esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Edge Computing for the Internet of Things in Aerospace
## 2 Related Works
### Satellite Constellation for IoT  
The typical satellite system that has been built for IoT is Orbcomm, ARGOS, LoRaWAN, etc.  
Orbcomm is a commercial global low-orbit satellite communication system. The system uses packet switching to achieve bidirectional short data transmission, providing global low-rate wireless data communication services [15]. It consists of 47 satellites, each of which covers a surface with a radius of 5100 km [16]. With the Orbcomm satellite communication system, users can carry out applications, including remote data collection, system monitoring, tracking and positioning of vehicle and mobile facilities, the transmission of short messages, sending and receiving emails, etc.  
ARGOS (the Advanced Research and Global Observation Satellite) is a satellite communication system for data collection and positioning established by France and the United States. The system uses satellites to transmit various environmental monitoring data and locates the carrier of the measuring instrument. It provides an excellent means of communication for hydrological and meteorological monitoring instruments at high latitudes [17]. ARGOS is a typical space IoT system that uses satellite networks to interconnect people, platforms, and sensors. It can quickly, accurately, and extensively collect water temperature and salinity profiles from 0 to 2000 m in the global ocean. It helps to understand the real-time changes of the ocean in a more detailed way, improve the accuracy of climate and ocean forecasting, and effectively defend against the threats posed by the increasingly severe global climate and marine disasters.  
Inmarsat Plc and LPWAN equipment manufacturer Actility jointly developed the LoRaWAN-based IoT in January 2017. The LoRaWAN network is the world's first global IoT network. With a backbone of terrestrial and satellite networks, the Inmarsat LoRaWAN network can deliver on its strategy to bring the IoT to every corner of the globe. At present, its early applications cover asset tracking, agribusiness, and oil and gas.  
Other planned satellite IoT systems are described as follows. Australian space technology company Fleet plans to launch 100 nanosatellites between 2018 and 2020. The US communications company Kepler plans to complete the deployment of a space network consisting of 140 Ku-band nanosatellites in 2022. HeliosWire plans to launch 30 satellites to build a space IoT, supporting 5 billion sensors with 30 MHz bandwidth. Russia's SPUTNIX plans to deploy about 200 IoT satellites in low Earth orbit in 2025.  
These IoT satellites can provide data communication services for sensors or ground equipment. They collect data from ground devices and forward them to the terrestrial cloud. However, the capability of processing in-orbit is weak. It will result in a large amount of communication data and a high latency.
## 2 Copula-Based Bayesian Reliability Models
### Characterizing the Component Dependence  
In reliability engineering, the assumption of component dependence is more realistic and therefore important in practice. In simplified terms, the concept of stochastic dependence implies that the state of some units can influence the state of others. However, the state can be given by the age, failure rate, failure probability, state of failure, or other condition measures. Several categories of stochastic dependence exist in the reliability engineering literature for two-component systems (see, e.g., [26]). For example, one of the categories (i.e., the Type I failure interaction) has to do with failure interactions which imply that the failure of Component 1 may induce the failure of Component 2 with a given probability (constant or time dependent) and the other way round. On the other hand, the Type II failure interaction means that every failure of Component 1 acts as a shock to Component 2, without inducing an instantaneous failure, but affecting its failure rate.  
The failure of the system considered in this study was formulated using a Poisson process with rate \(\Lambda_{1}\) for Component 1 and a Bernoulli process for Component 2 with conditional probability \(P_{2}\) given the failure of Component 1. It was assumed that \(\Lambda_{1}\) and \(P_{2}\) were distributed with probability distributions \(g_{1}(\lambda_{1})\) and \(g_{2}(p_{2})\), respectively. A joint probability prior distribution for the parameters (i.e., failure rate and failure probability) of the distributions (i.e., Poisson and binomial failure models) that describe the failure mechanism of the elements of the system mentioned above was constructed using the Bayesian reliability analysis approach. Subjective belief was applied for the value of these parameters to incorporate the uncertainty in the prior belief associated with the failure rate and the failure probability.  
Here, it is important to note that two interpretations of the prior distributions are considered in Bayesian reliability analysis. When empirical data are well available, prior distribution may be motivated empirically on the basis of observed data. In this case, it might be easier to justify the equivalence of dependence between reliability characteristics to the existing dependence between subjective beliefs about the reliability characteristics.  
However, when facing the situation characterized by lack of data, subjective interpretation might need to be considered and the degree of belief might not be given a frequency interpretation. While this subjective interpretation might be more controversial among reliability analysts, exploiting all a priori insights might still be very useful when empirical data is lacking or when dealing with rare events.
## 2 Related Work  
### The Classical Token Bucket Algorithm  
In the process of processing service requests on the single-server GIS platform, tasks are randomly submitted by clients, and there is usually no correlation or priority difference among these tasks. To address the bottleneck problem of task processing in single-server high-concurrency scenarios, existing research primarily relies on the classical token bucket algorithm [24], which regulates the processing rate of tasks by controlling the tokens, thereby reducing task processing latency and preventing sudden high-concurrency situations that could cause severe server blockage or downtime. Figure 1 illustrates the key steps of this method:  
1. The client sends requests, and each request forms a task on the server. All tasks form a queue, and they are processed according to FCFS (First-Come, First-Served) [25].
2. A token bucket with a capacity of \(b\) tokens is set up, and tokens are provided at a rate of \(r\) tokens per second. If the bucket is full, excess tokens are discarded.
3. Each arriving task must obtain a token from the bucket before it can be sent. After the task is sent, the token is removed from the bucket.
4. The server receives and processes the tasks which obtain tokens. If no token-holding tasks are waiting in the queue or if the waiting time exceeds a predefined timeout, the tasks are dropped.  
Figure 1: Principle of token bucket algorithm.  
It can be found that in the token bucket-based traffic-shaping approach for highly concurrent tasks, the rate \(r\) at which the system provides tokens is very important, and this value along with the load state of the system determines the speed at which the system can process task requests under high-concurrency conditions.
* When the task is relatively simple, the single task occupies fewer system resources, and it is appropriate to set a larger \(r\) value at this time to improve the system resource utilization and the average processing rate of the total task.
* When the task is more complex, the single task occupies more system resources; at this time, it is appropriate to set a smaller \(r\) value to improve the processing rate of the single task and the average processing rate of the total task.
# Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies
## 3 Laboratory Tests and Numerical Approach Method
### Mathematical Modeling Method
Then, based on the percolation model, they established a mathematical model of the release judgment condition by considering the initiation pressure gradient. Finally, combined with the gas-water two-phase flow mechanism, they derived the release time of the water-phase trap closure. The calculated release time of the water-phase trap closure in production using this model differed from the actual release time by only 0.1%.
# Assessment of Contact Pressures between a Mandibular Overdenture and the Prostodontic Area
## 1 Introduction  
Overdentures are used in rehabilitating the edentulous mandible. They are an economic compromise available to a wide range of patients [1, 2]. Their relatively affordable price and simple clinical management, together with substantial improvements to retention and stability, make them an attractive and realistic treatment option, both for the patient and the dentist. However, the multitude of available types of overdentures [3, 4, 5, 6] with respect to the number and locations of implants, as well as kinds of attachments (bars of various size, ball or \"locator\" attachments, telescopes), make a rational choice of the components difficult [7, 8]. Despite the abundant literature, restoration design is often based on clinical tradition without considering theoretical foundations. Such negligence accompanied by performance failures or the lack of follow-up care may lead to unexpected and uncontrolled prosthesis rotation. The clinical implications of the above phenomena may not only include deviations in precision attachment mechanics and a change at the level of peri-implant tissues but may also lead to atrophy of the denture-bearing areadistally to the implants. According to [PERSON]'s law, atrophy is caused by excessive pressure on the prosthesis, referred to as \"contact pressure\" in the research terminology of mechanics [9]. In the method applied in this study, the authors use the above term to describe the areas of strain and relief in a model of the edentulous mandible restored with the overdenture supported by two implants and a bar. A situation was assumed where the edentulous maxilla was fitted with the complete denture.  
In this paper, we assess the pressure between the overdenture located in the mandible supported by a bar retained on two implants and the prosthodonic area. Furthermore, the contact pressure distribution relative to the stiffness of polypropylene matrices was also studied.
# Observing APOD with the AuScope VLBI Array
## 3 Scheduling and Observations
However, a final schedule iteration is recommended shortly before a session, as the observation times may slightly change with updated orbit elements. VieVS was updated for the APOD observations to generate the station-specific AZEL tracking files enabling the AuScope antennas to continuously track satellites (see Section 2.1). The data points for the tracking files were determined in a 1 s interval with the calculations being based on the latest APOD orbit predictions provided by BACC about 12 h before an experiment. The observation plans were written to VEX formatted schedule files (see [[http://www.vlbi.org/vex/](http://www.vlbi.org/vex/)]([http://www.vlbi.org/vex/](http://www.vlbi.org/vex/))), which are standard in VLBI. They contain a full description of the experiments, including all receiver and recorder settings for the whole antenna network, and the observation time schedule. The participating stations extract all relevant information from the VEX file using the Field System program drudg. Drudg creates station-specific control files which contain all required commands to run the experiment fully automated by the Field System, including antenna motion control, calibration routines, and the setup of the signal chain. Only one major modification from this standard procedure was required to enable satellite tracking: Prior to satellite scans, the tracking mode of the ACUs had to be manually switched over from the star tracking mode, which is used per default for astronomical sources, to the AZEL tracking mode described in Section 2.1, and the prepared AZEL tracking files had to be loaded. These setup changes had to be done individually for all observing antennas. Manually changing the tracking mode only affected the antenna motion control, while the signal chain was still controlled by the Field System based on the setup parameters defined in the VEX file. To incorporate these additional steps, about 5 min of idling time was defined in the observation schedules prior and after APOD tracking. The raw data were recorded with Mark5B+ recorders and were shipped to the computation cluster in Hobart for correlation and post-processing.
###### Abstract  
The real-time precise point positioning (RT PPP) technique has attracted increasing attention due to its high-accuracy and real-time performance. However, a considerable initialization time, normally a few hours, is required in order to achieve the proper convergence of the real-valued ambiguities and other estimate parameters. The RT PPP convergence time may be reduced by combining quad-constellation global navigation satellite system (GNSS), or by using RT ionospheric products to constrain the ionosphere delay. But to improve the performance of convergence and achieve the best positioning solutions in the whole data processing, proper and precise variances of the observations and ionospheric constraints are important, since they involve the processing of measurements of different types and with different accuracy. To address this issue, a weighting approach is proposed by a combination of the weight factors searching algorithm and a moving-window average filter. In this approach, the variances of ionospheric constraints are adjusted dynamically according to the principle that the sum of the quadratic forms of weighted residuals is the minimum, and the filter is applied to combine all epoch-by-epoch weight factors within a time window. To evaluate the proposed approach, datasets from 31 Multi-GNSS Experiment (MGEX) stations during the period of DOY (day of year) 023-054 in 2018 are analyzed with different positioning modes and different data processing methods. Experimental results show that the new weighting approach can significantly improve the convergence performance, and that the maximum improvement rate reaches 35.9% in comparison to the traditional method of priori variance in the static dual-frequency positioning mode. In terms of the RMS (Root Mean Square) statistics of positioning errors calculated by the new method after filter convergence, the same accuracy level as that of RT PPP without constraints can be achieved.  
real-time precise point positioning; convergence time; ionospheric delay constraints; precise weighting 2018 8 (2018) 2537 doi:10.3390/app8122537 /
# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 1 Introduction
However, this algorithm fails to balance the needs of different services, as it tends to prioritize time-sensitive (high-QoS) services, leaving some low-QoS services without sufficient transmission resources for extended periods, thereby failing to meet their basic service quality requirements. In summary, given the challenges of existing load balancing methods in simultaneously ensuring load balance and maintaining the service quality of time-sensitive services, this paper proposes a satellite Internet service scheduling algorithm based on the spatiotemporal distribution of service traffic. First, based on the spatiotemporal traffic factor, a shortest-delay path is constructed for time-sensitive services, with bandwidth resources reserved. Second, the load balancing problem for non-time-sensitive services is modeled as an integer linear programming problem, which is NP-hard and difficult to solve within polynomial time. On this basis, a novel link weight based on the link load accumulation factor is designed, transforming the above optimization problem into a routing problem. This allows non-time-sensitive services to fully utilize satellite network resources for data transmission, thereby avoiding contention for resources needed by time-sensitive services. Additionally, a Virtual Private Network (VPN)-based implementation scheme is designed, where VPN updates are triggered periodically, enabling time-based load balancing. This ensures network scalability and supports the deployment of this solution in large-scale networks. The second section of this paper establishes the system model and the spatiotemporal distribution model of service traffic. The third section presents the corresponding mathematical models. The fourth section describes the graph load balancing routing algorithm based on link weight characterization and its implementation. The fifth section provides a simulation of the proposed algorithm. The sixth section discusses the application scenarios and limitations of the proposed algorithm, and the seventh section concludes the paper.
## 3 Experimental Results and Validation
### Sensitivity and Quality Factor  
Sensitivity and quality factors are very important sensor parameters. Equations (4) and (6) were used for the calculation of sensitivity and quality factors, where \(f_{c}\) is the center resonance frequency, and \(f_{b}\) is the bandwidth frequency [36]. The bandwidth frequency was obtained using Equation (5).  
\[Q=\frac{f_{c}}{f_{b}} \tag{4}\]  
\[f_{b}=f_{h}-f_{l} \tag{5}\]  
where \(f_{h}\) and \(f_{l}\) are higher and lower frequencies at \(-3\) dB of the center frequency, respectively. The sensitivity of the sensor was calculated using Equation (6) [36].  
\[S=\frac{f_{empty}-f_{sample}}{f_{empty}\;(\epsilon_{r}-1)} \tag{6}\]  
where \(f_{empty}\) is the unloaded resonant frequency and \(f_{sample}\) is the OUT resonant frequency. The highest sensitivity for our sensor was 4.6, whereas the highest quality factor was 39.28.
###### Abstract  
The effects of fermentation with lactic acid bacteria (LAB) and yeast on the aroma of samples were analyzed in this work. The volatile features of different soybean hydrolyses were investigated using both GC-MS and GC-IMS. Only 47 volatile flavor compounds (VFCs) were detected when using GC-IMS, while a combination of GC-MS and GC-IMS resulted in the identification of 150 compounds. LAB-yeast fermentation could significantly increase the diversity and concentrations of VFCs (\(p<0.05\)), including alcohols, acids, esters, and sulfurs, while reduce the contents of aldehydes and ketones. Hierarchical clustering and orthogonal partial least squares analyses confirmed the impact of fermentation on the VFCs of the hydrolysates. Seven compounds were identified as significant compounds distinguishing the aromas of different groups. The partial least squares regression analysis of the 25 key VFCs (ROAV \(>\) 1) and sensory results revealed that the treatment groups positively correlated with aromatic, caramel, sour, overall aroma, and most of the key VFCs. In summary, fermentation effectively reduced the fatty and bean-like flavors of soybean hydrolysates, enhancing the overall flavor quality, with sequential inoculation proving to be more effective than simultaneous inoculation. These findings provided a theoretical basis for improving and assessing the flavor of soybean protein hydrolysates.  
soybean protein hydrolysates; lactic acid bacteria; yeast; volatile flavor compounds; GC-MS; GC-IMS Article
## 4 Discussion
First, this study is limited to adult females aged 18-35 years, and further research is needed to explore cervical curvature in males and other age groups to improve generalizability. Second, the seventh cervical vertebrae were excluded from the analysis. In practice, seeing the lower cervical spine on routine lateral cephalometric radiographs is difficult. While three methods were used to address this limitation, the exclusion of C7 may impact the completeness of cervical curvature evaluation. To improve the evaluation of cervical curvature, future studies should consider adjusting imaging techniques to include the lower cervical spine. Third, various factors may influence cervical spine alignment, necessitating further investigation into biomechanical elements, such as daily life habits or job. Despite these limitations, this study is pioneering in illustrating the variance in cervical curvature types across vertical facial patterns. It has also been established that craniofacial features contribute to craniocerical posture and cervical curvature, although the exact nature of the cause-and-effect relationship remains unclear. Cervical posture is associated with facial soft-tissue aesthetics, while cervical spine curvature affects health-related quality of life [25]. Consequently, this study suggests that clinicians should meticulously evaluate craniocerical posture and cervical spine curvature using routine lateral cephalometric radiographs prior to orthodontic treatment.
## 3 Research Methodology
### Quality Assessment Method  
Unlike other metrics, decision quality is evaluated through a methodologically rigorous, multi-faceted approach designed to overcome the limitations of subjectivity encountered in previous research. The procedure can be broken down into the following key steps:  
**Initial Author Evaluation:** Each of the 2205 decision statements across all participants was initially assessed by one of the study's authors. The evaluations were completed using a Likert scale ranging from 1 to 5, with 1 representing the lowest quality and 5 representing the highest quality. These assessments established a foundational baseline for the study.  
**Machine learning Validation--Naive Bayes:** the study further trains and tests a Naive Bayes model using textual decision statements for each participant to validate the initial author evaluations concerning the quality of the decision statements.  
**Participant-Level Aggregation:** A single aggregated quality score for each of the 45 participants was calculated following the initial author evaluations. An average of 49 individual decisions corresponding to each participant were assessed by the authors in this study and were computed to generate this aggregated quality score. This aggregate score represents the cumulative decision quality for each participant during the experiment.  
**Expert Validation:** To further strengthen the robustness of these quality measurements, the averages of all quality scores for the 45 participants were subsequently reviewed and then validated by domain experts in the field. This triangulation between initial author assessments and expert evaluations significantly reduces subjectivity, hence strengthening the reliability of this study's findings [99; 100; 101].