esa-sceva/satcom-chunk-collection huggingface dataset:
# Pentaband Dual-Polarized Antenna for Multiservice Wireless Applications
## 1 Introduction
The proposed design offers a competitive performance compared to the other designs in the table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & \multicolumn{2}{c}{**Antenna Dimensions**} & \multirow{2}{*}{\
\begin{tabular}{c} **Operating** \\ **Bands** \\ **(GHz)** \\ \end{tabular} } & \multirow{2}{*}{**No. of Ports**} & \multirow{2}{*}{
\begin{tabular}{c} **Polarization** \\ **(LP/CP)** \\ \end{tabular} } & \multirow{2}{*}{**Isolation**} \\  & \(\mathbf{w\times 1\times h}\) & & & & \\  & **(mm\({}^{3}\))** & & & & \\ \hline
[19] & \(50\times 30\times 1.59\) & Dual & 2 & LP & 24,27 \\ \hline
[20] & \(38\times 37\times 1.6\) & Triple & 2 & LP & 20 \\ \hline
[21] & \(64\times 35\times 1.6\) & Multi & 2 & LP & 32.79 \\ \hline
[22] & \(55\times 45\times 1.57\) & Quad & 2 & LP & 23.5 \\ \hline
[23] & \(29.5\times 27\times 0.8\) & Penta & 2 & LP & 16 \\ \hline
[24] & \(-\times-\times 1.6\) & Triple & 4 & LP & 16 \\ \hline
[25] & \(150\times 75\times 1.6\) & Triple & 4 & LP & 17 \\ \hline
[26] & \(46\times 46\times 1.6\) & Triple & 4 & LP & 18 \\ \hline
[27] & \(104\times 104\times 0.5\) & Triple & 8 & LP & 16 \\ \hline
[28] & \(80\times 70\times 1.6\) & Quad & 4 & LP & 22 \\ \hline
[29] & \(117\times 65\times 0.762\) & Penta & 6 & LP & 14.5 \\ \hline
[33] & \(165\times 165\times 1.6\) & Triple & 4 & CP & 41 \\ \hline
[34] & \(60\times 60\times 1.6\) & Triple & 4 & Dual & 15 \\ \hline
[35] & \(50\times 70\times 0.762\) & Penta & 4 & Dual & 17 \\ \hline Prop. work & \(62\times 62\times 1.6\) & Penta & 4 & Dual & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of pentaband MIMO antenna with the antennas in the literature.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 4 Experimental Design
### Hybrid Algorithm Strategy Workflow  
The OpenStreetMap (OSM) data is modeled as a directed graph \(G=(V,E)\), where nodes \(V\) represent intersections or traffic signal-controlled junctions, and edges \(E\) denote road segments between these nodes. Each edge is annotated with multi-dimensional attributes including  
* **Static features:** path length, road classification (e.g., highway, residential), bidirectional accessibility.
* **Dynamic features:** real-time congestion levels, traversal cost penalties (e.g., 60 s delay for traffic light waiting).
* **Operational constraints:** predefined avoidance zones or prohibited areas.  
Nodes \(V\) are enriched with contextual attributes such as traffic signal presence, restricted zone status, and waypoint markers. During the global planning phase, criticalspatial constraints (e.g., avoidance points, restricted zones) are integrated into the A* algorithm's heuristic function to guide path exploration. The A* algorithm generates an initial optimal path by minimizing a composite cost function that balances distance, traffic penalties, and constraint violations.  
For dynamic adaptation, temporary obstacles (e.g., simulated traffic incidents) are injected into the navigation process. The road network graph is continuously updated with real-time edge attributes (e.g., congestion levels, temporary closures) and node states (e.g., signal timing changes). The Deep Q-Network (DQN) agent then performs local path refinement through reinforcement learning, leveraging its state space representation of adjacent nodes, edge weights, and obstacle distributions to optimize detour decisions while maintaining alignment with the global A*-derived trajectory.To validate the effectiveness of different strategies, we design three comparative experiments based on varying levels of global and local integration, as summarized in Table 3.  
This hybrid architecture enables synergistic coordination between deterministic global optimization and data-driven local adaptation within the structured graph representation. The specific experimental procedure is as follows:  
1. Initialize the map, including the start point, end point, and obstacle distribution.
2. Plan a global path using the A* algorithm.
3. Introduce dynamic obstacle regions during the simulation process.
4. As the agent traverses the path, it performs local optimization, obstacle avoidance, and rerouting through the DQN module, as illustrated in Figure 5.
5. Repeat the experiment 100 times and record the experimental data.
6. Conduct statistical analysis to compare the performance of each algorithm in terms of path length, planning time, and risk coefficient.
## 1 Introduction
It should be noted that since the recovery strategy of the lateral slow scanning (C-scan) data is the same as that of the B-scan data, only 2D reconstruction was studied in this work, but the method is still applicable to 3D reconstruction.
# Dialogue Enhanced Extended Reality:
## 7 Conclusions and Future Work  
This paper describes an architecture to develop natural and hands-free human-machine interaction systems for industrial environments, which can combine more classical Human Augmentation technologies (such as Virtual, Augmented, and Mixed Reality) with dialogue based interaction for process solving tasks. The proposed solution facilitates the capture, distribution, and communication of domain specific knowledge to operators in training and production phases. Two systems constructed using the same architecture are presented and initially evaluated in terms of usability and acceptance, one for a maintenance task and the other for assembly scenarios. The implemented systems were hands-free, multi-modal (including visual and speech interaction) and supported multiple technologies. They were well accepted by a group of non-expert operators unfamiliar to the task and a group of operators with cognitive disabilities. The results obtained can serve as a reference starting point in further research for new advances in the industrial sector.  
Given the number of participants in the groups selected for these initial validations, one of the main goals of future research is to further validate the proposed Interactive XR architecture with a wider sample of participants to ensure the significance of the evaluation. Testing additional systems in new industrial scenarios that require the combination of additional XR interfaces, like Kinesthetic-Bionic devices, Internet of Things, machine-sensors, and multi-operator scenario settings will be addressed to assert the flexibility of the proposed architecture. Future work also includes finding ways to easily adapt the language-specific modules (STT and NLU in the Interpretation Layer, and RTG and TTS in the Response Generation Layer) to multiple languages to overcome possible linguistic barriers for multinational companies.  
Conceptualisation, M.S., L.G.-S., and B.S.; data curation, L.G.-S.; funding acquisition, M.S., H.A., and J.A.; methodology, M.S., L.G.-S., and B.S.; project administration, M.S. and J.A.; resources, B.S., H.A., and J.A.; software, M.S., L.G.-S., B.S., and J.A.; writing--original draft, M.S., L.G.-S., B.S., and H.A.; writing--review and editing, B.S., L.G.-S., M.S., H.A., and J.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project has received funding from the European Union's Horizon 2020 research and innovation programme under the grant agreement with ID 723711 (MANUWORK) and by the Department of Economic Development and Infrastructure of the Basque Government under grant KK-2017/00080 (INICIAR).  
**Acknowledgments:** We would like to especially thank all the volunteers who kindly participated in the experimental sessions for their time and valuable feedback, and [PERSON] for comments that improved the manuscript. Also, we would like to thank [PERSON] for his advice on how to approach the manuscript and comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Experimental Method and Results
### Methods and Materials  
Several experiments were carried out to study the 1D linear location method. In the beginning, AE events were generated by the INTERUNIS-IT electronic simulator equipped with a UT-1000 broadband transmitting transducer. The experiments were performed on a steel plate with dimensions of 1000 \(\times\) 650 \(\times\) 7 mm presented in Figure 1.  
Registration of AE events and detection of their source location was performed using two receiving transducers \"VS150-RIC\" with a preamplifier connected to the system \"Vallen AMSY-6\". Those receiving transducers (AE sensors #1 and #2) were located on the surface of the steel plate with an 800 mm distance between them (\(B=800\) mm). The transmitting transducer was installed on the surface as well, but its coordinates were changed for each experiment \(X=100\), \(200\), \(\ldots 700\) mm in respect to the AE sensor #1.  
Before the experiments began, the parameters of measurement devices and the average wave velocity of AE signals \(V_{g}\) have been established. The standard methodology of AE monitoring is described in PB (Safety Rules) 03-593-03 <<The guidelines for acoustic  
Figure 1: Scheme of experimental determination of AE source location on the steel plate.  
emission monitoring of vessels, apparatus, boilers and pipelines(r) and in ASTM E 976-99. The thresholding level \(u_{th}\) in the current research has been calculated by the equation:  
\[u_{th}\geq u_{n}+6, \tag{1}\]  
where \(u_{n}\)--maximum noise amplitude, dB.  
Thus, the thresholding level equaled to \(u_{th}\) = 40 dB. The frequency bandwidth constituted \(\Delta f\) = 50-850 kHz. The wave velocity \(V_{g}\) was evaluated based on the pencil-lead-break experiment with acoustic signal generation. The pencil lead was broken outside the area between AE sensors #1 and #2 at a distance of 100 mm from AE sensor #1. The AE wave velocity in the steel plate was \(V_{g}\) = 4250 m/s with the distance between acoustic sensors \(B\) = 800 mm.  
The amplitude of generated AE signals varied from 55 to 100 dB due to the potential difference of the AE simulator in the voltage range of 10-300 V. The process of acoustic source imitation consisted of alternately generating 250 AE signals in each of the coordinates \(X\) = 100, 200,... 700 mm. The calculation of the positioning error (\(\gamma\)) of the AE transmitting transducer was carried out by the equation [18]:  
\[\gamma=\frac{|X_{real}-X|}{B}\cdot 100\%, \tag{2}\]  
\(X_{real}\)--installation coordinate of the AE simulator, \(X\)--experimentally calculated coordinate of the AE simulator, \(B\)--the distance between AE sensors #1 and #2.
## 3 Materials and Methods
### Experimentation  
The methodology is divided into a sequence of tasks, which begin with the data collection, followed by splitting the data for building the corpus and for validating. The validation data go through a previous classification using PoS patterns before entering the classification algorithm. For clearer interpretation, the results of the tagged data are presented on a map of the study area according to the related coordinates of the tweet. Finally, quality metrics are shown to validate the approach. The methodology is graphically illustrated in Figure 2.  
Figure 2: Outline of the proposed methodology.  
Data Collection  
In this phase, the model connects to the Twitter Streaming API using the Tweepy library to capture geo-referenced tweets across South America. The Streaming API has certain limitations, but it allows the download of 100% of tweets that meet the defined filter, as long as they are less than 1% of the global volume of publications at a given time [33]. The application was run on a server for a period of 10 months (from April 2019 to February 2020--up to the beginning of the pandemic) and data was stored in a MySQL database.  
A corpus related to land use categorization was not available in Spanish, so it was necessary to build one. The collected data was divided into two sets: the first set consists of 3870 tweets located in the historical center of Arequipa, that will then be used to identify the type of land use. The second set is used for the creation of the corpus and consists of 42,318 tweets located anywhere in South America.  
These tweets are previously processed to remove the duplicates, blanks, single word, and only numbers. As a result, we obtained a total of 24,995 messages that were semi-automatically categorized into a land use type according to their content and geographical coordinates, leaving a total of 4538 useful tweets in different languages, which were randomly split into training and test. The result was distributed into the defined categories and divided into subcategories to avoid data imbalance (see Table 2).  
Semi-automatic categorization consisted of two phases: automatic categorization using the Freeling library (successful in about 73%), and manual categorization of the remaining tweets, examining the presence of keywords (restaurant, school, park, house, etc.), along with the occurrence of specific sequence of tags (e.g., verb-preposition-noun).
Application specialists often consider hierarchical algorithms as more versatile than their partitional counterparts (for example, k-means or Gaussian mixture models) since the latter tend to work well only on data sets having isotropic clusters [4]. So in [5], we segmented astronomical images of different observing filters, that had first been matched such that they related to exactly the same fields of view and pixel resolution. For the segmentation we used a Markov random field and Gaussian mixture model; followed by a within-segment GMM clustering on the marginals. Further within-cluster marginal clustering could be continued if desired. For model fit, we used approximations of the Bayes factor: the pseudo-likelihood information criterion to start with, and for the marginal GMM work, a Bayesian information criterion. This hierarchical- or tree-based approach is rigorous and we do not need to go beyond the Bayes factor model evaluation framework. The choice of segmentation methods used was due to the desire to use contiguity or adjacency information whenever possible, and when not possible to fall back on use of the marginal. This mixture of segmentation models is a first example of what we want to appraise in this work. What now if we cannot (or cannot conveniently) match the images beforehand? In that case, segments or clusters in one image will not necessarily correspond to corresponding pixels in another image. That is a second example of where we want to evaluate different families of models. A third example of what we want to cater for in this work is the use of wavelet transforms to substitute for spatial modeling (e.g., Markov random field modeling). In this work one point of departure is a Gaussian mixture model (GMM) with model selection using the Bayes information criterion (BIC) approximation to the Bayes factor. We extend this to a new hierarchical context. We use GMMs on resolution scales of a wavelet transform. The latter is used to provide resolution scale. Between resolution scales we do not seek a strict subset or embedding relationship over fitted Gaussians, but instead accept a lattice relation. We focus in particular on the global quality of fit of this wavelet-transform based Gaussian modeling. We show that a suitable criterion of goodness of fit for cross-model family evaluations is given by Renyi quadratic entropy.
## 2 Materials and Methods
### Precision Assessment  
The analysis of the accuracy is an important step for the evaluation of the resulting classification because the users of the information, once the classification is performed, need to know how accurate the result is in order to use the data in their decision making. A minimum level of overall precision for a selected macro-class or class of at least 85%, according to the recommendations found in similar works [49; 60; 62; 63], was proposed in this study. The selected ratio between a training set and validation set was 70/30. This means that 70% of the available information was used for training purposes, whereas 30% was used for validation. Acceptable precision measurements used in this work included producer precision (PA > 85%), user precision (UA > 85%), overall precision (OA > 85%) and the Kappa coefficient (\(K\)) [49; 64; 65]. The reference values of the Kappa coefficient proposed by [PERSON] and [PERSON] [66] are shown in Table 4.  
For precision evaluation, a set of verification points are required. The sample should be designed to achieve low standard errors in precision estimates, and this is generally achieved by random selection of points. The number of samples should be calculated by  
\[N=\left(\frac{\sum_{i=0}^{N}(W_{i}-S_{i})}{S_{o}}\right)^{2} \tag{9}\]  
where \(N\) is the sample size, \(W_{i}\) is the mapped area proportion of the class \(i\), \(S_{i}\) is the standard deviation of the stratum \(i\) and \(So\) is the expected standard deviation of overall accuracy, often valued at 0.01 [67; 68; 69].
# Natural Frequency Prediction Method for 6R Machining Industrial Robot
## 2 Materials and Methods  
### Sample Preparation  
The axes of 6R robot are noted as \(a_{1}\), \(a_{2}\),..., \(a_{6}\), corresponding joint angles are \(q_{1}\), \(q_{2}\),..., \(q_{6}\). The mapping relation between natural frequency (\(f_{\rm F}\)) and configuration can be expressed as  
\[f_{\rm F}=\mathrm{f}(q_{1},q_{2},\ldots,q_{6}), \tag{1}\]  
With samples, a regression equation can be fitted to represent the mapping relation. However, it is unnecessary and inefficient to include the entire workspace of the robot as the configurations near the boundary could not be adopted in machining process. Therefore, range of variables is limited before selecting samples through DoE method. Then natural frequency information of each sample configuration is acquired through modal test.
## 6 Methods
### Segmentation Model
#### 6.3.3 Loss Function  
During the training process, the loss function assists in minimizing the error for each training sample. Because liver vessels make up a minor percentage of the liver, imbalanced foreground (the liver vessels) and background (the liver) classes frequently induce prediction deviation, biasing the classification to the background with more voxels. Therefore, we have experimented with different loss functions. We have used the Weighted Dice Loss function [38] as a final selection. It uses the dice coefficient to place penalties for the number of misclassified voxels, to solve the high-class imbalance problem and to enhance segmentation accuracy; see Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Function** & **Equation** & **Definition** \\ \hline Dice [39] & \(\frac{2\sum_{i}^{N}p_{i}g_{i}}{\sum_{i}^{N}p_{i}^{2}+\sum_{i}^{N}g_{i}^{2}}\) & Calculates the overlap of \(g_{i}\) and \(p_{i}\). \\ \hline Dice-BCE [40] & \(1-\text{Dice }+\lambda_{b}L_{BCE}\) & Evaluates the class prediction of each pixel vector, and then averages all pixels. \(\lambda_{b}\) is 0.5. \\ \hline Log Cosh Dice [40] & \(\log(\cosh(p_{i}-g_{i}))+\lambda_{l}*Dice\) & For smoothing variations, can be used for skewed dataset. \(\lambda_{l}\) is 0.5. \\ \hline Focal [40] & \(-\alpha_{t}(1-L_{BCE})^{\gamma}\log(L_{BCE})\) & It up-weights the contributions of difficult examples. Here, \(\gamma\) is 0.5, \(\alpha_{t}\) is 0.5. \\ \hline Dice Focal [40] & \(Dice+\lambda_{f}*Focal\) & Variant of Dice loss, with focus on difficult examples. \(\lambda_{f}\) is 0.5. \\ \hline Tversky [40] & \(\frac{\sum_{i}^{N}p_{i}g_{i}}{\sum_{i}^{N}p_{i}g_{i}+\beta_{t}\sum_{i}^{N}(1-p_ {i})g_{i}+\alpha_{t}\sum_{i}^{N}p_{i}(1-g_{i})}\) & Adds weight to False positives and False negatives, where \(\alpha_{t}\) is 0.5 and \(\beta_{t}\) is (1-\(\alpha_{t}\)). \\ \hline Weighted Dice [38] & \(1-\frac{\sum_{i}^{N}p_{i}g_{i}}{\sum_{i}^{N}p_{i}g_{i}+\alpha_{i}\beta_{t}\sum_ {i}^{N}(1-p_{i}^{2})\sum_{i}^{N}(1-g_{i}^{2})}\) & Data with unbalanced classes, where \(\alpha_{l}\) is 0.5 and \(\beta_{l}\) is 6 [38]. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Loss Functions: where \(g_{i}\) is the ground truth; \(p_{i}\) is the prediction.
## 2 Experimental Section
### Characterization  
The SEM images of typical samples were obtained with a JEOL JSM-5500 Scanning Electron Microscope (JEOL, Kyoto, Japan). Samples were gold coated using an Instrumental Scientific Instrument PS-2 coating unit. The SEM operating voltage was at 15.0 kV.  
FTIR spectra of samples were performed at 16 scans and a 4 cm\({}^{-1}\) resolution in transmission mode with a PerkinElmer 2000 Fourier transform infrared spectrometer (Buckinghamshire, UK). The samples were mixed with potassium bromide (KBr) and the scan range was fixed in the region between 400 and 4000 cm\({}^{-1}\).  
Thermal stability analysis (TG/DTG) of samples was carried out in a Perkin-Elmer thermoanalyzer (Yokohama, Kyoto, Japan). Samples of pure TOCgel and composites were heated in open platinum pans from 50 to 600 \({}^{\circ}\)C, under a nitrogen atmosphere, at a heating rate of 5 \({}^{\circ}\)C/min. Then, samples were heated from 600 to 1000 \({}^{\circ}\)C under air at a heating rate of 10 \({}^{\circ}\)C/min.  
XPS measurements were performed with a Kratos Ultra electron spectrometer (AXIS ULTRA, Kratos, Manchester, UK, 2000) using a monochromatic (AlK\(\alpha\)) X-ray source (\(k=1486.6\) eV) with a power of 225 W, at a take-off angle of 90\({}^{\circ}\) relative to the sample surface. The low-resolution survey scans were taken with a 1 eV step and 160 eV analyzer pass energy. High-resolution spectra were taken with a 0.1 eV step and 40 eV analyzer pass energy. The analysis area was less than 1 mm\({}^{2}\) and measurements were taken at two different locations on each of the touching faces of pure TOCgel and TOCgel/ZnO composite film. The collected data were analyzed using Vision software version 2.1.3 and CASA XPS version 2.3.15 (Kratos, Manchester, UK, 2013).  
Tensile Tests of pure TOCgel and TOCgel/ZnO composite films were performed on a universal testing machine (INSTRON 4201, Instron, Canton, MA, USA) at room temperature with a gauge length of 10 mm and cross-head speed of 10 mm/min. The average value of five replicates for each sample was taken.  
Finally, the photocatalytic activity of the pure ZnO and TOCgel/ZnO composite films in the degradation of MB (10 mM) was performed in air at ambient temperature. The same amount of each sample (50 mg) was immersed in 50 mL of MB solution and then exposed to ultra-violet irradiation from a 15W Hg lamp (model UVL-56, UVP, Upland, CA, USA) for 2 h. After that, the absorbance of MB was measured using a UV-vis spectrometer (Cary 5000 spectrophotometer, Varian, Melbourne, Australia). The photocatalytic efficiency was evaluated in terms of the decrease of the absorption peak intensity of MB at 665 nm which indicates the degradation of the MB during the UV-irradiation.