esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Non-Dominated Sorting Genetic Algorithms
### Local Search
#### 3.6.1 Acceptance Criteria for a Move  
In the multi-objective context, a solution \(Sol\) improves \(Sol^{\prime}\) if it dominates it. In what follows, \(Z_{1}(Sol)\) and \(Z_{2}(Sol)\) respectively denote the total travel cost and the total non-preference value of a solution \(Sol\), and \(w\) is a real number belonging to \([0,1]\).  
The criteria tested to decide if converting a solution \(Sol\) into \(Sol^{\prime}\) is an improved one define two different strategies (\(LS1\) and \(LS2\)).  
\(LS1\): the Pareto dominance: \(Sol^{\prime}\) improves \(Sol\) if \(((Z_{1}(Sol^{\prime})-Z_{1}(Sol)\leq 0)\) and \((Z_{2}(Sol^{\prime})-Z_{2}(Sol)<0))\) or \(((Z_{1}(Sol^{\prime})-Z_{1}(Sol)<0)\) and \((Z_{2}(Sol^{\prime})-Z_{2}(Sol)\leq 0))\).  
Figure 4: Contradictory positions of clients \(i\) and \(j\).  
However, it could be interesting to use an acceptance criteria based on the convex combination of the objective functions, such as done in \(LS2\). The main objective is to improve solutions in the first front conducting the search by descending values with emphasis on the extreme solutions, while preserving the space between solutions. These techniques were first introduced by [66]. For that, a pseudo-weight \(w\) must be computed for a given solution \(Sol\). In [66], the authors used Equation (5), in which \(Z_{c}^{min}\) and \(Z_{c}^{max}\) are respectively the minimum and the maximum values of criterion \(c\) (\(c=1,2\)).  
\[\mathbf{w}=\frac{\frac{Z_{1}(Sol)-Z_{1}^{min}}{Z_{1}^{max}-Z_{1}^{min}}}{\frac{Z_{ 1}(Sol)-Z_{1}^{min}}{Z_{1}^{max}-Z_{1}^{min}}+\frac{Z_{2}(Sol)-Z_{2}^{min}}{Z_{ 2}^{max}-Z_{2}^{max}}} \tag{5}\]  
Then, an improving move is accepted if \(w.(Z_{1}(Sol^{\prime})-Z_{1}(Sol))+(1-w).(Z_{2}(Sol^{\prime})-Z_{2}(Sol))<0\) with \(w\in[0,1]\).
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Packet Size
#### 4.1.5 Simulation Results of Rayleigh Propagation Model regarding Packet Size  
The Rayleigh propagation model is a multi-path propagation channel; hence, the mRSU throughput is better here. Additionally, the average latency of the mRSU is shorter in the Rician propagation model as compared to the sRSU. Because the mRSU is in motion with the nodes, the distance between them is reduced, and therefore the latency is reduced. Similarly, the throughput increases owing to the multi-path process since messages can be received from any hop.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 2 Related Work  
With reference to the published studies, there are different designs for RF energy harvesters were presented such as single [3; 55; 56], dual [57; 58; 59; 60], triple [61; 62; 63; 64; 65], quad [65; 66], six [67], and broad [68; 69; 70; 71; 72] bands. For instance, [PERSON], et al. [55], designed a circuit to harvest signals of GSM-1800 band. [PERSON] et al. [57], designed a system to work at the dual-band; GSM-900 and (Wi-Fi 2.45 GHz) bands. [PERSON] et al. [61], presented a triple-band rectenna to operate at the GSM-900, GSM-1800, and 2.1 GHz bands. [PERSON], et al. [65], introduced the quad-band RF harvester for the bands of 1.3, 1.7, 2.4, and 3.6 GHz. A wide-band system was designed [68], to collect power in the frequency range of 1.8 to 2.5 GHz.  
The increase of the harvested direct current (DC) power of the RF energy harvester is a goal for researchers by increasing the input frequency ranges or the input powers. However, a rectenna's high sensitivity is a significant limitation to go through this goal due to the non-linearity of Schottky diodes, which serve as a rectifier for the received RF signals to convert them into DC power. The diode's non-linearity appears when the received RF signal is varied in power level or frequency. The non-linearity leads to an increase of the reflected power to the receiving antenna and then degrading the RF-DC conversion efficiency, which results in a low harvested DC power [49; 62]. Moreover, most of the published work focused on the dual-band design, and limited studies handled the quad-band system.  
In this paper, both the mathematical and simulation approaches were used. These approaches were utilized to specify the rectifier input impedance under the variation of input power or frequency. This enabled us to overcome the Schottky diode's non-linearity challenge by designing a suitable impedance matching network. The authors adopted two scenarios in this study to investigate the quad-band concept to harvest the radiated power of the GSM-900 band, the GSM-1800 band, the Universal Mobile Telecommunications System (UMTS-2100) band, and the Wi-Fi 2.45 band. The first scenario introduced a quad-band voltage multiplier circuit with a single receiving antenna and four band-pass filters (BPFs) of elliptic type. The second scenario presented a quad-band voltage multiplier circuit with four receiving antennas. The proposed quad-band approach can harvest 2 mW DC power; moreover, the optimal RF-DC conversion efficiency of 74.846% was achieved in this study when the four tones were applied. The importance of this study compared to the literature review can be summarized in two points. The first point is to predict the rectifier input impedance theoretically and numerically, unlike most published studies, which took into account the simulation results only for the circuit input impedance. The second point is keeping the RF-DC conversion efficiency acceptable for the interested wide frequency range. Compared to the other published results, the work results are presented in Section 5 to clarify this study's importance.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 5 Conclusions  
In this paper, we investigated the effectiveness of the botnet detection model based on machine learning techniques using DNS query data. The experimental results on DGA botnet and FF botnet datasets show that most of the machine learning techniques used in the model achieved the overall classification accuracy over 85%, among which the random forest algorithm gives the best results with the overall classification accuracy of 90.80%. Based on this result, we propose to select the random forest algorithm for the proposed botnet detection model using DNS query data.  
In the future, we continue to test the proposed model with larger datasets and analyze the effects of the domain name features on the detection accuracy, as well as research and propose new features to improve the detection accuracy of the proposed model.  
[PERSON] raised the idea, initialized the project and designed the experiments; [PERSON] carried out the experiments under the supervision of [PERSON]; Both authors analyze the data and results; [PERSON] wrote the paper.  
This research was funded by the Ministry of Science and Technology, Vietnam grant number KC.01.05/16-20.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Machine Learning Algorithms**} & \multicolumn{5}{c}{**Measures (\%)**} \\ \cline{2-6}  & **PPV** & **FPR** & **TPR** & **ACC** & **F1** \\ \hline kNN (\(k=15\)) & 94.10 & 5.00 & 80.40 & 87.70 & 86.70 \\ C4.5 & 93.40 & 5.70 & 80.90 & 87.60 & 86.70 \\ RF (27 trees) & 94.40 & 4.80 & 80.90 & 88.10 & 87.20 \\ Naive Bayes & 83.90 & 17.10 & 89.10 & 86.00 & 86.40 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification performance of the detection model using T3 training set.  
**Acknowledgments:** This work has been supported by the CyberSecurity Lab, Posts and Telecommunications Institute of Technology, Hanoi, Vietnam and funded by the Ministry of Science and Technology, Vietnam grant number KC.01.05/16-20.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Characterisation of Biosorbents  
All biomass feedstocks were milled using a cutting mill (Retsch SM300). Subsequently, they were sieved in a sieve shaker for 15 min and split into five particle size ranges (<106 \(\upmu\)m, 106-212 \(\upmu\)m, 212-300 \(\upmu\)m, 300-425 \(\upmu\)m, 425-600 \(\upmu\)m) in accordance with BS EN ISO 17827-2:2016 [57; 58]. Proximate analysis was conducted using a TA Instrument Q500 Thermogravimetric Analyser (TGA) via the slow pyrolysis method [59; 60]. A LECO CHN 628 elemental analyser was used to estimate the carbon (C), hydrogen (H), and nitrogen (N) contents, and subsequently oxygen (O) content was obtained through difference [60]. A Bruker IFS66 (FTIR) was used to analyse the infrared spectra of the biosorbents. The Bruker IFS66 (FTIR) included a KBr beam splitter, Globar light source, deuterated triglycine sulfate (DTGS) room temperature detector, and Specac \"Goldengate Bridge\" diamond attenuated total reflection (ATR) attachment. A Micromeritics ASAP 2420 surface area and porosimetry system with CO\({}_{2}\) adsorbate was used to determine the surface area and micropore volume of the prepared biosorbents. Specific surface areas were calculated using the BET model at relative pressures of 0.025 to 0.030 (0.83-1.07 bar), and the Dubinin-Radushkevich model was used to calculate the micropore volume in conjunction with the Microactive Software V5.0 [61].
###### Abstract  
Estimates of Sun-Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O\({}_{2}\)) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal-sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument's spectral resolution. We demonstrate that for proximal-sensing scenarios compensating for atmospheric effects by simply introducing the O\({}_{2}\) transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O\({}_{2}\) absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O\({}_{2}\) absorption on the evaluated proximal-sensing SIF retrieval strategies, its dependency on surface pressure (\(p\)) and air temperature (\(T\)) was also assessed. As an example, we combined simulated spectral data with \(p\) and \(T\) measurements obtained for a one-year period in the Hyytiala Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of \(T\) and \(p\), if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature-dependent physiological responses of vegetation.  
sun-induced chlorophyll fluorescence (SIF); proximal sensing; O\({}_{2}\) transmittance; fraunhofer line discriminator (FLD); spectral fitting method (SFM); air temperature; atmospheric pressure 2018 10, 1551; doi:10.3390/rs10101551 www.mdpi.com/journal/remotesensing
# Voxel-Based Navigation: A Systematic Review of Techniques, Applications, and Challenges
## 4 Key Techniques for Voxel-Based Navigation
### Voxel Segmentation  
Semantic segmentation is crucial in voxel-based modeling for navigation tasks. While geometric segmentation divides 3D data, such as point clouds, meshes, or surfaces, into smaller, meaningful regions, semantic segmentation assigns each voxel a semantic label (e.g., \"car\", \"road\", \"pedestrian\"). This classification is essential for identifying navigable spaces and obstacles, which greatly enhances environmental understanding. In addition, semantic segmentation can also identify dynamic obstacles, further enhancing the system's understanding of the environment and enabling real-time updates. This allows the navigation system to adapt to dynamic environment changes, ensuring more robust and reliable navigation in constantly changing scenarios. As a result, semantic segmentation improves the efficiency, safety, and adaptability of voxel-based navigation systems, supporting their ability to navigate complex and changing environments.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 2 Background and Motivation
### GPU Profiler  
The GPU profiler provides information for the occupancy. It also identifies factors restricting the occupancy by inspecting the resources such as registers and LDS memory. In general, because improving the performance of an application requires understanding its behavior, application profiling is required. Traditionally, profilers such as Linux _perf_[20] and _gprof_[21] have been used to improve the performance of applications. However, these profilers can only analyze operations running on the CPU. For this reason, GPU vendors provide the GPU profiler [22; 23] to profile kernels. Those profilers can also  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**\# of VGPR usages** & \textless{}=24 & 25–28 & 29–32 & 33–36 & 37–40 & 41–48 & 49–64 & 65–84 & 85-127 & 128\textgreater{}= \\ \hline
**Active waves** & 10 & 9 & 8 & 7 & 6 & 5 & 4 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of active wavefronts by VGPR usage in the kernel.  
leverage the hardware performance counter in the device to gather information while the kernel executes.  
Table 2 is a profiling result of the kernel provided by OpenCV by using a GPU profiler. The profiler provides three categories: device information, GPU kernel information, and kernel occupancy summary. The device information includes GPU hardware specifications such as the number of CUs and the maximum number of wavefronts per CU. In addition, information related to the kernel is provided. For example, the kernel's hardware resource usage is identified, such as the VGPR usage per work-item and LDS memory usage per work-group. Lastly, the occupancy information is offered. Categories of _number of waves limited by_ * indicate the number of wavefronts that are activated under the influence of the corresponding factor. Among them, the category of _limiting factor(s)_ means factors affecting the kernel's performance significantly. Estimated occupancy is given at the bottom of the profiling results. In Table 2, the kernel has 30% occupancy because only up to 12 of the 40 wavefronts are activated owing to the VGPR. As described in Section 2.3, no results showed the SGPR as a limiting factor.
## 5 Results and Discussion
### Comparison between the Navigation Using Tracking Goal Grid Code and Using the Place Sequence (Trip)  
The difference between both navigational approaches is the time complexity and the accuracy because in the navigation through tracking of distant place grid code, only a single time direction, and the distance is required to calculate. However, in the case of obstacles, an agent may have to change its current path resulting in recalculating the distance and direction from the changed path. In contrast to grid code, in the case of a trip, this corresponds to each recalled place neuron of the learned trip, the direction, and the distance is required to calculate to move forward, which makes it time inefficient in processing.  
As far as accuracy is concerned, if the target grid code is far, then in case of tracking using goal grid code will be lower compared to using a learned trip for navigation. The results of the accuracy of both cases are shown in Figure 24, where the vertical axis representing the goal-tracking accuracy, and the horizontal axis represents the number of grid neurons used.  
The accuracy for tracking the goal location through the goal grid code is observed to be highly error-prone at a smaller number of grid neurons, due to the possibility of ambiguity in a large conjunctive grid pattern from a current to a goal location, but the accuracy has increased almost exponentially on increasing the number of grid neurons. The accuracy for the tracking using place sequence learner is observed to be less error-prone compared to the tracking through goal grid code. The reason behind  
Figure 23: Trip strength dynamics.  
Figure 24: Performance comparison on localization.  
this is the less ambiguity between the current and the nearby place grid code in comparison to the current and the goal grid code.
## 4 Effect of Heteroatoms-Metal Interaction in the Liquid Phase Oxidation of Benzyl Alcohol
(**a**) Nitrogen (green) and cobalt (red) elemental-mapping images and the corresponding line profile of Co@NC-4 sample; (**b**) N _1s_ and (**c**) Co 2p X-ray photoelectron spectroscopy (XPS) spectra of Co@NC-x; (**d**) Schematic illustration of Mott–Schottky-type contact of Co@NC; (**e**) The N/C mole ratio of Co@NC-x samples as determined by elemental analysis and the corresponding schematic structures; (**f**) Turnover Frequency (TOF) values for methyl benzoate (MB) production via aerobic esterification of benzyl alcohol and methanol over Co@NC-x. Reprinted with permission from reference [107]. Copyright 2017 American Chemical Society. \begin{table}
\begin{tabular}{r c c c c c c c c c} \hline \hline
**Entry** & **Catalyst** & **TOF (I/I/I)** & **Conversion (\%)** & **Solvent** & **Alcohol/Metal (mol \%)** & **T (\({}^{\circ}\)C)** & **pO2 (ATM)** & **Sel to Benzal-Dehyde** & **Reference** \\ \hline
1 & P4//PR24-PS & 7260 & n.d. & solventless & 35,000 & 120 & 1.5 & 62 & [101] \\
2 & P4//NR24-PS & 65,876 & n.d. & solventless & 35,000 & 120 & 1.5 & 64 & [101] \\
3 & P4/PR24-PS & 6076 & n.d. & solventless & 35,000 & 120 & 1.5 & 74 & [101] \\
4 & AuPd/N-P624-PS & 52,638 & n.d. & solventless & 35,000 & 120 & 1.5 & 75 & [101] \\
5 & Pd/NCTN73K & 11,475 & 94 (after 1 h) & solventless & 3000 & 80 & 2 & 76 & [52] \\
6 & Pd/NCTN73K & 7865 & 88 (after 1 h) & solventless & 3000 & 80 & 2 & 73 & [52] \\
7 & Pd/NCTN74K & 2387 & 35 (after 1 h) & solventless & 3000 & 80 & 2 & 68 & [52] \\
8 & Pd/C & n.d. & 20 (after 5 h) & solventless & n.d. & 125 & 1 & 99 & [105] \\
9 & Pd/B-C & n.d. & \(>\)99 (after 5 h) & solventless & n.d. & 125 & 1 & 99 & [105] \\
10 & Co8/NC-0.3 & n.d. & 3 (after 1 h) & CH3 OH & 180 & 60 & 1 & 12 & [107] \\
11 & Co8/NC-2 & n.d. & 31 (after 1 h) & CH3 OH & 180 & 60 & 1 & 52 & [107] \\
12 & Co8/NC-4 & n.d. & 58 (after 1 h) & CH3 OH & 180 & 60 & 1 & 77 & [107] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Activity of metal supported on functionalized carbon catalysts in benzyl alcohol oxidation.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 3 Challenges during Operations  
### Equipment Failure  
As any technological observation system, the ocean observatories suffer from equipment failure. The oceanographic equipment is exposed to one of the most energetic and corrosive environments on the planet, being the subject of environmental forcing throughout the field deployment period. The age of the equipment affects directly the endurance and the integrity of the system. The significance of the failure can range from unnoticed events to severe incidents that can lead to data loss or affect the station or platform integrity. In the last decade, the POSEIDON system has suffered from several cases of equipment failure that have caused gaps in the data flow and significant expenses for the repair or replacement of sensors and modules. The failures can be categorized as follows:  
1. Platform failure, where the hosting platform's (e.g., buoy) main components fail and cause the station data flow to be interrupted. These types of failures to the POSEIDON fixed stations network lead to emergency recovery of the station. Due to spare part availability and R/V operation costs, the platform failure causes a data gap. The Ferrybox system, operating in highly industrialized environment as a ship's machine compartment, suffers from component failures caused by the high temperatures and the vibrations;
2. Mooring line failure can lead to an uncontrolled drifting buoy that can be a danger to navigation safety or to sensor and equipment losses at sea (Figure 9). Similar incidents  
Figure 9: Drifting incident for the ATHOS buoy, December 2013.  
have happened to the POSEIDON system and although the drifting buoys were recovered, there were losses in the underwater sensors attached to the mooring line; 3. Cable and connector failure are quite frequent in the oceanographic stations [16]. The inductive modem coupler and the cabling of external sensors attached to SBE CTD units used in the POSEIDON fixed stations are typical and often examples of this type of failures can cause the loss of the underwater RT telemetry or problems with the CTD logger operation; 4. Mechanical failure of equipment includes the disruption of material and components such as moving parts and motors, housings, attachment brackets and sealing. Given the complexity of the ocean scientific stations, these failures are quite common and can have a major impact in some cases. Examples of failures for the above categories are presented in details in Figure 10.
## 3 Results  
### Tests with the 3D CGI Head  
A series of interpolations between frames were obtained using the 3D CGI head model and 3 ds Max with rotation angles in the range of 5-60\({}^{\circ}\). The interpolated images were compared with the actual images by evaluating the colors of the obtained pixels. Figure 2 shows the percentage accuracies of these images as a function of the rotation angle and indicates the poorly reconstructed areas in red. The results show that the interpolated images remain consistent to the original images for rotation angles up to 20\({}^{\circ}\). Beyond this angle, unnatural deformations appear on the face and neck and are no longer correctly reconstructed. We determined that the maximum acceptable angle between the two cameras with no or few deformations was 20\({}^{\circ}\).  
A test was then performed with seven horizontal images separated by 20\({}^{\circ}\) on a 120\({}^{\circ}\) circular arc, and three elevation levels separated by 20\({}^{\circ}\) (Figure 3a). First, a total of 128 images were interpolated between each pair of images horizontally; 64 images were then interpolated vertically. A final total of 99,072 images were obtained, among which only 21 were actual images (0.02%). The interpolation time of all images with a fast-graphics computer was 6 h. Hogels were then generated from these perspective images and recorded with a 250 \(\upmu\)m resolution one after another into a 15 \(\times\) 20 cm U04 holographic plate. The hologram was then developed using two chemical baths and sealed with optical glue to prevent variations in the emulsion thickness. When CHIMERA was illuminated 50 cm from the center of the hologram and at an angle of 45\({}^{\circ}\) with an RGB LED lamp, a fine full-color and full-parallax reconstruction of the 3D CGI face was generated, as shown in Figure 3b and Video S1. Observations show that the unnatural deformations present in some of the interpolated images are not visible in the final hologram.