esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### The Relationship Between Eye Tracking and Packaging in Consumer Research  
Visual attention plays a crucial role in consumer decision-making processes, particularly in environments characterized by high information density such as retail shelves. Eye-tracking technology has emerged as a powerful methodological tool to objectively measure visual attention and explore how packaging elements influence consumer behavior.  
Research has consistently shown that packaging design elements--such as color, typography, shape, and visual claims--capture attention and can influence product evaluation and purchase intention [63]. Eye-tracking metrics such as time to first fixation, fixation duration, and number of fixations on areas of interest (AOIs) are commonly used to evaluate which package attributes attract consumer attention and for how long.  
It has been demonstrated that visual saliency and branding elements on packaging significantly affect gaze patterns and ultimately influence brand choice. Findings indicate that consumers tend to fixate first and longer on visually salient packages, and that such visual prominence increases the likelihood of product selection [64]. Likewise, strong design features on packages have been shown to not only increase attention but also enhance memory and preference, particularly when integrated into coherent branding strategies [65].  
Moreover, eye-tracking studies have revealed that consumers do not process all available visual information on packaging but tend to rely on heuristic cues, often guided by top-down expectations [66]. This behavior underscores the importance of strategically placing key product attributes--such as sustainability claims or nutritional information--in visually dominant positions on the package.  
In the context of sustainable packaging, eye-tracking techniques have been used to investigate how eco-labels on food packaging affect consumer attention and choices. Findings suggest that eco-labels can influence purchasing decisions, but only when they are visually accessible and aligned with consumer values [67].  
Overall, the integration of eye tracking into packaging research enables a nuanced understanding of the visual mechanisms that drive product perception and choice. These insights are invaluable for marketers and designers aiming to create more effective, attention-capturing packaging in competitive marketplaces.
# Research on the Positioning Performance of GNSS with a Low-Cost Choke Ring Antenna
## 4 Conclusions  
The conducted experimental studies allowed for a preliminary assessment of the impact of the use of choke ring cover on the performance of GNSS antenna during long  
Figure 11: Selected measures of 2D position accuracy.  
Figure 12: Selected measures of 3D position accuracy.  
term operation. It is especially important in applications where long-term operation of the positioning system in conditions with the possibility of multipath effects is required.  
The designed and constructed choke ring antenna cover allowed us to obtain a positioning accuracy of approximately 0.02 [m], despite the presence of higher values of the DOP parameters compared to the dedicated antenna, a similar number of satellites in use, or higher positioning errors in the ECEF system.  
The obtained results confirm that the choke ring antenna can be used for positioning in non-open surroundings without a significant loss of accuracy in the RTK mode. It allows for determining the direction of future work, which could be researched on the influence of long-term external interference (EMI) on the operation of the GNSS with a choke ring antenna or the effect of the different cover material (e.g., aluminum) in the choke ring antenna on the damping of satellite signals in the context of the accuracy of the satellite system.  
Conceptualization, L.R., P.K. and K.C.; methodology, L.R., P.K. and K.C.; software, L.R.; validation, L.R., A.R., M.P. and T.M.; formal analysis, L.R., P.K. and K.C.; investigation, L.R., A.R. and M.P.; resources, A.R., L.R., M.P., P.K. and K.C.; data curation, L.R. and M.R.; writing--original draft preparation, L.R.; writing--review and editing, L.R., A.R., M.P., M.R. and T.M.; visualization, L.R., M.R. and P.K.; supervision, A.R. and T.M.; project administration, M.P. and T.M.; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript.  
This work was financed by the National Centre for Research and Development under research project DOB-SZAFIR/09/A/010/01/2021 (\"Inteligentny, zintegrowany system do lokalizacji, wstepnej oceny i pomocy medycznej poszkodowanym na polu walki wykorzystujacy geoinformacje i sensory biomedyczne\").  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
Figure A2: The number of satellites in use histogram: (**a**) GNSS antenna, (**b**) choke ring GNSS antenna.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Methodology  
Clustering algorithms are pivotal in machine learning for analyzing extensive, multidimensional datasets and hold substantial value in addressing big data analytical challenges [43]. The grid density clustering algorithm employed in this study is a density-based approach where the data space is segmented into grids, and clustering is achieved by evaluating the density of data points within these grids [32; 42]. Unlike traditional distance-based clustering methods, the grid density algorithm excels in identifying irregularly shaped clusters without the need for pre-defined cluster counts. This algorithm has demonstrated its efficacy across various domains, including image segmentation [41; 44] and spatial data analysis [40; 45]. Nonetheless, it requires the manual determination of grid dimensions and density thresholds, which introduces a degree of subjectivity. Given the complexities and uncertainties inherent in the development of the second-hand housing market, a reliance on traditional empirical methods or subjective judgments may prove inadequate. To address this, the study incorporates the Bayesian optimization algorithm to refine the selection of grid parameters scientifically. This algorithm treats grid dimensions and thresholds as hyperparameters [35], optimizing them through a Gaussian process model that treats the objective function as a black-box function [46]. This method facilitates more efficient hyperparameter estimation compared to traditional techniques [39; 47; 48; 46].  
Building on existing research and emphasizing an interdisciplinary approach, this study uniquely combines grid density clustering [32; 41; 43; 49], GScan [42], and Bayesian optimization algorithms [34; 36; 38; 39]. An enhanced density algorithm, the RScan algorithm, is introduced by integrating Bayesian optimization into the framework of grid density and GScan algorithms, marking a significant advancement in the application of machine learning techniques to the real estate sector.
# Fabric Sol-gel Phase Sorptive Extraction Technique: A Review
## 8 Conclusions and Future Outlook  
Fabric phase sorptive extraction is a newly developed technique used for isolation and preconcentration of different analytes, from various matrices where even untreated samples demonstrate high extraction efficiency, operational flexibility, simplicity, and a shortened sample pretreatment scheme. FPSE has successfully eliminated inherent errors of conventional sample preparation as well as excessive time consumption. Submerging the FPSE media directly into the sample solution for analyte extraction offers great flexibility and simplicity, decreasing drastically potential analyte loss. The FPSE is easy to use with chromatographic techniques (e.g., liquid chromatography and gas chromatography) coupled with various detection systems such as spectrometry, mass spectrometry and atomic absorption spectrometry.  
The inherent porosity of sol-gel sorbent and characteristic permeability of flexible cellulose or polyester fabric substrate result in rapid extraction of target analytes and complete the extraction equilibrium in a short time. Small volumes of organic solvent for elution purposes, elimination of solvent evaporation, and a sample reconstitution step, make the technique environmentally friendly and cost effective in accordance with Green Analytical Chemistry requirements. This review of the literature dealing with FPSE and its applications clearly shows that the technique has achieved great  
Figure 7: FPSE technique: The FPSE fabric (**up–left**); the construction of the minicolumn packed with the FPSE media in the form of disks in a row; the minicolumn with FPSE disks ready for use (**down–right**). Reproduced from [36] with permission of Elsevier.  
importance. Most of the applications have been developed for the selective extraction of drugs, pharmaceuticals, and various chemical compounds in water, and in biological and food samples.  
In the future, research will have the challenge to develop new sol-gel coatings and materials to determine any type of analyte in complex sample matrices. The new automated approach opens up the possibility of sample preparation and total analysis without human intervention, increasing throughput, and improving several analytical performance characteristics.  
[PERSON] wrote the review article and [PERSON] reviewed and revised the article. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
## 4 Experimental and Results Analysis
### Experimental Results and Analysis  
#### 4.3.1 Experiment 1  
The findings from Experiment 1 are presented in Table 6, showing the classification accuracy of different models across varying sequence lengths and effective sample sizes. Notably, the UniTS model exhibits a significantly superior performance across all available datasets compared to the other models.  
* Impact of Effective Sample Size: Datasets with larger effective sample sizes (Dataset A) lead to a higher classification accuracy compared to smaller sample sizes (Dataset B). This demonstrates that more representative data enable better model generalization. As shown in Table 6, the UniTS, THF_TSC, and LSTM_TSC models achieve 11.92%, 8.93%, and 2.58% higher classification accuracies on Dataset A compared to Dataset B, respectively. This illustrates the importance of larger sample sizes in improving the performance across all models.
* Impact of Sequence Length: Increasing the sequence length from 60 to 3600 significantly improves the classification accuracy, as longer sequences provide more contex  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Batch** & **Initial** & **Model** & **Hidden** & **Patch Length** \\  & **Size** & **Learning Rate** & **Layers** & **Layers** & **and Stride** \\ \hline UniTS & 32 & 0.0001 & 512 & 2 & 16 \\ UniTS\_SAF & 32 & 0.0001 & 512 & 2 & 16 \\ UniTS\_SAM & 32 & 0.0001 & 512 & 2 & 16 \\ UniTS-SinSpec & 32 & 0.0001 & 512 & 2 & 16 \\ THF\_TSC & 32 & 0.0001 & 12 & 12 & 25 \\ LSTM\_TSC & 32 & 0.0001 & 2 & 2 & none \\ \hline \hline \end{tabular}
\end{table}
Table 5: Models’ initial parameters.  
tual information. Specifically, the classification accuracies of the UniTS, THF_TSC, and LSTM_TSC models improve by 2.54%, 0.43%, and 5.94%, respectively, showing that longer sequence lengths enhance the models' performance. However, extremely long sequences (86,400) pose computational challenges for complex models, like UniTS and THF_TSC, which encounter memory-related errors. In contrast, the simpler LSTM_TSC model can handle longer sequences, but at the cost of reduced accuracy, achieving only 5.64%.
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 3 Materials and Methods
### Application of the Algorithm
The summary of the tests conducted in the laboratory stands for different lengths of the pattern are presented in Table 2. As it can be seen, the proposed method returns reliable results for the patterns consisting of five and three panels (90 cm and 54 cm respectively), but a one panel pattern turned out to be insufficient for the problem being considered.
## 4 Discussion
### Spatial Variability  
When plotting all samples according to their position inside the pitfalls, in each 35 well G1 group samples (high in carcinrite) clearly represent the 0-4 m, while G2 group samples (high in Ti-phases) the 4-8 m deep interval. Such obvious discrimination suggests differentiation of the red mud due to its density resulting mineralogical and so chemical zonation inside the pitfalls. Gravitational differentiation as a governing process had to cause carcinrite, a low density (~2.4 g/cm\({}^{3}\)) mineral tend to move upwards, while heavy minerals, first of all, the Ti-phases (anatase ~3.9 g/cm\({}^{3}\), rutile ~4.2 g/cm\({}^{3}\), ilmenite ~4.8 g/cm\({}^{3}\), titanomagnetic ~5.2 g/cm\({}^{3}\)) tend to sink. Of course, in accordance with the tight relationship between the REEs and these mineral phases, gravitational differentiation modifies the spatial distribution of the REEs as well. One can assume that other heavy minerals (e.g., zircon, apatite) behave in a similar way, these phases, however, are of a little amount and, consequently, thought to have a minor role in REE accumulation.  
The coarse grain fraction of the red mud studied contains not only single mineral grains but also goethite, hematite, and carcinrite as form well-cemented coarse particles. As the Fe-phases have a much higher density than carcinrite does, presence of these aggregates hinders carcinrite moving to the shallow zone of the pitfalls. Nevertheless, Ca enrichment in the upper zone, as well as the increased Fe concentration in the deep, confirms the influence of gravitational differentiation process (c.f. Table 1).  
All information considered the most essential geological processes that determine the spatial distribution of the major mineral phases and so the REEs in the Almasfuzito pitfalls are the following (Figure 5). (1) Because of their high density, the Ti-phases tend to subside and accumulate in the deepest horizons. (2) Cancrinite develops inside the pitfalls as a reaction product of kaolinite under the hyperalkaline condition. (3) Cancrinite forms well-cemented aggregates with goethite and hematite. (4) Because of its low density, carcinrite tends to rise, but as an aggregate-forming phase, it subsides instead. (5) At the topmost zone of the pitfall, carcinrite reacts with atmospheric CO\({}_{2}\) and forms calcite.  
Figure 4: Alkaline alteration products formed during the ageing of kaolinite in 5 M NaOH + 4 M SO\({}_{4}\) at 70 \({}^{\circ}\)C for (**a**) 2 days, (**b**) 7 days (reproduced after the Figure 3 of [20] with the permission of the Mineralogical Society of Great Britain and Ireland).
## 5 Results  
After implementing the DBSCAN technique with the number of clusters ranging from 2 to 10 as input variables, the silhouette score for each cluster was estimated, as depicted in Figure 4. In general, silhouette scores range from \(-1\) to 1, with values closer to 1 indicating a more optimal cluster configuration. The analysis revealed that with four (4) clusters, the silhouette score reached a peak value of 0.91. This optimal clustering was confirmed through parallel analysis using the K-means clustering technique, which yielded the same result.  
Choosing the four (4) clusters as the final priority group based the silhouette score result, the boundary values for each cluster, representing the minimum and maximum complaint processing rate totals, are summarized in Table 7. Clusters were evaluated based on their complaint processing rate totals, with corresponding priority levels of high, neutral, low, and very low assigned to clusters 1-4, respectively, as detailed in Table 7. Table 8 further delineates the types of complaint locations associated with each complaint type according to their designated priority level.  
To validate how closely the calculated complaint-processing priorities align with actual complaint-handling practices, we analyzed the frequency of reported civil complaint locations for each priority level. As depicted in Figure 5, locations categorized with a  
\begin{table}
\begin{tabular}{c c c} \hline \hline  
\"high\" priority predominantly include high-traffic and high-speed roads such as highways, national roads, and local roads. Conversely, the \"very low\" priority category frequently encompasses SOC facilities, residential streets, and railways. This distribution suggests that the data-based complaint-processing prioritization method proposed in this study aligns well with established social conventions and reflects practical traffic management needs effectively.
# Stimulation Montage Achieves Balanced Focality and Intensity
## 3 Results
### Robustness Study
#### 3.3.4 The Impact on Focality by Conductivity Uncertainty  
Similarly to the TE test, the scalp and CSF show minor and comparable effects on focality, usually inducing focality changes less than 10 mm. Figure 7A and Table 4 also reveal the main influence of skull conductivity uncertainty on focality. Figure 7B shows an example of focality analysis in a single-target experiment with our SBFI method. The skull-induced focality changes can be larger than 40 mm, but most of them happen when \(\sigma_{skull}\leq 0.005\) S/m. When \(\sigma_{skull}>0.005\) S/m, the focality change is less than 1.5 mm. This also occurs in the other scenarios (see Supplementary), but the tuning point is not always the same. The impact of the cortex on focality is more complicated, and varies significantly with algorithms and target types. The highest change is \(\sim 25\) mm, but sometimes the influence can be as weak as the scalp/CSF effects.  
Figure 6: (**A**) Maximum TE deviation from the expected value due to conductivity uncertainty. **Left**: single target. **Right**: synthetic multiple targets, from top to bottom are the results for target 1, 2, and 3, respectively. (Note: \(y\)-axis is log scale in the right column.) The effects of TE in general: skull \(>\) cortex \(>\) scalp \(>\) CSF. (**B**) Example of the TE changes. TE has non-monotonic and divergent changing forms. (Note: the \(y\)-axis starts at 4 mm.) Overall, TE tends to decrease when the skull/cortex conductivity increases, while scalp/CSF has no clear tendency. TE spans caused by the cortex are the largest, which indicates that cortex conductivity has a strong impact on TE.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tissue** & **\(\sigma\) (S/m)** & **Max TE (mm)** & **Min TE (mm)** & **Rate ((mm)/(S/m))** \\ \hline Scalp & 0.2–0.6 & 7.383 & 7.184 & 0.497 \\ Skull & 0.001–0.04 & 8.158 & 6.605 & 39.830 \\ CSF & 1.20–2.01 & 7.591 & 6.817 & 0.956 \\ Cortex & 0.05–0.71 & 9.743 & 5.900 & 5.822 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Target error results of the robustness test.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 3 Results
### Neural Network Model  
Among the alternatives for modeling time series are neural network-based approaches, which have proven effective in capturing complex and nonlinear patterns in data. In this study, an NNAR (13,1,7)\({}_{12}\) model was used, a variant of the autoregressive neural network specifically designed for time series. Based on this configuration, the model uses the last 13 lagged values of the series as input variables, performs first-order differencing to stabilize the series, and includes a hidden layer with seven neurons. The model was trained using the error backpropagation algorithm, which is based on the gradient descent method to optimize the neural network weights and minimize the objective error function. The seasonal component [12] maintains an annual periodicity, suitable for monthly frequency series. The logistic sigmoid function was used as the activation function; this choice was due to its traditional application in simple neural network architectures, widely used in time series analysis.  
The interpretation of Figure 11 is the following: in the top panel, the residuals fluctuate around zero without any systematic or visible pattern, indicating randomness. The ACF plot (bottom left) shows no significant autocorrelations at the lags, suggesting the absence of serial correlation. In the histogram (bottom right), the residuals are approximately normally distributed. Based on this, the NNAR (13,1,7)\({}_{12}\) model adequately captures the structure of the series, and it can be concluded that the residuals exhibit behavior consistent with white noise.
# Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
## 2 Design of Adaptive Detectors with Secondary Data
### Adaptive Complex Parameter Rao Detector with Secondary Data
 \tag{12}\]The FIM \(\mathbf{J}(\Theta)\) is calculated according to the JPDF \(f(\mathbf{z},\mathbf{z}^{\prime}|H_{1}\,)\) (2) and the theory of complex-valued matrix differentiation [40]. After some calculation, we obtain:  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta^{*}\partial\beta^{T}}=-\frac{1}{ \sigma^{2}}\mathbf{r}_{t}^{H}\mathbf{r}_{t} \tag{13}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta\partial\beta^{H}}=-\frac{1}{\sigma^ {2}}\mathbf{r}_{t}^{H}\mathbf{r}_{t} \tag{14}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta\partial\beta^{T}}=0 \tag{15}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta^{*}\partial\beta^{H}}=0 \tag{16}\]  
where \(f_{1}=f(\mathbf{z},\mathbf{z}^{\prime}|H_{1}\,)\). Since the equation \(E\left[\frac{\partial\ln f}{\partial A}\frac{\partial\ln f}{\partial\beta} \right]=-E\left[\frac{\partial^{2}\ln f}{\partial A\partial\beta}\right]=-E \left[\frac{\partial}{\partial B}\left(\frac{\partial\ln f}{\partial A} \right)\right]\) holds, we can obtain the first block \(J_{\Theta,\Theta_{r}}\):  
\[ \begin{array}{l}\mathbf{J}_{\Theta,\Theta_{r}}=E\left[\frac{\partial\ln f(\mathbf{z },\mathbf{z}^{\prime}|H_{1})}{\partial\Theta^{2}}\frac{\partial\ln f(\mathbf{z},\mathbf{z} ^{\prime}|H_{1})}{\partial\Theta^{2}}\right]\\ =E\left[\begin{array}{cc}\frac{\partial\ln f_{1}}{\partial\beta^{*}}\frac{ \partial\ln f_{1}}{\partial\beta^{*}}\frac{\partial\ln f_{1}}{\partial\beta^{ *}}\frac{\partial\ln f_{1}}{\partial\beta^{*}}\frac{\partial\ln f_{1}}{ \partial\beta^{*}}\end{array}\right]\\ =E\left[\begin{array}{cc}\frac{\partial\ln f_{1}}{\partial\beta^{*}}\frac{ \partial\ln f_{1}}{\partial\beta^{*}}\frac{\partial\ln f_{1}}{\partial\beta^{ *}}\frac{\partial\ln f_{1}}{\partial\beta^{*}}\end{array}\right]\\ =\left[\begin{array}{cc}\frac{1}{\sigma^{2}}\mathbf{r}_{t}^{H}\mathbf{r}_{t}&0\\ 0&\frac{1}{\sigma^{2}}\mathbf{r}_{t}^{H}\mathbf{r}_{t}\end{array}\right]\end{array}  \tag{17}\]  
In a similar way, we can obtain  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta^{*}\partial\alpha^{T}}=-\frac{1}{ \sigma^{2}}\mathbf{r}_{t}^{H}\mathbf{r}_{d} \tag{18}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta^{*}\partial\sigma^{2}}=-\left( \frac{1}{\sigma^{2}}\right)^{2}\left(\mathbf{r}_{t}^{H}\mathbf{z}-\alpha\mathbf{r}_{t}^{H} \mathbf{r}_{d}-\beta\mathbf{r}_{t}^{H}\mathbf{r}_{t}\right) \tag{19}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta^{*}\partial\alpha^{H}}=0 \tag{20}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta\partial\alpha^{H}}=-\frac{1}{ \sigma^{2}}\mathbf{r}_{d}^{H}\mathbf{r}_{t} \tag{21}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta\partial\alpha^{T}}=-\left(\frac{1} {\sigma^{2}}\right)^{2}\left(\mathbf{z}^{H}\mathbf{r}_{t}-\alpha^{H}\mathbf{r}_{d}^{H}\mathbf{ r}_{t}-\beta^{H}\mathbf{r}_{t}^{H}\mathbf{r}_{t}\right) \tag{22}\]  
\[\frac{\partial^{2}\ln f_{1}}{\partial\beta\partial\alpha^{T}}=0 \tag{23}\]  
Then, the second block \(\mathbf{J}_{\Theta,\Theta_{s}}\) can be calculated as follows:  
\[