esa-sceva/satcom-chunk-collection huggingface dataset:
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 2 The Internet of Things
### Development of Standards for the Future IoT
This working group has now been converted to an official subcommittee of JTC1--SC41 [33]. SC41 has 3 working groups--Sensor Networks, Internet of Things, and Wearable Technologies. IEEE formed an IoT Technical Community that runs conferences and also publishes the IEEE Internet of Things journal [34]. The IEEE IoT group is also engaging its stakeholders in standardization efforts, such as IEEE P2413 a draft standard for an architectural framework for the IoT [3]. P2413 describes IoT domains, and identifies commonalities between different domains. It also provides a model for data abstraction and quality. ITU initially established an IoT Global Standards Initiative [35] that aimed to promote a unified approach to development of technical standards for IoT. This initiative ended its activities in 2015 and ITU has now established a new Study Group on IoT and its applications including smart cities and communities. The Simulation Interoperability Standards Organization (SISO) is also looking at opportunities for IoT standardization from a modelling and simulation (M&S) perspective [36]. Many of the goals of the M&S community are similar to the goals of IoT: timely environmental data (for live simulations), representation of systems, and representation of human behavior. The IoT can provide these inputs to the virtual world through sensors embedded in the real world that provide live environmental feeds, and human biometric and behavior data. This would enhance representation of live entities in Live Virtual Constructive simulations. We note that SISO researchers have applied a process model (IEEE Std 1730) developed for distributed simulations to a simulation of IoT cyber security that can include real devices [37]. Various other organizations and industry consortia are working on aspects of IoT standardization under a range of projects and alliances. The main consortia include the AllSeen Alliance (includes CISCO and Microsoft), the Industrial Internet Consortium (includes Intel, Dell, and Samsung), Open Interconnect Consortium (includes IBM, Intel), and Thread (includes ARM and Samsung) [38]. These consortia are not mutually exclusive with some companies in several consortia. OneM2M is another initiative that is developing specifications for machine to machine services for IoT. This initiative also comprises the major SDOs IETF, IEEE, ISO/IEC, OGC, and ITU. Systems Engineering will also be critical for the IoT to succeed [39; 40]. Systems Engineering Standards such as ISO/IEC 15288, Software Engineering Standards including ISO/IEC 12207 and ISO/IEC 29110 for very small software development organizations [41] and Project Management Standards (e.g., ISO 21500) have been developed by SDOs in recent years. However, as will be discussed in Section 4.4, the IoT can be viewed as a Complex Adaptive System, and traditional Systems Engineering standards may break down for such systems [42]. Table 2 contains the profiles of five key traditional Systems Engineering standards.
## 4 Parameter Optimization
### Optimization Result
#### 4.3.2 Reconstruction Accuracy  
An ALS topographic mapping task is essentially a measurement of the topographic relief of an area. In its reconstructed point clouds, latitude and longitude often act as the coordinate frame, while elevation is the measured value. Therefore, we locate the proximity points in different point clouds by giving the latitude and longitude and use the elevation difference between the two as the reconstruction error.  
Though three types of points are used in global optimization, the criteria for determining the error are used for elevation.  
Finally, the vertical errors are around 0.3 m, different every time when solved through the PSO, and the minimum value is as low as 0.271 m, as shown in Figure 11. In contrast, whether assuming placement errors of 0 or optimizing using nonlinear LSM starting with random numbers, the elevation errors of the generated point cloud are all greater than 1 m.  
On the one hand, the optimization results of the placement errors have huge jitter and are coupled with each other; on the other hand, the point cloud reconstruction results are relatively stable, and their reconstruction errors are controlled within a small range. In fact, although we have been referring to the parameters to be optimized as placement errors, they are clearly not true installation angles. In other words, we have achieved highly accurate point cloud reconstruction without checking parameters.  
The placement error obtained by the PSO algorithm produces a significant effect on point cloud reconstruction. As far as the horizontal direction is concerned, it solves the  
Figure 11: The altitude errors in fifteen PSO solutions.  
misalignment problem between the strips and reflects the validity of the building target criterion shown in Figure 12.  
The difference before and after PSO is more intuitively shown in Figure 13, where a clear misalignment before the nonlinear optimization is shown in Figure 13b. The PSO has smoothed this issue out, as shown in Figure 13c.
# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 4 Proposed Multi-Level Traffic Load Balancing MDTLB
### Design Considerations
#### 4.2.4 Example of Proposed Scheme  
Suppose we have two paths, P1 and P2, which both have the capacity of 10 G, and were received for flows A, B, C, D, E, F, G, H, and I. As illustrated in Figure 8 and Table 4, the following events describe the system response for this scenario:  
1. Flow A started; the status of paths is unknown, so they are grey; the portion of good paths equals the portion of bad paths (0), so no need to change the dynamic thresholds, and our routing logic will choose any of the paths, let us say P1.
2. Flow B started; the judging of paths will be bad for P1 due to traffic and grey for P2; the routing logic will choose P2; the portion of bad paths is greater than the portion of good paths, so the dynamic threshold should be increased by 10%.
3. Flow C started and due to the change of the dynamic threshold from the previous step, the judging logic will be good for P1 and bad for P2; as a result, the routing logic will choose P1, and the portion of good paths equals the portion of bad paths, so no need to change the dynamic thresholds.  
4. Flow D started, the judging logic will be bad for P1 and P2; the routing logic will choose either P1 or P2, let us say P1; the portion of bad paths is greater than the portion of good paths, so the dynamic thresholds should be increased by 10%.
5. Flow E started, and due to the change of dynamic thresholds from the previous step, the judging logic will be very bad for P1 and good for P2; the routing logic will choose P2, and the portion of good paths equals the portion of bad paths, so no need to change the dynamic thresholds.
6. Flow F started, the judging logic will be very bad for P1 and bad for P2; the routing logic will choose P2, the portion of bad paths is greater than the portion of good paths, so the dynamic thresholds should be increased by 10%.
7. Flow G started, and due to the change of dynamic threshold from the previous step, the judging logic will be very good for P1 and good for P2; the routing logic will choose P1, and the portion of bad paths is smaller than the portion of good paths, so the dynamic thresholds should decrease by 10%.
8. Flow H started, and due to the change of dynamic threshold from the previous step, the judging logic will be good for P1 and good for P2; the routing logic will chose either P1 or P2, let us say P2; the portion of bad paths is smaller than the portion of good paths, so the dynamic thresholds should decrease by 10%.
9. Flow I started, and the judging logic will be good for P1 and bad for P2; thus, the routing logic will choose P1, and the portion of good paths equals to the portion of bad paths, so no need to change the dynamic thresholds.
## 3 Results and Discussion
### Confectionery Masses Characterization
#### 3.2.9 CIElab Chromatic Parameters of the Confectionery Masses  
The CIElab chromatic parameters of the confectionery masses during storage were evaluated. The results attest that on the 1 st day, the control was characterized by the high value of the luminosity L* (92.05). The negative value of component a* (\(-\)0.20) showed the presence of green hue, and the positive value of component b* (10.18)--yellow hue, due to the color of cocoa butter and skimmed milk powder. The chromaticity C* (10.18) demonstrated the low color intensity and the presence of gray shade. In addition, the hue angle H \({}^{\circ}\) (91.1) is in the second trigonometric quadrant, in which the yellow tone predominates. In the case of fortified samples, the use of aronia extract led to a decrease in L* 76.45 (CMAE), and the addition of aronia powder contributed to a sudden reduction in this value--19.74 (CMAEP). Due to the anthocyanins in aronia fruits, the color of the CMAEP darkened. In the case of a* values, in the samples with aronia, it varied between 32.47 and 36.17, demonstrating the presence of anthocyanins. The b* values were positive in the aronia samples and vary in a fairly large range. In CMAE, the b* value increased up to 36.41 due to the presence of yellow hue, but with the addition of aronia powder, this value was drastically reduced to 9.00 (CMAEP). The C* demonstrated the color intensity of the CMAE and CMAEP, being 53.37 and 36.66, respectively. The H\({}^{\circ}\) is in the first trigonometric quadrant, 52.5\({}^{\circ}\) (CMAE) demonstrating the presence of the orange hue, and 9.4\({}^{\circ}\) (CMAEP), the presence of red tone.  
The shelf life influenced the chromatic parameters of the confectionery masses. On the 50 th storage day, the L* value increased in all the samples studied: by 2.2% (PM); by 6.5% (CMAE) and by 17.4% (CMAEP). The a* values reduced to all the samples investigated. For PM, the a* component was negative (\(-\)1.08) due to the presence of green tone, and for the other samples the values were 25.00 (CMAE) and 28.14 (CMAEP), demonstrating the reduction in red tone compared to the initial values. The b* values were positive for all tested samples, but decreased, showing a decrease in yellow pigments. Furthermore, the C* values decreased due to the reduced color intensity of the tested samples. The H \({}^{\circ}\) values attest that in all the researched samples the color hue was lighter, but they remained in the same trigonometric quadrants described above (Table 4). Several authors have noted that the color intensity of confectionery masses with aronia, which was determined by the content of monomeric anthocyanins, decreased under the influence of temperature and storage time [67].  
The extract in combination with aronia powder can be successfully used in the technology of making confectionery masses as natural dyes, helping to increase the biological value of sugary products. In addition, this fact allows to expand the assortment of candies and fillings.
## 4 Results  
### Test Results According to Surface Treatment Method  
The flexural strength test results, as a function of surface processing, are shown in Figure 5. The flexural strengths of the control and sand-blasted specimens were 308.80 MPa and 355.91 MPa, respectively. The flexural strength of the sand-blasted specimens was higher than that of the control, indicating a higher bonding capacity for the GFRP panels. Generally, the fractures between the steel panels and the FRP composites occur in one or more of the following ways: (a) FRP and adhesive interface failure, (b) FRP delamination, (c) FRP rupture, and (d) steel yielding. In this study, the GFRP panel was manufactured using the same resin that was used to bond the steel panel to the GFRP [28; 29; 30; 31; 32]. Therefore, the only variable in our system was the shape and surface pre-treatment of the steel panel. The surface of our steel panel was roughened by sandblasting to reduce the occurrence of failure modes (a) and (c) [28]. Roughening the surface of the steel panel prevented interfacial failure and resulted in enhanced bending strength, relative to that of the control steel panel without any surface treatment.  
Figure 6 shows fracture deformation in specimens following long-span flexural tests. The control specimens exhibited fracture with interfacial delamination between the steel and GFRP panels prior to the occurrence of splitting cracks in the GFRP panel at the loading point (Figure 6a). In contrast, sand-blasted specimens (Figure 6b) did not exhibit interfacial delamination at the steel and GFRP bond interface, showing instead splitting fractures via cracking at the loading point. These data indicate that  
Figure 4: Flexural test set up.  
Figure 5: Flexural strength as a function of steel panel surface modification.  
the specimens incorporating sand-blasted steel showed better performance than those incorporating unmodified steel panels.
## 5 Conclusions  
In this paper, we have continued our research of the UEG and study the MDFs and the PCFs using the SMPMC method in a wide range of temperatures and densities: \(0.5\leq\theta\leq 4\), \(0.2\leq r_{s}\leq 36\). Thus, different thermodynamic states from an almost ideal gas (Boltzmann of Fermi) to a strongly non-ideal system with weak and significant degeneracy have been considered.  
Firstly, we have discovered that the single-particle MDFs start to exceed the Fermi distribution at high momentum values, when the UEG is strongly non-ideal. In the degenerate case \(\theta=0.5\), this occurs at \(r_{s}\gtrsim 8\) (\(\Gamma\gtrsim 9\)), in the semi-degenerate case \(\theta=1\)--at \(r_{s}\gtrsim 16\) (\(\Gamma\gtrsim 8\)), in the almost classical case \(\theta=4\)--at \(r_{s}=36\) (\(\Gamma\gtrsim 5\)). The deviation has the form of the \"quantum tail\" at high momenta and is reliable at \(p\lesssim p_{FS}\), where the value of \(p_{FS}\) is conditioned by the finite-size effects.  
We explain the phenomena of \"quantum tails\" as follows. The deviation of the MDFs from the Fermi distribution is always followed by the appearance of the short-range order on the PCFs; so, with increasing coupling strength, the UEG becomes more liquid-like and the potential wells surrounding the electrons appear. As a result, the spatial localization of the electrons increases, so the localization in the momentum space becomes weaker and the MDFs become wider. Besides, at high degeneracy, the electrons are tunneling from the potential wells more easily, so the \"quantum tail\" appears at higher values of \(\Gamma\) in comparison with the non-degenerate case.  
Secondly, we have obtained that the difference between the PCFs of the electrons with the same and opposite spin projections becomes negligible at high values of \(\Gamma\) in the degenerate case. Thus, the spatial structure of a strongly non-ideal UEG is basically conditioned by the Coulomb interaction rather than the exchange repulsion.  
Finally, we have calculated the average kinetic and potential energies for \(r_{s}\) up to 36, supplementing our results from [34] significantly.  
Conceptualization, A.L. and V.F.; methodology, A.L.; software, A.L.; validation, A.L., V.F. and P.L.; investigation, A.L.; visualization, A.L.; writing--original draft preparation, A.L.; writing--review and editing, V.F. and P.L.; supervision, V.F.; project administration, P.L.; funding acquisition, V.F. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the by the Russian Science Foundation, Grant No. 20-42-04421.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
We acknowledge stimulating discussions with [PERSON], [PERSON], [PERSON], and [PERSON] (Kiel).
## 4 Discussion  
The comparison results demonstrate that the incorporation of air cavities effectively reduces the equivalent loss tangent, resulting in a significant decrease in overall loss. Additionally, the use of a periodically nonuniform-width inner conductor compensates for the impedance mismatches introduced by the air cavities, thereby further improving the return loss. This design methodology is validated through its application to both an SICL and a stripline. Moreover, the methodology can be extended to other interconnects in integrated circuits, utilizing standard fabrication processes with wide tolerance. Ultimately, the proposed interconnect design holds substantial potential for reducing power consumption in integrated circuits, offering a promising solution for improving performance in high-frequency applications.
## 3 Results  
### Temporal and Spatial Variations in Livestock and Environmental Variables  
Before the statistical analysis, the satellite-driven climate and ET variables were evaluated against ground observations. All variables showed statistically significant (\(p<0.001\)) linear relationships with ground observations (Table 2), which confirmed the applicability of the satellite-derived climate and ET data in reflecting their seasonal and interannual variability found by ground observation.  
The percent change in livestock numbers and various explanatory variables showed distinct spatial and interannual variations from 2003 to 2010 (Figure 4). In particular, the 2009-2010 _Dzud_ was countrywide except for Southwest Mongolia (see the last map of the first column in Figure 4), resulting in a wide range of relative change rates from \(-93\%\) to \(+125\%\) across 337 _soums_. In 2010, livestock numbers decreased in 279 _soums_ (83%) but increased in only 58 _soums_ (17%). In addition to the countrywide _Dzud_ event in 2009-2010, considerable region-scale livestock reductions also occurred in the east during 2005-2006 and 2007-2008 and in the west during 2008-2009. On the other hand, the lumped spatial patterns of livestock increases and decreases are clear in the first column of Figure 4, suggesting that certain regional factors would play important roles in regulating the change. For example, negative z-scores in summer NDVI and precipitation (the second and third columns in Figure 4) were well coincident with the negative percent change of livestock numbers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Observation** & **Satellite** & **r** & **ME** & **RMSE** \\ \hline Monthly T (\({}^{\circ}\)C) & 0.85 (14.7) & \(-1.7\) (12.3) & 0.98 \({}^{\star}\) & \(-2.5\) & 0.06 \\ Ts & 18.1 (3.2) & 12.5 (3.8) & 0.84 \({}^{\star}\) & \(-4.9\) & 0.26 \\ Tw & \(-18.7\) (5.0) & \(-17.6\) (3.9) & 0.86 \({}^{\star}\) & +0.2 & 0.18 \\ \hline Monthly P (mm) & 15.5 (23.5) & 17.5 (23.7) & 0.93 \({}^{\star}\) & +1.8 & 0.17 \\ Ps & 41.4 (23.0) & 44.7 (23.8) & 0.90 \({}^{\star}\) & +2.9 & 0.90 \\ Pw & 2.4 (1.8) & 3.4 (2.1) & 0.76 \({}^{\star}\) & +0.9 & 0.09 \\ \hline Daily ET (mm) & 0.73 (0.77) & 1.37 (1.28) & 0.74 \({}^{\star}\) & +0.58 & 0.04 \\ \hline \end{tabular} \({}^{\star}\)Pearson correlation coefficient, significant at \(p<0.001\).  
\end{table}
Table 2: Comparisons of satellite-derived environmental variables with field observations. Mean and standard deviation in parentheses, Pearson correlation coefficient, mean bias (ME), and root mean square error (RMSE).
## 3 Results  
### Hairy Root Induction  
We selected hairy root lines on a medium containing kanamycin, resulting in 14 transgenic root lines and 2 control lines capable of fast and stable growth (Figure 1). The gene expression results of the hairy root lines are presented in Figure 2. These 14 lines (_ZmLC_) showed higher expressions than the control sample (GUS), especially hairy root lines 1 and 10. Homogenized line 10, thereafter LC10, was used to conduct further evaluations.  
Figure 1: Hairy root induction of _A. rugosa_ using _A. rhizogenes_. (**A**) Control hairy root harbouring GUS construct. (**B**) Transgenic hairy root harbouring _ZmLC_ construct. The scale bar represents 1 cm.  
Figure 2: qRT-PCR analysis of control hairy root lines expressing _GUS_ (C1 and C2) and transgenic hairy root lines expressing _ZmLC_ (#1, #2, #3, #4, #5, #6, #7, #8, #9, #10, #11, #12, #13, and #14). Asterisks denote statistical significance (\"\" \(p<0.01\), and \"\" \(p<0.001\)).  
Analysis of Phenylpropanoloid Biosynthesis Pathway Genes Expression in A. rugosa Grown under Light Condition  
The results of the qRT-PCR analysis are shown in Figure 3. The expression level of eight phenylpropanol pathway genes was higher in the LC-10 sample than in the control sample. The highest expression was obtained for the _CHS_ gene (2.7-fold higher), and the lowest expression was 1.28-fold for the _HPPR_ gene compared to the control. There were two genes, _RAS_ and _PAL_, with similar expression levels, and their expression was 2.48- and 2.44-fold higher, respectively, than that in the control. The expression of the _TAT_, _C4H_, and _4 CL_ genes was 1.69-, 1.40-, and 1.30-fold higher, respectively, than that in the controls.
## 3 Results
### Results of Synthesis
#### 3.4.1 Cornea Preservation Quantitative Parameters
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Author Year** & **Central Comal Thickness (CCT) (um) (Mean \(\pm\) sd) Optisol-CS** & **Central Comal Thickness (CCT) (um) (Mean \(\pm\) sd) Distinct Media** & \multicolumn{2}{c}{(\(y=0.026\))} \\ \hline  & Optisol-CS & & & Kerasave with amphoteric B and Optisol. & & (\(y=0.026\)) \\  & baseline: & & baseline: & & baseline: & \\  & 551 \(\mu\)m to 740 \(\mu\)m & & 551 \(\mu\)m to 740 \(\mu\)m & & 16 & (629 \(\pm\) 13) & 0.6281 \\  & 1 & 16 & (637 \(\pm\) 10) & 7 & 16 & (672 \(\pm\) 15) & 0.1399 \\  & 7 & 16 & (697 \(\pm\) 19) & 14 & 16 & (717 \(\pm\) 17) & 0.4543 \\ \hline  & Optisol-CS & 9 & 0.65 \(\pm\) 0.06 & [PERSON] medium & 9 & 0.69 \(\pm\) 0.05 & 0.001 \\  & Day c: & & Day 0: & & & \\  & 7 & 9 & 0.59 \(\pm\) 0.07 & 7 & 9 & 0.69 \(\pm\) 0.06 & 0.0001 \\ [PERSON] et al., 2000 [31] & 6 & 0.63 \(\pm\) 0.03 & 10 & 6 & 0.73 \(\pm\) 0.08 & 0.01 \\  & 14 & 4 & 0.60 \(\pm\) 0.02 & 14 & 4 & 0.87 \(\pm\) 0.04 & 0.0001 \\  & 21 & 2 & 0.69 \(\pm\) 0.02 & 21 & 2 & 0.87 \(\pm\) 0.03 & 0.02 \\  & Day c: & 9 & 0.65 \(\pm\) 0.06 & Day 0: & 9 & 0.69 \(\pm\) 0.05 & 0.001 \\ \hline  & Optisol-CS & & 550 \(\pm\) 50 & & Cornea Cold & & 550 \(\pm\) 50 \\  & baseline: & & baseline: & & & \\  & 7 & 12 & 8.5\% (\(\pm\)8.0) increase & 7 & 12 & 6.3\% (\(\pm\)8.2) & (\(y<0.05\)) \\  & 14 & 12 & 7.8\% (\(\pm\)7.5) & 14 & 12 & 5.3\% \(\(\pm\)7.33) & (\(y<0.05\)) \\  & 21 & 12 & 6.2\% (\(\pm\)7.17) & 21 & 12 & 3.3\% \(\(\pm\)5.5) & (\(y<0.05\)) \\  & 28 & 12 & 3.84\% (\(\pm\)6.15) & 28 & 12 & 2.86\% (\(\pm\)6.28) & (\(y<0.05\)) \\ \hline  & Optisol-CS & 22 & 526 \(\pm\) 10 & 0.006 & Kerasave & 22 & 571 \(\pm\) 12 & 0.006 \\  & baseline: & & baseline: & & & baseline: & \\  & Day 1-FK group & & 522 \(\pm\) 17 & 0.132 & Day 1-FK group & & 563 \(\pm\) 19 & 0.132 \\  & Day c: & 556 \(\pm\) 30 & 0.311 & Day 6: & 5 & 596 \(\pm\) 23 & 0.311 \\  & Day 12 & 5 & 594 \(\pm\) 30 & 0.756 & Day 12 & 5 & 608 \(\pm\) 32 & 0.756 \\ [PERSON] et al., 2020 [32] & DSARF group: & & 521 \(\pm\) 18 & 0.161 & DSARF group: & & 561 \(\pm\) 20 & 0.161 \\  & Day \(\&\) & 547 \(\pm\) 19 & 0.078 & Day 4: & 6 & 600 \(\pm\) 20 & 0.078 \\  & Day \(\&\) & 6 & 115 \(\pm\) 9 & 0.784 & Day 6: & 6 & 112 \(\pm\) 10 & 0.784 \\  & DMEK group: & & & & & & \\  & Day \(\&\) & 5 & 540 \(\pm\) 18 & 0.072 & Day 4: & 5 & 602 \(\pm\) 24 & 0.072 \\  & Day \(\&\) & 5 & not reported & & Day 6: & 5 & not reported \\ \hline \hline \end{tabular}
\end{table} 
Table 8: _Cont._The endothelial cell (EC) mortality was assessed in 114 corneas in three studies [22; 30; 31] (Table 9). A higher EC was observed for Optisol-GS (\(p\) = 0.002) when compared with the initial values for corneas treated with Kerasave (\(p\) = 0.033) [30]. On Day 14, the extent of mortality was greater in the peripheral area of corneas than in the central area for both storage groups (\(p\) = 0.0029 for OGS and \(p\) = 0.021 for Kerasave) [30]. Cornisol was comparable with Optisol-GS (17.4% vs. 15.7%; \(p\) = 0.83) regarding EC mortality at 14 days [22].