esa-sceva/satcom-chunk-collection huggingface dataset:
# HMB-I/O: Fast Track for Handling Urgent I/Os in Nonvolatile Memory Express Solid-State Drives
## 2 Related Work
### Support in the Block I/O Layer for NVMe SSDs  
The Linux block I/O layer has changed significantly in response to NVMe SSDs with numerous queues [2]. In a previous single-queue block I/O layer, all the I/O requests issued by processes running on each CPU core were sent to a single request queue. Consequently, a performance bottleneck occurred because of lock contention for the request queue. To alleviate this problem, a multiqueue block I/O layer employs two levels of queues--software queues and hardware queues. I/O requests issued by a process running on a CPU core are firstly sent to the corresponding software queue attached to the CPU core and then to the hardware queue mapped to the software queue. The I/O requests are finally sent to the submission queues in the NVMe controller, which are mapped to the hardware queues.  
The multiqueue block I/O layer is suitable for handling I/O requests in parallel by cooperating with NVMe SSDs with many queues. However, as this approach lacks QoS support, some I/O scheduling techniques for NVMe SSDs have been proposed. [PERSON] et al. [9] stated that write requests still affect the latency of read requests negatively in NVMe SSDs. They proposed a queue isolation technique that eliminates the write interference and improves the read performance by separating read and write requests. [PERSON] et al. [23] presented a scheme that efficiently implements a weighted round robin (WRR) scheduler, which is one of the NVMe features involved in providing differentiated I/O service. The submission queues could be marked as urgent, high, medium, or low in a WRR, and WRR support was implemented in a Linux NVMe driver. These studies tried to provide some QoS services for read requests or high-priority requests, but they could not solve the problems when SSDs are over-burdened.  
[PERSON] et al. [12] classified application types into online applications that are latency-sensitive and offline applications that are relatively less sensitive and provided separated I/O paths for each application type. In their approach, the multiqueue block I/O layer is bypassed to provide a low latency for I/O requests issued by online applications. In contrast, I/O requests issued by offline applications follow the original procedures of the multiqueue block I/O layer. Using this method, the average latency of servers running multiple applications was improved by 22%. It is similar to our approach in terms of the fact that application types are classified and then urgent I/O requests are directly processed by bypassing the multiqueue block I/O layer. However, our scheme utilizes the HMB space as a direct communication channel between the host and SSD, so it can provide a faster response for urgent I/O requests even when there are many pending I/O requests in the submission queues.
## 3 Results
### Principal Component Analysis (PCA)  
The principal component analysis is a statistical procedure to extract principle components and structures in data by using orthogonal transformation and by reducing dimensionalities of data, according to [PERSON] and collaborators [33]. It extracts the first two axes reflecting the variety of samples to the most extent and thus can reflect high-dimensional data variation in a two-dimensional graph, which reveals the simple principle embedding in complex data. The results of PCA analysis based on OTUs are shown in Figure 10. The more similar the community composition among the samples are, the closer the distance of their corresponding data points on the PCA graph. Thus, the thermophilic composts were quite similar, while the mesophilic composts were substantially different.  
Clustering analysis was used to construct a cluster tree to study the similarity among different samples. The unweighted pair-group method with arithmetic mean (UPGMA) is a hierarchical clustering method widely used in ecology to classify the samples. First, samples with the closest distance are clustered to form a new sample. Then, the average distance between the newly created sample and other samples is calculated, and the two closest samples can be found again to repeat the above steps. With time, the A1 relative abundance of the _Actinobacteria_ phylum decreased, whereas that of _Proteobacteria_ increased. In contrast, in all other composts, the opposite trend is observed (Figure 11).  
Figure 11: (**A**) UPGMA cluster tree based on Weighted Unifrac distance. (**B**) UPGMA cluster tree based on Unweighted Unifrac distance, which is plotted with UPGMA tree on the left and relative abundance in phylum map on the right (A1, thermophilic of Harmanli site; A2, mesophilic of Harmanli site; B1, mesophilic of Yasno pole site; B2, thermophilic of Yasno pole site).  
Figure 10: Unweighted pair-group method with arithmetic mean (UPGMA) (A, thermophilic of Harmanli site; B, mesophilic of Harmanli site; C, mesophilic of Yasno pole site; D, thermophilic of Yasno pole site).  
The Weighted Unifrac distance matrix and Unweighted Unifrac distance matrix were calculated before using for UPGMA cluster analysis. They were displayed with the integration of clustering results and the relative abundance of each sample by phylum in the figure, based on Unweighted Unifrac distance. The UPGMA tree is on the left and the relative abundance in phylum map showed that in the thermophilic compost of Harmanli, _Proteobacteria_ decreased, while _Actinobacteria_ and _Acidobacteria_ increased. In the rest of the compost stages, _Proteobacteria_ are the dominant species, and _Actinobacteria_ lower their abundance due to the PCoA (Figure 10A). Moreover, a correlation between the environmental factors and the bacteria at the class level was conducted (Figure 11A). _Actinobacteria_ and _Proteobacteria_ possessed a highly significant positive correlation with the processes in the thermophilic phase of Harmanli compost. In contrast, _Proteobacteria_ were mostly significant in the rest of the compost stages (Figure 11B).
## 6 Four-Zero Texture Model Validation
### Predictions for the \(V_{CKM}\) Elements
For \(|V_{ts}|\), the experimental central value is shown with a solid blue horizontal line at \(|V_{ts}|=0.03978\), and its experimental error with two blue dashed horizontal lines at \(\pm 0.00060\). The predictions for these two elements indicate that the data obtained with precisions of \(\chi^{2}<1\) and \(\chi^{2}<1\times 10^{-1}\) may be outside the experimental errors, so they are not good predictions. In contrast, the prediction with \(\chi^{2}<1\times 10^{-2}\) remains within the errors, being a good prediction. These characteristics are observed in the four case studies. When analyzing the predictions of the elements of the \(V_{CKM}\) matrix (\(|V_{ud}|\), \(|V_{cd}|\), \(|V_{cs}|\), \(|V_{tb}|\), \(|V_{td}|\), and \(|V_{ts}|\)), it was observed that only the solutions of the HE-DEPSO algorithm, with an accuracy of \(\chi^{2}<1\times 10^{-2}\), fit within the limits of experimental error. These solutions are considered valid and capable of reproducing the \(V_{CKM}\) matrix in all case studies and elements. Thus, it can be established that the 4-zero texture model is physically viable. It would be interesting, however, to explore the implications of this information obtained within the texture formalism.
# No-Wait Job Shop Scheduling Using a Population-Based Iterated Greedy Algorithm
## 4 Computational Results and Comparisons
### Calibration of the PBIG Algorithm  
There are four parameters in total to calibrate for the PBIG, namely \(p\), \(d\), \(pb\), and \(D\). A larger Design of Experiments (DOE) [34] is carried out based on the following factor: (1) parameter \(p\) tested at four levels: 4, 6, 8, 10; (2) parameter \(d\) tested at four levels: 2, 4, 6, 8; (3) parameter \(pb\) tested at four levels: 0.6, 0.7, 0.8, 0.9; (4) parameter \(D\) tested at four levels: 2, 4, 6, 8. We select the following seven instances with different sizes from each other: la06, la11, la21, la26, la31, la36, and swv01. Each instance is solved by the algorithm for each combination of the factors with five independent replications. The average RPD (ARPD) obtained by the algorithm is calculated as a response variable. The stopping criterion is the elapsed CPU time not less than 6 \(mn^{2}\) milliseconds (ms). The multi-factor Analysis of Variance (ANOVA) technique is used to analyze the computational results. The ANOVA results are shown in Table 2. It can be seen from Table 2 that the parameters \(p\), \(d\), and \(D\) are statistically significant, whereas the parameter \(pb\) is not. The means plots of these factors, together with least significant difference (LSD) 95% confidence intervals, are illustrated in Fig. 1. Recall that if the LSD intervals for two means are not overlapping, then the difference between the two means is statistically significant.  
Figure 1 suggests that for the values of parameter \(p\), 8 is statistically better than 4 and 6, although its difference from 10 is not significant. For the values of parameter \(d\), 4 is statistically better than 2 and 8. The values 6 and 8 are relatively better than 2 and 4 for the parameter \(D\), while no statistical significance is found for the parameter pb. Finally, the parameters of the algorithm are set as \(p=8\), \(d=4\), \(pb=0.7\), and \(D=6\).
# Experimental Setup and Graphical User Interface for Zero-Length Column Chromatography
## 4 Program Design  
### Graphical User Interface  
A GUI was programmed in LabVIEW software (National Instrument, USA) to perform repeated experiments at different flow rates. A National Instruments USB-6212 card data acquisition (DAQ) with analog inputs/outputs and digital inputs/outputs was used for manipulation and monitoring of the electromechanical devices (MFCs and FSVs) and acquisition of the FID's signals. Through this GUI it is possible to: configure the measurement routines, enter the experimental parameters, obtain the desorption curves and calculate the adsorption isotherms and diffusion coefficient.  
It should be noted that the GUI is designed to work with flame ionization detectors. However, the GUI was designed in such a way that it is scalable, i.e., it allows for new components, such as sensors, detectors, or more complex equipment. In the case of a mass spectrometer, these are generally supplied with their own data acquisition software; nevertheless, they can be configured and manipulated remotely via a physical interface (USB, GPIB, Ethernet connection cable) and a user-designed control program in a LabVIEW programming language.  
The functions available on the front panel designed for this paper will be explained according to the numbered blocks showed in Figure 3.  
* Consists of two tabs, the first one allows the user to enter the characteristics of the molecular sieve. In the second tab, by means of four control paddles, the study gases are chosen for each mass flow controller.
* It allows the user to configure the conditions for three different work routines: degassing, equilibrium, and desorption process. In this function, the mass flow rates of each MFC for each process are selected. The pressures of pure and  
Figure 3: Labview GUI of semi-automatic experiment setting.  
mixtures gases are entered by means of numerical indicators. Additionally, the temperature of the chromatography furnace in each process is indicated.
* This block consists of four switches that allow the user to choose which process will be stored, specify the storage path of the measurement parameters and the results obtained, and introduce the frequency of data storage.
* Plots the raw adsorption and desorption curve in real-time. By means of two switches, the user has the option to start and stop the acquisition of the response graph.
* This block consists of two sections. In the left one, the normalized curve of C/C0 vs. \(t\) is presented. This curve is obtained from the virgin desorption curve (see Section S2 of Supplementary Materials). In the right section, the adsorption capacity (\(n_{\text{ad}}\) vs. \(p\) is plotted. In this same section are also calculated: Henry's constant (\(K\)), time constant ratio (\(L\)), diffusion coefficient (\(D\)), and maximum amount adsorbed (\(n_{\text{ad}}\)). These calculations were performed using equations from Section S2 of the Supplementary Material.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 2 Basic Theory and Solution Method
### Frequency-Domain Analysis  
The steps involved in the frequency-domain analysis are as follows (Figure 1):  
1. Determining random time-domain waveforms of stress/strain at structural specific positions by testing.
2. Determining the frequency-domain curve by Fourier transform for time-domain waveforms and analyzing the load PSD.
3. Establishing the frequency-domain fatigue model.
4. Obtaining the S-N curve of the material by consulting literatures.
5. Calculating the structural fatigue life based on the Miner linear cumulative damage criterion.  
The frequency-domain analysis method of the PSD function can be divided into narrow-band and broad-band methods.  
Figure 1: Frequency-domain analysis.
## 3 Vision, Key Features and Challenges of 6G
### Challenges of 6G  
Even though several advanced features have been added to 6G networks to enhance the performance matrices in comparison with 5G networks, there are still some key challenges that must be addressed further. These challenges are broadly classified into two categories: (i) technological challenges that include high throughput, EE, connectivity flexibility, more intelligent optimization techniques, etc., and (ii) non-technological challenges including industry barriers, spectrum allocation, regulatory policies and standardization, etc. [200]. A few key challenges of the future 6G networks are summarized in Figure 6.  
In addition, due to the integration of the IoE, terrestrial and non-terrestrial communication networks in 6G, their different heterogeneous highlights must be considered to productively coordinate them. Heterogeneity is likewise present in the protocol that those communication networks will comply with. Thus, 6G is taking on the massive task of integrating a number of heterogeneous aspects [203]. Furthermore, due to the inclusion  
Figure 6: Challenges of 6G [5; 154; 200; 201; 202; 203].  
of mm-Wave and THz communication, 6G networks are facing several more open challenges, e.g., more sensitive low-power transmitter, new model architecture, advanced propagation techniques for better coverage and directional communication. The networks must also deal with system noise, channel fading and fluctuations [169; 203; 204; 205]. Several more challenges such as computational and processing resources due to the application of AI [206], a few ML application-related challenges [207], training issues and interoperability challenges [208], challenges in estimating the channel information by using reconfigurable intelligent surfaces (RIS) [209; 210] and computational and trade-off challenges due to the application of artificial neural networks (ANN) in the IoT [211] have been recognized for the future 6G networks.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 4 Experiments and Models
### Sentinel-1  
SAR is a method of creating images using radio waves. Due to the movement of the radar, a large artificial gap will be created when a gap in the synthesis of data is formed by taking measurement values at certain intervals and collecting these values at the same time [37]. The artificial aperture creates the equivalent of a large physical antenna, and it provides high resolution images. There are many different types of satellites with discrete bands or modes.  
Sentinel is the name of the program called Copernicus run by the European Space Agency (ESA). Sentinel-1 consists of 2 satellites (Sentinel-1A and Sentinel 1-B), which both share the same orbit. Sentinel-1A and Sentinel-1B were launched, respectively, in 2014 and 2016. Sentinel-1 carries a C-band, dual-polarized synthetic aperture radar that enables data collection day and night in all weather conditions. Sentinel-1 can collect data in 4 different modes, which are: wave (WV), strip-map (SM), extra-wide swath (EW) and interferometric wide swath (IW). The main mode is the IW mode, which acquires a total representation of the earth's surface and satisfies the mass of service demands [38]. This is the reason why IW has been used in this paper.
# A Potential \"Vitaminic Strategy\" against Caries and Halitosis
## 3 Results  
### Variation of Planktonic and Biofilm Growth  
All the notable variations induced by Vea(r) Oris treatments on the planktonic and biofilm growth of _S. mutans_ and _F. nucleation_ are described below.  
As shown in Figure 4, compared with the control, the planktonic growth of _S. mutans_ was significantly but only slightly increased by Vea(r) Oris at 5 or 10% (% \(v/v\)), with increments of 17 and 14% (\(p<0.01\)), respectively. Conversely, the 20% concentration did not exert a significant modulation of the planktonic bacterial growth (\(p\) = 0.90).  
Interestingly, all the Vea(r) Oris concentrations significantly reduced the biofilm growth of the cariogenic species _S. mutans_. A biofilm reduction of 26% was obtained with Vea(r) Oris at 5% (% \(v/v\)) and a decrement of 32% with Vea(r) Oris at 10 and 20% (% \(v/v\)) (\(p<0.01\)).  
Figure 4: Vea® Oris activity on _S. mutans_ growth. (**a**) The planktonic (gray triangles) and biofilm (blue circles) growth of _S. mutans_ untreated (0%, CTR) and treated with the indicated concentrations of Vea® Oris. The error bars indicate the standard deviation (SD) of the O.D. values. (**b**) Mean values and standard deviation (SD) of planktonic and biofilm growth; (**c**) statistical significance of all comparisons through ANOVA analysis with [PERSON]’s HSD test, \(p\leq 0.05\).  
Similarly, for _F. nucleatum_, as highlighted in Figure 5, Vea(r) Oris did not influence planktonic growth (_p_ > 0.05), whereas it significantly reduced the ability of the bacterium to form a biofilm, regardless of the applied concentration (_p_ < 0.01). Even more interesting is that all the tested concentrations of 5, 10, and 20% induced similar biofilm decrements, of 50, 52, and 40%, respectively.
## 4 Numerical Simulation
### Parametric Study Results and Discussions
#### 4.3.2 Effects of Pile Spacing  
To study the influence of pile spacing on the mechanical behavior of GRPS embankments, models with pile spacings of 1.6 m, 2 m, 2.5 m, and 3 m and embankment heights of 3 m were established, respectively. The numerically calculated stress distribution curves are depicted in Figure 13. As can be seen from the figure, the bifurcation point of the stress curve of X1/X2 continues to increase with the increase in pile spacing, the arch height also increases, and the influence range of the soil arch effect also expands. Because the arch foot is formed at the top of a pile, the distance between the arch foot and the pile increases. However, the rise-span ratio of stable soil arch structure shows little variation; that is, the arch height becomes higher when the arch foot spacing increases. A comparison of the magnitude of the vault stresses shows that the vault stresses are higher when the pile spacing is small and become smaller when the pile spacing increases. It can be seen that increasing the pile spacing increases the height of the arch and extends the range of influence of the arch effect, but the strength of the arch is weakened. On the contrary, when the pile spacing is small, the height of the arch will be reduced, but the arch structure will be more stable. The stress at the top of the pile is 240.2 kPa, 250.0 kPa, 219.4 kPa, and 216.5 kPa at pile spacings of 1.6 m, 2 m, 2.5 m, and 3 m, respectively. It is evident that when the pile spacing is small, the transfer of inter-pile stresses to the top of the pile is more noticeable, and the bearing performance of the pile can be effectively used.
## 3 The Biosynthesis of Artemisinin (Qinghaosu)
### Phase 3 (Dihydroartemisinin acid to Artemisinin)
#### 3.3.2 Artemisinic Acid (**473**) as a Late-Stage Precursor to Artemisinin
Artemisitiene (**497**) can then reportedly be converted into artemisinin (**495**) _in vivo_[240] [the end of **494** was proposed to be derived from Grob fragmentation of the vicinal diol **486**, which is in turn derived from arteannuin B (**462**)]. **4. Strategies for the Production of Artemisinin from _A. annua_ and Derived Systems  
Although artemisinin can be produced by chemical synthesis [171,228-239], the structural complexity of this target and the large number of steps involved in all published synthetic routes render this approach far too expensive for many in the Third World who are most affected by malaria. Various alternative possibilities for the production of artemisinin from _A. annua_ and derived systems have therefore been extensively investigated, and this topic has been amply reviewed [313-320]. Fourstrategies are considered in this review, all of which might benefit from a knowledge of the full biosynthetic pathway to artemisinin. These are: 1. Plant breeding programmes; 2. Plant tissue culture; 3. Endophytic fungi; and 4. Genetic engineering. The most successful strategy to date has been plant breeding, resulting in the cultivar \"Artemis\", which was registered in Switzerland in 1999, and contains up to 1.4% artemisinin (by comparison, the extremely variable yields obtained from wild varieties of _A. annua_ typically range between 0.01-0.5% artemisinin [321]). There is a potential for even higher yields in the future from on-going plant breeding programmes, such as that of the CNAP _Artemisia_ project at the University of York (UK) [195]. Recent attempts to produce artemisinin through fermentation, by genetically engineering several enzymes from _A. annua_ in to a microbial host, would seem to have the greatest promise for the cheap and reliable production of artemisinin. This approach, pioneered by the synthetic biologist Jay Keasling at UC Berkeley (USA), has so far yielded microbially-produced artemisinin acid (**473**), which is then transformed to artemisinin (**495**) in a separate chemical process [see Section 3.3b for a discussion of the confusion surrounding the true biosynthetic status of artemisinin acid (**473**)]. Amyris Biotechnologies and Sanofi-aventis are currently developing a commercial-scale manufacturing process, which is based on this semi-synthetic approach to artemisinin. The discussions in Sections 4.1-4.4 emphasize how a full understanding of both the phytochemistry of _A. annua_ and the biosynthesis of artemisinin can be helpful for improving the production of this important antimalarial drug by each of these four strategies.