esa-sceva/satcom-chunk-collection huggingface dataset:
# From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission
## 1 Introduction
### Triton Satellite Mission  
The Triton satellite is an experimental micro-satellite designed and manufactured by NSPO and was planned along with the FS-7 program. Hence, in the beginning, it continued to use the FORMOSAT serial number and subjoined a letter \"R\" for easier identification (FS-7R). In late 2018, the name \"Triton\" was officially given due to the GNSS-R mission. In addition to the GNSS-R mission, the Triton satellite mission is conceived as a technology demonstration mission, with the goal of demonstrating some key satellite bus technologies, including the onboard computer (OBC), the power control unit (PCU), H\({}_{2}\)O\({}_{2}\) propulsion, a fiber optical gyro, a space-grade GPSR, a micro-stepping solar array driving module, and some associated components. These are designed and supplied by domestic industries in order to increase the technology readiness level (TRL) through flight heritage so that a baseline design of a satellite platform with self-reliant components and technologies could be provided for future NSPO missions [10]. However, due to the reduction in the FS-7 program's scope (down from 12 + 1 satellites to 6 satellites), the Triton program became less dependent on the FS-7 program, even though the Triton satellite shares some common components with FS-7 satellites, such as a solar array, an S-band transceiver, a reaction wheel, a magnetometer, a coarse sun sensor, magnetic torquers, and a battery. The mass of the Triton satellite is less than 285 kg, and its deployed and stowed configurations are depicted in Figure 4. The dimension of the stowed configuration is approximately \(100\times 120\times 125\) cm. After separation from the launch vehicle, the solar panel will be deployed in space to generate an average power of 162 W.  
In this paper, the design and development of the in-house built space-grade NSPO GPSR will be described in the following section, which also includes the performance and environmental tests. This is followed by a presentation of the design, development, and features of the GNSS reflectometry payload. Some unique features of the NSPO self-developed GNSS-R payload will also be highlighted. In order to verify the design of the NSPO's self-developed GNSS-R receiver, several airborne flight experiments have been performed. The data analysis including link budget and data comparison would be  
Figure 3: Data distribution for 24 h coverage by FORMOSAT-7/COSMIC-2 (FS-7) constellation.  
discussed as well. The conclusion and the updated status of the Triton mission would be presented in the end.
## 5 Conclusions  
RL has been studied in various forms to train learning objects to achieve desired goals. It is proposed to design an algorithm to apply RL by mapping an environment with high complexity and time-sensitive dynamic changes to the network topology.  
To learn dynamically changing network topologies effectively, we designed a system to evaluate the status and update the rewards through human intervention. Unlike in the existing methods, quantitative and clear evaluations are made using real numbers. In addition, the reward value evaluated from human intervention is calculated by applying the SDG algorithm to establish an adaptive update strategy to improve the learning speed. This RL method is stable and effective and enables accurate reward updating through human intervention. After that, the system rewards and adaptive rewards estimated from the human evaluations are calculated and updated accordingly. To demonstrate the effectiveness of this technique, a basketball game was mapped to a competing network topology and investigated experimentally. The proposed framework was compared with the existing RL methods (binary evaluation, the SARSA method, and the MDP method) in the software environment. The proposed adaptive human evaluation strategy converged to the maximum reward value the fastest and produced a high Q-function value.  
In future research, methods of effectively learning two or more opposing objects in a physical environment should be considered, as interventions that deliver human evaluations directly to learning objects in physical environments must be designed with more sophisticated and advanced reward updating strategies.  
Methodology, J.K. and H.L.; software, J.K.; validation, J.K. and H.L.; formal analysis, J.K.; investigation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, J.K. and H.L.; visualization, J.K.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2018R1A6A1A03024003).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Adaptive Human** \\ **Evaluation Strategy** \\ \end{tabular} & \begin{tabular}{c} **Binary Evaluation** \\ \end{tabular} & \
\begin{tabular}{c} **SARSA** \\ \end{tabular} &
\begin{tabular}{c} **MDP** \\ \end{tabular} \\ \hline Human evaluation & Real number & Binary number & No intervention & No intervention \\ \hline Human intervention rate & \(\delta=0.8\) & \(\delta=0.8\) & No intervention & No intervention \\ \hline Average reward & 23.57 & 17.72 & 16.01 & 13.54 \\ \hline Q-function value & 24.78 & 21.51 & 19.14 & 16.12 \\ \hline Convergence time & & & & \\ of maximum reward value & \(t=9\) & \(t=87\) & \(t=97\) & \(t=92\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results of four RL methods.
## 3 CNQF Architecture  
The authors of [1] emphasize the distinction between _QoS mechanisms_ which support service differentiation (that determine _how_ to achieve performance objectives for heterogeneous flows) and _QoS management and control_ (concerned with _who_ should be entitled to preferential treatment from the network). CNQF mainly addresses the latter (QoS management/control) through its framework architecture which can be adapted to support heterogeneous underlying _QoS mechanisms_ (such as DiffServ, MPLS, WiMAX QoS, UMTS QoS _etc._) that may be deployed in the access and/or core segments of the transport layer. The logical subsystems that make up the CNQF framework include: _Resource Management Subsystems (RMS), Measurement and Monitoring Subsystem (MMS) and Context Management and Adaptation Subsystems (CAS)_. As shown in Figure 1, CNQF has distributed functional entities whose instances coordinate the resources of the transport network to enable closed-loop, scalable, end-to-end QoS control and resource management in converged networks (by leveraging existing QoS mechanisms in the transport plane).
## 4 Results and Discussion  
### Simulation of Different Construction Methods  
#### 4.1.1 Construction Impact Displacement Analysis  
In order to analyze the displacement response characteristics of the model after pile excavation, the X-direction and Z-direction displacements of the pipeline generated by the traditional percussive drilling pile excavation method and rotary drilling method are analyzed, respectively. The X-direction and Z-direction displacements of the oil pipeline by the two construction methods are shown in Figures 6 and 7, respectively.  
As can be seen from Figures 6 and 7, for the right bearing pile foundation construction of Wengcun No. 1 Bridge, the maximum horizontal displacements caused by conventional percussive drilling and rotary drilling for the pipeline were 2.04 mm and 0.22 mm, with a displacement increment of 1.82 mm, and the vertical displacements were 3.83 mm and 4.12 mm, with a displacement increment of 0.29 mm.  
In particular, in order to verify the accuracy of the simulation, displacement monitoring points in X and Z directions were added at a point closest to the pile foundation, and then the same monitoring points were added 1 m before and after that point, with the layout shown in Figure 8.
# Moodle LMS Integration with Amazon Alexa: A Practical Experience
## 4 Usability Evaluation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Survey Question** & **Possible Answers** \\ \hline Have you ever used a voice-activated device, similar to Alexa & Yes \\ Echo devices? & No \\  & Never \\  & Seldom \\ If so, how often do you use those devices? & Weekly \\  & Several times per week \\  & Daily \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial survey to students (Extract taken from [PERSON] et al. [23]). Finally, a feedback survey was designed and given to the students at the end of the course. This survey consisted of one multi-choice question and a second one with dichotomous reply options (yes/no), with three more open-ended questions. We did not implement reliability analysis as the survey aimed to get a qualitative opinion from the students (see Table 4). We gathered answers from 43 students (70.5% from the total amount of participants). The feedback given to us by these students allowed us to better understand their personal experience when interacting with the Alexa skill, as well to collect information on how to further improve it. One of the most interesting outcomes of the survey was to find out that a vast majority of the students (81.4%) would like to receive events through an Alexa-enabled device, but only 6 of the surveyed students (13.9%) did answer they used the Alexa skill to do so (See Figure 10). Figure 8: Screenshot of the Analytics section within the Alexa Developer Console. Figure 9: Statistical data deviated from initial survey (Source: own authors’ elaboration). Students would like to have more content on the current Alexa skill, which can enable them to get information regarding all their courses (different activities within the course, exam dates, material release dates, essay deadlines, etc. ), as well as other information regarding the school, such as upcoming special events (expositions, talks and conferences, etc.). In contrast, the survey indicates that Moodle is seen as a very complete and useful platform as is. Finally, the answers to the survey suggest that students are not as familiarized with voice technology as with other current technologies, as, for example, mobile apps, and voice technology is not yet integrated in their daily lives. This unfamiliarity is reflected in the existence of a certain level of insecurity from the students when it comes to utilizing technology, which in the case of Alexa devices, requires listening to the user continuously in order to activate and initiate the interaction with the user.
# Chinese Neural Question Generation: Augmenting Knowledge into Multiple Neural Encoders
## 4 Experiments
### Comparative Neural Question Generation Models  
We have explored the following models to compare with prior work from question generation in the literature:  
1. Trans-Rules: the rule-based Chinese question generation system [7], which transforms a declarative sentence into an interrogative question by using predefined rules. We modified the code so that it can generate questions based on a given passage and the answer.
2. s2s+ Att: baseline encoder-decoder based seq2 seq network with attention mechanism [12].
3. NQG++: the state-of-the-art neural question generation system extends s2s+Att by adding answer position and linguistic features to the embedding layer and a copy mechanism [11]. The Chinese word embeddings are pre-trained on large-scale Chinese Wikipedia and Baidu dataset [45].
4. NQG++MP: extends NQG++ model by adding a maxout pointer mechanism with a gated self-attention encoder [16].
5. NQG++PC: extends NQG++ by adding the passage compression step described in Section 3.1.
6. BERT-SQG: uses the pre-trained BERT model to encode passage and previous decoded question words and a single fully-connected layer to decode question words based on the hidden state of last token on the last layer in the BERT model [19].
7. mT5: uses the pre-trained mT5-base model, where the encoder handles the input passage and answer, and the decoder generates question words [41]. Bert4 keras library is used for the model implementation [46].
8. Single-encQG: our proposed model with only one BERT encoder compressed passage, answer and triples, and deploys a double attention-based decoder combining with the question word BERT representation.
9. Multi-encQG +TA/P: our proposed model with two encoders: one BERT encoder for passages and another BERT encoder for triples from the knowledge graph and answer embeddings.
10. Multi-encQG +PA/T: our proposed model with two encoders: one BERT encoder for passages and answer, and another BERT encoder for triples from the knowledge graph.
11. Multi-encQG +PT/A: our proposed model with two encoders: one BERT encoder for passages and triples, and another BERT encoder for the answer.
12. Multi-encQG ++: our proposed model with three encoders for passages, triples and answers, respectively.
13. Multi-encQG-triples: ablation test; the triple BERT encoder is removed from MultienQG++.
14. Multi-encQG-answer: ablation test; the triple Answer BERT encoder is removed from Multi-encQG++.
# Diagnosis of the Hollow Ball Screw Preload Classification Using Machine Learning
###### Abstract  
The prognostic diagnosis of machine-health status is an emerging research topic. In this study, the diagnostic results of hollow ball screws with various ball-nut preloads were obtained using a machine-learning approach. In this method, ball-screw pretension, oil circulation, and ball-nut preload were considered. A feature extraction was used to determine the hollow ball-screw preload status on the basis of vibration signals, servo-motor speed, servo-motor current signals, and linear scale counts. Preloads with 2%, 4%, and 6% ball screws were predesigned, manufactured, and operated. Signal patterns with various preload features, servo-motor speeds, servo-motor current signals, and linear scale counts were classified using the support vector machine (SVM) algorithm. The features of the vibration signal were classified using the genetic algorithm/k-nearest neighbor (GA/KNN) method. The complex and irregular model of the ball-screw-nut preload could be learned and supervised using the driving motion current, ball-screw speed, linear scale positioning, and vibration signals of the ball screw. The experimental results indicate that the prognostic status of the ball-nut preload can be determined using the proposed methodology. The proposed diagnostic method can be used to prognosticate the health status of the machine tool.  
ball screw; ball-nut preload; feature extraction; genetic algorithm; k-nearest neighbor; prognostic diagnosis; servo-motor current; support vector machine
## 3 Results and Discussion
### Assay 2: Effect of Healthy Stuffing in Acrylamide Concentration and Consumers Acceptance
Recent research has found similar results for the antioxidant capacity of these hydrocolloid fillers [43; 45]. As for the contaminant concentrations, there were no significant differences for the fillers 'Oregano' and 'Pimientos,' 'Garlic' and 'Thyme,' and for 'Mojo Picon' and 'Eggplant' at dilution 1. Similarly, there were no statistical differences for 'Sangaree' and 'Pimientos,' and for 'Garlic,' 'Mojo Picon,' 'Oregano' and 'Eggplant' for dilution 2 (Figure 3). The values observed after experimentation are consistent with the relationship between antioxidant compounds such as phenolic compounds and acrylamide levels. Previous research by the group has shown the inverse relationship between these two types of compounds. The higher the concentration of phenolic compounds, the greater the attenuation that occurs in acrylamide formation after high-intensity heat treatments [18; 45]. Therefore, the value of using a healthy filling in the preparation of California-style ripe black olives can be appreciated. Figure 3: Total phenol (mg GAE 100 g\({}^{-1}\)) of ‘Hojiblanca’ table olive variety stuffed with different aromatic hydrocolloids. Different lowercase letters mean a statistically significant difference between stuffed aromas ([PERSON]’s test \(p<0.05\) n = 3). Different capital letters mean significant differences between the different dilutions ([PERSON]’s test \(p<0.05\) n = 3). As for the organoleptic characteristics of the samples, the sensory analysis performed for dilutions 1 and 2 is shown in Table 4.  
For the panelists, Control 1 and 2 were rated as statistically equal in attributes such as 'Olive' and 'Fruity,' but their defect of 'Cooked' was considered statistically non-significant. Significant differences were observed for all fillers in almost all attributes, with the exceptions of the 'Dry Odor' and 'Cooked' defect. In the case of the 'Thyme' filler, it was found that for both positive and negative attributes presented by the sample, dilution 1 and 2 were statistically equal. With respect to the positive attributes, all stuffed olives presented the attributes of 'Olive' and 'Fruity,' with 'Herb' being the next most common and, finally, 'Wine' only presented in the 'Sangaree' stuffing. As for the negative attributes, 'Sangaree' and 'Thyme' were the fillings with the most attributes according to the tasters, although all at levels below 3.2. For all these reasons, the 'Mojo Picon,' 'Eggplant' and 'Garlic' fillings were the most appetizing to the tasters and, consequently, the ones that received the highest overall rating. The best rated filling was 'Mojo Picon' with 8.5 and 9.2 for dilution 1 and 2 respectively. The use of healthy fillers has been previously proposed together with the use of by-products accepted by consumers, looking for a sensory profile absent of bitterness and with adherence in the human diet [9; 23; 43].
## 6 Thematic Taxonomy of UWSNs
### Applications
#### 6.5.1 Scientific  
A wide range of applications for underwater wireless sensor networks in the scientific field is classified as environmental monitoring, ocean sampling, and, particularly, Great Barrier Reef activities. The environmental monitoring application serves to monitor the amount of pollution such as chemical and biological that is deposited on the seabed [2,8] and to conduct water quality observation that involves participation from people who are affected by using real-time notifications [45]. Moreover, in [12], robotic fish have been employed to measure oxygen levels in water as well as for pressure and temperature monitoring [70]. An ocean sampling application reported in [17] focuses on monitoring a large coastal area to study the ocean phenomena by applying underwater sensor vehicle technology at several locations. The collected data will be sent to the shore automatically for further observations. In [21], the researchers present a coral reefs application that combines the technology of the sensor network, big data, and the Internet of Things (IoT) to study the effects of ocean salinity, temperature, humidity, and pressure on coral bleaching and marine ecosystems. Long-term marine environmental monitoring can also be implemented using a combination of different types of agents and communication. The research team [105] conducted experiments at Biograd Na Moru, Croatia by combining Autonomous Surface Vehicles (ASVs), highly mobile artificial fish, and artificial mussels for data collection purposes.  
Figure 8: Applications of UWSNs.
## 10 Discussion  
### Choosing a Rational f(m) Function  
The use of the \(f(m)\) function (recall Equation (11)) in Equation (12) is deemed necessary because a variation of shear stress in the form of:  
\[\tau_{xz}=\tau_{xz,OB}\!\left(\frac{x}{x_{OB}}\right)^{m}, \tag{54}\]  
leads to unrealistic stress components in Zone II [34; 35]. The problematic behavior of Equation (54) is better interpreted considering that the one of the two halves of the symmetrical sand heap has been replaced by a rigid retaining wall which is translating away from the other half (half sand heap) until the latter to reach the active state (see Figure 5). During this translational movement, the outer zone of the sand heap (Zone I) gradually fails as it gradually loses its support; in this respect, the failure of the inner zone (Zone II, zone between line OB and OC in Figure 5) precedes. Thus, the shear stress \(\tau_{xz}\) on OB is not constant and equal to \(\tau_{xz,OB}\) but a portion of \(\tau_{xz,OB}\) which becomes lower and lower as the soil heap fails, i.e., \(\tau_{xz}=f(m)\cdot\tau_{xz,OB}\); \(f(m)\) ranges between 1 and 0 for the state at rest and the active state respectively.  
\(f(m)\) could have the following more generalized form:  
\[f(m)=1-\frac{a}{m}. \tag{55}\]  
However, if \(a>1\), for values of \(m\) close to unity Equation (55) leads to intermediate earth pressures (on the active side) greater than the respective earth pressures at rest; something that is not admissible. For any \(0<a<1\) Equation (55) gives intermediate earth pressures less conservative as compared to those obtained from Equation (12). Thus, Equation (12) seems to be the best choice for the function \(f(m)\).  
The two assumed shear stress functions given by Equations (11) and (54) are compared through the coefficient \(K_{xe}^{c-\varphi}\). The data used were \(c^{\prime}=0\) or 20 kPa, \(\varphi^{\prime}=30^{\circ}\), \(\gamma=18\)\(kN/m^{3}\), \(k_{h}=0.3\), and \(k_{v}=k_{h}/2\). As it is shown in Figure 6, Equation (11) is not only more rational (as explained above), but also gives more conservative results as compared to Equation (54).
# Surface Defect Detection Methods for Industrial Products: A Review
## 3 Surface Defect Detection Method of Industrial Products Based on Deep Learning
### Weakly Supervised Method
#### 3.3.2 Inexact Supervision Method  
Inexact supervision focuses on situations where the monitoring information is given, but the information is imprecise, that is, only contains coarse-grained labels. For more tasks that contain pixel-level labels, image-level labels are coarse-grained labels.  
Literature [77] based on the original ResNet-50 network, deleted the original fully connected layer and pooling layer and added two 1 \(\times\) 1 convolutions at the end of the network to obtain the corresponding feature map of the defect, which realized that only through the image tag completes the preliminary detection of cracks on the solar panel. Literature [78] developed a WSL framework composed of localization network (LNet) and decision network (DNet) for steel surface defect detection, where LNet uses image-level label training and outputs the heat map of potential defect locations as DNet input, DNet uses RSAM to weight the regions identified by LNet, the performance of the proposed framework is proven on real industrial data sets.  
At present, weakly supervised methods are relatively rare in the field of surface defect detection of industrial products, but due to the advantages of both supervised learning methods and unsupervised learning methods at the same time, the application prospects of this type of method are also broad.