esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural-Network-Based Synchronization Acquisition with Hankelization Preprocessing
## 2 System Model and Problem Formulation  
Throughout this paper, the synchronization signal is constructed using the ZC sequence [23], which is renowned for its exceptional correlation properties. The ZC sequence exhibits sharp autocorrelation peaks and uniform energy distribution, making it particularly suitable for robust synchronization signal detection in wireless communication systems [24]. The \(K\)-th ZC sequence, denoted as \(\mathbf{x}_{K}\in\mathbb{C}^{p}\), is expressed as:  
\[\mathbf{x}_{K}[n]=e^{-\frac{i\pi\kappa_{K}(n+1)}{p}}, \tag{1}\]  
where \(n\in\{0,1,\ldots,P-1\}\). Here, \(P\) represents the length of the sequence, chosen to be a prime number, and \(K\) is the root index that determines the characteristics of the sequence. Ingeneral, \(K\) is set as a prime number less than or equal to \(P-1\) to guarantee the correlation characteristic of ZC sequences.  
The objective is to reliably detect the presence of the desired signal embedded in noisy observations. The desired signal, represented by the ZC sequence, is known a priori. The problem is framed as a binary classification task, where the goal is to determine whether the desired synchronization signal is present within the received noisy signal \(\mathbf{y}\). The correlation function between \(\mathbf{y}\) and the ZC sequence \(\mathbf{x}_{K}\) is computed and transformed into a structured Hankel matrix via the inverse Fourier transform. This transformation effectively highlights the dominant features of the desired signal while suppressing noise and residual components. Singular values extracted from the Hankel matrix are then utilized as inputs to a NN, which performs binary classification to determine the presence or absence of the synchronization signal. This approach ensures reliable and accurate synchronization detection across a range of challenging scenarios.
# Efficacy of Maximally Expansion with Clear Aligner in the Mixed Dentition: A Systematic Review
## 4 Discussion  
The purpose of this systematic review was to evaluate dentoalveolar changes using Invisalign(r) First clear aligners in patients in the mixed dentition with maxillary transverse deficiency. Invasalign(r) First was created by Align Technology in 2018 in order to perform phase 1 of orthodontic treatment, which was the interceptive orthodontic treatment for growing patients in the mixed dentition.  
This systematic review included fourteen articles (retrospective studies, prospective studies, preliminary studies, randomized controlled trials, and case series), which were selected from a total of 265 records identified through online database searching and 36 records identified through manual searching. Several parameters were considered in order to assess dentoalveolar expansion, such as intercanine width, intercanine transpalatal width, intercanine dentoalveolar width, first and second intercediduous molar width, first and second intercediduous molar transpalatal width, first and second interpremolar width, first interpremolar mesial, distal, and transpalatal width, intermal dentoalveolar width, first interpolar width, space recovery, overbite, overjet, molar inclination, arch depth and perimeter, palatal volume, and surface area.
## 3 Proposed System
### Application of the Proposed System
Deployed in different parts of the city, the system can detect abnormal behavior in public spaces, anomalies in traffic flow, and safety issues in public facilities in real time. This allows city managers to quickly respond to issues and take the necessary actions to maintain safety and order in the city, including reducing the incidence of accidents and crime. Applying these systems will contribute toward improving people's safety in their daily lives and preventing dangerous situations from occurring. These systems can be widely used in various areas of society, from city centers to schools, commercial facilities, and smart cities.
## 7 Numerical Results  
The four structures with variable thickness made of FGMs depicted in Figure 4 are considered in the present section to evaluate their natural frequencies by the LGDQ method. As previously illustrated, the reference domain (or middle surface) is described by the corresponding position vector \(\mathbf{r}(\alpha_{1},\alpha_{2})\), whereas a proper expression is introduced to define the smooth thickness variation of the shells. With reference to Figure 4, it should be specified that the shell cross-sections affected by the thickness variation are represented, too. To this end, the local coordinate reference system is also depicted to understand more clearly the thickness variation (the principal axes are specified by the colors magenta, cyan and green, respectively). Different stacking sequences and volume fraction distributions are taken into account to characterize the mechanical features of the composites (Figure 5). The two isotropic constituents and their corresponding properties are listed in Table 1 for conciseness purposes.  
Firstly, the proposed method is validated by the comparison with the results available in the literature. Then, several parametric investigations are performed to show the effect of the variation of the volume fraction distributions on the natural frequencies. In these circumstances, the results are compared with the ones obtained by a three-dimensional finite element model (3D-FEM) for the limit cases. In other words, the commercial code Abaqus is employed only to evaluate the natural frequencies of composite structures made of isotropic layers. For this purpose, the three-dimensional model is obtained by using 20-node hexahedrons elements (C3D20). On the other hand, all the results shown in the following tables are obtained by the LGDQ method implemented in a MATLAB code [128]. In the present solution, the degrees of freedom are equal to \(3(N+2)\times(I_{N}\times I_{M})\), or \(3(N+1)\times(I_{N}\times I_{M})\) in case the Murakami's function is neglected in the HSDT.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constituents** & **Mechanical Properties** \\ \hline Stainless steel SUS304 & \(E_{M}=207.7877\) GPa, \(\
u_{M}=0.317756\), \(\rho_{M}=8166\) kg/m\({}^{3}\) \\ Silicon nitride Si\({}_{3}\)N\({}_{4}\) & \(E_{C}=322.2715\) GPa, \(\
u_{C}=0.24\), \(\rho_{C}=2370\) kg/m\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the two constituents.  
Figure 4: Definition of the shell structures and graphical representation of their cross-sections with variable thickness: (**a**) annular plate [90]; (**b**) elliptic cylinder; (**c**) doubly-curved shell of revolution with hyperbolic meridian; (**d**) doubly-curved panel of translation (a parabola slides over another parabola).  
Figure 5: Volume fraction distributions and lamination schemes for the shells with variable thickness under consideration: (**a**) Elliptic cylinder; (**b**) Doubly-curved shell of revolution with hyperbolic meridian; (**c**) Doubly-curved panel of translation.
## 4 Discussion
Finally, the findings of our study were derived from a single imaging installation and technology type, specifically focusing on one MRI machine and its accompanying AI from Philips. For future research, it would be beneficial to investigate machines from other manufacturers to broaden the understanding and applicability of our findings. Our acceleration factors were set in advance in collaboration with engineers. Since energy consumption is linked to operational time, identifying the optimal trade-off between scan duration reduction and image quality could lead to greater savings. The use of parallel imaging is also a way to reduce the sequence time and consequently the electrical consumption of an examination: Thus, by moving from a 16- to a 64-channel coil, we will be able to increase the acceleration factor and reduce the number of phase encodings, while maintaining a good Signal/Noise. Future research could evaluate AI-driven imaging systems from different vendors, assessing image quality to confirm their consistency, reliability, and any variations between technologies; additionally, incorporating other intelligent solutions that work in post-acquisition image processing to enhance sharpness and reduce noise and ensure compatibility with any make or model of MRI scanner, such as Subtle MR [29]. There is an opportunity for further optimization through AI programming, with the goal of improving the overall efficiency in examination scheduling and execution processes.
# Solution of Orifice Hollow Cathode Plasma Model Equations by Means of Particle Swarm Optimization
## 2 Plasma Model of Hollow Cathode
### Plasma Model Equations
#### 2.3.2 Orifice  
The system of equations for the cathode's orifice region writes a balance for the ion flux Equation (4) and for the power Equation (5), and states that pressure calculated by means of kinetic theory is equal to that computed with continuum flow and choked orifice,Equation (6). The plasma number density (\(n_{e,o}\)), the electron temperature (\(T_{e,o}\)), and the density of the neutrals (\(n_{n,o}\)) in the orifice region are the unknowns.  
\[\left\{\frac{q\pi r_{o}^{2}L_{o}n_{n,o}n_{e,o}K_{i}}{n_{ion}}=j_{i,o}\left[2\pi r _{o}L_{o}+\pi r_{o}^{2}\left(1-\frac{1}{4}\frac{1}{0.61}\sqrt{\frac{8}{\pi}} \right)\right]+j_{th,o}\pi r_{o}^{2}\right. \tag{4}\]  
\[R\dot{I}_{d}^{2}=\dot{n}_{ion}\langle e_{i}\rangle+\frac{5}{2}\frac{k_{B}}{q} l_{d}(T_{e,o}-T_{e,e}) \tag{5}\]  
\[\left(n_{e,o}+n_{n,o}\right)k_{B}T_{w,o}\left(1+\frac{\alpha T_{e,o}}{T_{w,o}} \right)=\frac{\dot{n}}{\pi r_{o}^{2}}\sqrt{\frac{R_{g}T_{w,o}}{\gamma}\left(1 +\frac{\alpha T_{e,o}}{T_{w,o}}\right)} \tag{6}\]  
Here, the plasma resistance is calculated as:  
\[R=\frac{\eta L_{o}}{\pi r_{o}^{2}} \tag{7}\]  
The plasma resistivity is generally computed as:  
\[\eta=\frac{(\
u_{ei}+\
u_{en})m_{e}}{n_{e}q^{2}} \tag{8}\]  
and collision frequencies are:  
\[\
u_{ei}=2.9\cdot 10^{-12}\left(\frac{n_{e}n\Lambda}{\left(\frac{T_{e}\ k_{B}} {q}\right)^{\frac{3}{2}}}\right) \tag{9}\]  
\[v_{en}=\langle\sigma_{en}\rangle n_{n}\left(\frac{k_{B}T_{e}}{n_{e}}\right) \tag{10}\]  
where the Coulombogarithm is:  
\[n\Lambda=23-\frac{1}{2}\log\left(\frac{10^{-6}n_{e}}{\left(\frac{T_{e}\ k_{B }}{q}\right)^{3}}\right) \tag{11}\]  
and the fit for the Maxwellian average elastic electron-neutral cross section (\([m^{2}]\)) proposed in ref. [13] (Table 1) for xenon is:  
\[Log_{10}(\langle\sigma_{en}\rangle)=\frac{p_{1}T_{e}^{*5}+p_{2}T_{e}^{*4}+p_{ 3}T_{e}^{*3}+p_{4}T_{e}^{*2}+p_{5}T_{e}^{*}+p_{6}}{T_{e}^{*2}+q_{1}T_{e}^{*}+q _{2}},T_{e}^{*}=Log_{10}\left(\frac{T_{e}\ k_{B}}{q}\right) \tag{12}\]
## 5 Communication in Vehicular Networks
### Communication Models  
Due to the incorporation of various infrastructural elements in the Internet of Vehicles (IoV), a multitude of communication models is evident. These encompass the exchange of information among onboard units (OBUs), roadside units (RSUs), base stations, or transportation infrastructure. Consequently, three distinct categories can be identified based on the three primary components within vehicular networks, as illustrated in Figure 3--namely, onboard communication, vehicle-to-vehicle (V2V), and vehicle-to-infrastructure (V2I). These categories collectively form what is commonly known as the V2X communication model.  
Onboard communication is facilitated by the OBU and, in the context of the IoV, also by the application unit (AU). The OBU establishes a communication link for the AU, thereby ensuring the functionality of various onboard applications.  
The significance of intervehicle communication cannot be overstated in the context of IoV applications. V2V communication plays a pivotal role by enabling the broadcast of operational data related to traffic conditions, including the detection of collisions or emergencies [64]. Moreover, it serves as a crucial mechanism for network operations, thus allowing data forwarding to the infrastructure when the vehicle is not directly within the coverage of the IoV infrastructure. This functionality transforms vehicles into relay nodes in V2X communication, thereby effectively expanding the communication range. The ability to share data among vehicles through V2V communication facilitates the creation of ad hoc vehicular clouds, as well as enables the pooling of computational resources for enhanced IoV system efficiency [64]. These vehicular clouds eliminate the need to offload complex computational tasks, such as environment map formation or path planning, to remote clouds, thereby reducing solution delays.  
Examining the last discussed communication model, V2I involves communication between vehicles and RSUs, thus establishing a vital channel for information exchange among vehicles and other networks [65]. This connection establishment from a vehicle to an RSU not only extends the communication range but also provides comprehensive information about the transportation environment. It allows for the processing and integration of data from individual components, thereby contributing to a more detailed understanding of the surroundings.  
The adoption of these communication models brings significant advantages compared to earlier vehicular networks. These models enable communication for vehicles beyond their immediate range, expand computing and storage capabilities through resource pooling, and support the development of intelligent applications by processing data collected from the transportation system.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 2 JSC Network Configurations
### Monostatic Deployment
#### 2.1.3 The Spurious Target Issue  
In multibeam array systems, the emergence of'spurious targets' is of significant concern. These can occur especially in the presence of strong beam sidelobes, e.g., during beam scanning. When sidelobes encounter strong reflections, they may cause the system to misinterpret the signal's origin, resulting in ghost targets at unintended angles [22]. In dense traffic or city environments, ghost targets can cause the system to misperceive object locations, leading to safety risks. Advanced pruning algorithms are required to differentiate real objects (e.g., pedestrians or vehicles) from spurious targets to enhance the sensing system's fidelity and mitigate the impact of unwanted artifacts caused by sidelobes.
# Leveraging the Internet of Things and Blockchain Technology in Supply Chain Management
## 5 Combining IoT with Blockchain Technology: Emerging Research Areas
### Traceability and Interoperability  
Blockchain technology is already used in combination with tamper-evident RFID-tags to aid in the verification of the provenance (e.g., geographic source or origin) and authenticity of bottles of fine wine. The tags are affixed to the cork and aim to eliminate attempts to refill wine bottles with a cheaper product. By logging the data on the Blockchain and crosschecking the traceability and provenance of the wine bottles, consumers can verify the history and authenticity of the purchased wine by inputting the product ID in the system [65]. It should be noted that absolute certainty on the authenticity of any food or drink product can only be achieved using science-based analytical testing methods of the product itself rather than relying on covert or overt security features on the outer package. Several projects are in development to apply a combination of Blockchain and IoT for enhanced traceability and interoperability. For example, firms such as UK-based Evrything; Slovenian-based OriginTrail, a Blockchain protocol provider; and Ambrosus, a Swiss-based Blockchain startup, aim to combine the Blockchain and the IoT for the food, consumer goods, apparel, and pharmaceutical sector. In doing so, their Blockchain-powered IoT networks will enable a secure and frictionless dialogue between sensors, distributed ledgers, and databases to optimize supply chain visibility and quality assurance [121; 122; 123]. In another example, Skycell, a company developing containers for refrigerated biopharmaceuticals, uses IoT sensors along with the Blockchain to monitor temperature, humidity, and location, thereby reducing temperature deviations to a level of less than 0.1% [124]. Given these current developments, we propose:  
* RP5: Blockchain technology positively impacts the traceability and interoperability of IoT solutions.
## 2 Materials and Methods  
The silica NPs used in this study were provided by Nyaocl Nano Technologies Inc. (Ashland, MA, USA). The DP 9711 product was acquired at a 30 wt. % concentration in deionized water (DIW) and with a pH of 3. The DP9711 product has a proprietary surface coating but [PERSON] and [PERSON] [45] reported that DP 9711 is coated with polyethylene glycol. For ease, these NPs are referred to as DP in this study. The NPs as claimed by the manufacturer have an average particle size of 20 nm. As and when required, the NFs used in this study were prepared from the stock fluid by diluting it with appropriate prines. Stevns Klint (SK) outcrop chalk cores were used as the porous media. SK chalk is 99% pure biogenic with a high porosity range of 45-50% and a relatively low absolute permeability of \(\approx\)4 mD [36]. SK chalk matrix material and its petro-physical properties resembles chalk reservoirs, which makes it useful in the analysis [46]. Calcite mineral powder acquired from Honeywell Riedel-de Haen was of analysis grade with a surface area of 0.23 (m\({}^{2}\)/g) calculated previously by the water adsorption isotherm [47]. The model oil used in this study was n-decane (n-C10) acquired from Merck (Drammensveien, Oslo, Norway). Stearic acid (SA) was added to n-decane at a concentration of 0.005 mol/L to prepare the synthetic oil. The properties of the synthetic oil are listed in Table 1, estimated from PVT Sim.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature \({}^{\circ}\)C** & **Viscosity (cP)** & **Density (g/mL)** \\ \hline
20 & 0.92 & 0.73 \\
50 & 0.5802 & 0.7683 \\
70 & 0.4812 & 0.7525 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Synthetic oil properties.  
Zetasizer Nano ZSP from Malvern Instruments (Malvern, Worcestershire, UK) was used to characterize the average hydrodynamic diameter and zeta potential of the NPs. Scanning electron microscopy was performed on a Supra 35 VP SEM by [PERSON] (Oberkochen, Baden-Wurttemberg, Germany) with an integrated EDXRF analyzer to visualize the adsorption of the NPs on the SK chalk samples treated with NF. NP concentration during isothermal static adsorption tests and NP dynamic adsorption in brines was measured using a dual beam UV-Vis 1700 spectrophotometer from Shimadzu Corporation (Nakagyo-ku, Kyoto, Japan). The schematic of the core flooding setup used in this study is shown in Figure 1. The concentration of the cations in the effluents was determined by a Dionex ICS-5000 ion chromatograph from Thermo Fisher Scientific (Waltham, MA, USA). Inductive coupled plasma and optical emission spectrometry (ICP-OES) was carried out by an Optima 4300 DV from PerkinElmer (Waltham, MA, USA)to quantitatively determine the trace amounts of elements eluted and to determine the NP concentration in the effluent for chalk cores injected with DIW.
## 5 The Result Analysis  
### CO2 Emission Factors of Materials and the Proportion of Quantity of Each Section in Tunnel  
From the CO2 emission factors of materials, as shown in Figure 9, it can be seen that different materials have different CO2 emission factors, therefore, during the design process, we could choose the materials with lower CO2 emissions, according to needs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment Variable** & **Data** \\ \hline w (carrying capacity) & 6 m\({}^{3}\) \\ e (production efficiency) & 95\% \\ t1 (loading time) & 4.5 min \\ t3 (unloading time) & 3 min \\ t4 (waiting time) & 8 min \\ l (average distance) & 3 km \\ v1 (travel speed with loading) & 15 km/h \\ v2 (travel speed without loading) & 20 km/h \\ \hline \hline \end{tabular} Note: For concrete lining, pumping time is considered, t3 is assumed as 24 min. For shot concrete, shotcrete time is considered, t3 is assumed as 45 min.  
\end{table}
Table 6: Mixer-truck variable data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment Variable** & **Data** \\ \hline T (carrying capacity) & 15 t \\ rt (density in natural stage) & 1.9 t/m\({}^{3}\) \\ L (volume conversion rate) & 1.25 \\ f (soil conversion factor) & 77\% \\ E (production efficiency of dump truck) & 90\% \\ Cms (cycle time for loading by loader) & 34.4 s \\ q (bucket capacity) & 1.72 m\({}^{3}\) \\ k (bucket coefficient) & 1.0 \\ Es (production efficiency of loader) & 60\% \\ t3 (unloading time) & 0.8 min \\ t4 (waiting time for loading) & 0.42 min \\ t5 (automatic cover installation and dismantling time) & 0.5 min \\ t 1 (average distance) & 6 km \\ v1 (travel speed with loading) & 15 km/h \\ v2 (travel speed without loading) & 20 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 7: Dump truck variable data.  
From the circle graph, as shown in Figure 10, it can be seen that the proportion of quantity of each section in tunnel, concrete lining, concrete slab (30 cm) and drainage way account for a large proportion; the three sections take over 76 percent.
## 3 Results and Discussions
### ECM Development and Parameter Identification
Figure 7: Parameter identification results of components in the equivalent circuit (**a**–**e**) represent the change of R\({}_{0}\), R\({}_{\text{P1}}\), R\({}_{\text{P2}}\), C\({}_{\text{P1}}\), and Cp\({}_{\text{2}}\) with SOC, respectively). Considering the trade-off between accuracy and complexity, the 6 th-order polynomial was chosen as the optimal option. The resulting SOC-OCV 6 th-order polynomial fitting relationship is as in Formula (7). \[\text{OC}V=12.408*x^{6}-30.346*x^{5}+24.227*x^{4}-5.7168*x^{3}-0.8549*x^{2}+1.04 36*x+3.42 \tag{7}\]  
Figure 9a presents a comparison between the simulated voltage output of the ECM and the measured voltage over a discharge cycle. The simulated voltage closely follows the trend of the real voltage, accurately capturing both the general downward trajectory and transient fluctuations observed in the experimental data. This alignment indicates that the ECM reliably represents the battery's voltage response during discharge. Figure 9b presents the difference between the simulated voltage and real voltage over time. This difference is generally confined within a narrow range of \(\pm\)0.03 V, which reveals the high accuracy of the ECM. At very low SOC levels, there is a slight underestimation, but the discrepancy remains minimal under 0.05 V. This level of accuracy confirms the robustness of the ECM parameters in effectively modeling the battery's behavior across different SOC levels. The battery underwent continuous charge-discharge cycling between a 15% and 95% SOC under varying charge rates of 0.5C, 1C, 1.5C, 2C, and 2.5C for 1000 h. Detailed data on the battery voltage, current, and SOC were collected, which served as the input data for the subsequent data-driven model training. Each data set was generated with a fixed charge rate and discharge rate throughout the cycling process. After 1000 h of cycling, the simulated battery capacity in Simulink was set to degrade to 80%. This capacity degradation was calculated using the software's default formula. Detailed data on the battery voltage, current, and SOC were collected, which served as the input data for the subsequent data-driven model training. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Polynomial Order** & **Fitting R\({}^{2}\)** & **Fitting RMSE** \\ \hline
4 & 0.9973 & 0.0143 \\
5 & 09994 & 0.0068 \\
6 & 0.9997 & 0.0050 \\
7 & 0.9998 & 0.0042 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of R\({}^{2}\) and RMSE for different polynomial orders used in the SOC-OCV fitting process. Figure 9: Comparison between the simulated voltage output of the ECM and the actual measured voltage: (**a**) simulated voltage verify; (**b**) simulated voltage difference.