esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### The VQoSRR Reservation Method  
Frequently changing traffic metrics may cause QoS-based routing to consume network resources and overburden the controller as a result of frequent routing updates. Hence, the proposed solution relied not just on QBR, but also on queuing mechanisms to achieve a balance between routing overhead and quality assurance and controlling the data flow.  
Figure 2: Dynamic re-routing algorithm (DR-RA) flowchart.  
Incorporating resource reservations with QoS routing leads to fine control over the route and resources; this allows better congestion management, thus reducing route updating and providing more stability for QoS routes. Additionally, this method also contributes to reducing latency, a factor that can impact video streaming QoE.  
The VQoSRR suggested defining a higher-level reservation control strategy with suitable administrative mechanisms to enable fairness to flow according to their requirements, coupled with a method to configure the underlying resources. The idea is to utilize the rate and per-class queueing reservation mechanism to prioritize flows. The task is to map application traffic to different QoS levels, with each assigned a rated weight then partitioned queues based on the level-specified rate weight. Afterward, each packet is marked to a class with the differentiated service code point (DSCP), and according to that, the network forwarders handle packets. Figure 3 shows the main components of the approach followed by this study to allocate resources for QoS flows and non-QoS flows. Further explanation is provided in the following sections.
## 2 Methodology  
### Multi-Layer Perceptron Technique  
The structure of a multi-layer perception network is mainly based on one input layer, information processing elements (neurons) in the hidden layer and one output layer (Figure 1) [22; 23]. In artificial neural networks (ANN) elements processing information, so-called artificial neurons are nothing else but the reflection of a real model of neurons (nerve cells), which is part of the nervous system responsible for processing and analysis of information in the human body. This information is processed inside the cells via inputs (dendrites). Then the processed signal is sent to other cells via axons. Information via inputs (dendrites) is processed inside the cell [24].  
Figure 1: Structure of multi-layer perceptron (MLP): (**a**) input layer; (**b**) hidden layer; (**c**) output layer. Description: —neuron index, n\({}_{i}\)—an “i” index neuron, w\({}_{i}\)—the weigh vector of “i” neuron connections, w\({}_{j}\)—the weigh vector of connections from the previous layer with neuron from the present layer, x—input descriptors, y—output descriptors.  
The most difficult stage seems to be determining the appropriate number of neurons in each layer. The characteristic of neurons determines both linear and non-linear dependency (logistic or hyperbolic, among others). A basic algorithm in supervised MLP learning is back propagation (BP) [25]. The algorithm is based on minimizing the error function or the sum of square errors, which is used to modify the vector of weights to determine the direction to decrease the errors made by the aforementioned network.
# Genesis of the Baiyangping Cu-Co and Pb-Zn Mineralizations in Lamping Basin, SW China
## 4 Results
### S Isotopes  
Nineteen samples of tetrahedrite and sphalerite were used to determine \(\delta^{34}\)S values, and the results are listed in Table 2 and plotted in Figure 6. The \(\delta^{34}\)S values range from +5.2% to +9.5%. The majority of the minerals had \(\delta^{34}\)S values peaking from +7% to +9%. The \(\delta^{34}\)S values of tetrahedrite range from +5.2 to +9.5%, whereas those of sphalerite range from +7.5% to +9%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample No.** & **Stage** & **Mineral** & \(\delta^{13}\)**C\({}_{\rm PDB}\)** & \(\delta^{18}\)**O\({}_{\rm PDB}\)** & \(\delta^{18}\)**O\({}_{\rm SMOW}\)** \\ \hline BYP15D02B3 & Cu–Co stage & Ankerite & \(-\)5.2 & \(-\)10.3 & +20.2 \\ BYP15D03B2 & Cu–Co stage & Calcite & \(-\)4.4 & \(-\)12.4 & +18.1 \\ FLC15D01B1-1 & Cu–Co stage & Calcite & \(-\)3.8 & \(-\)16.7 & +13.6 \\ FLC15D01B1-2 & Cu–Co stage & Calcite & \(-\)4.0 & \(-\)16.7 & 13.6 \\ FLC15D01B2-1 & Cu–Co stage & Calcite & \(-\)3.3 & \(-\)14.9 & +15.5 \\ FLC15D01B3-3 & Cu–Co stage & Calcite & \(-\)3.9 & \(-\)15.0 & +15.4 \\ FLC15D01B4 & Cu–Co stage & Calcite & \(-\)4.1 & \(-\)16.9 & +13.4 \\ FLC15D02B10 & Cu–Co stage & Calcite & \(-\)2.2 & \(-\)16.4 & +13.9 \\ FLC15D02B11-1 & Cu–Co stage & Calcite & \(-\)2.4 & \(-\)16.8 & +13.5 \\ FLC15D02B11-2 & Cu–Co stage & Calcite & \(-\)2.3 & \(-\)16.8 & +13.5 \\ FLC15D02B7-3 & Cu–Co stage & Calcite & \(-\)3.1 & \(-\)14.5 & +15.9 \\ FLC15D02B5 & Cu–Co stage & Calcite & \(-\)3.1 & \(-\)15.0 & +15.4 \\ FLC15D02B5 & Cu–Co stage & Calcite & \(-\)2.0 & \(-\)17.4 & +13.0 \\ FLC15D02B9 & Cu–Co stage & Calcite & \(-\)2.5 & \(-\)15.9 & +14.4 \\ FLC15D01B6-1 & Cu–Co stage & Calcite & \(-\)3.9 & \(-\)17.3 & +13.0 \\ FLC15D01B7 & Cu–Co stage & Calcite & \(-\)3.8 & \(-\)18.1 & +12.2 \\ FLC15D01B8-1 & Cu–Co stage & Calcite & \(-\)2.8 & \(-\)16.0 & +14.4 \\ LZP15D03B6-1 & Pb–Zn stage & Calcite & \(-\)3.0 & \(-\)14.5 & +15.9 \\ LZP15D03B6 & Pb–Zn stage & Calcite & \(-\)3.1 & \(-\)14.4 & +16.0 \\ LZP15D03B6-2 & Pb–Zn stage & Calcite & \(-\)3.0 & \(-\)14.4 & +16.0 \\ LZP15D03B7 & Pb–Zn stage & Calcite & \(-\)3.3 & \(-\)15.1 & +15.3 \\ LZP17D03B7 & Pb–Zn stage & Calcite & \(-\)2.9 & \(-\)14.6 & +15.8 \\ LZP15D01B6 & Pb–Zn stage & Calcite & \(+\)1.7 & \(-\)11.0 & +19.5 \\ LZP15D01B9 & Pb–Zn stage & Calcite & \(-\)1.1 & \(-\)9.7 & +20.9 \\ LZP17D01B9 & Pb–Zn stage & Calcite & \(-\)1.6 & \(-\)9.0 & +21.6 \\ LZP15D02B4-2 & Pb–Zn stage & Calcite & \(+\)1.8 & \(-\)8.6 & +22.0 \\ LZP15D03B1 & Pb–Zn stage & Calcite & \(-\)2.3 & \(-\)14.2 & +16.2 \\ LZP17D03B1 & Pb–Zn stage & Calcite & \(-\)2.5 & \(-\)16.0 & +14.4 \\ LZP15D02B4-1 & Pb–Zn stage & Calcite & \(+\)1.7 & \(-\)8.7 & +21.9 \\ LZP15D01B3 & Pb–Zn stage & Calcite & \(-\)1.8 & \(-\)11.1 & +19.4 \\ LZP15D02B6 & Pb–Zn stage & Calcite & \(-\)0.2 & \(-\)11.4 & +19.1 \\ LZP15D01B11 & Pb–Zn stage & Calcite & \(-\)2.1 & \(-\)9.8 & +20.8 \\ LZP17D01B11 & Pb–Zn stage & Calcite & \(-\)2.2 & \(-\)9.2 & +21.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(\delta^{34}\)S (%) isotopic data from the Baiyangping Cu–Co–Pb–Zn deposit.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 4 Results
### Analysis of Radar Measurements of UAV States  
The UAV flew continuously along the route and the radar performed track scanning of the specified target. Using the continuous radar base data corresponding to routes 3 and 4 as examples, we analyzed the actual operational state of the UAV. As can be seen from Figure 11, the UAV first hovered and then flew at a constant velocity from the start point  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Velocity (m/s)**} & **Slant Range** & **Elevation of** & & & & **Occentage of** \\  & **between UAV** & **Main Beam** & **UAV Flight** & **Elevation (m)** & **Deviation (m)** & **Beamwidth** \\  & **and Radar (m)** & **Center (m)** & **Elevation (m)** & **Elevation (m)** & **Occupied (\%)** \\ \hline +7 & 1211.933 & 297.524 & 296.337 & 21.193 & 1.187 & 5.60 \\ \(-\)10 & 1166.227 & 290.693 & 289.511 & 20.393 & 1.182 & 5.79 \\ +8 & 1231.907 & 300.509 & 299.265 & 21.542 & 1.244 & 5.77 \\ \(-\)10 & 1167.008 & 290.809 & 289.643 & 20.407 & 1.167 & 5.72 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The minimum deviation between the UAV elevation and the radar main beam center.  
Figure 10: Analysis of the UAV velocity excluding the acceleration and deceleration process. (**a**) 7 m/s; (**b**) 10 m/s; (**c**) 8 m/s; (**d**) 10 m/s.  
at 650 m to the endpoint at 1250 m. After reaching the endpoint, the UAV hovered for a while and then returned along the same route at different velocities. Throughout the entire operation, the intensity of the UAV was consistently above 45 dB, and its return velocity was faster than its forward velocity. This indicates that the radar measurement results are consistent with the actual motion state of the UAV and that the radar antenna remained focused on the UAV.  
Qualitative analysis of the radar velocity measurement results shows that the UAV repeatedly underwent a state transition from hovering to low velocity away, then to a high-velocity approach. For better visual interpretation, the experiment emphasized the display of effective data within a range of \(\pm\)0.5 m/s for each preset velocity. Based on Figure 12, pixels with UAV intensity values greater than 45 dB are identified as target pixels. Whether the UAV is hovering, moving away from the radar, or approaching the radar, the target pixels are generally close to the preset velocity. Furthermore, the closer the actual measurement results are to the preset velocity, the denser the distribution of target pixels. Therefore, the radar measurement results effectively reflect the velocity state of the UAV, indicating that the trend of the actual radar-measured velocity is generally consistent with the preset velocity of the UAV.  
Figure 11: Radar measurement results of the UAV flight status on round-trip routes. (**a**) 8 m/s; (**b**) 10 m/s.  
Figure 12: _Cont._
## 7 Black Start Capabilities through HVDC
### Transient Stability Improvement  
Taking into account that every VSC in an HVDC system is able to independentlycontrol its reactive power injections, a method is proposed in [61] for increasing or decreasing the electromagnetic torque of the generators of a system in order to improve the system's overall stability. This is particularly important when thinking about the development of future projects consisting of huge hybrid AC/DC grids.  
Besides controlling active power injections, the authors propose two different strategies for controlling reactive power injections in the system:  
1. A global control method, using as a reference the weighted average of the frequencies measured on the terminals of the AC system. This set point is determined as: \[W_{i}^{*}=\sum_{k=1}^{n}\alpha_{k}W_{k}\] (4) where: \(W_{i}\) = Frequency reference \(W_{k}\) = Frequency of the AC bus of converter \(K\) \(\alpha_{k}\in[0,1]\) In this strategy, each converter will inject, or consume, reactive power depending on whether its frequency is above or below the average.  
Figure 5: Cascade controller scheme for fast frequency support.  
A local frequency control strategy. Here, the \(Q\) injection of each converter is proportional to its local frequency deviation with respect to the nominal frequency of the system. This method reduces communication complications.
## 3 Results and Discussion
### SEM Analysis  
Figure 10 shows the SEM images of DSPB after 60 min of reaction. From the figure, it can be seen that as the hydration reaction proceeded, different products were formed on the surface of the particles, but the main hydration products were all the same, and the hydration mechanisms of the different groups were all basically the same. The degree of reaction of the C1 group was the lowest, the slag particle surface was slightly etched, only a small amount of gel and Aft was generated, and many flaky Ca(OH)\({}_{2}\) crystals were precipitated (Figure 10d). This may have been due to the absence of SS and the low pH, which resulted in slow dissolution of the slag. In contrast, the surfaces of the Control group and A3 particles were eroded to a greater extent, resulting in the formation of more flocculent precipitates (Figure 10a,b). High pH and abundant silicate oligomers accelerated the reaction.  
Numerous needle-like Aft crystals were observed as adhering to C-S-H gel surfaces, forming a highly polymerized, intertwined floc structure, which impacts rheological properties. Additionally, hydration products and precipitated Ca(OH)\({}_{2}\) crystals create irregular particle surfaces, leading to mutual hindrance during flow and increased flow resistance.  
Figure 10: SEM images of DSPB after 60 min of reaction. (**a**) Control; (**b**) A3-HCDA80; (**c**) B2-PCE1.2; (**d**) C1-S50.
# Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
## 1 Introduction
Aside from this, thermomechanical models have also been presented [2; 25; 26; 27]. Although such models could not predict the formation of individual ice lenses, they effectively examined the global response of freezing soils by introducing a porosity rate function with no hydraulic analysis. Meanwhile, due to the development of computer computational capabilities, prediction studies based on artificial neural networks are gaining traction. An ANN handles incomplete data and captures nonlinear and complex relationships among variables of a system. With these traits, ANNs have been recognized as a powerful tool for prediction. Similar to how ANNs are being applied in various engineering fields, the application of ANNs in the field of geotechnical engineering is also extending to various purposes, such as estimating ground surface settlement, in-situ permeability, undrained shear strength, thermal properties, and landslide susceptibility [28; 29; 30; 31; 32; 33; 34; 35]. However, among many kinds of research, only a fraction of the studies was about predicting frost heave behavior [35]. [PERSON] et al. [35] predicted frost heave ratio of saline soil using back-propagation neural network (BPNN) and generalized regression neural network (GRNN) approaches and compared the prediction performance between two approaches to obtain a relatively reliable model. Despite the significant progress brought upon by the aforementioned experimental, numerical, and statistical studies, several challenges remain. Most previous studies focused on the mathematical modeling of the freezing process accompanied by experimental validation, yet there remains a lack of in-depth analysis for freezing behavior from a geotechnical point of view. Most notably, the estimation of frost heave amount should be evaluated based on various geotechnical properties. Therefore, this study numerically evaluates the frost heave behavior of frozen soil at a specimen scale by considering important geotechnical parameters. A parametric study is also conducted to quantitatively analyze the effect of major geotechnical properties on frost heave behavior. In addition, after evaluating the sensitivity of each physical property to frost heave behavior via multiple statistical analyses, a prediction model based on an artificial neural network capable of practically estimating the frost heave ratio is finally presented.
## 1 Introduction
For the new normal era, wherein the user's lifestyle connectivity is regarded as important in the mobile environment, now and in the future, this study will help to respond to the changes that digital cultural spaces face. The instant sharing of the users' own singing on music service platforms is restricted due to the following reasons: (i) copyright and licensing agreements must be followed by music service platforms. If users' own singing data are allowed to be uploaded, this can constitute an infringement of copyrighted material, especially relating to songs that are under copyright protection. To prevent legal complications related to copyright laws, shared music is reviewed by music platforms; (ii) high-quality music files (audio and video) must be uploaded to the service platforms, otherwise, low-grade, poorly recorded, or unsatisfactory content will overload the system which will affect the overall experience of listeners; and (iii) community guidelines and standards must be followed by music platforms to suppress problems due to inappropriate or offensive content. In addition, spam and overload algorithm recommendations and bandwidth cost factors are significant reasons to restrict the direct uploading of users' content. In this work, a mobile-based social karaoke system is proposed to support group creation and music sharing activities using smartphones. The social music cloud existing in this model provides impromptu mobile singing and sharing services directly on the user's device panel. Instant data streaming and messaging facilities for karaoke users are allowed through touch and voice-based natural interfaces. The usability and stability of this model have been tested. Here, a three-step procedure is followed to implement the effective karaoke system, primarily the introduction of significant potential to foster cultural environments. Secondly, it offers user-friendly touch and voice interfaces and, finally, enhanced performance and management through the distribution process. The proposed work is beneficial for group activities without violating community guidelines and standards. Moreover, it reduces the overload of spam and irrelevant content on the music service platforms. The composition of this paper is as follows. In Section 2, both connectivity in the changing mobile environment and connectivity from music sharing social services are examined. In Section 3, the concept and structure of the mobile social karaoke system in a mobile environment are explained, and, in Section 4, the implementation and experiment are explained. In Section 5, conclusions are presented and future work is described.
# Dynamic Scheduling and Optimization of AGV in Factory Logistics Systems Based on Digital Twin
## 3 Logistics Scheduling Framework Based on Digital Twin
### Architecture of Digital Twin for Workshop Logistics System  
The dynamic scheduling architecture of a workshop logistics system based on digital twin is composed of the physical world, virtual world, digital twin engine and service management system, as shown in Figure 1. The physical world means the intelligent hardware devices, such as sensors, PLC (Programmable Logic Controller), RFID (Radio Frequency Identification) tags and RFID readers. The motion logic and performance of the physical world lays a foundation for the digital twin in the virtual world. At the same time, data acquisition tools can transmit real-time dynamic data to the digital twin engine. The physical world is the basis for the digital twin system, and it is also the ultimate target to be optimized for the digital twin system. The virtual world is composed of digital models and information systems. It is the virtual mapping of the physical world and synchronizes the data and models of the physical world. The virtual world provides the model and data for the digital twin engine, and receives data feedback from the digital twin engine. The digital twin engine is the engine to realize the real-time connection between virtual and real space, and it is also the core of the whole digital twin system. It is composed of an interactive drive, data storage and management, model management, an intelligent scheduling algorithm, a historical knowledge database and a communications system. The interactive drive connects the physical world, virtual world and the interactive interface of the service management system. Data management needs to be integrated with model management before intelligent computing analysis. The historical knowledge database contains the optimization data and theoretical data of basic algorithms recorded in previous historical iterations. The communication system is composed of a Web Service, database interface, a TCP/UDP (Transport Control Protocol/User Data Protocol), and an OPC UA (Object Linking and Embedding for Process Control Unified Architecture). The communication between the physical world, virtual world and service management system can be realized through the communication system. The service management system generally includes the WMS (Warehouse Management System), SCM (Supply Chain Management), MES (Manufacturing Execution System), RRP (Recommended Retail Price) and PDM (Product Data Management), which can realize the visual analysis of data, the scheduling of production plans and the management of the digital twin system. The visual data will be fed back to the physical world workshop and the virtual world workshop synchronously in real time and will also be recorded in the digital twin engine.
## 3 Seismic Fragility Theory  
Seismic fragility curves are widely used for structural safety assessments, seismic performance evaluations, and establishing disaster response standards to mitigate earthquake damage [25]. These fragility curves provide quantitative damage levels for given performance levels based on the magnitude of the input seismic load, making them a widely used method for the performance evaluation of structures. Based on seismic analysis results, the probability of damage for a given earthquake magnitude can be calculated and expressed using a log-normal distribution function. The median and log-standard deviation of the log-normal distribution function can be calculated using the maximum likelihood estimation, as shown in Equation (1).  
\[L=\prod_{i=1}^{N}\left[F(a_{i})\right]^{x_{i}}\left[1-F(a_{i})\right]^{1-x_{i}}. \tag{1}\]  
Here, \(F(\cdot)\) represents the seismic fragility curve, and \(a_{i}\) is the input earthquake magnitude (PGA). \(x_{i}\) takes a value of \(x_{i}=1\) if damage occurs at the \(i\)th instance; otherwise, it  
Figure 1: Statistical characteristics of ground motions. (**a**) Histogram of peak ground acceleration; (**b**) histogram of magnitude; (**c**) histogram of aís intensity; (**d**) histogram of vs30.  
takes a value of \(x_{i}=0\). \(N\) is the number of ground motions. The seismic fragility curve can be expressed as a cumulative distribution function, as shown in Equation (2) [4,26].  
\[F(a)=\Phi\left[\frac{\ln\left(\frac{\theta}{\beta}\right)}{\beta}\right]. \tag{2}\]  
Here, \(a\) represents the input variable, the earthquake magnitude (PGA), \(\theta\) is the median, \(\beta\) is the log-standard deviation, and \(\Phi[\cdot]\) is the cumulative probability function of the standard normal distribution.  
Various methods have been proposed for deriving seismic fragility curves. Techniques such as multiple stripe analysis, IDA, and cloud analysis are commonly used; this study employed the IDA method for deriving the seismic fragility curves. IDA is a popular method enabling toe estimation of the structural performance under various seismic loads. The main goal of IDA is to determine the relationship between the seismic intensity level, i.e., IM, and the corresponding engineering demand, i.e., structural response. The IDA can be explained with the following steps: (1) selecting a set of seismic ground motions; (2) selecting the appropriate intensity measure (IM) and engineering demand measure (EDM); and (3) scaling the selected the seismic ground motions, i.e., IM, and performing the nonlinear structural analysis using finite element method to obtain the structural response, i.e., EDM. Once structural analysis using IDA is completed, the constructing fragility curves can be estimated by optimization techniques to obtain two parameters of the fragility curves [14,22,23,26,27]. In this study, the magnitude of the ground motions considered for the fragility curve input ranged from 0.01 g to 5.00 g in increments of 0.01 g.