esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Protocol Adaptation for Modbus and 5G
### Protocol Adaptation for Modbus TCP and 5G  
In the same way, we detail the protocol adaptation between Modbus TCP and 5G. The Modbus data judgment process is shown in Figure 6. In the first step, we judge that the transaction identifier, protocol identifier, and unit identifier in the MBAP belong to the Modbus TCP protocol. Otherwise, we discard this data and wait for the next frame message. Then, we verify the correctness of the function code, data address, and data scope in turn. If not correct, we send the 01, 02, and 03 exception codes with the same meaning as Modbus RTU. Secondly, we resolve the valid data into the MBAP message header and PDU data. Thereafter, we separately extract and store the PDU data. In the third step, we encapsulate the resolved data by the 5G protocol stack and transmit it through the antenna. Fourth, we resolve the PDU data of the 5G data package in the receiver, according to the reverse process of 5G data encapsulation. The fifth step is to add the missing MBAP field.  
Figure 6: The judgment process of Modbus TCP data.  
In the above processes, we convert the Modbus data into the 5G data according to the 5G NR protocol stack. We obtain the Modbus PDU data sequentially through the SDAP sublayer, PDCP sublayer, RLC sublayer, MAC sublayer, and physical layer in the user plane protocol stack data link layer. The 5G data encapsulation and decapsulation process is presented in Figure 7. First, we send the Modbus PDU data to the application layer through a TCP/IP protocol. After that, we observe that each IP packet is given a label and perform the mapping of QoS flows to radio bearers in the SDAP [31] sublayer. Subsequently, the data are transmitted to the PDCP sublayer, which compresses, encrypts and protects the IP header [31]. Next, the data are added to the RLC header and sent in sequence according to different logical channels. We can also request to retransmit erroneous data. Afterward, we transmit the data to the MAC sublayer for selecting the transmission blocks and ensuring the instantaneous rate according to the link adaptive mechanism [32]. Ultimately, we encode, modulate, and check the wrong data.
## 3 Results and Discussion
### Influence of Phthalic Anhydride Solution Concentration on the Surface Properties of PPTA Fiber
#### 3.1.2 Study on Surface Element Compositions of Aramid Fiber before and after Modification
In order to further analyze the chemical states of surface elements on the PPTA fiber before and after modification, peak fitting was performed on the C1s spectrum, as shown in Figure 5. From Figure 5a, it can be observed that the functional groups on the unmodified PPTA fiber surface are mainly composed of C-C (284.5 eV), C-N (285.5 eV), and C=O (287.7 eV). After modification with phthalic anhydride, new functional groups, such as C-O (286.3 eV) and O=C-O (289.9 eV), were introduced onto the PPTA fiber surface, resulting in increased oxygen content and a higher proportion of polar oxygen-containing functional  
Figure 4: XPS full spectrums of PPTA fiber before and after modification: (**a**) PPTA; (**b**) PPTA-0.15; (**c**) PPTA-0.3; (**d**) PPTA-0.45. groups. This change enhances the chemical bonding strength between PPTA fibers and the DGEBA matrix and improves the wettability and adhesion property of the PPTA fiber surfaces, thereby enhancing the interfacial bonding strength in the PPTA fiber-reinforced composite materials.
## 3 AQM Framework for Smart City Applications  
By observing the trust, privacy, and confidentiality issues from a data quality point of view, it is suggested that quality properties in smart city applications could be checked and assessed by considering contextual data. In this research, smart city applications were modeled as agent systems. During the run-time of the modeled smart city application or agent system, the effective way to assess agent quality was by performing an analysis of agent interactions and social activities. The richness of the agent data captured during those activities has opened the opportunity for granular analysis using the data at critical points during the execution. The AQM framework is designed as an extension of the generic three stages of the existing verification process, i.e., modeling, specification, and verification [45]. The framework was proposed by adapting the TDQM model reviewed in Section 2. The TDQM principle was adapted to manage agent quality within agent systems. The framework coordinates the agent quality management processes into four stages, i.e., definition and specification, modeling and execution, verification and assessment, and analysis and improvement phases that can be implemented as a cycle. AQM checks and assesses the agent quality conditions in selected critical states and transitions. The quality checking process includes continuous monitoring during run-time in order for the quality specifications to be verified by considering contextual information, i.e., relating the agents to their individual profile status, social preferences, and content sharing activities that may affect agent quality properties.  
The quality assessments for an individual agent in a smart city environment can be used to detect agent quality properties violations during run-time. The integration of TDQM in order to check, assess, and manage agent quality, and to finally detect agent quality violations, is the new contribution that complements the existing proposed solutions for quality requirements in smart cities. Figure 1 illustrates the extension of the existing generic verification process and the integration of TDQM to form the new AQM framework. Multiple processes are organized into the four elements of AQM, which are:  
1. definition and specification,
2. modeling and simulation,
3. verification and assessment,
4. analysis and improvement.  
In the following subsections, each of the processes implemented in the proposed AQM are explained.
## 5 Specific Application of Topic Identification Models and Algorithms
### Application of LDA Theme Model and Its Derivative Models
#### 5.1.1 LDA Theme Model  
LDA was proposed by [PERSON] et al. in 2003. It is a three-layer Bayesian probability model, including a three-layer structure of words, topics and documents. The LDA topic model overcomes the shortcomings of sparse text matrices and ignores text semantics, and is one of the most effective methods for analyzing large unstructured document sets [36]. By maximizing the co-occurrence probability of words to find word clusters, Dirichlet  
Figure 4: Classification of topic identification models and algorithms.  
distribution is used to describe the document generation process and to cluster documents. The LDA topic model is the most widely used method in topic recognition, particularly in the fields of graphene [37], digital journalism [38], marine diesel engines [39], stem cell research [40] and so on.  
[PERSON] extended the LDA model to the online text stream and designed an online topic evolution mining model for network public opinion situation analyses [41]. [PERSON] used the LDA model to assess the topics of scientific and technological studies in combination with controlled vocabulary [42]. [PERSON] used LDA to realize the unsupervised automatic identification and acquisition of potential scientific and technological topics, and uncover topic distribution in a large number of patent texts [43]. [PERSON] added topic filtering on the basis of existing evolution methods and proposed a method to judge the evolution status of a topic [44]. [PERSON] et al. used the LDA algorithm to determine the clustering of potential technical topics in patent studies, and analyzed the distribution characteristics and evolution law of topics in various periods [45]. [PERSON] et al. used the LDA model to identify the research topics within fund projects and the texts of papers, and used the similarities to build correlations between topics [46]. [PERSON] et al. used the LDA model to identify topics, and obtained a time series data of topic changes in the field of information construction [40]. [PERSON] et al. used the LDA model to mine the topic features within the deep learning field, and subdivided the research topics [47]. [PERSON] et al. used the LDA model to identify emerging topics in graphene studies by the National Science Foundation of the United States [48].
# Study on the Influence of Groundwater Variation on the Bearing Capacity of Sandy Shallow Foundation
## 4 Conclusions  
In this study, the influence of water-level change on the bearing capacity of shallow foundation was studied by combining a model test and numerical simulation. The major conclusions are summarized as follows:  
Figure 13: The p–s curves for different water-level heights.  
The height of water level and the frequency of water-level fluctuation are negatively correlated with the bearing capacity of the foundation, and the relationship of change is nonlinear. The water-level fluctuation can strengthen the capillary effect and expand the range of saturated soil in the foundation.  
In this study, the influence of the water-level fluctuation was added to the traditional calculation of the foundation bearing capacity. A new expression form of pore water pressure was derived for sinusoidal fluctuation. The average error between the calculated bearing capacity and the test results is less than 8%. It shows that this theory can be used to calculate the bearing capacity of a foundation under water-level fluctuation.  
This model can well predict the change in the foundation bearing capacity due to the water-level change. It has a good early warning function for an engineered foundation that has been under a high groundwater level for a long time.  
This model also has some limitations. It is currently only applicable to sandy shallow foundations. In the future, the model will be expanded to apply to more types of foundations.  
Conceptualization, W.C.; methodology, W.C.; software, E.W.; data curation, E.W.; writing--original draft preparation, W.X.; writing--review and editing, S.Z.; project administration, W.C.; funding acquisition, E.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (project Nos. 42122051, 41972271, 41602314), the Fellowship of the China Postdoctoral Science Foundation (project No. 2021M692494), the Fundamental Research Funds for the Central Universities, WHUT (Wuhan University of Technology, project No. 3120620191), Sanya Science and Education Innovation Park of Wuhan University of Technology (project No. 2020 KF0007) and the National Innovation and Entrepreneurship Training Program for College Students (project No. S202010497183).  
Not applicable.  
The authors declare no conflict of interest.
# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
###### Abstract  
As cloud computing rises in popularity across diverse industries, the necessity to compare and select the most appropriate cloud provider for specific use cases becomes imperative. This research conducts an in-depth comparative analysis of two prominent cloud platforms, Microsoft Azure and Amazon Web Services (AWS), with a specific focus on their suitability for deploying object-detection algorithms. The analysis covers both quantitative metrics--encompassing upload and download times, throughput, and inference time--and qualitative assessments like cost effectiveness, machine learning resource availability, deployment ease, and service-level agreement (SLA). Through the deployment of the YOLOv8 object-detection model, this study measures these metrics on both platforms, providing empirical evidence for platform evaluation. Furthermore, this research examines general platform availability and information accessibility to highlight differences in qualitative aspects. This paper concludes that Azure excels in download time (average 0.49 s/MB), inference time (average 0.60 s/MB), and throughput (1145.78 MB/s), and AWS excels in upload time (average 1.84 s/MB), cost effectiveness, ease of deployment, a wider ML service catalog, and superior SLA. However, the decision between either platform is based on the importance of their performance based on business-specific requirements. Hence, this paper ends by presenting a comprehensive comparison based on business-specific requirements, aiding stakeholders in making informed decisions when selecting a cloud platform for their machine learning projects.  
cloud computing; machine learning; deep learning; object detection; Amazon web services (AWS); Microsoft Azure; cloud provider comparison +
Footnote †: journal:  
Article
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 4 Navigation Integration Enhanced by the ANN
### Intelligent Estimator Using an NN as a Replacement of KF for Data Fusion
Therefore, RNN networks are likewise realized via the time-back propagation algorithm. The \"gradient disappearing phenomena\" is caused by the activation function's derivative multiplying with the input data sequence as it gets longer. This phenomenon is explained by the structure of an RNN and occurs when the input data sequence gets longer [87]. The weights are no longer updated as a result of this disappearance, which makes it challenging for the model to learn distant information. However, RNN suffers from the well-known gradient explosion and dispersion issues that lead to local extrema.
## 2 Materials and Methods
### Model  
Figure 1 presents the framework of our model. Following the encoder-decoder architecture [11], we built a prediction network based on DA-RNN [16]. The encoder processes a multidimensional spatial attention mechanism that can adaptively capture the dynamic correlation between the Chl-a and other features time series. The decoder uses a temporal attention to adaptively select the relevant previous time intervals for making prediction. With the spatial-temporal attention mechanisms, the DA-RNN can catch the degree of the relevancy between input features and Chl-a and apply the attention of time dimension to catch dynamic temporal correlation.
## 2 A Generic Multi-Path and Multi-Channel Proposal for HWMP  
Figure 1 shows the proposed modified structure for the HWMP algorithm. On the one hand, the MSTAs are capable of storing multiple paths to every destination node in their routing tables. These paths are classified by a path selection policy with the objective of sending the data traffic with the highest priority over the best paths. On the other hand, to reduce the level of interference between MSTAs and increase the network performance, a different channel is assigned to each available path. Besides, a different channel will be reserved for control packets. In order to add these multi-path and multi-channel functionalities to the default HWMP protocol, several mechanisms are proposed. To evaluate their performance, all the proposals have been programmed and included in the basic ns-3 IEEE 802.11s module [5].
# Experimental Investigation of Controlled and Uncontrolled Rectifiers for Low-Power Wind Turbines
## 7 Experimentation with Small Wind Turbines and Integration of an Inverter  
The experiment in this section was performed on a small wind turbine emulator manufactured by [PERSON]. The emulator consists of a battery charge regulator panel, a synchronous generator panel, a three-phase rectifier, a panel with 230 V AC bulbs, an off-grid inverter panel, a variable resistor panel, and a panel for testing the operation of small servo motors.  
The emulator uses a synchronous generator with permanent magnets, such as small wind turbines. The generator has a nominal speed of 1000 rpm with 300 W rated power. Generators transform mechanical energy into electricity through electromagnetic induction. Hence, excitation and armature winding speed are critical. An armature winding (usually three-phase) is symmetrically arranged in slots around the generator's stator. The armature induces a voltage when magnetic flux changes. Rotor windings excite, and the excitation winding generates magnetic flux from a direct current. The rotor's mechanical motion rotates the excitation's magnetic field. Even with the current, stator winding conductors create an electromotive force.  
The charge controller has been specifically designed for small wind farm installations. It guarantees that the linked battery is fully charged. Suppose the battery cannot be charged anymore. In that case, the load resistor converts the extra energy into heat, which ensures that the motor that serves as the wind generator model is always loaded and prevents it from spinning at excessively high speeds. The panel includes, in addition to the charging regulator, connectors for connecting the wind generator, a rectifier with protection, a protection resistor, a battery, and a connection for external loads. The rectifier converts the three-phase input voltage to a DC voltage. Figure 15 shows the experimental setup of the wind turbine system and the output waveforms. The wind generator was controlled to power a 25 W incandescent lamp. The power from the wind generator is first rectified and controlled with the charge controller, and then the power is inverted from DC to AC through a 230 V, 50 Hz inverter. The sample of output waveforms at each stage is shown in Figure 15.
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 2 Materials and Methods
### General Network Architecture  
Remote-sensing images contain a wealth of semantic information that can aid in the accurate classification of pixels. The codec network structure is well-suited for this task due to its powerful feature extraction and context-understanding abilities. With this structure, the encoder can convert the input image into a high-dimensional feature vector that contains detailed information about the road and global features in the remote-sensing image. This rich semantic information can then be utilized by the decoder to improve pixel classification accuracy.  
This paper presents a model with an overall network structure divided into two parts: the encoder network structure for feature extraction (left half of Figure 1) and the decoder network structure for feature map recovery (right half of Figure 1). The encoder network utilizes part of the DarkNet network and the ASPP module. On the other hand, the decoder network employs the same decoder as the SegNet network. The ASPP module uses a spatial pyramid pool-like structure that aims to acquire more accurate feature information using multi-scale information. This approach helps in extracting fine and narrow road information and refining the contextual information for better results.  
Figure 1: Overall network structure.  
The structure of DANet follows a coder-decoder pattern, where the encoder is based on the DarkNet network structure, and the decoder is similar to the SegNet network. Additionally, to capture feature information on a wider scale, two ASPP structures have been included at different locations within the network.
## 2 The Applications in Smart Grid Based on Time Synchronization
### Summary of Applications  
It should be noted that each application has connections with others. They can not be separated directly. The detailed time information requirements depend on a specific case; for example, the fault location detection may use a traveling wave, or DFRs data, or PMUs measurements. Different methods can give different location errors, and the corresponding time accuracy requirements should be considered differently. In general, the time information requirements are shown in Table 1[32].  
The IEC Standard 61850-5 [12] classifies time synchronization requirements for various applications and services, similar to Table 1. It defines six synchronization accuracy classes, ranging from Class A (accuracy within \(\pm\)1 us) to Class F (exceeding 1 s) [42].
## 5 Results and Discussion
### Parametric Studies
#### 5.3.2 Operating Pressure  
The design operating pressure for E-STR reactor is 70 atm, while previous calculations were performed under 28 atm. In this section, the operating pressure is raised to 70 atm. The result is compared with that of the base case in Table 10.  
Under the higher operating pressure at 70 atm, there is no unreacted char at the exit of the gasifier, and the mole fractions of H\({}_{2}\) and CO increase about 4 percentage points; moreover, the HHV level is also higher. Temperature distributions along the centerline of the reactor are compared in Figure 6. The maximum temperature after the second stage increases from 3400 \(K\) to about 4000 K (Figure 6), and the exit temperature from 1463 K to 1532 K (Table 10), indicating the effect of significantly increased pressure. For reference, the adiabatic temperature of solid carbon can reach about 4667 K under atmospheric condition [39].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Species** & **Base** & \begin{tabular}{c} **70 atm** \\ **100\% O\({}_{2}\)** \\ \end{tabular} & \begin{tabular}{c} **28 atm** \\ **50\% O\({}_{2}\)** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Mole fraction \\ (\%) \\ \end{tabular} } & CO & 36.91 & 40.81 & 21.89 \\  & H\({}_{2}\) & 24.48 & 29.21 & 19.97 \\  & CO\({}_{2}\) & 10.07 & 10.82 & 9.15 \\  & H\({}_{2}\)O & 20.61 & 10.52 & 34.13 \\  & CH\({}_{4}\) & 2.27 & 2.84 & 5.35 \\  & Volatiles (G) & 1.05 \(\times\)\(10^{-6}\) & 1.3 \(\times\)\(10^{-9}\) & 6.1 \(\times\)\(10^{-5}\) \\  & Volatiles (L) & - & 0 & 0 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Unreacted Char (\%) \\ Temperature (K) \\ \end{tabular} } & 20 & 0 & 82 \\  & 1463 & 1532 & 1100 \\  & HHV (MJ/kg) & 8.15 & 9.38 & 5.73 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Effect of operating pressure and oxygen amount on syngas composition and exit temperature.  
Figure 6: Comparison of temperature distributions under two pressures and two oxygen supply conditions.
## 3 Taxonomy
### Non-Al-Based Methods
#### 3.2.3 System Design  
In this sub-section, articles that designed a system but were not implemented fully are discussed. This sub-section includes seven (\(n=7\)) articles. In the first article, a real-time IoT-based smart river monitoring system was designed with the use of unmanned aerial vehicles (UAVs) or drones and lower power wide area communication technology [16]. In the second article, a real-time IoT-based drinking water quality monitoring system was designed using ZigBee and pH, turbidity, temperature, broken-down oxygen, and conductivity sensors [38]. Another study utilized ZigBee with Wasp note to design a real-time IoT-based continuous water monitoring and water quality controlling system using pH, temperature, turbidity, dissolved oxygen, Calcium, and Chloride sensors [39]. In the next study, an integration between amphibious UAV and hovercraft was designed and a prototype was developed for an IoT-based monitor water quality system in a large area utilizing pH, DO, electrical conductivity, and turbidity sensors [40]. In this study, real-time IoT-based water quality monitoring was proposed for fish pond owners to maintain normal water levels in fish tanks using pH and ultrasonic sensors [41]. In this study, a remote real-time water and soil quality monitoring system was designed using pH, turbidity, electrical conductivity, and moisture sensors [42]. In the last study, an IoT-based portable drinking water quality monitoring system was proposed [43]. This prototype system consists of various types of parameters to analyze the taste, odor, color, Nitrate, Floride, Lead, Arsenic, and Chromium.