esa-sceva/satcom-chunk-collection huggingface dataset:
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 5 Evaluation
### Test 2: Effective Throughput  
To evaluate the performance of eTorii in terms of throughput, we compared it against Equal-Cost Multi-Path (ECMP) [35], a standardized and well-known protocol in the field of DCNs.  
More specifically, we evaluated both protocols in a Spine-Leaf 4 \(\times\) 4 topology (that is, with 4 Spine nodes \(S\) and 4 Leaf nodes \(L\)), with 20 hosts per Leaf node (as depicted in Figure 8). The traffic load is based in the flow patterns illustrated in Figure 10 with random inter-arrival times, and source-destination flows randomly generated in which the source and destination differ and belong to separate ToR switches, i.e., different Leaf nodes. Their flow size distributions are Web Search [2] and Data Mining [36] (derived from flow size measurements in previous works related to data centers). Furthermore, we defined four scenarios with increasing average traffic load in links (10%, 20%, 40% and 60% with respect to the their nominal capacity). This setup aims to prove that the relative performance is not worsen by the traffic increase in the network. Finally, all network links are set to 100 Mbps and the total execution time of the tests is 1800 s, considering a transitory time of 800 s, that is, we only evaluate the 1000 s afterwards, when the environment is stable. Each scenario is executed 10 times. Table 1 summarizes the setup.  
The results are illustrated in Figure 11, which show that eTorii obtains similar throughput values to ECMP. In particular, the two columns depict the two types of traffic, divided into the three types of flows (\"elephant\", \"rabbit\" and \"mouse\"), and eTorii has a better throughput with the \"elephant\" (around 3% and 13% higher) and \"mouse\" flows (around 8% and 12% higher), while it remains similar or even lower for the \"rabbit\" flows. The advantage of eTorii is that DCNs are usually characterized by elephant and mouse flows, as they represent the majority of traffic, while rabbit flows remain less frequent. Therefore, the overall performance of eTorii would be better in DCNs if compared to ECMP.  
The provided results are a lower bound since a distributed implementation of eTorii will obtain better results since it will not suffer from the delays derived from the SDN in regards to communication and processing time, which are at least equal to the Round Trip Time (RTT) between each device and SDN control plane, through the established control channel, to install the SDN rules derived from the HLMAC assignment.  
Figure 9: Percentage of selected routes, grouped by source ToR.
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 1 Introduction
Due to the enormous international influence of FAST, in order to avoid a tragedy such as that of the Arecibo telescope, its monitoring and early-warning system must be able to detect danger accurately and effectively in the first place and promptly remind on-site staff to take necessary measures to avoid accidents. This paper develops an early-warning system for FAST in terms of two parts, including pre-evaluation and health monitoring (PHM) systems. The model establishment parameters of the FAST reflector are studied, combined with mechanical simulation technology (MST), and an accurate model of the FAST reflector is established. The rainflow counting method is utilized to obtain the fatigue-stress amplitude curve of cables. The information is displayed in the system through MATLAB so that the pre-evaluation can be controlled visually. Development of the system has now been completed. The system can evaluate the safety condition of the FAST cable net at any time, promptly warn of possible accidents on the cable net, and predict the remaining working life of every cable, which effectively guarantees the stable operation of FAST. With these data, on-site staff can replace damaged components at the first opportunity. A field test also proved that the system effectively improved maintenance efficiency and reduced maintenance costs.
# Personalization of the 3D-Printed Upper Limb Exoskeleton Design--Mechanical and IT Aspects
## 5 Discussion
### 3D Printing Techniques  
Complex requirements for hand exoskeletons often require unconventional solutions, e.g., modification of a 3D printer to produce three new materials that meet many often conflicting requirements [83]. The importance of solving problems related to the hand exoskeleton is shown by the study of a soft hand exoskeleton designed for spacewalk astronauts to avoid hand fatigue in pressurized EVA gloves (reducing the stiffness of the suit glove). This exoskeleton is based on a set of six flexible actuators with shape memory wire (SMA) as the actuator. The small volume and high force-to-weight ratio made positional and force control testing for healthy, trained users crucial here. Thus, the exoskeleton on hand is a real space supertechnology [84].  
Efficiency usually depends on optimizing the combination of technological parameters and material properties. Computational optimization of the 3D printing process using artificial intelligence allows you to balance technical requirements with user safety constraints [85]. The research results so far show that the mere fulfillment of the initial functional requirements by the hand exoskeleton is insufficient, and mechanical complexity (e.g., limited numbers of actuators), susceptibility to modifications (including in CAD) and the ability to adapt to different hand sizes are important [86].
## 3 Test Results and Discussion
### Strength and Deformation Properties
On the other hand, by comparing Figure 7a,b, it can be found that the inclination angle also has a significant effect on the deformation of holes or inclusions. The deformation of the hole with \(\alpha=0^{\circ}\) is more sensitive to the change of the filling mode, and the resistance effect of filling on hole deformation improves with the time. This indicates that as the load increases, the inclusion will gradually produce a passive supporting effect on the surrounding rock matrix, especially the inclusion with better strength and stiffness, which can effectively improve the stress state of the surrounding rock, thereby improving the strength and deformation behavior of the specimens with inclusions [10; 32]. Table 3 lists the deformation values of \(0^{\circ}\) and \(45^{\circ}\) holes under different filling modes at the stresses of 10, 20, 30 and 40 MPa. It can be seen that under the same filling mode, no matter how the applied stress changes, the deformation of the \(45^{\circ}\) hole is always greater than that of the \(0^{\circ}\) hole, indicating that the structural stability of the \(45^{\circ}\) hole is worse than that of the \(0^{\circ}\) hole, and large deformation is more likely to occur in \(45^{\circ}\) hole under external force, which results in the mechanical properties of pre-holed  
Figure 7: Layout of virtual extensometers around \(0^{\circ}\) (**a**) and \(45^{\circ}\) (**b**) square hole and the variation in hole apertures with filling mode. specimens with \(\alpha=45^{\circ}\) is lower than those with \(\alpha=0^{\circ}\). In addition, for the \(0^{\circ}\) hole, the deformation of the hole after filling is significantly reduced, and the reduction degree increases with the increase of stress. However, for the \(45^{\circ}\) hole, the resistance effect of Filling Mode B on hole deformation is poor, while that of Filling Mode C is relatively obvious.
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods
### Classifier
#### 3.6.1 Random Forest  
RF is an ensemble learning model consisting of a set of decision tree classifiers \(\{f_{k}(x,\theta_{k})|k=1,\;2,\cdots,n\}\)[31], and the specific implementation process is to use a randomized with put-back approach (Bootstrap method) to extract the training set \(\theta_{k}\) from the original sample set \(\theta\); then to use the sampled training set \(\theta_{k}\) to train the decision tree \(f_{k}(x,\theta_{k})\). When a new sample \(x\) is input to the random forest, all decision trees \(f(x)\) classify the new sample separately, and finally determine by voting the classification results:  
\[Y=F(x)=\operatorname*{argmax}\sum_{k=1}^{n}I(f_{k}(x)=y), \tag{2}\]  
where \(Y\) is the final result of the classification, \(F(x)\) is the classification model, \(f_{k}(x)\) is a single decision tree classifier, \(y\) is the result of a single decision tree classification, and \(I(^{\circ})\) is the characteristic function.  
RF has the advantages of high prediction accuracy, fast training speed, strong resistance to noise and outliers, and generates training sets by random sampling to reduce overfitting and improve generalization ability.
## 2 Materials and Methods
### RNA Extraction and Sequencing  
RNA was isolated from each sample with three independent biological replicates using a Biospin Plant Total RNA Extraction Kit (Hangzhou Bioer Technology Co. Ltd., Hangzhou, China) following the manufacturer's instructions. The cDNA library preparations for next-generation sequencing were generated from 1 ug RNA of each \(D\). _officinale_ leaf sample using NEBNext(r) Ultra(tm) RNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA). Each cDNA library was purified with AMPure XP system (Beckman Coulter, Beverly, MA, USA) and its quality was assessed by using Agilent RNA 6000 Nano Kit on the Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA), then the cDNA libraries were sequenced and analyzed in Biomarker Technologies Co., Ltd. (Beijing, China). Based on sequencing-by-synthesis (SBS) technology, cDNA libraries were sequenced using the Illumina high-throughput platform. The cleaned reads generated by our cDNA libraries were mapped to the genome of \(D\). _officiale_ with HISAAT2 software [17; 18]. The transcripts with mapped reads were assembled using StringTie [19]. Fragments Per Kilobase of transcript sequence per Millions mapped reads (FPKM) was calculated to assess gene expression levels. The expression level of each gene was calculated with the DESeq2 package [20]. The genes with a fold change (FC) \(\geq\) 2 and false discovery rate (FDR) \(<\) 0.01 by pairwise comparisons were defined as differentially expressed genes (DEGs). A Kyoto Encyclopedia of Genes and Genomes (KEGG) database ([[https://www.genome.jp/kegg/pathway.html](https://www.genome.jp/kegg/pathway.html)]([https://www.genome.jp/kegg/pathway.html](https://www.genome.jp/kegg/pathway.html)), accessed on 1 March 2021) was used for KEGG pathway analyses. KEGG category enrichment with a \(q\) value (corrected \(p\) value) \(<\) 0.05 was considered significantly enriched. The protein-protein interaction (PPI) analysis was performed by STRING database ([[http://string-db.org/](http://string-db.org/)]([http://string-db.org/](http://string-db.org/)), accessed on 1 March 2021) and was visualized using Cytoscape [21]. The heat maps for DEGs were plotted by using TBtools [22]. Raw sequence reads can be downloaded from the website ([[https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA715099](https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA715099)]([https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA715099](https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA715099)), accessed on 1 March 2021).
# 40 W All-Fiber Er/Yb MOPA System Using Self-Fabricated High-Power Passive Fiber Components
## 4 Conclusions  
In conclusion, we have demonstrated an all-fiber high-power CW-operating MOPA system, in which we have used self-fabricated passive fiber components: pump and signal power combiners in the configuration (\(5+1\)) \(\times\) 1 using SM signal fibers and in the configuration (\(6+1\)) \(\times\) 1 using LMA-type signal fiber, and also an MFA for connection of the SM fiber (\(8.2/125\)\(\upmu\)m) with the LMA-type fiber (\(20/130\)\(\upmu\)m). The presented MOPA system consisted of three stages: pre-amplifier (EDFA based on erbium-doped active SM fibers), medium-power amplifier (EYDFA based Er/Yb co-doped active SM-DC fiber) and high-power amplifier (EYDFA based on Er/Yb co-doped active LMA-type DC fiber). The maximum achieved CW power was approximately 44 W at the wavelengths of 1555 nm and 1563 nm in the presence of the pumping at the power of 144 W and the wavelength of 915 nm. The presented MOPA setup confirmed functionality of our self-fabricated high-power passive fiber components and their capability for stable operation in high-power radiation regimes. Further research on the high-power passive components will lead to improvement of their transmission parameters, leading to improved efficiency of MOPA systems.  
[PERSON] and [PERSON] designed the experimental MOPA system; [PERSON] designed and fabricated high-power passive fiber components; [PERSON] and D.S. performed the MOPA system demonstration and characterization; [PERSON], [PERSON] and K.M.A. analyzed the data; [PERSON] and D.S. wrote the manuscript.  
[PERSON] and K.M.A acknowledge support from the National Science Centre (NCN), Poland under the project \"Pump and signal power combiner with improved signal transmission efficiency\" (decision No. UMO-2015/19/N/ST7/01511); [PERSON], [PERSON] and [PERSON] acknowledge support from the Wroclaw Research Centre EIT+ under the project \"The Application of Nanotechnology in Advanced Materials\"--NanoMat (POLG.01.02-02-002/08-00) financed by the European Regional Development Fund (Innovative Economy Operational Programme, 1.1.2); [PERSON] and [PERSON] acknowledge support from the statutory funds of WrUST (grant No. 0401/0137/16); [PERSON] acknowledge partial support from the action COST MP1401 \"Advanced Fibre Laser and Coherent Source as tools for Society, Manufacturing and Lifescience\" (2014-2018).  
The authors declare no conflicts of interest.
## 4 Discussion
### Effect of MPs on Fish Intestinal Microbiota  
In this study, diversity or composition of intestinal microbiota was not significantly changed by virgin MPs exposure. However, PCA analysis revealed that MPs in water shaped the bacterial community composition by separating Group A and B from the control group. The relative abundance of Proteobacteria and Actinobacteria gradually increased compared with the control group at the phylum level. The dominant genera in the gut of largemouth bass were Mycoplasma in healthy largemouth bass, while they were decreased after MPs treatment. [PERSON] et al., (2020) also found the gut microbiota in juvenile guppy (_Poecilia reticulata_) were dominated by Proteobacteria and Actinobacteria, and exposure to MPs resulted in an increase of Proteobacteria at phylum level [44]. Similar results were found in zebrafish gut, Proteobacteria significantly decreased (\(p<0.05\)) after exposure to 5 \(\upmu\)m microbeads for 21 d [45]. Proteobacteria were more prevalent in contaminated environments with higher MPs concentrations [46]. The growth of Proteobacteria and other bacteria were encouraged by the inflammation induced oxidation that connected with microbiota dysbiosis [47]. Collectively, Proteobacteria could potentially be pathogenic bacteria phylum in fish gut when affected by MPs.  
Moreover, the general resilience of fish intestinal microbiota was found in a study of common carp (_Cyprinus carpio_) exposed to environmentally relevant concentrations of MPs for 30 d and followed by MPs excretion for another 30 d [48]. We tried to use experiments to simulate real aquacultural situations and found that the fish themselves can cope with the pressure of MPs more than our expectation. Nevertheless, we should also acknowledge that the absence of biofilm can be advantageous in reducing the variables that can affect the gut microbiological diversity, thus simplifying the interpretation of the results.
# Chemical and Enzymatic Treatment of Hemp Biomass for Bioethanol Production
## 3 Results and Discussion
### Ethanol Fermentation  
After optimizing both SHF and SSF processes, ethanol fermentation in the bioreactor was performed, as presented in Figure 4.  
In the SHF process the ethanol concentration achieve higher values than it was noted in the case of the SSF process, while the increasing the fermentation time does not change the ethanol yield. The highest concentration of ethanol was observed at 48 h of the SHF process and it was 10.51 \(\pm\) 0.08 g/L, which is 21.02 g/100 g of hemp biomass. In the SSF process the highest concentration of ethanol was observed at 96 h and it was 6.5 \(\pm\) 0.02 g/L, which is 13g/100 g of hemp biomass. The similar study presented [PERSON] et al. [34]. In their work conversion lignocellulosic biomass to bioethanol was carried out through pretreatment, saccharification and fermentation processes. Their results showed that the SHF process gave a higher concentration of ethanol (8.11 g/L) compared to the SSF process (3.95 g/L). SHF as a process alternative in an industrial bioethanol plant has both potential and limitations. The main advantage of SHF is the possibility to optimize the process steps separately, especially to be able to run the enzymatic hydrolysis at an optimal temperature, with respect to enzymes. In addition, the SHF process brings the possibility of removing the insoluble solids after enzymatic hydrolysis, which allows to perform liquid fermentation, facilitating the reutilization of the fermentative microorganisms [35]. The SSF process, however, consists in lower energy consumption and thus lower costs of bioethanol production. Due to this advantage, it is often the preferred method of obtaining lignocellulosic bioethanol [11].  
Figure 4: Ethanol yield after fermentation process for separated hydrolysis and fermentation (SHF) and simultaneous saccharification and fermentation (SSF).
# A Kernel Least Mean Square Algorithm Based on Randomized Feature Networks
## 5 Simulations and Results
### Nonlinear Channel Equalization
#### 5.2.1 Time-Varying Channel Equalization  
The time -varying nonlinear channel used in this simulation is often encountered in satellite communication links, as mentioned in [31; 32]. The linear channel impulse response is \(h=[h_{0},h_{1},h_{2}]^{T}\). Here, the channel impulse response \(h=[0.3482,0.8704,0.3482]\) is used; such a channel is more likely to be encountered in a real communication system. For the time-varying channel model, the transfer function is defined as  
\[H(z)=(h_{0}+h_{0}(j))+(h_{1}+h_{1}(j))z^{-1}+(h_{2}+h_{2}(j))z^{-2} \tag{39}\]  
where \(h_{0}(j)\), \(h_{1}(j)\), and \(h_{2}(j)\) are the time-varying coefficients and are generated by using a second-order Markov model, in which a white Gaussian noise source drives a second-order Butterworth low-pass filter. To generate the time-varying coefficients, the simulation code can be found in [33]. The output of the nonlinear channel is described by  
\[r(n)=x(n)+0.2x^{2}(n)+v(n) \tag{40}\]  
where \(v(n)\) is 20-dB white Gaussian noise.  
To determine the optimum value of the kernel parameter for a time-varying channel, the dimension of the KLMS-RFN is set to 50. The range of \(\sigma\) is chosen as 1.2 to 2.6. The MSEs of the KLMS-RFN over 500 Monte Carlo simulations are shown in Figure 8. The optimum value of the kernel parameter for equalizing the time-varying channel used in this simulation is 2. The same rule is used, i.e., that small and large kernel parameters can result in a reduction in accuracy.  
Figure 8: The final MSE curve of the KLMS-RFN algorithm with a varied kernel parameter \(\sigma\) from 1.2 to 2.6 in time-varying channel equalization.  
Figure 7: The structure of the nonlinear channel.  
Next, we compare the performance of the KLMS-RFN in equalization with the QKLMS and QKLMS-FB. Their kernel parameters are chosen as 2. All step sizes are set to 0.1. The number of dimensions of the KLMS-RFN is 50. The values of \(\gamma\) of the QKLMS and QKLMS-FB are both 0.3. The \(\beta\) and budget \(D\) of the QKLMS-FB are 0.96 and 25, respectively. The MSE curve after 100 independent trials is shown in Figure 9. The network size grown curves of the QKLMS and QKLMS-FB are shown in Figure 10. The network size of the QKLMS-FB achieves its fixed budget, which is lower than the final network size of the QKLMS. The run time curves of the QKLMS, QKLMS-FB, and KLMS-RFN are also given as shown in Figure 11. After 7000 iterations, the total run times of the QKLMS, QKLMS-FB, and KLMS-RFN in time-varying channel equalization are 0.1816 s, 0.6749 s, and 0.0745 s, respectively. The final MSEs of the QKLMS, QKLMS-FB, and KLMS-RFN (\(M=50\)) are \(-11.55\) dB, \(-10.97\) dB, and \(-12.10\) dB, respectively. At the same coverage speed, the KLMS-RFN shows the best performance.  
Figure 10: The network size growth curves of the QKLMS and QKLMS-FB algorithms in time-varying channel equalization.  
Figure 9: The learning curves of the QKLMS, QKLMS-FB, and KLMS-RFN algorithms in time-varying channel equalization.
## 4 Experiments and Analysis  
This section firstly introduces the datasets used in the experiments: Underwater Image Enhancement Benchmark (UIEB) dataset [5], non-reference underwater image dataset U45 [48], and Enhancement of Underwater Visual Perception (EUVP) dataset [43]. Secondly, we describe the experimental setup and implementation details. To demonstrate the feasibility and superiority of the proposed method, we compared LPS-Net with traditional methods [9; 36; 49] and state-of-the-art deep learning underwater methods [5; 14; 15; 16; 17; 18; 40; 22; 38; 40; 50]. Finally, we conducted an analysis of the objective data presented in the tables and provide detailed visualizations to demonstrate the advanced and effective nature of our method.
## 2 Materials and Methods
### Instrumentations and Analytical Conditions  
ERDA analysis was performed using an H-class ultra-performance liquid chromatography (UPLC) system (Waters, Milford, MA, USA). An Acquity BEH C\({}_{18}\) column (2.1 mm \(\times\) 100 mm, 1.7 \(\mu\)m) \"Waters, Milford, Massachusetts, USA\" was used for separation and determination of EDRA. A mixture of 0.1% formic acid in acetonitrile and water (0.1% formic acid) (65:35) was used as a mobile phase and eluted at flow rate 0.25 mL/min. The outlet of the column was coupled to a Waters TQD Class Mass Spectrometer (Waters, Milford, MA, USA) with an electrospray ionization (ESI) source operated in the positive ion mode. Mass spectrometry parameters were adjusted at optimized conditions for separation of EDRA and IS Multiple reaction monitoring (MRM) mode was selected and used, in which the ion transitions for EDRA and IS were \(m/z\) 447.2 \(\rightarrow\) 86.0 and \(m/z\) 441.1 \(\rightarrow\) 84.0, respectively (Figure 2). The details of compound and instrumental parameters were illustrated in Table 1. Each of EDRA and IS was separated at about 1.23 and 1.02 min, respectively, with a total run time of 2.0 min.  
Figure 1: Chemical structure of Erdafitinib (**A**) and Ibrutinib (**B**).  
Figure 2: Product ion spectra of the ERDA (**A**) and IS (**B**) in positive ionization mode.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **ERDA** & **IS** \\ \hline I. Compound Parameters & & \\ \hline Precursor ion (\(m/z\)) & 447.2 & 441.1 \\ \hline Product ion (\(m/z\)) & 86.0 & 84.0 \\ \hline Dwell time (s) & 0.05 & 0.05 \\ \hline Cone Voltage (V) & 38 & 50 \\ \hline Collision energy (eV) & 30 & 40 \\ \hline II. Instrument Parameters & & \\ \hline Collision gas flow rate (mL/min) & 0.1 & 0.1 \\ \hline Nitrogen flow rate (L/h) & 600 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass optimization parameters for ERDA and IS.