esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sample Preparation  
We used titanium substrates which were 15 mm diameter Grade 2 Titanium circular shape plates (99.6 at%, Grade 2). The plates were mechanically polished to #1200 grit level using Struers LaboPol-35 polishing machine with StruersLaboForce-Mi device (Struers, Copenhagen, Denmark), followed by chemical polishing with a corresponding proportional solution (silica gel: 5 mL; hydrogen peroxide: 0.77 mL; nitric acid: 0.02 mL; and hydrogen fluoride: 0.01 mL) for titanium to produce a mirror-likesurface. The substrates were then cleaned in acetone two times for 10 min each in an ultrasonic cleaning bath at room temperature, followed by rinsing with distilled water for 20 min and air drying.  
To achieve a titanium surface covered by silver nanoparticles with different sizes, the pure titanium surface was modified in three stages:  
Stage I--first annealing (I) of Ti samples to create TiO layer;  
Stage II--applying physical vapor deposition (PVD) method to deposit silver layer with different thicknesses on Ti/TiO surface;  
Stage III--second annealing (II) of Ti samples (already covered with Ti and Ag layers) at different temperatures for different times to achieve silver nanoparticles on the surface.  
Creation of silver nanoparticles on titanium followed procedures described below.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 6 Resin Adhesive Systems
### Composition of the Adhesive Systems
#### 6.1.2 Cross-Linking and Functional Monomers  
There are two main types of monomers: the functional monomers that correspond to the dentinal primers and the cross-linking monomers that correspond to the adhesive resin [48] (Figures 11 and 12).  
1. Cross-linking monomers  
All cross-linking monomers are hydrophobic, designed to stabilize the HL and interact with the overlying restoration material, as well as functional monomers. Acrylic systems are tasteless, colorless, and present an easy radical for polymerization reaction [166]. Different kinds of polymerizable groups, and hence resin systems exist:  
Figure 10: Higher magnification of dentin etched with phosphoric acid gel 37%. Amounts of small debris (silica) are clearly observed [154].  
* Acrylates (-CH\({}_{2}\)-CH=COO-): compounds characterized by double bonds that exhibit higher reactivity than methacrylates, causing problems in shelf-life and biocompatibility, and containing an ester group [48].
* Methacrylates (-CH\({}_{2}\)-C(CH\({}_{3}\))=CH\({}_{2}\)): compounds containing acrylates (double bonds), methyl group (-CH\({}_{3}\)), and ester group (Example: UDMA, Ethylene glycol dimethyl-acrylate, TEGDMA, or Bis-GMA) [167]. Both acrylates and methacrylates are susceptible to water degradation because of the presence of an ester group (R\({}_{1}\)-CO-OR\({}_{2}\)) in their formulations [69].
* Methacrylamides: compounds composed of a methacrylate group and an amide group (R\({}_{1}\)-CO-NH-R\({}_{2}\)) instead of an ester group [48].  
Methacrylamides are more resistant to water degradation compared to methacrylates, primarily due to the presence of the amide group [49].
# Space-Efficient, Fast and Exact Routing in Time-Dependent Road Networks +
## 2 Materials and Methods
### Shortcut Unpacking Data
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Queue Pops**} & \multicolumn{4}{c}{**Evaluated Travel Time Functions**} & \multicolumn{4}{c}{**Running Time [ms]**} \\ \cline{3-13}  & & **Basic** & \(\star\) **Corridor** & \(\star\) **Lazy** & \(\star\) **A*** & **Basic** & \(\star\) **Corridor** & \(\star\) **Lazy** & \(\star\) **A*** & **Basic** & \(\star\) **Corridor** & \(\star\) **Lazy** & \(\star\) **A*** \\ \hline Ber & Monday & 167.4 & 38.2 & 1,605.0 & 618.6 & 99,629.0 & 5403.3 & 1762.4 & 674.9 & 8.6 & 0.6 & 0.6 & 0.3 \\  & Tuesday & 167.4 & 38.1 & 1603.6 & 635.2 & 1080.25 & 5224.1 & 1767.4 & 691.5 & 8.8 & 0.6 & 0.6 & 0.3 \\  & Wednesday & 167.4 & 38.6 & 1640.4 & 6437.1 & 1019.381 & 5405.3 & 1786.6 & 702.0 & 8.9 & 0.6 & 0.7 & 0.3 \\  & Thursday & 167.4 & 38.9 & 1647.9 & 6425.0 & 1015.841 & 5409.8 & 1799.8 & 701.8 & 8.8 & 0.6 & 0.7 & 0.3 \\  & Friday & 167.4 & 37.8 & 1959.1 & 69.9 & 9412.5 & 5061.0 & 1722.9 & 674.0 & 8.5 & 0.5 & 0.6 & 0.3 \\  & Saturday & 167.1 & 26.1 & 926.5 & 491.5 & 8647.0 & 2214.5 & 967.4 & 514.1 & 6.4 & 0.3 & 0.3 & 0.2 \\  & Sunday & 167.1 & 24.7 & 864.1 & 476.3 & 847.66 & 1865.9 & 895.3 & 495.1 & 6.1 & 0.2 & 0.3 & 0.2 \\ \hline Ger06 & Monday & 492.3 & 66.6 & 2494.0 & 727.0 & 75167.93 & 235.42 & 3029.0 & 853.0 & 42.8 & 1.8 & 1.4 & 0.5 \\  & midweek & 492.3 & 79.7 & 3323.2 & 831.0 & 518.721.3 & 3174.08 & 3838.0 & 995.1 & 46.4 & 2.3 & 1.7 & 0.6 \\  & Friday & 491.9 & 629.9 & 2349.2 & 731.3 & 780.0813 & 214.232 & 2665.4 & 848.3 & 42.6 & 1.6 & 1.2 & 0.5 \\  & Saturday & 490.7 & 24.1 & 334.4 & 211.1 & 5413.41 & 2457.5 & 360.0 & 223.6 & 26.8 & 0.4 & 0.3 & 0.2 \\  & Sunday & 490.0 & 20.2 & 219.2 & 163.5 & 5030.094 & 1599.4 & 226.8 & 169.1 & 24.2 & 0.3 & 0.2 & 0.2 \\ \hline SynEur & Low & 742.8 & 341.3 & 6626.2 & 1704.8 & 4871.956 & 9749.099 & 16521.1 & 4730.7 & 201.8 & 39.9 & 5.1 & 2.0 \\  & Medium & 746.6 & 461.8 & 1720.9 & 3.796.9 & 57424.62 & 1596.041 & 403.5 & 3506.3 & 3998.8 & 253.0 & 69.4 & 13.1 & 4.0 \\  & High & 749.7 & 554.1 & 33,572.70 & 7018.4 & 6142.257.3 & 2031,399.6 & 60234.2 & 15,685.2 & 289.2 & 96.4 & 25.5 & 6.9 \\ \hline Ger17 & Tuesday & 510.3 & 143.4 & 18,450.0 & 3099.2 & 2,1007.318 & 164372.5 & 19,910.5 & 3495.5 & 169.7 & 13.7 & 9.1 & 1.7 \\ \hline Eur17 & Tuesday & 861.6 & 229.3 & 39,714.8 & 6876.5 & 9,951,623.1 & 806,727.8 & 43,581.1 & 7911.0 & 808.6 & 62.3 & 20.8 & 4.1 \\ \hline Eur20 & Tuesday & 871.0 & 335.6 & 62,677.7 & 7231.9 & 10,527/072.7 & 1,222,655.6 & 70,145.4 & 8844.7 & 8132.2 & 92.9 & 33.7 & 4.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Query performance with different optimizations. We report the number of nodes popped from the queue, the number of evaluated travel time functions and the running time. All values are arithmetic means over 100,000 queries executed in bulk with source, target and departure time drawn uniformly at random.
## 3 Materials and Methods
### Feature Reduction
#### 3.1.2 Lasso  
Least Absolute Shrinkage and Selection Operator (LASSO) [21] is a feature reduction technique that also helps with reducing overfitting and is effective when the dataset has multicollinearity. LASSO is an enhancement to Linear Regression by adding penalties to reduce overfitting. Lasso performs L1 regularization, also known as L1-norm, which adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function (the difference between the real and the predicted values). Given a response vector \(y\in\mathbb{R}^{n}\), and a matrix \(X\in\mathbb{R}^{n\times p}\) of predictor variables, the LASSO regression coefficients are defined as: \(\hat{\mathbf{\beta}}=\text{argmin}_{\beta_{0},\ldots,\beta_{p}}\sum_{i=1}^{n}(Y_{i }-\sum_{j=1}^{p}(X_{ij}\beta_{j}))^{2}+\lambda\sum_{j=1}^{p}|\beta_{j}|\). The L1 regularization combats overfitting by shrinking less important features' coefficients to 0 and eliminating them from the model. Hence, the LASSO procedure encourages models with fewer parameters.
# Diagnosis of the Multiepitope Protein rMLEEISH3 for Canine Visceral Leishmaniasis
## 1 Introduction
Such recombinant proteins offer several advantages, including consistent standardization, improved reproducibility, absence of contaminants from other parasite antigens, and the possibility of large-scale production. Furthermore, multi-epitope antigens allow for a broader spectrum of immune response and are especially useful for detecting antibodies, even in dogs with low parasitemia [17]. The aim of this study was to evaluate the diagnostic performance of the chimeric protein rMELEISH3, which combines selected epitopes of immunogenic proteins _from L. infantum_, by applying an in-house ELISA protocol designed to detect IgG antibodies in serum derived from naturally infected dogs. The rMELEISH3 protein is a novel, independently designed molecule, structurally independent of previously described MELEISH antigens, and was constructed using bioinformatics strategies for epitope prediction and design. This is the first study to investigate its potential for the serological diagnosis of canine visceral leishmaniasis.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 4 Conclusions  
To improve the traffic efficiency, we proposed a short-term traffic state prediction model based on the data acquisition strategy of MEC-assisted V2X network. The model combines the advantages of GCN and GRU soft-attention mechanism to analyze the spatiotemporal characteristics of traffic data. In addition, we design an intelligent roadside platform to verify the proposed model. The main conclusions are summarized as follows:  
1. This paper fuses multi-source information between intelligent OBUs, roadside sensors and traffic signal controller to accurately perceive the traffic state based on the V2X communication.
2. The prediction model considers the spatiotemporal dependence of all vehicles at vertexes of the intersection network. The proposed model can effectively extract vertexes features from the intersection, which greatly improves the prediction accuracy of the model. Based on the data acquisition strategy of the MEC-assisted V2X network, the comparative experiment reveals the effectiveness of our proposed model.
3. This paper mainly analyzes the traffic operation state of the single intersection, which limits the usage of the proposed model in extended scenarios and may pose a challenge to the adaptability of the model. In further research, we will study the traffic state prediction problems with the regional intersections to explore the efficiency and effectiveness of data support and implementation scheme in CVIS, as well as the adaptability of the model in these scenarios.  
Conceptualization, P.W. and J.Z.; methodology, X.L.; software, X.L.; validation, X.L., Y.W. and T.W.; formal analysis, P.W.; investigation, P.W.; resources, P.W.; data curation, X.L.; writing--original draft preparation, P.W. and X.L.; writing--review and editing, J.Z.; visualization, P.W.; supervision, P.W.; project administration, P.W.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Natural Science Foundation (Grant number 4212034).  
Not applicable.  
All data and models used during the study appear in this article.  
The authors would like to thank [PERSON] and [PERSON] for their technical assistance with the experiments and analyses, and thank the University of Exeter for the publication support of this paper.  
The authors declare no conflict of interest.
## 3 Results
### Temporal Dynamics of NPP and ET for Different Vegetation Types  
Generally, the NPP and ET of the vegetation in Tianshan North Slope region show both an increasing trend in response to the changes in climate and CO\({}_{2}\) concentration under all three RCP scenarios during 2006-2055 (Figures 8 and 9). In order to compare the sensitivities of different vegetation types to the changes under the three RCPs, we used the normalized rate. By doing so, the linear fitting rate of change is divided by the mean annual value (Table 4). Figure 8: The temporal variation of annual NPP for different vegetation types from 2006 to 2055 under RCP2.6 (**a**), RCP4.5 (**b**) and RCP8.5 (**c**). TNF: temperate needle-leaf forest; TBF: temperate broadleaf forest; GRS: grass; NPS: non-pheatophyte shrub; PS:pheatophyte shrub; CRP: crop; STL: settlement; SPT: Summer pasture. Figure 7: The spatial patterns of annual ET difference between 2006 and 2055 under RCP2.6 (**a**), RCP4.5 (**b**), and RCP8.5 (**c**). Figure 8 shows that there is a clear pattern in the NPP of different vegetation types during the study period. Under all three RCPs, the CRP has the highest NPP in Tianshan North Slope while the NPS has the lowest NPP. The NPP of each vegetation type in Tianshan North Slope shows an increasing trend with various rates under each RCP scenario. The changing rate of NPP for each vegetation type increases from RCP2.6 to RCP4.5 to RCP8.5 (Figure 8). Under each RCP, the annual NPPs of NPS, PS, and GRS are more sensitive to the changes in climate and CO\({}_{2}\) concentration than the other vegetation types, while those of the TBF and CRP are less sensitive to the changes. The NPP of NPS is the most sensitive to changes in climate and CO\({}_{2}\) concentration with an increasing rate of 1.16% and 1.52% under RCP2.6 and RCP4.5 respectively (Table 4). Among the eight vegetation types, the NPP of TBF is the least sensitive to the changes in climate and CO\({}_{2}\) concentration with an increasing rate of 0.50% and 0.69% under RCP2.6 and RCP4.5, respectively. Under RCP8.5, the most and the least sensitive vegetation types are PS and CRP with respectively an NPP increasing rate of 2.33% and 0.80%. For the ET, CRP has the highest annual value and NPS has the lowest value under each RCP scenario (Figure 9). Considering the fact that the CRP and NPS are distributed in the basin with similar climate conditions, therefore, the considerable difference in ET between CRP and NPS is induced by the irrigation for CRP. Similar to the dynamics of NPP, the ET of each vegetation type presents an increasing trend under all RCP scenarios (Figure 9). Different from the NPP dynamic, the ET of PS is most sensitive to the changes in climate and CO\({}_{2}\) concentration. Among the eight vegetation types,
## 15 Physical Meaning of the Frequency Shift Due to Velocity  
Clearly, the average velocity of the droplet causes a positive shift of the pulsation frequency. The fact that the frequency increases with speed is not in contradiction with relativity theory, because relativistic effects such as time dilation will show up in the asymptotic derivations as terms of a fourth asymptotic order. Terms of this order were neglected in the present theory. The frequency shift \(\Delta\omega\) due to velocity, defined as:  
\[\Delta\omega=k\omega_{0}.{v_{av}}^{2} \tag{31}\]  
does have a clear physical meaning, but there is the question of how it will manifest itself in actual practice. In [7] it is shown that this shift automatically takes control of matters in the case of a droplet moving back and forth inside a potential well, and leads to energy quantisation in agreement with the usual results of QM. However, the shortest way to get to our present goal, viz. to a quantitative verification of the equations of motion, is to use the droplet theory for a derivation of [PERSON]'s equation. This may be done by imagining an experiment where we let the electromagnetic field induced by a static--but pulsating--droplet \((v_{av}=0)\) interfere with that due to a moving droplet. We will find low frequency beats (\"low frequency\" compared with the pulsation frequency) in the combined field, with the beat frequency \(\Delta\omega\) according to Equation (31).
## 2 Modeling and Simulation
### Simulation Model  
The mechanistic modeling of the controllable pitch propeller system is cumbersome and lacks intuitive clarity, and many parameters in the resulting transfer function are challenging to measure. In addition, the Simulink/SimHydraulics toolbox contains considerable hydraulic modules and commercial component-based modules that are commonly used, allowing for the realization of the physical modeling of hydraulic and hydro-mechanical systems. Herein, the Simulink/SimHydraulics toolbox is adopted for the physical modeling to establish an accurate and user-friendly mathematical model of the controllable pitch propeller system. The parameters of the hydraulic cylinder and the load settings are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameter Value** \\ \hline Mass & 4.5 kg \\ Coefficient of spring elasticity & 10 N/m \\ Damper damping & 250 N/(m/s) \\ Hydraulic cylinder A end area & 0.006 m\({}^{2}\) \\ Hydraulic cylinder B end area & 0.003 m\({}^{2}\) \\ Piston stroke & 19.6 cm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hydraulic cylinder and load parameters.  
Figure 4: Flow characteristic curves of the four-blade pitch paddle torque coefficient.  
The simulation model of the hydraulic system is presented in Figure 5. When the external control signal is input to the three-position four-way valve using the digital-to-analog converter module, the direction of the hydraulic fluid between the pipelines is changed, pushing the hydraulic cylinder. The mass block, spring, and damper simulate the external force on the pitch paddle blade in seawater and are connected to the hydraulic cylinder, achieving a better replication of the operating environment at sea and a more realistic simulation.  
Considering that obtaining the state information of the controllable pitch propeller system is challenging, the identification target is set as the transfer function of the input and output signals. Then, this function is transformed into the state space during the system identification. The input and output signals are imported using MATLAB's system identification toolbox. The zeros and poles of the transfer function are set to be two and three, respectively.  
After the identification, the final transfer function obtained aligns well with the model input and output, representing 93% conformity (Figure 6). It can be found that the conclusion is consistent with the engineering accuracy and systematically identifies the physical model of the controllable pitch propeller. The transfer function can be expressed as:  
Figure 5: Hydraulic system simulation model of the controllable pitch propeller.  
\[\phi=\frac{2.1408s^{2}+0.2282s+0.0278}{s^{3}+1.3793s^{2}+0.1654s+0.0144} \tag{8}\]
# Gathering Big Data in Wireless Sensor Networks by Drone +
## 6 Algorithm for Big Data Gathering during Drone Movement  
The algorithm proposed here receives a tour created by the Incremental or Decremental heuristics and defines the sensor nodes that will send their data to the drone when it is moving. A tour created by these heuristics is a sequence of hovering points and a subset of sensor nodes for each hovering point. All nodes in a subset have to send data to the drone when it is over a hovering point. The proposed algorithm creates a subset of sensor nodes for each path between two consecutive hovering points on tour. The nodes in these subsets have to send their data to the drone during its movement. This reduces the hovering time (here named \(T_{\textit{collecting}}\)) and consequently reduces the total time to gather all data (here named \(T_{\textit{total}}\)). Furthermore, the proposed algorithm guarantees that the drone will stay a minimum time inside each sensor node's radio range in these subsets. This time must be greater or equal to the time each node needs to transmit all data stored in its memory.  
Figure 7: Example of the Local Search. (**a**) The initial tour \(R_{i}=\{PDP_{3},PDP_{5}\}\). (**b**) \(PDP_{3}\) was replaced by \(PDP_{2}\) and \(T_{\textit{total}}\) is calculated. (**c**) \(PDP_{3}\) was replaced by \(PDP_{6}\) and \(T_{\textit{total}}\) is calculated again. After that, the Local Search replaces \(PDP_{5}\) with \(PDP_{2}\), \(PDP_{4}\) and \(PDP_{6}\); however, the given example does not present it.  
For instance, consider the example presented by the Figure 8. It shows the sensor nodes \(s_{1}\) and \(s_{2}\) and two consecutive hovering points \(p_{1}\) and \(p_{2}\) that are part of a tour \(P\) created by one of these heuristics. Each sensor node has \(r\) meter of radio range, \(m\) bits of data storage in its memory and link with \(b\) bits per second. The algorithm proposed here has to create the subset of sensor nodes \(d_{1\leftrightarrow 2}\), such that during the drone flight from \(p_{1}\) to \(p_{2}\), it stays inside the radio range of all nodes in \(d_{1\leftrightarrow 2}\) for a period of time greater or equal to \(\frac{m}{b}\) seconds. Since we consider the drone flying straight to each hovering point in constant speed \(dspeed\), the path between \(p_{1}\) and \(p_{2}\) is a line. Figure 8 shows that this line is totally out of the radio range of the sensor \(s_{1}\), but part of it is inside the radio range of the sensor \(s_{2}\). Considering \(ds_{2}\) as the length of this part, the sensor \(s_{2}\) can be part of \(d_{1\leftrightarrow 2}\) only if \(ds_{2}\) is long enough to drone move over for at least \(\frac{m}{b}\) seconds. Node \(s_{1}\) is not in the subset \(d_{1\leftrightarrow 2}\).  
The example presented here has only two hovering points and two sensor nodes. However, a tour created by the aforementioned heuristics can be composed of a sequence of several hovering points. The algorithm proposed here analyzes each sensor node for each two consecutive hovering points on tour.
# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 1 Introduction
### Short Literature Review  
Automation industries perform movement and transportation with a robot that requires a collision-free path from the starting position with orientation, to the goal position with orientation. A variety of path planning algorithms exist for achieving the motions of the robots by creating collision-free paths. One of the most common and powerful algorithm types chosen to create collision-free paths is sampling-based algorithms. Even though the completeness guarantees are weaker in these algorithms [18], they provide probabilistic completeness, have collision detection methods in the configuration space and apply a discrete search that utilizes the samples. There exist other combinatorial algorithms [19] for creating collision-free paths, but these algorithms exhibit a high computational time, resulting in real-time implementation difficulties within industries. If the right settings are chosen for the sampling-based algorithms, they are easy to implement and they overcome the challenges of combinatorial algorithms [14]. Sampling-based algorithms are classified into two categories: active and passive [20]. Any algorithm which gets its best feasible path on its own without any extra search algorithms is active. For example, the RRT algorithm constructs the tree and finds the path between the start and goal points on its own, independently of any graph search algorithms. The algorithms which create the mesh or graph and add a combination of the search algorithm to pick the best feasible path are called passive. For example, PRM creates the roadmap and can fetch a path from the start to the goal based on any graph search algorithms implemented. For example, the article by A*, [PERSON] evaluates both passive and active sampling-based algorithms.