esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Design of the Control System
### Model Predictive Control Considering Driving Style and Slope
An adaptive model predictive control model considering driving style as shown in Figure 8 was constructed by means of Carsim, MATLAB/Simulink. The joint simulation platform is divided into four main parts, including the upper model predictive control part, the lower speed controller part, the vehicle model part, and the fuzzy inference part. The upper controller, lower speed controller and fuzzy inference sections were implemented in the main runtime environment of MATLAB/Simulink, and the vehicle model section was built in the Carsim 2019.1 simulation software. The vehicle model is used to transmit the vehicle configuration and parameter states to the upper model predictive control and fuzzy inference logic in real time during simulation time. The upper controller transmits the desired acceleration decision to the lower control strategy based on the results of the fuzzy inference, the desired speed sequence, and the actual vehicle longitudinal speed. The lower-level speed controller obtains the corresponding brake cylinder pressure or throttle opening according to the desired acceleration and inputs it to the vehicle model, which determines the braking or driving state of the vehicle according to the input signal, forming closed loop control. This section takes the fuzzy inference module and uses driving style, slope, rate of change in acceleration, and speed as inputs to the fuzzy controller and thus obtains the control quantity weights \(Q\) for the model predictive control, which in turn expresses the effect of driving style on vehicle control. In this condition, the vehicle is simulated for uphill and downhill conditions at 0% and 5% slopes.
# Interface Shear Performance between Porous Polyurethane Mixture and Asphalt Sublayer
## 4 Test Method
### Test Conditions  
Asphalt pavement is subject to different climate conditions during its service life, including high temperature in summer, freezing-thaw in winter, and water infiltration during rainfall. At rainy days, water can drain vertically through the OGFC and laterally flow to pavement shoulder at wet weather condition due to the internal interconnected voids, which cause less moisture-related distress in OGFC pavement. However, it is documented that the open and interconnected pores in porous asphalt mixtures are easier to be damaged by the volume change due to freezing and thaw as compared to dense-graded asphalt mixtures [13]. Thus, shearing test was applied to composite specimens at different temperatures (\(-18\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C and \(60\)\({}^{\circ}\)C) and after freezing-thaw cycles. The details of test matrix were presented in Table 3. At each freezing-thaw cycle, the composite specimens were frozen under \(-18\)\({}^{\circ}\)C for four hours and then immersed in water bath at \(15\)\({}^{\circ}\)C for four hours. This process was repeated for four times.  
Although the shear strength may indicate the shear resistance of interface for the purpose of ranking, the interface between surface layer and sublayer of asphalt pavement sustains repetitive shear force due to traffic loading. Therefore, stress-controlled inclined shear fatigue test was conducted to evaluate fatigue life of the interface in the composite specimen under repeated loading cycles. The inclined shear test setup shown in Figure 2 was used for fatigue test. A sinusoidal load wave with the frequency of \(10\) Hz and peak stress of \(0.4\) MPa without rest time between cycles was applied at \(25\)\({}^{\circ}\)C for shear fatigue test. The loading frequency and magnitude was selected based on the typical shear stress magnitude experienced at near-surface under truck loading at highway speeds [24]. The cyclic loading in the fatigue test was applied until that the interface in composite specimen failed. Although currently there is no standard available for shear fatigue test, the similar fatigue test approach has been used in the previous work as the more realistic test to represent the failure process of pavement interface under repeated traffic loading [31; 32].  
It is noted that uniform shear stress in the interface was assumed in the calculation of shear stress. In the real case, the shear stress may have stress concentrations due to the stiffness difference between asphalt binder and aggregate. This should be investigated with microstructure-based model in future work.
## 1 Introduction and History of Cryogenic Lasers
### The Modern Era
The first use of a Ti:Al\({}_{2}\)O\({}_{3}\) cryogenically-cooled amplifier was reported in [31]; the second-stage amplifier, pumped with 25 W of 527 nm power from doubled Nd:YLF lasers, was cooled to 125 K, resulting in an increase of the thermal focal length from 0.45 to 5.6 m, by a factor of about 125. This approach has been discussed and used by a number of researchers [32; 33; 34; 35]. The use of cryogenic-cooling in ultrafast lasers will be further discussed later in this article.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
###### Abstract  
The degree-constrained Steiner problem in graphs is well known in the literature. In an undirected graph, positive integer degree bounds are associated with nodes and positive costs with the edges. The goal is to find the minimum cost tree spanning a given node set while respecting the degree bounds. As it is known, finding a tree satisfying the constraints is not always possible. The problem differs when the nodes can participate multiple times in the coverage and the constraints represent a limited degree (a capacity) for each occurrence of the nodes. The optimum corresponds to a graph-related structure, i.e., to a hierarchy. Finding the solution to this particular Steiner problem is NP-hard. We investigate the conditions of its existence and its exact computation. The gain of the hierarchies is demonstrated by solving ILPs to compute hierarchies and trees. The advantages of the spanning hierarchies are conclusive: (1) spanning hierarchies can be found in some cases where spanning trees matching the degree constraints do not exist; (2) the cost of the hierarchy can be lower even if the Steiner tree satisfying the constraints exists.  
graph theory; degree-constrained Steiner tree; Steiner hierarchy; homomorphism; NP-hard problem; exact computation; approximation 05C05; 05C07; 05C60; 90-10
## 2 Sensors  
Sensors are the key components of BSN, as they connect the physical world with electronic systems. They are mainly used to collect the information about physiology and the surrounding environment. Sensor nodes, which have a sensor as their main part, are responsible for processing information by format conversion, logical computing, data storage, and transmitting. One sensor node generally comprises a sensor module, processor module, wireless communication module, and power supply module [36]. The sensor module is responsible for collecting the status of measurands and converting data from physical quantities to electrical signals. The processor module is responsible for controlling the sensor nodes. The wireless communication module, consisting of network layer, MAC layer and wireless transceiver in the physical layer, is responsible for communication among sensors and computers. The power supply module is responsible for providing energy for entire the sensor node.
# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 2 PPP Mathematical Model and Satellite Selection Algorithms
### Dual-Frequency Multi-GNSS PPP
\right., \tag{3}\]where superscript \(r\), \(m\), \(T\) denote receiver, satellite, and satellite system, respectively; \(p_{r,1}^{1,T}\) and \(l_{r,1}^{1,T}\) denote observed minus computed (OMC) values of pseudorange and carrier phase observables on the carrier frequency \(f_{1}\), respectively; \(\mathbf{u}_{r}\) is the unit vector of the component from the receiver to the satellite; \(\mathbf{1}\) is a vector of \(2\times m\) rows and one column, of which each element is one, corresponding to the receiver clock parameter \(d\vec{t}_{r}^{T}\); \(M_{W}\) is the wet mapping function; \(\mathbf{x}\) is the vector of the receiver position increments relative to the a priori position; \(dt\) is the receiver clock offsets; \(Z_{W}\) is the zenith wet delay; \(\vec{I}_{r,1}^{s,T}\) is the LOS ionospheric delay on the frequency \(f_{1}^{m,T}\); \(\gamma_{j}^{T}\) is the frequency-dependent multiplier factor (\(\gamma_{j}^{T}=\left(f_{1}^{s,T}/f_{j}^{s,T}\right)^{2}\)), which is independent of the satellite pseudorandom noise (PRN) code; \(d_{j}^{s,T}\) is the frequency-dependent satellite UCD; \(d_{r,j}^{s,T}\) is the frequency-dependent receiver UCD with respect to satellite \(s\); \(\lambda_{j}^{s,T}\) is the carrier wavelength on the frequency band \(j\); \(N_{r,j}^{s,T}\) is the integer phase ambiguity; \(b_{r,j}^{s,T}\) and \(b_{j}^{s,T}\) are the frequency-dependent receiver and satellite uncalibrated phase delays (UPDs); \(\varepsilon_{r,j}^{s,T}\) and \(\xi_{r,j}^{s,T}\) are the sum of measurement noise and multipath error for pseudorange and carrier phase observations; in matrix \(\mathbf{K}\), the element for the corresponding \(p_{r,1}^{1,T}\) is 1, while the element for \(l_{r,1}^{1,T}\) is \(-1\), corresponding to the ionospheric parameter \(\vec{I}_{r,1}^{T}\); \(\mathbf{R}_{1}\) and \(\mathbf{R}_{2}\) is the matrix corresponding to the ambiguity parameters \(\overline{\mathbf{N}}_{r,1}^{T}\) and \(\overline{\mathbf{N}}_{r,2}^{T}\), respectively, the element for the corresponding \(p_{r,1}^{1,T}\) is 0, while for \(l_{r,1}^{1,T}\) is 1; \(\mathbf{Q}_{L}\) denotes the stochastic model of OMC observables. Note that all the variables in Equations (1) and (2) are expressed in meters except the ambiguity and UPDs in cycles.
## 3 Results and Discussion  
### Ultrasound Pulses  
Under the conditions of the present study, the acoustic intensity of the treatments with 50% amplitude for 1 and 2 min was 42 and 52 W/cm\({}^{2}\), respectively, while for treatments with 100% amplitude, the intensity was 71 for 84 W/cm\({}^{2}\) for 1 and 2 min, respectively. Henceforth, the treatments are identified as UPG-42, UPG-52, UPG-71, and UPG-84, respectively, and CG (control gelatin). During the ultrasonication of the gelatins, a maximum increase of 0.8 \({}^{\circ}\)C was recorded, ensuring that the conformational changes in the proteins were due to cavitation and not because of a temperature increase. On the other hand, the pH of the UPG decreased by 0.78 units, showing a significant difference concerning the CG. However, although the pH decreased as the intensity of the treatments increased, there were no significant differences between them (\(p<0.05\)). The decrease in pH has previously been associated with transitional changes that result in the deprotonation of acidic amino acid residues. This can affect intramolecular interactions and the conformation of protein chains by exposing new residues to the aqueous medium. The decrease in pH for the samples treated with ultrasound has been previously reported for vegetable and animal proteins, including fish gelatin [6].
## 7 Potential Anti-Virulence Agents for Black Rot Disease in Plants  
The virulence of _X. campestris_ is regulated by several factors, including the synthesis of extracellular enzymes and extracellular polysaccharides (EPS), both of which are controlled by the HD-GYP domain regulator RpfG and its cognate sensor kinase RpfC [56]. This two-component signal transduction system perceives and transduces the DSF (diffusible signal factor), whose synthesis is dependent on RpfF [76]. Mutations in _rpfG_, _rpfC_, or _rpfF_ reduce virulence, highlighting their role in disease progression [56; 77].  
_X. campestris_ is a Gram-negative, plant-pathogenic bacterium responsible for black rot disease in cruciferous crops such as cabbage, broccoli, and cauliflower. It infects plants through natural openings like hydathodes and stomata or via wounds, spreading systemically through the xylem. Once inside, _X. campestris_ produces extracellular polysaccharides (EPS), degradative enzymes, and virulence factors that disrupt water transport, causing wilting, chlorosis, and necrotic V-shaped lesions on leaves [78].  
A key breakthrough in understanding _X. campestris_ pathogenesis was the demonstration that the HD-GYP domain of RpfG functions as a phosphodiesterase that hydrolyzes c-di-GMP [77]. This finding established a mechanistic link between c-di-GMP signaling and the regulation of virulence, EPS production, and biofilm formation in _X. campestris_. Consequently, targeting RpfG with small-molecule inhibitors presents a promising strategy for developing anti-virulence agents to control black rot disease in economically important crops.
###### Abstract  
Molecular hybridization (MH) of heterocyclic rings has enabled scientists to design and develop novel drugs and drug-like candidates. In our previous work, considering the importance of MH, we synthesized different kinds of chloropyrazine-tethered pyrimidine derivatives (**22-40**) containing either substituted phenyl or heteracryl rings at position-6 of the pyrimidine ring and evaluated their antitubercular activity. Herein, we report the antimicrobial and antiproliferative activities of **22-40**. The antiproliferative activity of the target hybrids was superior to the antimicrobial activity. However, some compounds showed greater antimicrobial activity than the standard drugs. For instance, among the nineteen derivatives, compound **31** containing a 2''/4''-dichlorophenyl ring, showed the most potent antibacterial and antifungal activities (MIC 45.37 uM), followed by compounds **25** and **30** bearing 4''-nitrophenyl and 2''/4''-difluorophenyl scaffolds with minimum inhibitory concentrations (MIC) values of 48.67 uM and 50.04 uM, respectively. Compound **35**, containing a bioisosteric 2''-pyridinity ring, showed the most potent antiproliferative activity against the prostate cancer cell line (DU-145) with an IC\({}_{50}\) value of \(5\pm 1\) ug/mL. Additional testing of compounds **22-40** on human normal liver cells (LO2) indicated that the compounds were more selective to cancer cell lines over normal cells. Further, molecular docking of the most potent compound **35** against dihydrofolate reductase (DHFR) (PDB ID: 1U72) had a good binding affinity with a docking score of \(-6.834\). The SwissADME program estimated the drug-likeness properties of compound **35**. Hybrid **35** is a potential lead molecule for the development of new anticancer drugs, whereas **31** is a promising antimicrobial lead candidate.  
molecular hybridization; heterocyclic rings; chloropyrazine; pyrimidine; antiproliferative activity; antimicrobial activity; cytotoxicity; molecular docking; drug-likeness
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 2 Background  
### Rpl  
As already mentioned, this research proposes a scheduler for TSCH-based WSNs that can allocate additional cells during traffic bursts. The additional number of cells is computed based on the subtree size. This latter information can be obtained from the routing protocol. The de facto routing protocol in WSNs, RPL [4], is optimized for convergecast traffic and was proposed by the Internet Engineering Task Force (IEFT). RPL organizes the network in a destination-oriented directed acyclic graph (DODAG) with the help of control messages. The DODAG information object (DIO) message is sent from the root to the nodes in the network, and it advertises the existence of the RPL tree and its properties. The nodes that receive this message compile a list of candidate parents with the help of an objective function and choose a preferred parent from the list. The subtree of a node consists of the children of the node and all the descendants of the children. Since we are interested in the size of the subtree, RPL is set to work in storing mode. In this case, as a response to the received DIO message, the nodes send a destination advertisement object (DAO) message to their preferred parent. This DAO message is forwarded upward by the ancestors until it reaches the root. Based on the information in the DAO message, each node can keep a routing table with destination and next hop entries for all the nodes in its subtree, as shown in Figure 1. The subtree size of a node is the number of entries in the routing table of that node. In Figure 1, the subtree size of node 3 is 3 (nodes 4, 5 and 6 form the subtree). Once nodes are part of the tree, they also broadcast the DIO messages. Nodes might request to join the network before receiving the DIO message by broadcasting a DODAG information solicitation (DIS) message.  
The DIO messages are issued periodically with the help of the trickle timer algorithm. The role of the trickle timer is to tune the frequency at which control messages are sent in a network. If inconsistencies in the network are observed or if the connectivity changes, the rate at which DIO messages are sent is increased [5].
## 2 Formulation
### Radial Basis Surrogate Model  
To populate the data obtained initially from high-fidelity CFD simulations, a low-fidelity surrogate model named Radial Basis Function (RBF) is adopted in the present  
Figure 1: Sign criteria for (**a**) force coefficients and angle of attack; and (**b**) for aeroelastic forces and displacements.  
study. The principle behind RBF is to mimic human-like response functions, which are processed through acquired learning and experiences. The radial basis functions used for surrogate training are Gaussian, cubic, multi-quadratic and inverse multi-quadratic; for the present study, the multi-quadratic function is adopted. The mathematical expression for the multi-quadratic RBF is given by the following formula:  
\[K(r)=\sqrt{r^{2}+p^{2}}, \tag{3}\]  
where the distance measured from the central point is represented as \(r\), while \(p\) is a constant shape parameter. The directional independence of the \(r\) RBF adds to the mathematical complexity in the modelling between the input and output parameters.  
The MATLAB inbuilt function 'newrb' is used to develop the surrogate. A set of input and output matrices obtained from the CFD simulations is fed into the model. The other parameters involved are as follows: spread constant, error goal, maximum number of neurons and the number of neurons in the display. The spread constant is chosen as unity so that the response by the neurons to the various input variables is similar and high sensitiveness over specific input variable or variables is avoided. The error goal aims to ensure that the surrogate is efficiently trained while not effecting the accuracy.
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 2 Advances in Visible Light Communications
One of the main advantages of VLC technology is that it does not interfere with machinery or medical devices. Currently, if devices in factories or medical institutions have to be online (even on a local network), they are usually connected via Ethernet or optical cables, as these devices tend to be highly sensitive to any RF interference. However, this approach has its limits, since it is not always  
Figure 3: Pure WiFi environment vs. WiFi and light fidelity (LiFi) zone. WiFi signals pass through individual walls, thus limiting the reusability of the channels. convenient to wire movable machines. Therefore, an alternative, which lies in VLC systems or LiFi, could significantly improve the reliability or even the portability of said devices [50]. Multiple complications were encountered during the development of the previously mentioned VLC based systems. Among them is the receiver sensitivity, as most of the photodetectors were designed for fiber optics, where the beam is precisely focused on the photodiode itself. Therefore, the sensitivity tends to be low, and multiple solutions are currently being explored. Since it is practically impossible to increase the size of the photodiode itself without influencing the resulting bandwidth, many teams tend to develop optical concentrators. [PERSON] et al. presented their own concentrator, which managed to improve the communication coverage of an on-off keying (OOK)based system by approximately 15 to 55% [51]. A fluorescent concentrator can be even used to implement a MIMO system, as presented by [PERSON] et al. [52]. Apart from different concentrators, a fly-eye receiver can be used [53] or even an advanced form of an angle diversity receiver, which is often combined with beam steering technology [54; 55]. [PERSON] et al. [56] compared APD and single photon avalanche diode (SPAD)based receivers, which are also often used in experiments. APD showed promising results, but it tends to be prior for mass deployment. Beamforming is another area that is currently explored not only in VLC, but also in 5G and the new WiFi 6 [47; 57; 58]. This technology can significantly improve resource allocation or the reusability of frequency bands. [PERSON] et al. presented their own VLC beamforming solution called LiBeam, which managed to improve network spectral efficiency significantly, thus increasing the throughput of the whole platform [57]. VLC integration into 5G networks was also tested by [PERSON] et al. [59]. VLC systems and light propagation can be somewhat reliably simulated by employing ray-tracing technologies, which are currently on the rise, as GPUs with dedicated ray-tracing hardware are available. [PERSON] et al. introduced their own ray-tracing platform for the simulation of VLC channel modeling [60].
## 2 Materials and Methods
### System Environment and Hardware Configuration  
All the image-processing algorithms and experiments in this study were constructed and executed under the Windows 11 operating system environment, and the experimental platform was a Lenovo P720 workstation equipped with 256 GB of operating memory, an NVIDIA RTX 5000 GPU and an Intel Xeon Gold 6142 CPU (2.6 GHz). The experiments were conducted in Python 3.7 programming language and developed and debugged in PyCharm integrated development environment. In the specific implementation, this study constructs and optimises the XGBoost model based on the Scikit-learn framework, and also extracts the key information for overlapping fog droplets in the image by using the functions in the OpenCV library.