the input signal.  
To convert the center band of \(\upsilon_{k}(t)\) to its corresponding baseband, the single-sided spectrum of \(\upsilon_{k}(t)\) is computed and multiplied by \(e^{-\beta t}t\):  
\[\left[\left(\delta(t)+\frac{j}{\pi t}\right)*\upsilon_{k}(t)\right]e^{-j^{ \omega_{k}}t} \tag{1}\]Then, to calculate the square norm of the demodulation gradient:  
\[\left\{\begin{array}{c}\{\begin{array}{c}\{\begin{array}{c}\min_{\{v_{k}\},\{ \omega_{k}\}}\{\sum\limits_{k}\|\partial_{t}[(\delta(t)+\frac{j}{\pi t})*v_{k}(t )]e^{-\mu kt}\}\|_{2}^{2}\end{array}\}\\ \end{array}\right.\begin{array}{c}\begin{array}{c}s.t.\ \sum\limits_{k}v_{k}\ (t)=f(t)\end{array} \end{array} \tag{2}\]  
In Formula (2), \(\{v_{k}\}=\{v_{1},\cdots,v_{k}\}\) represent the decomposed intrinsic mode function (IMF) components, \(\{\omega_{k}\}=\{\omega_{1},\cdots,\omega_{k}\}\) represent the central frequency of each component, \(\delta(t)\) represents the Dirac delta function, \(j\) is the imaginary unit, and \(f(t)\) is the original signal. The unconstrained optimization is achieved by extending the Lagrange expression:  
\[\begin{array}{c}L(\{v_{k}\},\{\omega_{k}\},\lambda)=&\begin{array}{c}\alpha \sum\limits_{k}||\partial_{t}[(\delta(t)+\frac{j}{\pi t})*v_{k}(t)]e^{-\mu kt} \|_{2}^{2}+|f(t)\\ &-\frac{1}{k}u_{k}(t)\|_{2}^{2}+\lambda((t),f(t)-\frac{1}{k}u_{k}(t))\end{array} \end{array} \tag{3}\]  
By employing the alternating direction multiplication operator for the intrinsic modal parameters and IMF central frequency, Equation (4) is utilized to iteratively update \(\overset{\cdot(n+1)}{u_{k}}\) until the optimal solution of the function is attained:  
\[\overset{\cdot(n+1)}{u_{k}}(\omega)=\frac{\overset{\cdot}{f(\omega)-\sum \limits_{i\
eq k}\overset{\cdot}{u_{i}}(\omega)+\frac{\lambda(\omega)}{2}}}{1+2 \alpha(\omega-\omega_{k})} \tag{4}\]  
In Equation (4), \(\omega_{k}^{(n+1)}\) is iteratively updated by Equation (5):  
\[\omega_{k}^{(n+1)}\ (\omega)=\frac{\int_{0}^{\infty}\omega\overset{\cdot}{u_{k}} (\omega)|^{2}dw}{\int_{0}^{\infty}\overset{\cdot}{u_{k}}(\omega)|^{2}du} \tag{5}\]  
\(\lambda^{n+1}\) is updated iteratively according to Equation (6):  
\[\lambda^{n+1}=\lambda^{n}+\tau(f-\sum u_{k}^{n+1}) \tag{6}\]  
By iteratively applying the cyclic Equations (2)-(5) until the convergence condition expressed in Equation (7) is met, the optimal decomposition of the original signal \(f(t)\) is accomplished:  
\[\sum\limits_{k}\|u_{k}^{n+1}-u_{k}^{n}\|_{2}^{2}/\|u_{k}^{n}\|<2 \tag{7}\]
## 3 Finite Element (FE) Model
### Parametric Study  
This section describes the parametric studies performed to derive the \(p\)-multipliers and group effect parameters of group piles in sand. A suite of nonlinear 3D analyses was performed to investigate the effect of different pile configurations (\(3\times 3\), \(4\times 4\), and \(5\times 5\)), pile head fixity (free and fixed), _S/D_ (3, 4, 5, and 6), and friction angle (\(\varphi\)) of sand (\(30^{\circ}\), \(35^{\circ}\), and \(40^{\circ}\)). The depth of the soil profile was set to 15 m. The soil profile was assumed to be composed of uniform soil with a constant \(\varphi\), but the shear wave velocity (\(V_{s}\)) was varied with depth to account for its dependence on confining pressure. It is first calculated by determining the overburden and energy corrected SPT blow count (\(\left(N_{1}\right)_{60}\)) and then converting to \(V_{s}\). Values of \(\left(N_{1}\right)_{60}\) were back-calculated from \(\varphi\) using the correlation of [PERSON] and [PERSON] [PERSON] and [PERSON] (1997) presented in Equation (2):  
\[\varphi^{\prime}=\sqrt{20\left(N_{1}\right)_{60}}+20 \tag{2}\]  
\(N_{1(60)}\) blow count was converted to \(N_{60}\) using the equation of [PERSON] and [PERSON] [PERSON] and [PERSON] (1998). Finally, the \(V_{s}\) profile was calculated using the empirical equation of [PERSON] et al. [PERSON] et al. (1999) for sand. The calculated shear wave velocity used in the simulations is shown in Figure 3. \(V_{s}\) is shown to increase with depth. The unit weight of the soil was set to 18 kN/m\({}^{3}\). The water table reduces the effective stress of soil and also the stiffness of the \(p\)-\(y\) curve. However, because the water table is most often below the \(10D\) critical depth of influence, it was assumed that the soil is dry.  
Figure 2: Average soil resistance for a single pile and leading row in a \(3\times 3\) free-head pile group at \(y=50\) mm.
## 5 Simulation Results and Analysis
### Packet Delivery Ratio (PDR) with Obstacles  
In Figure 13, we show the comparison of PDR in an environment with obstacles with the AODV, DFM, LADTR, and ICBM-UAV. ICBM-UAV performs better than LADTR, AODV, DFM, and Epidemic routing protocol, because none of them had been considered in an obstacle situation. Initially, the PDR of ICBM-UAV in an environment with obstacles is the same as the environment without: 67%. With the increase in number of the UAVs, the PDR of ICBM-UAV also increases. LADTR performs higher than the epidemic and AODV, as they use ferrying UAVs and multi-hop forwarding of the packet.
## 3 Results and Discussion
### Fluorescence EEM and Fluorescence Regional Integration (FRI)
The C2 excitation wavelength was close to the border (250 nm) of regions V and III, such that the peak (C2) may have properties close to the fulvic acid-like substance (P\({}_{\rm III,n}\)). The C3 is attributed to protein-like (P\({}_{\rm II,n}\)) substances. In the present study, the LHS had higher P\({}_{\rm III,n}\)/P\({}_{\rm II,n}\) ratios than HHS, and the P\({}_{\rm III,n}\)/P\({}_{\rm II,n}\) ratios had a strong positive correlation with the HIX. The high HIX values in LHS are attributed to the composition of protein- and fulvic acid-like substances compared to HHS. Moreover, the P\({}_{\rm III,n}\)/P\({}_{\rm II,n}\) ratio of AD (1.08 \(\pm\) 0.08) was significantly higher than FD (0.99 \(\pm\) 0.06, \(p\) = 0.006). The ratios had a significantly negative correlation with the SUVA\({}_{254}\) (r = \(-\)0.46, \(p\) = 0.024) for the total SHS (Figure 4). However, when the SHS was separated into two groups, LHS and the other group, which included BHS, HHS, and MHS, the SUVA\({}_{254}\) had an insignificant correlation with the P\({}_{\rm III,n}\)/P\({}_{\rm II,n}\) ratios for both SHS groups (r = 0.17-0.19,  
Figure 3: FRI percentages of size-fractionated SHS considering the AD and FD samples as pooled samples. Figure 4). This suggested that the SHS aromaticity was not developed to the extent of the humification in SHS. The HHS and MHS had a higher SUVA\({}_{254}\) and P\({}_{V\!\!,\alpha}\) percentage than LHS. One possible reason is that the HHS contains aromatic lignin resulting from the POM.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 8 Discussion
The proposed covert channel primarily finds its application in Wi-Fi networks. To prevent the covert \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Stations** & **Scenario** & **Throughput [Kbps]** & **Delay [ms]** & **Jitter [ms]** & **Frame Efficiency** \\ \hline \multirow{4}{*}{1 STA} & 1—w/o retrans. & 4.8 & 0.4 & 0.002 & 100 \\  & 2—with retrans. & 4.8 & 0.4 & 0.002 & 100 \\  & 3—with SWP & 4.8 & 0.4 & 0.002 & 100 \\ \hline \multirow{4}{*}{11 STAs} & 1—w/o retrans & 2.98 & 3.23 & 2.51 & 62 \\  & 2—with retrans. & 4.15 & 4.25 & 3.09 & 86.27 \\  & 3—with SWP & 4.78 & 0.69 & 0.37 & 99.29 \\ \hline \multirow{4}{*}{21 STAs} & 1—w/o retrans & 2.3 & 3.5 & 2.7 & 49 \\  & 2—with retrans. & 3.46 & 4.5 & 3.2 & 71.93 \\  & 3—with SWP & 4.77 & 0.75 & 0.38 & 99.11 \\ \hline \multirow{4}{*}{31 STAs} & 1—w/o retrans & 2.19 & 3.51 & 2.79 & 45 \\  & 2—with retrans. & 3.12 & 4.54 & 3.21 & 64.96 \\  & 3—with SWP & 4.66 & 0.83 & 0.54 & 96.8 \\ \hline \multirow{4}{*}{41 STAs} & 1—w/o retrans & 1.98 & 3.42 & 2.76 & 41.18 \\  & 2—with retrans. & 3.05 & 4.55 & 3.2 & 63.39 \\  & 3—with SWP & 4.52 & 0.88 & 0.61 & 93.86 \\ \hline \multirow{4}{*}{51 STAs} & 1—w/o retrans & 1.95 & 3.4 & 2.7 & 41 \\  & 2—with retrans. & 2.87 & 4.56 & 3.19 & 59.79 \\  & 3—with SWP & 4.37 & 0.91 & 0.62 & 90.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the three simulated scenarios based on the highest achieved throughput. frame from being targeted, one can deploy the proposed covert channel to send messages to the AP without establishing an association or transmitting any meaningful data frames that could draw attention. Another significant application of the proposed covert channel is in monitored networks, where all data traffic is under surveillance. In such scenarios, the covert channel can be employed to send secret messages that remain imperceptible within the network, bypassing detection mechanisms. Furthermore, the covert message can serve as an authentication mechanism for device authentication. This application becomes particularly relevant in highly secure networks, where the MAC address can be easily spoofed, potentially compromising the true identity and legitimacy of a station. By utilizing the covert channel as an authentication message, a station can provide genuine proof of its legitimacy in a private network.
## 2 Materials and Methods
### Characterization of Photocatalysts  
The crystalline phases of GO and TiO2@rGO were analyzed by powder X-ray diffraction (P-XRD, Rigaku) operating with CuK\(\alpha\) radiation. Fourier transform infrared (FT-IR/ATR, Bruker Vertex 70)) spectra were performed to analyze the chemical groups in the structure of the nanocomposite materials obtained. To confirm the crystalline phase of GO and TiO2@rGO, Micro-Raman analyses were performed using a Bruker SENTERRA spectrometer with an Olympus microscope. X-ray photoelectron spectroscopy (XPS) performed on SPECS Phoibos 150 with AlK\(\alpha\) radiation (1486.74 eV) was used for elemental composition determination. Binding Energy (BE) was corrected using the main peak of reduced GO as a reference, set at 284.4 eV. C 1s spectra of GO and rGO were normalized to get similar intensity to the other carbon spectra. Scanning electron microscopy and energy-dispersive X-ray spectroscopy (SEM/EDX) were carried out with the aim of obtaining microstructural characteristics and elemental mapping of the samples. Measurements were made using Hitachi TM4000 plus tabletop SEM-EDX facility with 15 kV field energy in the backscattered electron (BSE) imaging mode and FEG-SEM model JSM-7600F from Jeol at 10 kV in the secondary electron (SE) mode. The bandgap energy of the prepared nanocomposite powders was acquired from UV/Vis/NIR spectroscopy measurements using a Perkin Elmer Lambda 950 spectrophotometer.
# How Secure Are Two-Way Ping-Pong and LM05 QKD Protocols under a Man-in-the-Middle Attack?
###### Abstract  
We consider a man-in-the-middle attack on two-way quantum key distribution ping-pong and LM05 protocols in which an eavesdropper copies all messages in the message mode, while being undetectable in the mode. Under the attack there is therefore no disturbance in the message mode and the mutual information between the sender and the receiver is always constant and equal to one and messages copied by the eavesdropper are always genuine. An attack can only be detected in the control mode but the level of detection at which the protocol should be aborted is not defined. We examine steps of the protocol to evaluate its security and find that the protocol should be redesigned. We also compare it with the security of a one-way asymmetric BB84-like protocol in which one basis serves as the message mode and the other as the control mode but which does have the level of detection at which the protocol should be aborted defined.  
quantum cryptography; quantum key distribution; two-way communication pacs: 03.67.Dd; 03.67.Ac; 42.50.Ex
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 2 Estimating Interference with 2D VA
### Estimating Interference  
After calculating and determining the survivor path (in Section 2.2), the state and input branch at each step of the survivor path can be identified. The state contains the information of \(v[j,k-1]\) and \(v[j,k]\), and the output branch contains the information of \(v[j,k+1]\). Therefore, ISI can be achieved using \(v[j,k-1]\) and \(v[j,k+1]\), and it can be written as:  
\[IS[j,k]=v[j,k-1]+v[j,k+1]. \tag{27}\]  
Considering \(IS[j,k]\) in Equation (28), we subtracted it from the signal, \(z[j,k]\), to create a signal that is only distorted by 1D VI. Then, the VI signal was detected by the 1D VA. Using Equation (19), the VI signal was derived as follows:  
\[\begin{split} s_{v}[j,k]=& z[j,k]-IS[j,k]\\ =&\sum_{n=-1}^{1}a[j-n,k]g_{n,0}+w_{\mathbf{F}}[j,k]. \end{split} \tag{28}\]  
On the other hand, if \(v\) is assigned along the horizontal direction to Equations (25)-(27), the ITI estimator can be created. Thus, the signal \(IT[j,k]\) can be achieved, and the signal \(s_{h}[j,k]\) can be acquired as follows:  
\[IT[j,k]=v[j-1,k]+v[j+1,k]. \tag{30}\]  
\[s_{h}[j,k]=\sum_{m=-1}^{1}a[j,k-m]g_{0,m}+w_{\mathbf{F}}[j,k]. \tag{31}\]  
In the proposed model (in Section 3), the signals \(IS[j,k]\) or \(IT[j,k]\) are the output of the estimator. If 1D VA is applied according to the vertical direction, the estimator is used with  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(a[j-1,k-1]\) & \(a[j-1,k]\) & \(a[j-1,k+1]\) & \\ \(a[j,k-1]\) & \(a[j,k]\) & \(a[j,k+1]\) & \(v[j,k-1]\) & \(v[j,k]\) & \(v[j,k+1]\) \\ \(a[j+1,k-1]\) & \(a[j+1,k]\) & \(a[j+1,k+1]\) & & \\ \hline \(-1\) & \(-1\) & \(-1\) & \(-r-2t\) & \(-1-2p\) & \(-r-2t\) \\ \hline \(-1\) & \(-1\) & \(1\) & \(-r\) & \(-1\) & \(-r\) \\ \hline \(1\) & \(-1\) & \(-1\) & & & \\ \hline \(-1\) & \(1\) & \(-1\) & \(r-2t\) & \(1-2p\) & \(r-2t\) \\ \hline \(1\) & \(-1\) & \(1\) & \(-r+2t\) & \(-1+2p\) & \(-r+2t\) \\ \hline \(-1\) & \(1\) & \(1\) & \(r\) & \(1\) & \(r\) \\ \hline \(1\) & \(1\) & \(-1\) & & & \\ \hline \(1\) & \(1\) & \(1\) & \(r+2t\) & \(1+2p\) & \(r+2t\) \\ \hline \end{tabular}
\end{table}
Table 1: Results of \(\mathbf{v}\), depending on \(a[j,k]\), from Equations (22)–(24).  
Equations (28) and (29) to find the \(ISI[j,k]\) and remove the horizontal interference. If 1D VA is applied according to the horizontal direction, the estimator is used with Equations (30) and (31) to find the \(ITI[j,k]\) and remove the vertical interference. \(ISI[j,k]\) or \(ITI[j,k]\) are selected according to circumstances of the channel. This is mentioned in the simulation section (Section 4).
## 4 Path Ambiguity Solution  
In this section, two methods are presented to solve the path ambiguity problem, which is caused by wrong signal association during the user's motion, which can be observed from Figure 7. One is based on pseudorange (PR) comparison and the other on a Doppler aiding decision. These two methods benefit from a single visible GNSS satellite. The pseudorange comparison method compares the pseudorange estimated from the GNSS signal and the corresponding value computed by using the known satellite position and the receiver position in the alternative paths. The Doppler aiding decision exploits the Doppler effect in the GNSS signal to assist the receiver velocity and trajectory decision.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 3 Results
### Parameter Analysis for \(b\)  
The blocksize of the subcubes for local restoration of LRMR is denoted by \(b\). The parameter choice is significant, since naturally, the size of the subcubes provides accuracy and computational effort to the restoration. We vary the starting value for the blocksize parameter \(b\) between 15 and 25. Smaller values for \(b\) have not provided successful computations and errors in PSNR. The other parameters are kept as \(r=7\), \(p=0.15\), and \(s=8\). Figure 11 shows the PSNR values for different values of \(b\). The PSNR values are linearly decreasing with respect to increasing blocksize. Figure 12 shows the computational times in seconds in MATLAB. Apart from the large leap at the end and the smaller leap at the beginning, the computational time regarding the change in blocksize seems not to be affected significantly, although a weak trend towards increasing PSNR with increasing blocksize seems to be visible. We present the computational results for the Indian Pines data set.  
Figure 11: PSNR plot for different values of \(b\).  
Figure 12: CPU time plot for different values of \(b\).  
In the following, we present the computational times in seconds in MATLAB and Python in Table 3.  
Again, the computational effort is better in MATLAB R2022a.