esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion  
### Temperature Distribution on Keyhole Wall Surface  
The temperature distribution on the surface along the keyhole wall calculated from experimental research is depicted in Figure 8. The temperature range was around 1573-2073 K. The maximum temperature of 2073 K occurred at the bottom surface of the keyhole. This result can be explained by (1) the heat transfer from the arc plasma to the weld pool and (2) the subsequent thermal conduction in the weld pool. Generally, the total heat flux on the surface of a base metal (workpiece) consists of the heat flux from electron condensation and thermal conduction. With an opening keyhole, the energy transfer on the surface along keyhole wall consists of electron condensation and thermal conduction, whereas the energy transfer anywhere within the weld pool only occurs due to thermal conduction. In this case, the arc plasma column with very high temperature attached to the surface along the keyhole wall. The center of the arc plasma column was closer to the workpiece on the bottom surface rather than the top surface due to the small keyhole diameter at the bottom surface. Therefore, the energy from electron condensation transferred to this region was much larger than elsewhere within the weld pool [26]. As a result, the temperature on the surface along the keyhole wall was higher than at other positions within weld pool, and the temperature on the bottom surface was higher than on the top surface.  
We conducted many experiments to confirm that the temperature distribution tendencies for both pure Ar and Ar + 0.5% O\({}_{2}\) were similar, meaning that the temperature at the bottom surface was higher that at the top surface. Therefore, to discuss the results in this work, only the temperature distribution in the case of pure Ar is presented.  
Figure 7: Relationship between theoretical radiation intensity ratios to temperature on the surface along the keyhole wall.
## 2 Background Overview
### Cloud, Fog, and Edge Computing  
Cloud computing offers storage and computation facilities that are placed remotely, at an extended distance from vehicles, typically in cloud data centers. They offer unlimited storage and computational capability that can be accessed from anywhere. Therefore, vehicles can send and retrieve huge amounts of data at any time and place, without being concerned about their limited storage capability. However, when it comes to time-sensitive applications, the distant cloud is not able to fulfill the latency requirements.  
Due to the dynamic nature of vehicles, it is necessary to deliver the applications with minimal delay and ensure uninterrupted service. To address these issues, a computing paradigm that takes place closer to the vehicles is needed. Fog computing is physically located somewhere between the edge layer and cloud layer and bridges the gap between the two. It ensures abundant storage, computational and network resources, real-time communication, high bandwidth, high mobility support, and context awareness [29; 30; 31]. Moreover, because of the proximity to the vehicles, fog computing is a good solution for services that require high QoS.  
On the other side, smart vehicles have a considerable amount of storage and computing capabilities, and therefore can be considered a form of edge computing. While some resources are reserved for the operating system, other available resources can be used for delivering VANET applications and processing data obtained from sensors. Processing and analyzing data at the edge will avoid the massive traffic flow in the core network, which occupies much of the already limited network resources.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Hybrid Detection Position Sensorless Method
#### 3.3.2 Hybrid Detection Method Based on Multiple Features  
Another hybrid method is to use multiple means to estimate the rotational speed and locate the special position to make up for the shortcomings of the traditional single method. Pulse injection is combined with flux linkage [156; 157]. Multiple inductive features [158; 159]. Mutual inductance-based methods [164; 165] are proposed to obtain the rotor position based on the induced voltage generated by the mutual inductance effect between the motor phases, which is a typical hybrid detection technique. During SRM operation, the mutual inductance voltage is formed between the conducting phase and the non-conducting phase, by detecting the change accompanying the mutual inductance voltage when the rotor position changes. After that, the rotor position is estimated by combining the characteristics of the inductance. However, the back EMF can adversely affect the accuracy of the estimation. In [166], a high frequency pulse is injected into the tail of the excitation current. The current waveform and flux linkage waveform are shown in Figure 13. During the start-up and low-speed phases, the inductance is divided into multiple regions, a linear region of which is selected for rotor position estimation. In the medium and high speed regions, the excitation current flux linkage and the current generated by the injection pulse are compared for rotor positioning. This method enables four-phase operation for the full speed range and start-up phase.  
This type of scheme deserves further study, and its unique advantage is that it can make up for the inherent shortcomings of the original method by introducing new methods. It has huge advantages in speed regulation range, four-phase operation, and no pre-stored parameters.  
Figure 13: Schematic diagram of the hybrid method based on flux linkage and current waveforms. (**a**). Normal excitation mode. (**b**). excitation mode with chopping.  
The differences between the various methods can be easily obtained by comparing the Table 2. In general, some methods are effective and widely used in certain speed ranges, e.g., pulse injection in the start-up phase, inductance-based methods in the mid-to-high speed range, and observer-based, add-on-based methods in the full-speed range. In order to find a control strategy without pre-storage, in the full speed range, independent of the speed/torque control strategy and with high position estimation accuracy, it is extremely important to study the electromagnetic characteristics and control theory of the motor to obtain the rotor position signal indirectly.
## 2 Materials and Methods
### Architecture Design and Implementation
#### 2.1.2 Software Design  
The host system was based on the LabVIEW2018 (32 bit) integrated development environment to develop a CCT monitoring platform. The platform was responsible for data analyzing and processing in real time to make a judgment about the real condition of a refrigerated freight car internal environment and the state of product quality, sending alarm messages if necessary. Meanwhile, the monitoring platform could achieve data storage and retrieval in real time by interacting with the Alibaba Cloud database. LabVIEW software always comes with built-in library functions in software standards such as TCP/IP and ActiveX to support data transmission through a 4G signal [12; 13]. The system could present real-time information on the interface platform to directly view by users through texts, graphs, tables, curves, and other forms. In this way, the platform significantly improved the visualization of the monitoring interface. It broke through the limitations of the local area networks to improve the data sharing ability with the help of cloud databases.  
Figure 2: The physical architecture of the data acquisition module.  
The CCT monitoring center is the general control interface of the platform (Figure 3). Users and managers can obtain information about the current vehicle operations in general from the interface. Simultaneously, managers can check the state of connection between the database and the platform and change the platform's TCP/IP listening status through the monitoring center interface.
## 5 Conclusions  
The study presents a hybrid framework that leverages deep learning to effectively identify various Sella Turcica (ST) morphologies in full lateral cephalometric (lat ceph) radiographic images, including bridging, circular, oval, and flat. The framework employs an encoder-decoder network to iteratively refine the network output by merging it with the input data and passing it through it to Attention Gating Modules (AGM) and Squeeze-and-Excitation (SE) blocks. This process allows for the identification of anatomical characteristics within the radiographic images. To enhance the feature maps and prioritise regions of interest, the proposed framework utilised RrCL and AGMs instead of relying solely on conventional convolutional layers.  
By accurately extracting data from the encoder-decoder network, the proposed framework facilitates precise segmentation of anatomical characteristics. This assists healthcare professionals in predicting various dentofacial anomalies associated with ST structures.  
Moving forwards, enhancement to the anatomical feature segmentation pipeline could involve using synthetically generated samples, produced via generative adversarial networks, to enhance performance. Further improvements may include downstream tasks such as ST structure classification and the identification of dentofacial anomalies related to the defined ST structures. This approach will contribute to a more comprehensive computer-aided diagnosis (CAD) for cephalometric radiographs. Moreover, to bridge the gap between high performance and interpretability, our future work will consider incorporating explainable AI techniques. This approach can help elucidate the decision-making process of our deep learning model, making its predictions more transparent and interpretable to healthcare professionals. Additionally, a more advanced segmentation and classification method will be investigated to achieve more accuracy and reliability in anatomical structure segmentation and dentofacial anomaly classification.  
Conceptualization, K.S.S., M.J., P.K., A.A. and A.L.; methodology, K.S.S.; validation, M.J., V.K. and A.L.; formal analysis, V.K. and J.P.; investigation, K.S.S., M.J. and A.L.; resources, M.J., V.K. and A.L.; data curation, K.S.S., M.J. and V.K.; writing--original draft preparation, K.S.S. and P.K.; writing--review and editing, K.S.S., P.K., J.P., A.A. and A.L.; supervision, M.J., J.P., V.K., A.A. and A.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData sharing not applicable. The data are not publicly available due to privacy regulation.  
The authors are grateful to the Oral Health Sciences Centre, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, for providing cephalometric data under the ethical clearance number IEC-09/2021-2119, and the faculty of the School of Science, RMIT University, Melbourne, for their valuable assistance.  
The authors declare no conflict of interest.
## 1 Introduction  
The animal body is inherently metagenomic, not only in relation to the eukaryotic genome that makes up the body, but also the genomes of the microbiomes colonizing the surface of the body, which includes bacteria, archaea, fungi, protozoa, and viruses [1]. A growing body of research has shown that commensal microbial communities interact with almost all physiological aspects of the host in health and disease [2; 3; 4; 5; 6; 7]. The microbiome within the gut is the most widely studied because its microbial biomass exceeds that of other bodily habitats by a large order of magnitude, and it is separated from the host only by a single layer of epithelial cells [5]. Numerous studies have shown that gut microbiota dysbiosis contributes to the onset of many diseases, from gastrointestinal and metabolic disorders to immune and neuropsychiatric diseases [8; 9; 10; 11].  
In this context, fecal microbiota transplantation (FMT), an important means of regulating the composition and functions of the gut microbiota [12], is often used in studies of the gut microbiota. Clinically, FMT, also known as fecal transplantation, is a procedure in which stool from a healthy donor is placed into another patient's intestine [13]. However, in experimental studies, its definition is broader. Common research modes include transplanting wild-type (WT) mice or healthy human microbiota into disease-model recipients, [14; 15; 16] transplanting disease model microbiota into recipients [17; 18; 19], or even transplanting a customized combination of microbiota (selective microbiota transplantation, SMT) to achieve specific experimental purposes [20]. Some studies have also applied a combination of several modes, for example, the inclusion of a model group that acts as both the donor and recipient to control for handling and allows for the analysis of confounding factors that may affect the experimental groups [21].  
In clinical situations, there are already systematic FMT treatment guidelines in place that are constantly being updated and improved [22; 23; 24]. There have been many reportson donor selection, the separation of fecal bacteria, the frequency of FMT, and infusion procedures [13; 22]. Nevertheless, only a few studies have been designed to explore the methodology of FMT, and even fewer have provided standardized FMT procedures for use in animal models used in research [25], creating difficulties for many beginners in microbiota research. Therefore, the aim of the present review is to provide a simple and repeatable FMT protocol, as well as a summary of the literature references for each adaptable step to aid in customizing microbiota. Moreover, necessary analyses related to FMT, as well as common patterns among studies that have developed this technique to investigate the disease mechanisms related to the intestinal ecosystem, are discussed.
## 2 Materials and Methods
### Equilibrium Isotherms  
Determination of adsorption isotherms was performed according to ASTM D3860-98 [50] by adding different amounts of PACs to a series of 100 mL flasks, containing the same initial concentration used in the batch adsorption tests for both organic dyes and phenolic compounds (Table 1). The flasks were kept under stirring in a six-place magnetic plate at 800 rpm and room temperature (\(20\pm 2\)\({}^{\circ}\)C) until equilibrium, keeping the pH between 5 and 6. Freundlich and Langmuir models were considered in this study, as they are the most frequently used to describe the adsorption of organic dyes and phenolic compounds [52; 53; 54; 55], even if other models have been considered in the literature [56]. The parameters in the [PERSON] (Equation (5)) and Langmuir (Equation (6)) equations were determined through a linearization procedure.  
\[\ln q_{e}=\ln k_{F}+\frac{1}{n}\;\ln C_{e}\;(\textit{linearized Freundlich}) \tag{5}\]  
\[\frac{C_{e}}{q_{e}}=\frac{1}{k_{L}Q_{0}}+\frac{C_{e}}{Q_{0}}\;(\textit{linearized Langmuir}) \tag{6}\]  
where \(q_{e}\) is the adsorption capacity (mg/g), \(C_{e}\) is the residual concentration, \(k_{F}\) and 1/n are the Freundlich constants, \(k_{L}\) is the Langmuir constant, and \(Q_{0}\) is the maximum saturation capacity at the isotherm temperature (mg/g).
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 2 Related Work
### Class-Imbalance Learning
#### 2.2.1 Data-Level Methods  
Data-level methods need to pre-process the data by increasing or decreasing the number of samples to balance the imbalanced data, such as under-sampling methods (e.g., random under-sampling, RUS) and oversampling methods (e.g., synthetic minority oversampling technique, SMOTE [40]). RUS remove the majority class samples, while over-sampling methods add synthetic minority class samples to make the data balanced.  
SMOTE makes the discriminant boundary of the classifier expand from the minority class to the majority class. However, since SMOTE randomly selects the minority class samples to generate synthetic minority class samples, this may cause overlapping problems or over-fitting problems. To deal with this problem, many researchers have made their efforts to propose new methods based on SMOTE, such as Borderline-SMOTE [41], MWMOTE (Majority Weighted Minority Oversampling TEchnique) [42], and ADASYN (Adaptive Synthetic) [69]. Borderline-SMOTE only over-samples the minority class samples on the boundary, because its basic assumption is that samples on the boundary are more likely to be misclassified by the classifier than samples away from the boundary. MWMOTE selects minority classes on the border based on their weighting factors. The larger the minority class weight, the greater the probability of being selected. Additionally, it samples the selected minority classes. ADASYN generates synthetic samples for minority classes that are easily misclassified. Moreover, the bigger the weight of minority classes on the boundary, the more synthetic samples generated. These over-sampling methods try to avoid generating noisy samples; however, numbers and regions of synthetic samples to generate are limited, since synthetic samples are only allowed to be introduced near the centre of minority samples clusters or the boundaries.
## 2 End-to-End QoS in WSNs
### Related Research  
The related research mainly aims at protocols that ensure reliable data delivery either via multipath routing [12],[13] or with a transport protocol that is suitable for WSNs [14, 15, 16, 17]. These proposals introduce protocols that adapt to the network conditions, but the actual causes for the unreliability are not addressed in the literature.  
Due to the data centric nature of the WSNs, few papers define the reliability as a probability that data is sensed in every region of the monitored area with a required minimum accuracy. In [18], reliability is defined based on estimated data generation intervals and the probability of a node failure. A model that analyzes reliability when several nearby nodes fail simultaneously due to the same reason is presented in [19]. The effect of the number of active reporting nodes to the reliability is modeled in [20]. The paper finds an optimal number of reporting nodes noting that too many nodes increase traffic and thus cause collisions and data loss, while too few nodes do not provide enough information.  
An empirical study on reliability in outdoor and indoor WSNs is presented in [6]. The study finds that a network commonly has unequal link error rates and packet loss rates worse than 50% are common with heavy traffic. The results are based on Berkeley motes and TinyOS MAC that uses Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol. The authors note that a CSMA protocol with virtual carrier sensing, such as S-MAC [2], can eliminate hidden node problems and thus have better performance. While the study presents practical behavior in WSNs, it does not consider causes or offer remedies to the unreliability.  
Buffer space requirements when disseminating data from a single source to multiple nodes are studied in [21]. The paper proposes a buffer management algorithm that aims to reduce buffer requirements by buffering a packet only in a subset of all nodes. While the resulting delivery latencies and reliability is studied, the paper does not consider the effect of link errors. A queuing model for mesh networks utilizing sleeping is presented in [22]. The model considers the trade-off between wakeup probability, traffic arrival probability, queue size, and latency. In [23], end-to-end latency and reliability when transmitting a batch of packets is modeled. The effect of different single hop retransmission policies and PER is analyzed.  
[PERSON] _et al._[24] study acknowledgments, buffering, bit error rate, and encoding schemes in WSNs. Reliability is evaluated using a CSMA-based MAC with flooding, Ad hoc On demand Distance Vector (AODV), and geographic routing protocols. The study suggests using hop-by-hop acknowledgments, message buffering, error correction scheme, and adjusting radio transmission range based on network density to reduce interference. While the study considers link errors, it does not evaluate the end-to-end latency caused by the different schemes.
## 2 Operation and Control
### Communications and Automation  
Automation and communications must evolve to face the current challenges of the \(SG\), as well as future challenges. Potential subareas will be explained:  
* The current scenario has a large volume of data through the communications network associated with the SG. Therefore, the communications infrastructure must be robust, secure and reliable.
* The protocols that govern the elements of automation and communication must be standardized. This standardization will allow a better operation and control of the SG.
* The scenario based on big data arises in the SG, since the existence of a large volume of data and heterogeneous, requires the presence of advanced management tools. Big data must conform to the needs of the SG.  
After the review of the works, the author has considered grouping them in the following subsections: Advanced Metering Infrastructures, Big Data, Communication Architecture, Standards and Communications Protocols and Automation and Control and New Conditions.
## 6 Simulation Results and Analysis
### Simulation Results and Analysis
These findings confirm the algorithm's robustness under dynamic bandwidth constraints and validate its practical applicability in real-world spaceborne communication environments. These extended experiments further support that the AIFG algorithm not only provides adaptive framing under differentiated QoS demands but also maintains high levels of delay control, bandwidth efficiency, and throughput under fluctuating MPDU configurations and varying relay link conditions. This reinforces the algorithm's suitability for deployment in dynamic space networking environments with heterogeneous service profiles. \begin{table}
\begin{tabular}{c c c c} \hline
**MPDU Size (Bytes)** & **Average Delay (ms)** & **Multiplexing Efficiency (\%)** & **Throughput (Kbps)** \\ \hline
800 & 7.23457 & 96.110 & 559.608 \\
1200 & 9.27896 & 98.992 & 566.066 \\
1500 (Default) & 10.77100 & 99.494 & 566.453 \\
1800 & 12.05860 & 97.593 & 525.173 \\ \hline \end{tabular}
\end{table} 
Table 6: Performance comparison under different MPDU sizes (delay requirement: 20 ms). \begin{table}
\begin{tabular}{c c c c} \hline
**Relay Link Bandwidth (Between Satellites) (Mbps)** & **Average Delay (ms)** & **Multiplexing Efficiency (\%)** & **Throughput (Kbps)** \\ \hline
3 & 11.0130 & 96.588 & 566.859 \\
6 (Default) & 11.3376 & 99.523 & 575.183 \\
9 & 11.0255 & 97.558 & 569.503 \\
15 & 11.0319 & 96.951 & 567.063 \\
20 & 11.2763 & 99.044 & 574.157 \\ \hline \end{tabular}
\end{table} 
Table 7: Performance comparison under different satellite link bandwidth (delay requirement: 20 ms).