esa-sceva/satcom-chunk-collection huggingface dataset:
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 1 Introduction
### ImageCLEF Caption Detection Task
#### 1.8.2 ImageCLEF Caption Detection Task 2018  
The 2018 ImageCLEF Caption Detection task, a continuation of the 2017 edition [22], focused on clinical descriptions to limit dataset diversity. External data usage was restricted,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **BLEU-Score** \\ \hline NLM & 0.5634 \\ NLM & 0.3317 \\ PRNA & 0.3211 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Caption prediction presentation of runs exploiting external resources; the exact type of third-party substances indicated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **F1-Score** \\ \hline NLM & 0.1436 \\ NLM & 0.1418 \\ AAI & 0.1583 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Concepts detection presentation in terms of F1-scores without the use of external resources.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **F1-Score** \\ \hline NLM & 0.1718 \\ NLM & 0.1648 \\ AAI & 0.1583 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Concepts detection presentation of runs exploiting external properties; the exact type of third-party material is indicated.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Team** & **F1-Score** \\ \hline ISIA & 0.2600 \\ ISIA & 0.2507 \\ ISIA & 0.2454 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Caption forecast performance in terms of BLEU scores without external properties.  
and the challenge emphasized identifying general topics from biomedical images based on large-scale training data. The task maintained two subtasks: concept detection, requiring automatic extraction of UMLS CUIs, and caption prediction, generating descriptive captions (Tables 7 and 8):  
Several methods were developed for concept detection. One approach applied a traditional bag-of-visual-words model using ORB descriptors, with keypoints clustered via k-means (k = 4096) using FAISS [23]. Generative Adversarial Networks (GANs) [24] and their hybrid autoencoding variants [25] were also explored to learn latent visual features. Open-source tools like LIRE were used to index images, combined with Latent Dirichlet Allocation (LDA) for concept grouping.  
In total, fifteen runs were submitted [26]. ImageSem achieved the best performance, reaching an F1-score of 0.0928 in concept detection and a BLEU-Score of 0.2501 in caption prediction. Their method, based on image retrieval and transfer learning, highlighted challenges in fully capturing biomedical image semantics.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 3 Results
### Journal Distribution and Academic Influence  
The following tables show the ranking of the top 10 journals that publish peer-reviewed articles on remote sensing of phytoplankton blooms according to the total number of published articles (Table 2) and total citations (Table 3). There is an evident dominance of USA journals regarding publication of this research topic, with 6 journals. The other 4 journals are European (England, Netherlands, Switzerland, and Germany).  
Although both tables may look similar, they share only six journals. Four of the journals with the most published articles (Table 2) do not reach a high enough number of total citations to be included in the ranking of the top 10 journals according to total citations (Table 3). These journals are Remote Sensing of Environment, Journal of Marine Systems, and Harmful Algae.  
Figure 4: Ecoregions according to [32] and number of published papers in each ecoregion.  
Figure 5: Temporal evolution of number of peer reviewed articles published divided in marine and inland waters. Seven publications were developed in both type of waters and were not included.  
The remote sensing of phytoplankton blooms literature originated from 219 journals. The ten journals with the highest number of publications (Table 2) accounted for 45.44% of the total number of papers. Among these, 118 journals had only one publication on this topic each, which accounted for 53.9% of all the journals. Overall, 177 journals had less than five publications, which accounted for 80.8% of all the journals and 22.5% of the total number of papers.  
\"Journal of Geophysical Research: Oceans\" is the journal with more published articles and is the most cited journal with 13.7% of the total citations. \"Remote Sensing\" is the second journal in number of published articles, but is not among the top ten most cited journals. \"Global Biochemical Cycles,\" which is not one of the top ten publishing journals (only 1.46% of total analysed articles), accounts for 5.21% of total citations and is the fourth most cited journal. This happens similarly with \"Nature,\" which is the fifth most cited journal thanks mainly to only two papers with 1089 and 200 citations, respectively, both published about the Southern Ocean in 2000.  
The calculation of the Citation Index showed that 26% of the papers had null or low relevance, meaning a low citation number, while 51% had high or very high relevance. Relevance was similar for marine and inland studies.
# Robust and Efficient Corner Detector Using Non-Corners Exclusion
###### Abstract  
Corner detection is a traditional type of feature point detection method. Among methods used, with its good accuracy and the properties of invariance for rotation, noise and illumination, the Harris corner detector is widely used in the fields of vision tasks and image processing. Although it possesses a good performance in detection quality, its application is limited due to its low detection efficiency. The efficiency is crucial in many applications because it determines whether the detector is suitable for real-time tasks. In this paper, a robust and efficient corner detector (RECD) improved from Harris corner detector is proposed. First, we borrowed the principle of the feature from accelerated segment test (FAST) algorithm for corner pre-detection, in order to rule out non-corners and retain many strong corners as real corners. Those uncertain corners are looked at as candidate corners. Second, the gradients are calculated in the same way as the original Harris detector for those candidate corners. Third, to reduce additional computation amount, only the corner response function (CRF) of the candidate corners is calculated. Finally, we replace the highly complex non-maximum suppression (NMS) by an improved NMS to obtain the resulting corners. Experiments demonstrate that RECD is more competitive than some popular corner detectors in detection quality and speed. The accuracy and robustness of our method is slightly better than the original Harris detector, and the detection time is only approximately 8.2% of its original value.  
corner detection; Harris corner detector; non-corners exclusion; features from accelerated segment test +
Footnote †: journal: Applied Sciences
# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 4 Deep Learning Methods for Nested NER
### Span Labeling Approaches  
The span labeling approach addresses the nested NER problem by assigning labels to all candidate spans likely to be NEs (Figure 3b,c). This approach detects all NEs simultaneously, mitigating the error propagation problem. In this section, we classify span labeling  
Figure 3: Overview of three approaches for nested NER. For each approach, the diagram shows how an input sequence \(\{x_{1},\ \dots,\ x_{n}\}\), where \(x_{i}\) indicates the \(i\)-th word in an input sentence, is transformed through encoding and decoding layers to predict nested NEs. The encoding layer consists of a text vectorization, which converts words or tokens into numerical vectors, and a context layer that uses neural networks such as LSTM or transformer to capture broader linguistic context. The decoding layer then predicts NEs based on the context from the encoding layer. (**a**) Layer-based approach. \(h_{i}\) and \(\hat{h}_{i}\) refer to the initial and latent embeddings for \(x_{i}\), respectively. \(\gamma_{layer_{i}}\) denotes the output from \(i\)-th NER layer. (**b**) Enumeration model of span-based approach. \(\hat{h}_{(i,j)}\) indicates the latent embedding for the span from \(x_{i}\) to \(x_{j}\), and \(y_{(i,j)}\) refers to a class assigned to that span. (**c**) Boundary model of span-based approach. \(\hat{h}_{is}\) represents the latent embedding for \(i\)-th candidate span determined by the boundary detection layer. \(y_{s_{i}}\) denotes the label assigned to the \(i\)-th candidate span.  
approaches into three categories: enumeration, boundary, and MRC. The enumeration and boundary models are distinguished by how they generate candidate spans. The enumeration model sets all spans shorter than a certain length as candidates, whereas the boundary model uses neural networks to generate candidate spans. The MRC model differs from the other two by incorporating information about entity type using queries.
## 5 Challenges and Open Issues
### Technology Demands
#### 5.1.4 Digital Inclusion and Accessibility  
Digital accessibility for inclusive higher education emphasizes equality, so that all learners are to be able to independently acquire the same knowledge, engage with the same spaces, interactions, and technologies, and to enjoy the same services within the same timeframe regardless of any diversity factors (e.g., ability, language, socioeconomic status, culture, religion) [76; 77]. Equitable access to learning experiences in the metaverse goes beyond providing affordable wearable devices, to adopting proactive designs and inclusive strategies for constructing the learning experiences; and to enforcing relevant legislation and regulations to ensure equity and to prevent the emergence of a metaverse divide [71; 78].
## 2 Model
### 1D Array of Helmholtz Resonators
\begin{array}{cc}1&Z_{\Delta l_{slit}}^{(n) }\\ 0&1\end{array} \right],\] (11) with \(Z_{\Delta l_{slit}}^{(n)}=-i\omega\Delta l_{slit}^{(n)}\rho_{0}/\phi_{t}^{(n) }S_{0}\) the characteristic radiation impedance, where \(\omega\) is the angular frequency, \(\phi_{t}^{(n)}=w_{s}^{(n)}/w_{t}\) is the total porosity and \(S_{0}^{(n)}=w_{t}L_{s}^{(n)}\). \(\Delta_{slit}=\Delta l_{1}+\Delta l_{2}\) is the length correction, where \(\Delta l_{1}\) is the length correction given by the pressure radiation at the discontinuity from the neck duct to the cavity of the Helmholtz resonator, and \(\Delta l_{2}\) comes from the radiation at the discontinuity from the neck to the principal waveguide (see reference [38]) given by Equation (4) and Equation (5). It is important to note that, due to the symmetry of each resonator, the radiation correction of the slit to the free space must be applied at both sides of the structure. * The transfer matrix for the air cavity placed behind the Helmholtz resonator, \(M_{w}^{(n)}\), is defined in the following form \[M_{w}^{(n)}=\left[ \begin{array}{cc}\cos\left(k_{0}L_{w}^{(n)}\right)&iZ_{0 }\sin\left(k_{0}L_{w}^{(n)}\right)\\ \frac{L_{0}}{Z_{0}}\sin\left(k_{0}L_{w}^{(n)}\right)&\cos\left(k_{0}L_{w}^{(n) }\right)\end{array} \right],\] (12)where \(k_{0}\) and \(Z_{0}\) are the wavenumber and the acoustic impedance of the air. In this part of the system we consider that the width of the tube is large enough to neglect the effect of viscothermal losses. The total transfer matrix of the system can be built by multiplying the transfer matrix corresponding to the different building blocks as follows:  
\[\mathbf{M}=\prod_{n=1}^{N}\mathbf{M}_{b}^{(n)}=\prod_{n=1}^{N}M_{\Delta_{shift}} ^{(n)}M_{s}^{(n)}M_{HR}^{(n)}M_{s}^{(n)}M_{\Delta_{shift}}^{(n)}M_{w}^{(n)}. \tag{13}\]  
In our case we are considering the reflection problem and, thus, at \(x=L\) we consider a Neumann boundary condition, i.e., an acoustically rigid wall by means of assuming \(v(x=L)=0\). It is worth noting here that the total length of the system is given by \(L=\sum_{n=1}^{N}L^{n}=\sum_{n=1}^{N}\left(L_{s}^{n}+L_{w}^{n}\right)\). By considering this, we can obtain the reflection coefficient by using the following expression,  
\[R=\frac{M_{11}-Z_{0}M_{21}}{M_{11}+Z_{0}M_{21}}. \tag{14}\]  
Thus, the absorption coefficient is directly calculated as follows,  
\[\alpha=1-|R|^{2}. \tag{15}\]
# Performance Analysis of Ground Target Detection Utilizing Beidou Satellite Reflected Signals
## 3 Experimental Campaign  
### Description of the First Experiment  
The experiment using the BeiDou reflected signal to detect ground targets has higher requirements for the surrounding environment. Because this experiment is a preliminary verification of the technology, if the surrounding environment is complex, the reflected signal received by the antenna will have interference, which will affect the analysis of the results. Based on this, the selected experimental site is located in the suburban area of Beijing, whose latitude and longitude are 39.69\({}^{\circ}\) and 116.69\({}^{\circ}\), respectively. There are no tall buildings around it, which avoids the influence of surrounding environment on the BeiDou signal.  
A LHCP antenna with a gain of 12 dB was mounted on a triangular frame with a height of 1.4 m. The antenna was placed nearly downward horizontally, whose direction was west (seen in Figure 6), and it was able to receive the GPS L1 and BeiDou B1 signals at the same time. In this experiment, only the reflection signal of BeiDou B1 was collected. At the same time, a 3 dB omnidirectional Right-Handed Circularly Polarized (RHCP) antenna was placed vertically on the top of the triangle. It should be noted that, in this experiment, the function of the RHCP antenna was just to acquire the signal quality of the BeiDou satellite in real-time. As shown in Figure 4, the BeiDou reflected signal was received by a dual-channel collector and stored on a computer hard disk in real-time. The sampling frequency of the dual-channel collector was 16.368 MHz, and the digital intermediate frequency was 4.992 MHz. This was previously successfully applied in the typhoon experiment at the Yangjiang site [22]. In addition, there were about 8 M bytes of buffer data in the collector, which needed to be skipped during data processing.  
Figure 6: Measurement setup for ground target detection using BeiDou reflected signals. The metal box is placed in the footprint of the left-handed circularly polarized (LHCP) antenna in the experiment.  
In this experiment, metal boxes were selected as the target, and the size of box is shown in Table 1. In order to verify the feasibility of using the BeiDou B1 frequency signal for ground target detection, a total of 70 s of data were collected, and the target was placed in the footprint of the LHCP antenna for two time periods (10-20 s and 40-55 s). For other periods, the metal box was removed. This allowed the difference between the two cases to be compared. It should be noted that the target placement time started from the time of signal acquisition.
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 4 Experiments
### Experimental Results
#### 4.3.1 Compared with Typical Algorithms
Because under the background of low signal-to-noise ratio, people pay more attention to the intelligibility of speech, that is, the STOI evaluation index. This shows that RRSENet can improve the intelligibility of speech under low signal-to-noise ratio. In addition, the \"Avg.\" of RRSENet under the PESQ and STOI evaluation indicators is higher than the other four comparative experiments. Therefore, the enhanced speech of RRSENet obtains the best speech quality and intelligibility, which also shows that RRSENet is good at the generalization ability on the mismatched noise test set is better [48]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & \multicolumn{4}{c}{**PESQ**} & \multicolumn{4}{c}{**STOI**} \\ \hline
**SNR** & \(\mathbf{-5}\) **dB** & \(\mathbf{0}\) **dB** & \(\mathbf{5}\) **dB** & **Avg.** & \(\mathbf{-5}\) **dB** & \(\mathbf{0}\) **dB** & \(\mathbf{5}\) **dB** & **Avg.** \\ \hline Noisy & 1.537 & 1.843 & 2.083 & 1.821 & 66.40 & 77.56 & 84.92 & 76.30 \\ LogMMSE & 1.579 & 1.906 & 2.176 & 1.887 & 66.77 & 78.25 & 85.52 & 76.84 \\ Wiener & 1.710 & 2.180 & 2.610 & 2.166 & 65.15 & 77.84 & 85.90 & 76.29 \\ TCNN & 1.969 & 2.510 & 2.830 & 2.436 & 80.87 & 89.11 & 92.71 & 87.56 \\ AECNN & 2.013 & 2.557 & 2.945 & 2.505 & 81.33 & 89.45 & 92.99 & 87.92 \\ HDMNet & 2.122 & 2.654 & 3.013 & 2.596 & 82.03 & 89.52 & 93.09 & 88.22 \\ RLSENet & 2.151 & 2.692 & 3.039 & 2.627 & 83.06 & 90.18 & **93.40** & 88.80 \\ RRSENet & **2.220** & **2.750** & **3.051** & **2.674** & **83.17** & **90.19** & 93.30 & **88.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental results of different network models under seen noise conditions for PESQ and STOI. **BOLD** indicates the best result for each case. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & \multicolumn{4}{c}{**PESQ**} & \multicolumn{4}{c}{**STOI**} \\ \hline
**SNR** & \(\mathbf{-5}\) **dB** & \(\mathbf{0}\) **dB** & \(\mathbf{5}\) **dB** & **Avg.** & \(\mathbf{-5}\) **dB** & \(\mathbf{0}\) **dB** & \(\mathbf{5}\) **dB** & **Avg.** \\ \hline Noisy & 1.419 & 1.603 & 1.868 & 1.630 & 65.05 & 72.99 & 82.10 & 73.38 \\ LogMMSE & 1.439 & 1.654 & 1.941 & 1.678 & 65.23 & 73.66 & 82.67 & 73.85 \\ Wiener & 1.617 & 2.030 & 2.387 & 2.011 & 64.50 & 76.20 & 84.90 & 75.20 \\ TCNN & 1.785 & 2.054 & 2.359 & 2.066 & 74.76 & 83.71 & 89.88 & 82.79 \\ AECNN & 1.771 & 2.066 & 2.437 & 2.091 & 74.28 & 83.50 & 89.79 & 82.52 \\ HDMNet & 1.937 & 2.234 & 2.611 & 2.261 & 77.99 & 85.06 & 90.25 & 84.43 \\ RLSENet & 1.899 & 2.204 & 2.586 & 2.230 & 77.10 & 85.62 & 90.97 & 84.56 \\ RRSENet & **1.959** & **2.276** & **2.649** & **2.295** & **78.87** & **85.99** & **91.03** & **85.29** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of different network models under unseen noise conditions for PESQ and STOI. **BOLD** indicates the best result for each case.
## 3 Experiments and Analysis of Results
### Intelligent Setting of Indoor Temperature and Humidity  
#### 3.3.1 Reward Value  
The experiment sets the relevant initial parameters, learning and training based on the DDPG algorithm, deriving the change of reward values during the training process, and observing its convergence. According to most users' demand for indoor thermal comfort, the average reward value reflects the overall trend of the reward value. The trend of reward value change during the training process is shown in Figure 10 and the algorithm converges in a certain exploration process. The results show that the reward values in the online room temperature regulation model based on the DDPG algorithm fluctuate in the initial condition and finally stabilize. Initially, the controller learns and takes a trial-and-error approach to explore the appropriate temperature and humidity, resulting in fluctuating reward values. The DDPG algorithm updates the strategy during training, and the effect improves with each training iteration, eventually converging. During training, the reward values fluctuate due to noise and changes in the thermal environment during each iteration that affect energy consumption and thermal comfort. After about 75 rounds of training and learning, the reward value converges to a stable level.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 5 Proposed Network Deployment
### First Phase: Search
#### 5.1.1 Derivation of Parameters  
To generate a static network using potential fields, the sum of potential fields must be zero; that is, \(\vec{P}_{a}+\vec{P}_{r}=0\) (from Equation (4)) for each UAV. We thus need to find values of \(c_{a},c_{r},e_{a},e_{r}\) such that, when \(d_{ki}=\sqrt{3}\ U_{gr}\), \(\|\vec{P}_{a}\|=\|\vec{P}_{r}\|\) as shown by Equation (7). It has been shown that, for \(d_{ki}=\sqrt{3}\ U_{gr}\), the coverage is optimal [15]. Equation (7) is derived from Equation (4) by substituting \(P_{k}=0\).  
\[\begin{array}{lll}\vec{a_{ki}}c_{a}d_{ki}^{a}+\vec{a_{ik}}c_{r}d_{ik}^{e_{r} }&=&0\\ \vec{a_{ki}}c_{a}d_{ik}^{\vec{a_{ik}}}&=&\vec{a_{ik}}c_{r}d_{rk}^{e_{r}}\\ c_{a}(\sqrt{3}l_{gr})^{1}&=&c_{r}(\sqrt{3}l_{gr})^{-2}\\ c_{r}&=&c_{a}(\sqrt{3}l_{gr})^{3}\end{array} \tag{7}\]  
In the equation, the directions of unit vectors \(\vec{a_{ki}}\) and \(\vec{a_{ik}}\) are opposite to each other (\(\vec{a_{ik}}=-\vec{a_{ki}}\)). Earlier work in potential-fields-based movement control has shown that the attractive potential should be proportional to the distance and the repulsive potential field should be inversely proportional to the distance squared to provide collision-free cohesive movement [41; 42]; thus, \(e_{a}\) and \(e_{r}\) are set to \(1\) and \(-2\), respectively.  
In the simulation experiment, \(U_{gr}=100\) meters, and Equation (7) shows the relationship between \(c_{r}\) and \(c_{a}\). Assuming that \(c_{a}=1\), we obtain \(c_{r}=3\times\sqrt{3}\times 100^{3}\).
# Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User
## 6 Limitations and Future Work  
It is noteworthy to mention that the system model in this work is analyzed at the user level (i.e., a single primary user and a single secondary user) using an amplify-and-forward relaying technique and this is considered the limitation of this study. Therefore, this work can be extended in the future by considering more primary and secondary users to investigate the performance of the proposed system model at the network level and to study the impact of the design parameters such as the number of relays on the system performance. However, the same analysis can be followed to analyze the system model for multiple users. On the other hand, other relaying techniques such as decode-and-forward can be considered to study the impact of such a technique on the system performance. Moreover, this work can be extended by allowing the relays to serve more  
Figure 5: OPs of CRN with mobile end user under different vehicular speeds.  
than one secondary user, since not all relays are in use and only one of them is selected for transmission.  
The authors contributed equally to this work where the individual contributions are as follows; conceptualization, M.F.A.-M., M.A.K. and M.A.; methodology, M.A.K., M.A. and K.A.D.; software, L.N.I. and M.F.A.-M.; validation, L.N.I, M.F.A.-M., M.A.K. and K.A.D.; writing--original draft, L.N.I. and M.A.; supervision, M.F.A.-M. and M.A.K.; writing--review and editing, L.N.I., M.A.K., M.A. and K.A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors have no conflict of interest to declare.
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy
## 3 Rotation Angle Measurement Principle and Error Analysis  
### Rotation Angle Measurement Principle  
Two theodolites and two plane mirrors were used to measure the rotation angles of the optical unit (Figure 4). The rotation angles around the \(x\)- and \(z\)-axes of the unit were obtained from the azimuth and pitch angles of theodolite1, respectively. At the same time, the rotation angles around the \(x\)- and \(y\)-axes of the unit were obtained from the azimuth and pitch angles of theodolite2, respectively. The rotation angle around the \(x\)-axis was measured by the two theodolites at the same time, so that the two theodolites could calibrate each other. In order to obtain the re-orientation accuracy, the unit was inserted into and extracted from the frame several times, and the re-orientation accuracy was found by recording the azimuth and pitch angles of theodolites.  
Figure 4: Measurement principle and coordinate systems. Two mirrors were fixed on the unit and were perpendicular to each other. The \(y\)-axis and the \(z\)-axis were parallel to the central lines of the two mirrors, respectively. In addition, two theodolites were placed on the center lines of the two mirrors, respectively.