esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Dog Emotions Based on Posture Analysis Using DeepLabCut
## 2 Chosen Approach
The resulting images were subsequently filtered and standardized in terms of size and quality. An object detector was then applied to all images identifying the area where the dog is most likely located. Cropping the images to this area ensures optimal input for landmark detection and prevents potential detection errors. A landmark detector is then used to capture as many relevant reference points of the dog images as possible. The objective of landmark detection algorithms is to be robust to changes in perspective, changes in size, and motion of the object being observed [17]. From the detected points, the pose or essential aspects of the pose can be computed by relating the points. In particular, coordinates for the animal's joints are necessary to reconstruct its pose as accurately as possible. For this purpose, the landmark detector _DeepLabCut_ by [PERSON] et al. [18] was used, which was previously trained with the _Stanford Extra Data Set_ by [PERSON] et al. [19] and the _Animal Pose Data Set_ by [PERSON] et al. [20]. Finally, the extracted landmark points are used to train two models to detect dog emotions. First, a neural network that uses raw coordinates from the landmark detector as training data and second, a decision tree that was trained using calculated pose metrics:  
1. **Tail Position:** Angle between tail and spine;
2. **Head Position:** Elevation degree of head;  
Figure 1: Workflow diagram of the proposed approach. * **Ear Position:** Angle of ear in relation to line of sight;
* **Mouth Condition:** Opening degree of mouth;
* **Front Leg Condition:** Bending degree of front leg(s);
* **Back Leg Condition:** Bending degree of back leg(s);
* **Body Weight Distribution:** Gradient degree of spine.
# Mitigating Electrochemical Effects in Resistive Frost-Detection Sensors
## 2 Materials and Methods
### Image-Based Quantification of Electrode Deposits  
To complement visual observations and resistance measurements, a computer vision tool was developed in Python 3.11.9 using OpenCV 4.11.0 to quantify the visual accumulation of electrochemical byproducts on the sensor electrodes over time. The analysis was based on time-lapse images taken during the experiment.  
For the left electrode, which accumulated a black deposit, grayscale image analysis was applied. Each image was cropped to focus on the electrode region, and contrast enhancement was performed using CLAHE (Contrast-Limited Adaptive Histogram Equalization). The number of pixels below a fixed intensity threshold (grey level \(<\) 25) was then counted to estimate the amount of black material deposited on the electrode surface.  
For the right electrode, which developed a blue precipitate attributed to copper hydroxide, color filtering was used to isolate the deposit. After image cropping and contrast enhancement, red-dominant and black areas were masked out. The remaining pixels were counted to approximate the area covered by the blue substance  
These image-based metrics provided a method to track the visual progression of material accumulation on each electrode throughout the experiment.
## 6 Trusted Routing Operation in AODV
### Trust Updating Rule  
When we talk about trust assessment then trust update is very important because it also counts good history of nodes and recommendation. We keep updating our repository due to dynamic nature of mobile ad hoc networks.  
* If node \(A\) had successful communication with node \(B\) then its update the trust value by incrementing trust in that node. By successful communication we mean normal packet forward or RREP with in the time interval.
* If node \(A\) had failed communication with node \(B\) then it degrades trust values by decrementing the update counter.
* Every time field of successful or failed event is changed, opinion space values are recalculated using Equation (5).
* If node \(B\)'s routing entry is deleted from node \(A\) routing table due to expiry, then new opinion will set as \(\omega_{B}^{A}=(0,0,0,1)\).
# An Improved NSGA-II Algorithm Based on Adaptive Weighting and Searching Strategy
## 2 Preliminaries  
### Multi-Objective Optimization  
In engineering, we often encounter design and decision problems under multiple criteria or multiple design objectives, which are often contradictory for finding the best design solution to satisfy these objectives, which is the multi-objective optimization problem [8]. As an example of minimizing MOPs, define the relevant concepts of MOPs as follows:  
**Definition 1**.: _Multi-objective optimization problem. For a MOP with n-dimensional decision variables and m-dimensional objectives, the mathematical model is defined as_  
\[\left\{\begin{array}{l}\min F(x)=(f_{1}(x),\quad f_{2}(x),\ldots,\quad f_{ \text{m}}(x),)^{T}\\ M_{i}(x)\geq 0,\quad\text{i}\in\{1,2,\ldots,\text{p}\}\\ N_{j}(x)=0,\quad\text{j}\in\{1,2,\ldots,\text{q}\}\end{array}\right. \tag{1}\]  
_where \(\mathbf{x}=(\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{n})\in\Omega\) is the decision variable, and \(\Omega\) is the decision space. \(F(x)\) is an objective function consisting of multiple mutually contradictory sub-functions, and M(x) and N(x) are constraint functions with independent variable x._  
**Definition 2**.: _Pareto optimal solutions [9]. In the context of multi-objective optimization, objective functions conflict with each other or cannot be compared in a simple way. The solution of a problem that is optimal in the first objective function may appear to be the worst solution in the second objective function, so people refer to a series of solutions that cannot be simply compared in the computation of multiple objective optimization problems as non-dominated solutions or Pareto optimal solutions._  
**Definition 3**.: _Pareto dominate. Supposing that \(x_{1},x_{2}eX\) are two decision variables in the objective space that satisfy the constraints, the definition of the dominance between the two solutions can be expressed as \(x_{1}\) dominating \(x_{2}\) if_  
\[\{\forall\mathrm{i}\in\{1,2,\ldots,\mathrm{m}\},f_{1}(x_{1})\leq f_{1}(x_{2}) \}\cap\big{\{}\exists\mathrm{j}\in\{1,2,\ldots,\mathrm{m}\},f_{j}(x_{1})<f_{j}( x_{2})\big{\}}. \tag{2}\]  
_Therefore, a design \(x\) is said to be non-dominated [10] if no other feasible design dominates it._
## 2 Configuration of the Dual Linear-Motor Differential-Drive System  
Figure 1 shows the configuration of the dual linear-motor differential-drive system [26], components as follows: 1--Fixed end of upper linear-motor drag chain, 2--Upper linear-motor actuator, 3--Moving end of upper linear-motor drag chain, 4--Upper linear-motor drag chain, 5--Upper linear-motor table, 6--Linear guide rail, 7--Proximity switch, 8--Mechanical limit block, 9--Upper linear-motor end cover, 10--Upper linear-motor stator, 11--Upper linear-motor assembly, 12--Under linear-motor stator, 13--Under linear-motor actuator.  
This paper provides a dual linear-motor differential-drive micro-feed servo system. Due to the two high-speed \"macro movements\" that operate above the critical creeping speed, the \"micro movements\" are obtained through the differential speed of the upper and under linear motors. Therefore, it can eliminate the unavoidable and low-speed nonlinear creeping phenomenon caused by the inherent properties of the traditional electromechanical cal servo-system structure, and enable the system to have a lower stable speed limit and achieve accurate micro-feed control. The upper linear motor drives the upper table to perform linear motion, while the under linear motor drives the under table to perform linear motion. According to the given motion requirements, specific algorithms are assigned to the upper linear motor and under linear motor motion commands to control the motion of the upper and under table, respectively.  
The linear motion speed of the upper table driven by the upper linear motor alone along the axial direction is represented by V1, while the linear motion speed of the under table driven by the under linear motor alone along the axial direction is represented by V2. Under the drive of the dual linear motors, the differential composite speed of the two tables is close to zero, that is \(\Delta=\mathrm{V1}-\mathrm{V2}\approx 0\), which avoids the creeping phenomenon caused by the low-speed movement of the table when driven by a single linear motor, allowing the dual linear-motor differential-drive system to achieve a high-precision micro-feed motion that a linear-motor single-drive servo system cannot achieve.
## 1 Introduction
### Paper Contributions  
This paper delves into the algorithm designs of modified LSTM-based congestion-aware EFL (MLSTM-CEFL) to address the aforementioned problem statements, structuring the solutions into three contributing phases as follows:  
1. The system architecture is given to handle the virtualization approach for orchestrating resource pooling properties within the NFV infrastructure (NFVI). The framework divides the plane into participants, edge aggregators, and controllers, strengthened by a modified LSTM (MLSTM) algorithm for predicting congestion levels. The structural planes enhance interactivity and interface connections to facilitate scalability for the multi-service IIoT model aggregation.
2. MLSTM-CEFL is introduced for collecting feature inputs, predicting congestion rates, determining high-impact latency-efficient conditions, and optimizing the final model learning objectives. The procedural flow of feature collection and EFL-based IIoT model communications are presented in this paper.
3. The proposed reliable model aggregation policy is presented by considering the output of quality of service (QoS) guarantees and orchestration capabilities in configuring proactive congestion detection and prioritizing model flows. The simulation is implemented with DL policy modelling in a TensorFlow and Keras-based environment and network topology in Mininet and MiniNFV.
# Analysis of _In Vivo_ Plant Volatiles Using Active Sampling and TD-GC\(\times\)GC-TOFMS
## 1 Introduction
Gas chromatography-mass spectrometry (GC-MS) is a robust and sensitive platform for plant metabolomics that can detect and identify hundreds of plant metabolites [30; 31; 32; 33; 34; 35; 36]. However, GC-MS has limitations such as a limited separation capacity and resolution, which results in the coelution of compounds that can lead to significant and/or less intense volatiles going unnoticed, poor quantification, and reduced mass spectral quality [37; 38; 39]. Comprehensive two-dimensional gas chromatography (GC\(\times\)GC) drastically enhances the separation capacity and resolution compared to one-dimensional GC [37; 40; 41; 42]. Further advancements in plant metabolomics have been made as a result of using GC\(\times\)GC-MS [43; 44; 45]. In this field, GC\(\times\)GC-MS has been used to evaluate antibacterial activities of plants, plant pathogens, and plant-pest interactions and profiling food headspace volatiles [18; 46; 47]. In this study, an _in vivo_ active sampling method targeting plant headspace VOCs onto TD tubes was validated. As SPME is frequently used to extract plant headspace VOCs and can be implemented into the _in vivo_ sampling system, some comparisons to SPME sampling were performed in order to demonstrate the different ranges and abundances of compounds collected with the two techniques. Thermal desorption with two-dimensional gas chromatography-time-of-flight mass spectrometry (TD-GC\(\times\)GC-TOFMS) was used to separate and identify the VOCs emitted by various plants. Three species of plants (two varieties of tomatoes) were used in this study: _Solanum lycopersicum_ (Little Napoli tomato),_Escultentum lycopersicon_ (Sugar Rush tomato), _Vaccinium cormbosum_ (Patriot highbush blueberry), and _Mentha villosa_ (Mojito mint). These plants were chosen due to their accessibility to the research team and to highlight the versatility of the _in vivo_ sampling system by collecting headspace samples from various species. Furthermore, two tomato varieties were chosen to demonstrate the robustness of the _in vivo_ sampling system to differentiate volatiles from similar plants. To our knowledge, this is the first description of a reusable setup for the _in vivo_ sampling of plant volatiles onto a TD tube with active sampling to collect plant VOCs using TD-GG\(\times\)GC-TOFMS for the analysis. The overarching goal of this study was to provide an additional extraction technique that targets secondary plant metabolites and other headspace VOCs that allow for the analysis of a whole plant without causing damage and mimic nature as closely as possible.
## 2 Materials and Methods
### Synthesis of Metal-CA and Measurement of CA Activity  
The metal-CA hybrid carrier assay was synthesised according to the [PERSON] research group's protocol, which produced hybrid nanoflower carriers for CO\({}_{2}\) capture [25]. Briefly, the procedure used was the following: 0.6 mL of metal phosphate solution (Ti, Co, Mn, Cu, Ca and Zn) of 120 mM was added into 100 mL of phosphate-buffered saline (PBS, 0.1 M, pH 7.4) solution containing 10 mg of CA and was incubated at 4 \({}^{\circ}\)C for 2.5 days, as illustrated in Figure 2. The red and blue dots in Figure 2 represent phosphate and metal ions, respectively, in the reaction. The activity of the CA enzyme was determined spectrophotometrically by mixing p-nitrophenyl acetate (p-NpA) hydrolysis at room temperature in  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **Value** & **Test/Standard** \\ \hline Liquid limit: \% \(w/w\) & 63 & Cone penetrometer; BS 1377-2: 1990 [30] \\ Plastic limit: \% \(w/w\) & 33 & BS 1377-2: 1990 [30] \\ Plasticity index: \% \(w/w\) & 30 & BS 1377-2: 1990 [30] \\ Gravel content (\%) & 4 & BS 1377-2: 1990 [30] \\ Sand content (\%) & 20 & BS 1377-2: 1990 [30] \\ Silt content (\%) & 37 & BS 1377-2: 1990 [30] \\ Clay content (\%) & 39 & BS 1377-2: 1990 [30] \\ Soil classification & CH & BS 1377-2: 1990 [30] \\ Organic matter content: \% \(w/w\) & 3.9 & Loss of ignition: ASTM D2974-14 [31] \\ Moisture content: \% \(w/w\) & 47.5 & BS EN ISO 17892: Part 1: 2014 [32] \\ In situ bulk density (g/cm\({}^{3}\)) & 1.78 & BS EN ISO 17892: Part 2: 2014 [33] \\  & 7.7 & BS ISO 10390-2005 [34] \\ Natural CaCO\({}_{3}\) content (\%) & 4.82 & ASTM D4373-21 [35] \\ Undrained shear strength, S\({}_{\rm u}\) (kPa) & 5.5 & Unconfined compression test BS EN ISO 17892-7:2018 [36] \\ \hline \end{tabular}
\end{table}
Table 1: Basic physicochemical properties of a sandy silty clay soil from Prickwollow, East Anglia.  
Figure 1: Particle size distribution of the soil used in this study.  
a reaction mixture (1.35 mL) containing freshly prepared 3 mM p-nitrophenyl acetate in phosphate buffer (0.13 M and pH 7.2). Activity for p-NpA hydrolysis was determined using a method that [PERSON] and the group proposed in 1966 [37]. The reaction proceeded for 5 min, and the change to A\({}_{348}\) per minute was measured. Then, the CA activity was characterised by the amount of p-nitrophenyl produced per unit of time, and enzyme activity was expressed in terms of U (see Equation (6)):  
\[\text{CA activity}\bigg{(}\frac{\text{U}}{\text{mL}}\bigg{)}=\frac{(A_{348} \text{T}-A_{348}\text{B})\times 1000}{5\times Volume} \tag{6}\]  
where \(A_{348}\)T is the final reading of absorbance; \(A_{348}\)B is the initial uncatalysed reaction at a wavelength of 348 nm; and 1 U (\(\upmu\text{mol}/\text{min}\)) is defined as the amount of the enzyme that catalyses the conversion of one micromode of substrate per minute.
# Optimal Interplanetary Transfer of Solar Wind Ion Focusing Thruster-Based Spacecraft
## 1 Introduction
The propulsion system also includes a cathode (or other suitable electron source) to prevent the thruster from accumulating a net charge. Since the SWIFT cone axis always points to the Sun, the functions of collecting and directing the solar wind ions are separated, and for this reason the SWIFT concept has interesting similarities with the Forward's SPT configuration. So far, however, a reasonable estimate of the SWIFT capabilities is still lacking and is confined to the preliminary analysis in Ref. [34], where [PERSON] et al. have reported some parametric studies about flyby missions to Venus and Mars. The aim of this paper is to fill that gap and quantify the SWIFT's performance in a systematic way, by translating the analysis into the solution of a minimum-time interplanetary transfer problem. In this case, the optimal (i.e., minimum-time) trajectories are obtained with an indirect approach by enforcing the necessary conditions for optimality according to the Pontryagin's maximum principle [35]. In particular, the Hamiltonian associated to the problem is maximized at any time by maximizing the projection of the propulsive acceleration vector along the direction of the Lawden's primer vector [36; 37]. This paper is organized as follows. The next section describes the spacecraft dynamics in a heliocentric mission scenario and provides an extension of the thrust model recently discussed in Ref. [34]. The analytic expression of the spacecraft propulsive acceleration vector is then used in Section 3 to optimize the spacecraft transfer trajectory in a circle-to-circle coplanar orbit transfer, while Section 4 discusses the optimal results obtained by numerically simulating two potential (and simplified) interplanetary missions towards Mars and Venus. Finally, the last section summarizes and highlights the main outcomes of this work.
## 5 Conclusions  
An ultra-wideband electromagnetic multi-layered composite absorber with resistive conductor linked with two metasurfaces is proposed. The proposed absorber has good absorption property at the target frequency band with quadruple absorption points of resonance. To meet the ultimate goal, a genetic algorithm was used as the design method for the two metasurfaces. Through the proposed optimization algorithm, pixelated pattern generation and EM analysis are iteratively performed until an optimized result is obtained with the proposed cost function. As a result, ultra-wideband absorption performance is achieved as an integrated composite material. In addition, for TE-polarization, through the proposed mechanism, a bandwidth that satisfies 90% absorption can be achieved from 2.2 to 18 GHz of which the fractional bandwidth is 156% for not only the normal but also the oblique incidence angle (0\({}^{\circ}\) to 30\({}^{\circ}\)). To verify the proposed absorber design and performance, the absorptivity was measured by the NRL arch method with an anechoic environment. Through the measurement and full-EM simulation, the proposed absorber was confirmed, and comparable results were obtained. Furthermore, from this study, the proposed composite absorber of this design method has an extremely high applicability to actual structures such as tanks, airplanes, ships, etc. It was confirmed through environmental tests that the proposed absorber is reasonable for various applications. In addition, the proposed silk-screen printing method provides the lowest complexity of the fabrication process among methodologies adopted to fabricate multilayer structures. This increases the possibility of mass and large area production. Therefore, the proposed design method may break through the conventional concept that the number of absorption resonances coincides with that of the layers. Finally, based on the proposed metasurfaces, the based composite absorber achieves a down-to-earth breakthrough in realizing a low-profile, lightweight, durable, large-area expandable, and low-cost EM wideband absorber in the RF stealth field. Conceptualization, C.L. and K.K. ; Methodology, K.K. and P.P. ; Software, C.L. and K.K. ; Validation, C.L., K.K., and Y.J. ; Formal analysis, Y.J. and J.J.; Investigation, P.P., Y.J., and J.J.; Resources, Y.J. and J.J.; Data curation, P.P. ; Writing--original draft, C.L. ; Writing--review and editing, K.K. and H.L. ; Visualization, T.C. ; Supervision, T.C. and H.L. ; Project administration, T.C. and H.L. ; Funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the Center for Advanced Meta-Materials (CAMM) funded by the Ministry of Science and ICT as Global Frontier Project (CAMM- No. 2014M3A6B3063700). The authors declare no conflict of interest.
## 4 Experiment
### Experimental Results and Analysis
Over time, while the validation loss exhibits a minor increase, the validation accuracy continues to rise slightly. This suggests that the framework maintains its generalization ability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Method** &
\begin{tabular}{c} **Inference** \\ **Time (ms)** \\ \end{tabular} & **Acc (\%)** & **MPre (\%)** & **MRec (\%)** & **MF1 (\%)** & **GM (\%)** \\ \hline \multirow{8}{*}{D0} & MLLF+MKB [19] & - & 89.3 & - & - & - & - \\  & CNNs+Transfer learning [12] & - & 96.0 & - & - & - & - \\  & FNSTC [32] & - & 98.5 & - & 98.2 & 98.4 & - \\  & GAEnsemble [33] & 106 & 98.8 & 98.9 & 98.8 & 98.8 & - \\  & **Our method** & **74** & **99.7** & **99.5** & **99.7** & **99.6** & **99.5** \\ \hline \multirow{8}{*}{IP102} & ResNet-50 [31] & - & 49.4 & - & 40.1 & - & - \\  & DMF-ResNet [35] & - & 59.1 & - & 58.1 & - & - \\  & ResNet152+ViT+Swin-T [36] & - & 65.6 & 60.9 & 59.7 & 60.3 & - \\  & Pre-trained Inception-v3 [37] & - & 67.9 & - & - & 60.6 & - \\  & SGDL-Net [38] & - & 71.2 & - & - & 63.9 & - \\  & IRNV2 [34] & - & 71.8 & **65.9** & 63.2 & 64.1 & - \\  & DGrad-Adam+CNNs+EM [29] & - & 73.6 & - & - & 72.9 & **84.7** \\  & EFLM+AFFM+SV [20] & - & 73.9 & - & - & **73.6** & - \\  & **Our method** & **74** & **74.1** & 64.0 & **69.9** & 65.9 & 59.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of our multi-task framework with previous methods. (“-” indicates that the original study did not provide the corresponding metric result. The bold highlights indicate the best results. F1 scores reported in the original studies are assumed to be the macro-average F1 scores.)
# Machine Learning and Deep Learning Based Model for the Detection of Rootkits Using Memory Analysis
## 4 Methodology
### Data Acquisition  
In this step, data are collected to build the rootkit detection model. This involves capturing the memory image (including RAM) using the Dumpit.exe tool. The generated memory dumps serve as the primary data source for further analysis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Learning** & **Performance** & **Dataset** & **Strengths** & **Limitations** \\ \hline \multirow{6}{*}{[PERSON] et al. [15]} & \multirow{6}{*}{CNN} & Accuracy, & \multirow{6}{*}{Dampware10 [22]} & Comprehensive & Limited \\  & & & dataset with & explanation of \\  & & & malware and & dataset creation \\ \cline{1-1}  & & precision, recall, & & benign samples. & process. Limited \\ \cline{1-1}  & & & & Inclusion of GIST & discussion on \\ \cline{1-1}  & & ROC-AUC & & descriptors and & feature extraction \\ \cline{1-1}  & & & & HOG features. & methods. \\ \cline{1-1}  & & & Handles feature & \\ \cline{1-1}  & & False positives, & & selection & Could be \\ \cline{1-1}  & & False negatives, & & automatically. & computationally \\ \cline{1-1}  & & Accuracy, F1- & & Resistant to & expensive. \\ \cline{1-1}  & & Score, Precision & & over-fitting. & \\ \cline{1-1}  & & RF, DT, k-NN, & Accuracy, F1- & & CIC-MalMem- & High accuracy and \\ \cline{1-1} [PERSON] et al. [17] & Naive Bayes, SVM, & Score, Precision, & 2022 & fast classification. & \\ \cline{1-1}  & Logistic Regression & & Recall & [24] & \\ \cline{1-1}  & & TPR, FPR, AUC, & & Integration of & Limited \\ \cline{1-1} [PERSON] et al. [18] & RF, DT, Bayesian & F-measure, & [18] & memory forensics & exploration of \\ \cline{1-1}  & & Accuracy & & analysis. & feature selection. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Overview of Methodology for Rootkit Detection using Memory Analysis.  
Figure 2: Tools Used for Rootkit Detection using Memory Analysis.