esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 3 Theoretical Framework and Novel Methods
### Bluetooth Communication Model  
The Bluetooth Classic protocol utilized in this framework operates in the 2.4 GHz ISM band with Frequency-Hopping Spread Spectrum (FHSS) modulation, providing a theoretical maximum throughput of 2-3 Mbps. However, practical limitations including protocol overhead, acknowledgment packets, and error correction reduce the effective bandwidth to approximately 700-800 kbps for sustained bidirectional communication [5; 13]. Recent developments in wireless technologies for flexible and wearable sensing by [PERSON] et al. [9] provide additional insights into optimizing wireless communication for resource-constrained applications.  
The communication model must accommodate both low-latency control commands and bandwidth-intensive audio data within this constrained channel. The bidirectional data flow can be represented as  
\[R_{total}=R_{c}+R_{a}\leq C \tag{4}\]  
where \(R_{c}\) is the data rate for control commands (rover to controller and controller to rover), \(R_{a}\) is the data rate for audio streaming (rover to controller), and \(C\) is the effective channel capacity.  
Given that control commands are represented as single ASCII characters sent at a maximum frequency of 100 Hz, \(R_{c}\) is negligible at approximately 1 byte \(\times\) 100 Hz = 800 bps. The audio data rate \(R_{a}\), after compression to 8 bits per sample, is approximately 128 kbps (16,000 samples per second \(\times\) 8 bits), falling well within the channel capacity while leaving sufficient headroom for variable channel conditions. This approach is consistent with findings by [PERSON] et al. [10] regarding networking architectures and protocols for IoT applications in smart cities.  
The probability of successful packet delivery \(P_{s}\) over a noisy channel can be modeled as  
\[P_{s}=(1-p)^{L} \tag{5}\]  
where \(p\) is the probability of bit error and \(L\) is the length of the packet (in bits). At 64 bytes audio packet size of the data (512 bits), for the typical bit error rates (from \(10^{-3}\) to \(10^{-5}\)) of Bluetooth and for different wireless channel conditions, it is found that the packet delivery probability is between 99.95% and 59.9%. They thus need to provide efficient support for buffering to handle a possible loss of packets without causing a disturbance in the audio, which should be uninterrupted from a user's perspective. Analogous problems and solutions were investigated by [PERSON] et al. [14] in the creation of a wireless Bluetooth-low-energy and lightweight neural recording system.
# Adaptive Attentional Network for Few-Shot Relational Learning of Knowledge Graphs
## 1 Introduction
This paper believes that the weights of entities for different task scenarios should also be different, and at the same time, the impact of the same attribute relationship triplet on the weight of the source entity should also be greater than that of entities with different attribute relationship three groups. In order to solve the above problems, this paper proposes an adaptive attentional network for few-shot relational learning of knowledge graphs (AAF), whose purpose is to learn a matching function that accurately infers real entities. Specifically, firstly, the Transformer mechanism [13] and the hierarchical attention mechanism are mixed to obtain the high-level domain aggregation information of entities. This module can capture the feature effects of adjacent entities in different task scenarios and different levels on the source entity. Next, to improve the source entity information, the neighbor encoder synthesizes the deep and surface features of the source entity through the domain structure. Then, a noise detection module is used to filter out unreasonable candidate entities. Finally, the matching processor first builds the negative triples, and the trained parameters are then optimized through a loss function to further infer the true entity pairs for the new relationship. Our contributions are as follows:  
1. We propose an adaptive attention mechanism to process different task scenarios by researching the features of triples in different task scenarios and at different levels in few-shot scenarios. Moreover, in order to obtain more entity feature information, we enhance the multi-level aggregation mechanism of entity surface features and deep features in the knowledge graph. Therefore, we can improve the accuracy of inference through more accurate entity feature information. 2. We design an adaptive attentional network for the few-shot relational learning model of knowledge graphs (AAF), using Transformer and hierarchical attention to learn the latent features of entities in different scenarios. The neighbor encoder updates the source entity feature information to adapt it to the reasoning process of different task scenarios. Noise check to filter unreasonable entities in the candidate set. Finally, the matching processor infers the ground truth by the candidate entity similarity scores. 3. We conduct extensive experiments to evaluate our model on the NELL-One dataset. The experimental results show that our model outperforms the baseline model in both one-shot and few-shot task scenarios, proving the effectiveness of the entity-enhanced encoder and noise checker.
# Multi-Feature Extension via Semi-Autoencoder for Personalized Recommendation
## 2 Related Work
### Deep Learning-Based Recommendation Methods  
At present, the existing methods based on deep learning have become effective resources for learning feature representation and provide various methods for improving RS performance [22]. Deep learning can disentangle the underlying explanatory factors behind the data and has made remarkable progress in learning effective feature representations for recommendation [23]. For example, [PERSON] et al. offered two models to solve the incomplete and complete cold-start problem for new items, which combine CF methods to incorporate content features into cold-start item rating predictions [24].  
With the rapid development of deep learning, various approaches, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and AE, have been extensively utilized in the recommended system [25]. For example, [PERSON] et al. proposed an integrated CNN-RNN framework to model and analyze patient-physician-generated data [26], but the model still relies on considerable labeled data and the training time is prolonged. In addition, [PERSON] and [PERSON] conducted a comprehensive and systematic investigation of RS based on deep learning, which revealed that AE is the most widely used deep learning framework until 2020 [27]. AEs are deep learning architectures for unsupervised learning that can efficiently recover data from a reduced encoded representation to the original input representation [28; 29]. Due to the fast convergence rate and the no label requirement, [PERSON] et al. proposed an Autoencoder-based Recommendation system (AutoRec) [30]. It encodes the user or item representation and leverages the generalization ability of the autoencoder to make recommendations. Along this line, several variants of autoencoder, such as denoising autoencoder [31], variational autoencoder [32], and stacked autoencoder [33] were proposed by later researchers and applied to recommendation systems.  
Along this line, [PERSON] et al. proposed a Hybrid Collaborative Recommendation model based on Semi-Autoencoder (HCRSA) [8], which can generate personalized recommendations with auxiliary information and learned nonlinear features of users and items; the details are shown in Section 3.1. Moreover, [PERSON] et al. proposed a Personalized Recommendation method via KG (PRKG), which obtains single side information through KG [34]. These works are close to ours, but with the following two main differences: (i) Considering less auxiliary information available, we utilize the KG to obtain more side feature information as additional attributes for the recommendation; (ii) since the semi-autoencoder models ignore the hidden relationships between mining features, we introduce the LSI model for hidden relationships mining among multiple features, which can be used as a favorable basis for the recommendation. We also transform the feature information into a low-dimensional feature vector, which helps to reduce the sparsity of the input model data.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 6 Conclusions  
MALDI-TOF MS has rapidly become a transformative technology in the field of food microbiology, offering unparalleled speed, accuracy, and efficiency in the identification of microorganisms associated with foodborne disease, spoilage, and fermentation. Its capacity to generate species-specific proteomic fingerprints with minimal sample preparation and at a low operational cost has led to its widespread adoption in both industrial and regulatory laboratories.  
This review has highlighted the multifaceted applications of MALDI-TOF MS in food microbiology, including the detection of foodborne pathogens, identification of spoilage organisms, and its emerging role in food authentication and traceability. These applications have significant implications for food safety monitoring, outbreak response, shelf-life prediction, and the verification of food origin and labeling claims. When integrated into food-quality assurance programs, MALDI-TOF MS strengthens microbial surveillance systems and supports a proactive approach to food safety.  
Nonetheless, several limitations remain, including dependence on curated databases, limited strain-level discrimination, and challenges related to complex sample matrices and mixed cultures. Moreover, the current necessity for cultured isolates precludes direct-from-sample applications in many cases, limiting its utility in scenarios requiring immediate or culture-independent diagnostics. Continued technological and methodological innovations--including database expansion, enhanced spectral resolution, improvedsample-preparation protocols, and hybrid diagnostic workflows--are expected to address many of these challenges.  
Looking forward, the integration of MALDI-TOF MS into digitalized food safety ecosystems, its potential for direct food matrix analysis, and its application in microbial metabolomics and viral detection represent exciting frontiers. Regulatory standardization and the development of globally harmonized protocols will be essential for the broader adoption and legal acceptance of MALDI-TOF MS in official food control programs.  
In conclusion, while not without its challenges, MALDI-TOF MS stands at the forefront of modern food microbiology. As the food industry continues to prioritize rapid, reliable, and cost-effective microbial diagnostics, MALDI-TOF MS will remain a cornerstone technology, advancing food safety, quality, and authenticity, in an increasingly complex global food system.  
Conceptualization, M.I.K. and M.K.; methodology, M.I.K.; software, M.I.K.; validation M.I.K., M.K. and B.W.-R.; formal analysis, M.I.K., M.K. and B.W.-R.; investigation, M.I.K.; resources, M.I.K.; data curation, M.K.; writing--original draft preparation, M.I.K.; writing--review and editing, M.I.K., M.K. and B.W.-R.; visualization, M.I.K.; supervision, M.I.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.
## 5 Discussion
### Effects of Ensemble  
The Ensemble method achieves a better accuracy of 98.5% than Fixed Patch Attention with 94.2% and Fixed Tile Attention with 95.1. This improvement hints that the Ensemble method is more suitable for precise liver and tumor region segmentation. The Ensemble method is able to utilize multi-scale feature extraction and attention mechanisms, extracting information from diverse perspectives and finer feature distinctions. Further, the Ensemble method demonstrates superior performance in Dice scores of 97.03. Fixed Tile Attention scores a Dice of 94.8%, and Fixed Patch Attention achieves 94.1%. This higher Dice score signifies that the Ensemble method produces segmentations that closely align with actual anatomical boundaries, herein reflecting greater accuracy in delineating the liver regions. The liver results for Fixed vs. Ensemble methods are shown in Table 5.  
The Fixed Patch Attention and Fixed Tile Attention methods are greatly outperformed by the Ensemble method in tumor segmentation. It achieves a higher accuracy of 97.2% and a Dice score of 75.4% compared to 94.3% and 70.3% for Fixed Patch Attention and 95.7% and 71.1% for Fixed Tile Attention. This enhancement is due to the Ensemble method's capacity to amalgamate multi-scale feature extraction and dual-attention mechanisms, allowing it to discern intricate tumor characteristics and extensive spatial context. The liver tumor results for Fixed vs. Ensemble methods are presented in Table 6.  
The Ensemble method's ability to fuse Patch and Tile Attention mechanism outputs at multiple scales generates fine-grained local features and context patterns that aid in improving segmentation quality. The method shows a considerably better performance due to the integration of multi-scale features and the effective employment of dual-attention mechanisms that make the best of fixed-size methods. These fixed approaches may miss subtle details or fail to adapt to the varying complexities of medical images.
## 1 Introduction
### Infrared Thermography (IRT)  
Structural defects in concrete bridge decks are broadly categorized into surface and sub-surface defects. Inspection of surface defects such as cracks can be detected by visual inspection or cameras. On the other hand, delamination is a sub-surface anomaly not visible within the visible wavelength spectrum. IRT applications have grown rapidly in recent years and have been used in studies such as those investigating historic structures [29] and medical studies [30]. IRT has been significantly explored in assessing and detecting delamination in bridge decks [6; 11; 14].  
The theory behind IRT technology is based on the thermal emission and absorption between two different material mediums with different thermal properties. The material's temperature and emissivity control the amount of radiation being emitted. This relationship was developed and shown by the Stefan-Boltzmann Equation in Equation (1) [31].  
\[\text{E}=\varepsilon\;\sigma\;\text{T}\;^{4} \tag{1}\]  
where E = Total surface radiation (W.m\({}^{-2}\)), \(\varepsilon\) = Emissivity, \(\sigma\) = Stefan-Boltzmann constant, and T = Temperature of material (in Kelvin). Material mediums with higher emission capacity are more suitable for IRT tests. Emissivity ranges from 0 to 1 and changes due to the variability of surface texture, temperature, and emission characteristics [29]. Therefore, it is common for defective regions to have different emissivity from sound regions. IRT applications are either conducted by active or passive thermography. Active means require introducing an artificial heating or cooling source to stimulate temperature differences before testing, while passive thermography does not require a heat source. Field tests are usually conducted during the available heating source of the sun. This is usually the case for large structures such as bridge decks. Other NDE methods such as microwave thermography, pulsed-eddy-current-simulated thermography, flash thermography, vibro-thermography, sonic thermography, and laser thermography are passive-based and have been highlighted in [PERSON] et al. [14], where an external source of heat is introduced. Delamination, an internal defect, causes a thermal gradient (\(\Delta\)T), resulting in surface temperature difference due to different heat conduction rates between the sound and defective portions. A 0.2-0.5 \({}^{\circ}\)C thermal contrast is suitable for possible detection [11; 32; 33].  
Past studies have elaborated and established several factors and conditions that adversely affect the reliable outcome of delamination detection using IRT. Some of these are sizes and depth of delamination, materials preset within the delamination, concrete's thermal properties, the presence of overlays, data collection time, height of data collection, speed of UAS data collection platform, surface conditions, solar loading, ambient wind speed, ambient temperature, and sensor resolution [11; 14; 22; 34; 35].
## 3 Results and Discussion
### Removal of Lead from Aqueous Solution  
#### 3.4.1 Effect of Contact Time  
Figure 8 shows the effect of contact time on the adsorption capacity (q\({}_{\rm t}\)) of studied clays to Pb (II). In the case of palygorskite-rich clays (viz., C1, C3 and C4), q\({}_{\rm t}\) increases continuously with increasing contact time until equilibrium reached at 5 h and then remained constant. These results indicate that the equilibrium is reached very slowly, which is comparable with the results reported by [PERSON] et al. [37] for pure palygorskite, which was found to be 30 min for concentrations of Pb (II) from 20-100 mg/L. A similar trend was observed with montmorillonite-rich clay, where the equilibrium time (150 min) was longer than that (100 min for Pb concentration = 150 mg/L) observed by [PERSON] et al. [38]. The difference may be due to the not well exposed adsorption sites on palygorskite because of existence of other minerals on the surface of this type of clays (see SEM, Figure 6b). It may also be referred to the involvement of palygorskite in competitive adsorption with other minerals. However, the adsorption of Pb onto the calcite-rich clay (C5) reaches the equilibrium faster (namely at 31 min) than either the palygorskite- or montmorillonite-rich clays. This ensures that the adsorption nature of Pb (II) onto C5 clay is clearly different than that observed on other clays, which is due to the existence of high amounts of calcite in this clay [39].  
Figure 8: Effect of contact time on Pb (II) adsorption onto clays in the study; agitation speed (130 rpm), temperature (25 \({}^{\circ}\)C), pH 6 and initial adsorbate concentration (300 mg/L).
## 2 Materials and Methods
### Method of Calculation  
All DFT calculations were performed using a Gaussian 09 molecular package [27] using the B3 LYP [28] and M06-2X [29] methods in combination with the 6-311++G(d,p) basis set [30; 31]. The initial geometry generated from standard geometrical parameters was  
Figure 1: Structures of the investigated pyridones **1–9**.  
minimized without any constraint in potential energy surface and with default convergence criteria. For the optimization of the radical species, an unrestricted approach was applied. The optimized structures were confirmed to be real minima by frequency calculation (no imaginary frequency).  
The solvent effects of water were taken into account with a conductor-like polarizable continuum model (CPCM) [32] through a reoptimization of the gas-phase geometries of all investigated species. The absorption spectra of pyridones were simulated using the TD-DFT method with water as a solvent.  
To understand the role played by the differences between the structural and electronic features of these molecules, the following properties were calculated: (1) the energy of the highest occupied molecular orbital (HOMO); (2) the energy of the lowest unoccupied molecular orbital (LUMO); (3) vertical (VIE) and adiabatic (AIE) ionization energies.  
For an electrochemical oxidation reaction where the pyridone is in the protonated or deprotonated molecular form P with net charge n, the reaction is presented as follows:  
\[\text{P}^{\text{n}}-\text{e}^{-}\rightarrow\text{R}^{\text{n+1}}+\Delta\text{ E}_{\text{ox}}\]  
where \(\Delta\text{E}_{\text{ox}}\) can be represented as vertical (VIE) or adiabatic (AIE) ionization energies.  
The electrooxidation reaction is mimicked using these two approaches, starting from a fully optimized pyridone molecule/ion (initial state) [33]:  
1. Vertical transition of pyridone molecular form, where the final state preserves the geometry of the initial state: \[\text{VIE}=E_{0}\left(\text{R}^{\text{n+1}}\right)-E_{0}\left(\text{P}^{ \text{n}}\right)\] (1) where VIE represents vertical ionization energy, \(E_{0}\left(\text{R}^{\text{n+1}}\right)\) is the energy of the pyridone radical (oxidated state) with preserved geometry of the optimized pyridone form in its initial state, and \(E_{0}\left(\text{P}^{\text{n}}\right)\) is the energy of the optimized pyridone form in its initial state.
2. Adiabatic transition of the pyridone molecular form where the final radical state is energetically relaxed to its minimum: \[\text{AIE}=E_{0}\left(\text{R}_{\text{opt}}^{\text{n+1}}\right)-E_{0}\left( \text{P}^{\text{n}}\right)\] (2) where AIE is adiabatic ionization energy, \(E_{0}\left(\text{R}_{\text{opt}}^{\text{n+1}}\right)\) is the energy of the optimized pyridone radical (oxidated state), and \(E_{0}\left(\text{P}^{\text{n}}\right)\) is the energy of the optimized pyridone form in initial state.
## 2 Materials and Methods
### MIP Image Assessment  
Figures 6 and 7 present the MIP images generated by applying the slice-wise masks shown in Figures 2-4 to the 3D volume. After the masks are applied, a MIP is performed on the resulting 3D dataset, providing a final MIP that excludes non-intracranial arteries. When the masks (Figures 2-4) accurately preserve only the intracranial arteries, the MIP images (Figures 6 and 7) clearly show reduced interference from external carotid branches and highlight the internal carotid and vertebral systems. In order to evaluate images produced by the segmentation step, a dedicated software application was developed (Figure 6). This software can load a folder containing DICOM files for axial images acquired by MRA, and upon pressing the \"start processing\" button, it automatically converts each DICOM file into a JPEG image, applies the segmentation model, and incorporates the resulting mask back into the DICOM data. It then saves a three-dimensional matrix reflecting the applied segmentation, effectively providing an automated workflow from raw images to a fully segmented 3D volume. As a preview, the software displays MIP  
images viewed in axial, sagittal, and coronal orientations side by side, enabling a direct visual comparison of MIP images without segmentation versus those with segmentation applied. This interface helps verify how effectively undesired regions have been removed.  
Using this software environment, MIP images were generated for all 20 subjects under two conditions: one without segmentation and one with the segmentation masks. The degree of removal of key external carotid branches, including the superficial temporal, middle meningeal, and occipital arteries, was assessed qualitatively. Specifically, complete  
Figure 6: Overview of the developed software for automated segmentation and MIP image generation.  
Figure 7: Comparison of MIP images from different views. (**a**) Sagittal MIP image, (**b**) coronal MIP image, and (**c**) axial MIP image, and corresponding segmented MIP images (**a’-c’**).  
absence of removal was assigned a score of 0, removal of approximately 50% of the original depiction was scored as 1, removal of about 75% was scored as 2, removal of about 90% was scored as 3, and complete removal was scored as 4. The scores were determined by consensus among four experienced radiological technologists. Each test case was evaluated according to this scoring system, and for each fold in the five-fold cross-validation, the mean removal score and its 95% confidence interval were calculated.