esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Valid Inequalities
#### 3.4.1 ATSP Inequalities  
Apart from the subtour elimination constraints, probably the best-known family of facet-defining inequalities for the ATSP goes by the name of \(D^{+}_{k}\)-inequalities [2; 34]. These inequalities are derived based on the compound variables \(y\) on a complete directed graph \(D=(V,A)\). To simplify notation, for sets \(S\), \(T\subseteq V\) we let  
\[\delta(u,T)\coloneqq\{(u,v)\in A\mid v\in T\}.\]  
The \(D^{+}_{k}\)-inequality for a sequence \((v_{1},\ldots,v_{k})\) of \(2\leq k<n\) distinct vertices is given by  
\[\sum_{j=1}^{k-1}y_{v_{j},z_{j+1}}+y_{v_{k},v_{1}}+2y(\delta(v_{1},\{v_{3},\ldots,v_{k}\}))\] \[\qquad\qquad\qquad\qquad+\sum_{j=4}^{k}y(\delta(v_{j},\{v_{3}, \ldots,v_{j-1}\}))\leq k-1.\]  
The separation of \(D^{+}_{k}\)-inequalities involves the enumeration of possible sequences in a Branch-and-Bound-like fashion. Nonetheless, the separation works well in practice, since many of the possible sequences can be pruned.
## 4 Experiments
### Moving Object Segmentation  
In order to validate the comprehensive performance of motion segmentation, this section compares our method with the current mainstream 3D motion segmentation methods on the SemanticKITTI dataset for metrics evaluation and validation, as shown in Table 5 below. The evaluation results in this section are only for the IoU (Intersection over Union) of the motion objects, where the structure indicates the representation of a point cloud, and RV means the range view image.  
It can be seen that the point-based methods PointNet and EmPointMovSeg have lower \(IoU^{MOS}\) than our method in motion segmentation. The voxel-based method Cylinder has a higher \(IoU^{MOS}\) than the point-based methods, but has no obvious advantage in running speed. The range-view image methods usually have faster speeds. Considering that the scanning speed of Lidar in real scenarios is 10 Hz in general, the RV structure has a clear advantage in these models, since it exceeds the scanning speed of Lidar and can achieve the effect of real-time operation. LMNet achieves the highest FPS among the RV methods. The accuracy of RV methods can be improved when the neighboring frame increases from 1 to 9, but the FPS decreases significantly. The 10-frame point cloud LMNet has an 8% improvement in \(IoU^{MOS}\) over the 2-frame point cloud, but a 73.9% reduction in speed. MotionSeg3D achieves the highest \(IoU^{MOS}\), but the FPS is only 8.55. Our method achieves the third-highest \(IoU^{MOS}\) and the second-fastest speed. Thus, although MotionSeg3D achieves a higher \(IoU^{MOS}\), the performance increase comes at the cost of a significant speed reduction, and our method guarantees faster speeds while performing better than LMNet with similar speeds.
# Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load
## 4 Numerical Calculation and Analysis of Subgrade Deformation
### Impact of Traffic Load  
Three traffic loads are applied in the numerical calculation according to test conditions. It can be seen from Figure 23 that in any depth range of new and old roadbed, the relationship between vertical displacement and traffic load is a roughly linear distribution. The settlement of roadbeds increases exponentially with the increase in traffic load. The closer the distance is to the new and old subgrade pavement, the higher the slope of the settlement curve is, the more strongly affected by the traffic load, which also verifies the test conclusion. This rule also verified by [PERSON]'s model test [22].
# Lead Monoxide Nanostructures for Nanophotonics: A Review
## 4 Conclusions and Outlook  
In the past twenty years, black-phosphorus-analog PbO has achieved great progress due to its versatile advantages, such as its low cost, facile fabrication, high surface area, suitable bandgap, and good radiation-absorbing properties. In this review, PbO nanostructures with different dimensionalities were summarized in detail, and their fundamental properties and optoelectronics/photonics applications were also reviewed. However, although the application prospects of PbO nanostructures in nanophotonics are interesting and promising, the exploration of PbO nanostructures in the field of optoelectronics/photonics is still relatively new compared to that of other black phosphorus analogs, such as Te [13; 14], Se [6], Bi [4], antimony [12], etc. Firstly, it is crucial to take the mass production and process integration of PbO nanostructures into consideration for the development of low-cost, facile, and ecofriendly synthetic methodologies to meet the demands of industrial application. Secondly, functional PbO nanostructures, such as doped PbO or PbO-based heterostructures, which are capable of significantly improving the performance of PbO nanomaterials, are a research hotspot. Thirdly, the multifunctional performance based on PbO nanostructures (surface-modified 2D PbO, 2D PbO/polymer, etc.) for multidisciplinary applications, such as photodetection with a self-cleaning ability and nonlinear photonics with a self-healing ability, is a burgeoning field because of the superior properties in each disparate component.  
**Funding:** The research was supported by the National Natural Science Foundation of China (grant nos. 61805147 and 52103316) and the Science and Technology Innovation Commission of Shenzhen (grant nos. JCYJ20180305125141661).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Nonlinear photonics of the PbO QD/PS-composite-based SA. (**a**) Saturable absorption experimental data obtained from the open aperture Z-scan technique at 1064 nm. (**b**) The nonlinear transmission curve of the \(\beta\) PbO QD/PS-composite-film-based SA at 1064 nm. (**c**) Schematic diagram of the YDFL with the PbO QD/PS-composite-film-based SA. (**d**) Output power versus pump power. (**e**) Long-term stability under water immersion for 70 min. (**f**) The shifts of the central wavelength and 3 dB bandwidth within 70 min, when the pigtails were completely immersed in water. Inset shows the photograph of the fiber pigtails under water immersion. Reproduced with permission from Reference [17]. Copyright 2019, The Royal Society of Chemistry.
## 3 Proposed System
### Optimal Precoding Using Block Bi-Diagonalization
Here, both the thermal noise and shot noise are Gaussian and signal independent. The signal degradation occurs due to interferences caused by the thermal noise, shot noise and phase noise. In the proposed system, the BBD is developed before the LED transmitter. Therefore, the interference in the indoor MIMO-VLC data transmission is eliminated by using the BBD-based precoding in the MIMO-VLC transmitter. In addition, the transmission over the MIMO-VLC system is improved by using the QAM modulation. The mitigation of interference in the indoor MIMO-VLC system helps to reduce the BER. The combination of MIMO-VLC with BBD precoding improves the reliability of communication performances.
## 3 Results
### Surface Chemical Bonding States  
Figure 3a presents the high-resolution XPS survey spectrum of the TiO sample. It indicated that different chemical compositions of O, Ti, and C could be detected in the oxide layer using XPS. The O 1s, Ti 2p, and C 1s peaks were observed at \(\approx\)530 eV, \(\approx\)458 eV, and \(\approx\)285 eV, respectively. The regional scan of the O 1s peak taken from the oxide layer of the TiO sample is displayed in Figure 3b. The binding energy revealed that three characteristic components were mainly bonded on the surface layer after the plasma treatment. The three dominant peaks at \(\approx\)531 eV, \(\approx\)530 eV, and \(\approx\)529 eV corresponded to O-Ti, Ti-O\({}_{2}\), and O-Ti-O, respectively. The three dominant peaks proved that there was an oxide layer  
Figure 2: The SEM micrographs of investigated samples: (**a**) control TiO sample, (**b**) HDPA-SAM/TiO sample, (**c**) NDPA-SAM/TiO sample, (**d**) PUA-SAM/TiO sample, and (**e**) PHA-SAM/TiO sample.  
formation. It was also found that the concentration percentage of O and Ti in the surface layer of the TiO sample was around 32.4% and 10.1%, respectively. The result demonstrated that the TiO film could be completely synthesized on the substrate surface. There was no presence of P in the surface layer of the TiO sample. However, additional peaks assigned to P could be detected at \(\approx\)133 eV (as indicated by black arrows) on the SAMs' coated surfaces, as illustrated in Figure 4. The concentration percentage of P in the surface layer of the HDPA-SAM/TiO, NDPA-SAM/TiO, PUA-SAM/TiO, and PHA-SAM/TiO samples was around 2.3%, 4.1%, 1.4%, and 2.6%, respectively. Figure 5a displays the high-resolution XPS spectrum of O 1s of the HDPA-SAM/TiO sample. Clearly, the bonding states of P=O (\(\approx\)532 eV), P-Ti-O (\(\approx\)530 eV), and O-Ti (\(\approx\)529 eV) were detected in the surface layer using XPS. In the regional scan of the P 2p peak (Figure 5b), the binding energy indicated that the component characteristics of P-O (\(\approx\)133 eV) and P=O (\(\approx\)132 eV) were bonded in the surface layer. A similar tendency of the bonding states could also be found in the surface layer of the NDPA-SAM/TiO, PUA-SAM/TiO, and PHA-SAM/TiO samples. Accordingly, the XPS results demonstrated that the SAMs of phosphonate could be formed on the surface of the TiO layer through Ti-O-P linkages.  
Figure 3: (**a**) The high-resolution XPS survey spectrum of the TiO sample and (**b**) the regional scans of O 1s peaks taken from the TiO sample surface.  
Figure 4: The high-resolution XPS survey spectra of the SAMs-coated samples.  
Figure 5: (**a**) the high-resolution XPS spectrum of O 1s of the HDPA-SAM/TiO sample and (**b**) the regional scans of P 2p peaks taken from the HDPA-SAM/TiO sample surface.
# A Cyanobacteria-Based Biofilm System for Advanced Brewery Wastewater Treatment
###### Abstract  
Algal/cyanobacterial biofilm photobioreactors provide an alternative technology to conventional photosynthetic systems for wastewater treatment based on high biomass production and easy biomass harvesting at low cost. This study introduces a novel cyanobacteria-based biofilm photobioreactor and assesses its performance in post-treatment of brewery wastewater and biomass production. Two different supporting materials (glass/polyurethane) were tested to investigate the effect of surface hydrophobicity on biomass attachment and overall reactor performance. The reactor exhibited high removal efficiency (over 65%) of the wastewater's pollutants (chemical oxygen demand, nitrate, nitrite, ammonium, orthophosphate, and total Kjeldahl nitrogen), while biomass per reactor surface reached 13.1 and 12.8 g\(\cdot\)m\({}^{-2}\) corresponding to 406 and 392 mg\(\cdot\)L\({}^{-1}\) for glass and polyurethane, respectively, after 15 days of cultivation. The hydrophilic glass surface favored initial biomass adhesion, although eventually both materials yielded complete biomass attachment, highlighting that cell-to-cell interactions are the dominant adhesion mechanism in mature biofilms. It was also found that the biofilm accumulated up to 61% of its dry weight in carbohydrates at the end of cultivation, thus making the produced biomass a suitable feedstock for bioethanol production.  
Keywords:Leptolyngbyu sp.; attached growth; brewery wastewater; carbohydrates; bioremediation; supporting materials; hydrophobicity; attachment efficiency; biofilm photobioreactor +
Footnote †: journal: Applied Sci.
# Transient Nanoscale Tribofilm Growth: Analytical Prediction and Measurement
## 4 Discussion
This can be seen to occur even though there is ample adsorbed film coverage as shown in \[\Delta E_{mech}=\tau\Delta V \tag{2}\]3  
The inclusion of an adsorption model in the prediction of tribofilm growth offers an opportunity for the development of competitive/antagonistic additive technology, where boundary active additives compete for adsorption sites. It has been observed that certain additive combinations provide harmonic/synergistic or disruptive/antagonistic relationships. In the case of MoDTC and ZDDP, it has been noted that there is a harmonic/synergistic relationship in automotive lubricant applications where low shear strength MoS2 layers form within the ZDDP-based tribofilm, offering a slip plane which reduces friction, whilst ZDDP offers exceptional wear protection [34; 58]. However, if the ratio of adsorption rates between the ZDDP and MoDTC are altered, then the ratio of the tribofilm composition can be tailored accordingly. MoDTC is one of several lubricant-soluble polar molecules capable of producing a MoS2 tribofilm. Therefore, it may be possible to make a selection based on the predicted performance by the expounded model. Alternatively, detergents such as calcium sulfonate have been shown to disrupt the growth of ZDDP, leading to essential compromises in lubricant formulation [59; 60]. These relationships are well-suited for predictions with a competitive adsorption model as they allow a limited number of surface sites to be accounted for prior to the additive activation event. As a result, it is possible to determine a fractional content of subsequent layers in combined MoDTC and ZDDP competition and the growth of ZDDP in competition with a detergent molecule as well as other surfactant combinations. [PERSON] and [PERSON] [61] developed an early competitive adsorption model, based on the work of [PERSON] [62] and a similar model also exists for Langmuir-based adsorption [63]. Similarly, the current approach allows for discrepancies between additives' performances observed for different surface materials or coatings. In a typical fully formulated lubricant, there are many species competing for bonding/adsorbing to a finite number of surface sites, and variation in the surface forces is likely to cause significant differences in tribofilm growth and subsequent lubricant system performance in mixed and boundary regimes of lubrication. The analytical model proposed shows reasonable correlation to the test results in \[\Delta E_{mech}=\tau\Delta V \tag{2}\]4 However, the average results were generated from a limited number of tests supported by observed trends [12].
## 2 Materials and Methods
### The Triple Structure Siamese Network for Velocity Spectra Lateral Target Tracking
#### 2.2.1 Triple Siamese Network Structure  
The traditional Siamese network always uses the initial image as the tracking target. However, the velocity spectrum varies in the lateral direction. With the deepening of the tracking process, the information contained in the initial sample is insufficient to track the subsequent targets. We solve this problem by adding an update of target features in the tracker. The velocity spectrum samples at the initial position provide the most basic characteristics of the target and play a leading role in the tracking process. With the lateral movement of the target position, tracking by fusing the previous position features is better than using only the initial samples. Based on this consideration, we proposed a triple structure Siamese network based on the traditional Siamese network. We added an update branch of the current target and used the current prediction result as the target to track the velocity spectrum of the next position. During the tracking process, the current target will be updated with the result of target tracking. The structure of the triple velocity spectrum tracking network is shown in Figure 3.  
Different from the traditional Siamese network, the triple network consists of three branches: an initial branch \(I\) with an initial target as an input, a search branch \(S\) with a search area as an input, and an update branch \(U\) with a tracking result at the previous location as an input. The backbone models in the three branches share the same CNN architecture. Through the same network model, the responses of \(I\), \(S\), and \(U\) are \(\varphi(I)\), \(\varphi(S)\), and \(\varphi(U)\), which are embedded into the feature space of subsequent tasks. \(I\) is the  
Figure 3: Structure of the triple velocity spectrum tracking network.  
initial branch, and the specified initial velocity spectrum is used as the sample. \(I\) remains unchanged throughout the tracking process. \(U\) is the update branch, that is, the previous target tracking result. \(U\) will be updated with the tracking process. In order to embed the information of these branches, we use the feature map of the updated branch and the feature map of the search area to perform cross-correlation operations. Similar to the traditional Siamese network, the triple network uses a full convolution network for feature extraction, and each channel also generates a corresponding mapping response \(R\). Since there are three branches, the mapping results of two channels are generated, which are:  
\[R_{1}=\varphi(S)*\varphi(I) \tag{2}\]  
\[R_{2}=\varphi(S)*\varphi(U) \tag{3}\]  
where \(*\) represents a cross-correlation operation. \(R_{1}\) is the mapping result of the initial branch and the search branch, and \(R_{2}\) is the mapping result of the update branch and the search branch. In order to use the two-mapping information, the mapping results are fused weighted \(R_{f}\):  
\[R_{f}=a_{1}R_{1}+a_{2}R_{2} \tag{4}\]  
where \(a_{1}\) and \(a_{2}\) are the weight coefficients, and \(a_{1}+a_{2}=1\).
## 6 EFPOC Algorithm Complexity  
To estimate the presentation of the EFPOC algorithm, a complexity analysis is made in Table 2. The existing BER is based on the LUT for dynamic channel allocation of channel selection; the price of the LUT is high usually in terms of time and space. The complexity study is done on the EFPOC algorithm while taking the logic expense of operations once implemented in the hardware. It is taken that n represents the number of channel presently being idle in IN-BNC and T is the time engaged in finding the free channels that must be engendered. The algorithmic complexity of the EFPOC algorithm for the channel selection has been calculated, and the complexity obtained is O(n\({}^{2}\)). This manages the dynamic channel selection in a rapid manner compared to the existing BER-based ranking system.  
Figure 6: Pseudo code of the proposed Enhanced Flower Pollination-based Optimized Channel selection (EFPOC) algorithm.  
The EFPOC algorithm when used in the dynamic channel selection of idle PUs consumes less processing time, thereby enabling the APs to allocate channels to a higher number of OP-BAN in less time [22; 23].
# Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks
## 3 Numerical Study
### Train Characteristics  
In this study, the ICE 2 train was selected in the numerical calculations. One axle of the quarter bogie model (Figure 4) was considered. The vehicle body and the secondary suspension were neglected because they contributed to the complexity of the calculations and were not relevant for the analysis of the contact forces. The ICE 2 mechanical parameters can be found in [37]. In the calculations, only a single defect was adopted in order to understand the influence of a flat on the dynamic track behavior without having the superposition of the effects.  
Furthermore, all calculations were performed with the nonlinear contact model between the wheel and the rail, because it was expected that the variation of the dynamic load would be considerable. The considered contact stiffness was \(C_{\rm H}=1.04\times 10^{11}\) N/m\({}^{2/3}\), as indicated in the literature review.  
In this paper, the track dynamic behavior was evaluated for a single newly formed wheel flat with different depths introduced to one wheel of the vehicle. A newly formed wheel flat was considered because, according to [13], fresh flats produce higher impact loads on the track than rounded flats.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component/Type of Element** & **Parameter** \\ \hline rail UIC 60 & \(E=210\) GPa; \(I=3055\) cm\({}^{4}\) \\ (beam Euler & \(\
u=0.3\); \(A_{t}=76.84\) cm\({}^{2}\) \\ Bernoulli) & \(m_{r}=60.34\) kg/m \\ \hline pad & \(k_{p,est}=22.5\) kN/mm \\ (spring and dashpot & \(k_{p,din}=40\) kN/mm \\ in parallel) & \(c_{p}=8\) kN/m \\ \hline embedded sleeper & \(m=30\) kg \\ (punctual mass) & \(e=0.65\) m \\ \hline slab & \(E=34\) GPa; \(\
u=0.2\) \\ (plane-stress & \(\rho=25\) kg/m\({}^{3}\) \\ 8-node elements) & \(h_{1}=0.24\) m \\ \hline cement treated base & \(E=10\) GPa; \(\
u=0.3\) \\ (plane-stress & \(L=2.5\) m; \(\rho=25\) kg/m\({}^{3}\) \\ 8-node elements) & \(h_{2}=0.30\) m \\ \hline foundation & \(k_{f}=130\) kN/mm\({}^{2}\) \\ (Winkler foundation) & \(c_{f}=30\) kN/m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical and mechanical characteristics of the slab track components.  
Figure 4: Quarter bogie model: ICE 2 locomotive.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 2 Introduction of Wireless Body Area Network
### Challenges in WBAN Routing Protocol Design  
In view of the particularity of WBAN, the IEEE 802.15 working group set up a TG6 working group for WBAN, in 2007, to study the standardization of the WBAN technology. In 2012, the first WBAN standard proposed in the world, IEEE802.15.6, was officially released. The formulation of this standard also promoted the rapid development of WBAN.  
WBAN is a technology applied to human body, so its network environment is very complex. Since the physiological data collected have a great impact on human life and health, it is very important to design routing for the body area network. Based on the above analysis, the following problems and challenges must be considered in routing design [9; 10]:  
1. Dynamic changes of the topological structure: Wireless transmission involves body surface transmission, body transmission, and free space transmission. Channel conditions are complex. Shadow effect caused by human motion should also be considered. The distance and relative position between nodes will also change with the movement of limbs. Considering the time-variation of the topology and the possible problems [11], reliable routing mechanism should be designed to adapt to the dynamic topology.
2. Energy efficiency: WBAN is a technology applied in human body, some sensors are implanted in the body and must be replaced by surgery. It is not enough to supply power only through micro-batteries. Now, although RF (Radio Frequency), EM (Electro Magnetic) or energy harvesting can be used for the power supply, energy efficiency design should also be carried out at the source. Therefore, energy efficiency of single node and energy balance of the whole network must be considered in the routing design, so as to prolong the lifetime of the network, as far as possible.
3. Node temperature: Nodes generate heat when they work, which can cause damage to the key tissues and organs of the human body [12; 13]. Therefore, the temperature of nodes must be taken into account in the routing design, to avoid this kind of situation.
4. Various Qos requirements: Nodes in the WBAN generate different types of data, which must be processed differently, to ensure the Qos requirements of different types of data, such as emergency data, delay sensitive data, reliability sensitive data, and general data.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 2 Ultra-Tight GPS/INS Integration and RISS
### Velocity Equations  
After the azimuth and pitch angles are obtained, forward vehicle velocity can be projected into East, North, and Up velocities as the following:  
\[v_{e}=v_{o}\sin A\cos p \tag{5}\]  
\[v_{n}=v_{o}\cos A\cos p \tag{6}\]  
\[v_{u}=v_{o}\sin p \tag{7}\]