esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Peak and Residual Shear Stress as a Function of Shearing Time  
Figure 7 presents the shear stress curves dependent on the shearing speed under different drainage conditions. The shearing speeds applied were 0.1, 1, 10, and 100 mm/s. The main purpose of the selected shear speed is to examine the shear stress at the initiation of landslide movement. To ensure the reliability of the measurements, the lowest speed (i.e., 0.1 mm/s) was applied repeatedly five times. It is generally believed that the shear stress decreases with a decrease in shearing speed. Compared with the highest speed (i.e., 100 mm/s), the peak value is similar with speeds of 1 and 10 mm  
small; for example, the peak value is approximately 40 kPa, and the residual shear stress is 10-15 kPa in both drainage conditions. It is also interesting that the shear stress seems to decrease from high to low shear stress and stabilize when the speed has the lowest value. In addition, the difference in shear stresses dependent on the shear speed applied in the drained condition is higher than that in the undrained condition; the difference is approximately 30 kPa in the drained condition and 20 kPa in the undrained condition. This means that the interaction and rolling and the fracture effect between particles in the drained condition are more significant actions than those in the undrained condition.  
Figure 8 presents the influence of shear speed on shear stress in the drained and undrained conditions. As previously discussed, the shear stress is dependent on the shear speed, regardless of the drainage condition. The increase in shear stress in the drained condition is much higher than that in the undrained condition (i.e., the slopes obtained are 0.24 and 0.08 in the drained and undrained conditions, respectively). The shaded box zones are the range observed for peak and residual shear stress obtained at the shear speed of 0.1 mm/s. The range obtained from the peak shear stress is much larger than that from the residual shear stress. This may be because the shear resistance can be influenced by  
Figure 6: Grain size distribution obtained from the top, middle, and lower parts of the ring shear cylinder after shearing. The order is the same as shown in Figure 5.  
Figure 7: Shear stress and shearing time: (**a**) drained and (**b**) undrained conditions.  
the development of the slip surface. The maximum range of peak and residual values was approximately 25-35 and 10 kPa, respectively, under both drainage conditions. The difference is very small in the residual state; as expected, the shear resistance in the residual state is measured along the existing slip surface. For a given normal stress and shear speed, it seems that the residual shear stress is not strongly dependent on the drainage condition.
## 3 Observational Data Description, Assimilation and Experimental Design
### Sentinel Observations for the Silvi Marina Test Case
In case the InSAR DEM is higher than the WRF DEM, a negative correction to the model ZTD has to be applied, namely  
\[\Delta\text{ZTD}=-10^{-6}\left[\sum_{k=1}^{k_{0}}(Z_{k}-Z_{k-1})\cdot N_{k}+( Z_{obs2}-Z_{k_{0}})\cdot N_{K_{0}+1}\right], \tag{3}\]  
where \(k_{0}\) corresponds to the last model level that is below the InSAR DEM, \(Z_{obs2}\). To find the best resolution of the InSAR ZTD to be assimilated, the modeled ZTD calculated on the innermost domain is compared with the InSAR ZTD map resampled on its grid (1.5 km resolution), after the correction due to the height differences between the DEMs described above. The GFS-driven OL (taken as an example) simulation has a BIAS of \(-0.15\) cm and a RMSE value of 2.4 cm with respect to the InSAR ZTD. The simulations assimilating InSAR ZTD at 1.5, 4.5 and 13.5 km resolution have a BIAS of \(-0.69\), \(-0.27\) and \(-0.35\) cm and a RMSE of 1.70, 1.64 and 1.92 cm respectively. Thus, all the simulation assimilating the InSAR ZTD at different resolutions (1.5, 4.5 and 13.5 km) reduce the RMSE but increase the BIAS. Since the RMSE is reduced at most with the assimilation of the InSAR ZTD at 4.5 km resolution, this thinning resolution is chosen for the InSAR assimilation in this case. Future work is planned to estimate the effects on the rain forecast of assimilating the InSAR ZTD maps at different resolutions and with different uncertainties.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
## 2 Materials and Methods
### Evaluation  
The reconstructions are evaluated by calculating the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) for the test data. We set the background values of ground truth and reconstructions to \(-1000\) HU to focus the analysis on the body region where the relevant anatomy is located. For the evaluation, the slices of the test dataset are divided into three categories: no metal, moderate metal artifacts, and severe metal artifacts, with 106, 748, and 137 slices, respectively. This separation allows one to evaluate the reconstructions when no metal is present. A slice is assigned to the severe metal artifact category if the FBP yields an SSIM value of less than 0.7, and to the moderate metal artifact category if the SSIM value of the FBP is greater than or equal to 0.7. The SSIM threshold is chosen, such that the number of slices with severe metal artifacts is similar to the number of slices without metal.
## 2 Effect of Floating DC Bus on System Stability  
This section discusses the stability degradation that can possibly arise in a floating DC bus microgrid supplying a CPL. The present paper is based on a previous work [33] published by the authors about the criticality of a DC floating bus powering a hybrid electric vehicle. Nevertheless, the same conclusions can be extended to any islanded DC microgrid with a floating bus, supplying a high-bandwidth destabilizing load. In this regard, the simplified microgrid under study is described in Section 2.1, while initial considerations about the effect of a floating DC bus on the stability margin are proposed in Section 2.2. Then, two methodologies for large-signal analysis are introduced in Section 2.3, whereas numerical simulations are proposed in Section 2.4 for validating, through the system dynamic response analysis, the methodological approach used for the stability evaluation. In Section 2.5, the dynamic numerical simulations are used to verify the basin of attraction (BA) and demonstrate the validity of the Lyapunov analytical approach.
# Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling
## 4 Constructive Heuristics
Table 2 summarizes the main procedures used in each one of the constructive heuristics. **Example 1**. Consider the initial sequence with four jobs: \(\{J_{3},J_{2},J_{1},J_{4}\}\). The insertion neighborhood results in \((n-1)^{2}=9\) sequences:  
* Initially, inserting the first job \(J_{3}\): \(\{J_{2},J_{3},J_{1},J_{4}\}\), \(\{J_{2},J_{1},J_{3},J_{4}\}\), \(\{J_{2},J_{1},J_{4},J_{3}\}\);
* Then, inserting the second job \(J_{2}\) only in the positions not considered yet: \(\{J_{3},J_{1},J_{2},J_{4}\}\), \(\{J_{3},J_{1},J_{4}\}\), \(\{J_{2}\}\); for example, it is not necessary to insert \(J_{2}\) in the first position because the sequence resulting was already listed; and
* Next, the third job \(J_{1}\) is inserted and, lastly, the fourth one \(J_{4}\): \(\{J_{1},J_{3},J_{2},J_{4}\}\), \(\{J_{3},J_{2},J_{1}\}\), \(\{J_{4},J_{3},J_{2},J_{1}\}\), \(\{J_{3},J_{4},J_{2},J_{1}\}\). Starting again from the initial sequence \(\{J_{3},J_{2},J_{1},J_{4}\}\) of the same example, the permutation neighborhood results in \(n(n-1)/2=6\) sequences:  
* First, the first two jobs are permuted: \(\{J_{2},J_{3},J_{1},J_{4}\}\); then the first and third jobs: \(\{J_{1},J_{2},J_{3},J_{4}\}\); and the first and fourth jobs: \(\{J_{4},J_{2},J_{1},J_{3}\}\);
* Next, from the initial sequence, the second and third are permutated: \(\{J_{3},J_{1},J_{2},J_{4}\}\); and the second and fourth: \(\{J_{3},J_{4},J_{1},J_{2}\}\); and
* Lastly, from the initial sequence, the third and fourth jobs are permutated: \(\{J_{3},J_{2},J_{4},J_{1}\}\). _Heuristic H1_  
* Order jobs according to the EDD rule (in the case of a tie, use the lower \(\sum p_{\bar{k}}\)). * For the first two jobs, apply the timing adjustment procedure to find the best partial sequence (between these two possibilities) with the lower \(n_{JIT}\). * For h = 3 to \(n\), do: Keeping the relative positions of the jobs of the partial sequence, insert the \(h\)th job of the order defined in Step 1 in all possible positions and apply the timing adjustment procedure in each insertion; consider the new partial sequence with the best \(n_{JIT}\) (in the case of a tie, use the upper position). **Algorithm 2. ** Pseudo-code of heuristic H1. _Heuristic H2_  
* Order jobs by the EDD rule (in the case of a tie, use the lower \(\sum p_{\bar{k}}\)). * For the first two jobs, apply the timing adjustment procedure to find the best partial sequence (between these two possibilities) with the lower \(n_{\|IT}\). * For h = 3 to \(n\), do:
* Add the \(h\)th job of order defined in Step 1 in the last position of the partial sequence;
* Considering the insertion neighborhood with \((h-1)2\) partial sequences and applying the timing adjustment procedure, determine that with the best \(n_{\|IT}\);
* From the best solution obtained so far, considering the permutation neighborhood with \(h(h-1)/2\) partial sequences and applying the timing adjustment procedure, determine that with the best \(n_{\|IT}\). **Algorithm 3. ** Pseudo-code of heuristic H2.
## 4 Experimental Results and Discussion
### Analysis of Time Efficiency  
In order to evaluate the time efficiency of our method, we carried out experiments with a different number of detection rays and SMPL models. All experiments in this section were done on a laptop with Intel(R) Core(TM) i5-3230M processor. The most time-consuming part of our technique is self-intersection detection as is described in Algorithm 1. The highlight of our method is that the time complexity is linear to the number of triangles. The number of detection rays has almost no effect on the elapsed time as is shown in Table 2. As can be seen in the Table 2, the number of detection rays was increased from \(128\times 128\) to \(2048\times 2048\) but the elapsed time remained approximately constant.  
In the next experiment, we fixed the number of detection rays and changed the number of SMPL models to test the performance of our method with growing number of triangles. A single SMPL model has about 13,000 triangles. As is shown in Table 3, the elapsed time grows about 30 ms for each additional SMPL model added to the mesh. Result of this experiment demonstrated that the time complexity of our proposed algorithm is linear to the number of triangles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Per Vertex Error (mm)** & **Percentage of Vertices in Self-Intersection** \\ \hline RE only & 257.54 & 15.22\% \\ RE+LR [17] & 452.72 & 7.64\% \\ RE+SA [22] & 186.98 & 0.87\% \\ RE+SPT (ours) & **140.31** & **0.23\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of baseline method, other state-of-the-art methods and our proposed approach on reduced test set of UP-3D [24].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Detection Rays** & **Elapsed Time (ms)** \\ \hline \(128\times 128\) & 53.84 \\ \(256\times 256\) & 54.96 \\ \(512\times 512\) & 56.76 \\ \(1024\times 1024\) & 58.32 \\ \(2048\times 2048\) & 59.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumed by an iteration with different number of detection rays. The number of SMPL models is 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of SMPL Models** & **Elapsed Time (ms)** \\ \hline \(1\) & 56.76 \\ \(2\) & 89.80 \\ \(3\) & 119.25 \\ \(4\) & 149.58 \\ \(5\) & 186.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time consumed by an iteration with different number of SMPL models. The number of detection rays is \(512\times 512\).  
Our proposed method is obviously more computational compared with traditional methods, but it is worthwhile to apply this method because of the accuracy and generality of our approach. Moreover, the time efficiency of our method is totally acceptable according to the experimental results.
## 5 Continuous Isochoric Shear  
Next, isochoric (volume preserving) shear tests are conducted on 50 of the 250 dense specimens and 30 of the 100 loose specimens. The tests are strain-rate controlled. The principal strain rates are \(\dot{\epsilon}_{1}\,\geq\dot{\epsilon}_{2}\,\geq\,\dot{\epsilon}_{3}\). Firstly, the intermediate principal strain rate ratio \(b=\frac{\dot{\epsilon}_{2}-\dot{\epsilon}_{3}}{\dot{\epsilon}_{1}-\dot{\epsilon} _{3}}\) is randomly chosen between 0 and 1, \(\dot{\epsilon}_{1}\,\) is fixed at 1 s\({}^{-1}\), thus \(\dot{\epsilon}_{2}\,\) and \(\dot{\epsilon}_{3}\,\) can be calculated from \(b=\frac{\dot{\epsilon}_{2}-\dot{\epsilon}_{3}}{\dot{\epsilon}_{1}-\dot{ \epsilon}_{3}}\) and the volume-preserving condition (\(\dot{\epsilon}_{1}+\dot{\epsilon}_{2}+\dot{\epsilon}_{3}=0\)). The simulations are conducted until the largest principal strain is at least 30%.  
Figure 5 gives the evolution of stress and strain for a loose specimen (\(\phi\) = 0.6016 and \(p^{\prime}\) = 0.01529) when sheared with \(b\) = 0.5. From the plot of stress components (Figure 5a), it can be seen that the mean stress \(p^{\prime}\) will decrease first, the specimen reaches the critical state after about \(\dot{\epsilon}_{1}\) = 15%, and the stress components keep constant at the critical state even with continuous shear deformation (Figure 5).  
The critical state density and pressure for these 80 specimens are shown in Figure 6. Some specimens reach liquefaction (\(p^{\prime}\approx 0\)) before reaching the critical state. The density  
Figure 4: Specimens in this study.  
Figure 5: An isochoric shear test.  
and pressure of other specimens lie on a line called the critical state line in soil mechanics. From Figure 6, this critical state line collapses with the loosest possible jammed state line from isotropic compression tests. Therefore, it is concluded that shearing will cause the rearrangement of grains, contacts and fabric, after sufficient deformation, which will finally lead to a critical \(p\)-\(\phi\) relationship, which is also the loosest jammed state from isotropic compression tests. More theoretical studies are required to explore what affects the relationship between density and pressure.  
The stress state of specimens at the critical state is plotted on the octahedral profile in Figure 7. It is shown that the critical state stress state is well described by a Coulomb-type equation and the corresponding bulk internal friction angle is about 21\({}^{\circ}\) for the studied granular material (diameter = 0.17 mm and surface friction \(\mu\) = 0.5).
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.4.1 Coverage Optimization Problem  
During the deployment optimization process, the proposed method expands the coverage range by adjusting the sensing direction of stationary sensors and repairs coverage holes by redeploying mobile sensors. We aim to reduce redundancy and maximize coverage. The objective function can be expressed as:  
\[f_{1}=\max(COVR) \tag{12}\]  
\[f_{2}=\min(REDR) \tag{13}\]  
In order to minimize redundancy and maximize coverage, the effective coverage area of all sensors should be increased as much as possible. So, the objective function can also be expressed as:  
\[f_{3}=\max(\sum_{i=1}^{n}A_{i}) \tag{14}\]  
Therefore, when adjusting the sensing direction, we take the coverage rate as the optimization objective to solve for the optimal working direction of the sensor. When redeploying mobile nodes, we also consider coverage as one of the objective functions to further improve network coverage.
###### Abstract  
The World Health Organization recently stated that new sources of antibiotics are urgently required to stem the global spread of antibiotic resistance, especially in multiresistant Gram-negative bacteria. Although it was thought that many of the original sources of antibiotics were exhausted, innovative research has revealed promising new sources of antibiotic discovery in traditional medicine associated with _Streptomyces_. In this work we investigated the potential of a specific limestone grassland soil, associated with Irish folk medicine, as a new source of antimicrobial discovery. Using selective enrichment and isolation techniques on a limestone grassland soil sample obtained from Boho, West Fermanagh, we isolated _Streptomyces_ sp. CJ13. This bacterium inhibited the growth of a broad range of pathogens in vitro including Gram positive _Staphylococcus aureus_ (MRSA 43300) and Gram negative multiresistant _Pseudomonas aeruginosa_ (PA01), as well as the anaerobic bacteria _Propinibacterium areas_ and the yeast _Starmerella bunbolica_. Genome sequencing and phylogenetic analysis revealed _Streptomyces_ sp. CJ13 to be closely related to an unclassified _Streptomyces_ sp. MJM1172, _Streptomyces_ sp. Mg1 and two species known as _Streptomyces_ sp. ICC1 and ICC4 from a karst region in British Columbia. The closest type species to _Streptomyces_ sp. CJ13 was _Streptomyces lacendulae_ subspecies _Iaverdulae_. Analysis of _Streptomyces_ sp. CJ13 whole genome sequence using the secondary metabolite prediction tool antiSMASH revealed similarities to several antibiotic gene synthesis clusters including salinichelin, mediumycin A, weishannycin, combamide, heat stable antifungal factor and SAL-2242. These results demonstrate the potential of this alkaline grassland soil as a new resource for the discovery of a broad range of antimicrobial compounds including those effective against multiresistant Gram negative bacteria.  
soil; antibiotics; _Streptomyces_; traditional medicine; MRSA
# Cervical Cancer Diagnostics Using Machine Learning Algorithms and Class Balancing Techniques
### Research Hypotheses  
According to the presented literature overview and by using the identified research gap, the following questions can be asked:  
* Is it possible to apply data balancing techniques to increase the classification performances of ML classifiers for cervical cancer diagnosis?
* How does the combination of the class balancing technique and ML classifier influence the classification performances?
* How does the combination of the class balancing technique and ML classifier influence the generalization performances?
* Is it possible to use developed methods for tentative cervical cancer screening?  
The novelty of this paper can be divided into two main parts. The first main novelty is the definition of the algorithmic foundation for the development of an online tool for questionnaire-based cervical cancer screening. The presented method is intended for the public health system, where the application of AI algorithms will provide initial cervical cancer screening, based on a questionnaire. For this research, a publicly available data set is used for the investigation of given hypotheses. The collection of new data set is defined as one of the future investigations. The second main novelty is the examination of the different combinations of class balancing techniques and ML-based classification algorithms. The determined combinations will be used to develop a questionnaire-based cervical cancer survey.  
This paper is divided into seven sections. In the Section 2, a comparison of standard diagnostic procedures and tentative AI-based screening is presented. Section 3 is dedicated to the data set description and the description of class balancing techniques that are used in this research. In the Sections 4 and 5, a brief description of ML classifiers used is provided, followed by a detailed overview of the research methodology. Sections 6 and 7 are dedicated to the results, discussion, and conclusions.
## 4 EBCRP: Energy-Balanced Cluster-Routing Protocol  
Figure 3 shows the main procedure of the proposed EBCRP.  
Based on the sensing region \(R\) with randomly deployed sensor nodes, the sensor nodes are first grouped into clusters evenly using PSO with five mutation operators. In this step, the numbers of clusters and their centroids are optimized, in which some sensor nodes are selected as the centroids of clusters and other sensor nodes are assigned to their respective nearest centroid. Additionally, the sensor node clustering scheme is optimized according to the following two objectives: minimizing the average distances between sensor nodes and their centroids, and maximizing the balance index of the number of sensor nodes between clusters. To increase the diversity of search, five mutation operators are proposed for optimizing the sensor node clustering scheme. Finally, the data collection task is performed based on the clustering results. In each round, the sensor node with the highest residual energy in each cluster is selected as a CH. Each CH is responsible for aggregating data from its cluster members and sending these data to the sink. The residual energy of all sensor nodes is also updated in each round until the end of the network lifetime.
###### Abstract  
Building a large-scale Mach-Zehnder-based silicon photonic switch circuit (LS-MZS) requires an appropriate choice of architecture. In this work, we propose, for the first time to our knowledge, a single metric that can be used to compare different topologies. We propose an accurate analytical model of the signal-to-crosstalk ratio (SCR) that highlights the performance limitations of the main building blocks: Mach-Zehnder interferometers (MZI) and waveguide crossings. It is based on the cumulative crosstalk and total insertion loss of the LS-MZS. Four different architectures: Benes, dilated Benes, switch and select, double-layer network were studied for the reason that they are mainly referenced in the literature. We compared them using our developed SCR indicator. With reference to the state-of-the-art technology, the analysis of the four architectures using SCR showed that, on a large scale, a high number of waveguide crossings significantly affects the performance of the switch matrix. Moreover, better performance was reached using the double-layer-network architecture. Then, we presented a \(2\times 2\) MZI using two electro-optic phase shifters and a waveguide crossing realized in LETI's silicon photonics technology. Measured performances were quite good: the switch circuit had a crosstalk of -31.3 dB and an insertion loss estimated to be less than 1.31 dB.  
Keywords:signal-to-crosstalk ratio; switch architectures; Mach-Zehnder-based silicon photonic switch circuit; waveguide crossing +
Footnote †: journal: Applied