esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multi-Connectivity Model Formulation
### Problem Definition
It is worth mentioning that in the problem of Equation (2) the effects of mobility, propagation and interference are implicitly captured in the term \(\gamma(\beta_{m,n})\). Specifically, as the UE moves, its propagation and interference conditions with respect to the different cells changes and, therefore, the involved cells modify the amount of bandwidth to be allocated to the UE so that the traffic fraction \(\beta_{m,n}\) can be transmitted.
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 3 Proposed Methodology
### Fingerprint Feature Matching  
The feature matching process mainly aims to determine the reference point in one image and to detect a matching point in another image. After extracting the SURF features and calculating the descriptors (feature vectors), our goal in the matching phase is to find geometrically consistent feature matches across all fingerprint images. Therefore, if a feature from a fingerprint image is matched with the corresponding feature in another database image, their descriptor should be matched to find the closest matching feature. This can be carried out in various ways, but the most common and popularly accepted method to define similarity is the use of the pairwise Euclidean distance between these descriptors. Hence, two fingerprint feature vectors match, if and only if the distance between them is less than a specified distance threshold.  
Due to not all the feature points being matched properly, there are points that do not fit the plan. To effectively tackle this problem, we use the RANdom SAmple Consensus (RANSAC) algorithm to detect and remove points that fall outside the threshold as outliers (i.e., error feature points) to finally determine the matching area exactly. The basic idea behind the RANSAC algorithm is to find a model where the data points are suited to the model (inlier features) and the non-suited points are eliminated. Feature points extracted from fingerprint images are compared together using proximity metric using normalized Euclidean distance to construct an index-pair matching matrix. Then, a multi-feature fusion of local SURF and Harris features is performed and outliers in the matched features are removed by applying the RANSAC algorithm. In Figure 8, a sample of correctly matched points for a pair of fingerprint images is shown.  
Finally, the similarity or matching percentage (\(P\)) can readily be computed by the following formula:  
\[P=\frac{|CF|}{|TF|} \tag{17}\]  
where \(CF\) is correct matching features after applying the RANSAC algorithm (inliers) and \(TF\) is the total matching feature number in the index matching pair matrix.  
Figure 8: A sample of correctly matched points for a pair of fingerprint images.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation
### Constraints
#### 2.2.1 Power Balance Constraint  
In actual industrial power generation, the power demand keeps changing all the time. To meet the power balance constraint is an important and foremost task in the scheduling. The power balance constraint is an equality constraint, shown as the following Formula (4):  
\[\sum_{j=1}^{n}P_{j,t}u_{j,t}=P_{D,t} \tag{4}\]  
where \(P_{D,t}\) is the power demand at time \(t\). In this paper, the power loss in the transmission process is ignored. It is denoted that the generation output power of the unit \(j\)th at time \(t\) should be balanced with the load demand of the system, which means the supply should be equaled to the demand. Otherwise, the frequency and voltage may vibrate and lead to black out or other security problem [30; 31].
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 2 Resource Constrained Job Scheduling  
The resource constrained job scheduling (RCJS) problem consists of a number of nearly independent single machine weighted tardiness problems that are only linked by a single shared resource constraint. It is formally defined as follows. A number of jobs \(\mathcal{J}=\{1,\ldots,n\}\) must execute on machines \(\mathcal{M}=\{m_{1},\ldots,m_{l}\}\). Each jobs \(i\in J\) has the following data associated with it: a release time \(r_{i}\), a processing time \(p_{i}\), a due time \(d_{i}\), the amount \(g_{i}\) required from the resource, a weight \(w_{i}\), and the machine \(m_{i}\) to which it belongs. The maximum amount of resource available at any time is \(\mathcal{G}\). Precedence constraints \(\mathcal{C}\) may apply to two jobs on the same machine: \(i\to j\) requires that job \(i\) completes executing before job \(j\) starts. Given a sequence of jobs, \(\pi\), the objective is to minimise the total weighted tardiness:  
\[T(\mathbf{\updelta})=\sum_{i=1}^{n}w_{\pi_{i}}\times T(\pi_{i}),\quad\text{where} \quad T(\pi_{i})=\max\{0,c_{\pi_{i}}-d_{\pi_{i}}\}, \tag{1}\]  
where \(c_{\pi_{i}}\) denotes the completion time of the job at position \(i\) of \(\pi\), which--given \(\pi\)--can be derived in a well-defined way.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
###### Abstract
[PERSON] et al. demonstrated a conversion gain of \(-\)24.5 dB at 1 GHz and an IIP3 of 22.3 dBm at 5 GHz using aligned carbon nanotube array-based devices [27]. It is worth noting that conversion gain and IIP3 are relatively low for carbon-based RF mixers in the sub-6 GHz band, so it is particularly important to continue to improve the frequency conversion capability of carbon-based RF mixers. Furthermore, randomly oriented carbon nanotube film-based RF devices have developed for several years with remarkable progress, while demonstrations of frequency conversion devices such as mixers with performances compatible with Ku band amplifiers are still lacking [21]. In this work, we designed and fabricated RF FETs based on randomly oriented carbon nanotube films and constructed an active mixer with the transistor as the core. The frequency conversion capability of the mixer in the X and Ku bands was explored. The wider gate width of the transistor allowed for lower internal resistance, which allowed for better and easier impedance matching with the test system. CNT-based RF transistors with an on-state current of 30 mA and peak transconductance of 34 mS achieved a cut-off frequency (\(f_{T}\)) and maximum oscillation frequency (\(f_{max}\)) of 78 and 60 GHz, respectively. Experiments and analyses demonstrated that CNT-based active mixers have better conversion gain of \(-\)11.4 dB\(-\)\(-\)17.5 dB at 10 GHz to 15 GHz for the X and Ku band and an IIP3 of about 18 dBm, which is compatible with a CNT-based RF amplifier in terms of process and performance. It is worth noting that, different from previous related works on the pursuit of limit RF performance, this work is a preliminary attempt to determine the actual performance of RF electronic devices without an on-chip matching network and tuning instrument assistance in order to promote the real performance of RF electronic devices based on randomly oriented carbon nanotubes. The CNT-based mixer used in this work is compatible with the CNT-based RF amplifier [21] and silicon-based CMOS, which heraldsthe potential for building practical RF transceiver systems in satellite communications and future system-on-chip integration on silicon.
## 3 Results and Discussion
### Spectral Characteristics of Meat Samples  
Figure 5 shows the spectral characteristics of three types of meat sample: sirloin, tenderloin, and turkey. The spectra from the meat samples showed varying intensity in the region from 458 to 950 nm. The variation in the visible spectrum (458-700 nm) is mostly caused by the characteristics of the sample's skin, color, and darkness of the meat; whereas, the variation towards the near-infrared (NIR) region is due to the chemical components in the samples.  
The region at 762 nm is the third overtone of the O\(-\)H stretching, which is linked to the presence of water; the region at 930 nm is the third overtone of the C\(-\)H stretching, linked to the presence of fat in the muscle of the meat [24]. In comparison with the tenderloin spectra, the turkey and sirloin samples showed higher spectral-absorbance intensity. This might be due to the presence of higher fat and moisture content in the samples. The fat and moisture values in the tenderloin samples were 14.73% and 12.77%, respectively, which were lower than the turkey and sirloin samples, showing lower spectral absorbance (Figure 5). However, the turkey spectra showed relatively higher optical reflection in the visible region of the spectrum due to the outside skin color.  
Figure 4: Selection of light-emitting diode (LED) wavebands using hyperspectral imaging (HSI) data based on beta coefficient plot. The selected wavebands were further used for multispectral system: (**a**) Selected regions for moisture content; (**b**) Selected regions for fat content; (**c**) Selected regions for protein contents. The dashed lines correspond to the central wavelengths.
# Risk Assessment of Large-Scale Infrastructure Projects--Assumptions and Context
## 2 Materials and Methods
### Data  
Table 1 presents the research sample projects with their basic characteristics. It states the undiscounted economic investment costs (i.e., investment costs excluding VAT reduced by a conversion coefficient 0.807), economic internal rate of return (ERR), economic net present value (ENPV), and cost benefit ratio (BCR), which was calculated according to the following relation:  
\[\frac{\text{B}}{\text{C}}=1+\frac{\text{ENPV}}{\text{IC}} \tag{1}\]  
where:  
BCR: Cost Benefit Ratio  
ENPV: Economic Net Present Value  
IC: Discounted Investment Costs  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Name of the Project** & **IC** & **ERR** & **ENPV** & \\  & **\(\boldsymbol{\xi}\)** & **\%** & **\(\boldsymbol{\xi}\)** & **BCR** \\ \hline P1 & Vestec connection & 73,655,517 & 13.15\% & 134,141,506 & 2.90 \\ P2 & I/22 Draženov-Horazd’ovice & 253,477,033 & 5.67\% & 25,929,610 & 1.11 \\ P3 & I/27 Kaznejov, bypass & 91,192,128 & 9.50\% & 74,002,422 & 1.83 \\ P4 & I/13 Ostrov-Smilov, & 141,082,434 & 5.88\% & 19,811,383 & 1.15 \\ P5 & I/13 Ostrov-Smilov, & & & & \\  & left bank & 116,820,770 & 7.52\% & 50,193,343 & 2.01 \\ P6 & I/26 Horsovsky Tyn & 50,375,269 & 5.60\% & 4,578,849 & 1.09 \\ P7 & D0 Brezineves-Satalice var. 1 & 371,886,072 & 39.45\% & 2,576,573,157 & 8.28 \\ P8 & D0 Brezineves-Satalice var. 2 & 434,933,917 & 30.46\% & 2,395,820,591 & 6.92 \\ P9 & D0 Brezineves-Satalice var. 3 & 757,919,450 & 17.89\% & 1,934,644,942 & 3.81 \\ P10 & I11–Hradec Kralove, tangent & 111,776,135 & 17.24\% & 336,621,090 & 4.15 \\ P11 & I/18 Pribram-bypass var. 1 & 28,417,453 & 14.20\% & 54,410,634 & 2.96 \\ P12 & I/18 Pribram-bypass var. 2 & 49,497,029 & 13.21\% & 74,973,161 & 2.61 \\ P13 & I/50 Bücovice & 78,579,450 & 7.56\% & 32,937,152 & 1.44 \\ P14 & I/36 Trnová-Fablovka-Dubina & 53,652,370 & 19.20\% & 190,286,624 & 4.73 \\ P15 & I/11 Nové Sedlice-Opava Komarov & 91,436,523 & 5.52\% & 7,834,232 & 1.09 \\ P16 & I/26 Holysov, bypass & 56,624,471 & 9.19\% & 42,452,457 & 1.80 \\ P17 & D10 Praha-Kosmonosy & 361,367,050 & 5.72\% & 35,994,616 & 1.11 \\ P18 & I/67 Bohumin-Kavrina & 83,937,876 & 5.33\% & 4,067,671 & 1.05 \\ P19 & D43 Boitov-Stare Mésto & 56,624,471 & 9.19\% & 42,452,457 & 1.80 \\ P20 & D27 Prestice-Klatovy & 128,638,259 & 5.12\% & 22,333,326 & 1.02 \\ \hline \end{tabular} Source: Feasibility Studies of Investment projects, The State Fund for Transport Infrastructure SFDI, authors’ own processing.  
\end{table}
Table 1: Basic economic data on research sample projects.  
Qualitative risk analysis is generally based on expert opinions on the risks that threaten a particular investment project. Lists of risks are usually created, based on the knowledge of the issues addressed, which contain risks that are relevant and common for the given type of projects. A risk register was created in the Czech Republic for the purposes of risk assessment of the road infrastructure projects specified above [7]. The list of risks according to the risk register is given in Table 2.
# DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model
## 3 Methods  
### Conception  
The Two-cascade TSM proposed in the literature [15] is an improvement on the traditional TSM, which significantly enhances the model's ability to extract long-term information. However, when attempting to cascade more TSMs within the same residual block, the experimental results did not meet expectations. The authors speculate that this might be due to the excessive temporal shifting operations, which cause the originally adjacent feature maps in different channels to become sparse in the temporal dimension, thereby weakening the model's performance.  
Figure 2: The two convolution forms of depthwise separable convolution. (**a**) Pointwise convolution and (**b**) depthwise convolution.  
Based on this observation, this paper proposes a new hypothesis: if a feature fusion step is added after each temporal shift to combine the feature maps at the same temporal position before and after the shift, could this further enhance the model's ability to extract longer-term temporal information? Based on this, this paper designs the DualCascadeTSF module, which enhances the ability to extract long-term temporal information without adding extra parameters. It is hoped that through the method of feature fusion [2], the correlation between features in the temporal dimension can be strengthened without increasing the number of parameters, thereby compensating for the information sparsity caused by multiple shifts in the Two-cascade TSM design.  
Moreover, to make the designed model more lightweight, this paper chooses to replace traditional ResNet with MobileNetV2 as the base model. MobileNetV2, with its unique inverted residual structure and linear bottleneck design, significantly reduces the number of parameters and computational complexity while maintaining performance. Therefore, combining the DualCascadeTSF module with MobileNetV2 can not only achieve higher accuracy but also effectively reduce the model size and improve the calculation speed, making it more suitable for deployment on resource-constrained edge devices.
# Review on Fast Pyrolysis of Biomass for Biofuel Production from [PERSON]
## 3 Biomass Generalities
### Oil Palm (Elaeis guineensis) Waste  
Solid waste from the palm tree includes trunks, leaves, empty bunches, fruit waste, and the cake that is a co-product of oil extraction from the nuts of the palm fruit. The press cake contains 6-15% oil, while the solvent-extracted cake contains no more than 3% oil [45]. The main components and some of the work carried out on oil palm waste are given in the tables below (Tables 2 and 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**N\({}^{\circ}\)** & **Compound** & **Range** \\ \hline
1 & Glycolaldehyde & 1–13.7 \\
2 & Acetol & 2.6–8.6 \\
3 & Ethanoic acid & 2.5–8.7 \\
4 & 4-ethylguaiacol & 0–0.1 \\
5 & Methyl & 0.3–0.5 \\
6 & Propanoic acid monophechols and monofurans & 0.2–2.8 \\
7 & Syringol & 0–0.4 \\
8 & 2(5H)-furanone & 0.1–0.8 \\
9 & Guaiacol & 0.1–0.5 \\
10 & Creosol & 0.1–0.5 \\
11 & Eugenol & 0.1–0.6 \\
12 & Methyl-2(5H)-furanone & 0–0.2 \\
13 & Levoglucosan & 3–6.5 \\
14 & Syringylaldehyde Monosugars & 0–0.1 \\
15 & Cyclopenten-one & 0.1–0.2 \\
16 & Phenol & 0–0.9 \\
17 & Catechol & 0.2–0.9 \\
18 & 4-methylcatechol & 0–0.5 \\
19 & Vanillin & 0–1.5 \\
20 & Furfural & 0.1–0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Principal pyrolysis oil compounds determined and quantified by GC/MS (wt%) [44].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Composition (\%db)** & **OPT** & **OPF** & **EFB** &
\begin{tabular}{c} **Presscake of** \\ **Mesocarp** \\ \end{tabular} \\ \hline Lignin & 18.1 & 18.3 & 21.2 & 14.1 \\ Hemicellulose & 25.3 & 33.9 & 24 & - \\ Holocellose & 76.3 & 80.5 & 65.5 & - \\ \(\alpha\)-cellulose & 45.9 & 46.6 & 41 & 20.5 \\ Alcohol-benzene & 1.8 & 5 & 4.1 & - \\ Ashes & 1.1 & 2.5 & 3.5 & 4.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main constituents of oil palm waste [48; 49].
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work  
Adaptive Control is a method employed to manage uncertainties in system parameters and environmental variations [45; 46]. For uncertain nonlinear systems, Adaptive Control adjusts the controller parameters in real-time to achieve the desired performance. In short, Adaptive Control can be classified into three types: MRAC, self-tuning, and gain scheduled. The first one, MRAC, is based on a stability perspective, with stability theory as its foundation. The second, self-tuning control, is grounded in an optimization perspective, originating from the first Self-Tuning Regulators (STRs) and controllers. Finally, gain scheduling is an approach to controlling nonlinear systems that employs a family of linear controllers, each designed to ensure satisfactory control at different operating points of the system. Adaptive Control encompasses two main strategies frequently discussed in the literature: Model Reference Adaptive Control (MRAC) and Self-Tuning Regulator Control (STRC).
# Review of Power Electronics Technologies in the Integration of Renewable Energy Systems
## 3 Literature Survey
### Power Systems Connected to Variable-Speed Hydro-Wind Turbines  
For power systems connected with variable-speed hydro-wind turbines, as shown in Figure 14, gearboxes, converters, and transformers have emerged as essential elements of modern RE infrastructure. Electric power can be generated by extracting the power both from hydro plants and wind energy systems. Variable-speed hydro-wind turbines are designed to adapt to changing wind and water conditions, maximizing energy production. The turbines are connected to gearboxes, which help increase the rotational speed of the turbine shafts to match the generator's requirements. Gearboxes play a vital role in optimizing power generation by ensuring that the turbine operates at its most efficient speed [91; 92; 93; 94].  
Figure 13: Fuel-cell-powered inverter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference Number** & **Author Details, Year of Publication** & **Proposed Work** \\ \hline
[43] & [PERSON] et al., 2020 & High-gain converter in FC EVs \\
[72] & [PERSON] et al., 2024 & AI in wind farm monitoring \\
[53] & [PERSON] et al., 2024 & Intelligent fault-tolerant PV converter \\
[15] & [PERSON] et al., 2023 & Cuckoo search for an energy park \\
[17] & [PERSON] et al., 2022 & Economic strategy for PV + battery system \\
[74] & [PERSON] et al., 2022 & Moth–flame optimization for control \\
[73] & [PERSON] et al., 2023 & Flatness-based control for DC motor \\
[14] & [PERSON] et al., 2022 & VSC-HVDC simulation \\
[75] & [PERSON] et al., 2019 & Open-circuit detection \\
[77] & [PERSON] et al., 2015 & Forward-flyback boost \\
[76] & [PERSON] et al., 2018 & Harmonic/DC current mitigation \\
[78] & [PERSON] et al., 2013 & IC design for energy harvesters \\
[79] & [PERSON] et al., 2018 & Power-optimizer-fed inverter \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._They also provide mechanical protection by absorbing excessive loads and preventing damage to the turbine components. PE converters convert the AC power generated by the wind turbines at the variable frequency into a constant-frequency power, offering suitable options for transmission and distribution. These converters use power electronics to control the flow of electricity and maintain a consistent output. They also enable the integration of renewable energy sources into the existing power grid, ensuring a smooth and reliable power supply [98; 99; 100; 101; 102; 103; 104]. Transformers play a vital role in transmitting electricity efficiently over long distances and distributing it to consumers at appropriate voltage levels. Transformers help minimize power losses during transmission and ensure that the electricity is delivered safely and reliably to end-users.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 1 Introduction
Unlike traditional ERP or multi-plane projections, SCP preserves pixel-wise scale and topology on the spherical surface, offering a unified and geometry-preserving alternative for downstream tracking tasks. * We introduce a dense optical flow tracking framework tailored for spherical imagery. To the best of our knowledge, this is the first system to perform dense pixel-motion estimation directly on a spherical pixel structure. Our method integrates a nonorthogonal gradient operator specifically adapted to the sphere, enabling smooth and spatially consistent flow fields over the entire 360\({}^{\circ}\) FoV. * We present a fully integrated visual odometry pipeline that combines spherical projection and dense flow tracking into a real-time system. Extensive experiments on both public and custom datasets demonstrate that our method not only achieves superior accuracy under high-speed motion but also offers improved robustness compared to existing fisheye and panoramic odometry methods. The rest of this paper is organized as follows. In Section 2, a brief review of odometry algorithms based on panoramic images is given. Section 3 presents the details of the proposed SCP method, and the experiments are conducted and analyzed in Section 4. Section 5 concludes the paper.
## 4 Results
### Effect of Moisture Content on Mechanical Properties
#### 4.2.4 Conventional True Triaxial Stress Path  
Figure 18 presents the curves of the \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) deviatoric stresses as a function of axial strain (\(\varepsilon_{1}\)), lateral strains (\(\varepsilon_{2}\), \(\varepsilon_{3}\)), and volumetric strain (\(\varepsilon_{v}\)) for sandstone with different moisture contents in conventional triaxial tests. It is evident that under stress Path I, as the water content increased, the stress-strain curves of the sandstone specimens exhibited distinct nonlinear characteristics, particularly during the crack propagation and plastic deformation stages. The higher the moisture content, the more pronounced the swelling phenomenon in the sandstone, resulting in a significant decline in its mechanical properties. Specifically, the maximum deviatoric stress values for the dry, 30% saturated, and saturated states were 135.2, 133.5, and 124.8 MPa, respectively. This indicates that the moisture caused a significant deterioration in the mechanical properties of the sandstone, with the maximum deviatoric stress decreasing by 7.7% under saturated conditions.  
The experimental results indicated that under varying moisture contents, the initiation stress, closure stress, and damage stress of the sandstone decreased with an increase in the water content. This effect was particularly pronounced at higher moisture contents, where the damage stress was significantly reduced. During the true triaxial loading process, the presence of moisture accelerated the expansion and through-cracking of existing fractures, leading to a substantial decrease in the strength and stability of the sandstone. Consequently, the failure modes became more complex. As shown in Figure 19, the macroscopic failure patterns of the sandstone specimens under different moisture conditions in the conventional triaxial tests exhibited distinct differences. In the dry state, failure primarily occurred owing to shear stress, with fractures along the main shear plane. In contrast, at 30% saturated  
Figure 17: Failure modes of sandstone with different water contents: (**a**) dry; (**b**) 30% saturated; (**c**) saturated.  
Figure 18: Three-dimensional deviatoric stress–strain relationship diagrams: (**a**) dry; (**b**) 30% saturated; (**c**) saturated.  
and saturated moisture contents, the fractures became more intricate, forming a three-dimensional crack network characterized by dense and interwoven fracture patterns.