esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 5 Summary  
Our survey assessed 80 latest articles on robotic emotion recognition of two emotional categories--discrete and valence-arousal and discussed the applicability of different sources and ML models in robots. For both categories, our survey found neural network-based methods, especially CNN, performed the best. To be specific, for the discrete category, the highest accuracy of 99.90% was achieved by CNN. Another neural network-based method, LSTM, was the best performer, with accuracies of 99.40% and 96.88% for valence and arousal, respectively. The majority of the experiments that used neural networks had accuracies above 80%. Besides neural network models, SVM can be an alternative model, as this model has been widely used by numerous researchers, with ease of implementation and accuracies of 80% to 99.82%. From the signal sources, Imaging signals were the most proficient and widely used source. Within the VA category, the top eight best-performing models used brain signals as signal sources, showing the great potential of brain signals for this recognition. Despite this, imaging and brain signals also perform well for VA and discrete categories. Among the signal sources, tactile signals performed worst in both categories, which gives an indication of the cautious usage of tactile signals for human emotion recognition. It is also noticeable that fusion signals performed comparatively better than individual signals. In terms of applicability, brain signals need sophisticated acquisition devices and data processing procedures, while imaging signals can be readily used in ML models. Therefore, to apply within humanized robots for emotion monitoring, we believe imaging sources could be the first choice. Therefore, ML methods, neural networks and SVM, and signal sources of facial imaging should be most promising for further research on emotion monitoring, with some focus on using fusion signals to make the model more robust.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 2 State-of-the-Art AIS Solutions
Nonetheless, the NPS algorithm has not yet been validated in a realistic IoT architecture with differing datasets. This work aims to bridge this gap and therefore offer a comprehensive study of AISs in IoT malware detection under realistic conditions. The methodology of leading AIS solutions, NPS and MNSA, is similar and comprises two stages: the detectors generation stage and the detection stage (see Figure 1). In the detectors generation stage, two different sets of detectors are generated: the negative detectors, represented as \(C_{Neg}\), which do not match self-data, and the positive detectors, represented as \(C_{Pos}\), which match self-data. In the detection stage, if an incoming file matches one of the negative detectors, it is tagged as a malicious file. In contrast, if an incoming file matches one of the positive detectors, it is tagged as a benign file.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Spectral Data Acquisition  
The methodology began with collecting and analyzing rock spectra from the Freixeda study area. In total, 16 rock samples were acquired and placed in an oven, where they were subjected to a temperature of 50 degrees Celsius to eliminate any traces of liquid on the surface [34]. This drying step is essential to avoid spectral distortions caused by surface moisture, ensuring that the spectral measurements accurately reflect the mineralogical composition of the samples [35].  
The spectral information obtained from the rock samples was collected using Analytical Spectral Devices (ASDs). The ASD FieldSpec 4 spectroradiometer is a portable, battery-operated spectrometer with a spectral range of 350-2500 nm. It offers a spectral resolution of 3 nm at 700 nm (VNIR), 10 nm at 1400 nm (SWIR 1), and 10 nm at 2100 nm (SWIR 2). A spectralon (Labsphere) plate was used for reflectance calibration. The spectralon plate has a maximum reflectance higher than 99% for the 400 to 1500 nm range and higher than 95% for the 250 to 2500 nm range [14]. In total, 48 spectra were collected.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**PlanetScope**} & **SR** & \multirow{2}{*}{**Sentinel-2**} & **SR** & \multirow{2}{*}{**ASTER**} & **SR (\(\mu\)m)** & **Landsat 9** & \multirow{2}{*}{
\begin{tabular}{c} **SR** \\ **(\(\mu\)m)** \\ \end{tabular} } \\  & **(\(\mu\)m)** & & & & & & \\ \hline Coastal Blue (B1) & 0.431–0.452 & Band 1 & 0.433–0.453 & - & - & Coastal Aerosol & 0.435–0.451 \\ Blue (B2) & 0.465–0.515 & Band 2 & 0.458–0.523 & - & - & Blue (B2) & 0.452–0.512 \\ Green I (B3) & 0.513–0.549 & - & - & - & - & - \\ Green II (B4) & 0.547–0.583 & Band 3 & 0.543–0.578 & Band 1 & 0.52–0.60 & Green (B3) & 0.533–0.590 \\ Yellow (B5) & 0.600–0.620 & - & - & - & - & - \\ Red (B6) & 0.650–0.680 & Band 4 & 0.650–0.680 & Band 2 & 0.63–0.69 & Red (B4) & 0.636–0.673 \\ Red Edge (B7) & 0.697–0.713 & Band 5 & 0.698–0.713 & - & - & - \\ NIR (B8) & 0.845–0.885 & Band 8A & 0.755–0.875 & Band 3N and 3B & 0.78–0.86 & NIR (B5) & 0.851–0.879 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral range correspondence between PS and other multispectral satellite sensors (Sentinel-2, ASTER, and Landsat 9).
## 1 Introduction
In this paper, our dynamic programming algorithm is capable of solving the \((p,d,\ell,b)\)-clique transversal problem in \(O(n(\omega(G))^{4})\) time for a distance-hereditary graph \(G\), where \(\omega(G)\) is the cardinality of a maximum clique in the graph \(G\) (when the context is clear, we also use \(\omega\) interchangeably). We develop this dynamic programming algorithm by employing binary ordered decomposition trees--specifically, \(PTF\)-trees--of distance-hereditary graphs. The idea is to leverage the tree decomposition, a structural characterization of distance-hereditary graphs, to represent the graph as a tree structure where dynamic programming can be efficiently applied to the nodes. Tree decompositions, which capture the hierarchical nature of distance-hereditary graphs, facilitate breaking down the problem into smaller, more manageable subproblems that can be solved iteratively. For specific examples of dynamic programming algorithms applied to distance-hereditary graphs, readers can refer to research papers [8; 20; 21; 22; 23], which address various optimization problems using similar decomposition-based approaches. To guide the reader through the contributions of this paper, the rest of the paper is organized as follows:  
* In Section 2, we provide the necessary background and preliminary definitions required for understanding the subsequent sections. This includes the recursive definition of distance-hereditary graphs and detailed explanations of the four clique transversal problems. The recursive definition introduces the construction of a binary ordered decomposition tree, known as a \(PTF\)-tree, which is central to the development of our algorithm;
* Section 3 introduces the \((p,d,\ell,b)\)-clique transversal problem, details the problem's constraints and parameters, and explains how it generalizes the four specific clique transversal problems;
* In Section 4, we identify how the optimal solution to the \((p,d,\ell,b)\)-clique transversal problem can be composed of the solutions to its subproblems and define the recursive formulas using the recursive definition of distance-hereditary graphs;
* Section 5 presents our dynamic programming algorithm for solving the \((p,d,\ell,b)\)-clique transversal problem. We guide the reader through the design of the algorithm, illustrating how the value of an optimal solution is computed in a bottom-up manner using the recursive formulas. Additionally, we provide a detailed analysis of the algorithm's time complexity.
# Pasteurization of Foods with Ultrasound: The Present and the Future
## 3 Microbial Inactivation

Table 1: Microbial inactivation in food products using ultrasound. Milk processing with ultrasound continues to be a topic of interest. Recently, several critical reviews about the use of ultrasound for milk pasteurization have been published [15; 23; 60; 61]. New milk sources, like goat, camel milk, or even the latest plant-based milk, are in the scope of ultrasound research. Ultrasound has also been tested against thermoduric spore-formers microorganisms in dairy products. It is well known that _Bacillus_ spp. is one of the main problems during milk storage, shortening the shelf-life [62] considerably. However, also, _Bacillus_ spp. is one of milk's most heat-resistant species. [PERSON] et al. [56] studied the inactivation of spore-formers vegetative cells in milk using ultrasound (5000 W, 20 kHz, 80% amplitude, 10 min). The results showed an excellent inactivation of _B. coagulans_ (4.53) and _A. flavourhens_ (4.26); when thermal pasteurization (63 \({}^{\circ}\)C, 30 min) was followed by ultrasound, both microorganisms were fully inactivated. Using ultrasound as a unique technology to pasteurize milk is not feasible without alternative hurdles like heat or pressure. However, ultrasound can modify some functional properties of milk and dairy products [23]. It can also promote the growth of microorganisms, speed up fermentation processes in dairy products, like cheese or yogurt, or increase yield [63; 64; 65]. However, the use of ultrasound for milk and dairy products needs to be optimized in processing conditions to deliver those benefits that ultrasound can offer and minimize some drawbacks related to cavitation.
# Control of Ribosome Synthesis During the Cell Division Cycles of _E. coli_ and _Synechococcus_
## 1 Introduction  
_E. coli_, a chemoheterotroph, and _Synechococcus_, a photoautotroph, are on opposite ends of the microbial nutritional categories. Basic differences in the nutritional process of chemoheterotrophs and photoautotrophs lie in their methods of generating energy and in the nature of the carbon sources needed for cell replication. In chemoheterotrophs organic compounds serve as sources of both energy and carbon. The energy source for photoautotrophs is radiant energy, and the carbon source for biosynthesis of cellular molecules is mainly CO\({}_{2}\). Chemheterotrophs can take in organic substrates from the environment to support cell growth, while photoautotrophs must produce all cellular molecules and cellular structures from inorganic compounds to support cell growth. Chemoheterotrophs can grow in light or dark conditions, while photoautotrophs grow in light conditions only. Nevertheless, there are similarities in biosynthetic paths of building block molecules, the synthesis of macromolecules, and the assembly of macromolecules to form basic cell structures in both bacteria. The cell cycle events that occur sequentially in the cell division cycle of _E.coli_ and _Synechococcus_ are genome replication, genome segregation and cell septum formation, suggesting that these cell events are coordinately regulated events ([PERSON] and [PERSON], 1995; [PERSON], 2001; [PERSON], 2003). However, ribosome synthesis in _E. coli_ is stimulated by the quality of external nutritional environments, or by growth phases, or growth rates (see reviews by [PERSON] et al., 1995; [PERSON] and [PERSON], 1996; [PERSON] et al., 1996; [PERSON], 1999). In _Synechococcus_, ribosome production occurs early in the cell cycle followed by genome replication, genome segregation and cell septum formation ([PERSON], 1979). It is obvious that the regulation of ribosome synthesis in _E. coli_ and _Synechococcus_ as described above is not controlled by similar mechanisms. What is less obvious is the complexity of the regulatory mechanisms involved in ribosome synthesis in _E. coli_ and _Synechococcus_. Comprehensive and authoritative reviews on the regulation of ribosome synthesis in _E. coli_ have been published by authors indicated above. The aim of this article, however, is to focus on current issues regarding the control of rRNA and r-protein syntheses in ribosome formation in _E. coli_ and _Synechococcus_.
## 2 The Theory of the Smooth Local Subspace Projection Noise Reduction and PE  
### The Theory of Smooth Local Subspace Projection Noise Reduction Method  
Smooth projection noise reduction [28; 29; 30] which is based on SOD, is applied to phase space reconstruction of the noisy signals generated by mechanical systems. The basic principle is that noises can divulge to a higher dimensional phase space of the tangent space of its attractor. Here, the data are embedded into a higher \(d\) dimensional space, then are projected to \(k\) dimensional tangent subspaces to achieve the purpose of denoising. The limitation of trajectories on the subspace strengthens the smoothness of the time series which has been filtered, thus obtaining a better denoising effect. The time series with noises \(\left\{x_{i}\right\}_{i=1}^{n}\) is embedded into a \(d\) dimensional phase space, which is composed of vector value trajectories \(\left\{\mathbf{X}_{i}\right\}_{i=1}^{n-(d-1)\tau}\) generated by a delay coordinate, as shown in Equation (1).  
\[\mathbf{X}_{i}=[x_{i},\ x_{i+\tau},\ldots,x_{i+(d-1)\tau}]^{T} \tag{1}\]  
where \(\tau\) is delay time, which is usually determined by using the average mutual information method [44]. The selection of global embedding dimension \(d\) and tangent subspace dimension \(k\) is one of the most important steps. False nearest neighbor method is often used to estimate minimum embedding dimension \(D\). In addition, the embedding theorem [45] provides a method for choosing the minimum embedding dimension: The minimum embedding dimension should be two times larger than the fractal dimension of its attractor.
## 4 Simulation Results and Analysis  
In this section, we provide the numerical simulation results to illustrate the performance of the proposed WE-I-A algorithm, where relevant analysis and evaluation are conducted. Simulations using MATLAB are designed, and the main parameters are listed in Table 2. The path-loss model and shadow fading are considered for both cellular uplink users and D2D pairs. In order to demonstrate the generality of the experiment, simulation results are evaluated by averaging 500 independent experiments.
# Correlations Between Phenolic Composition and Perceived Astringency of Wines
## Materials and Methods
### Proanthocyanidin Content  
The proanthocyanidin content (PAC) of the wine samples was determined by using a slight modification of the vanillin-acetic acid assay described by [PERSON] et al. (1998). To each well of a 96-well microplate, 30 \(\mu\)L of the wine sample and a dilution buffer were transferred and 150 \(\mu\)L of a vanillin working solution (0.5% in 4% HCl in acetic acid) was added. The microplate was covered with a lid and incubated at 25 \({}^{\circ}\)C for 4 min on a microplate reader (shaking on for 3 min, off for 1 min, end with shaking off). The absorbance of the reaction mixture (Abs1) was recorded at a wavelength of 500 nm against the blank solution consisting of the dilution buffer and the vanillin working solution. The absorbance of the reaction mixture (Abs2) consisting of the wine sample and 4% HCl in acetic acid solution was recorded using this method as well. The absorbance value Abs1 subtracted from Abs2 was used to estimate the proanthocyanidin content. The PAC was estimated using a (+)-catechin (C) calibration curve (y = 0.0061x + 0.0018, \(R^{2}\) = 0.9988). Different concentrations of catechin in a dilution buffer (10, 20, 40, 80, 160, 200, 300 \(\mu\)g/mL) were used for the construction of the calibration curve. The PAC was expressed as \(\mu\)g/mL catechin equivalent (CE).
## 3 Methodology and Implementation  
This chapter details the proposed methodology adopted in designing a disaster victim verification system supported by a mobile application, ID Victim. The work was divided into three stages: A--architecture and principle of operation of the people identification module, B--compilation of the neural network module, C--implementation of a mobile application.
## 3 Results
### Icephobic Properties
#### 3.5.2 Freezing Delay Time  
The FDT recorded for the reference sample was 3 min. The other modified coatings showed values for this parameter from 14 min to 50 min. Thus, it can be concluded that the addition of functionalized polysiloxanes increased the FDT and consequently improved the anti-icing properties of the surface of the epoxy-silicone coatings. The longest FDT was recorded for the sample MOD4, which also obtained the lowest ice adhesion value. The FDT for this sample increased 17 times compared to that of the unmodified sample; the FDT for the MOD 4 sample was 50 min. The other samples also showed a significant increase in the FDT, as the time values for these samples increased 5, 6, and 8 times. A previous work [32] also observed an increase in the FDT after modification with polysiloxanes of a polymer resin.
# Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control +
## 3 Design and Implementation
The full mitigation can be described as:  
\[z_{\text{i}}[n]+\text{j}z_{\text{q}}[n]=h\big{(}x_{\text{i}}[n]+\text{j}x_{ \text{q}}[n],l\big{)} \tag{8}\]  
where \(h(\cdot,l)\) is a non-linear function with memory to map the inputs to the outputs of the IM for the \(l\)-th channel of the HDDM. The HW implementation is done in register-transfer level (RTL) code (i.e., very-high-speed integrated circuit hardware description language (VHDL)), allowing the deployment on different FPGA technologies or even as an application-specific integrated circuit (ASIC) intellectual property (IP). The central processing blocks are the DFT and inverse discrete Fourier transform (IDFT)--both based on a standard Cooley-Tukey radi-2 FFT core. The HDDM FFT bit width is driven by the input signal dynamic range and constrained by resource utilization and timing requirements. In addition to the FFT, the windowing and the reconstruction modules are also optimized for digital HW implementation. The windowing is based on a combination of bit shifts, to avoid instantiating multiplier blocks. The reconstruction module adder is implemented using a transposed structure, which puts the adder in the pipeline [13]. The AGC implementation is described in detail in [11]. This article avoids multipliers and dividers through the left and right shifting. It constrains the size of the window of samples to a power of two, which does not introduce any relevant limitation. The HDDM hardware module has been implemented on two FPGA-based GNSS dual-band receivers, having different costs, form factors, and target applications. A summary of the resource utilization on different Xilinx devices is given in [11].
## 6 Conclusions  
In this paper, we have proposed a STAR-RIS-aided NOMA system by deploying STAR-RISs on the UAV. The active beamforming at the BS and the passive beamforming at the STAR-RISs are optimized to maximize the achievable rate for the users. Rician fading was adopted to model the channel with altitude-dependent Rician factors. Based on the model, two baseline schemes were proposed and discussed. By leveraging the SDR techniques, simulation results showed that the BER and achievable rate performance improvements are achieved by applying the proposed scheme when compared to the baseline schemes. Despite these good results, the proposed scheme has several limitations. First, it considered only a two-user scenario and assumed perfect channel state information, which may not be practical. Furthermore, although convergence was verified in simulations, a formal theoretical analysis remains to be discussed. Future work could investigate developing robust optimization methods that account for imperfect channel state information and environmental uncertainty, improving the resilience of the system in practical deployments. The insights presented in this work are expected to contribute for the future and practical deployments of RIS-assisted systems to address the implementation challenges in future networks.  
Conceptualization, J.L.; software, J.L.; validation, Y.P.; writing--original draft preparation, J.L.; writing--review and editing, J.L., Y.P., M.M.M., and F.A.-H.; visualization, J.L.; supervision, Y.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Science and Technology Development Fund, Macau, SAR (0085/2024/RIA2). Furthermore, this research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2025-71).  
No new data were created or analyzed in this study.  
The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2025-71).  
The authors declare no conflicts of interest.  
Figure 4: BER of the proposed scheme and baseline 2 scheme versus the number of STAR-RIS elements when \(h_{uav}=10\) and \(r=5\).
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 2 Method  
The manipulator can perform not just a single task, but it can also perform two or more tasks with different priority by using null space projection. For a single task, the relationship between the torque and force is expressed as follows [2]:  
\[\Gamma=J^{T}F \tag{1}\]  
where \(\Gamma\in R^{n}\) and \(F\in R^{m}\) denote the torque and the force vector, respectively. \(J^{T}\in R^{n\times m}\) is the Jacobian transpose matrix of the task. If we apply this equation into two simultaneous tasks (e.g., \(\Gamma=J_{1}^{T}F_{1}+J_{2}^{T}F_{2}\)), each task will interfere with each other, because the priority of tasks are not assigned to the excess DoF. In order to prioritize the two tasks into primary and secondary tasks simultaneously, without interfering with the primary task, the null space projection should be applied [3]. The corresponding equation of the torque with the null space projection is given in (2):  
\[\Gamma=J_{p}^{T}F_{P}+N_{P}^{T}J_{S}^{T}F_{S} \tag{2}\]  
\[N_{P}^{T}=I-J_{p}^{T}J_{P}^{T+} \tag{3}\]  
where \(N_{P}^{T}\in R^{n\times n}\) and \(J_{p}^{T+}\in R^{m\times n}\) denote the null space projector matrix and Moore-Penrose pseudoinverse matrix of primary task Jacobian transpose, respectively. I is the \(n\times n\) identity matrix. The subscripts \(P\) and \(S\) identifies the primary and the secondary tasks. In this paper, we deal with only two tasks, primary and secondary priorities, but for the general situation, it could be extended further. Based on the Equation (2), the motion generated by the secondary task force, \(F_{S}\), cannot interfere with the motion of the higher  
Figure 1: Actual model of the 7-DoF manipulator. The infinitely possible solutions of the 1-DoF secondary task are shown when performing the positioning and orienting at the end-effector as the primary task.  
priority due to the null space projector \(N_{P}^{T}\). That is because the null space projector \(N_{P}^{T}\) constrains the joint motion that is available to \(F_{S}\) due to the assigned priority.