esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
Instrument recognition using machine learning has emerged as a dynamic and evolving research area, marked by a variety of approaches ranging from traditional feature engineering to modern deep learning architectures. Early methods focused on extracting acoustic descriptors, such as those developed by [PERSON] et al. [11], who introduced hybrid MFCC-PCA features for timbral modeling. Building on this, [PERSON] et al. [12] advanced cepstral analysis by utilizing UDC/MelUDC representations, combined with RBF-SVM classification for improved recognition. In a similar vein, [PERSON] et al. [13] pioneered harmonic spectral peak detection to isolate instruments within polyphonic audio contexts. The shift towards deep learning brought about significant innovations, including [PERSON] et al.'s [14] temporal max-pooling networks designed for detecting polyphonic activity and [PERSON] et al.'s [15] implementation of ConvGRU-enhanced CNNs applied to Mel-spectrograms for more accurate instrument identification. As the challenge of audio degradation and polyphonic interference persisted, modern approaches incorporated adaptive data augmentation techniques. For instance, [PERSON] et al. [10] introduced auxiliary classification networks for generating synthetic training data, while [PERSON] et al. [16] leveraged WaveGAN-powered multi-task frameworks to simultaneously address pitch and instrument recognition. The field continues to progress with the application of transfer learning strategies [17], which utilize pre-trained auditory models to boost the generalization capabilities of new models. In parallel, Multi-instance multi-label (MIML) formulations [6] have reframed polyphonic analysis as multi-label segmentation over temporal instances, providing a more comprehensive solution. This methodological convergence reflects a systematic effort to tackle the core challenges of instrument recognition by combining feature optimization, architectural advancements, and data-centric innovations, all aimed at improving robustness in complex auditory environments.  
Instrument recognition research has increasingly incorporated transfer learning and multi-instance multi-label frameworks while also exploring various generative neural networks for data augmentation beyond the use of Wave-GAN. Although generative adversarial networks (GANs) and their variants continue to be prominent in the field, their practical deployment in pattern recognition systems is limited by training instability. Alternative approaches, such as diffusion models [18] and flow-based architectures [19], have shown superior image generation quality but come with substantial computational costs, making them less feasible for instrument recognition tasks. In contrast, variational Autoencoders (VAEs) [20] provide greater architectural flexibility and training stability, allowing for their effective integration as embedded subsystems tailored to specific tasks. This advantage is further extended by Conditional VAEs (CVAEs) [21], which enable label-conditioned data synthesis, offering a more controlled approach to data generation [22].
# A Fully Automatic DEXP Method for Gravity Data and Its Application on a Potash Salt Deposit
###### Abstract  
We developed an improved depth of extreme point (DEXP) method, characterized as an effective and rapid imaging method that can estimate the depth and distribution of a source quickly. Its main purpose is to solve various challenges. The automatic calculation aspect of the traditional method is often limited; namely, there is a problem with achieving automatic and reliable processing when the observed surface presents undulating topography, and this problem cannot be ignored. Therefore, we propose the addition of the constant method and the hypothetical observed surface method to achieve improvements in the traditional method. Firstly, we test the improved method on the synthetic models to demonstrate its notable advantage: the achievement of a fully automatic calculation without requiring any other additional information such as structural index (SI) values and threshold values. Meanwhile, we also demonstrate its ability and reliability to handle undulating topography with acceptable accuracy for imaging results. Furthermore, we verify the robustness of the improved method by applying it to real gravity data from the potash salt deposit in the Sakhon Nakhon basin, Laos. In this case, the improved DEXP method effectively identified the location of the potash deposit. Moreover, combined with the optimal edge detection method, gravity prospecting for potash salt deposits exhibited significant advantages.  
improved DEXP method; undulating terrain; automatic calculation; depth estimated; potash salt deposit
## 4 Proposed Protocol
### CH Selection
#### 4.1.2 Step 2: Selection of CHs
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Linguistic Variables** & **Membership Functions** \\ \hline \multirow{2}{*}{\(f_{CH_{1}}\)} & VL (very low), VH (very high) & Trapezoidal \\  & L (low), M (medium), H (high) & Triangular \\ \hline \multirow{2}{*}{\(f_{CH_{2}}\)} & F (few), N (numerous) & Trapezoidal \\  & M (medium) & Triangular \\ \hline \multirow{2}{*}{\(f_{CH_{3}}\)} & N (near), F (far) & Trapezoidal \\  & M (medium) & Triangular \\ \hline \multirow{2}{*}{\(f_{CH_{4}}\)} & L (low), H (high) & Trapezoidal \\  & M (medium) & Triangular \\ \hline \multirow{2}{*}{\(CH_{probability}\)} & VP (very poor), VS (very strong) & Trapezoidal \\  & P (poor), \(B_{ave}\) (below average), Ave (average), & Triangular \\ \multicolumn{1}{c}{\(A_{ave}\) (above average), S (strong)} & Triangular \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input and output parameters. Figure 2: FIS for FQA. In the proposed FQA, we used the fuzzy logic model to select CHs based on probability values. The node with the highest probability values emerges as the winner and takes the CH position. Every node sends messages containing the node ID and probability value to all other nodes in the range after the fuzzy system generates the probability values. They receive a message with the node ID and probability from other nodes and use this information to select a CH. The function mapping between input and output variables is constructed based on the knowledge base consisting of if-then rules, and the final fuzzy rules are shown in Table 2.  
Figure 4: Membership function for the output variable. Figure 3: Membership function for the input variables: (**a**) residual energy; (**b**) number of neighboring nodes; (**c**) distance to the BS; (**d**) node centrality.
# Data-Driven Reinforcement-Learning-Based Automatic Bucket-Filling for Wheel Loaders
## 5 Automatic Bucket-Filling Algorithm
### State and Reward Representation  
The appropriate state and reward function should be set to optimize action selection. The state needs to be able to reflect the characteristics of the environment when the agent interacts with the environment and the dimensionality should not be too high to avoid the curse of dimensionality. Lift force is the most important feature affecting the lift and tilt commands [13] and lift force is related to the lift cylinder pressure. Besides, the velocity of wheel loaders is of significance to the choice of throttle command. The tilt cylinder pressure can be used as a redundant feature. Thus, in this study, we defined the state using a three-dimensional vector consisting of velocity, tilt cylinder pressure, and lift cylinder pressure, which are expressed as:  
\[S=\left\{s=\left[V_{loader},P_{lift},P_{tilt}\right]\right\}, \tag{6}\]  
where \(V_{loader}\) is the velocity of wheel loaders, \(P_{lift}\) is the lift cylinder pressure, and \(P_{tilt}\) is the tilt cylinder pressure.  
It can be seen from Figure 11 that the bucket-soil interaction force mainly depends on the amount of loaded soil. Therefore, the bucket-soil interaction force can directly translate into the amount of loaded soil and is important for the bucket-filling of wheel loaders. In order to encourage the loader to improve the bucket digging force and fuel economy during the training process, the reward function should be composed of the negative value of fuel consumption and the bucket-pile interaction force. Because the bucket-soil interaction force is difficult to measure directly and has a positive correlation with lift cylinder pressure, we use the lift cylinder pressure as a part of the reward function to represent digging force. The bigger bucket digging force demands increased fuel consumption. Therefore, a trade-off is necessary between the fuel consumption and bucket digging force. The reward function is expressed as follows:  
\[R(s,a)=-0.1*J(s,a)+kP_{lift}(s,a), \tag{7}\]  
where \(J(s,a)\) is the fuel consumption from the current state \(s\) to the next state \(s^{\prime}\) when the agent takes action \(a\), \(P_{lift}(s,a)\) is the lift cylinder pressure of next state \(s^{\prime}\), and \(k\) is a constant to control the priority of the fuel economy and bucket weight and \(k=0.2\).
## 4 Evaluation Results and Discussion
### Performance Analysis
#### 4.2.2 Comparative Path Evaluation Analysis  
Deriving from the portrayed performance-oriented literature [34; 35; 36; 37; 38], the cases of the SP-based and the one-dimensional delay-sensitive solutions are investigated. Consequently, their results are compared against the Wii-enabled QUAKE strategy to identify performance differences. The first examined scenario considers the mapping of \(VNR_{1}\) upon an SN path, from source node \(B\) to the destination node \(F\). Initially, all 357 possible paths are calculated, and then the studied strategies impose their evaluation and ordering methodologies upon the candidate paths.
# Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition
## 5 Tattoo Removal
### Quality Metrics  
To evaluate the quality of the different tattoo removal models, we use three different metrics commonly used in the literature:  
* Peak signal-to-noise ratio (\(PSNR\)) is a measurement of error between an input and an output image and is calculated as follows: \[PSNR(X,Y)=20\cdot\log_{10}\left(\frac{MAX_{I}}{\sqrt{MSE(X,Y)}}\right)\] (2) where \(MAX_{I}\) is the theoretical maximum pixel value (i.e., 255 for 8 bit channels) and \(MSE(X,Y)\) is the mean squared error between the ground truth image \(X\) and the inpainted image \(Y\). The \(PSNR\) is measured in decibel, and a higher value indicates better quality of the reconstructed image.
* Mean Structural Similarity Index (\(MSSIM\)) as given in [56], is defined as follows: \[MSSIM(X,Y)=\frac{1}{M}\sum_{i=1}^{M}SSIM(x_{i},y_{i})\] (3) where \(X\) and \(Y\) are the ground truth image and inpainted image, respectively, \(M\) is the number of local windows in an image and \(x_{i}\) and \(y_{i}\) are the image content of the \(i\)'th local window. The \(SSIM\) over local window patches \((x,y)\) is defined as: \[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+C_{1})(2\sigma_{xy}+C_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2}+C_{1})+(\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})}\] (4) where \(\mu_{x}\) and \(\mu_{y}\) are the mean values of the local window patches \(x\) and \(y\), respectively; \(\sigma_{x}^{2}+\sigma_{y}^{2}\) are their local variances and \(\sigma_{xy}\) is the local covariance of \(x\) and \(y\). \(C_{1}\) and \(C_{2}\) are constants set based on the same parameter settings as [PERSON] et al. [56], i.e., \(C_{1}\approx 6.55,C_{2}\approx 58.98\). \(MSSIM\) returns a value in the range of 0 to 1, where 1 means that \(X\) and \(Y\) are identical.  
Figure 12: The encoder of the network is based on ResNet34, and the decoder consists of four main blocks and utilizes PixelShuffling [52]. The loss function is a combination of feature loss (perceptual loss) from [53], gram matrix style loss [54], and pixel (L1) loss. For the gram matrix loss and the feature loss, blocks from a pre-trained VGG-16 model are used [51; 55].  
Visual Information Fidelity (_VIF_) is a full reference image quality assessment measurement proposed by [PERSON] and [PERSON] in [57]. _VIF_ is derived from a statistical model for natural scenes as well as models for image distortion and the human visual system. \(VIF(X,Y)\) returns a value in the range of 0 to 1, where 1 indicates that the ground truth and inpainted images are identical. We use the pixel domain version as implemented in [58].  
We estimate the different quality metrics both on portrait images, i.e., where the entire face is visible and on the inner part of the face (corresponding to the area covered by the 68 dlib landmark points; see Figure 3) where we focus on only the area from the eyebrows to the chin; these regions are shown in Figure 13.
## 3 Development of the Multi-Objective AutoML Model
### Sensitivity Analysis of the Multi-Objective AutoML Model  
Sobol sensitivity analysis, a variance-based global method, was employed to quantify each input variable's contribution and their interactions to the output variance. Total variance \(D(f)\) is decomposed into first-order terms and higher-order interaction terms (Equation (5)). The first-order index \(S_{i}\) and total-order index \(S_{Tj}\) (including all interactions involving \(x_{i}\)) are defined in Equations (6) and (7) [24].  
\[D(f)=\sum_{i=1}^{p}D_{i}+\sum_{i<1}^{p}D_{ij}+\cdots\sum_{i<j<\cdots<p}^{p}D_{ ij\cdots p} \tag{5}\]  
where \(D(f)\) is the total variance of the model output; \(D_{i}\) is the first-order effect of the input variable \(x_{i}\); and \(p\) is the number of input features.  
\[S_{i}=\frac{D_{i}}{D(f)} \tag{6}\]  
\[S_{Ti}=\frac{D_{i}+\sum_{i<1}^{p}D_{ij}+\cdots\sum_{i<j<\cdots<p}^{p}D_{ij \cdots p}}{D(f)} \tag{7}\]  
where \(S_{i}\) is the first-order effect, which indicates the influence of a single variable on the total variance; \(S_{Tj}\) is the total-order effect, which indicates the total contribution proportion of the variable and all its interactions to the total variance. Based on the results of the Sobol analysis, the input variables exhibited varying degrees of sensitivity across the three output parameters (Figure 7).  
For the \(E\), a strong univariate dominance is observed. Variable \(y_{6}\) shows a first-order sensitivity index (\(S_{i}\)) and total effect index (\(S_{Tj}\)) both close to 1 with \(S_{i}\) = 0.998 and \(S_{Tj}\) = 0.998, while the influence of all other inputs is negligible. This indicates that \(E\) is almost entirely determined by \(y_{6}\) alone, with no significant interaction effects. In addition, \(k_{s}/k_{n}\) is primarily influenced by \(x_{6}\) with \(S_{i}\) = 0.806 and \(S_{Tj}\) = 0.912, while \(y_{6}\) also contributes to some extent. The remaining variables show low sensitivity. The gap between \(S_{Tj}\) and \(S_{i}\) for \(x_{6}\) and \(y_{6}\) suggests that interaction effects are present, particularly for \(y_{6}\). What is more, \(E_{d}\) exhibits a multivariate influence pattern. \(x_{8}\) is the dominant variable with \(S_{i}\) = 0.441 and \(S_{Tj}\) = 0.440, followed by \(x_{3}\) and \(x_{2}\). Other inputs such as \(x_{7}\), \(x_{4}\), and \(y_{1}\) also have minor contributions. Overall, this output is influenced by the cumulative effects of  
Figure 7: Sobol sensitivity indices for input features with respect to (**a**) \(E\); (**b**) \(k_{s}/k_{hi}\); and (**c**) \(E_{d}\).  
multiple variables. The Sobol sensitivity analysis thus elucidates the dominant factors and interaction mechanisms underlying the model output, offering critical guidance for parameter prioritization and the refinement of inverse modeling strategies.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions
### Results of Self-Attention LSTM  
Secondly, Table 4 shows the results of the experiment in Section 3.4.2, performed in order to choose a self-attention for the LSTM. The table compares the LSTM only and the LSTM with self-attention (SA-LSTM). Based on the experiment, the MAE of the SA-LSTM was 26.89, which was better than that of the LSTM only.
# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 2 Proposed Internet of Things (IoT) Glucose Monitoring System  
The glucose estimation method described in the present work consists of a non-invasive optical analysis in which a laser beam is pointed to the user's fingertip, while for capturing the transmitted light response, an RPi camera was used.  
Previous works use different body tissues (e.g., forearm, ear lobe, finger, cheek) for non-invasive glucose measurement. However, due to its high capillary density and its ease of implementation [31], the fingertip was selected as the most convenient for the present study. For this reason, the proposed system was instrumented on a glove, where the Raspberry Pi Zero (RPi) board was placed in an acrylic case attached to the carpal area, whereas a 3D-printed case housing the image acquisition setup was placed at the index fingertip.  
The proposed system considers four stages, as depicted in Figure 1. Correspondingly, the materials and methods used to this aim are explained in detail below.
# The Total Phenolic Content and Antioxidant Activity of Nine Monofloral Honey Types
###### Abstract  
Honey is well known for its antioxidant and antimicrobial properties, which significantly contribute to its high demand among consumers. While there is plenty of information available about the antioxidant potential of honey, there is still a lack of research specifically focused on monofloral honeys, as most studies have been based on market samples. To address this issue, in the present study we analyzed the total phenolic content and antioxidant activity of nine monofloral honey types produced in Greece: fir, chestnut, citrus, erica, cotton, Jerusalem thorn, pine, oak and thyme, in comparison with manuka honey. The samples were collected from beekeepers applying the appropriate beekeeping practices. In total, ninety-six representative monofloral honey samples meeting the microscopic, physicochemical, and sensory characteristics were analyzed. Oak honey stood out as the darkest type (L* = 33.67) with the highest total phenolic content (203.75 mg GAE/100 g) and antioxidant activity (106.2 mg AAE/100 g). Chestnut honey closely followed, having also the highest electrical conductivity (16.67 mg/ cm). Although manuka honey had a high total phenolic content, its total antioxidant activity was found to be medium-low compared to fir, pine, and erica honeys. Citrus honey, being the lightest in color (L* = 37.2), exhibited the lowest total antioxidant activity (6.36 mg AAE/100 g). Statistical analysis revealed significant positive correlation between total antioxidant activity and electrical conductivity (r*e = 0.587, p*e = 0.000), and negative correlation between total antioxidant activity and L* parameter (r*aL = \(-\)0.424, p*aL = 0.000). Similar correlations were also observed regarding total phenolic content (p*pe = 0.457, p*e = 0.000, r*pL = \(-\)0.455, p*pL = 0.000). In conclusion, oak and chestnut honeys seem to have a high antioxidant potential, that should be further explored, to highlight their value and help promote them worldwide.  
monofloral honey; total phenolic content; total antioxidant activity; electrical conductivity; color +
Footnote †: journal: Applied Sciences
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 2 Design of UWB Antenna Unit
### Key Parameters Analysis  
In the design process of the UWB antenna, it can be seen from Formula (3) that the main parameters affecting the lowest resonant frequency of the antenna working band are w, fw1, fw2, gw, combined with the simulation software for detailed parameter optimization to get the optimal size, focusing on the influence of w, fw1, fw2, gw on the reflection coefficient, as shown in Figure 5. The green line in Figure 5 represents S\({}_{11}=-10\) dB.  
Since the length and width of the radiating patch of the UWB antenna are close to each other and the structure is approximately square, only one parameter w is discussed. The feed gap depends on the size of the interval between the front patch structure and the reverse ground, which can be calculated by \(\mathrm{gap}=\mathrm{fw1}+\mathrm{fw2}\;-\;\mathrm{gw}\). The antenna size is optimized and fine-tuned by simulation software to obtain the final determined width of the radiating patch w = 10.1 mm, the height of the backside ground gw = 4.81 mm, and the length of the stepped ground fw1 + fw2 = 6.4 mm.
# Strength Development of Metakaolin-Based Alkalial-Activated Cement
## 4 Conclusions  
Metakaolin is a commonly used precursor for alkali-activated cements due to its amorphous nature and high reactivity. In this study, however, a metakaolin sample of relatively poor reactivity was used as the main precursor, and there was a need to use more reactive co-binders to improve the final reactivity and resulting strength development.  
The following conclusions can be drawn:  
* The compressive strength of metakaolin-based geopolymer cement depends upon both the Si/Al ratio of the mix design as well as the activator concentration. By varying both of these parameters in a systematic way, it would be possible to optimize the obtained strength.
* When an initially low reactive metakaolin material is used as precursor, the use of co-binders such as a more reactive metakaolin and BFS should be added to the mix design.
* The co-binders ensure early strength development via precipitation of K-A-S-H and C-A-S-H gels, and also by enabling subsequent strength development due to improved dissolution of the low-reactive metakaolin. Significant increases in compressive strength, in fact, a doubling in some cases, were observed from 7 days to 28 days of curing, which indicated that the poorly reactive metakaolin did dissolve and contribute to the final strength.
* Similar strength development trends were found for both co-binders, but overall, somewhat higher strengths were observed for BFS as a co-binder than metakaolin.  
Conceptualization, T.V.; Methodology, B.L. and T.V.; Formal Analysis, B.L.; Investigation, B.L.; Writing--Original Draft Preparation, T.V. and B.L.; Writing--Review and Editing, T.V. and B.L.; Visualization, B.L. and T.V.; Project Administration, T.V.; Funding Acquisition, T.V. All authors have read and agreed to the published version of the manuscript.  
This work was performed as a part of the project Development of Environmentally Friendly Metakaolin-Based Geopolymers for Oil Well Cementing and Well Abandonment (MGeo), funded by the Research Council of Norway (grant number: 328733).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
Figure 10: Compressive strength of MKB-based cement samples with 10% GGBFS with varying KOH concentrations. Si/Al ratio = 1.2 (**left**) and Si/Al ratio = 1.6 (**right**) in the initial mix design.  
**Acknowledgments:** The authors would like to thank our project partners in Brazil, TECCIM, and UFRN for fruitful discussions throughout the project period.  
**Conflicts of Interest:** [PERSON] and [PERSON] were employed by the company Saferock AS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.