esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Next Steps for Data and Webmap Access in the Maltese Islands  
CRISOLA will not make it simply by employing the Plan4 all methodology and operating around that structure's available specifications; it needs new data feeds to ensure that the dynamic social structures are backed by new and updated protocols and actual datasets. This is coming in the form of the next phase for web mapping and dissemination in Malta as currently being developed through an extensive Maltese ERDF project [57] which entails the integration of physical, social and environmental data within a comprehensive Shared Environmental Information System [58] structure as proposed by the EC. Having experienced instances of lack of data, a dearth of metadata on one hand and a relentless drive to take up new technologies, the Maltese scenario is ripe to integrate all the three CRISOLA issues (together with other thematic natural-social-physical environments) through the employment of hi-end technology located both _in situ_ and remotely. The resultant output will employ data-capture and scanning technologies that may yet make inroads in the social and planning operational fields. Through a planned complete terrestrial LIDAR scan combined with a bathymmetric acoustic scan up to one nautical mile from the baseline coast and the inclusion of bathymetric LIDAR ensuring consistency in data acquisition across the two surveys, the baseline data will be created for the eventual overlaying of the thematic data which will combine the three CRISOLA themes. Integrated with oblique imagery and in-situ data trawling, the end result should enable the production of a unique model of the islands for use across the scientific platforms.  
The envisaged free dissemination of the data through Aarhus-compliance to the general public and the scientific community should enable the launching of integrated research across the spatial and social themes. Urban and environmental planners can undertake urban modeling scenario-building for strategic, regional and local areas, with the add-on integration of social, economic and criminological issues leading to healthier and safer localities. In addition, the data will be integrated with impact-analysis of baseline studies for air, water, noise, soil and radiation themes and their impacts on high population/urban density Malta. It is envisaged that the project outputs will serve as a base for the creation of updated data for the crimemaps, which outputs will also be disseminated to other geoportals inclusive of the Plan4 all geoportal in a harmonized format, thus ensuring homogeneity across the datasets. Thus the base landuse-related data requirement will be supplied through the ERDF project and the social data will be delivered through the Census 2011, which would require georeferencing and structuring in order to allow integration with the Plan4 all requirements. The next step would require the integration of the crimemaps within the Plan4 all mapserver which calls for integration of the original (pre-geoclip) spatial datasets within the metadata profile to ensure that the data fits within the geoportal.
## 5 Case Studies: Validating the CILS Framework
### Case Study One: Busuu
#### 5.1.2 Application of the CILS Framework in Busuu  
Applying the CILS within Busuu represents a groundbreaking step in leveraging AI to enhance language learning through tailored, culturally aware instruction. By integrating the CILS framework, Busuu has significantly advanced its capabilities in delivering personalized language education that respects and incorporates the cultural nuances of each language it offers.  
Incorporating the CILS into Busuu involves using sophisticated AI algorithms to continuously analyze user interactions and performance data. This analysis enables the platform to offer a more nuanced and effective learning experience that adjusts to the learner's needs in real time. For example, if a learner struggles with specific grammatical structures or cultural concepts, the CILS can intervene by providing customized exercises and explanatory content that target these weaknesses.  
The CILS enhances Busuu's social learning features by facilitating more meaningful interactions between learners and native speakers. AI-driven insights from the CILS allow native speakers to provide feedback that is not only linguistically corrective but also culturally informative, thus enriching the learning process. This dual-focused feedback helps learners understand how language is used and why certain linguistic choices may be more appropriate in specific cultural contexts.  
The integration of the CILS into Busuu enables the platform to simulate complex language use scenarios that involve cultural subtleties, preparing learners for real-world interactions in a safe and controlled environment. These scenarios challenge learners to apply their language skills in culturally diverse situations, improving their ability to communicate effectively across cultural boundaries.  
By applying the CILS framework, Busuu has transformed from a simple language learning tool into a comprehensive educational ecosystem that prioritizes cultural intelligence as much as linguistic accuracy, offering users a holistic approach to language learning that is both innovative and highly effective [38].  
Figure 6 illustrates the interface of the Busuu platform, showcasing the CEFR (Common European Framework of Reference for Languages) levels from A1 to C1. This screenshot depicts the detailed learning paths for English language learners, highlighting the progressive structure of lessons designed to enhance various language skills such as listening, reading, speaking, and writing. The adaptive learning features, facilitated by AI, personalize the educational content to match each learner's proficiency level, thereby improving engagement and learning outcomes.
# Block Recovery Rate-Based Unequal Error Protection for Three-Screen TV
## 2 Background
### Reed-Solomon (RS) and Raptor FEC Codes  
Reed-Solomon (RS) based unequal loss protection algorithm is performed [5; 6; 7; 8; 9] in the FEC assigner. Here, we denote the layered unit data (block) in a temporal layer \(t\) of a quality layer \(q\) as \(B(t,q)\). When the number of FEC packets and video data are represented by \(F(t,q)\), the reconstruction mechanism of the RS code is composed of continuous \(B(t,q)\) video packets and \(F(t,q)\) parity packets. If the minimum number of successfully received packets is more than \(B(t,q)\), then the original source packets can be reconstructed. The FEC assignment scheme is proposed to maximize the block recovery rate (BRR). A similar local hill-climbing search method is utilized for assignment of FEC packets for each block with low complexity.  
The Raptor FEC, developed by [PERSON] in 2001, is also called a rateless code because the amount of encoded data is not fixed in advance [10]. It is used in the multimedia broadcast and multicast services (MBMS), digital video broadcasting-handheld (DVB-H), and reliable multicast transport (RMT) working group [11]. The encoding processes of the Raptor codes are as shown in Figure 4. The processes consist of two steps: (i) pre-coding, which encodes the input symbols using a traditional erasure correcting code; and (ii) Luby transform (LT) coding [12], which creates the needed number of encoding symbols. The LT code makes the Raptor codes have low computational complexity.  
In the \((n,k)\) systematic Raptor codes, \(k\) source symbols are pre-coded to \(k\) intermediate symbols, and then pre-coded to \(k+s\) pre-coding symbols, where \(s\) is the number of redundant symbols of pre-code. Finally, pre-coding symbols are encoded into \(n\) encoding symbols using LT-code.
# Antimicrobial Activity of Human C-Type Lectin Domain Family 3 Member A (CLEC3A)
## 3 Results
### Antimicrobial Activity  
The antimicrobial activity of CLEC3A, CLEC3A Ex23, and CLEC3A Ex3 was investigated using viable count assays. The gold standard AMP LL-37 and the CLEC3A-derived AMP Ex12 were used as positive controls. CLEC3A Ex2, which is solely the amphipathic alpha-helix of Ex12 without the positive charge of the N-terminus, was used as a negative control. We could show an outstanding reduction of _P. aeruginosa_ colonies using CLEC3A. The MIC50 of CLEC3A is statistically significantly different to the MIC50 of the gold standard AMP LL-37 (\(p\) = 0.0192) and the CLEC3A-derived AMP CLEC3A Ex12 (\(p\) = 0.0254. CLEC3A Ex23 and CLEC3A Ex3 also demonstrated an antimicrobial activity against _P. aeruginosa_ and _S. aureus_ whereas none of the examined proteins showed an antimicrobial activity against _MRSA_ (Figure 3). As CLEC3A carries a Twin-Strep-tag(r) and CLEC3A Ex23 and CLEC3A Ex3 carry a poly His-tag, we used COMP (with a Twin-Strep-tag(r) and Matt3 (carrying a poly His-tag) as controls to rule out an effect of the tags. Matn3 did not show an antimicrobial effect against _P. aeruginosa_ or _S. aureus_. COMP did not demonstrate an antimicrobial effect against _S. aureus_. However, we found a slight decrease in the bacterial colony count of _P. aeruginosa_. To validate that the tag has no effect, we used ORF8, another Twin-strep-tagged protein (Supplementary Figure S2).  
Figure 3: Viable count assays. (**A**) Antimicrobial activity of LL-37, CLEC3A Ex2 (Ex2), CLEC3A Ex12 (Ex12), CLEC3A Ex3 (Ex3) (**\(p\) = 0.0041), CLEC3A Ex23 (Ex23), and CLEC3A (* \(p\) = 0.0192) against _P. aeruginosa_. Concentrations shown on the horizontal axis were rounded for CLEC3A Ex3, CLEC3A Ex23 (maximal concentration = 9.6 uM), and CLEC3A (maximal concentration = 4.8 uM). (**B**) Antimicrobial activity of LL-37, CLEC3A Ex2 (Ex2), CLEC3A Ex12 (Ex12), CLEC3A Ex3 (Ex3), CLEC3A Ex23 (Ex23), and CLEC3A against _S. aureus_. (**C**) Antimicrobial activity of LL-37, CLEC3A Ex2 (Ex2), CLEC3A Ex12 (Ex12) (* \(p\) = 0.0389), CLEC3A Ex3 (Ex3), CLEC3A Ex23 (Ex23), and CLEC3A against MRSA. Each experiment was performed three times (n = 3). Averages and standard deviations are shown. The bar charts show the determined MIC50s. All MIC50s were compared to the MIC50 of the gold standard AMP LL-37 and the statistical significance was calculated using GraphPad Prism 9 and a paired ANOVA followed by multiple comparisons and Dunnett tests. Statistical significance between the MIC50 of LL-37 and CLEC3A Ex12 was calculated by using an unpaired \(t\)-test. ns = not significant. na = not applicable.
# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 3 Pipeline Overview
### Surface Sample and Normal Estimation  
For each potentially nonempty leaf of the octree, all points in a configurable neighborhood radius of the voxel center are collected in image space, using the same image space projection optimization, such as FusionMLS [15]. A surface sample and normal are generated for each voxel using a neighborhood-based local surface estimation method for point clouds. In this work, we utilize principal component analysis (PCA) [66] as a high-speed method and second-degree moving least squares (MLS) [50] for better quality. We apply statistical outlier removal to filter noise points and false positives by discarding every voxel, which does not contain enough points in the neighborhood.  
The result of the first two steps is equivalent to performing voxelization, statistical outlier removal, and normal estimation, on the whole, merged point clouds of all depth cameras, which is part of many open-source libraries for point cloud processing (e.g., Open3D [67]). Our optimizations that exploit the compute resources of GPUs and the 2.5D structure of point clouds generated from depth images, which were also used in previous works [15; 25], make the process efficient enough to perform in real time.
## 3 Workflow and Preliminaries  
In this section, we provide a brief review of the main concepts, assumptions and constraints associated with our proposed approach.  
Figure 1 illustrates our workflow, annotated with the section numbers where we describe the individual steps. All figures within this article use a consistent color scheme. All computation related parts in magenta. All hardware specific parts in purple. Everything related to observed execution is colored in green. Data dependency related parts in brown. The simulation parts are visualized in blue. The Figure 1 can be split into three columns, the system description, the characterization and modeling and the evaluation of our analysis. The system we describe consists of software described by a computation model (Magenta, Section 3.2) and hardware described by an architecture model (Purple, Section 3.1). For analysis, we characterize the hardware and software via measurements (Green, Section 3.3). This results in performance models for the computational (Blue, Section 4.1) and for the communicational (Blue, Section 4.2) aspect of our system. With these performance models, we then can simulate different system configurations with respect to its timing behavior (Blue, Section 4.3). In the evaluation Section 5, we compare the results of our analysis with the actual observed timing behavior of the simulated system setup.  
Figure 1: Workflow showing all modeling, characterization and evaluation steps.
# Directional Support Vector Machines
## 10 Conclusions  
Several concepts in real-life applications are represented by directional variables; from periodic time representation on calendars to compass directions. Traditional classifiers, which are unaware of the angular nature of these variables, might not properly model the data. Thereby, the study of directional classifiers is relevant for the machine learning community. Previous attempts to address classification tasks with directional variables focused on generative models [8] and discriminative linear models (logistic regression) [4].  
In this work, we proposed several instantiations of directional-aware support vector machines. First, we modified the SVM decision function by considering parametric periodic mappings of the directional variables using cosine and triangle waves. Then, we proposed an extension of the model with triangular waves in order to allow asymmetric margins on the circle. The kernelized versions of these models were proposed as well. Furthermore, we analyzed and demonstrated the expressiveness of each proposed alternative.  
In the experimental assessment, the relevance of the proposed models was evaluated, being able to achieve competitive results in most datasets. As expected, when compared to other shallow directional classifiers, kernelized models built on top of the triangle wave attained the best results in larger datasets, due to their large expressive power, which we have proven theoretically. One extra experiment combining a directional feature transformation and a deep neural network showed very promising results and clearly motivates further research.  
Since the additional parameters involved in our asymmetric SVMs (in both kernelized and non-kernelized versions) have a periodic impact on the decision boundary or are constrained to a specific domain, using gradient-based optimization techniques may result in sub-optimal models. While this problem was circumvented by using fine-tuning from simpler models, there is research room for the design and exploration of optimization techniques specific for these models. Furthermore, deep multiple kernel learning [23] is an unexplored research line in directional data settings that may lead to a unified framework combining directional kernel machines and deep neural networks.  
[PERSON] and [PERSON] motivated the problem and designed the proposed models; [PERSON] conceived the mathematical proofs in the Appendix and wrote most parts of the paper; [PERSON] and [PERSON] conducted the experiments; [PERSON] supervised the work.  
This work was partially financed by the ERDF (European Regional Development Fund) through the Operational Programme for Competitiveness and Internationalisation (COMPETE) 2020 Programme and by National Funds through the Portuguese funding agency, FCT (Fundacao para a Ciencia e a Tecnologia) within project \"POCI-01-0145-FEDER-028857\" and also by Fundacao para a Ciencia e a Tecnologia within Ph.D. Grant Numbers SFRH/BD/93012/2013 and SFRH/BD/129600/2017.  
The authors declare no conflict of interest.
# MCST Scheme for UAV Systems over LoRa Networks
## 3 System Model
### Link Capacity Model  
Then, the transmission rate [bit/s] from node \(i\) to node \(j\) is computed according to Shannon's capacity and is given as:  
\[R_{ij}=B\cdot log_{2}\big{(}1+SINR_{ij}\big{)} \tag{4}\]  
With the assumed system model including the channel model, interference model and link capacity model, the capacity improvement of the LoRa network is investigated through the MCST scheme.  
Figure 3: An example of SNR-based interference model.
###### Abstract  
Fault diagnosis is a top-priority task for the health management of manufacturing processes. Deep learning-based methods are widely used to secure high fault diagnosis accuracy. Actually, it is difficult and expensive to collect large-scale data in industrial fields. Several prerequisite problems can be solved using transfer learning for fault diagnosis. Data from the source domain that are different but related to the target domain are used to increase the diagnosis performance of the target domain. However, a negative transfer occurs that degrades diagnosis performance due to the transfer when the discrepancy between and within domains is large. A multi-objective instance weighting-based transfer learning network is proposed to solve this problem and successfully applied to fault diagnosis. The proposed method uses a newly devised multi-objective instance weight to deal with practical situations where domain discrepancy is large. It adjusts the influence of the domain data on model training through two theoretically different indicators. Knowledge transfer is performed differentially by sorting instances similar to the target domain in terms of distribution with useful information for the target task. This domain optimization process maximizes the performance of transfer learning. A case study using an industrial robot and spot-welding testbed is conducted to verify the effectiveness of the proposed technique. The performance and applicability of transfer learning in the proposed method are observed in detail through the same case study as the actual industrial field for comparison. The diagnostic accuracy and robustness are high, even when few data are used. Thus, the proposed technique is a promising tool that can be used for successful fault diagnosis.  
deep learning; fault diagnosis; industrial robot; prognostics and health management (PHM); spot welding; transfer learning +
Footnote †: journal: Applied Sciences
# Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures
###### Abstract  
In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing.  
Keywords:edge-preserving filtering; bilateral filtering; non-local means filtering; acceleration; denormalized number; SIMD; vectorization; CPU microarchitecture +
Footnote †: journal: Applied Sciences
# Transfer Accuracy of Two 3D Printed Trays for Indirect Bracket Bonding--An In Vitro Pilot Study
## 3 Results
### Bracket Bonding Accuracy (Angular Measurements)
#### 3.3.2 Impact of the Tray Type  
Angular discrepancies were higher for hard (median: 2.49\({}^{\circ}\), Q1-Q3: 1.32-3.91\({}^{\circ}\)) compared to the soft (median: 1.77\({}^{\circ}\), Q1-Q3: 0.94-3.01\({}^{\circ}\)) trays (\(p\) < 0.001). When splitting the data, significant differences were found in the crowding group, i.e., for angulation at front teeth (\(p\) = 0.001), canines (\(p\) < 0.001) and premolars (\(p\) = 0.009), and for torque at front teeth (\(p\) < 0.001), and in the no crowding group for angulation at canines (\(p\) < 0.001). For all these differences the transfer with the soft tray was more accurate (Table 1).
###### Abstract  
Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike those of shallow-water acoustic communication channels) are poorly understood. Based on a channel measurement experiment in a typical deep-sea incomplete sound channel environment in the South China Sea, this paper analyzes the structural and spatiotemporal characteristics of high-frequency underwater acoustic channels in the direct-arrival and shadow zones. The channel multipath vertical structure, amplitudes of different path clusters, root mean square (RMS) delay spread, and channel temporal coherence are investigated at different depths. The randomness of different path clusters is quantified as the coefficient of variation. The channels in the shadow zone are characterized by a complex multi-path structure, high RMS delay spread, and low temporal coherence. On the contrary, the channels of the direct-arrival zone show multi-path convergence, low RMS delay spread, and high temporal coherence. Understanding the dynamic changes in these parameters can guide the statistical modeling of channels and the design of communication algorithms in different zones in deep-sea high-frequency acoustic communication scenarios.  
deep-sea high-frequency acoustic communication; channel multipath structure; coefficient of variation; RMS delay spread; channel temporal coherence Article