esa-sceva/satcom-chunk-collection huggingface dataset:
# Compressive Sampling with Multiple Bit Spread Spectrum-Based Data Hiding
## 4 Experimental Result
### Noisy Environment  
In the noisy environment, our proposed method was robust to additive noise attack as confirmed mathematically in Section 3.5. Nevertheless, it was necessary to know how robust the method was if the additive noise attacks the encoded audio by simulation. We analyzed the detected watermark quality represented by BER and the reconstructed audio quality represented by ODG as two performance parameters affected by the additive noise. In the simulation, we used 50 clips with 50 iterations for each clip, \(M=23\), \(r=6\), and \(p=r\). The additive noise parameter or the input parameter for the simulation was SNR, as described in (45), whose range was 0 to 40 dB. ODG and BER as the performance parameters obtained were averaged, as displayed in Figure 7. Decreasing the noise power  
Figure 6: BER in relation to \(N_{l}\) and \(r\) using a different Hadamard matrix between encoding and decoding.  
or increasing the SNR rose the reconstructed audio quality or ODG and the detected watermark quality or BER.  
We embedded the watermark image with the letters \"ITB\" and a resolution of \(20\times 35\) to understand the interpretation of the value of BER. The detected watermarks are displayed in Table 7 with various BER. We used one selected clip as the audio host using parameters \(M=256,r=100\), and \(p=r\). The original watermark image is shown at the very bottom of Table 7, since its BER was zero. We used the additive noise as the attack with various SNR from 0 to 55 dB. The detected watermark was interpretable as \"ITB\" when the SNR of the noise was more than 25 dB or its BER was less than 10%. Thus, the maximum acceptable BER for the detected watermark was up to 10%. In Figure 7, BER less than 10% could be achieved on an SNR of 10 dB and above. This meant that the detected watermark was already interpretable when the noise power was still half of the signal power. Furthermore, ODG was already more than \(-1\). These results confirmed the robust proposed method of additive noise. The reconstructed audio was also robust to the additive noise since the ODG already achieved more than \(-1\) when the SNR was still 10 dB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline SNR & BER (\%) & Detected Watermark \\ \hline
0 & 24.1 & \\
5 & 18.7 & \\
10 & 17.4 & \\
15 & 14.3 & \\
20 & 11.7 & \\
25 & 8.8 & \\
30 & 6.1 & \\
35 & 5.7 & \\
40 & 5.6 & \\
45 & 3.6 & \\
50 & 0.4 & \\
55 & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The detected watermark in certain SNR.  
Figure 7: Additive noise effect and the detected watermark in certain SNR.
# Bio-Sourced and Biodegradable Membranes
## 3 Biodegradable Antifouling Membrane Properties
### Cellulose Acetate (CA) Membranes  
Cellulose acetate (CA) is among the natural polymers with excellent mechanical stability. In light of their high hydrophilicity, antifouling, and film-forming properties, cellulose and its derivatives have attracted considerable interest as potential membranes for water/wastewater filtration [162, 163, 164, 165, 166, 167, 168, 169, 170, 171]. Several membrane filtration applications have been conducted with cellulose-based membranes, including separating bacteria, salt, dye, protein, oil, and heavy metal from the aqueous solutions [172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 180, 183, 185, 187, 188, 189, 180, 184, 186, 187, 188, 189, 181, 182, 184, 185, 186, 187  
fouling was reported for both contaminants (2.2% for humic acid and 7.8% for copper), demonstrating the superior antifouling property of the prepared biodegradable membrane. [PERSON] et al. [178] compared the adsorption of blood proteins by CA membranes and non-biodegradable polyethersulfone (PES) membranes. The adsorption of albumin protein by PES membrane was about 2.5 times that of CA membrane. Protein adsorption decreased by 50% (<0.5 \(\upmu\)g.com\({}^{-2}\)) for the modified CA membrane with a biocompatible phospholipid polymer (poly(2-methacryloyloxyethyl phosphorylcholine (MPC)-co-n-butyl methacrylate (BMA))).
## 4 Empirical Insights from Sectorial and Regional Studies
### Nordic Countries Study for AI Adoption in Manufacturing and Retail SMEs  
[PERSON] et al. [114] investigate how AI implementation unfolds within manufacturing SMEs in Nordic countries using a resource orchestration lens. Their qualitative study reveals how firms must strategically coordinate, develop, and leverage internal and external resources to extract value from AI technologies. Technologically, the SMEs examined struggled with limited data maturity, outdated legacy systems, and integration challenges, aligning closely with the technological barriers outlined in the TOE framework. Organizationally, success was linked to proactive leadership and internal champions who could bridge technical and operational divides. In addition, employee resistance and lack of digital skills were reported as key inhibitors, mapping to DOI attributes such as perceived complexity and lack of trialability.  
A critical contribution of the study lies in its emphasis on dynamic capability building: firms that succeeded in AI implementation actively reconfigured internal processes and cultivated strategic partnerships with technology providers. Environmentally, government support and regional innovation networks played an enabling role, especially when cross-organizational collaboration was required.  
Although the study is qualitative, it includes proxy performance outcomes such as increased production efficiency, reduced waste, and shorter lead times, illustrating tangible business benefits. These findings reinforce the importance of tailoring AI strategies to sector-specific resource constraints and support the adaptability of the TOE-DOI framework to high-precision, capital-intensive SME contexts such as manufacturing.
# Quantifying Perceived Facial Asymmetry to Enhance Physician-Patient Communications
## 2 Materials and Methods
### Facial Landmarks and the Corresponding 3D Coordinate System  
Twenty facial landmarks, including eight medial and six bilateral pairs, as shown in Table 1, are identified from the acquired facial image [6]. Each landmark, denoted as \(\mathbf{L}(i)\), is associated with an ID (\(i\)). IDs with star superscript indicate bilateral landmarks.  
To mathematically construct the 3D coordinate system, the mid-sagittal plane of the face is defined as the orthogonal to the vector \(\vec{Ex_{r}}\vec{Ex_{l}}\) that connects right and left Esccanthi (ID = 9*; \(Ex_{l}\) and \(Ex_{r}\)) and passes through Nasion (ID = 2; \(N\)). The intersection of the mid-sagittal plane and the vector \(\vec{Ex_{r}}\vec{Ex_{l}}\) is denoted as \(N^{\prime}\), which can be considered as the projection point of \(N\) along the mid-sagittal plane. The proposed head coordinate system is thus defined as (\(N\), \(\vec{x}\), \(\vec{y}\), \(\vec{z}\)), where \(N\) is the origin; \(\vec{x}=\vec{Ex_{r}}\vec{Ex_{l}}/\|\vec{Ex_{r}}\vec{Ex_{l}}\|\) (pointing to the right); \(\vec{y}=N^{\prime\prime}N/\|N^{\prime}N\|\) (pointing into the head); \(\vec{z}=\vec{x}\times\vec{y}/\|\vec{x}\times\vec{y}\|\) (pointing upwards). The coordinates of all 20 landmarks can then be determined against (\(N\), \(\vec{x}\), \(\vec{y}\), \(\vec{z}\)). Note that these 20 facial landmarks are later identified as 14 facial features (ID = 1, 2, 3,... 14).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Landmarks** & **ID (\(i\))** & **\(\mathbf{L(i)}\)** & **Definition** \\ \hline Glabella & 1 & \(G\) & Most prominent midline point between eyebrows \\ Nasion & 2 & \(N\) & Deepest point of nasal bridge \\ Promasale & 3 & \(Prn\) & Most protruded point of the apex nasi \\ Subnasale & 4 & \(St\) & Midpoint of angle at columella base \\ Labial (superius) & 5 & \(Ls\) & Midpoint of the upper vermilion line \\ Labial (inferius) & 6 & \(Li\) & Midpoint of the lower vermilion line \\ Stomion & 7 & \(Sto\) & Midpoint of the mouth orifice \\ Menton & 8 & \(Me\) & Most inferior point on chin \\ Exocanthion & 9* & \(Ex\) & Outer commissure of the eye fissure \\ (left and right) & & \\ Endocanthion & 10* & \(En\) & Inner commissure of the eye fissure \\ (left and right) & & \\ Alar curvature & 11* & \(Al\) & Most lateral point on alar contour \\ (Left and right) & 12* & \(Ch\) & Point located at lateral labial commissure \\ Zygion & 13* & \(Zy\) & The most lateral extents of the zygomatic arches \\ (left and right) & & \\ \hline \hline \end{tabular}  
* Bilateral landmarks.  
\end{table}
Table 1: Twenty facial landmarks, abbreviations, and definitions.
# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 6 Discussion
### Toward Improving Human Parameters in Visualization Recommendation Systems  
Taken together with previous work, the findings in this study provide empirical evidence that numeracy and need for cognition each has positive correlation with visualization literacy. In the following section, we introduce a subset of implications of our study for the other related research activities in the field of data visualization.  
There are growing interests in developing visualization recommendation engines. Studies like those by Voyager [68], VizDeck [69], and VizRec [70] attempt to provide a ranked list of data visualizations following user inputs, data properties, and visualization types for adaptive exploration depending on users' tasks in hand. However, we still lack an understanding of the human parameters in this avenue of research. Most current recommendation engines rely on static sets of perceptual and cognitive disciplines and user preferences, which cannot reflect individual users' current and potential capabilities of using data visualizations. As shown in this study, visualization literacy is related to various types of user cognitive characteristics. Specific evidence of user cognitive characteristics along with their relationships to visualization literacy can be an important consideration in building visualization recommendation systems, especially user models in the systems. Therefore, we need to investigate how we use the nature of visualization literacy within the pipeline of visualization recommendation systems.  
Some studies show procedural learning, which tracks the user's analysis of visualizations and adapts them so that visualization systems can nudge the users about undiscovered trends and patterns or optimal data visualization types for further exploration. Furthermore, other studies have found the effects of individual factors on task performance with data visualizations e.g., [22; 71], and they provided rationales to create visualization systems that are adaptive to individual differences. However, few studies have accounted for the aspect of the user's ability to perform tasks with the recommendedand adapted visualizations and their aptitude for learning new data visualization types. Visualization literacy plays a central role in procedural understanding and users' task performance when confronted with data visualizations. The recommendation and adaptation may need to be more specific, not only by highlighting visual elements to nudge the user toward better performance but also by guiding users to learn how to manipulate interfaces and to unpack visual outcomes. More research efforts are needed to reveal users' mental steps and cognitive behaviors that can help people learn more advanced visualizations so that the system can timely diagnosis users' misconceptions, estimate their understanding, and provide appropriate guidance, feedback, suggestions, and instructions.
# Comparison of Thermal Response for RF Exposure in Human and Rat Models
###### Abstract  
In the international guidelines/standards for human protection against electromagnetic fields, the specific absorption rate (SAR) is used as a metric for radio-frequency field exposure. For radio-frequency near-field exposure, the peak value of the SAR averaged over 10 g of tissue is treated as a surrogate of the local temperature elevation for frequencies up to 3-10 GHz. The limit of 10-g SAR is derived by extrapolating the thermal damage in animal experiments. However, no reports discussed the difference between the time constant of temperature elevation in small animals and humans for local exposure. This study computationally estimated the thermal time constants of temperature elevation in human head and rat models exposed to dipole antennas at 3-10 GHz. The peak temperature elevation in the human brain was lower than that in the rat model, mainly because of difference in depth from the scalp. Consequently, the thermal time constant of the rat brain was smaller than that of the human brain. Additionally, the thermal time constant in human skin decreased with increasing frequency, which was mainly characterized by the effective SAR volume, whereas it was almost frequency-independent in the human brain. These findings should be helpful for extrapolating animal studies to humans.  
human safety; thermal time constant; temperature elevation; vasodilation; bioheat transfer equation +
Footnote †: journal: International Journal of Environmental Research and Public Health
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 2 Definitions and Basic Concepts for Cooperative Drone-Based Counter-UAS Systems
### Drones  
A drone or unmanned aerial vehicle (UAV) is an aircraft with no human pilot on-board. It is the central element of a UAS, which is the set comprised of the aircraft and all the other elements supporting the service of a drone. In detail, a UAS is made up of the main following components:  
* Airframe, which is the mechanical part of the vehicle, including the propulsion system;
* Navigation and motion sensors that collect the information about the drone position and its flight trajectory;* Flight control system (FCS), which controls the propulsion system and the servos in order to apply a flight trajectory;
* Payload, which is the specific equipment to accomplish a given mission;
* Ground control station (GCS), which is a computer system or a network of computer systems on the ground, which monitor and control UAS operation;
* Communication infrastructure, which is the set of data links and related equipment for the communication between the vehicle and the GCS (or other external elements).  
There are different classifications of drones according to several parameters, such as weight, altitude, endurance, degree of autonomy, etc. Reference [3] provides a survey of the main classification of drones. For example, the military domain includes a NATO UAS classification system, which is shown in Figure 1. It sets three classes, based on the weight. The classes are further divided according to other parameters, such as the employment, the operating altitude and the mission radius. According to such a classification system, mini drones are Class I drones with a weight less than 15 kg, whereas microdrones are Class I drones with a maximum energy state less than 66 J.  
A slightly different classification for micro-, mini and small UAVs (sUAS, NATO Class I) is described in [5] and shown in Table 1. The latter also reports endurance and payload capabilities, as well as weight, altitude, range and same example platforms available on the market.  
Figure 1: NATO’s UAS classification system [4].
## 4 Experiments and Analysis of Results
### Limitation of the PCES-YOLO  
As demonstrated in the preceding research, PCES-YOLO achieves significant improvements in PCB defect detection accuracy while concurrently reducing model parameters. However, the integration of large-kernel convolutions within the ConvNeXtBlock module introduces a marginal increase in computational load compared to YOLOv11n, resulting in a slight reduction in inference frame rate, as listed in Table 9. It is expected that employing  
Figure 10: Comparison results of visualization experiments on the DeepPCB dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **P/\%** & **R/\%** & **mAP50/\%** & **mAP50–95/\%** \\ \hline YOLOv9t & 97.0 & 94.8 & 98.3 & 74.2 \\ YOLOv10n & 94.9 & 93.9 & 98.1 & 76.9 \\ YOLOv11n & 97.3 & 96.3 & 98.7 & 77.1 \\ PCES-YOLO & 98.0 & 96.9 & 98.9 & 77.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental comparison results on the DeepPCB dataset.  
updated versions of PyTorch, optimizing batch configurations, and improving the acceleration method can effectively mitigate this frame rate decrease. Consequently, the impact on practical deployment scenarios is anticipated to be minimal. Moreover, Table 9 shows that PCES-YOLO's GPU memory usage is lower than that of YOLOv11n and YOLOv11s, making it suitable for deployment on devices with limited resources.
# Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification
## 3 Proposed RF Fingerprinting-Based Emitter Identification Method
### Attacker Emitter Detection
Set \(i\gets i+0.5\times W_{E}\) Step 2: (Calculate the spectrogram)  Calculate the spectrogram \(\mathbf{s}_{Feature}\) of the SF with Equation (13) with respect to the BOI, \(BW_{\text{BOI}}\). Step 3: (Perform emitter classification) \(i+W_{Ext. }<length(\mathbf{s})\)  Estimate the emitter IDs from the decision rule using either the base classifier (18) or ensemble approaches in Equation (20). Step 4: (Perform outlier detection)  Scale the output vector for temporal scaling factor \(T_{s}\) with Equation (22) and detect the outliers with Equation (23) Output: Return the authenticated baseband hop signals \(s_{h}^{\text{k}}(t)\)
```  
**Algorithm 2** Proposed RFEI algorithm.
# Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review
## 3 Major Initialisation Methods
### Probability Distributions
The findings of those authors showed that PSO and CS are more sensitive to the initialisation scheme used, whereas DE was less susceptible to the initialisation scheme used. In addition, PSO relies on a greater population size, whereas CS requires a lesser population size. DE does well with an increased number of iterations. The Beta, Rayleigh, and exponential distributions are great performers as the results showed that they greatly influence the convergence of the optimisers used. [PERSON], [PERSON], and [PERSON] [31] incorporated Latin hypercube sampling (LHS) to initialise four (4) optimisers; namely, the evolution strategies (ES), covariance matrix adaptation (CMA), elitist covariance matrix adaptation (ECMA) and differential evolution (DE). They use these optimisers to investigate the relation between the geometry of the structural components, and their service life. They aimed to improve the service life of structural components under fatigue. Their choice of LHS instead of the random Monte Carlo simulation optimised the number of samples needed to calculate the problem regarding the formulation of the statistical quantities. The stochastic fractal search (SFS) technique was used in the work of [32] to improve the performance of the multi-layer perceptron neural network. It was used to obtain the optimal set of weights and threshold parameters. The hybrid approach was tested on EEE 14- and 118-bus systems, and the results were compared with other non-optimized MLP (optimized MLP based on genetic algorithm (MLP-GA) and Particle Swarm Optimization (MLP-PSO)). The precision was up by 20-50%, and the computational time was down by 30-50%. However, SFS tends to ignore local search; the correct balance between the global and local search is desired. Similarly, the levy-flight was replaced by stochastic random sampling of simpler fat-tailed distributions enhanced with scaled-chaotic sequences to boost cuckoo search (CS) performance in solving the complex wellbore trajectories problem [33]. Probability distributions generally suffer from issues such as equiprobable disjunct intervals and errors in correlations between variables. We summarise efforts in this category in Table 3.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 5 Results and Discussion
### YOLOv8-Pose Model Inconsistencies
#### 5.4.2 Inconsistency Analysis for the 13-Class Dataset  
For the smaller dataset, the inconsistency matrix reveals that specific gesture pairs still show significant confusion, while classification accuracy is generally higher than in the 47-class case. Moreover, motion-based signs such as \"Kim\" and \"Bar\" exhibit higher inconsistency scores, suggesting that optical flow introduces noise rather than useful motion cues in these cases. Therefore, the model performs best for gestures with distinct hand shapes, while signs with similar postures but different motion paths are more prone to errors.
## 2 Materials and Methods
### Differential Scanning Calorimetry (DSC)  
The crystallinity of each film was studied using a differential scanning calorimeter (DSC series Q200, TA Instruments, New Castle, DE, USA). Experiments were carried out on neat and nanocomposite films, before and after 30 days of accelerated aging. All measurements were performed under a nitrogen atmosphere (50 mL.min\({}^{-1}\)). Specimens of 5-8 mg, weighed using a microbalance (Sartorius micro PRO 11), were placed in aluminum sample pans (Tzero Aluminium Hermetic pan, TA Instruments). The thermal protocol was defined according to the polymer. The PBSA samples were heated up to 150 \({}^{\circ}\)C, maintained at 150 \({}^{\circ}\)C for 5 min, cooled to \(-\)35 \({}^{\circ}\)C and finally reheated to 150 \({}^{\circ}\)C. In the case of PHBV, samples were heated up to 200 \({}^{\circ}\)C, maintained at 200 \({}^{\circ}\)C for 5 min, cooled to \(-\)30 \({}^{\circ}\)C and finally reheated to 200 \({}^{\circ}\)C. Temperatures were adjusted at a rate of 10 \({}^{\circ}\)C.min\({}^{-1}\). The crystallization temperature (T\({}_{\rm c}\)) determined from cooling ramp, melting temperature (T\({}_{\rm m}\)) and melting enthalpy (\(\Delta\)Hm) obtained from first heating ramp, were determined from the DSC curves using the Universal Analysis 2000 software by TA Instruments. The crystallinity degree (X\({}_{\rm C}\)) of neat and nanocomposite polymers was determined by the equation:  
\[\rm{X_{C}(\%)}=\frac{\Delta H_{m}}{\Delta H_{m}^{0}}\times\frac{100}{w}, \tag{1}\]  
where: \(\Delta H_{m}\) is the melting enthalpy of the polymer matrix, w is the polymer weight fraction in the sample and \(\Delta H_{m}^{0}\) is the theoretical melting enthalpy of the polymer assumed to be 100% crystalline, \(\Delta H_{m}^{0}\) = 146.0 J.g\({}^{-1}\) for PHBV [18] and \(\Delta H_{m}^{0}\) = 116.9 J.g\({}^{-1}\) for PBSA [19].