esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Site Spatial Parametric Study  
In this research, a Quantum Geographic Information System with OpenStreetMap data and the Open Route Service (ORS) were employed, and questionnaire data were used to analyze and determine the average travel distances for various driving scenarios in Hail City. The analysis encompassed different zones--north, middle, and south--within the city. It was found that the average driving distance within these zones extends up to 37 km. Consequently, this distance has been adopted as the upper limit for the study, ensuring that the research accurately reflects the typical driving patterns. Table 13, outlining the trip durations in hours for these lengths, offers a more granulated perspective. In the conducted study, the ORS was utilized to ascertain both the fastest and shortest routes for various travel scenarios within Hail City. This approach enabled an evaluation based on the average distances, aligning with the specific objectives of the research. In correlation with L, determined to be approximately 77 km/day through a comprehensive survey, it was observed that a maximum one-way distance of 37 km, accounting for a round trip of approximately 74 km, corroborates the survey findings. This concurrence between the ORS-derived data and the survey results substantiates the accuracy of the average distances used in the analysis, providing a robust foundation for the study's conclusions. Such a methodology underscores the effectiveness of integrating advanced geospatial tools with empirical data collection in urban transportation research.
## 5 Decoupled Criteria for GNSS Code Tracking Evaluation
### Equivalent Gabor Bandwidth  
Equation (25) is defined as the 'Equivalent Gabor bandwidth' in our previously published paper [14], used for code tracking evaluation under unmatched receiving. Literally, it can be seen as a generalization of the original Gabor bandwidth [10], in order to support unmatched receiving. It reflects the capability of a signal to resist WGN in code tracking. From Equation (25) we can see that performance is only influenced by the local signal, mismatch of local despreading waveform and receiving bandwidth. The cross-spectrum \(\mathcal{X}_{s,\delta}(f)\) in Equation (21) depicts the mismatch between the local despreading signal and the received signal, which is not considered in the previous work.  
Also, the generalized evaluation criterion equivalent Gabor bandwidth Equation (25) can degenerate to the original Gabor bandwidth, as defined in Equation (26), which is for the local signal \(\delta\). Therefore, with the proposed signal evaluation criteria, the low bound of code tracking error is simplified as:  
\[\sigma_{\text{NELPL,LB}}^{2}=\frac{B_{L}(1-0.5B_{L}T)}{\left(2\pi\right)^{2}} \left(\frac{1}{\frac{C_{\delta}}{N_{0  
From the code tracking error bound Equation (28) we can see that, by observing the unmatched criteria equivalent Gabor bandwidth \(\varpi\) and the anti-interference rate \(\
u\), one can get the essential abilities to resist the WGN and interference of a signal, regardless of the specific hypothetical assumption of SNR or SIR, making the results more universal. Therefore, the evaluation results are more persuasive in the comparing of the inherent performance among different signals.  
It should be noted that, the evaluation criteria defined in Equations (24) and (25) consider the mismatch between local despreading code chip waveform and the received spreading code chip waveform. By letting the local spectrum match the received signal spectrum, the proposed evaluation criteria in Equations (24) and (25) are also available for the evaluation of matched receiving.  
In the following two sections, the proposed criteria are used to evaluate ACE-BOC and MBOC signals, from which some instructive suggestions are derived for signal design and receiver configuration. As a summary of the mathematical model and evaluation theory, the important parameters used in this paper are provided in a nomenclature list.
## 4 Energy Detection Model  
SS, as the most demanding activity of CR, enables SU (unlicensed users) to adapt to the environment by detecting spectrum portions unused by licensed network users (PU). The problem of SS can be mathematically expressed as a binary hypothesis [8; 17; 44]:  
\[ \begin{gathered} H_{0}:y_{i}(n)=w_{i}(n),\\ i=1,\ldots,\ M,\ \ n=1,\ \ldots,\ N\ \text{ if PU is absent}\\ H_{1}:y_{i}(n)=x(n)+w_{i}(n),\\ i=1,\ldots,\ M,\ \ n=1,\ \ldots,\ N\ \text{ if PU is present}\end{gathered}  \tag{1}\]  
where \(w_{i}(n)\) is the noise signal received by the \(i\)-_th_ SU, \(y_{i}(n)\) is the signal received by the \(i\)-_th_ SU during the \(n\)-_th_ sample, \(x(n)\) is a transmitted signal from \(i\)-_th_ PU impacted by the temporary amplitude gain of the channel at the point of discrete time sample \(n\), and \(N\) is the overall sample quantity (number of samples) used in the sensing. Period (number of samples) and \(M\) is the overall number of SUs [45; 46; 24]. In Table 2, all descriptions of parameters used in this work are presented. In Figure 3, a general block diagram of the detector based on the ED method is shown. The received signal \(y_{i}(t)\) consists of the signal of PU \(x(t)\) and the noise signal \(w_{i}(t)\). In the ED process, the received signal is passed through a band-pass filter (BPF) in order to select the signal bandwidth of interest and to remove out-of-band noises [24]. An analogue to digital converter (ADC) transforms the received signal in the digital domain and feeds it into two sub-processes (Figure 3). Figure 2: Different approaches to dynamic resource allocation in OFDM systems. In the first sub-process, the SU (cognitive radio) performs sampling and digital signal processing (Figure 3). The received signal is estimated via the magnitude square of the fast Fourier transform (FFT) and the result of this sub-process is the energy test statistic \(\tau_{i}(n)\) obtained for an average of \(N\) samples, expressed as [45; 47]:  
\[\tau_{i}(n)=\frac{1}{N}\sum_{n=1}^{N}\lvert y_{i}(n)\rvert^{2} \tag{2}\]  
In the second parallel sub-process (Figure 3), the noise variance is evaluated according to SNR variations. Since modulation schemes like OFDM are designed to employ frequency diversity to provide robustness against fading, in this analysis, frequency-selectivity of
## 2 Systematic Working of the Proposed System  
The configuration of the developed solar-fed BLDC motor using a modified LUO converter is shown in Figure 1. The proposed modified LUO converter operates with a single switch in a continuous conduction mode. This increases the efficiency and suppresses the ripple in the output voltage. The single switch operation reduces the switching stress of the converter. The continuous conduction mode reduces the heat produced in the power semiconductor devices.  
Figure 1: Proposed solar-based system for motor pumping.  
The proposed GWO-optimized algorithm reduces the maximum power tracking time and makes the output voltage nearly constant. This constant voltage is fed to the three-phase electronic commutator, which converts the DC into three-phase AC voltage. For find the rotor position, three hall sensors are set up in the rotor displaced by 60\({}^{\circ}\) mechanicals. The input current is continuous because of the modified LUO converter, and this makes the BLDC motor make a soft starting. The proportional-integral (PI)-based motor speed control achieves a constant speed of operation [26]. This makes the proposed work highly efficient in a way that could easily be carried out in a low-cost budget. The proposed system is developed using the dsPIC30F2010 microcontroller by Microchip Technology Inc., USA, and the experimental results are verified using the MATLAB Simulink tool by MathWorks, USA.  
This system consists of a 1000 W solar panel, insulated-gate bipolar transistor (IGBT)-based modified LUO (M-LUO) converter with an active snubber circuit and IGBT-based VSI with a 3 phase, 1 horsepower (HP) BLDC motor employed with a Hall Effect sensor. Additionally, a dsPIC30F2010 controller is used as a pulse generator, which is controlled by the GWO algorithm. The driver circuit drives the IGBT switch. The system functions when the input from the solar array voltage is given to the modified, LUO converter for maintaining constant voltage, with less ripple content delivered to the inverter system.  
The output of the LUO converter is used as an input for the GWO algorithm. For tracking the maximum power, the proposed GWO algorithm is advantageous. This algorithm produces PWM pulses based on the variation of the PV panel voltage, thereby adjusting the duty cycle of the M-LUO converter. In the M-LUO converter, the elementary LUO converter is added with one extra inductor. This converter acts as a second-order low pass filter, and therefore it does not require another filter circuit. This converter with the GWO algorithm provides a constant voltage to all the three phases of the electronic commutator. Switching is done with the help of Hall Effect voltage sensors. The three-phase voltage source inverter converts the DC voltage into AC voltage to provide power to the BLDC motor. The inverter system works at a fundamental frequency to eliminate the high-frequency noise in the inverter output, and high efficiency is thus attained.
## 4 Dataset Observation and Prepossessing
### Padding and Binary Masking  
The trajectories in the training dataset were padded to the maximum length across different trajectories from all four locations. This was a crucial step for efficient batch processing during training. Let \(X_{i}\) represent the trajectory of the \(i\)-th example, and \(T_{i}\) be its original length. The trajectories were padded to a length of \(T_{\text{max}}\), where \(T_{\text{max}}=\max_{i}T_{i}\).  
Additionally, a binary mask was created for each trajectory to indicate the presence of observed points (1) and missing points (0) in the incomplete trajectories. The binary mask is denoted as \(M_{i}\), where \(M_{i,t}=1\) if the point at time step \(t\) is observed and \(M_{i,t}=0\) if it is missing.
## 3 Results  
### Coatings' Characterization  
#### 3.1.1 Roughness  
Figure 3 shows the mean values of roughness measured on C and S samples before and after the application of the coatings. The roughness of the SR sample before the sandpaper treatment was 0.39 \(\upmu\)m which increased to 1.3 \(\upmu\)m after the P40 treatment, as reported in Figure 3. After the application of coating 1 (S1), the roughness did not change significantly, but it did after the application of coating 2 (S2) and achieved a Ra = 3.1 \(\upmu\)m. Whereas sample CR had a roughness of 3.7 \(\upmu\)m, which did not appreciably change after the application of both coatings since only a slight increase of the roughness could be measured, and only for C1 (from 3.7 to 4.1 \(\upmu\)m).
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 2 Structure, Classification, and Function of CRISPR-Cas Systems
### Classification  
CRISPR-Cas systems are usually classified by the _cas_ loci structure. The current classification includes 2 large classes, 6 types, and 33 subtypes [23]. Class 1 (types I, III, and IV, including 16 subtypes) includes CRISPR-Cas systems with multi-subunit effector complexes, while in class 2 systems (types II, V, and VI, including 17 subtypes), all functions of the effector complexes are performed by a single protein, such as Cas9 in type II or Cas12 in type V [23]. Type I, II, and V systems target DNA, type VI systems--RNA, and type III systems target both DNA and RNA [24; 25]. Type IV systems are quite unique: as mentioned above, they are mostly located on plasmids, prophages, and mobile genetic elements, and use type I systems' adaptation module for acquiring new spacers. These systems are hypothesized to be involved in competition between plasmids [20]. Most CRISPR-Cas systems can be unambiguously classified into one of six main types. However, there are a number of organisms whose _cas_ loci do not fit into the current classification due to their complex modular structure and variability. For example, the target of Cas13d is not DNA, but RNA. Nevertheless, modern approaches that consider many aspects, such as phylogenetic similarity, particularly the phylogeny of _cas1_ as an obligatory element of all CRISPR-Cas systems discovered to date, allow them to be classified into the two classes and six types mentioned above. The CRISPR-Cas classification is constantly evolving: back in 2015 it consisted of only 5 classes and 16 subtypes [26]. Considering the exceptional importance of type II CRISPR-Cas systems in biotechnology, it is worth noting that they are quite rare and have been found exclusively in bacterial genomes [23].  
The class 2 endonucleases Cas9, Cas12, and Cas13 are widely used as genome editing tools [27]. This finds application in the treatment of genetic diseases, the creation of genetically engineered microorganisms and plants, and the fight against pathogens' drug resistance [28; 29; 30]. Currently, these nucleases are among the most used, although they have certain drawbacks. In this regard, there is a constant search for new Cas nucleases.
## 3 Solution Method Based on Improved BES Algorithm
### Improvement of Bald Eagle Search Optimization Algorithm
#### 3.2.3 Levy Flight Mechanism  
Levy Flight [20] attempted to use versatility and diversity to speed up the optimization process, so as to help the algorithm find the search location effectively and avoid falling into a local minimum.  
The foraging activities of most animals also have characteristics of Levy Flight. For example, most foraging time is spent around known food sources, and sometimes it is necessary to fly long distances to find other food sources. The BES algorithm shows good performance in continuous search space problems, but it is not enough in optimization problems. To overcome the above problems, the Levy Flight mechanism was combined with the BES algorithm to improve the overall optimization efficiency of the algorithm. Levy Flight can increase the diversity of the bald eagle population, make it converge earlier, and improve the efficiency of skipping local optimal solutions. At the same time, Levy Flight optimized the diversification of search subjects, enabling the algorithm to efficiently explore the search location and avoid falling into the local minimum.  
The Levy Flight mechanism is combined with the BES algorithm by adjusting the search agent with the factor \"\(LF\)\". The mathematical calculation of this factor is as follows:  
\[LF(\gamma)=0.01\times\frac{\mu\times\sigma}{|v|^{\frac{1}{\gamma}}} \tag{37}\]\[\sigma=\left(\frac{\Gamma(1+\gamma)\times\sin(\frac{\pi\gamma}{2})}{\Gamma(1+ \frac{1+\gamma}{2})\times\gamma\times 2^{(\frac{\gamma-1}{2})}}\right)^{\frac{1}{ 7}} \tag{38}\]  
where, \(\mu,\upsilon\) is a random number (0,1).  
The Levy function is inserted into the third step, the Swooping stage. The revised new group calculation is as follows:  
\[P_{i,new}=\left\{\begin{array}{l}P_{best}+P1\times F\times P_{i,new}r_{1}<0. 5\\ P_{best}+P1\times F\times st\times r_{1}\geq 0.5\end{array}\right. \tag{39}\]  
\[st=LF\times(P_{best}-LF\times P_{i,new}) \tag{40}\]  
where, \(P1\) and \(LF\) are constants, and \(r_{1}\) is a random number of (0,1).
# Application of OCT in the Gastrointestinal Tract
## 3 OCT Applications in the GI Tract
### Imaging of Microvasculature  
In 2014, [PERSON] et al. demonstrated a 3D OCT angiography (OCT-A) using the biospeckle effect to reveal the subsurface vasculature of BE without exogenous contrast agents [90]. A video introduction of this promising technology in the gastrointestinal tract is available [36]. Revelation of subsurface vasculature could be clinically useful in order to highlight areas to minimize bleeding during an APC hybrid lift ablation (Erbe Elektromedizin, Tubingen, Germany) or peroral endoscopic myotomy, for example. Microvasculature irregularities also appear to be correlated with dysplasia in BE [91; 92].  
Additionally, OCT-A may help identify specific areas to treat in the rectal wall of chronic radiation protopathy (CRP) which may develop consequent to pelvic irradiation for various malignancies [93; 94]. OCT also may help identify other vascular abnormalities such as [PERSON]'s lesions, often buried deeply in the submucosal architecture and a source for intermittent but brisk blood loss [95]. Additionally, en face OCT findings have been studied in patients with gastric antral vascular ectasia (GAVE), also a cause of upper GI bleeding, before and after RFA treatment. [PERSON] et al. demonstrated OCT/OCT angiography's (OCTA's) ability to differentiate GAVE-associated tissue architecture and microvasculature before and after RFA, namely identifying regions of incomplete ablation hidden under coagulum [96]. Lastly, instantaneous real-time imaging may be used in the future to assess vascular changes associated with food allergies in the gut that are increasingly appreciated as potential causes of diarrhea or irritable bowel syndrome (IBS).
## 4 Proposed Method
### Differentiated Embedded Pilot Insertion Scheme  
We propose a differentiated embedded pilot insertion scheme that incorporates in the DD plane distinct pilot symbols related to modulation, which are surrounded by zero-padded guard bands, as illustrated in Figure 3. While preserving the inherent advantage of no interference between data and pilot signals as in existing pilot insertion schemes [34], it can further facilitate the neural network model designed hereafter to extract modulation-related features from the received signals.  
Specifically, we employ differentiated pilot values for different modulation schemes. We define the set of modulation schemes as \(\mathcal{M}=\{\text{BPSK},\text{QPSK},\text{8 PSK},\text{16 QAM},\text{64 QAM},\)  
256 QAM} and the corresponding pilot set as \(\mathcal{P}=\{P_{M_{i}}\}_{M_{i}\in\mathcal{M}}\), where \(P_{M_{i}}\) is the unique pilot symbol associated with modulation \(M_{i}\in\mathcal{M}\). The specific values for \(\mathcal{P}\) depend on the channel types (e.g., PDCCH, PDSCH, PUCCH, PUSCH) and the utility of the reference signals (e.g., CSI-RS, SRS), and can be set according to rules indicated in Release 18 of the 3 GPP NR specification [44].  
In the embedded pilot design, the pilot symbols (i.e., \(P\in\mathcal{P}\)) are placed at specific positions in the DD domain grid, and the guard symbols (i.e., 0) are configured around the pilots to isolate them from data symbols (i.e., \(D\)) [35]. This configuration can be expressed as  
\[x_{\text{dd}}[k,l]=\begin{cases}P,&\text{if }(k,l)\in\mathcal{F}_{p},\\ 0,&\text{if }(k,l)\in\mathcal{G}_{p},\\ D,&\text{otherwise},\end{cases} \tag{8}\]  
where \(\mathcal{F}_{p}\) denotes the set of pilot positions in the DD grid, while \(\mathcal{G}_{p}\) represents the guard symbol region surrounding the pilot, and its size is constrained by the maximum delay and maximum Doppler shift.  
The DD-domain structure based on the above differentiated embedded pilots can capture characteristics of different modulation schemes and provide additional information on modulation, thus allowing signals to be well-distinguished from each other. Therefore, the classification accuracy at the receiver can further be improved.
## 5 Discussion  
This paper introduces the use of MTV methods to enhance the traditional LSRTM, with the objective of improving the imaging quality of subsurface structures. In comparison to the conventional TV method, MTV offers notable advancements in stability and accuracy while maintaining a computationally efficient framework.  
However, it is important to acknowledge the limitations inherent in the current study. Specifically, the work was confined to permittivity contrast media models, assumed uniform background media, and was highly sensitive to the selection of the parameter \(\lambda_{1}\), which substantially influences imaging efficacy. Future work should address these limitations by exploring models that incorporate dispersive or anisotropic media, thus aligning more closely with real-world scenarios. In addition, rigorous experimentation on parameter selection is essential to validate the robustness of the proposed method.  
Moreover, the LSRTM-MTV algorithm demonstrates potential applicability beyond the scope of this study, particularly in other imaging techniques requiring the minimization of similar objective functions. Future studies could further investigate these avenues, possibly engaging in comparative assessments with other state-of-the-art techniques to establish the method's relative merits.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 4 Experimental Evaluation
### Experimental Results
#### 4.2.3 Varying the Number of Processing Nodes with Constant Per-Node Workload  
The third metric we considered, the size up, offers another perspective on the scalability of the algorithm execution. We kept constant the amount of work each node had to perform, but gradually increased the number of nodes executing the algorithm in parallel. This way, as the degree of parallelism increased, it also increased the total amount of work the system had to execute. This experiment can give us a useful insight into the efficiency of the algorithm execution settings, which in our case, also included the effectiveness of using vector hardware instructions. Figure 6 plots the results. We used the same reference datasets used for the previous experiment, with the further differentiation in three sizes introduced in the previous paragraph. The dashed lines in the picture represent the average execution time with two nodes, and the solid lines with four nodes. We observe that the performance trend is comparable with the results in both the previous paragraphs.  
Figure 5: K-means scale-up: the workload execution time for an increasing number of nodes and dataset sizes. Error bars represent the variance across different experiment runs.  
Figure 6: K-means size-up: the workload execution for an increasing number of processing nodes, but a constant per-node workload. Error bars represent the variance across different experiment runs.  
However, there is a significant difference between the three datasets: in line with the results of Figure 3, the dataset with the highest number of dimensions (red lines) is the fastest under any conditions, yet it is the hardest to scale-up linearly. Again, the reason is to be attributed to the higher communication overhead, as explained above. On the other hand, the dataset with the largest number of rows (green line) is not only the slowest, but also the hardest to scale. This is not a surprising result: as we previously commented, a larger number with smaller records puts the system under a higher load. When we increase the size of such dataset, the system load increases more than linearly and consequently the total execution time also increases.
###### Abstract  
In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an 'agile' optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.  
team orienteering problem; real-life optimization; parallel computing; biased randomization; smart cities; unmanned aerial vehicles +
Footnote †: journal: Applied Sciences