esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
2-VP, 2-Vinylpyridine; 4-HB, 4-hydroxybenzoic acid; 4-VP, 4-vinylpyridine; AA, acrylic acid; ABCHC, 1,1-azobis(cyclohexanecarbonitrile); AIBN, 2,2'-azobisisobutyronitrile; AM, acrylamide; APTES, 3-aminopropyltriethoxysilane; APTMS, 3-aminopropyl trimethoxysilane; AT, allythiourea; BET, Brunauer-Emmett-Teller; BisAA, \(N\),\(N\)-methylebasicrylamide; BMPA, isobutyl methylphosphonate; CH\({}_{3}\)CN/ACN, acetonitrile; CHCl\({}_{3}\), chlorofoam; CMPA, cyclohexyl methylphosphonate; DCF,diclofenac; DCM, dichloromethane; DES, diethylstilbestrol; DHT, 5-dihydrotestosterone; DMAEA, 2-dimethylaminoethyl acrylate; DMF, dimethylformamide; DTA, differential thermal analysis; DTG, derivative thermogravimetry; DVB, divinylbenzene; EDMA, ethylene dimethylacrylate; EGDMA, ethylene glycol dimethylacrylate; EMPA, ethyl methylphosphonate; FM, functional monomer; FTIR, fourier-transform infrared spectroscopy; H\({}_{2}\)O, water; HAc-MeOH, acetic acid-methanol; HEMA, 2-hydroxyethyl methacrylate; HPLC, high-performance liquid chromatography; IAP, IL anion-exchange polymers; IMAP, ionic liquid-based molecularly imprinted anion-exchange polymer; IMPA, isopropyl methylphosphonate; IR, infrared; MAA, methacrylic acid; MeOH, methanol; MIPMs, molecularly imprinted polymer microspheres; MISPE, molecular imprinted solid phase extraction; MMIP, magnetic molecularly imprinted polymer; MISM, molecularly imprinted silica monolithic; MIPs, molecularly imprinted polymers; MPA, methylphosphonic acid; MRDT, maximum rate decomposition temperature; MUF, melamine-urea-formaldehyde; Mw, molecular weight; NIPMs, non-molecularly imprinted polymer microspheres; NIPs, non-inprinted polymers; NSAIDs, non-steroidal anti-inflammatory drugs; OPP, organophosphorus pesticides; PMPA, pinacolyl methylphosphonate; PrG, propyl gallate; PTA, pentaerythnitol triacrylate; SEM, scanning electron microscopy; SPE, solid phase extraction; SSA, specific surface area; TEOS, tetraethoxysilicane; TEOS, tetraethyl orthosilicate; TG, thermogravimetric analysis; THF, tetrahydrofuran; TMPTA, trimethylolpropane triacrylate; TRIM, trimethylolpropane trimethacrylate.
# DRUNet: A Method for Infrared Point Target Detection
## 3 Proposed Network  
### Network Architecture  
CNN-based methods for detecting small infrared targets face two main challenges.  
1. Unlike visible images, infrared images lack information, such as color, texture, and contours, especially in deep networks, where small targets are easily overwhelmed by complex environments or confused with pixel-level impulse noise.
2. Resolution and semantics conflict. A complex low SNR ratio background often obscures small infrared targets. Deep networks learn more semantic representations by gradually shrinking feature sizes, which is inherently counterintuitive because they are learning more semantic representations by gradually decaying the feature size. To detect these small targets with a low false alarm rate, the network must have a high-level semantic understanding of the whole IR image.  
In this paper, the network design is considered from three aspects for the detection task of small infrared targets.  
1. Down-sampling scheme: Many studies emphasize that the acceptance domain of predictors should match the target scale range when designing CNNs. If the down-sampling scheme is not recustomized, it is difficult to retain the features of small IR targets as the network goes deeper [26]. Therefore, our proposed method maintains an up-sampling rate of 2 and a down-sampling rate of 0.5 when performing feature extraction.
2. Suitable image attention enhancement module: Since ordinary visible image targets are relatively large and the pixels occupied by the targets are more widely distributed in the graph, the existing attention modules tend to aggregate global or long-term contexts. However, infrared small targets occupy fewer pixels, so it is necessary to choose the appropriate module when using attention modules; otherwise, it is impossible to optimize the network performance while increasing the model complexity.
3. Feature fusion methods: Feature fusion is mostly studied in a one-way, top-down manner to fuse cross-layer features and to select appropriate low-level features based on high-level semantics. However, only using top-down modulation may not work because small targets may already be overwhelmed by the background.  
Based on the above three points, we propose an effective IR small target detection algorithm called DRUNet (Dense-Rest-UNet) network. Figure 1 shows the overall structure of the algorithm proposed in this paper; it can be divided into two parts: the feature extraction network and the prediction head. The feature extraction network structure is similar to the dense nested network structure of UNet++ [27], but unlike UNet++, we propose an attention enhancement module based on the residual structure called ECA-Rest Block, which becomes the base block of the dense nested network. We design the prediction head to predict the feature map obtained from the feature extraction network and generate a heatmap to predict the coordinates of the target location. The next part of this section will explain each part of the algorithm in detail.
# Exploring the Impact of Multitemporal DEM Data on the Susceptibility Mapping of Landslides
## 6 Conclusion  
In the present paper, DEM data for 1992, 2000, and 2009 are obtained to evaluate the susceptibility of landslides. Based on the DEM-dependent factors and the same DEM-independent factors, the susceptibility results are evaluated using LRM.  
From the assessment, we find that the results are different by using DEM data from different times. The rapid decrease in the area of level IV from 2000 to 2009 is noticeable. The RNA for level IV in 2009 is larger than in 1992 and 2000. The area of increased susceptibility is much larger than that of decreased susceptibility based on the results of the assessment level differences from 1992 to 2000, which differ from the results from 2000 to 2009.  
From the assessment results, we conclude that the DEM data have an impact on the susceptibility assessment in the study area. It is also worth noting that the influences of the specific values of the elevation differences on the assessment results are obtained. Meanwhile, the influence of historical landslide events on the susceptibility assessment is obtained by analyzing the hazard data and the differences between the assessment results. The most obvious finding to emerge from the analysis is that the assessment level of the area with historical landslide events decreases. The present study goes some way towards enhancing our understanding of the impacts and the mechanism of multitemporal factors on susceptibility.  
[PERSON], [PERSON], and [PERSON] designed the study. [PERSON] and [PERSON] performed the in situ investigation. [PERSON] and [PERSON] wrote the manuscript. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the National Key R&D Program of China (Grant No. 2018 YFC1505401), the National Natural Science Foundation of China (Grant No. 51478483, [PERSON]; and Grant No. 41702310, [PERSON]), and the Natural Science Foundation of Hunan (Grant No. 2018 JJ3638) These financial supports are gratefully acknowledged. Thanks to the Scholarship of Central South University for Undergraduate Students Studying Abroad.  
The authors declare no conflict of interest.
## 7 Simulations and Results
### Patient Attendances  
Figure 7 shows a comparison of actual (black) data compared to ARIMA (green), Prophet (yellow) and GRNN (purple) predictions for the Patient Attendances indicator. The observed data contain a greater variance than Patients in Department, and this can alter the performance of prediction models. The COVID-19 pandemic caused a similar change in data behaviour to Patient Attendances, and GRNN was also the only model that quickly adapted to the change and accurately predicted the timeseries variables during this period.  
Figure 4: Comparison of Actual and ARIMA with residual distribution chart, January 2020-November 2020.  
Figure 5: Comparison of Actual and Prophet with residual distribution chart, January 2020-November 2020.  
The following figures breakdown this information and show the residual distributions.  
Observing the residuals charts at the bottom of Figures 8-10, in the pre-lockdown phase ARIMA appears to be the best fit model as the residuals are concentrated closer to the x axis, whilst GRNN appears to have greater residuals and less accuracy in this phase. However, when the pandemic changed the data, GRNN was the only one of the three models to quickly adapt and predict with accuracy almost instantaneously, whilst both ARIMA and Prophet overestimated for approximately a 5 weeks period. In the post-lockdown phase, after all three models learned the new data behaviour, the performance is similar.
# Research Challenges for the Internet of Things: What Role Can OR Play?
## 3 How can OR Support the Future IoT?
### Self-Organizing Systems and the IoT  
Self-organizing systems are systems that evolve into ordered systems from interactions among components of an initially disordered system. Self-organization concepts date from the 1940s and 1950s when neural networks were first proposed [97]. Scale-free computer networks can be self-organizing. CAS include self-organization as one of their characteristics. Wikipedia is an example of a self-organizing system; there is no central editor and numerous, mainly anonymous contributors [106]. It is continually created and updated with its reference articles tending to become more comprehensive and reliable due to editing. Blogs can also be considered as self-organizing systems [106].  
[PERSON] and [PERSON] [107] showed how self-organization is needed to organize smart appliances in an IoT application such as a smart home to enhance usability and thus gain consumer support. Object recognition, IoT sensing devices and network integration are all required for such self-organization. [PERSON] et al. [108] described a self-organized system for online shopping. The online system self organizes, adapting to users' needs by monitoring IoT data to analyze user behavior and modify the price and presentation of products and services.  
IoT networks will need to be self-organizing [109] so that they can continue to operate as designed. IoT devices must monitor their environment so that when a failure occurs, they can connect to neighboring devices and cooperate, establish communication paths and then recover from local faults to restore normal operations. Challenges include cross layer design for self-organization, heterogeneity in computation and communications of IoT networks, multi-channel radio communications, load balancing, and delay tolerant networking.
## 4 Evaluation
### Evaluation and Comparison Framework
Then, the NOMA utilization ratio is given by: \(|S|/(N_{CH}\times N_{RSU})\). * CPU time (s): CPU time represents the time taken to compute the decisions, measured in seconds. MATLAB's built-in tic and \(\tt{toc}\) functions were used for measurement [36]. Specifically, the tic function records the current time before the method begins execution and the \(\tt{toc}\) function calculates the elapsed time using the recorded value, immediately after the method completes execution. When each of the considered methods is invoked to compute a decision, the tic function is called before the method begins, and \(\tt{toc}\) function is called immediately after the method finishes, to measure the time taken for computation. Results were validated across diverse configurations and compared against baseline methods. Each scenario configuration was evaluated and compared over 10 iterations, with the corresponding results reported as mean and standard deviation values, where available.
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 1 Introduction
The prime node notifies _agreed_ transactions to the coordinator. Finally, the coordinator sorts all common and agreed transactions by time order and requests the prime node to generate a new block containing all of processed transactions. For the evaluation of our approach, we conducted three experiments for answering three research questions. We measured performance of the PBFT algorithm with and without our approach. In an experiment, the PBFT equipped with our approach obtained an average of 4.75 times better performance than only using the PBFT. In addition, we applied our approach to _Hyperledger Besu_ using the IBFT (Istanbul Byzantine Fault Tolerance) consensus algorithm and showed a 61.81% performance improvement, compared to using only IBFT. We also presented correlation of performance and trouble transactions associated with transaction issue interval and the number of blockchain nodes. The contributions of our approach are summarized as follows:  
* We propose a novel coordination technique to improve the scalability and performance of consensus algorithms, which is applicable to diverse BFT-based consensus algorithms. * Our approach was implemented and applied to PBFT and Hyperleder Besu consensus algorithm, opened as an open source project for public access. * We performed three experiments in response to three research questions and showed the feasibility of our approach. The remainder of this paper is organized as follows. Section 2 presents BFT-based consensus algorithms as background and related work for improving the scalability of BFT-based consensus algorithms. Section 3 proposes our coordination technique and explains four steps for achieving the scalable BFT-based consensus algorithm in detail. Section 4 presents the evaluation of our approach by responding to our three research questions. Section 5 concludes our paper and discusses future works.
###### Abstract  
Degradation rate is an important property to evaluate bioabsorbable metallic material; however, values vary depending on the method of measurement. In this study, three different methods of measuring corrosion rate are compared. The degradable samples to analyze corrosion rates include pure magnesium (Mg), lab produced Mg-Zn-Ca alloy (47-7-2), Mg-Zn-Zr-RE (rare earth) alloys (60-13, 60-14), Mg-Zn-Ca-RE alloy (59B), and pure zinc (Zn). A audiometer was used to measure hydrogen evolution from the reaction of degradable alloys in Hank's Balanced Salt Solution (HBSS). Corrosion rates based on volume loss of tested alloys in 30 days were calculated using Micro-computed tomography (micro-CT). Final mass change due to corrosion and corrosion removal was measured with a scale. We observed that the corrosion rates indicated by hydrogen evolution were high initially, and slowed down sharply in the following measurements. The corrosion rates of tested alloys calculated by volume loss and mass loss from high to low are: 60-13 \(\approx\) 60-14 \(\approx\) 47-7-2 \(>\) 59B \(>\) Mg \(>\) Zn (\(p<\) 0.05). The results provide instruction to experimental methodology to measure corrosion rates of degradable alloys.  
corrosion rate; mass loss; volume loss; hydrogen evolution; magnesium; zinc +
Footnote †: journal: Applied Sciences
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 2 User Stories
### Story: Medicine Dispenser  
A patient with prescribed medication would benefit from having a medicine dispenser device to remind them and keep track of when the medicine needs to be taken. Depending on the type of medicine, the system would prevent doubling the dosage when the care receiver does not notice or ignores the alerts and does not take the previously dispensed dose on time. A healthcare provider would benefit from a centralized system that would track compliance with the prescribed treatment. The doctor would use the same system to adjust the prescribed frequency of the medicine already in the dispenser. Thus, it is required for the dispenser to be connected to the system via the healthcare gateway. The pharmacy and the distributor would have a complete inventory. They will refill all the medicine dispensers in the area with minimal trips while ensuring that all medicine will be taken before the expiry date.
# Commuted PD Controller for Nonlinear Systems: Glucose-Insulin Regulatory Case
## 2 Materials and Methods  
### Dynamical Model of the Glucose-Insulin Regulatory System  
The main objective of this paper is to design a robust PD controller for an artificial pancreas able to stabilize the glycemia blood level around its basal value and prevent hypoglycemia from appearing when the administrated insulin is not well assimilated by the patient.  
In the recent literature, there are some glucose-insulin regulatory mathematical models [29]. In this paper, we consider the minimal model called the _Bergman minimal model_[30], widely used in literature (see, for instance [4; 5; 6; 10] and references therein). This is due to its simplicity. This model is as described follows:  
\[\dot{x}_{1}(t) = -p_{1}(x_{1}(t)-G_{b})-x_{1}(t)\cdot x_{2}(t)+D(t), \tag{1}\] \[\dot{x}_{2}(t) = -p_{2}x_{2}(t)+p_{3}(x_{3}(t)-I_{b}),\] (2) \[\dot{x}_{3}(t) = -n(x_{3}(t)-I_{b})+\gamma[x_{1}(t)-\sigma]^{+}t+u(t), \tag{3}\]  
where \(x_{1}(t)\), \(x_{2}(t)\) and \(x_{3}(t)\) are the blood plasma glucose concentration (mg/dL), the insulin's effect on the net glucose disappearance (1/min) and the insulin concentration in plasma (\(\mu\)U/mL), respectively. \([x_{1}-\sigma]^{+}\) means that when \(x_{1}(t)\) is greater that \(\sigma\), the value is taken to be \([x_{1}-\sigma]\), otherwise, its value is zero. [PERSON] et al. [30] describe this factor as the feedback response of pancreatic insulin to variations in glucose concentration in the bloodstream. \(G_{b}\) and \(I_{b}\) are the basal value levels of pre-injection level of glucose (mg/dL) and the pre-injection level of insulin (\(\mu\)U/mL), respectively. These values can be also called the subject's baseline glycemia (\(G_{b}\)) and the subject's baseline insulinemia (\(I_{b}\)) [19]. Remark that \(\sigma\) is greater than \(G_{b}\). \(D(t)\) represents the exogenous factors, and \(u(t)\) defines the insulin injection rate being the control input. Parameters \(p_{1}\), \(p_{2}\), \(p_{3}\), \(n\) and \(\gamma\) are the system parameters. These are described, for instance, in [6; 19]. Additionally, \(x_{1}(0)\), \(x_{2}(0)\), and \(x_{3}(0)\) are the corresponding initial conditions. Finally, \(x_{1}(t)\) is the available output system for the control algorithm.  
The term \(D(t)\) represents the rate at which glucose enters to the bloodstream from intestinal absorption after food intake. In oral glucose tests with nondiabetic subjects, the model aims to produce the desired effect that the plasma glucose level rises too rapidly (from the resting level) to a maximum in less than 30 min and then falling to the base level after 2-3 h [20]. Moreover, the exponential function also produces the desired behavior for the model.  
According to [6], \(D(t)\) is supposed to be reduced to zero or to some constant value in finite time, and it represents the rate at which glucose is absorbed by the regulatory system. One possible mathematical representation of \(D(t)\) is as follows [4; 6; 18; 20]:  
\[D(t)=Ae^{-Bt},\quad B>0. \tag{4}\]  
Therefore, the mathematical model (1)-(3) will be taken as a reference pancreas system.
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 1 Introduction  
At the 8 th session of the Maritime Safety Committee (MSC) in March 1964, the IMO first discussed automation in ships [1]. Since then, development and research on the automation of ships have been ongoing, and now the maritime industry is preparing for the operation of MASSs based on technology development. Several MASS research projects have been developed, conducted and are ongoing globally: Maritime Unmanned Navigation through Intelligence in Networks [2]; DNV-GL Revolt [3]; Kongsberg maritime autonomous shipping [4]; NYK Maritime Autonomous Surface Ships Trial [5]; Korea Autonomous ship project [6]; Yara Birkeland [7]. The first autonomous cargo ship will be in operation soon after 2020.  
Various research projects and studies on MASSs are facing challenges because of the complicated operation concept and new technologies. To identify and address these issues, IMO has conducted MASS regulatory scoping exercise (RSE) in order to accommodate technological development for MASS [8] and considered human factors, especially for an autonomous ship without crew [9]. There has been much research to analyze problems and challenges and to suggest possible solutions: problem identification in the remote control center for MASS operation [10]; design-based solutions and operation-based solutions in order to resolve the human factor challenges [11]; MASS application of current STCW 1978 convention requirements [12]; deep learning as a decision support system for MASS operation [13]; an artificial neural network decision-making system to help optimize the use of fuel for ships [14]; STPA for the risk assessment of autonomous ships [15]. Currently, these studies are being conducted in a wide variety of fields. Therefore, it can be very helpful for researchers who are performing related research to check how far research has gone on various fields of MASSs that are currently underway and to suggest future research directions based on this.  
In this study, necessary technologies and considerations are identified through a literature review for practical operation of the MASS, and the future direction of the research should be based on the identification of the current challenges facing the safe operation of MASSs. To this end, this study basically considers eight techniques, which were identified in a previous study [16] for MASS operation, that are different from conventional vessels. The results of the IMO MASS RSE were analyzed in order to identify further issues, taking into account the importance of the application of the IMO international convention. In addition, this study analyzes decision support systems, designs of MASSs, the communication system and cybersecurity of MASSs, maintenance and repair, hazard analyses for MASSs that were identified through previous studies, and IMO MASS RSE. Through this, this study will cover all issues regarding MASS operation and suggest the direction of research.
###### Abstract  
In this paper, a winding inter-turn fault (ITF) diagnosis algorithm for electrical machines in integrated motor and drive (IMD) systems is proposed based on impedance analysis using space voltage vectors. An ITF alters the stator resistance, causing an imbalance in the motor's impedance depending on the phase connection. This impedance asymmetry can be effectively utilized for fault diagnosis. However, in IMD systems, direct impedance measurement through mechanical terminal access is difficult due to the integrated structure. To address this, the impedance of the induction motor is analyzed electrically, without the need for physical disconnection, allowing practical implementation within integrated systems. The specific phase angle of the space voltage vector in the three-phase inverter is analyzed to replicate the electrical conditions of mechanical terminal configurations. Based on this approach, a fault diagnosis algorithm was developed by analyzing the variation in stator current and impedance with respect to space voltage vector angles. The effectiveness of the proposed method was verified through experimental validation using a 12 kW three-phase induction motor and terminal box.  
fault diagnosis; integrated motor and drive (IMD); inter-turn fault (ITF); three-phase inverter; space voltage vector +
Footnote †: journal: Applied sciences