esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Major Limitations to the Use of Microalgae and Cyanobacteria in Agriculture  
Although the beneficial effects of microalgae and cyanobacteria (or their extracts) in agricultural practices is undeniable, one of the major limitations to the use of these microorganisms or compounds is the high costs typically associated with microalgal/cyanobacterial biomass production and compounds' extraction. For example, autotrophic growth of microalgae and cyanobacteria can be quite expensive and biomass productivities achieved are not very high. Moreover, biomass recovery after the production process can be costly and energetic-demanding, representing a large fraction of the overall biomass production costs. On the other hand, application of microalgal/cyanobacterial extracts requires the extraction of the interest metabolites, being the most commonly used methods very expensive [7; 96]. The high costs associated with microalgal biomass production and processing contribute to an increase in the biofertilisers/biostimulants/biopesticides cost to values that cannot compete with the actual price of chemically-based compounds. For these reasons, applications with higher revenues (e.g., obtention of bioactive compounds for the nutraceutical, pharmaceutical and cosmetics' industries) are typically preferred for microalgal/cyanobacterial biomass.  
Another problem arising from the use of microalgal and cyanobacterial extracts is related with the sustainability of the most commonly used extraction procedures. Usually, extraction of the bioactive compounds requires and disposes large amounts of organic solvents.  
Finally, the use of microalgal/cyanobacterial products is not broadly applied in agriculture because little is known about the interactions established between microalgae/cyanobacteria (and their extracts), plants and the environment. Although a lot of studies have been carried out in this area, the great diversity of photosynthetic organisms already isolated and identified and the infinite number of metabolites that can be extracted from them difficult a deep understanding of these interactions and, hence, their application in agriculture [7].
## 3 Results
### Ablation Study  
In this part, we discuss the effect of each part in ERGCN. To test the contribution of each part in ERGCN, we conduct the ablation studies in WIKI and ICEWS18. To test the importance of graph embedding, we remove the global unit in our approach, named as \(ERGCNwtg\). To illustrate the essential context semantic information, we remove the learnable weight \(W_{r,2}\) in ERGCN, named \(ERGCNwtr\). To demonstrate the necessity of the semantic interaction, we remove the sub-query when training, named \(ERGCNwtc\). The further discussion of the contributions of each part in ERGCN is reported in Tables 4 and 5.  
To illustrate how the global embedding affects the results, we conduct experiments without the global model. The results are denoted as \(ERGCNwtg\). It can be seen that removing the global embedding results in a significant decline in the performance on WIKI and ICEWS18. When we remove the global embedding, ERGCN will lose lots of temporal dependencies around the whole graphs and the model will only focus on learning the neighbor structural information.  
After removing the independent weight on relation types from the model, the model becomes \(ERGCNwtr\). Therefore, our ERGCN becomes similar to the other studies where entities and relations share the same transform weights in training process. Since the correlations between various entities and relations are usually different, after removing the weight on relation types from the model, we will lose specific features between certain combinations of facts. The results prove our prediction as the results decrease by about 1-2% in WIKI and ICEWS18.  
The results are labeled as ERGCN wtc, where the relation constraint between entities and relations are removed. The relation constraint can be seen as interactions between entities and their linked relations, which helps the model obtains the combination features of facts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MRR** & **hit1** & **hit3** & **hit10** \\ \hline
**ERGCN** & **51.28** & **44.52** & **55.78** & **62.28** \\ ERGCN wtg & 40.87 & 31.21 & 48.71 & 54.36 \\ ERGCN wtr & 49.09 & 42.71 & 53.41 & 59.07 \\ ERGCN wtc & 49.12 & 42.67 & 53.55 & 59.22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation studies on WIKI.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MRR** & **hit1** & **hit3** & **hit10** \\ \hline
**ERGCN** & **28.29** & **18.46** & **32.60** & **47.47** \\ ERGCN wtg & 26.97 & 17.31 & 31.94 & 46.71 \\ ERGCN wtr & 27.13 & 16.68 & 31.18 & 47.07 \\ ERGCN wtc & 26.88 & 16.59 & 30.98 & 46.77 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation studies on ICEWS18.
## 1 Introduction
The last few years' foci have been shifted to a low cost and earth abundant transition metal, transition metal oxide, transition metal sulfides, transition metal nitrides, and transition metal carbides [37]. The most common metals used in these catalysts are iron, nickel, copper, and cobalt [38]. The catalysts prepared by these metals' combinations have an exceptional ability for electrochemical water splitting and show improved reaction kinetics, fast charge transfer, and high conductivity. Cobalt based catalysts have been frequently used in water splitting and electrocatalysis [39; 40; 41]. The catalysts' conductivity can be further enhanced by adding some dopants, such as nitrogen [42]. Melamine is a well known and common precursor for the preparation of nitrogen-doped carbon [43; 44; 45]. This study aims to synthesize an effective electrocatalyst for the oxygen evolution reaction (OER). Copper oxide-cobalt oxide/nitrogen-doped carbon hybrid (Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN) composite is synthesized by a simple two-step method using low cost precursors. The material is characterized by different analytical techniques and applied for electrochemical water splitting.
# No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm
## 3 Population-Based Iterated Greedy Algorithm
### Iterated Greedy Procedure
#### 3.1.2 Local Search  
The local search is performed on the candidate solution found by the DC. In the local search of the IG algorithm by [PERSON] (2018), a job \(s\) is extracted from the permutation \(\pi\) and inserted into the other \(n-1\) possible positions. Let \(\pi_{binset}^{s}\) denote the permutation of the best insert move, namely the permutation resulting in the best makespan among the \(n-1\) permutations. If \(\pi_{binset}^{s}\) is better than \(\pi\), \(\pi\) is replaced with \(\pi_{binset}^{s}\). The process is then repeated for another job, and it terminates when no improvement occurs for all jobs. [PERSON] et al. (2019) improved this local search by avoiding redundant search, and developed insert-based local search (IBLS). Here we introduce the IBLS for the makespan criterion. The procedure of the IBLS is shown in Algorithm 2. The IBLS employs a random permutation at the very beginning to make the local search more stochastic.  
```
1:\(\pi_{R}\) = a permutation generated randomly
2:\(i=0,h=1\)
3:while (\(i<n\))
4: let \(s=\pi_{R||i}\)
5: find \(\pi_{binset}^{s}\)
6: if (\(\pi_{binset}^{s}\) is better than \(\pi\))
7:\(\pi=\pi_{binset}^{s}\)
8:\(i=1\)
9:else
10:\(i=i+1\)
11:endif
12:\(h=(h+1)\) % \(n\)
13:endwhile
```  
**Algorithm 2** Insertion-based local search (IBLS).
# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 2 Related Work
### Deep-Learning-Based Medical Image Segmentation  
After the success of UNet on medical image segmentation tasks, there have been many studies trying to utilize its architecture or applying additional techniques to improve the performance [8; 9; 10; 11]. For example, UNet combined with self-attention gates [8] on coarsely extracted feature maps from the encoding path is used for multi-organ segmentation. This gating method relieved the noisy response in skip-connection, capturing more precise textural and structural information on the input image patches. Bottleneck Feature supervised BS-UNet [9] connects its network with two UNets for liver and tumor segmentation. The first network is UNet with no skip-connection, which works as an auto-encoder learning the feature information from labeled data. The second network is the original UNet that performs the segmentation task. GIU-Net [12] utilize a graph-cut algorithm for multi-organ segmentation (skip-connection), which modified the structure of the skip-connection and used it to solve the task. Targeting organs-at-risk (OARs) is also crucial work in medical facilities. In [11], the convolution layers of UNet are replaced in the context aggregation block to learn from wide-range features to perform segmentation of OARs in cervical cancer.  
Recently, research to improve medical image segmentation performance is still in progress. For instance, some studies adapt the UNet architecture as the backbone and exploit other deep learning techniques. UTNet [13] utilize a transformer encoder block and a decoder block as the last part of each convolution layer. Ref. [14] extracts fine-grained features by utilizing various sizes of the receptive field. It inserted a channel attention (CA) block into skip connection and applied a hybrid dilated attention convolutional (HDAC) layer to the last encoder output. X-Net [15] proposes an X-shaped network that can learn in parallel using two different branches: the CNN branch and transformer branch. As the Vision Transformer rises in various fields of the vision task, many studies utilizing vision transformers as the backbone have been conducted for medical image segmentation. In [16], a U-shaped network with a vision transformer base encoder for brain tumors and abdominal organ segmentation of 3D image patches is proposed, and a network consisting of pure transformers is introduced in [17]. The authors, inspired by Swin Transformer [18] and UNet, built an encoder-decoder architecture composed of only transformers using Swin Transformer blocks.
# An Intelligent Access Channel Algorithm Based on Distributed Double Q Learning
## 1 Introduction
Reference [20] proposes a multi-user reinforcement learning algorithm, but each user adopts the single-user reinforcement learning method [21] without considering the cooperation and interference between users. The convergence speed is slow. In reference [22], a novel collaborative distributed Q-learning mechanism is designed for resource-constrained communication devices, so that they can find idle subframes for their transmission and take the occupancy order of subframes as the global competition cost. This algorithm only considers the time domain subframes, without considering the frequency domain channel. Reference [23] presents a distributed Q-learning based on the decoupling (DQL) scheme. Each device separately uses two Q-learning mechanisms to find a spare subframe and code book (a collection of all the available frequency hopping codes). The scheme has strong scalability and can expand the number of users to hundreds, but it does not consider the external disturbance problem. References [20; 21; 22; 23] are all distributed Q-learning algorithms. As each user conducts distributed computing and distributed executing, and with moderate hardware performance requirements, it displays strong extendibility and processing capacity. Distributed Q-learning was applied to multi-user scenarios anti-interference decision making. The paper puts forward a distributed double Q-learning algorithm. Each communication node uses two Q-learning. According to the automatically received feedback signal, users choose the optimal subframe and channel at the same time to avoid malicious external jamming and mutual interference between the users and improve the network overall throughput and reduce the dimension of action set. To improve the convergence of the algorithm. The main contributions of this paper are as follows:  
1. A distributed double Q-learning communication anti-jamming algorithm (DDQL) is proposed, which can effectively avoid the interference between users and external malicious jamming. The algorithm reduces the dimension of action set and shortens the convergence time of the algorithm. 2. It is proved that the channel subframe binary optimization problem can be equivalently transformed into two unary optimization problems. The rest of the article is organized as follows. The system model is presented in Section 2, while the problem is formulated. Section 3 introduces the anti-jamming algorithm of intelligent communication based on distributed double Q-learning, while the simulation results and analyses are given in Section 4. The final part is the summary.
###### Abstract  
This paper presents a method for analyzing the symbol error rate (SER) of a UAV cellular data network based on an IRS. This method can change the distances between the BS, IRS, UAV, and the elevation of the UAV to observe their impacts on the SER; then, a two-dimensional curve diagram of the parameters and the SER is drawn to determine the deployment position of the UAV. Finally, by flexibly changing the maximum angle of the UAV's transmitted signal and the height of the UAV, the coverage area of the UAV's cellular data network can be obtained through three-dimensional imaging, function fitting, and the extreme value of the condition function. As a relay of a cellular data network, a UAV can be flexibly deployed at any effective position at a low altitude to compensate for the limited coverage of the base station and the poor quality of the user's received signal. In particular, an IRS deployed on the surface of a building can generate an equivalent line-of-sight channel to reflect the signal from the base station to the UAV. Using an IRS to change the signal phase and amplitude with the flexible deployment of UAVs can give priority to solving the problems of cellular data network coverage and signal quality in urban scenarios.  
[PERSON]  
A _et al._ Article
## 5 Improve Obstacle Avoidance Path Planning for RRT
### Comparison of RRT Before and After Improvement  
The improved RRT algorithm after adding the gravitational field is compared with the original RRT algorithm, and simulation experiments were carried out to analyze and verify the validity and superiority of the improved RRT algorithm, with red representing the starting point (0, 0, 0) and blue representing the endpoint (700, 800, 1000), using three types of models, orange cylinders, pink spheres, and violet cuboids, as obstacles. The simulation test results are shown in Figure 12, where Figure 12a depicts the planning path of the RRT algorithm before improvement, and Figure 12b depicts the planning path of the improved RRT algorithm.  
The improved front and rear RRT algorithms are compared in a three-dimensional space of 1000 \(\times\) 1000 \(\times\) 1000, with a starting point of (0, 0, 0) and an ending point of (700, 800, 1000). The collision-free path from the starting point to the endpoint was planned in a three-dimensional space with obstacles. It can be seen from Figure 12 that the path planning of the RRT algorithm with gravitational field extends rapidly towards the endpoint to avoid random diffusion of random trees. At the same time, the path planning search time of the RRT algorithm with gravitational field from the start to the endpoint is 2.17 s, while the path planning search time of the RRT algorithm without improvement is 7.34 s. Therefore, it can be concluded that the search orientation of the improved RRT algorithm can shorten the operation time and improve the search efficiency.  
Figure 11: Comparison of RRT improvements. (**a**) Guided by the presence of a gravitational field. (**b**) RRT extended chart. (**c**) Improved RRT extended chart.  
The overall algorithm flow chart is shown in Figure 13.  
Figure 12: Comparison of RRT algorithms for planning paths. (**a**) RRT planning path before improvement. (**b**) Improved RRT planning path.  
Figure 13: Algorithm flow chart.
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 5 Experiments
### Experiments with Simulated Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Noise Type** & **ENR** & **AEC [8,30]** & \begin{tabular}{c} **AEC [8,30]** \\ **+PFRES** [27] \\ \end{tabular} & \begin{tabular}{c} **AEC [8,30]** \\ **+HDRES** [28] \\ \end{tabular} & \
\begin{tabular}{c} **AEC [8,30]** \\ **+Proposed RES** \\ \end{tabular} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} \multirow{2}{*}{White} \\ \end{tabular} } & clean & 8.59 & 13.14 & 9.58 & **14.41** \\ \cline{2-5}  & 20 dB & 7.94 & 12.56 & 8.96 & **13.53** \\ \cline{2-5}  & 15 dB & 7.34 & 12.03 & 8.29 & **12.52** \\ \cline{2-5}  & 10 dB & 6.25 & 10.85 & 7.09 & **11.04** \\ \hline \multirow{2}{*}{Babble} & 20 dB & 7.87 & 12.71 & 8.89 & **13.53** \\ \cline{2-5}  & 15 dB & 7.30 & 12.07 & 8.26 & **12.67** \\ \cline{2-5}  & 10 dB & 6.21 & 10.86 & 7.07 & **11.32** \\ \hline \multirow{2}{*}{Factory} & 20 dB & 7.90 & 12.36 & 8.91 & **13.55** \\ \cline{2-5}  & 15 dB & 7.36 & 11.91 & 8.32 & **12.78** \\ \cline{2-5}  & 10 dB & 6.28 & 10.92 & 7.13 & **11.44** \\ \hline \multicolumn{2}{c}{Average} & 7.31 & 11.94 & 8.25 & **12.68** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Echo return loss enhancement (ERLEs) in various echo-to-noise ratio (ENR) conditions for the acoustic echo canceller (AEC) without residual echo suppression (RES), with the power filter-based RES (PFRES) [27], with the harmonic distortion RES (HDRES) [28], and with the proposed RES during the far-end single-talk periods for the simulated data. The numbers in the bold face indicate the best performances.
# The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors
###### Abstract  
Optical fiber sensors have been extensively adapted as structural health monitoring devices. Due to the existence of the adhesive layer, a portion of the strain is absorbed by the adhesive. As a result, the structural strain sensed by the optical fiber is underestimated and required to be corrected. An analytical solution is presented through which it is possible to establish the relationship between the strains in the host structure and the surface bonded optical fiber sensor. Experimental measurements based on the Mach-Zehnder interferometric technique were performed to validate the theoretical prediction and reveal the differential strains between the optical fiber strain sensor and test specimen. Parametric studies show that the percentage of the strain in the test specimen actually transferred to the optical fiber is dependent on the bonding length of the optical fiber and the adhesive. The strain transfer is increasing from 56% to 82% as the bonding length increases from 5 cm to 12 cm with the epoxy adhesive. The general trend of the strain transfer obtained from both experimental tests and theoretical predictions shows that the longer the bonding length and the stiffer the adhesive, the more strain is transferred to the optical fiber.  
optical fiber strain sensor; Mach-Zehnder interferometric; strain transfer; bonding length Article  
The Effects of Adhesive and Bonding Length on the Strain Transfer of Optical Fiber Sensors  
11
## 4 Conclusions  
The optimization design of mirror structures using the method proposed in this paper is conducive to realizing complex objective functions, which can help the design to meet designers' demands as well as avoid the problems of inefficient and ineffective optimization caused by changing the structure parameters manually. Moreover, compared with methods using optimization tools such as OptiStruct [2; 7; 8; 9], it is not necessary to transform the multi-objective optimization problem into a single-objective optimization problem, and the interference of weight is excluded. In particular, this method could be flexibly combined with any advanced optimization algorithm and it is not limited to the built-in algorithms of the software used, which offers new ideas and possibilities to structural designers. In this paper, the optimization efficiency was improved by combining the population genetic superiority and inferiority characteristics in the non-dominated sorting genetic algorithm, and the Pareto optimal solution set was obtained for multi-objective optimization, which verified the effectiveness of this method. Future research will continue on the multi-objective optimization of mirror structures under more complex constraints and working conditions with advanced intelligent optimization algorithms.  
Conceptualization, L.S.; methodology, L.S.; software, L.S. and P.W.; validation, B.Z., Z.G. and P.H.; formal analysis, L.S.; investigation, Y.W.; resources, B.Z.; data curation, P.W.; writing--original draft preparation, L.S.; writing--review and editing, Z.G.; visualization, P.H.; supervision, B.Z.; project administration, Y.W.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62205337.  
Not applicable.  
Figure 7: Surface shape error in \(Z\) direction.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 5 Evaluation of the Proposed Scheduling Heuristic
### Comparison of Performances Between the Heuristic and the Priority Rules  
Figure 6 displays a local situation where several operations, represented by black dots, are candidates to be assigned to different machines according to the proposed heuristic (called HET), the ETT, and the TTE heuristics. There, the abscissa and the ordinate inform, respectively, for each candidate solution, its additional energy cost (\(\delta e\)) and resulting delay (\(\delta d\)). According to the description of the three different decision processes, the heuristic selects a local solution that has intermediate performance in terms of additional energy and delay (\(\delta e_{ETT}\leq\delta e_{H}\leq\delta e_{TTE}\) and \(\delta d_{TTE}\leq\delta d_{H}\leq\delta d_{ETT}\)).  
To illustrate the propagation of this property, let us consider a small flexible job shop, displayed in Figure 7, with three machines (\(m_{1}\), \(m_{2}\), and \(m_{3}\)) with two jobs (\(J_{1}\) and \(J_{2}\)), composed of two operations (\(O_{11}\) and \(O_{12}\)) and (\(O_{21}\) and \(O_{22}\)). The operations \(O_{11}\) and \(O_{21}\) can be performed either with machine \(m_{1}\) or with machine \(m_{2}\), while operations \(O_{12}\) and \(O_{22}\) are performed with machine \(m_{3}\). The processing and transfer costs (delays and energies) are given by \(d/e\) in the figure.  
Figure 6: Example of local decision space.  
Figure 7: Small flexible job shop (job 1 in black; job 2 in orange).  
The application of the heuristic and the two priority rules leads to the schedules displayed in Figure 8, with the performances displayed in Table 5.  
Given the above, it appears that the proposed heuristic produces performances that are midway between minimizing the make-span and minimizing the processing and transfer of energy. This result appears to be favored by two conditions: on the one hand, the existence of an inverse relationship between time and energy required on two machines capable of carrying out the same operation (the faster machine expending more energy), and, on the other hand, if the structure of the production plan induces between the machines of the job shop, a rank in accordance with the solution building process of the heuristic.