esa-sceva/satcom-chunk-collection huggingface dataset:
# Mixup Based Cross-Consistency Training for Named Entity Recognition
## 4 Experiments
### Low Resource Environments  
To demonstrate that the proposed framework is robust to the size of strongly labeled data, we test the performance with the sub-split of 20%, 40%, 60%, and 80% of strongly labeled data. For a fair comparison between experiments, we used the same sample indices when splitting datasets.  
As shown in Figure 6, the proposed framework demonstrated better performance than the BERT baseline on all experiments. Particularly for NCBI-disease and LaptopReview, while the performance of BERT baseline dramatically decreased on 20% of the sub-sampled strongly labeled dataset, the proposed framework relatively maintained stable performances. Furthermore, we also observed that as the strongly labeled data size decreases, the performance decline decreases. Based on experimental results under low-resource environments, the proposed framework showed similar performances to environments where sufficient datasets are available, even with limited datasets. This demonstrates that the proposed framework would save the cost of assigning datasets for domain-specific NER tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**BC5 CDR-Chem**} & \multicolumn{3}{c}{**BC5 CDR-Disease**} & \multicolumn{3}{c}{**NCBI-Disease**} & \multicolumn{3}{c}{**LaptopReview**} \\  & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** & **Pre** & **Rec** & **F1** \\ \hline BERT baseline & 90.88 & 92.08 & 91.38 & 83.44 & 85.40 & 84.32 & 83.69 & **90.91** & 86.86 & 79.76 & 79.74 & 79.72 \\ mixup baseline & 90.95 & 92.49 & 91.21 & 83.65 & 85.53 & 84.11 & 85.94 & 87.76 & 86.60 & 80.56 & 77.49 & 78.54 \\ mixup-CCT & 91.98 & 92.78 & 92.19 & **86.69** & 85.46 & **85.86** & **88.64** & 89.23 & 88.82 & 83.40 & 78.09 & 80.46 \\ proposed & **92.12** & **93.14** & **92.23** & 84.19 & **86.81** & 85.03 & 87.98 & 90.25 & **88.92** & **84.85** & **80.90** & **82.24** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the test set’s span-level Precision/Recall/F1-score when the F1-score for the Dev set was the highest during training. The best performance is bolded. Pre, rec, and F1 mean a Precision, Recall, and F1-score, respectively.
# Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model
## 3 Pulmonary Fissure Detection
### Multiple Section Model  
As observed in Figure 5, the clutter-like airways or vessels labeled with yellow may appear as curvilinear structures in a single cross-section (e.g., the sagittal slice in Figure 5b, but appear as ellipse-like structures in the coronal, axial or diagonal planes. In contrast, pulmonary fissures labeled with green appear as curve-lines in the cross-sections. Therefore, we can use the shape features to suppress clutters and highlight the fissure representation.  
To achieve this purpose, a morphology and connected component-based region operator like the Matlab function'regionprops' was used to calculate the selected object property [17; 18] such as the major axis length \(H\) and minor axis length \(W\). We used \(S\) to represent each object in the axial, coronal, sagittal or diagonal planes. To suppress the ellipse-like structures, a line measure criterion was defined:  
\[W(S)/H(S)\leq T_{L} \tag{13}\]  
Based on the fact that the oblique fissure is oriented at approximately 45 degrees relative to the \(Z\) (vertical) axis and the angle that the horizontal fissure makes with the \(X\)-\(Y\) plane is small in most cases [19; 20], we only used the line measure criterion to suppress clutters in seven specific planes. In other words, the planes marked with yellow and green color in Figure 6 must not be taken into account. This is because pulmonary fissures may appear as ellipse-like structures in the planes.  
Figure 4: Clutters suppression. (**a**) \(N_{\mathrm{i}}\). (**b**) \(F^{\mathrm{A}}\). (**c**) \(F^{\mathrm{S}}\). (**d**) \(F^{\mathrm{C}}\).  
Figure 5: Investigate the shape feature of various pulmonary structures: (**a**) 3D visualization of a human lung, and its (**b**) sagittal slice, (**c**) coronal slice, (**d**) coronal slice.  
After using the line measure criterion to suppress the ellipse-like structures in multi-sections, we subsequently used the volume sorting scheme to pick out the surface patches as candidate pulmonary fissures [13]. The results corresponding to \(F^{\mathrm{A}}\), \(F^{\mathrm{S}}\) and \(F^{\mathrm{C}}\) are respectively shown in Figure 7a-c. It can be seen that parts of fissures are undetected. To overcome the problem, a morphological OR operation was used to integrate all of the results. Finally, we followed the work of [PERSON] et al. [13] and used the volume sorting scheme to isolate the fissure patches for pulmonary fissure segmentation, and the integration results are shown in Figure 7d.
## 1 Introduction
Compared with the traditional  
Figure 2: Surface roughness measuring direction influences of (**a**) profile when incline is 8\({}^{\circ}\); (**b**) profile in ideal measuring direction; (**c**) profile when incline is 4\({}^{\circ}\); (**d**) profile error when incline is 8\({}^{\circ}\) and (**e**) profile error when incline is 4\({}^{\circ}\). stylus tracing method, this novel method can be implemented with improved efficiency for surface roughness evaluation purpose. Moreover, the proposed method is also promising for finding artificial intelligence applications to meet the surface roughness evaluation demands in industrial fields. The major contributions of this research article are summarized below. 1. A texture skew correction method, based on the combination of an improved Sobel operator and Hough transform, is proposed for the surface texture direction adjustment. The results show that the proposed method is able to correct texture skew. 2. The paper proposes an intelligent surface roughness evaluation method based on machine-vision-enhanced artificial intelligence technology for 2D images. After 2D-DTCWT filtering of adjusted images, ResNet is employed for the surface texture pattern recognition. Owing to the engagement of ResNet in artificial feature learning, the model does not rely on prior knowledge. 3. Surface roughness estimation using the proposed method was performed on the images of milled metal surfaces made from the material spheroidal graphite cast iron 500-7. The high estimation accuracy of surface roughness revealed by the experimental results shows that the proposed method has good generalization ability. The rest of the paper is organized as follows. The image processing techniques, including skew correction and 2D-DTCWT, are briefly described in Section 2. The intelligent learning method based on ResNet is presented in Section 3. The framework of the proposed method is explained in detail in Section 4. The model training process and the validation experiment with results are shown in Section 5. Functions of the elements within the proposed method are discussed in Section 6. Some concluding remarks are given in Section 7.
Therefore, their use in inverse kinematics and obstacle avoidance yields multiple solutions within an acceptable error range, and far enough from possible obstacles. Additionally, once the training has been completed, the inference time is independent of the configuration of the robot, proving useful for real-time controllers. In the following sections, different DNN architectures will be reviewed, studying their use and precision when dealing with inverse kinematics, motion control and planning for robotic manipulators, with and without obstacles. More specifically, the key challenges addressed by the reviewed approaches are their precision, architectural and computational complexity, time cost and real-world applicability. To select the references included in this paper, Google Scholar has been used as the main scientific search engine, filtering by year and prioritizing on well-established publishers such as MDPI, IEEE, Elsevier, and Springer. The search has been conducted using keywords such as inverse kinematics, motion control, motion planning, robotic manipulator, DNN and each specific type of DNN architecture (e.g., MLP, CNN). Other relevant reviews devoted to the application of neural networks in robotics are [8, 21, 22, 23, 24, 25]. The sections are organized as follows: In Section 2, solutions to the IK problem involving DNN are presented. They are classified in supervised learning, unsupervised learning and Deep Reinforcement Learning, and also by the type of DNN used. This is a conventional classification within the machine learning community, which focuses on the problem statements before delving into the set of techniques or algorithms that serve to solve them. The obstacle avoidance problem is studied next, first from a motion control perspective in Section 3 and then from a motion planning one in Section 4. Section 6 also includes a brief review on the use of transformers. The results and trends found are discussed in Section 5, and some concluding remarks are presented in Section 6.
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## 2 Related Work
[32] have been developing new functionalities for the service by extending the information delivery aspect so that MEC apps contribute with prognostic data about the behavior of the UE like our proposal also does. RNIS needs improvements to collect and expose contextual information about UE networks. Current implementations show the importance of using RNIS in 4G and 5G contexts; however, the services still need to be improved. While some of these earlier works [16; 18; 28; 29; 30; 31; 32] carry out tests on the best brokers to be used in the development of the service, other works focused on the development of new functionalities for RNIS, such as the ability to obtain information from MEC apps. However, the efficiency of information delivery is ignored, with the LTE context generally being prioritized. Another point is that there is no open source implementation to compare performance, making it difficult to analyze the proposed services. However, in contrast to the literature, our proposed service aims to improve the delivery of RNIS information to interested consumers (MEC services and MEC apps), even with a new source of information provided by MEC apps in which the RNIS needs improvement to ensure efficient delivery of information to its consumers, especially in the context of B5G networks.
## Materials and Methods
### Biological Material  
Trichoderma Strains and Their Phylogenetic Relationships  
Four strains of _Trichoderma_ (F12, F26, F1020, and F58) belonging to the Mycology Laboratory of the University of Pavia were utilized in this study. The fungal strains were isolated from four different environments: F12 and F26 from hydrocarbon-contaminated soils in Ecuador and Pakistan, respectively; F1020 from the rhizosphere of a plant growing in a hydroponic setting (toxin free) in Italy; and F58 from soil polluted with pesticides. The Mycology Laboratory previously used F12 and F1020 for tolerance tests with different types of hydrocarbon substances. They were chosen for this work to study their potential in the treatment of used engine oil. The strains were grown on potato dextrose agar (PDA) Petri dishes and incubated at 27 \({}^{\circ}\)C for one week. After this time, all the fungal strains were identified by morpho-dimensional examination under a light microscope, based on the nature of the mycelium and the different reproductive structures. Molecular characterization was performed to confirm the morphological identification. The fungal genomic DNA was extracted using the NucleoSpin Plant II by [PERSON] (Bethlehem, PA, USA), with one hour of incubation at 65 \({}^{\circ}\)C after the addition of RNase, and then subjected to PCR amplification of the internal transcribed spacer (ITS) region of the ITS1-5.8S-ITS2 rDNA gene. The PCR reaction was performed on a Thermocycler Bio-Rad T100 in 20 \(\upmu\) reaction mixture containing 1\(\times\) DREAM Taq Green PCR MasterMix reaction buffer (Thermo Scientifics, Pittsburgh, PA, USA), 2 \(\upmu\)I (5 \(\upmu\)M) of each primer, 2 \(\upmu\)I of DNA sample, and 4 \(\upmu\)L of Nuclease Free water. The PCR program was as follows: denaturation by heating for 5 min at 95 \({}^{\circ}\)C, then 35 cycles of 30 s at 95 \({}^{\circ}\)C, 45 s at 50 \({}^{\circ}\)C, and 1 min at 72 \({}^{\circ}\)C, and a final elongation step for 10 min at 72 \({}^{\circ}\)C. The primers used were ITS1 (5'-TCCGTAGG TGAACCTGCGG-3') and ITS4 (5' TCCTCCGCTATTTGATAGC-3') [34]. PCR products were purified with ExoSAP-IT (Applied Biosystems, Foster City, CA, USA) according to the manufacturer's protocol. The amplified and purified DNA was sent to BMR Genomics (Padova, Italy), and the sequences were compared with target sequences using BLAST online ([[https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)]([https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/))) and MEGA X 10.1.7.  
The DNA sequences generated in this work, together with representative ITS sequences of other _Trichoderma_ strains, were aligned using MEGA X [35]. The ITS sequences of 28 strains belonging to seven species were downloaded from Genebank. To show the phylogenetic positions of our strains, strains belonging to the _Trichoderma_ clade _Pachybasium_ A and the _Pachybasium_ B clade _Lixixixi/catoptron_ were included in this analysis. The maximum likelihood tree was obtained with 100 bootstrap replications, using the Jukes-Cantor model. The rates among sites were gamma-distributed with invariant sites (G+I).
# Energy Prediction of Access Points in Wi-Fi Networks According to Users' Behaviour
## 3 An Energy Prediction Proposal
### Current Network Status and Relationship between Energy and Roamings  
We start from a full Wi-Fi infrastructure composed of a set of APs (left side of Figure 1). We collect two kinds of data along the time for a high number of network users: energy use in each AP, and number of roamings that users establish in them. From these data sets we build a matrix of roamings and an array of energies.  
The matrix \(CR\) (_Current Roamings_) is composed of \(S\) rows and \(I\) columns, where \(S\) is the number of network users and \(I\) is the number of access points. Each \(cr_{s,i}\) in the matrix represents the number  
Figure 1: Methodology for predicting the energy in the access points after removing one of them.  
of roamings that user \(s\) has registered in the AP \(i\) during the period considered. From this matrix, we form the array \(TCR\) (_Total Current Roamings_) with the sum of each column, that gives us the total number of roamings established in the corresponding AP.  
The array \(TCE\) (_Total Current Energy_) is composed of the total energy used in each AP along the period, which has a certain contribution of the users' activity determined by the number of roamings.  
We can establish a relationship between \(TCR\) and \(TCE\) or, in other words, between users' activity and energy. A first approach to the energy impact in the APs due to the users' activity would be to consider a function based on point coefficients. Thus, for each AP \(i\) there is a coefficient \(C_{i}\) that connects proportionally the number of roamings (\(TCR_{i}\)) and energy (\(TCE_{i}\)), as we express in (1). This proportional function based on point coefficients allows us to make a reasonable prediction of the energy if the corresponding number of roamings in the AP changes.  
\[TCE_{i}=C_{i}\,TCR_{i}=C_{i}\,\sum_{s=1}^{S}cr_{s,i} \tag{1}\]  
Finally, the longer spent collecting data and more network users, the higher the accuracy of the prediction model, because of the users' habits are better registered by means of the number of roamings established in each AP.
###### Abstract  
The automatic detection, segmentation, localization, and evaluation of the optic disc, macula, exudates, and hemorrhages are very important for diagnosing retinal diseases. One of the difficulties in detecting such regions of interest (Rols) with computer vision is their symmetries, e.g., between the optic disc and exudates and also between exudates and hemorrhages. This paper proposes an original, intelligent, and high-performing image processing system for the simultaneous detection and segmentation of retinal Rols. The basic principles of the method are image decomposition in small boxes and local texture analysis. The processing flow contains three phases: preprocessing, learning, and operating. As a first novelty, we propose proper feature selection based on statistical analysis in confusion matrices for different feature types (extracted from a co-occurrence matrix, fractal type, and local binary patterns). Mainly, the selected features are chosen to differentiate between similar Rols. The second novelty consists of local classifier fusion. To this end, the local classifiers associated with features are grouped in global classifiers corresponding to the Rols. The local classifiers are based on minimum distances to the representatives of classes and the global classifiers are based on confidence intervals, weights, and a voting scheme. A deep convolutional neural network, based on supervised learning, for blood vessel segmentation is proposed in order to improve the RoI detection performance. Finally, the experimental results on real images from different databases demonstrate the rightness of our methodologies and algorithms.  
biomedical image processing; retinal image segmentation; feature selection; texture analysis; convolutional neural network; optic disc; macula; exudates; hemorrhages +
Footnote †: journal: Computer Vision and Pattern Recognition
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 5 IoT, UAVs, and Satellites in the Emerging 5G Technology
### UAV/Satellite and 5G  
The 3 GPP carried out studies aimed at defining the possible role of Non-Terrestrial Networks (NTNs) in the 5G ecosystem in Release 15 [46] and is proposing solutions from the networking viewpoint to allow the integration between non-terrestrial and terrestrial networks in Release 16 [47]. The main difference among these solutions is related to which functionalities are implemented on-board non-terrestrial objects. They can act as relay nodes between 5G User Equipment (UE) and the 5G access point (5G-gNB) aimed at extending the 5G Radio Access Network (RAN) coverage or as a backbone support. Besides, an additional study is investigating the possible employment of Satellite Communication (SatCom) networks as active nodes in the 5G access operations, i.e., to employ satellites able to generate 5G cells acting as 5G gNBs [48]. Each of these solutions has its pros and cons, and it is not clear yet which could be the best one for the IoT use case.  
Several research projects are ongoing to investigate the employment of satellites and UAVs in 5G and develop and test solutions to be implemented in the future within the 5G network. The work in [49] presented the results of the ongoing SAT5G project funded by the European Commission [50] about satellite communications positioning in 5G scenarios for enhanced Mobile Broadband (eMBB). The aim is to provide cost-effective plug and play SatCom solutions and to accelerate the 5G deployment in all geographies. They selected four different satellite use cases for eMBB, and their efforts focused on (1) offering efficient multicast/broadcast content delivery to network edges, (2) providing 5G connectivity to premises to complement the terrestrial ones, (3) providing 5G connectivity to moving platforms such as airplanes, and (4) providing 5G as a fixed backhaul in areas where it is difficult to deploy terrestrial connections such as lakes, islands, etc. The work in [51] introduced a 5G-oriented network architecture based on satellite communication and Mobile Edge Computing (MEC) to aid eMBB applications and guarantee the QoE for live streaming applications. The evaluation was done by using a real satellite link. Another project called SATis5 presented a demonstrator for the integration of satellite and terrestrial 5G networks [52]. A recently kicked-off EU project addresses the integration of UAVs into 5G, 5G!Drones [53]. Its ultimate aim is to design, implement, and run various examinations of UAV use cases on top of the 5G infrastructure and to validate the 5G KPIs. The use cases to be considered are UAV traffic management, public safety/savings lives, situation awareness, and connectivity during crowded events.
## 2 Literature Review
In transit, the usual observation periods are the ones in which the number of vehicles in operation is constant (e.g., morning and afternoon commuter periods or midday and evening periods), which results in several profiles during the day. But sometimes this is not good enough, since a profile can be too short (not enough data to describe the sample precisely) or too long (traffic conditions change too drastically to describe the sample precisely). [PERSON] et al. [4] showed that for 15 min aggregations, travel time shows a normal distribution, while for 60 min aggregations, log-normal distribution becomes more accurate and that for commuter periods, normal distribution is more suitable, concluding that travel time distribution mainly depends on temporal data aggregations. There are many studies in the literature describing the most influential disturbance factors. Travel distance was shown to be a significant disturbance factor, since the increase in travel distance increases the probability of non-usual events such as accidents or vehicle breakdowns [30]. Stop density can be a disturbance factor because a higher number of stops results in a higher number of vehicles that did not have to make the stop [31]. Signalized intersections are also a disturbance factor because vehicles lose time waiting at a red light, and different approach moments result in different wait times [30]. Driver behavior is a crucial disturbance factor because drivers departing too early naturally produce higher travel times and create uneven supply [26]. Travel time is even under the influence of spatial, social and economic characteristics [32], because in industrial areas, passenger demand is less irregular than in city centers. Weather conditions, especially rain, can contribute to supply and demand and cause travel time discrepancy and variability. Times during the day also contribute, because commuter periods have more variations in demand. Although there are many studies dedicated to transit travel time variability, they often fail to put the problem into a traffic perspective. The research rarely detects whether the average time travel values ensure an efficient transport process. Disturbance factors are not observed as local obstacles or present at entire network portions, despite this is easily understood from a traffic perspective. And variability is observed mostly in bus transport, since it often shares the roads with private cars. Therefore, if compared to previous research, the novelty of this research is in:  
* Tram traffic in mixed traffic conditions as a case study;
* Modeling minimum travel time to base the research upon; this ensures observing both travel time discrepancy and variability;
* Putting the focus into classifying the indicators as transverse and longitudinal, which helps better understand the predictors from a traffic perspective.