esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation results
### Damper Performance Assessment from Free Decay Response  
The efficiencies of the passive damper and of the semi-active damper with control force constraints are assessed from simulated free decay responses adopting the logarithmic decrement method  
\[\delta_{i}=ln\left(\frac{V_{i}}{V_{i+1}}\right) \tag{22}\]  
where \(V_{i}\) denotes the anti-node cable displacement amplitude of period \(i\) during the free decay response and \(V_{i+1}\) is the anti-node amplitude of the subsequent period. This yields the damping ratio as follows  
\[\zeta_{i}=\frac{\delta_{i}}{\sqrt{4\;\pi^{2}+\delta^{2}}}-\zeta_{cable} \tag{23}\]where the cable inherent damping ratio is subtracted from the damping ratio obtained from the free decay response.  
The damping assessment from the free decay response is selected because the cable damping due to the semi-active damper cannot be determined in a different way due to the nonlinearities of clipping and control force constraints. Because of these nonlinearities the peak-to-peak damping ratio is not fully constant as for linear transverse cable dampers. Therefore, the damping of the free decay response is assessed from the exponential fit of the free decay response between decay start and 10% of the steady state amplitude [29; 30; 34]. Figure 7 depicts examples of the exponential fits for a passive damper being optimized to the excited mode (10), a passive damper being optimized to the first four targeted modes (12) and for the semi-active damper with the consideration of the control force constraints. Figure 7c shows that the local peaks during the free decay response match well the exponential fit. Hence, the nonlinearities due to clipping and control force limitations of the semi-active damper are minor and therefore do not falsify the damping determination by the exponential fit method.  
Figure 7: Simulated free decay responses of mode 1 for a cable with (**a**) passive viscous damper optimized to mode 1, (**b**) passive viscous damper optimized to modes 1 to 4, (**c**) clipped VDNS with control force constraints.
## 3 Results
### Comparison with the State-of-the-Art Larra Phenotype Classification Method  
In the previous study by [PERSON] et al. [20], they published the zebrafish larvae image dataset along with a supervised phenotype classification algorithm using a randomized trees classifier, which is a conventional machine learning method dedicatedly adapted to the zebrafish larvae images. They also used a two-tier classification strategy quite similar to ours, and therefore the major difference between our and their method is whether the deep learning approach was used. We refer [PERSON]'s method as the state-of-the-art baseline method and conducted the comparison based on the same training images (as listed in Table 2) and testing images (as listed in Table 1). Figure 5 plots the classification accuracies of the two methods. The accuracy values of the baseline method were collected  
Figure 4: Comparison between (**a**) the separable convolution architecture and (**b**) the conventional convolution architecture. Different colours of the input feature map indicate different feature channels. k is the number of feature channels and this figure uses k = 4 as an example.  
from [PERSON]'s original paper [20]. Our method achieved evident accuracy improvement over the baseline method for most phenotypes. For the phenotypes which are easy to recognize (dead and chorion), our method yielded an accuracy of 1.0 which is quite promising. [PERSON]'s method also obtained accuracy over 0.95 for these phenotypes but did not reach 1.0. The biggest improvement is with hemostasis, for which our method obtained an accuracy of 0.73 vs. 0.51 of the baseline method. Normal and short tail are the two phenotypes for which our method yielded slightly lower accuracies than the baseline method, but the differences (0.010 and 0.005 for normal and short tail, respectively) were too small to be considered significant.
## 2 Methodology  
### Wavelet Packet Transform (WPT)  
Wavelet transform (WT) is an efficient tool for signal analysis in the time-frequency domain. As an extension of WT, WPT further decomposes the detailed information in the high-frequency region [37]. This ability makes it possible for WPT to be applied to remove Gaussian noise from the original signal. Based on the multi-resolution analysis (MRA) [38], the procedure for a 3-level signal decomposition is shown in Figure 1. The load series S is broken down into approximation (A) components and detail (D) components.  
Most of the load data in STLF have complex detail components due to the volatile environment. A more precise forecast may be available after neglecting these components [39]. The wavelet bases of Daubechies (db) and Coiflets (coif) are proved to be capable of treating the load data [40]. In this paper, wavelet families of db2-db5 and coif2-coif5 are involved in data processing.
## 2 Single-Polarized 3D Absorber
### Alternative Design without the Reflecting Plate  
Unlike the conventional 2D absorbers, the absorption of the proposed absorber depends on the resonances of the 3D notch slot. It is therefore easy to understand that the reflecting plate is not mandatory. In other words, the absorber still works without the reflecting plate at the expense of small leakage (the transmission coefficient is not zero without the reflecting plate).  
The simulated S-parameters of the proposed absorber without the reflecting plate is shown in Figure 5 (black curves). Two resonances can be observed, and \(\left.\right|S_{11}\left|\right.\) is below \(-10\) dB from 2.45 GHz to 10.10 GHz. However, \(\left.\right|S_{21}\left|\right.\) gradually increases as the frequency increases. \(\left.\right|S_{21}\left|\right.\) is around \(-15\) dB below 6 GHz, but it increases to \(-5\) dB at 10 GHz. It is fortunate that \(\left.\right|S_{21}\left|\right.\) can be decreased by simply optimizing the structure (\(L\), \(L_{p}\) and \(h\)). It can be seen that after optimizing, \(\left.\right|S_{21}\left|\right.\) is smaller than \(-10\) dB below 10.6 GHz. Therefore, an absorption band (\(\left.\right|S_{11}\left|\right.\)\(\leq-10\) dB and \(\left.\right|S_{21}\left|\right.\)\(\leq-10\) dB) is obtained from 2.6 GHz to 10.6 GHz (121.21% FBW), which is nearly the same as that of the absorber with the reflecting plate.  
Figure 4: Current distribution of (**a**) the original lossy slotline and (**b**) the proposed 3D absorber.  
Figure 3: Simulated \(\left.\right|S_{11}\left|\right.\) of the proposed absorber with different structure parameters: (**a**) \(L_{s}\) and (**b**) \(L_{m1}\).
## Appendix A The Bit Error Rate in Non-AWGN Channels  
The appendix mainly analyzes the bit error rate in non AWGN channels. In Section 3.2.3, we conducted an analysis of the bit error rate (BER) in the context of an Additive White Gaussian Noise (AWGN) channel, demonstrating that when there is little difference in decoder performance, the Bit-Flipping with Path Loss (BPL) decoding based on the path selection criterion proposed in this paper yields a lower BER than both the original Belief Propagation (BP) decoding and BPL decoding based on traditional path selection criteria.  
If the channel is not an AWGN channel, it is difficult to determine the probability distribution of the soft information after decoding because the BP decoding result of polar codes is generated through iterations. However, an analysis of the BER can still be conducted. For any information bit, let us assume the transmitted codeword is 1. After decoding, the output soft information is \(\lambda\). If \(\lambda<0\), the decoding is incorrect; if \(\lambda\geq 0\), the decoding is correct. Let \(\lambda_{i}\) be independent and identically distributed; the mean value of the distribution is \(\mu\), the variance is \(\sigma^{2}\), and \(\Phi\) represents the cumulative distribution function.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Anchor-Based Methods  
Anchor-based methods use a set of anchor sequences, which are typically a subset of the input sequences, to guide the alignment of the remaining sequences in the dataset [27]. Through aligning sequences to the anchor sequences, the algorithm aims to preserve the overall structure and evolutionary relationships among the sequences. An anchor is a similar region of two or more genomes chosen based on their conservation, significance, or known biological relationships.  
Anchor-based methods provide a framework for aligning the remaining sequences by anchoring them to known conserved regions or motifs present in the anchor sequences. These methods can improve the accuracy and efficiency of the alignment process via leveraging known conserved regions or motifs present in the anchor sequences. Overall, they may reduce the complexity of aligning large datasets and improve the scalability of the alignment algorithm. However, selecting appropriate anchor sequences is crucial for the success of anchor-based methods, considering that they cannot be too divergent or unrepresentative of a given dataset; otherwise, they may lead to suboptimal alignments and poor performance in cases where the anchor sequences do not adequately capture the diversity or complexity of the input dataset.  
Some algorithms perform local alignments on each consecutive pair of anchors that are separated by a non-similar region smaller than a given length \(k\). The algorithms join all the anchors and the non-similar regions together. Different algorithms use this method either to perform a local alignment on chromosomes or a specific DNA sequence or to perform a global genomic alignment. In this section, we enumerate some of the most used algorithms that involve anchor-based methods and explain their computational steps.
# Cover Time in Edge-Uniform Stochastically-Evolving Graphs +
###### Abstract  
We define a general model of stochastically-evolving graphs, namely the edge-uniform stochastically-evolving graphs. In this model, each possible edge of an underlying general static graph evolves independently being either alive or dead at each discrete time step of evolution following a (Markovian) stochastic rule. The stochastic rule is identical for each possible edge and may depend on the past \(k\geq 0\) observations of the edge's state. We examine two kinds of random walks for a single agent taking place in such a dynamic graph: (i) The Random Walk with a Delay (RWD), where at each step, the agent chooses (uniformly at random) an incident possible edge, i.e., an incident edge in the underlying static graph, and then, it waits till the edge becomes alive to traverse it. (ii) The more natural Random Walk on what is Available (RWA), where the agent only looks at alive incident edges at each time step and traverses one of them uniformly at random. Our study is on bounding the cover time, i.e., the expected time until each node is visited at least once by the agent. For RWD, we provide a first upper bound for the cases \(k=0,1\) by correlating RWD with a simple random walk on a static graph. Moreover, we present a modified electrical network theory capturing the \(k=0\) case. For RWA, we derive some first bounds for the case \(k=0\), by reducing RWA to an RWD-equivalent walk with a modified delay. Further, we also provide a framework that is shown to compute the exact value of the cover time for a general family of stochastically-evolving graphs in exponential time. Finally, we conduct experiments on the cover time of RWA in edge-uniform graphs and compare the experimental findings with our theoretical bounds.  
dynamic graphs; random walk; cover time; stochastically-evolving; edge-independent; temporal graphs +
Footnote †: _Algorithms_
# Performance Analysis of Hard-Switching Based Hybrid FSO/RF System over Turbulence Channels
## 2 System Model  
Considering a hybrid FSO/RF system in which it is assumed that FSO link is having Gamma-Gamma fading, whereas, RF link follows Rayleigh fading. FSO utilize IM/DD with OOK scheme. For simple receiver implementation, it is assumed that only one of the links is active at a certain point. A simple mathematical expression that presents the channel from FSO transmitter to the photo-detector is given as [12; 13]:  
\[y=\eta P_{o}x+w, \tag{1}\]  
where \(\eta\) is the efficiency of a photodetector, which for simplicity is assumed to be equal to one, \(x\) represents the transmitted FSO symbols, \(P_{o}\) is the optical power and \(w\) is considered Gaussian distributed random variable with unitary variance and zero mean. For detection, low and stationary aerosol scattering or light haze is assumed with transmission efficiency \(\eta\) comparable to unity. The signal-to-noise ratio at FSO receiver can be expressed as [6]:  
Figure 1: System block diagram.  
Figure 2: Transition diagram for hard-switching.  
\[\gamma_{FSO}=\frac{E_{s}\mu^{2}R^{2}P_{FSO}^{2}G_{FSO}^{2}}{\sigma_{FSO}^{2}}l^{2}, \tag{2}\]  
where \(E_{s}\) is the average symbol energy, \(\mu\) is the modulation index, \(R\) is the photodetector's responsivity, \(P_{FSO}\) is the optical power transmitted, \(\sigma_{FSO}\) represents the shot noise variance at photodetector which can be modeled as additive white Gaussian noise (AWGN), \(l\) stands for the received optical irradiance and \(G_{FSO}\) is the attenuation of optical power which can be estimated by the Beers-Lambert law.  
Point-to-Point RF link operating at 58 GHz is assumed as the backup link for the above introduced FSO channel. The specific RF frequency band is comparatively less sensitive to deep fades due to the rain [2]. For short links-range, i.e., shorter than 2 km, the overall performance of the hybrid system practically depends only on the FSO channel and not on the supporting RF channel.  
Using Friis transmission equation, the channel power gain is given as [12]:  
\[G_{RF}=G_{tx}G_{d}G_{rx}, \tag{3}\]  
where, \(G_{rx}\) and \(G_{rx}\) are the transmitter's and receiver's antenna gains, respectively and \(G_{d}\) is the directive gain of the isotropic antenna.
## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Requirements Debt--Questions 43 to 45  
* Question 43: Have you thoroughly defined the objectives, scope, stakeholder needs, expectations, decision goals, and insights of the AI system to ensure alignment with business objectives and user expectations? Stakeholder: Organizers Score: 5 Justification: Clearly defining the objectives, scope, stakeholder needs, and expectations is fundamental to the success of any AI system. If these aspects are not well-defined, it can lead to requirement debt, where the system does not meet the necessary criteria for success due to ambiguous or incomplete requirements.  
Example: If the competition platform's goal is not aligned with the stakeholders' expectations, it may result in a system that is technically sound but fails to engage users or meet business objectives.
* Question 44: Have you thoroughly addressed the technical aspects of the AI system, including the selection of appropriate AI techniques, algorithms, and models to achieve desired functionality and performance, as well as specifying quality attributes, trade-offs, metrics, and indicators to measure and evaluate system performance effectively? Stakeholder: Organizers Score: 5 Justification: Neglecting technical aspects such as AI techniques and models can result in a system that doesn't meet performance expectations, leading to requirement debt. Technical requirements are essential for system functionality.  
Example: Choosing an overly complex model for a simple game could result in longer training times and difficulty in interpreting the model's decisions, making it harder to debug and improve.
* Question 45: Have you monitored and retrained the AI system with new data as needed? Stakeholder: Organizers and Participants Score: 4 Justification: Continuously monitoring and retraining the AI system with new data is important for maintaining its relevance and performance, which can prevent the accumulation of technical debt due to model staleness or performance degradation.  
Example: In a game competition platform, if new strategies or game updates are introduced, the AI must be retrained to understand these changes. Failing to do so can result in a system that performs poorly and requires significant refactoring later on.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 5 Comparative Analysis
### Communication Analysis  
The estimated cost of communication is shown in Table 7. For comparison, the size of the identities is assumed to be 16 bytes long, timestamps to be 4 bytes long, SHA-1 hash outputs to be 20 bytes long [53], the cost for an ECC point is \((20+20)=40\) bytes, random numbers to be 20 bytes long, and the symmetric encryption/decryption block size is 16 bytes [54; 55]. Figure 6 also displays the communication costs of the various protocols.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & [49] & [50] & [51] & [52] & Our \\ \hline Sensor node anonymity & ✓ & ✓ & ✓ & ✓ & ✓ \\ Ephemeral Secret Leakage (ESL) & ✓ & ✗ & ✓ & ✗ & ✓ \\ Protection against RA & ✓ & ✓ & ✓ & ✓ & ✓ \\ Efficient protocol design & ✗ & ✓ & ✓ & ✗ & ✓ \\ Stolen verifier attack & ✗ & ✓ & ✗ & ✗ & ✓ \\ Stolen \(SN\) attack & ✓ & ✓ & ✓ & ✓ & ✓ \\ Untraceasbility & ✓ & ✓ & ✓ & ✓ & ✓ \\ Parallel \(SK\) attack & ✗ & ✓ & ✓ & ✓ & ✓ \\ Reply Attack & ✓ & ✗ & ✓ & ✓ & ✓ \\ Sensor nodes \(IA\) & ✗ & ✓ & ✓ & ✓ & ✓ \\ Server \(IA\) & ✓ & ✓ & ✓ & ✓ & ✓ \\ MITM attack & ✓ & ✓ & ✗ & ✓ & ✓ \\ Insider attack & ✓ & ✓ & ✗ & ✓ & ✓ \\ Mutual Authentication & ✓ & ✓ & ✗ & ✓ & ✓ \\ Formal automated security verification & ✓ & ✓ & ✓ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Functionality characteristic comparison.  
Figure 5: The findings of the study performed utilizing OFMC and the CL-AtSe back end.  
The communication cost of \(MSG_{1}=\langle RND_{3}^{\prime},AUTH_{n},PR_{ID_{n}},T_{SN}\rangle\) is \(\langle 20+20+16+4=60\rangle\) bytes, \(MSG_{2}=\langle PR_{ID_{4}},RND_{4}^{\prime},AUTH_{TA,s},Z_{TA,s},T_{TA}\rangle\) is \(\langle 16+20+20+20+4=80\rangle\) bytes, \(MSG_{3}=\langle SK_{sn},RND_{6}^{\prime},RND_{7}^{\prime},AUTH_{s},T_{s}\rangle\) is \(\langle 20+20+20+20+4=84\rangle\) bytes, and \(MSG_{4}=\langle RND_{6}^{\prime},Z_{TA,n},AUTH_{TA,n},T_{s},T_{TA}^{+}\rangle\) is \(\langle 20+20+20+4+4=68\rangle\) bytes, respectively.  
Adding together all these results, 292 bytes is the overall communication expense of the newly implemented protocol during the login and authentication process. Table 7 denotes that the presented protocol's communication cost is minimized as compared to the protocol in [51], and somewhat greater than that in [49, 50, 52], but this is acceptable because the presented protocol offers greater security than all previously compared protocols, as shown in the Table 6. Figure 6 also illustrates the new protocol's communication costs.
## 5 Simulation Results and Discussion
### BER vs. SNR  
We first compare the BER of different detection algorithms for MIMO systems low-precision ADCs. Figure 2 compares several detectors for a scenario with \(K=20\) UEs and \(N=30\) antennas at the BS. For the MMB-LR-SIC scheme, we set \(L=2\) and \(L=4\) to test detectors with different performance and complexities. In general, increasing the number of candidates \(L\) improves BER but also increases the complexity. The performance of all the detectors improve with as ADCs of higher resolutions are used, especially when the number of bits \(b\) is increased from 2 to 3. At low SNR where the noise is dominant, the difference in the BER is small for different ADCs. However, as the SNR increases, the quantization error dominates and there are significant gaps between different ADC choices. It is also clear that nonlinear signal detectors such as the LR-SIC can significantly reduce the SNR required for achieving a given BER, especially when the target BER is low and the ADC resolution is low. This indicates that more advanced symbol detectors are required for achieving better performance.  
Figure 3 compares the performance when the the number of BS antennas is increased from \(N=30\) to 60. It is seen that the trends are similar to Figure 2. It is also interesting to see that nonlinear signal detectors still provide better performance, showing that they can improve over linear detectors even when a large number of BS antennas are employed. However, as well known from the past massive  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Minimum distance: \(d_{\text{min}}\) & 35 m \\ Large-scale fading: \(\beta_{k}\) & \(10^{-3.53}/d_{k}^{3.76}\) \\ Transmission bandwidth: \(B\) & 20 MHz \\ Channel coherence time: \(T_{C}\) & 10 ms \\ Channel coherence bandwidth: \(B_{C}\) & 180 kHz \\ Total noise power: \(B\sigma^{2}\) & \(-96\) dBm \\ FOM & 1432.1f/conv – step \\ Uplink PA efficiency at the UEs: \(\eta\) & 0.3 \\ Sampling rate: \(f_{s}\) & 20 MHz \\ Carrier frequency: \(f_{c}\) & 2 GHz \\ Power per RF chain the BS: P\({}_{\text{BS}}\) & 0.4 W \\ Power per RF chain the UE: P\({}_{\text{UE}}\) & 0.1 W \\ P\({}_{\text{FIX}}\) & 18 W \\ P\({}_{\text{SYN}}\) & 2 W \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Parameter.  
Figure 2: BER versus the receiver SNR for \(K=20,N=30\).  
MIMO studies, the performance gaps between linear and nonlinear receivers diminishes when the condition number of the channel is improved as more antennas are used at the receiver. Meanwhile, the signal processing complexity increases when nonlinear SIC is employed as compared with the linear MMSE detector.  
The above SNR-BER analysis suggests that the different receivers have different error rates versus complexity tradeoff. However, it challenging to select among the different candidates. In the following, we use the EE as a performance metric to compare the different detectors under different scenarios of applications.
# Decolorization of Orange-G Aqueous Solutions over C\({}_{60}\)/Mcm-41 Photocatalysts
## 2 Experimental  
### Photocatalysts: Preparation and Characterization  
The samples containing 1 and 3 wt % of C\({}_{60}\) (1C\({}_{60}\)/MCM-41, 3C\({}_{60}\)/MCM-41) were prepared following the dry impregnation procedure [31]. The photocatalysts with 6, 9, and 12 wt % C\({}_{60}\) (6C\({}_{60}\)/MCM-41, 9C\({}_{60}\)/MCM-41, 12C\({}_{60}\)/MCM-41) were synthesized by wet impregnation [32]. The MCM-41 mesoporous silica was synthesized according to the method reported in [31]. The impregnating solutions were prepared by dissolving the proper amount of C\({}_{60}\) (Merck, Darmstadt, Germany, 99.5%) in 1,2-dichlorobenzene (for synthesis-Merck, Schuchardt, Germany). The impregnatedsamples were heated at 180 \({}^{\circ}\)C for 4 h in air in order the final catalysts to be obtained. The catalysts had been exhaustively characterized in previous contributions of our group [31; 32] using N\({}_{2}\) adsorption-desorption measurements (Tristar 3000 Micromerities, Norcross, GA, USA), diffuse reflectance spectra (Varian Cary 3 equipped with an integration sphere, Australia), X-ray diffraction patterns recorded in the 2\(\theta\) range of 2-10\({}^{\circ}\) (Siemens D-500 X-ray diffractometer, Houghton, MI, USA) and in the range of 10-110\({}^{\circ}\) (ENRAF NONIOUS FR 590 diffractometer, Holland), infrared spectra (Perkin-Elmer16 PC FT-IR spectrophotometer, Waltham, MA, USA), thermo gravimetric analysis (LabsysTM TG of Setaram, Caluire, France) and transmission electron microscopy (JEOL 2011, New Brunswick, Canada).