esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SOA-Enabled HONoC Architecture
### Constrained Optimization Problem in SOA-Enabled HONoC  
In the HONoC, laser source power mainly depends on the total loss experienced by the optical signal in the longest path. The laser source power level is determined to ensure that the signal power at the receiver is greater than the sensitivity of the photodetector. Therefore, we reduce the burden of the laser source power by using the SOA, which is easy to integrate and of relatively low processing cost. Appropriately placing SOAs across the signal path is an effective way to save the laser source output power in large-scale HONoCs if the additional power consumption of SOAs is reasonably smaller than the reduced amount of the laser source power.  
As the total power consumption of the SOA-enabled HONoC depends on the location, spacing and number of SOAs, an efficient SOA placement is needed to achieve the goal. Furthermore, the SOA placement also affects the worst-case SNR because SOAs can amplify the crosstalk noise power as well  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Value** \\ \hline \(\Gamma\) & Light confinement factor & 0.4 \\ \(a_{1}\) & Constant & \(6.7\times 10^{-16}\) cm\({}^{2}\) \\ \(n_{0}\) & Transparency carrier concentration & \(1.2\times 10^{18}\) cm\({}^{-3}\) \\ \(L\) & Length of the SOA active region & 10 \(\mu\)m \\ \(l_{0}\) & Threshold input current & 5 \(\mu\)A \\ \(\alpha\) & Loss in the SOA active region & 10 cm\({}^{-1}\) \\ \(\Delta\lambda\) & SOA gain linewidth & 95 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definitions and values of SOA parameters.  
as the desired optical signal power. Therefore, the constrained optimization problem for implementing the SOA-enabled HONoC in an \(m\times n\) mesh is defined as follows:  
\[\text{Minimize}:\ \sum_{total}P_{laser}+\ \sum_{total}P_{SOA}\] \[\text{Subject to}:\ P_{sensitivity}\leq min\ \left\{PL_{(x_{0},y_{0})(x_{1},y_{1})}^{ SOA}\right\} \tag{6a}\] \[min\ \left\{SNR_{(x_{0},y_{0})(x_{1},y_{1})}^{ SOA}\right\}\leq min \ \left\{SNR_{(x_{0},y_{0})(x_{1},y_{1})}^{ SOA}\right\}\] (6b) \[x_{0},\ x_{1}\in\{1,\ \ldots,\ m\},\ y_{0},\ y_{1}\in\{1,\ \ldots,\ n\}\]  
Here, \(P_{laser}\) is the laser source power consumption of the SOA-enabled HONoC, \(P_{SOA}\) is the total power consumption of the SOAs, \(P_{sensitivity}\) is the sensitivity of the photodetector, \(PL_{(x_{0},y_{0})(x_{1},y_{1})}^{ SOA}\) is the power loss of the optical signal traveling from the router \((x_{0},y_{0})\) to the router \((x_{1},y_{1})\) in the SOA-enabled HONoC, \(SNR_{(x_{0},y_{0})(x_{1},y_{1})}\) is the SNR of the path from the router \((x_{0},y_{0})\) to the router \((x_{1},y_{1})\) in the conventional mesh-based HONoC and \(SNR_{(x_{0},y_{0})(x_{1},y_{1})}^{ SOA}\) is the SNR of the path from the router \((x_{0},y_{0})\) to the router \((x_{1},y_{1})\) in the mesh-based HONoC with SOAs.
# Cross-Modal Adaptive Interaction Network for RGB-D Saliency Detection
###### Abstract
A cross-modal adaptive interaction network (CMANet) is proposed to address the RGB-D SOD problem. This network integrates and enhances multi-scale features from both modalities, propagating them to subsequent layers while adaptively preserving  
Figure 2: Comparison of our model with other models. crucial information at each level. Simultaneously capturing the global context, it preserves image details and spatial information, thereby yielding more accurate output results;
2. We propose a cross-modal feature integration module (CMF), which mainly fuses information from different modalities (such as RGB and depth) to capture the complementary characteristics between the two modalities. Through this module, the model can simultaneously utilize the visual features such as color and texture of RGB images, as well as the spatial structure information of depth images, to generate more discernible and representative feature maps;
3. We propose an adaptive feature fusion module (AFFM) that utilizes the intrinsic features of both modalities and adaptively integrates them with the outcomes from the previously shared network layer. This module merges features of different scales into the full-scale feature map, enhancing the model's ability to perceive multi-scale information;
4. Extensive experiments show that our cross-modal adaptive interaction network (CMANet) model outperforms 17 previous state-of-the-art methods on four public RGB-D SOD datasets. The organization of this paper is as follows: In Section 2, we review two related works, namely RGB salient object detection and RGB-D salient object detection. Subsequently, in Section 3, we provide a detailed description of the proposed cross-modal adaptive interaction network (CMANet). In Section 4, extensive experiments are conducted to verify the effectiveness of CMANet, along with detailed experimental setups and results. Finally, Section 5 concludes this paper and discusses future research directions.
# Trajectories and Resource Management of Flying Base Stations for C-V2X
## 4 Aerial Operation
### UAV Trajectory Planning
```
Data:\(Q\), vehicle positions and data-rate demand Result: Next trajectory point Create unserved vehicles set \(\mathcal{V}\); create cluster set \(\mathcal{K}\), create cost function vector \(\mathbf{C}\); initialize time instant \(t=t_{0}\); while\(t<=\)end of simulationdo check vehicle \(v\) data-rate demand; for each vehicle \(v\) in the scenariodo if\(v\) is not servedthen add \(v\) to \(\mathcal{V}\);  end if run centroid-linkage (LIPGMC) clusterization algorithm for the set \(\mathcal{V}\) to find \(K=|\mathcal{K}|\) clusters; for each cluster \(i=\{1,...,K\}\)do compute the centroid point of cluster \(i\); compute cluster \(i\) distance to \(Q\), \(d_{i}\); compute the number of vehicles inside cluster \(i\), \(\delta_{i}\); estimate sum throughput for cluster \(i\), \(S_{i}\);  end for compute \(d_{\text{max}}\), \(\delta_{\text{min}}\), \(S_{\text{min}}\); for each cluster \(i=\{1,...,K\}\)do compute cost function \(C_{i}\); add \(C_{i}\) to \(\mathbf{C}\);  end for \(C_{k}=\text{min}(\mathbf{C})\); select centroid of cluster \(k\) as next path step; \(t=t+d_{k}/s\);  end for
```  
**Algorithm 2**Unmmaned aerial vehicle (UAV) trajectory definition.
# Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications
###### Abstract  
This paper presents a novel frequency reconfigurable antenna design for sub-6 GHz applications, featuring a unique combination of antenna elements and control mechanisms. The antenna is composed of an outer split-ring resonator loaded with an inner spiral resonator, which can be adjusted through the remote control of PIN diode or Single Pole Double Throw (SPDT) switches. The compact antenna, measuring 22 \(\times\) 16 \(\times\) 1.6 mm\({}^{3}\), operates in broadband, or tri-band mode depending on the ON/OFF states of switches. The frequency reconfigurability is achieved using two BAR64\(-\)02V PIN diodes or two CG2415M6 SPDT switches acting as RF switches. SPDT switches are controlled remotely via Arduino unit. Additionally, the antenna demonstrates an omni-directional radiation pattern, making it suitable for wireless communication systems. Experimental results on an FR-4 substrate validate the numerical calculations, confirming the antenna's performance and superiority over existing alternatives in terms of compactness, wide operating frequency range, and cost-effectiveness. The proposed design holds significant potential for applications in Wi-Fi (IEEE 802.11 a/n/ac), Bluetooth (5 GHz), ISM (5 GHz), 3G (UMTS), 4G (LTE), wireless backhaul (4G and 5G networks), WLAN (IEEE 802.11 a/n/ac/ax), 5G NR n1 band, and Wi-Fi access points due to its small size and easy control mechanism. The antenna can be integrated into various devices, including access points, gateways, smartphones, and IoT kits. This novel frequency reconfigurable antenna design presents a valuable contribution to the field, paving the way for further advancements in wireless communication systems.  
reconfigurable antenna; split ring resonator; sub-6 GHz +
Footnote †: journal: Applied  
**Citation**: [PERSON]; [PERSON]; [PERSON] Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications. _Appl. Sci._**2023**, _13_, 8719. [[https://doi.org/10.3900/app13187919](https://doi.org/10.3900/app13187919)]([https://doi.org/10.3900/app13187919](https://doi.org/10.3900/app13187919))
# Remote Sensing Analysis of Geologic Hazards
## 3 Landslides
### Monitoring Activities  
[PERSON] et al. [33] applied MTInSAR to Sentinel-1 and COSMO-SkyMed to measure the velocity time series of two landslides that occurred in 2013 and 2019 in southern Italy, which caused damages to buildings and roads. Their results evidenced the presence of nonlinear displacements in correspondence of some key infrastructures. They concluded that the analysis of accelerations and decelerations of persistent scatter objects corresponding to structures affected by recent stabilization measures helps to shed new light in relation to known events that occurred in the area of interest.
## 2 Wireless B5G Difficulties with DL-Based Mm-Wave  
To achieve directional beamforming and allow the high path loss to be offset by the beamforming gain, massive antenna arrays can be employed at the BS and UE sides. Massive antenna arrays and beam management are necessary for the best BS-UE beam pair selection to handle significant route loss in mm-wave communication. To satisfy the demands of B5G systems, where dense antenna integration is supported by mm-wave's short wavelength to improve network performance, DL in the wireless physical layer can facilitate effective signal processing.
# Solving Multi-Document Summarization as an Orienteering Problem
## 2 Related Work
### Graph-Based Approaches  
Representing texts as graphs has become a widely used approach in the application of text processing [32]. In the field of text summarization, many studies have followed the graph-based approach and used different graph representations. For example, graph nodes can represent different types of textual units, such as sentences [33] or words [34]. Moreover, the edges between the nodes can be represented using different types of relationships, such as using the cosine similarity measure [33]. Furthermore, different kinds of graphs have been used, such as the bipartite graph between documents terms and sentences [35]. Several graph-based algorithms, such as random walk [36] and spreading activation [34], have been used to give a score for each node.  
[PERSON] et al. [37] proposed a system that creates a semantic graph by connecting the nodes of the logical forms of text sentences with bidirectional edges that represent the semantic relationships and produces the summary by extracting and merging part of the logical forms. [PERSON] et al. [38]proposed an iterative reinforcement solution that combines ideas similar to PageRank [39] and the HITS [40] graph-ranking algorithms. [PERSON] and [PERSON] [33] proposed a new sentence centrality measure, LexPageRank, based on the concept of prestige or centrality in the social network field. This measure is similar to the PageRank [39] method, except that the edges in the sentence similarity graph are undirected edges that are added based on a predefined threshold.  
[PERSON] and [PERSON] [41] proposed an algorithm that starts by a clustering step where the input documents are ordered chronologically based on time references extracted from the text and then grouped based on their position on the time line. Then, it ranks the sentences in each cluster separately using FairTextRank algorithm, which is an iterative extension of the PageRank [39] algorithm. The summary is constructed by selecting sentences from each cluster. Overall, graph-based summarization approaches have been shown competitive with the other state-of-the-art approaches. For example, the graph-based summarization approach proposed by [PERSON] et al. [42] outperformed the best three participating systems in DUC 2003 and DUC 2004 competitions.
## 2 Materials and Methods  
The three-dimensional models were composed of a human mandible, implants, and their prosthetic components. The human mandible was obtained from the Renato Archer Information Technology Center database (Campinas, SP, Brazil), and the Rhinoceros 4.0 NURBS Modeling for Windows program (Robert McNell & Associates, Seattle, WA) was used for the construction of two three-dimensional models. The images in the CTI database, acquired from computerized tomographies, were previously approved by the Ethics Committee (#183/2015 (10 April 2015)).  
Two groups were analyzed: in the first group (M1), two implants were implanted in the inter-foramen region separated from each other by 14 mm. The second group (M2) was composed of three implants placed at a distance of 9.7 mm from each other. On the implants in both groups were installed aesthetic conical abutments and a metallic crown in NiCr.  
Figure 1 details the mandible with the holes where the implants were placed in each model.  
The implant employed in this study was an internal conical hexagon, 13 mm of length and 3.5 mm of diameter, while the aesthetic conical abutment had a length of 1 mm; both were manufactured by Implacil De Bortoli (Sao Paulo, SP, Brazil).  
To simulate teeth, an acrylic resin platform was developed, which was used to apply the loads on the model. The assembly of the model was done on Biocad Philosophy, simplifying the morphology of the mandible (Figure 2).  
Figure 1: Image of the arrangement of the implants in the two models studied: (**a**) group M1 with two implants; (**b**) group M2 with three implants.  
Regarding the bar, the height was 7 mm, with a cantilever length of 14 mm. The assembly of both models were exported to ANSYS software version 12.1 (Houston, USA). The material properties employed are detailed in Table 1[8].  
A complete osseointegration was added to both the trabecular and the cortical bone with the implant [9]. The models were subjected to an axial load of 100 N distributed throughout the region on the cantilever from the distal implant with reference to the midline and to a 30 N axial load on the implants in the inter-foramen region, both in model 1 and in model 2, as shown in Figure 3[10, 11].  
\begin{table}
\begin{tabular}{c c c} \hline
**Component** & **Poisson’s Ratio** & **Young’s Module [GPa]** \\ \hline Cortical bone & 0.3 & 14.7 \\ Trabecular bone & 0.3 & 0.49 \\ Implants and abutments (titanium alloy) & 0.33 & 117 \\ Acrylic & 0.3 & 3.8 \\ Bar & 0.33 & 205 \\ \hline \end{tabular}
\end{table}
Table 1: Material properties.  
Figure 3: Loading configuration.  
Figure 2: Assemblies employed in this study: (**a**) group M1; (**b**) group M2.  
A symmetry in the median sagittal plane and a fixed support in the posterior surface were considered as boundary conditions in both models.  
Tetrahedral elements with 10 nodes were employed in both models (Figure 4); we obtained the following elements and nodes for models 1 and 2, respectively: 106218 and 116688 elements; 170513 and 189695 nodes.
# Frame-Based Phone Classification Using EMG Signals +
## 2 Materials and Methods
### Acquisition Setup
#### 2.1.1 Recording Procedure  
Each session is recorded in a soundproof room using a silent computer in an attempt to reduce interference with the audio and EMG signals as much as possible. The EMG signals are recorded with a Quattrocento bio-electrical amplifier at a sampling frequency of 2048 Hz, and the voice is captured with a Neumann TLM103 (diaphragm) microphone with a sampling frequency of 16 kHz.  
For the acquisition and synchronization of the audio and EMG signals, we use publicly available software ([[https://github.com/cognitive-systems-lab/EMG-GUI](https://github.com/cognitive-systems-lab/EMG-GUI)]([https://github.com/cognitive-systems-lab/EMG-GUI](https://github.com/cognitive-systems-lab/EMG-GUI)), accessed on 1 March 2022), which also includes a user interface. Additionally, a camera captures a video of the facial movements, which is meant to provide supplementary data and allow multi-modal experiments in the future, such as automatic lip reading. For this paper, the video data are not considered. See Figure 1 for a photo of the complete acquisition setup.  
In order to reduce inter-session variability in audio and video as much as possible, the positions of the subject, microphone, and video camera are kept constant for all sessions. Furthermore, a personalized 3D mask (Figure 2) is used to ensure that the electrode locations remain constant throughout all sessions. Prior to the first session with each speaker, we locate the positions of the electrodes using reference points and a measuring tape. To give an example, to locate the risorius, or laughing muscle, we position the first electrode adjacent to the corner of the mouth and place the second electrode in the direction of the  
Figure 1: Acquisition setup: (1) electromyographic (EMG) signal amplifier; (2) silent computer; (3) computer screen; (4) camera; (5) microphone; and (6) audio interface.  
earlobe on the same side of the face. We mark three points: one on each outer side of both electrodes and one in the middle. This process is repeated for all eight electrode pairs, resulting in a total of 24 reference points. A 3D-printing professional then creates a 3D scan of the face and prints a mask with holes corresponding to the reference points. During subsequent sessions, we draw the points again on the subject's face using the holes and place the electrodes accordingly.  
Prior to each recording session, speakers are instructed to articulate their speech slightly more than usual. A supervisor is always present in the room to ensure the correct pronunciation of the utterances.
# Constructing Condition Monitoring Model of Harmonic Drive
## 3 The Proposed Approach
### Monitor Model Architecture Diagram  
The process of the proposed monitoring model is shown in Figure 1. In this study, two different datasets were used for model training and evaluation of the model's ability; these were the DCASE 2022 task 2 Dataset [13], which is called the DCASE 2022 Dataset (as discussed below), and the Harmonic Drive Dataset, which contains the harmonic drive operation sounds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Top-1 Accuracy** & **Params (M)** & **FLOPs (B)** \\ \hline EfficientNet-B7 & 84.7\% & 66 & 38 \\ EfficientNetV2-XL & 87.3\% & 208 & 94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance results of EfficientNetV2-XL and EfficientNet-B7 on the ImageNet Dataset.  
For the DCASE 2022 Dataset, data preprocessing was performed first. For each type of machine operating sound sample, the multi-layer Discrete Wavelet Transforms (DWT) were used to remove noise; then, the logarithmic Mel spectrogram was used to extract the features, and finally, the feature data were entered into the deep neural network for training.  
For the Harmonic Drive Dataset, each sound sample was cut to increase the number of samples first, and the same process was subsequently applied.
# A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection
###### Abstract  
Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.  
feature selection; correlation changes; curve registration; sensor clustering +
Footnote †: journal: Applied Sciences
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## Appendix A  
The identified parameter values for thruster 1 in the speed range 249.86-353.47 rpm are shown in Table A1. The means (\(\mu\)) of residual values are omitted (\(\mu\approx 0\)). The VIF values were computed by using only the residuals that are together in the models: Time domain model had features no. 1-3, the bearing model had features no. 4-8 and the gear model had features no. 9-11. The low values indicate that the models did not suffer from high multicollinearity.  
The identified covariance matrices for the time domain model (\(\Sigma_{1}\)), bearing model (\(\Sigma_{2}\)) and gear model (\(\Sigma_{3}\)) of Thruster 1 were as follows:  
\[\Sigma_{1}=\left(\begin{array}{cccc}1&359.40\times 10^{-3}&173.87\times 10^{-3} \\ 359.40\times 10^{-3}&1&617.53\times 10^{-3}\\ 173.87\times 10^{-3}&617.53\times 10^{-3}&1\end{array}\right)\,,\]  
\[\Sigma_{2}=\left(\begin{array}{cccc}1&213.16\times 10^{-3}&115.86\times 10^{-3} &244.48\times 10^{-3}&20.74\times 10^{-3}\\ 213.16\times 10^{-3}&1&-121.86\times 10^{-3}&-70.23\times 10^{-3}&16.63\times 10^{-3} \\ 115.86\times 10^{-3}&-121.86\times 10^{-3}&1&85.52\times 10^{-3}&26.46\times 10^{-3} \\ 244.48\times 10^{-3}&-70.23\times 10^{-3}&85.52\times 10^{-3}&1&164.39\times 10^{-3} \\ 20.74\times 10^{-3}&16.63\times 10^{-3}&26.46\times 10^{-3}&164.39\times 10^{-3} &1\end{array}\right),\]  
\[\Sigma_{3}=\left(\begin{array}{cccc}1&108.25\times 10^{-3}&592.75\times 10^{-3} \\ 108.25\times 10^{-3}&1&110.76\times 10^{-3}\\ 592.75\times 10^{-3}&110.76\times 10^{-3}&1\end{array}\right)\,.\]  
The identified parameter values for thruster 2 in the speed range 296.14-459.94 rpm are shown in Table A2. The low VIF values indicate that the models did not suffer from high multicollinearity.  
The defined covariance matrices for the time domain model (\(\Sigma_{1}\)), bearing model (\(\Sigma_{2}\)) and gear model (\(\Sigma_{3}\)) of thruster 2 were as follows:  
\[\Sigma_{1}=\left(\begin{array}{ccc}1&-208.53\times 10^{-3}&-39.62\times 10^{-3} \\ -208.53\times 10^{-3}&1&773.87\times 10^{-3}\\ -39.62\times 10^{-3}&773.87\times 10^{-3}&1\end{array}\right)\,,\]  
\[\Sigma_{2}=\left(\begin{array}{ccc}1&162.78\times 10^{-3}&127.74\times 10^{-3} &584.39\times 10^{-3}&168.96\times 10^{-3}\\ 162.78\times 10^{-3}&1&16.66\times 10^{-3}&90.11\times 10^{-3}&264.24\times 10^{-3} \\ 127.74\times 10^{-3}&16.66\times 10^{-3}&1&105.84\times 10^{-3}&368.07\times 10^{-3} \\ 584.39\times 10^{-3}&90.11\times 10^{-3}&105.84\times 10^{-3}&1&100.01\times 10^{-3} \\ 168.96\times 10^{-3}&264.24\times 10^{-3}&368.07\times 10^{-3}&100.01\times 10^{-3} &1\end{array}\right),\]  
\[\Sigma_{3}=\left(\begin{array}{ccc}1&167.14\times 10^{-3}&335.51\times 10^{-3} \\ 167.14\times 10^{-3}&1&105.94\times 10^{-3}\\ 335.51\times 10^{-3}&105.94\times 10^{-3}&1\end{array}\right)\,.\]