esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The hardware implementation of MUSIC was a major challenge of this paper. Implementing it in its intrinsic form on an FPGA would have required a lot of resources and computation time. Thus, our first intervention addressed the covariance matrix. Taking advantage of the Hermitian symmetry characteristic of the covariance matrix, the multiplication operations performed in parallel concerned just half of the matrix, and the other half was reconstituted by a simple sign change. The other intervention concerned the choice of an EVD decomposition technique with a low computational load and good accuracy. The best candidate was the Jacobi method. This iterative method led to the development of a new serial architecture, allowing the decomposition into real eigenvalues and eigenvectors. The performance of the proposed processor is improved using the systolic architecture. We have achieved very satisfactory results, with a Virtex-E FPGA resource allocation of less than 50% and a processing time per turn of 15.42 \(\upmu\)s Our immediate action following this research will be to extend the exploitation of our FPGA in real use cases in several sectors such as medical, aeronautical, automotive and space.  
Conceptualization, R.G. and W.A. and I.E.G.; methodology, R.G. and A.S.; software, R.G., W.A. and A.S.; validation, J.F. and I.E.G.; formal analysis, R.G. and A.S.; investigation, I.E.G. and J.F.; resources, J.F. and A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Umm Al Qura University under grant no 22 UQU4361156 DSR01.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **This work** & **EVD [39]** & **EVD [15]** \\ \hline Device Type & xcv1000e & Stratix EP1S25 & Virtex-6 \\ Word Length (bits) & 32 & 16 & 16 \\ Frequency (MHz) & 125 & 110 & 160 \\ Slices & 4640 & – & 5675 \\ LEs & – & 4045 & – \\ Latency (us) & 15.42 & 16.69 & 50.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of our design and selected previous pieces of research.
# Wavelength Selection for Satellite Quantum Key Distribution
## 4 QKD Performance
### Filtering Techniques  
To improve the signal-to-noise ratio and the secret key rate, several filtering methods can be utilized. A narrow temporal filter temporarily opens to allow signal photons through to the detector and closes once the signal has passed, blocking out the remaining noise photons. Precise synchronization is required to effectively block out noise. As such, a practical filter of 1 ns is chosen. Several light sources contribute to the accumulated background noise, including solar, lunar and terrestrial radiance, which results in a spectrum of continuous wavelength noise. Much of this noise can be blocked using a narrow spectral filter, which selectively allows light of certain wavelengths to pass through. We use a 1 nm spectral filter. Two spatial filtering strategies are proposed in [58], a diffraction limited (DL) strategy and a turbulence limited (TL) strategy. The key difference between the strategies is whether the field stop grows to accommodate the turbulence broadened beam spot (TL) or remains constant at the diffraction limit (DL). We adopt the turbulence limited strategy in which the size of the field stop is varied to always allow 84% of the signal through. The choice of spatial filter determines the solid-angle field of view (_FOV_), which is given by [58]  
\[\Omega_{FOV}=\pi\bigg{(}1.22\ \frac{\lambda}{D_{r}}\Big{[}1+\Big{(}\frac{D_{r}} {r(\lambda)}\Big{)}^{5/3}\Big{]}^{3/5}\bigg{)}^{2} \tag{16}\]  
\(\Omega_{FOV}\) increases with \(\lambda\), which makes spatial filtering significantly more effective at shorter wavelengths.
## 3 Results
### Photoinactivation of P. aeruginosa by PACT  
Synthesis of the studied phthalocyanine Pc+ was based on procedures recently reported by our group [29; 31]. The octaiodide salt of the magnesium(II) phthalocyanine derivative Pc+ was synthesized and characterized by mass spectrometry (MALDI TOF, the Advanced Chemical Equipment Instrumentation Facility at the Faculty of Chemistry, Adam Mickiewicz University in Poznan), UV-vis (Hitachi UV-vis U-1900 spectrometer) and NMR spectroscopy (Bruker 400 spectrometer) [29]. Herein presented studies indicate antimicrobial photocytoxicity of Pc+ against all tested strains of _P. aeruginosa_. Pc+ at a concentration of \(1.0\times 10^{-4}\) M, activated by light [L(+)Ps(+)], revealed a significant decrease in the number of bacterial cells compared to the untreated control group (L(-)Ps(-)). Representative images of _P. aeruginosa_ quantification after treatment with Pc+ at a concentration of \(1.0\times 10^{-4}\)M in the dark (dark toxicity) and after irradiation with light (photocytotoxicity) are presented in Figure 2. For _P. aeruginosa_ isolated from the lower respiratory tract of patients with pneumonia, effective bactericidal action (Figure 3) with reductions varying from 3.9 log (strains no. 10 and 22) to 5.7 log (strain no. 23) was demonstrated (Figure 3A). The results of the studies performed on _P. aeruginosa_ strains isolated from chronic wound infections (Figure 3B) revealed a decrease in the number of viable cells varying from 4.6 log (strain no. 30) to 5.5 log (strain no. 42). It is worth noting that no significant statistical differences were observed in the degree of microbial reduction either between strains isolated from the lower respiratory tract and chronic wounds, between strains resistant and sensitive to allbeta-lactam antibiotics used in the study, or between strains producing different degrees of biofilm (\(p>0.05\)). In the dark, no apparent toxicity was noted in the same conditions (Figure 3A,B). The influence of light was also assessed by the irradiation of a set of samples without a photosensitizer. The control indicated no change in the number of viable cells as compared to the non-irradiated samples (data not shown).  
Figure 2: Photocytotoxic effect of Pc+ against _P. aeruginosa_ through incubation and plate counting method. Representative plates with bacterial colonies of _P. aeruginosa_. (**A**)—number of _P. aeruginosa_ in the control sample not containing the photosensitizer and not irradiated (L- Ps-); (**B**)—number of _P. aeruginosa_ in the non-irradiated test containing the photosensitizer (dark toxicity (L- Ps+)); (**C**)—number of _P. aeruginosa_ in the irradiated test containing the photosensitizer (photocytoxicity (light toxicity) (L+ Ps+)).  
In the studies, the differences in morphology of _P. aeruginosa_ colonies were observed. Colonies isolated from Pc+-treated but non-irradiated samples were smaller and smoother compared to those isolated from non-Pc-treated controls.
## 4 Numerical Experiment
### Influence of the Stability of the Interval Fuzzy Capacity on Routing Optimization  
The range of an interval fuzzy number can reflect its stability and is determined by its upper and lower bounds. The stability of the interval fuzzy number is enhanced when the two bounds are closer to each other. In this section, we fix the interval fuzzy carbon tax rate, since it is determined by the practical market and cannot be changed by the receiver or the IMO. Therefore, we only discuss the influence of the stability of the interval fuzzy capacity on the routing optimization.  
We still assume that the receiver prefers a high RT and set the optimization levels as 0.9 and 1.0. We increase the lower bounds and decrease the upper bounds of the interval fuzzy capacities of the intermodal network from 0% to 15% in steps of 5%, and provide the routing optimization under different interval fuzzy capacity ranges, as illustrated in Figure 10.  
As we can see from Figure 10, improving the stability of the interval fuzzy capacity by narrowing its range from the lower bound to the upper bound reduces the TCs of the PIMR that yields a high RT to a certain degree. When the stability of the interval fuzzy capacity is improved through separately increasing the lower bound and decreasing the upper bound by 15%, the TCs of the PIMR are reduced by 7% for the confidence level of 0.9 and 10.7% for the confidence level of 1.0.  
Figure 10: TCs of PIMRs under different ranges of interval fuzzy capacities.  
Therefore, when building a water-rail-road intermodal network, besides considering the capacity sufficiency, the IMO should consider the stability of the capacity. Transportation modes with more stable capacities should be used to carry out transit processes, and transfer nodes with more stable capacities should be adopted to realize the transfer process between different transportation modes.
## 1 Introduction
### Contribution  
This paper delves into the exploration of different machine learning models, with the objective of evaluating their performance in addressing issues related to data traffic security. While existing state-of-the-art studies tend to focus on a limited selection of machine learning models, offering minimal comprehensive comparisons in terms of the effectiveness of available solutions, this research stands out by conducting extensive testing of numerous machine learning models. This approach allows for a critical evaluation of the strengths and weaknesses inherent in each model. Many existing papers in the literature propose models tailored exclusively for either binary classification or multi-class problems. However, this article takes a broader approach by testing the selected models for both binary and multi-class classification scenarios, significantly enriching the depth of the technical analysis presented. The contributions made by this study can be summarized as follows:  
* A thorough analysis of multiple machine learning algorithms has been conducted to effectively handle the processing of large volumes of network traffic data. These algorithms can be scaled to accommodate the expanding sizes of networks, resulting in the development of a robust and efficient intrusion detection system.
* The impact of dataset selection on the performance of intrusion detection systems has been explored in this study. By comparing the performance of intrusion detection systems across three distinct datasets (KDD 99, UNSW-NB15, and CSE-CIC-IDS 2018), a better understanding of the influence of dataset choice on system performance has been achieved.
* This research has made significant contributions to the advancement of effective feature engineering techniques, which are essential for constructing successful machine learning models. These techniques encompass feature selection, feature scaling, and feature normalization, enhancing the overall effectiveness of the models.  
Figure 1: Schematic representation of the machine learning workflow.  
Additionally, this study proposes an analysis of the computational time required by different models, a factor often overlooked in existing literature. This inclusion enriches the performance analysis of the models across the selected datasets.
# High-Resolution Mapping of Subsurface Sedimentary Facies and Reservoirs Using Seismic Sedimentology
###### Abstract  
This investigation presents the current status of seismic sedimentology, along with seismic geomorphology, as applied to the high-resolution (<5 m) mapping of sedimentary facies and hydrocarbon reservoirs in the subsurface. Seismic sedimentology involves the joint investigation of seismic lithology and seismic geomorphology. The high-resolution (as thin as one meter) interpretation of depositional units on lithofacies and paleo-landforms can be achieved by following a comprehensive workflow focusing on three mandatory steps (evaluating and improving data quality; selecting right attributes, preferably 90\({}^{\circ}\) seismic trace with frequency fusion; and making use of horizontal resolution on striatal slices) and two optional steps (guiding interpretation with seismic models and applying machine learning techniques). Seismic sedimentology is set to improve through enhanced calibration using well and outcrop data, along with regional and local geological models. Furthermore, there will be a deeper integration between geological and geophysical disciplines, as well as advancements in high-resolution geophysical acquisition and processing techniques.  
seismic sedimentology; seismic geomorphology; seismic resolution; spatial resolution; thin bed Article
# _Synechococcus elongatus_ PCC 7942 as a Platform for Bioproduction of Omega-3 Fatty Acids
## 4 Discussion
Here, we report SDA levels up to 5.7% at 24 h post induction, increasing to 14.8% at 48 h (Figures 5 and 6). To reach these levels, besides overexpression of desaturases, we manipulated the FA synthesis pathway by modifying the dosage of what looks like the limiting step in Se7942, the FabF reaction. Above 48 h post induction, SDA yields did not further improve, suggesting that we could have reached the maximum level of SDA that can be accumulated in Se7942 membranes. In a recent publication, SDA levels higher than the above were obtained using a new strategy: overproduction of Vipp1 (vesicle-inducing protein in plastids) [34]. These authors reported SDA yields of 10%, 17%, and 27% of total lipids in _Anababena_ sp. PCC 7120, Ss7002, and _Leptolyngbya_ sp. strain BL0902, respectively. In cyanobacteria, Vipp1 promotes thylakoid membrane biogenesis and maintenance [54], and its overexpression in Ss6803 increases the thylakoid membrane abundance [55]. It is tempting to speculate that Vipp1 overexpression may allow an additional increase in SDA production in the strains described in this work by increasing the number of thylakoid membranes where omega-3 FAs could be accumulated. In cyanobacteria, DesD, also known as \(\Delta 6\) desaturase, is the enzyme that catalyzes the addition of a double bond at the 6 th carbon-carbon bond position in FAs [56]. This enzyme is required for the conversion of ALA into SDA or LA into GLA (Figure 1) [57]. In human cells, it is well established that ALA is the preferred substrate for \(\Delta 6\) desaturase, and its affinity for ALA is 2-3 times greater than for LA [58]. In Ss6803, on the contrary, \(\Delta 6\) desaturase displays a higher affinity for LA than for ALA, favoring the production of GLA [57], as demonstrated by heterologously expressing DesD in Ss7002 [27] in _Anabaena_ sp. PCC 7120 [57] or in Se7942 (by also coexpressing DesA) [57]. In our experiments, we did not detect GLA when SDA was produced by DesABD coexpression (Figure 6), supporting the idea that Ss6803 DesD has a higher affinity for LA rather than for ALA. Taken together, our data suggest that synthesis of SDA in Se7942 is accomplished by \(\Delta 15\)-desaturation of GLA, not by \(\Delta 6\)-desaturation of ALA. Thus, we propose that the sequence of desaturation reactions to produce SDA in Se7942 would be \(\mathrm{SA}\rightarrow\mathrm{OA}\rightarrow\mathrm{LA}\rightarrow\mathrm{ GLA}\rightarrow\mathrm{SDA}\) (catalyzed by DesC, DesA, DesD, and DesB, respectively) (Figure 1).
## 3 Sliding-Mode Controller
### Switching Frequency and \(H\) Calculation  
The switching frequency \(F_{sw}\) imposed by the SMC is calculated from the ripples of the signals into the switching function. The stable operation of the SMC switching function (41) guarantees that \(\Psi=0\), which imposes the following closed-loop dynamics:  
\[i_{1}\cdot(1-d)=i_{2}\cdot d\quad\wedge\quad(i_{1}+i_{2})-i_{pv}=i_{r} \tag{53}\]  
Therefore, the average values of both the first and second components in (41) are equal to zero. However, the current ripples of \(i_{1}\) and \(i_{2}\) are in phase, as reported in (23) and (24); thus, their magnitudes are additive: \(i_{1}+i_{2}-i_{pv}-i_{r}=\delta i_{1}+\delta i_{2}\) and \(i_{1}\cdot(1-d)-i_{2}\cdot d=\delta i_{1}\cdot(1-d)-\delta i_{2}\cdot d\). Finally, the ripple of the switching function, which corresponds to the hysteresis band \(H\), is calculated from (41), (23), and (24) as follows:  
\[H=\frac{v_{pv}\cdot d}{2\cdot F_{sw}}\cdot\left[\frac{2-d}{L_{1}}+\frac{1-d}{ L_{2}}\right] \tag{54}\]  
Figure 8: Hysteresis band to limit the switching frequency.  
Figure 9: Switching circuit implementing the control law (51).  
It must be pointed out that \(H\) must be calculated for the maximum practical value of the switching frequency supported by the MOSFETs used in the NEC boost converter.
## 3 Metasurfaces
### Applications of Metasurfaces  
The interaction of surface plasmon with the electromagnetic wave leads to a phase discontinuity across the metasurface. Since elements on a metasurface can be spatially varied, this variation can cause the currents on the surface to lead (or lag) depending on the individual resonant element. This localized phenomenon allows us to tailor the wavefronts as they pass through a metasurface and leads to a variety of applications.
_Article_  
Active Sidewall Panels with Virtual Microphones for Aircraft Interior Noise Reduction  
[PERSON] (c)  
German Aerospace Center, Institute of Composite Structures and Adaptive Systems,  
38108 Braunschweig, Germany; [EMAIL_ADDRESS]  
Received: 12 August 2020; Accepted: 26 September 2020; Published: 29 September 2020  
**Featured Application: The proposed active sidewall panel with integrated structural actuators and sensors (smart lining module) is intended for application of cabin noise reduction in propeller driven regional aircraft like ATR 42, ATR 72, DHC-8 Q400 or in new energy efficient regional or short-range aircraft with counter rotation open rotor (CROR) or electric propeller engines. Since the smart lining modules are autonomous and geometrically compliant with their passive counterparts, they can be readily integrated into the aircraft.**  
**Abstract: This work deals with the reduction of aircraft interior noise using active sidewall panels (linings). Research work done in the past showed that considerable reductions of the sound pressure level (SPL) in the cabin are possible using structural actuators mounted on the lining and error microphones distributed in front of the linings. However, microphones are undesirable for error sensing because they are not suitable for the realisation of an integrated and autonomous active lining (smart lining module). Therefore, the goal of the present work is the replacement of the microphones by structural sensors. Using the structural sensors as remote sensors in combination with an acoustic filter, virtual microphones can be defined. The present study relies on experimental data of a double-walled fuselage system which is mounted in a sound transmission loss facility. Simulation results based on measured time data and identified frequency response functions are provided. Different configurations of virtual microphones are investigated regarding the SPL reduction and the induced vibration of the lining panel.**  
**Keywords: aircraft; interior noise; active noise control; sensors; actuators; optimization; remote sensors; virtual microphones**
## 4 Software-Defined Network (SDN)
### SDN for Smart Cities  
Traditionally, reconfiguring a network and adding features are exhaustive because of the fully distributed control. However, SDN is much simpler due to the centralization control, which allows the controller to overcome these complexities. Deploying an SDN for smart cities relieves the tasks of big data management, quality of service (QoS) guarantee, security and privacy concerns, heterogeneity, and communication resilience [35].  
It implements the needs of IoT-based smart city networks by providing routing efficiency, managing the network, and optimizing resources by virtualizing IoT devices of the access network [42]. Deploying an SDN for smart cities solves many issues with traditional networks, including scalability, heterogeneity, interoperability between IoT devices, lack of dynamic services, bandwidth reduction, quality of services improvement, and adapting new services [43]. These achieved solutions can be classified into management, security, and architectural solutions. The management solution specifies how the management layer's applications should coordinate with the control layer. It also specifies the management procedure network administrators use in addition to SDN controllers.  
Using an SDN, network service providers can easily configure and reprogram devices for smart cities [44]. Access to user equipment and network nodes is governed by several security parameters defined by the security solution. An SDN-based IoT architecture delineates distinct service layers in the control and data planes to better manage network traffic. The data plane illustrates a system for routing data to their intended destination [45].  
By deploying SDN technology, smart cities can have a more adaptive, secure, and efficient network structure, enabling the efficient implementation of diverse smart city applications and services. Table 3 summarizes the benefits of implementing SDN for smart cities [42; 43; 44].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Value Added** & **SDN for Smart Cities** \\ \hline Resources optimization & With an SDN, network resources can be dynamically allocated based on demand and usage patterns. This resource optimization ensures efficient bandwidth use and minimizes latency, allowing smart cities to operate smoothly and have real-time responses. \\ \hline Enhancing network security & An SDN enables robust security policies and network segmentation. Segmented networks can isolate sensitive data and critical infrastructure from potential threats, preventing unauthorized access and mitigating security risks. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Benefits of implementing SDN for smart cities.
# A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
## 5 Experiments
### Baselines and Evaluation Metrics  
For our baselines, we used SVM (support vector machine) and LR (logistic regression) classifiers because they have been shown to be effective in previous studies [3; 4; 43]. For features, studies showed that n-gram-based models performed very well in hate detection tasks (word and characters). Therefore, for both classifiers, we tested different n-gram features with 5-fivefold cross-validation on the training set. We found that character n-gram features (\(n\) = 1-4) yielded the highest F1 score. We used Python scikit-learn library [44] to implement both models. Optimal combination of the model hyperparameters was optimized using a grid search using fivefold cross-validation on the training set and with F1 scoring metric.  
For evaluation, we calculated true negatives (TN), true positives (TP), false negatives (FN), and false positives (FP) by comparing predicted and true labels. Then, we used macro-averaged precision (P), recall (R), accuracy, and F1-measures as in the following equations:  
P = TP/(TP + FP)  
R = TP/(TP + FN)  
Accuracy = TP+ TN/(TP+TN +FP + FN)  
F1 = 2 X (P XR)/(P + R)  
We also used area under the receiver operating characteristic curve (AUROC), which can be used as an indicator of how good the model is for distinguishing the given classes. ROC is a curve with true positives rate (TPR, sensitivity) plotted on the vertical axis and false positives rate (FPR = 1, specificity) plotted on the horizontal axis for various thresholds.  
Moreover, since our hate speech data collection is relatively imbalanced and given the serious consequences of failing to detect a hateful content, we also reported our results using positive class recall.