esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
###### Abstract  
The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10-20% compared to the benchmark algorithms.  
IoT; WSN; clustering protocol; routing protocol; energy efficiency +
Footnote †: journal: Sensors
# Re-Optimized Design for the ADR-Based Dual-Loop Controller of 100 m Class Aperture Radio Telescope
## 3 Re-Optimized Design of the Dual-Loop Controller  
The controller design of a servo system is usually carried out under the context of the triple-loop control structure: current-, velocity-, and position-loops. Figure 2 shows the block diagram of the 4 DOF antenna servo system, which is driven by multiple DC motors (denoted as \(nk\)) in the context of a triple-loop control structure. Where \(\rm G_{p}\), \(\rm G_{v}\), and \(\rm G_{c}\) denote the position-, the velocity-, and the current-loop controller, respectively. The current-loop controller related to the motor driving performance generally does not need a special design; in addition, the magnitude of the closed current-loop transfer function is unity within the velocity-loop bandwidth since the bandwidth of the current-loop is much higher than that of the velocity- and position-loops. So, the antenna servo controller design mainly focuses on the velocity- and the position-loop controllers. The position feedback point depends on the actual need and is usually chosen at the base. Figure 2 also presents the wind loads (in terms of torque \(\tau_{\rm wind}\)) acting on the antenna dish.  
Figure 2: Block diagram of multiple-DC-motor-driven 4 DOF antenna servo system with a triple-loop controller.  
We have designed an active disturbance rejection-based dual-loop controller on this 4 DOF model [27]. To further improve the performance of the servo control system of the large radio telescope, and thus, the pointing accuracy, we have re-optimized the designed controllers, i.e., the velocity controller re-optimization and position controller simplification.
## 7 Conclusions  
This article introduced a novel approach for learning to optimise the coverage of an area by a swarm of UAVs, i.e., automating the design of swarming behaviours in the context of area coverage missions. As a first step, a multi-objective optimisation problem has been defined to formally describe the objectives of the task to cover an area with a swarm of UAVs. This problem, called the Coverage of a Connected-UAV Swarm with 3 Objectives (CCUS3O), considers the coverage rate, the coverage time and the swarm connectivity. A CCUS3O instance represents a scenario for an area coverage mission. To automatically obtain the distributed heuristics necessary to tackle CCUS3O, a novel hyper-heuristic has been developed. Based on multi-objective Q-learning, also referred to as QLHH-II, it has been experimented on a set of CCUS30 instances. 50 instances from the class \(20\times 20/10)\) (10 UAVs on a \(20\times 20\) grid) have been used for the training. The generated heuristic has then been executed on 50 new instances of five different classes. The proposed hyper-heuristic has revealed a better convergence than existing techniques by generating a heuristic not only with better performance but also with more consideration of the multi-objective aspect. After processing a factorial experiment to determine an  
Figure 10: Example of fronts obtained with different heuristics for two instances, from the classes \((20\times 20/10)\) on the **left** and \((25\times 25/10)\) on the **right**.  
efficient parameterisation, experimental results demonstrate that while trained on a single instance class, the generated heuristic has shown to outperform on other classes a manually designed problem-specific heuristic along with state-of-the-art techniques designed for the coverage by a swarm of UAVs. In addition, empirical evidence of the good stability of the model and the better balance obtained on both objectives, coverage speed and swarm connectivity, has been provided.  
Future works will first consist of analysing other types of scalarisation techniques, such as Chebyshev. In the next step, following the empirically assessed good stability of the model, i.e., ability to generate a heuristic that performs well in unknown instances, the final objective of this work will be to study QLHH-II in dynamic environments. In a later stage, QLHH-II will be extended to tackle more diverse classes of problems.  
Conceptualisation, G.D. ([PERSON]); methodology, G.D. ([PERSON]); software, G.D. ([PERSON]); validation, G.D. ([PERSON]); formal analysis, G.D. ([PERSON]); investigation, G.D. ([PERSON]); resources, G.D. ([PERSON]); data curation, G.D. ([PERSON]); writing--original draft preparation, G.D. ([PERSON]); writing--review and editing, G.D. ([PERSON]) and G.D. ([PERSON]); visualisation, G.D. ([PERSON]); supervision, G.D. ([PERSON]), E.-G.T. and P.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 4 Experimental Results and Analysis  
We compared SDPL-VIO with the state-of-the-art methods: SVO [17], PCSD-VIO [22], PL-VIO [11] and VINS-Mono [10] on the EuRoC datasets [36]. The EuRoC datasets were collected by various sensors installed on the MAV, and divided into three series of flight scenarios, including a series of motion sequences ranging from easy to difficult. First, the comparison results of the accuracy and robustness performance based on the datasets are shown in Section 4.1. Then, to demonstrate the computation performance of SDPL-VIO, the time usage statistics results are illustrated in Section 4.2. Finally, the loop-closure detection capability of SDPL-VIO is evaluated in Section 4.3.
###### Abstract  
Cooperative communications advancements in Vehicular-to-Everything (V2X) are bolstering the autonomous driving paradigm. V2X nodes are connected through communication technology, such as a short-range communication mode (Dedicated Short Range Communication (DSRC) and Cellular-V2X) or a long-range communication mode (Uu). Conventional vehicular networks employ static wireless vehicular communication technology without considering the traffic load on any individual V2X communication technology and the traffic dynamics in the vicinity of the V2X node, and are hence inefficient. In this study, we investigate hybrid V2X communication and propose an autonomous and intelligent technology selection algorithm using a decision tree. The algorithm uses the information from the received Cooperative Intelligent Transport Systems (C-ITS) Cooperative Awareness Messages (CAMs) to collect statistics such as inter vehicular distance, one-way end-to-end latency and CAM density. These statistics are then used as input for the decision tree for selecting the appropriate technology (DSRC, C-V2X PC5 or 5G) for the subsequent scheduled C-ITS message transmission. The assessment of the intelligent hybrid V2X algorithm's performance in our V2X test setup demonstrates enhancements in one-way end-to-end latency, reliability, and packet delivery rate when contrasted with the conventional utilization of static technology.  
hybrid; 5G; C-V2X PC5; DSRC; technology selection; communication; CAMINO; CAM; DENIM; V2X
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 4 Conclusions  
The current study aimed to explore the effect of the view azimuth angle on two VENuS-derived VI values (i.e., NDVI and REIP) used in image processing for agricultural applications. The effect of the view azimuth angle on the NDVI value for field crops was more significant than its effect on REIP, while in the urban area, there was no difference between the VIs. The main finding is that in a fully developed field crop canopy, the REIP was less affected by the view azimuth angle than the NDVI. This finding provides additional evidence for preferring the REIP to the NDVI, which is more affected by high above-ground biomass. Currently, as the four red-edge bands are freely available by VENuS as well as Sentinel-2, the REIP has demonstrated its advantage in the view azimuth angle for field crops, in addition to its higher sensitivity to increasing vegetation biomass. Therefore, the REIP, when applicable, is recommended for field crop studies and monitoring. Specifically, the findings of the current study are crucial for processing vegetation properties in the forthcoming era of satellite constellations.  
M.S.: methodology, software, data curation, writing, and visualization; Y.T.: methodology, software, data curation, writing, and visualization; A.K.: methodology, writing, and funding acquisition; I.H.: conceptualization, methodology, data curation, writing, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This study was partially supported by the Hebrew University of Jerusalem's Intramural Research Fund in Career Development.  
Not applicable.  
The satellite imagery used are available on VENuS site [[https://venus.bgu.ac.il/venus/](https://venus.bgu.ac.il/venus/)]([https://venus.bgu.ac.il/venus/](https://venus.bgu.ac.il/venus/)) (accessed on 15 December 2021).  
The authors declare no conflict of interest.
## 5 IPv6 in Networking
### Address Space Exhaustion  
IPv6 addresses the issue of address space exhaustion, a significant limitation of IPv4. The limited number of IPv4 addresses creates bottlenecks as more devices connect to the network. IPv6's vast address space eliminates these scalability challenges, ensuring that every device participating in load-sharing or link-failure scenarios can be uniquely identified with its IP address. This research emphasizes the practical implications of adopting IPv6 in networks where dynamic and efficient address allocation is critical for seamless operation.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 2 Network Slicing in 5G
### Service Layer  
The service layer consists of specific services that are supported by the network operator or third party. It is linked to management and network orchestration (MANO) via an interface that allows it to create the dedicated slices for the specific application. The 5G network slicing architecture is shown in Figure 1. The services layer shows the supported applications and each application is depicted as a service instance.
## 2 Materials and Methods
### Microbiome Taxonomic Profiling Analysis  
Microbiome taxonomic profiling analysis was performed at CJ Bioscience using cell pellets consisting of 50 mL of the sample obtained via centrifugation at 10,000 rpm for 20 min (CJ Bioscience, Inc., Seoul, Republic of Korea). Total DNA was extracted using the FastDNA Spin kit (MP Biomedicals) according to the manufacturer's instructions [21]. PCR amplification was performed using fusion primers targeting the V3-V4 regions of the 16S rRNA gene with the extracted DNA. The fusion primers 341F (5\({}^{\prime}\)-AAT GAT ACG GCG ACC ACC GAG ATC TAC AC-XXX XXX XX-TCG TCA GCG TC-AGA TGT GTA TAA GAG ACA G-CCT ACG GGN GGC WGC AG-3\({}^{\prime}\); the underlined sequence indicates the target region primer) and 805 R (5\({}^{\prime}\)-CAA GCA GAA GAC GGC ATA CGA GAT-XXX XXX XX-GTC TCG TGG GCT CGG-AGA TGT GTA TAA GAG CA G-GAC TAC HVG GGT ATC TAA TCC-3\({}^{\prime}\)) were used for bacterial amplification. The fusion primers were constructed in the following order: P5 (P7) graft-binding, i5 (i7) index, Nextera consensus, sequencing adaptor, and target region sequence. Amplifications were carried out under the following conditions: initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 25 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, primer annealing at 55 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 30 s, with a final elongation at 72 \({}^{\circ}\)C for 5 min. The PCR product was confirmed through 1% agarose gel electrophoresis and visualized using a Gel Doc system (BioRad, Hercules, CA, USA). The amplified products were purified using CleanPCR (CleanNA; Waddinxveen, The Netherlands). Equal concentrations of purified products were pooled together, and short fragments (non-target products) were removed using CleanPCR. Quality and product size were assessed on a Bioanalyzer 2100 (Agilent, Palo Alto, CA, USA) using a DNA 7500 chip. Mixed amplicons were pooled, and sequencing was carried out at CJ Bioscience, Inc. using the Illumina MiSeq Sequencing system (Illumina, San Diego, CA, USA) according to the manufacturer's instructions.  
The secondary analysis which includes diversity calculation and biomarker discovery was conducted using in-house programs of CJ Bioscience, Inc. The alpha diversity indices (ACE, Chao1, Jackknife, Shannon, NPShannon, Simpson, and Phylogenetic diversity), rarefaction curves, and rank abundance curves were estimated [22; 23; 24; 25; 26; 27; 28]. To visualize the sample differences, beta diversity distances were calculated using Generalized UniFrac algorithms [29; 30; 31; 32; 33] and were displayed using principal coordinate analysis (PCoA) and dendrograms. With functional profiles predicted using PICRUST and MinPath algorithms [34; 35], taxonomic biomarkers and functional biomarkers were identified using statistical comparison algorithms (LDA Effect Size-LEfSe and Kruskal-Wallis H Test) [36; 37]. All analyses mentioned above were performed in EzBioCloud 16S-based MTP, which is a CJ Bioscience's bioinformatics cloud platform.
# Voting-Based Contour-Aware Framework for Medical Image Segmentation
## 2 Method
### Voting Strategy  
The difficult level of samples can be measured by uncertainty, and minimizing the loss function is the goal of supervised segmentation, which means the predictions are close to the ground truth. As depicted in Figure 1c, we employed a voting strategy in our framework to pick up the high-confidence images as inspired by [41]. In this paper, we deemed high-confidence samples as easy samples in each mini-batch and used these samples to update the target network weights. Due to the random data shuffling in the training process, the model can select different samples in each iteration. We simultaneously initialized three of our contour-aware networks with identical architecture. Each network will be the target to learn the easy samples selected by their respective voting committees. For each target network, it has its voting committee composed of its peer two networks. In this case, the information is shared with the committee, which can result in convergence at a robust minimum. Note that, due to the three networks initialized with different conditions, they could learn different representations from the same samples; thus, the three networks will not produce the same predictions in each mini-batch of data.  
In our work, we measured the agreement by computing the difference in loss values between the two networks in the committee, and for those samples with small loss differences, we deemed them to be high-confidence samples. We filtered out the high-confidence samples by choosing those showing agreed predictions of the two networks in the voting committee. The lower difference value means the predictions of the voting committee for this sample are more consistent, which also means the high confidence sample is more likely to obtain similar predictions from the voting committee. For the target network, its voting committee measures the agreement of samples according to the following equation:  
\[\mu=\operatorname{argmin}\left(L_{Seg}\left(Y_{j},f_{v1}\left(P_{j},W\right) \right)-L_{Seg}\left(Y_{j},f_{v2}\left(P_{j},W\right)\right)\right) \tag{6}\]  
where \(\mu\) denotes the agreement degree of the voting committee for each sample, \(L_{Seg}\) denotes cross entropy loss of semantic segmentation, and \(Y_{j},\ f_{v1},\ f_{v2}\) denote the semantic ground truth and the semantic predictions of voting committee networks.  
In this manner, we selected a certain proportion of the samples for the target network as usable samples and discarded the remaining samples in each mini-batch. The target network will only update its parameters by learning those useful samples. We describe the optimization details in Algorithm 1.
## 5 Reinforcement of Steel Beams (Plates) Using Fe-SMA
### Application of Reinforcing Steel Beams (Plates)  
The reinforcement of steel components contains two dimensions: repairing local cracks and improving the overall bearing capacity. The applications of reinforcing steel beams (plates) with Fe-SMA are summarized in Table 6. Steel beams (plates), such as steel bridges, have problems such as welding defects, residual stress, and stress concentration. Fatigue cracking is prominent under vibration and alternating load. Therefore, reinforcements should be applied to improve the fatigue-bearing capacity of cracked steel components.  
On the other hand, Fe-SMA can improve the overall bearing capacity of steel beams and satisfy the increasing load demand when steel beam capacity is insufficient or significant damage occurs. For the dimension of entire beams, friction clamps, with better applicability, can connect Fe-SMA to the strengthened beams through friction constraints, avoiding weakening the structural section and introducing vulnerable sources, as shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensions** & **Joining Methods** & **Activation Ways** & **Reinforcement Effect** \\ \hline Steel plate [98] & Bolt anchorage & Electrical resistance & Introduced a compressive stress of 74 MPa in the steel plate and considerably increased the yield load and carrying capacity of the steel plate. \\ \hline Cracked steel plate [94] & Bolt anchorage & Electrical resistance & Introduced a compressive stress of 71 Mpa in the steel plate. Under the stress amplitude of 75 Mpa, the fatigue life of the strengthened steel plate increased by 2.8 times, and the fatigue crack propagation is inhibited after the width of Fe-SMA strips is doubled. \\ \hline Cracked steel plate [106] & Adhesive bonding & Heat gun & Three mechanisms of added stiffness, bridging mechanism, and prestressing mechanism contributed to retarding the fatigue crack propagation. The fatigue life of repaired steel plate was 3.51 times higher than that of the unrepaired steel plate. \\ \hline INP300 steel beam & Friction clamp & Electrical resistance & Compressive stresses of 10-30 Mpa were applied to the bottom flange and upward deflections of 0.7–2.2 mm were achieved of the repaired steel beam. After \(2\times 10^{6}\) loading cycles, neither slippage of the clamping system nor prestress loss of Fe-SMA were observed. \\ \hline Double-angle connection of & Friction clamp & Electrical resistance & The activated Fe-SMA strips could realize complete fatigue crack arrest and greatly improve the fatigue performance of the joint. For the unreinforced joint, the crack propagates up to 50\% of the connection depth after \(2\times 10^{6}\) loading cycles. \\ \hline IPE240 and INP300 & & After reinforcement, upward deflections of 89 mm at the mid-span of the steel beams with 5.3 m-span [104] & Nail-anchor & Electrical resistance \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reinforcement applications of steel beams (plates) using Fe-SMA.
# Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms
## 4 Evaluation
### Evaluation Data  
We used the Slakh2100-split2 (Slakh) [62] and the RWC Popular Music Database (RWC) [63] for evaluation because these datasets include ground-truth beat times. The Slakh dataset contains 2100 musical pieces in which the audio signals were synthesized from the Lakh MIDI dataset [64] using professional-grade virtual instruments, and the RWC dataset contains 100 Japanese popular songs. All music signals were sampled at 44.1 kHz. The onset times of BD, SD, and HH (\(M=3\)) were extracted as ground-truth data from the synchronized MIDI files provided for these datasets. To make ground-truth drum scores, each onset time was quantized to the closest datum time (the justification of this approach is discussed in Section 4.4). Only musical pieces in which the drum onset times and tatum times had been annotated correctly as ground-truth data were used for evaluation. For the Slakh dataset, we used 2010 pieces, which were split into 1449, 358, and 203 pieces as training, validation, and test data, respectively. For the RWC dataset, we used 65 songs for 10-fold cross validation, where 15% of training data was used as validation data in each fold. Since here we aim to validate the effectiveness of the language model-based regularized training for self-attention-based transcription models on the same dataset (Slakh or RWC), investigation of the cross-corpus generalization capability (portability) of language models is beyond the scope of the paper and left as future work.  
For each music signal, a drum signal was separated with Spleetter [5] and the tatum times were estimated with madmom [2] or given as oracle data. The spectrogram of a music or drum signal was obtained using short-time Fourier transform (STFT) with a Hann window of 2048 points (46 ms) and a shifting interval of 441 points (10 ms). We used mel-spectrograms as input features because they have successfully been used for onset detection [11] and CNN-based ADT [14]. The mel-spectrogram was computed using a mel-filter bank with 80 bands from 20 Hz to 20,000 Hz and normalized so that the maximum volume was 0 db. A stack of music and drum mel-spectrograms was fed into a transcription model.