esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Hardware Resource Consumptions  
This subsection analyzes the hardware resource consumptions of the original C++ implementation (\(capt\_vhls\_ori\)) with that of the optimized versions (\(capt\_vhls\_opt\_*,*=1,2,3,\ldots,D_{\text{opt}}\)). The Zynq development and evaluation board (part: xc7z020 clg484-1) of Xilinx is used as the target device. The element utilizations of different implementation versions are listed in Table 5. According to the element utilization percentage, the implementation optimizing is mainly constrained by the number of DSP48 Es, so the optimizing goal of (16) can be rewritten as  
\[\begin{split}&\min G_{\text{opt}}\\ &\text{s.t.}\ \ C_{\text{opt}}^{\text{DSP48E}}\leq C_{\text{av}}^{ \text{DSP48E}}\end{split} \tag{17}\]  
where \(C_{\text{opt}}^{\text{DSP48E}}\) is the overall DSP48E consumptions, \(C_{\text{av}}^{\text{DSP48E}}\) is the available DSP48E number of the target device. In this case the DSP48E elements are used to generate the single- or double-precision floating point operating instances. Table 6 specifies the DSP48E-based instances of different optimizing versions. \(fadd\) and \(fmul\) refer to single-precision floating point adders and multiplexers, and they are allocated for PLS regressions (Lines 7-9 in Algorithm 4). The number of these two elements are multiplied with the raising of \(D_{\text{opt}}\). \(add\) is the double-precision floating point adder, and \(dexp\) outputs the value of \(e\) raised to  
Figure 7: Diagnostic error rate of different implementations (see Table 4 for the definition of acronyms).  
the input value's power. Despite of these operations are frequently invoked, HLS enables hardware reuse by binding one instance to multiple operations, so the resource cost can be well economized. The relationship between the DSP48E consumptions \(C^{\text{DSP48E}}_{\text{opt}}\) and the optimizing depth \(D_{\text{opt}}\) in this paper can be formulated as  
\[C^{\text{DSP48E}}_{\text{opt}}=\frac{1}{2}C^{\text{DSP48E}}_{\text{add}}D_{ \text{opt}}+C^{\text{DSP48E}}_{\text{full}}D_{\text{opt}}+C^{\text{DSP48E}}_{ \text{add}}+C^{\text{DSP48E}}_{\text{deep}} \tag{18}\]  
According to Table 6, the DSP48E number costed by \(fadd\), \(fmul\), \(dadd\) and \(dexp\) instances are \(C^{\text{DSP48E}}_{\text{fadd}}=2\), \(C^{\text{DSP48E}}_{\text{fmult}}=3\), \(C^{\text{DSP48E}}_{\text{add}}=3\) and \(C^{\text{DSP48E}}_{\text{deep}}=26\), so (18) can be simplified to \(4\times D_{\text{opt}}+29\). Considering that the \(D_{\text{opt}}\) must be an integer divisor of the iteration number \(m=16384\), according to (17), the optimal optimizing depth in this case is \(D_{\text{opt}}=32\).
# Securing IoT Devices against Differential-Linear (DL) Attack Used on Serpent Algorithm
## 3 Literature Review
This study focuses on securing Serpent, found on IoT devices, against DL attacks by using new 32-bit S-Boxes and a new function developed from three magic numbers in such a way that _DLCT_ will be cumbersome to construct when using new 32-bit S-Boxes. During the study, the analysis of the results showed that DL started with _DLCT_. Therefore, by blocking the construction of _DLCT_, it was believed that the DL attacks would be impossible. Additionally, a newly generated function called Blocker is introduced in this study. A Blocker Function uses a 32-bit output values from S-Box as \(state32 hold\) and delivers a new value, \(statehold\), as an output. A Blocker Function also turns \(P\) value, \(M\) value, and \(Q\) value into unfactorizable polynomials. Random numbers and XOR operators are utilized for complexity and confusion in order to prevent reverse engineering for attackers. The XOR operator and \(rand()\) change the values of the variables inside a Blocker Function. The random numbers and XOR operators also provide a problematic input range when invaders reverse a Blocker Function to calculate the exact information utilized in that situation. The values of \(M\), \(P\), and \(Q\) are also continually maintained as unfactorizable polynomial variables, which are non-linear and hard to reverse, in order to construct _DLCT_ using any machine or computer. The random numbers and XOR operators include inventing hidden, unseen, and unchangeable variables for intruders. A Blocker Function produces a unique 32-bit S-Box suitable for the new Magic Serpent Algorithm. A Blocker Function distracts the attacker since it comprises many mathematical random numbers and XOR operators. Additionally, most mathematical XOR operators and random numbers are irreversible. For more mathematical characteristics of a Blocker Function and C++ explanations, refer to Section 4 and Figure 1.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)
#### 5.3.9 Comparison of Cooperative Reliability-Based Energy-Efficient Routing Protocols (CO-RE2 RPs)  
Table 10 depicts, the basic characteristics of CO-RE2 RPs for UWSNs. According to the aforementioned discussion, the main function of the energy efficient routing protocols are to supply a reliable communicating data packets and are also more energy efficient. These E2 RPs can reduce the multiple route fading issues without utilizing various antennas. The energy saving approach is the main focus of the all-aforementioned energy efficient routing protocols with the selection of proper repeater nodes. Thus, the selection of repeater nodes in the network is necessary to increase the energy efficient communicating data packets transmission with very high reliability. A high network diversity gain can be generated by a reliable repeater node. In the network the best repeater nodes selection, theoretically improves the performance of the network and the main objective is cooperative routing, such as data packet delivery ratio, network throughput and reduce energy consumption. The overall network's concern is energy efficiency. However, a lot of the present routing protocols has used the remaining energy parameters, when selecting the repeater nodes in the network. As shown in, when choosing a repeater node in the network, both the remaining energy and the connection quality assessment are very important. Furthermore, the choice of many repeater nodes, as opposed to one, is the next popular route to ensuring the guaranteed communicating data packet delivery with low bit error rate. The main disadvantages of these routing protocols are that they compromise with network delay and ensuring communicating data packet reliability. Moreover, the communicating data packet duplication issue, that arises from multi-path routing leads to excessive energy use. Furthermore, in sparse networks the energy efficiency is limited. In this case, the communicating nodes must send communicating data packets over a considerable distance. Therefore, during the simulation results, a dense to sparse size of the network is understoodto demonstrate the protocol flexibility. As opposed to, if the expense is not a major obstacle, it is better to use several sink nodes than just using a single sink node.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Surface Model Generation
#### 4.2.1 Epipolar Rectification  
For epipolar rectification of the Pleiades push-broom images our approach in Reference [54] is modified that initially was designed for SAR images. However, we show that it can also be applied to push-broom geometries by supporting the RPC sensor model within the framework. This aspect highlights this method since a single implementation is applicable to multiple geometries. For details of the rectification method we refer to Reference [54]. Since, the underlying transformations from image to epipolar rectified geometry are non-linear and also incorporate iterative image-to-map transforms (e.g., solved via [PERSON]'s method and linearization), pixel-by-pixel evaluation is computationally expensive. Especially, as the epipolar Pleiades images are huge, ca. 50,000 \(\times\) 50,000 pixels for a full scene, an approximation is desired. It can be observed that the transformation is a locally slowly changing continuous function. Thus, we propose an algorithmic extension by a rigorous evaluation on grid points only. Transformation results between the grid points are then achieved by bilinear interpolation. We set the grid size such that the maximal pixel shift in column and line direction in comparison to the rigorous transformation is below 1/1000 of a pixel and thus negligible. For the test site Ljubljana, this accuracy is achieved when using a grid size of 250 pixels. We reduce it to 100 pixels to ensure errors below 1/1000 of a pixel also for any other possible scene, thus reducing the transformation effort by a factor of 10,000. Since the epipolar rectification maps an image from the original resolution to the same resolution, the GSD does not influence the mapping accuracy and thus the grid size does not need to be adjusted w.r.t. the GSD. Actually, the error limit of 1/1000 of a pixel can be seen as no error as we are working on the image domain. We could also set this limit to 1/100 of a pixel which then corresponds to a grid size of 700 pixels and thus a speedup of 500,000. To avoid recalculation of those grids they are also stored to be used later in the spatial point intersection. Those grids are called forward and backward addresses. Figure 8 depicts the geometric relation between stereo input images, their epipolar versions, forward and backward addresses and both disparity maps gathered in the matching step (cf. Section 4.2.2). For instance, a pixel coordinate from image 1 given in Figure 8 can be transformed into the epipolar version of image 1 by using the forward addresses of this image. Overall, the proposed approximation results in a significant speedup, thus extending the approach in Reference [54].
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
## 4 Discussion
Data-balanced versions mostly seem to outperform the unbalanced versions in this study, as seen in the figures of the second part of this study. A possible limitation of this study is that class-level separations could decrease the balance among data classes. Oversampling and down-sampling methods could not give sufficient repairments of the proposed methods. For instance, if the data have more unbalanced classes, this would require a more significant percentage of oversampling of the minority class or a more substantial percentage of down-sampling of the majority class. We plan to apply the analysis to some other databases in future work. We plan to compare the emotions as changing parameters and emotions in VA/VAD spaces. We are also planning to try some other classifiers. In future applications, the obtained methods  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Class** & **Accuracy 1** \\ \hline \multirow{3}{*}{[12]} & 3-class classification & 63.47 (V) \\  & (Low, medium, and high) & 69.62 (A) \\ \cline{2-3}  & & 63.57 (D) \\ \hline \multirow{3}{*}{[15]} & 73.43\% (V) \\ \cline{2-3}  & 2 classes & 72.65\% (A) \\ \cline{2-3}  & & 69.3\% (D) \\ \hline \multirow{3}{*}{[16]} & 2D and 3D emotion models \\ \cline{2-3}  & 4 emotional states (VA model) & Best accuracy of 79.1\%. \\ \cline{2-3}  & 8 emotional states (VAD model) & Highest accuracy of 93\% \\ \hline \multirow{3}{*}{ \begin{tabular}{c} Our Study \\ \end{tabular}  } & Binary classification for cross-comparisons of 8 emotional states from VAD space (A& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B& B&B&B&B&B&B&B&B&B&B&B&B&B&B&B&B&BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB  
and information could be used with modifications for clinical research on emotion analysis. For example, this might involve evaluating a group's emotions that appear due to stimuli applied according to the experimental design. Another example is BCI technologies, which have important contributions to medical applications. For example, BCI devices, such as wheelchairs, robotic arms, and drones, can easily turn left and right and move forward based on left and right hand motor imagery. Neural activities related to inhibition (P300) can be applied as a stop command [52]. [PERSON] et al. [52] developed a model for identifying the neural activities of human inhibition using a phase-locking value method. Their results imply that these activities can be used as a stop command in BCI technologies and to identify the symptoms of attention deficit hyperactivity disorder patients in clinical research. Furthermore, as future applications, the classification and comparison of emotions could be used in many areas, including smart health technologies, psychology, robot technologies, simulation systems, game technologies, smart homes, driver systems, etc.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 4 Results and Discussion
### Antenna Position Optimization
#### 4.1.2 Design 2: Close-In Side Lobe Level Minimization of a 10-Element LAA  
For minimizing the CSLL (Side lobe adjacent to main lobe), the antenna position is optimized using the MAOA optimization algorithm. The fitness function for this design is provided in Equation (28), where \(\theta_{\text{AS}}\) represents the total region of the side lobes, while \(\theta_{\text{NS}}\) is the region of close-in side lobes. The weight factors w\({}_{1}\) and w\({}_{2}\) add more degrees of freedom to design a sophisticated antenna system. In this design, \(\theta_{\text{AS}}\) = [0\({}^{\circ}\), 76\({}^{\circ}\)] and [104\({}^{\circ}\), 180\({}^{\circ}\)] and \(\theta_{\text{NS}}\) = [69\({}^{\circ}\), 76\({}^{\circ}\)] and [104\({}^{\circ}\), 111\({}^{\circ}\)] are the values decided for the fitness function, and both weight factors are taken to be unity.  
\[\text{fitness function}=\min(w_{1}*\max\{20\text{log}|\text{AF}(\theta_{ \text{AS}})|\}+w_{2}*\max\{20\text{log}|\text{AF}(\theta_{\text{NS}})|\}) \tag{28}\]  
The simulation results are mentioned in Table 2, highlighting the optimized element positions along with the CSLL. The highest reduction of \(-34.50\) dB in the near side lobe is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Optimized Element Positions (\(\times\lambda\))**} & \multirow{2}{*}{**PSLL (dB)**} \\ \cline{2-2} \cline{4-7}  & **I** & **II** & **III** & **IV** & **V** & \\ \hline ULA & \(0.2500\) & \(0.7500\) & \(1.2500\) & \(1.7500\) & \(2.2500\) & \(-12.97\) \\ \hline PSO [4] & \(0.2146\) & \(0.5999\) & \(1.0611\) & \(1.5870\) & \(2.2500\) & \(-19.72\) \\ \hline GOA [3] & \(0.3360\) & \(0.4196\) & \(1.0126\) & \(1.4166\) & \(2.1000\) & \(-21.31\) \\ \hline SMO [29] & \(0.2360\) & \(0.5280\) & \(1.0070\) & \(1.4710\) & \(2.1260\) & \(-20.25\) \\ \hline MAOA & \(0.2047\) & \(0.4072\) & \(0.8284\) & \(1.1676\) & \(1.7464\) & \(-21.82\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimized positions for positive half elements and PSLL of design \(1\).  
Figure 6: Array pattern of a 10-element LAA for design \(1\).  
attained with MAOA. The result is benchmarked with other nature-inspired optimization algorithms and shows a reduction of 21.53 dB from conventional ULA, 4.26 dB from ALO, 3.50 dB from PSO, and 3.20 dB from GWO-optimized antenna array. The array pattern for this design is illustrated in Figure 7.
# IoT-Based Smart Surveillance System for High-Security Areas
## 1 Introduction  
Currently, the popularity of real-time smart monitoring has significantly increased due to the growing demand for security systems for safeguarding property. This is because of the increasing crime rate concerning theft, larceny, burglary, and other offences. In 2021, the Federal Bureau of Investigation (FBI) reported 2,976,659 larceny/theft offenses and 586,564 burglary offenses. Most of these crimes occur in homes and buildings, with larceny/theft and burglary accounting for 60% and 81%, respectively [1], as shown in Figure 1.  
Moreover, the rate of crimes committed in vacant houses is increasing daily. In these cases, the owner of a house is unable to receive information on suspected movement near their house [2]. According to the National Incident-Based Reporting System (NIBRS) and the Summary Reporting System (SRS), an effective security system is required to mitigate the loss of property. Proactive security measures can significantly reduce the rate of property loss due to larceny/theft and burglary. The existing security systems framework is based on ultrasonic identifiers, microwave indicators, CCTV, photoelectric finders, infrared locators, etc. The many parts of this framework are very costly, environmentally unfriendly, harmfulto the human body, constrained by Line of Sight (LoS) and privacy, and require greater memory capacity and complex circuitry [3]. To address all these issues, an IoT-based smart, cost-effective SS-HSA was developed that utilizes frequency-based identification and alerts the person concerned by generating an SMS message or call through a GSM module [4]. It has been identified through a literature review that frequency-based identification systems are not used of detecting moving or suspected objects behind obstructions. The proposed SS-HSA aims to mitigate property loss rates via the real-time frequency-based identification of moving objects and alerting the individual concerned so that they take necessary actions in a timely manner. The major contribution of the proposed study is given herein.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions  
Even though the centralized and distributed MAC protocols for mmWave communication have been extensively scrutinized, this research area still faces several challenges. This section highlights and points out some outstanding research questions and problems of mmWave MAC protocols, as shown in Figure 9.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 5 Conclusions  
Massive MIMO systems may be able to achieve better spectral and energy efficiency, but signal detection is still an issue that needs to be investigated. This paper introduced the Gauss-Seidel iterative technique for large MIMO communication, which is based on Jacobi iteration and shared processing conjugate gradient. To increase convergence and boost BER performance, CG and Jacobi iterations are used with GS iterations. Theoretical and simulation studies show that the CJGS algorithm performs substantially better than existing detection systems for various antenna designs, requiring only a finite number of cycles to obtain the desired results. The proposed algorithm requires one iteration to achieve the desired BER performance when the receiving and transmitting ends have 256 and 64 antennas, respectively, if the correlation coefficient at the transceiver end of the channel is 0, and the other iterative algorithms require two iterations. Similarly, if the correlation coefficient at the transceiver end of the channel is 0.3, the proposed algorithm requires one iteration to achieve the desired BER performance, while the other iterative algorithms require more than five iterations. For this reason, the suggested algorithm in this study is not considerably impacted by the channel correlation. The CJGS iterative method achieves a reduction in computational complexity by maintaining its computational complexity at O(U\({}^{2}\)). Even if the research in this study unfortunately only takes into account the influence of channel correlation and antenna setup, precoding and soft decision techniques can be introduced in later studies to further increase the reliability of detection.  
Software, L.C.; investigation, H.L.; writing--original draft, L.C.; writing--review and editing, D.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Ningxia, grant number 2023 AAC03741. This research was funded by the Science and Technology Plan of Gansu Province, grant number 23 YFGA0047.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request due to privacy.  
The authors declare no conflict of interest.
## 1 Introduction
Specific thicknesses of SiO\({}_{2}\) were thus identified as being optimal for graphene and monolayer TMDs, since they maximize the optical contrast in the visible range [50; 55]. Similar studies were conducted for hBN. For instance, [PERSON] et al. [59] identified a color scale in the visible range for hBN flakes from a few nm to hundreds of nm on 90 nm SiO\({}_{2}\)/Si substrates, and experimentally and theoretically determined the optical contrast. [PERSON] et al. [60] experimentally and theoretically characterized the optical contrast for different wavelengths, from blue to red, of thin hBN flakes on 290 nm SiO\({}_{2}\)/Si substrates. [PERSON] et al. [61] and [PERSON] et al. [62] probed the visibility of thin hBN on Si and SiO\({}_{2}\)/Si substrates coated with polymers. [PERSON] et al. [63] studied the visibility of mono- to few-layer-thick hBN on SiO\({}_{2}\)/Si substrates with thicknesses of 290 nm and 90 nm and calculated the wavelength-dependent optical contrast for hBN monolayer as a function of SiO\({}_{2}\) thickness. [PERSON] et al. [64] showed that red-light-filtered imaging can be used to quantify the number of layers of thin hBN flakes on 90 nm SiO\({}_{2}\)/Si substrates. [PERSON] et al. [55] acquired images of a multilayered hBN flake which was picked up and transferred on several SiO\({}_{2}\)/Si substrates with different SiO\({}_{2}\) thicknesses, establishing, for each of them, a color scale for hBN in the \(0-100\) nm range. While these studies provide very valuable information in a specific parameter space, there are still gaps, and, furthermore, a comprehensive overview and practical guide is still missing. Here, we systematically probe the hBN color and contrast experimentally for a wide range of hBN thicknesses, from a few layers to bulk-like crystals, and on three different SiO\({}_{2}\)/Si substrates. Our results, combined with those of previous works, allow us to identify the best substrates for the observation of hBN flakes with desired thicknesses from the monolayer limit to \(\simeq\)200 nm. Our work thus provides a complete overview of the visibility of hBN flakes on SiO\({}_{2}\)/Si substrates, which can be used in a predictive manner to facilitate the observation of hBN flakes with desired thicknesses.