esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Mesh-Tree Grid Map Topological Representation  
#### 3.1.1 Topological Assumptions  
Per geometrical, spatial characteristic, and traffic constraint requirements in an HDS system, and also by referring to the mapping methods in [37; 38; 39], we set four major assumptions for our mesh-tree grid map topological representation method.  
Assumptions for Geometric and Spatial Structures  
* Uniform Grid Discretization  
The warehouse's layout is discretized into a uniform grid, where each cell corresponds to either a storage unit or a transportation space. Both main tracks and sub-tracks are discretized using the same grid unit, forming a structured mesh-tree topology.
* 2D Plan Modeling  
Only the ground floor is considered, and vertical elements, such as hoisters, are ignored.
* Tree structure abstraction  
Main tracks are modeled as the 'backbone' of a tree, while sub-tracks are considered 'branches'. In this way, the whole layout is segmented into a tree structure to simplify the subsequent path planning process.  
Assumptions for Connectivity and Traffic Constraints  
* Main Track Priority  
All paths originate from a start point on main tracks. Navigation prioritizes movement along the main track until reaching an appropriate sub-track/storage area. Main track movement is constrained to lateral (horizontal) directions.
* Sub-Track Movement  
Movements along sub-tracks are constrained to longitudinal (vertical) directions and generally from the main track to the sub-tracks. Movements can retreat from the sub-tracks to the main track when the path is blocked.  
* Jumping MechanismTo change from one sub-track to another, shipping equipment must return to the main track and then access the target via another sub-track. This is referred to as the 'jumping' mechanism. Direct switching between sub-tracks horizontally is not permitted.
* Restricted to Orthogonal Movement  
Only four orthogonal direction (up, down, left, right) movements are allowed. Diagonal or non-grid-based transitions are excluded to maintain grid consistency and simplicity.  
Mathematical and Algorithmic Assumptions
* Uniform Node Cost  
Assuming uniform distance and effort for movement between adjacent nodes, each grid node is assigned an equal transportation cost or computational cost in the algorithm.
* Random Obstacle Distribution  
Obstacles are assumed to be randomly distributed across both main and sub-tracks, with an upper bound of 25% coverage [40] to simulate realistic congestion or blockage scenarios.
* Feasibility of Reachability  
In the absence of obstacles, all positions are assumed to be reachable. If a destination is blocked by obstacles and all jumping attempts fail, the algorithm returns \"No Solution.\"  
Functional and Modeling Constraints
* Vehicle-to-Grid Matching  
The size of four-way shuttles and conveyors is assumed to match the size of a single grid cell, which is also equal to the size of a standard storage unit.
* Single-Agent Assumption  
The planning process assumes only one vehicle is active at a time. No inter-vehicle conflicts, collisions, or multi-vehicle corporations are considered.
## 5 Simulation Results and Discussion
### FER Performance
With the \((4,8)\) bit width settings, the proposed RC-MIM-QSMS decoder also outperforms both the RC-MIM-QMS decoder\({}^{\text{R2}}\) and the RC-FAID decoder by at least 0.2 dB for all simulated code rates. In the high SNR region, the proposed RC-MIM-QSMS decoder even shows slightly better error floor performance compared to the \((\infty)\)-SBP decoder for both the 802.11n LDPC codes and the 5G LDPC code. This is because there are degree-two VNs in the Tanner graphs of the simulated codes, which results in trapping sets due to the cycles being confined among these degree-two VNs [32]. These trapping sets become the most harmful objects and cause error floor in the high SNR region for the BP decoder. Similar phenomena are also observed in the literature [8; 10; 14; 15; 16], which show that the MIM quantization schemes can assist to mitigate the negative impact of certain harmful objects in the code structure. Figure 2: The FER performance of different decoders for the length-1296 IEEE 802.11n LDPC codes with code rates 2/3, 3/4, and 5/6. (a) \(R_{c}=2/3\). (b) \(R_{c}=3/4\). (c) \(R_{c}=5/6\).
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 2 Methodology and Equations
### Physical Augmentation Factor of Precision (PAFP)
\begin{array}{cccc}\mathrm{ P_{11}}&\mathrm{P_{12}}&\mathrm{P_{13}}&\mathrm{P_{14}}\\ \mathrm{P_{21}}&\mathrm{P_{22}}&\mathrm{P_{23}}&\mathrm{P_{24}}\\ \mathrm{P_{31}}&\mathrm{P_{32}}&\mathrm{P_{33}}&\mathrm{P_{34}}\\ \mathrm{P_{41}}&\mathrm{P_{42}}&\mathrm{P_{43}}&\mathrm{P_{44}}\\ \end{array} \right] \tag{12}\]  
Then, its PDOP value can be expressed as:  
\[\mathrm{PDOP_{B}}=\sqrt{\frac{\mathrm{P_{11}+P_{22}+P_{33}}}{k}}=\frac{ \mathrm{PDOP}}{\sqrt{k}} \tag{13}\]  
It is seen that the ratio of the improved PDOP value to the original PDOP value is \(\frac{1}{\sqrt{k}}\). When the original PDOP value is constant, the improved PDOP value is inversely proportional to the square root of the number of downlink frequencies. This ratio is called the physical augmentation factor of precision (PFAP). Then, when each visible satellite sends down multiple-frequency navigation signals, the standard deviation of the positioning error in three-dimensional space is:  
\[\sigma_{\mathrm{B}}=\sqrt{\sigma_{\mathrm{x}}{}^{2}+\sigma_{\mathrm{y}}{}^{2} +\sigma_{\mathrm{z}}{}^{2}}=\frac{\sqrt{\mathrm{P_{11}+P_{22}+P_{33}}}}{k} \times\sigma_{\mathrm{e}\_{B}} \tag{14}\]
## 2 Related Work
For instance, the trajectory of the vehicles is used in [7, 10]. The authors in [11] propose the Improvement GPSR routing protocol (I-GPSR), which incorporates distance, vehicle density, moving direction and vehicle speed to make packet forwarding decisions. A new data forwarding mechanism, which uses a store-carry-forward scheme besides the position information and moving direction of the nodes, was presented in [10]. Nevertheless, none of the existent proposals takes the available bandwidth into account as a metric to make forwarding decisions. The reason could be that obtaining a fairly simple, but accurate, model to calculate the available bandwidth in VANETs is a difficult task. Our proposal includes several metrics to optimize the selection of the next forwarding node in a geographic-based protocol. These metrics are vehicle density, trajectory, distance to destination and available bandwidth. The trajectory of a node consists of its moving direction and speed, which are used to estimate how fast a node gets near to or goes away from a destination. Then, we weight those four metrics into a single multimetric score. Furthermore, we evaluate the performance of each single metric compared to the global multimetric value and also to some combination of them.
# An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques
###### Abstract  
Electric two-wheelers are becoming increasingly popular across the world, particularly in cities where their small size and flexibility make them a viable option for navigating congested streets. One of the most challenging aspects of e-mobility on two-wheelers is precisely calculating their range. This might be an issue for riders who must go long distances or who have limited access to charging stations. Various factors can influence an electric two-wheeler range, making it challenging to predict how far it can travel on a single charge. To tackle this problem, most of the manufacturers offer range predictions based on both test data and real-world usage scenarios. However, these estimates are customized for specific vehicle models and testing parameters that may not apply in all circumstances. Additionally, it can be challenging to obtain comprehensive technical specifications for two-wheelers available in the market, as most manufacturers do not provide detailed technical information. Hence, it is crucial to address the challenge of range prediction for two-wheelers in general, which can be advantageous for riders. In this paper, we discuss the precise prediction of the remaining range of electric two-wheelers even without knowing detailed e-mobility technical information. An application is also developed only for this research purpose, which can provide navigation services. Our approach concentrates on user behavior, weather, road conditions, and the vehicle's performance history, which is gathered through the application. The collected data are used to train the selected ML model on the cloud. We applied various machine learning algorithms before deploying in the cloud where the SVM algorithm demonstrated outstanding performance, with a mean absolute error of 150 m for an average distance of 7.46 km. Furthermore, the model's performance was evaluated after deployment and tested having 130 m error on average.  
e-mobility; electric two-wheeler; range prediction; machine learning algorithms Article  
An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques  
[PERSON]; [PERSON]; [PERSON] An Application to Predict Range of Electric Two-Wheeler Using Machine Learning Techniques. _Appl. Sci._**2023**, _13_, 9840. [[https://doi.org/10.3390/appj3105840](https://doi.org/10.3390/appj3105840)]([https://doi.org/10.3390/appj3105840](https://doi.org/10.3390/appj3105840))  
1
## Abstract  
In a series of previous studies, we demonstrated that the photodynamic therapy (PDT), as a widely used tool for treatment of glioblastoma multiforme (GBM), also site-specifically opens the blood-brain barrier (BBB) in PDT-dose and age-related manner via reversible disorganization of the tight junction machinery. To develop the effective protocol of PDT-opening of the BBB, here we answer the question of what kind of photosensitizer (PS) is the most effective for the BBB opening. We studied the PDT-opening of the BBB in healthy mice using commercial photosensitizers (PSs) such as 5-aminobenzvelanic acid (5-ALA), aluminum phthalocyanine disulfonate (AlPoS), zinc phthalocyanine (ZnPc) and new synthetized PSs such as galactose functionalized ZnPc (GalZnPc). The spectrofluorimetric assay of Evans Blue albumin complex (EBAC) leakage and 3-D confocal imaging of FITC-dextran 70 kDa (FITC) extravasation clearly shows a revisable and dose depended PDT-opening of the BBB to EBAC and FITCD associated with a decrease in presence of tight junction (TJ) in the vascular endothelium. The PDT effects on the BBB permeability, TJ expression and the fluorescent signal from the brain tissues are more pronounced in PDT-GalZnPc vs. PDT-5-ALA/AlPoS/ZnPc. These pre-clinical data are the first important informative platform for an optimization of the PDT protocol in the light of new knowledge about PDT-opening of the BBB for drug brain delivery and for the therapy of brain diseases.  
Keywords:blood-brain barrier; photodynamic effects; photosensizers; drug brain delivery +
Footnote †: journal: Applied Physics Letters
## 2 Materials
### Satellite Data
#### 2.2.4 Topography  
The topography of the surface also influences the variation in the SM. With the GEE platform, topographic parameters, such as elevation, slope, aspect, and hill shade are obtained from the ALOS DSM Global 30 m dataset [67].
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 2 Microstructure  
### Interface Structure  
NMMFs with high interfacial density and controllable length scale are always fabricated via physical vapor deposition (PVD) and electro-deposition (ED). Starting from a substrate (rigid Si or compliant polymer), two different metals deposit on each other alternately, until the expected layer thickness is attained. Between two phases, an atomically sharp interface can be formed, which has been classified into three groups: coherent interface, semi-coherent interface and incoherent interface.  
Layers of materials between coherent interfaces are with identical crystallographic structure and approximate lattice parameter, commonly appeared in fcc/fcc multilayers, such as Cu/Ni [17]. The slip systems are nearly continuous across the adjacent layers; dislocations can pass across the coherent interfaces (Figure 1). But the small lattice mismatch of the interface can result in high coherent stresses, leading to the enhanced hardness/strength of multilayers.  
The same as coherent interfaces, there is the same crystallographic structure in two layers with semi-coherent interfaces, but with larger lattice mismatch. Semi-coherent interfaces are commonly appeared in multilayer systems which have large lattice mismatch. Misfit dislocation is often introduced to relieve the severe lattice distortion. Consequently, the dislocations at the interface attributes to the interface barrier. On the other hand, semi-coherent interface are likely to transform to coherent interface as \(h\) is small enough (smaller than the critical thickness \(h_{c}\)). Information about interface structure evolution with the variation of \(h\) are shown later in Section 2.2.2.  
The interfaces with distinct lattice structures, which possess a high lattice mismatch, are incoherent interfaces. Incoherent interfaces are commonly appeared in fcc/bcc multilayers [18; 19]. TEM photos  
Figure 1: HRTEM image of full coherent interfaces [17].  
(Figure 2a) and molecular simulations (Figure 2b) have shown that the interfaces possess a well-defined pattern of misfits (Figure 2c,d).
# Radiofrequency Performance Analysis of Metal-Insulator-Graphene Diodes
###### Abstract  
This work presents the performance projection of a metal-insulator-graphene diode as the building block of a radiofrequency power detector, highlighting its rectifying figures of merit. The analysis was performed by means of a computer-aided design tool validated with experimental measurements of fabricated devices. Transient simulations were used to accurately determine the detector output voltage, while particular consideration was given to suitable convergence of the non-linear circuit response. The diode was analyzed in both ideal and non-ideal cases, with the latter accounting for its parasitic effects. In the non-ideal case, the diode exhibited a tangential responsivity of 26.9 V/W at 2.45 GHz and 31.9 V/W at 1.225 GHz. However, when parasitic elements were neglected in the ideal case, the responsivity significantly increased to 47.3 V/W at 2.45 GHz and 38.7 V/W at 1.225 GHz. Additionally, the diode demonstrated a non-linearity of 6.64 at 0.7 V and an asymmetry of 806.6 in a bias window of \(\pm\)1 V, which resulted in a competitive value compared to other state-of-the-art rectifying technologies. Tangential responsivities (\(\beta_{\mathrm{v}}\)) of graphene diodes at less-studied frequencies in the gigahertz band are presented, showing a high \(\beta_{\mathrm{v}}\) value of 63.7 V/W at 1 GHz.  
graphene; diode; radiofrequency; power detection; MIG +
Footnote †: journal: Applied sciences
## 2 Results
### Mechanical and Genetic Determinants of Substrate Invasion
(Although Dbp4 is not currently annotated as a transcription factor, it was included in the mutant collection [28].) The _dbp4A/A_ mutant was also defective in biofilm formation under our conditions. These studies are consistent with the model that either or both biofilm formation and hyphal morphogenesis are required for substrate invasion. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Gene** & \multicolumn{2}{c}{**Functional Description Form the _Candidida_ Genome Database**} & **Mutant Phenotype** \\ \hline \multirow{3}{*}{_BRG1_} & Transcription factor; recruits Hda1 to hypha-specific promoters; Tn mutation affects & \\  & filamentation; Hap43-repressed; Spider and flow model biofilm induced; required for & \\  & Spider biofilm formation; Bcr1-repressed in RPMI a/a biofilms & \\ \hline \multirow{3}{*}{_TEC1_} & TEA/ATTS transcription factor; white cell pheromone response, hyphal gene & \\  & regulation; required for Spider and RPMI biofilm formation; regulates BCR1; Cph2 & \\  & regulated transcript; alkaline, rat catheter, Spider, flow model biofilm induced & \\ \hline \multirow{3}{*}{_NDT80_} & Ortholog of Ndt80; meiosis-specific transcription factor; activator of CDR1 induction & \\  & by antifungal drugs; required for wild-type drug resistance and for Spider biofilm & \\  & formation; transcript induced by antifungal drug treatment & \\ \hline \multirow{3}{*}{_ROB1_} & Zn(II)2 Cys6 transcription factor; required for Spider model biofilm formation; & \\  & mutant displays abnormal colony morphology and invasive growth; caspfungin & \\ \cline{1-1}  & repressed; flow model biofilm induced; rat catheter biofilm repressed & \\ \hline \multirow{3}{*}{_DPB4_} & Putative DNA polymerase epsilon subunit D; null mutant is viable but slow-growing and & \\  & displays abnormal invasive growth on SD and YPD media; Spider biofilm repressed & \\ \hline \multirow{3}{*}{_EFG1_} & bHLH transcription factor; required for white-phase cell type, RPMI and Spider & \\  & biofilm formation, hyphal growth, cell-wall gene regulation; roles in adhesion, & \\ \cline{1-1}  & virulence; Cph1 and Efg1 have role in host cytokine response; binds E-box & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transcription factor genes that are required for hyphal invasion.
## 3 Results
### Engine Performance Characteristics
#### 3.1.2 Brake Specific Fuel Consumption (BSFC)  
Figure 3 shows the BSFC of Moringa oleifera biodiesel blends and diesel at various engine speeds. Diesel fuel shows lower BSFC as compared to biodiesel blends. The MOB20 blend has the highest average BSFC, with a value of 0.6115 kg/kWh. The MOB10 and MOB20 blends have a higher average BSFC than diesel by 7.03% and 12.75%, respectively. In general, biodiesel have a larger HHV due to the fuel-borne oxygen. Hence, a higher amount of fuel mass needs to be injected from the fuel injection pump into the engine due to biodiesel's higher density than diesel. More biodiesel needs to be injected into the combustion chamber for the same power output as diesel according to volumetric efficiency. The higher kinematic viscosity of Moringa oleifera biodiesel blends is the leading cause of poor air-fuel mixing resulting from slower fuel atomization. Higher density and lower calorific values than diesel are factors that lead to the higher BSFC for biodiesel blends, especially those containing higher concentrations of biodiesel [54].
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 4 Experiments
### Experimental Preparation
#### 4.1.4 Experimental Settings  
The evaluation consists of internal comparison and external comparison. The internal comparison focuses on the parameter settings in the STS feature extraction. The external comparison aims to compare the performance with different feature selection methods and model building methods. Table 5 summarizes the experimental settings. Both the courier dataset and the MTA bus dataset are split into a training set (consisting of 90% of the data) and a testing set (10% of the data), respectively. The courier dataset contains 60 days' data for training, and six days for testing. The MTA bus dataset contains 81 days' data for training, and nine days for testing. The amount of testing samples meet the demand of real-life applications.
## 5 Conclusions  
FA-PAA-coated MnO nanoparticles with ultrasmall particle diameters (d\({}_{\rm avg}\) = 2.7 nm) were prepared through a simple one-pot polyol method in this study. The results are summarized as follows:  
1. The PAA coating conferred good colloidal stability and low cellular cytotoxicity, while FA conferred cancer-targeting ability on the nanoparticles.
2. The FA-PAA-coated MnO nanoparticles exhibited a high value of r\({}_{1}\) = 9.3 s\({}^{-1}\)mM\({}^{-1}\) (r\({}_{2}\)/r\({}_{1}\) = 2.2) owing to the strong interactions between the numerous Mn\({}^{2+}\) species on the nanoparticle surfaces and the many water molecules around the nanoparticles attracted by PAA.
3. The effectiveness of the FA-PAA-coated MnO nanoparticles as a cancer-targeting T\({}_{1}\) MRI contrast agent was proved by the strong positive contrast enhancements of the brain cancer site compared to those of the normal brain tissue.  
Methodology, S.M. and S.-L.H.; conceptualization, S.M.; formal analysis, S.M., H.Y., S.L., T.T., M.Y.A., A.K.A.S., D.Z. and Y.L.; investigation, S.M., H.Y., J.-A.P., S.K., J.-U.Y., H.C. and K.-S.C.; data curation, S.M., H.Y., J.-A.P., S.K., J.-U.Y., H.C. and K.-S.C.; writing--original draft preparation, S.M.; writing--review and editing, G.-H.L.; supervision, G.-H.L. and Y.C.; funding acquisition, G.-H.L. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Basic Science Research Program (grant number 2016R1D1A3B01007622 to G.-H.L. and 2020R1A2C2008060 to Y.C.) of the National Research Foundation funded by the Ministry of Education, Science, and Technology.  
The in vivo animal imaging experiments were performed according to the rules and regulation of the animal research committee of the Korea Institute of Radiological and Medical Sciences (approval number: Kirams2018-0072 and approval data: 9 January 2019).  
Not applicable.  
The data presented in this study are available on request from the corresponding authors.  
We thank the Korea Basic Science Institute for allowing us to use their XRD machine.  
The authors declare no conflict of interest.
## 2 Preliminaries
### Traditional DCC Method  
The schematic diagram of the traditional DCC method for multi-PMSMs is shown in Figure 1. \(F_{i}(s)\) is the synchronization error compensator, and \(G_{\mathrm{s},i}(s)\) is the speed tracking controller. The synchronization error compensator \(F_{i}(s)\) is designed as:  
\[F_{i}(s)=K\sum_{j=1,j\
eq i}^{N}\frac{J_{i}}{J_{j}}\big{(}\omega_{m,j}-\omega_{ m,j}\big{)} \tag{7}\]  
\(K\) is the compensation coefficient, with \(K=1\) in the experiment. \(J_{i}\) and \(J_{j}\) represent the moments of inertia of motors \(i\) and \(j\), respectively. Since the parameters of the three motors used in the experiment are identical, it follows that \(J_{i}/J_{j}=1\). The speed-tracking controller uses a PI control algorithm:  
\[G_{s,i}(s)=K_{P,i}+\frac{K_{I,i}}{s} \tag{8}\]  
Traditional multi-PMSM DCC control lacks advantages in terms of flexibility. To achieve synchronized control, the speed information of all motors in the system must be obtained. Additionally, as the number of motors in the speed regulation system increases, modifications to the original coupling structure are required. Therefore, traditional multi-PMSM DCC control struggles to meet real-time computational requirements.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Inorganic Corrosion Inhibitors
#### 3.2.1 Anodic Corrosion Inhibitors  
The term \"passivation inhibitor\" also refers to anodic inhibitors. They lead to a lower anodic response. They support the metal surfaces that are preventing the anode reaction, specifically. They also create a coating that is adsorbed on metal surfaces. Typically, these inhibitors react with the corrosion product first generated to form the previously noted cohesive and insoluble film. The anodic reaction is affected by the corrosion inhibitors and corrosion potentials of the examined metals. When corrosion inhibitors interact with the metal ions on their anode, they generate insoluble and impermeable metallic ions hydroxide coatings. When using anodic inhibitors, it is crucial to take into account the high inhibitor molecule concentrations in the solution. The film generated cannot completely cover the metal surface if the inhibitor concentration is minimal. Localized corrosion results from this. Anodic corrosion inhibitors include nitrates, molybdates, sodium chromates, phosphates, hydroxides, and silicates [56].