esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Automated Guided Modular Vehicle
## 2 Background Overview
### Navigation Approaches
the most efficient ones for this type of task [19]. Their inner layers extract and filter the information with which it is trained. CNNs correlate input images to control and actuate the variables of the AGV. Thus, the training process efficiency is increased. Only a few thousand data points are required to track. The DNN generalizes an optimal solution after being trained with a valid dataset [29; 30; 31]. Valid datasets depend on the complexity of the track [30]. Usually tens of thousands of examples are needed [32]. The CNN is used to train these sets of images, performing sequential tasks of convolutions, batch normalizations, and max pooling activations in order to take important image descriptors. These extractions of image features are commonly taken using feature extraction methods, such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), and others [33]. These methods often have image rotation invariance extractors as well as a low computational cost. For a BC/IL approach, a high number of features need to be extracted and acquired in very short periods of time. CNNs use the same approach as a conventional feature extractor, but they perform more tasks in the color and intensity histograms. These features are labeled with either the steering wheel angle, gas pedal intensity, brake force, or all three of them. The system captures images and labels them with the aforementioned parameters while the users drive. CNNs extract features from the images and correlate them with the specified parameters. They do this by inputting the features extracted from the camera into the model, which outputs a label. Output data is used to give the control information of the vehicle. This approach requires large quantities of images but does not need in-field training as in the DRL approach. This implies that safety is only at stake when a model with 95%+ accuracy is tested. Both methods can be trained and tested in simulated or real-world environments. DRL converges faster in simulations because of the lower complexity of the environment [34]. After an acceptable performance, it can be tested in a real-world environment. However, extra safety measures must be considered, such as inertia, friction, braking, and acceleration, which represent some factors that cannot be accurately simulated. BC and IL need human intervention to be trained. They offer a more straightforward solution; however, they require much more time to acquire enough data to function. The quality and amount of data, the DNN architecture, reward functions, and computer power determine how much time is needed to train both approaches [19; 32]. The ego vehicle will perform well if the testing scenario resembles the training data.
## 2 Smart and Connected Healthcare Systems  
The term \"connected health\" encompasses digital healthcare solutions that enable remote monitoring, encompassing continuous health monitoring, emergency response, and alarm functionality. The primary objectives of connected health are to enhance the quality and efficiency of healthcare by enabling self-care and supplementing it with remote care. This concept originated in telemedicine, wherein users receive timely feedback regarding their health status as required. A smart healthcare solution operates autonomously, while a connected healthcare approach offers clinical feedback to users. The smart and connected healthcare sector is classified based on how it is consumed by end users, which determines the economy of the industry. Figure 2 illustrates the broad classification of this sector, taking into account services, medical devices, technologies used, applications, system management, and end users. The integration of connectivity technologies enables the expansion of applications within the healthcare system. Wireless technologies facilitate the integration of small devices, enabling remote health monitoring via the IoT [33]. However, it is important to maintain constant internet connectivity and support heavy data traffic in a hospital setting where a healthcare network is managed using Wi-Fi and cables.  
Smart and connected health refers to digital healthcare solutions and systems that are fully connected and accessible remotely [34]. The National Science Foundation (NSF) and the National Institutes of Health (NIH) initiated this concept in 2013 to promote the development and integration of innovative information technology approaches in healthcare [35]. The research team aimed to foster multidisciplinary collaboration and generate innovative \"smart\" ideas to enhance scientific collaborations in the field. Technology, particularly artificial intelligence, has played a crucial role in advancing healthcare delivery within the framework of smart cities. Smart and connected health research involves various disciplines, including medical informatics, public health, big data, bioengineering, and the telecommunications industry. In complex healthcare scenarios, smart and connected health requires resource-aware, time-constrained, complex, and secure healthcare transactions involving multiple stakeholders. Furthermore, smart and connected health has the poten  
Figure 1: Organization of this Research Paper.  
tial to revolutionize healthcare by accelerating treatment and testing procedures, reducing physician visits, efficiently responding to emergencies and pandemics, and enhancing the quality of patient care [36].
# Design of True Time Delay Millimeter Wave Beamformers for 5G Multibeam Phased Arrays
## 3 Numerical Results
### Rotman Lens Results
#### 3.2.1 Rotman Lens with Four Dummy Ports  
The classical Rotman lens topology studied herein consists of three input, three output, and four dummy ports, as depicted in Figure 10, with dimensions \(22.334\times 17.182\times 0.3\) mm\({}^{3}\). The reduction of dummy ports number results in a reduced efficiency. Figure 21a-f shows the magnitude and phase distributions of the designed lens. Although the device seems to work satisfactorily in the frequency range 27-30 GHz, it has a prohibitive response in the second half of spectrum that ranges from 30 to 33 GHz. This unwanted behavior is due to the ineffective absorption of the incident waves on the side walls of the lens. These waves do not get absorbed, thereby exciting standing waves in the parallel plate region. Therefore, this deficiency affects the array factor of the feeding antenna array. Figure 22 reveals this distortion showing the occurring beam squint (i.e., undesired shifts of the beam's pointing angle) at 27 GHz (dotted lines) and at 30 GHz (continuous lines). The magnitudes of the input reflection coefficients (return loss) are depicted in Figure 23 and as expected they present poor matching.  
Figure 21: _Cont._
###### Abstract  
Utilizing antenna diversity techniques has become a well-known approach to improve the performance of wireless communication systems. Multiple antenna arrays with half-length spacing, such as a uniform linear array (ULA), have been taken into consideration. Since 60 GHz is an unlicensed frequency band and ideal for local propagation, it is where the technology is being used. The transmitter and receiver both accomplish QAM modulation and demodulation. The performance in terms of bit error rate (BER) was tested in MATLAB simulation software for all antenna diversity scenarios: the single input and single output (SISO) DWT, multiple input and single output (MISO) DWT, single input and multiple output (SIMO) DWT, and multiple input and multiple output (MIMO) DWT. The MIMO DWT was shown to be the best of them. The performance of MIMO OFDM using various wavelets was also simulated, and the performance of the Haar wavelet transform was 2 dB better than that of the other wavelet transform. Compared to simulation results, the analytical results showed good agreement with little discrepancy.  
SISO; SIMO; MISO; MIMO; BER; antenna arrays Article
## 5 Conclusions  
The IPSO-BiLSTM model constructed in this study has achieved significant results in the field of network security situational awareness. The optimization of BiLSTM parameters by IPSO, combined with the encapsulated Wrapper feature selection algorithm, effectively improves the recognition rate and accuracy of the model The recognition rate of some attack types in the experiment was quite high. At the same time, the model structure is compact and reduces the computational cost, which has obvious advantages in tasks with high real-time requirements.  
Future research can be carried out in various aspects: first, expanding the application scenarios, testing and optimizing the model in different data sets and industry networks to enhance the model's versatility and adaptability; second, exploring better feature selection and extraction techniques, improving optimization algorithms, and improving the model's performance; and third, strengthening the model's interpretability research, and analyzing the model's decision-making mechanism with the help of visualization and other means, so as to provide intuition for the protection of cybersecurity and promote the development of cybersecurity technology.  
Conceptualization, P.Z.; Software, P.Z.; Validation, P.Z., S.L. and S.W.; Formal analysis, Y.C.; Resources, W.Z.; Data curation, P.Z.; Writing--original draft, P.Z.; Writing--review & editing, P.Z., Y.C., S.L., S.W. and J.B.; Visualization, B.L. and S.L.; Supervision, Y.C. and W.Z.; Project administration, W.Z. and B.L.; Funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [UNSW-NB15 dataset--OneDrive].  
The authors declare no conflict of interest.
## 2 Proposed TDoA-Based Method
### Application Scenario and Analytical Model
\begin{array}{c}s_{0i}=A_{0} \cdot\mathrm{G}_{i}(\vartheta,\varphi)\cdot\mathrm{e}^{\mathrm{i}\left(\alpha _{i}-\alpha_{0}\right)}\\ \mathrm{G}_{\mathrm{i}}(\vartheta,\varphi)\in\Re\end{array} \]  
Figure 1: Scheme of an ideal Uniform Circular Array in a tridimensional space. where each \(s_{0i}\) is the amplitude-phase measurement of sinusoidal signal received by i-th antenna element, having directional power gain \(\mathrm{G}_{i}(\theta,\varphi)\), obtained using an ideal coherent receiver chain. All phase measurements shall be consistent between themselves, or rather they have to keep their sorting order considering the signal propagation path (Figure 1). By this the receiver chains for each antenna must share the same phase reference \(\alpha_{0}\). As it will be shown in Section 3.1, a SBA architecture is a feasible solution to enable such measurement conditions. As proven in [16; 21] and indicated in [23; 24], the information related to signal DoA is kept within the steering vector through the ratio between its own terms, or rather such information is strictly related to steering vector direction on its \(N\)-dimensional vectorial space. Considering the implementation of an ML estimator able to compare steering vectors directions more than single components absolute values, as, for instance, applying a MUSIC [21; 23; 24] or a minVAR estimator [16] in place of a typical LSE estimator [18], every term of any measured steering vector can be divided for a constant factor. By this, it is possible to choose a generic R-th reference antenna between the available ones and put  
\[\overline{\mathbf{S}}=\frac{1}{s_{0R}}\cdot\overline{\mathbf{S}}_{0}=\left( \begin{array}{c}s_{1}\\ s_{2}\\ \vdots\\ s_{N}\end{array} \right)\quad\text{with}\;\;s_{iR}=\left(\frac{\mathrm{G}_{i}( \vartheta,\varphi)}{\mathrm{G}_{R}(\vartheta,\varphi)}\right)\mathrm{e}^{ \mathrm{i}\left(\alpha_{i}-\alpha_{R}\right)} \tag{5}\]  
where \(\mathrm{G}_{R}=\mathrm{G}_{R}(\vartheta,\varphi)\) but \((\vartheta,\varphi)\) is fixed within a specific measurement, so \(\mathrm{G}_{R}\) is a constant value for all i-th elements. Dealing with an incoherent sensing chain, as, for instance, the one for RSSI-based DoA positioning system [29; 30; 31; 32], only the magnitude of each \(s_{i}\) term is taken in account, so the information related to phase displacement between different antennas is disregarded reducing the amount of available information and so the potential DoA estimation accuracy.
## 3 Results  
### Absorption, Photoluminescence and Time-Resolved Photoluminescence Measurements  
Figure 2 shows the absorption coefficient for temperature ranging from 90 K to 220 K. At \(\lambda\) = 3.4 \(\upmu\)m, the absorption coefficient \(\alpha\) reaches 4800 cm\({}^{-1}\) at 150 K, in agreement with other values reported elsewhere [7, 20, 28]. The absorption band edge shows a wavelength red-shift with increasing temperature, while the absorption value remains relatively constant.  
PL spectra, presented in Figure 3, display a shift in PL peak from 4.89 \(\upmu\)m to 5.70 \(\upmu\)m in the temperature range (77-250 K). Such values confirm that the Ga-free T2 SL structure is relevant for the MWIR spectral domain.  
Figure 3: PL spectra of the Ga-free InAs/InAs\({}_{0.63}\)Sb\({}_{0.37}\) T2 SL structure between 28 K and 250 K. Inset shows the temperature dependence of the T2 SL’s bandgap where the solid line is a fit curve using Einstein oscillator’s equation.  
Figure 2: Absorption coefficient versus wavelength of InAs (4.17 nm)/InAsSb (1.42 nm) T2 SL at various temperatures.  
The T2 SL's bandgap energy, extracted from the PL peak, as a function of temperature (E\({}_{\text{g}}\)(T)) is shown in the inset of Figure 3, and a fitting curve (solid line) was plotted using the Einstein Oscillator model [8] through the equation E\({}_{\text{g}}\)(T) = E\({}_{0}-\alpha\)\(\times\) (T\({}_{\text{E}}\)/(exp(T\({}_{\text{E}}\)/T) \(-\) 1)), where \(\alpha\) = 2.013 \(\times\) 10\({}^{-4}\) meV/K stands for the slope of the high-temperature linear asymptote, E\({}_{0}\) = 258 meV stands for the energy gap at T = 0 K, T\({}_{\text{E}}\) = 117.6 K stands for the Einstein temperature and T is the absolute temperature. This fitting of the bandgap as a function of temperature will be used to analyze dark current density measurements performed on devices (see Section 3.3).  
TRPL measurements were performed on the XBn T2 SL structure (Figure 1b) to determine the minority carrier lifetime as a function of temperature (Figure 4). A lifetime value of 1 \(\upmu\)s was extracted from these measurements for temperature ranging from 90 K to 220 K. As a consequence, since this value remains constant in this range of temperature, the minority carrier lifetime is limited by SRH processes [35]. Moreover, a lifetime value as high as 1 \(\upmu\)s at 150 K validates the MBE growth of the Ga-free XBn InAs/InAs\({}_{0.63}\)Sb\({}_{0.37}\) T2 SL structure sample ready for device processing.
# Cluster-Based Transmission Diversity Optimization in Ultra Reliable Low Latency Communication
## 4 Simulation Results
### Reliability Analysis  
A total of 90 subcarrier channels are used to analyze reliability, and 6-clusters are considered to configure C-OFSMA. In Figure 7, for 7-packet duplication with 5000 pkt/s arrival rate, the proposed C-OFSMA scheme earns the reliability of 99.95555% where the OFSMA scheme earns 99.949165%. At higher arrival conditions, for 5-packet duplication, the C-OFSMA scheme is showing better performance than the OFSMA scheme with 7-packet duplication. At 5000 pkt/s arrival rate, the used C-OFSMA scheme has the reliability of 99.9513% for 5-packet duplication and the OFSMA system has 99.9463%. As usual, 3-packet duplication showed poor results. The advantage of the C-OFSMA scheme is also true for 3-packet duplication by comparing the OFSMA scheme.
## 3 Results
### Catalyst Characterization
#### 3.2.3 XPS Measurements  
The XP spectral analysis results are listed in Table 2. The Al 2p peak was located at binding energy (BE) 75,1 \(\pm\) 0.1 eV in the spectra of all samples. The BE of Au 4f\({}_{7/2}\) peak in the range 84.2-84.5 eV indicated the presence of metallic gold in all gold-containing samples [23]. The Pd 3d\({}_{5/2}\) spectra of Pd-based samples were characterized by component at BE around 336.7 \(\pm\) 0.1 eV, typical of Pd\({}^{2+}\), thus excluding the probability of any PdAu alloy formation. The comparison of the XPS derived Au/Pd atomic ratio with the analytical ratio (1) revealed a surface enrichment of palladium. Quite interesting, the surface segregation of palladium was less in the bimetallic over pure CeO\({}_{2}\) support, the only bimetallic formulation that demonstrated better activity as compared to the monometallic ones. With respect to the Au/(Ce+Al) atomic ratios, not listed in the Table 2, a slight decrease from the analytical value of 0.007 to 0.005 was observed in the presence of palladium, probably due to partial covering of gold as a result of sequential palladium deposition over the gold catalysts. On the contrary, with respect to the Pd/(Ce+Al) atomic ratios, not listed in Table 2, the highest XPS derived Pd/(Ce+Al) atomic ratios of 0.044 and 0.041 as compared to the analytical value of 0.006 were obtained for Pd/CeAl and Pd/YCeAl. These ratios evidence a better surface dispersion of Pd, thus explaining the highest activity of these catalysts. Actually, the highest atomic ratios Pd/support and Au/support were observed for Pd/Ce (0.14) and Au/Ce (0.06) in good agreement with the capability of ceria to well disperse the supported noble metals. The highest activity of Pd-Au/Ce among all bimetallic formulations could be also related to abundance of finely dispersed gold and PdO particles on the surface of ceria. Important information was received from the ratios Y/Pd and Y/Au. The analysis showed that the atomic ratios Y/Au are much larger than the analytical one, whereas the ratios Y/Pd are very close to the analytical one (0.28), disclosing the tendency of yttrium to occupy sites close to the palladium. The analysis of Ce 3d spectra detected both Ce\({}^{4+}\) and Ce\({}^{3+}\) oxidation states on the surface of all samples. Based on general agreement about the relationship between concentration of Ce\({}^{3+}\) and oxygen vacancies formed for the charge compensation, the atomic ratios Ce\({}^{3+}\)/(Ce\({}^{4+}\)+Ce\({}^{3+}\)) were calculated and reported in Table 2. The values in the range 0.22-0.41 were higher than the typical values of 0.06-0.10 for bulk ceria, reflecting defective structure with oxygen vacancies formation on the surface of all catalysts [31]. The highest ratios were observed for the most active samples. The binding energy of Y 3d\({}_{5/2}\) at 158.0 \(\pm\) 0.2 eV was observed in the spectra of Y-containing samples and attributed to Y\({}^{3+}\)[23]. The role of Y-doping on higher defectivity of ceria was also clearly emphasized. The effect was better expressed in case of Pd/YCeAl (0.38) and undoped Pd/CeAl (0.25).
## 3 Life-Cycle Assessment (LCA) of Integrated Microbial Electrochemical Systems  
### Mfc-Fo  
Integration of a conventional MFC with a forward osmotic (FO) membrane results in a more efficient system, in terms of bioelectricity production, called osmotic MFC (Os-MFC). Researchers used the International Life Cycle Data System (ILCDS) to calculate the environmental impacts of the working integrated systems. This was performed to characterize the effect of using the Os-MFC system to treat 1 L of wastewater [46]. The system's life cycle is divided into five stages for analysis: raw material extraction and material processing, system fabrication, pretreatment, operation, post-treatment and end of life (EoL). The exploitation of raw materials has a significant impact on metrics, such as global warming potential (GWP) and abiotic depletion potential. The human toxicity potential (HTP) and ozone depletion potential (ODP) are factors in the EoL stage (ODP). Ultimately, the integrated Os-MFC system has a greater environmental impact than a stand-alone traditional MFC system.
## 3 Results
### MGD Characterization with Carbon Ions at CNAO  
For each test, a total number of 108 ions were delivered, and during the beam delivery, the total number of counts measured by the three chambers were stored every 100 ms. Figure 4 shows the counts measured by the three chambers with the bias voltage ranging from 13 to 400 V and at two different beam intensities 0.6 \(\times\) 107, and 3 \(\times\) 107 carbon ions/s. As expected, at lower bias voltage, the counts of the three chambers are overlapped, while the effect of different gaps appears at larger voltage due to the larger charge collection efficiencies. Indeed, chamber 1 reaches the saturation at 120 V for all the beam intensities, and its measurement could be used as a reference ionization charge to calculate the relative efficiencies.  
Figure 5a shows the counts normalized to the detector gap as a function of V/d2. Data from all the chambers and for the three intensities, 0.6 \(\times\) 107, 2 \(\times\) 107 and 3 \(\times\) 107 carbon ions/s, are reported. The error bars were computed from multiple measurements in identical conditions. The measurements from different chambers reach the saturation at around 800 V/cm2. We observe that the counts per unit of thickness depend on the beam intensity and that, at a given intensity, they follow the same curve for the three chambers. Therefore, the fits with Equation (5), using the Boag-Wilson function for continuous beam (Equation (1)) for \(f\), were evaluated and are shown in the same figure. The rate \(S\) of volumetric ionization, as resulting from fits, shows a good linearity as a function of the measured beam current as reported in Figure 5b.  
Two calibration curves for chamber 1 are obtained, one from the comparison with chamber 2 (relative efficiency f1/f2 = Q1d2/Q2d2d2d1) and the other with chamber 3 (f1/f3 = Q1d3Q3d3d1), as shown in Figure 6. The efficiencies f1 are computed as the ratio between the measured counts per unit thickness (Counts/d) and Csat.  
The calibration curve is determined with a fit to the Figure 6 data with a multiple parameters function where x corresponds to the relative measured charge in units of thickness (Q1d1/Q1d1d1d1) and \(a\), \(b\), \(c\), and \(d\) are ad hoc parameters to describe the data points:  
\[f_{1}(x)\ =\ ax+\frac{1}{b+e^{\frac{(-x+6)}{2}}} \tag{7}\]  
Figure 4: Integral counts measured by the three chambers as a function of the bias voltage for two beam intensities: (**a**) 6 \(\times\) 106 and (**b**) 3 \(\times\) 107 carbon ions/s.  
\(Q_{1}^{\prime}\)d\({}_{2}\)/\(Q_{2}^{\prime}\)d\({}_{1}\) = 0.6594 when IC1 is compared to IC2, and Q\({}_{3}\)d\({}_{1}\)/Q\({}_{1}\)d\({}_{3}\) = 0.4445 for IC1 compared to IC3.  
Inserting these values in Equation (6) and using the parameters as from the fits, we extract f\({}_{1}\) = 0.9466 from fit A and f\({}_{1}\) = 0.9428 from fit B. The total deviation between the two corrected IC1 readouts is less than 0.5%.