esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Response Models
### Model Fitting
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{8}{c}{D-Flow Response Models} & \multicolumn{8}{c}{T-Funnel Response Models} \\ \hline  & **Full** & **Fractioned A** & **Fractioned B** & **Small** & **Full** & **Fractioned A** & **Fractioned B** & **Small** \\ \hline \multirow{2}{*}{**Model Terms**} & **D-Flow** & **D-Flow** & **D-Flow2** & **D-Flow** & **1/(T-Funnel)** & **1/(T-Funnel)2** & **1/(T-Funnel)2** & **1/(T-Funnel)1** \\  & [mm] & [mm] & [mm] & [mm] & [mm] & [mm\({}^{-1}\)] & [mm\({}^{-1}\)] & [mm\({}^{-1}\)] & [mm\({}^{-1}\)] \\ \hline Independent & 343.75 & 338.36 & 1.18\(\times\)108 & 343.9 & 0.0688 & 0.0684 & 1.7845 & 0.0683 \\ Vw/Vc & 8.92 & 4.5 & 5415.09 & 6.22 & **0.0115** & **0.0103** & **0.6005** & **0.012** \\ Sp/p & 4.58 & NS & 1681.59 & 5.69 & 0.005 & 0.0035 & 4.9076 & 0.0035 \\ Vw/Vp & **19.42** & **16.28** & **11 336.31** & **18.69** & **0.0248** & **0.0223** & **2.8959** & **0.0266** \\ Vs/Vm & \(\underline{-}\)**34.92** & \(\underline{-}\)**37.37** & \(\underline{-}\)**22 962.6** & \(\underline{-}\)**34.7** & \(\underline{-}\)**0.0302** & \(\underline{-}\)**0.0325** & \(\underline{-}\)**1.4802** & \(\underline{-}\)**0.0281** \\ Vs/Vs & \(-\)1.42 & NS & \(-\)403.69 & NS & \(-\)0.0016 & NS & \(-\)0.1572 & 0.0003 \\ (Vw/Vc) \(\times\) & \(-\)5.36 & NS & \(-\)3375.31 & NS & NS & NS & \(-\)0.7822 & NS \\ (Vw/Vc) \(\times\) & \(-\)2.89 & NS & NS & NS & \(-\)0.002 & NS & NS & 0.0047 \\ (Vw/Vc) \(\times\) & NS & 4.51 & 2211.16 & 5.16 & \(-\)0.0037 & \(-\)0.0031 & NS & \(-\)0.0028 \\ (Vw/Vp) \(\times\) & 3.11 & NS & NS & NS & NS & NS & 0.003 \\ (Vw/Vp) \(\times\) & 2.98 & NS & 1966.69 & NS & \(-\)0.0061 & \(-\)0.007 & NS & \(-\)0.0111 \\ (Vs/Vs) & 1.89 & NS & NS & NS & NS & NS & NS \\ (Sp/p) \(\times\) & 1.89 & NS & NS & NS & NS & NS & NS \\ (Sp/p) \(\times\) & NS & NS & NS & NS & NS & NS & 0.0055 \\ (Vs/Vs) & NS & NS & NS & NS & NS & NS & NS & NS \\ (Vw/Vp) \(\times\) & \(\underline{-}\)**20.67** & NS & \(-\)2257.79 & \(\underline{-}\)**19.79** & \(-\)0.0017 & NS & \(-\)0.4979 & \(-\)0.002 \\ (Sp/p)2 & NS & NS & 1038.64 & NS & NS & NS & NS & NS \\ (Vw/Vp)2 & NS & NS & \(-\)1037.04 & NS & 0.0011 & NS & \(-\)1.1401 & 0.0011 \\ (Vs/Vm)2 & NS & \(\underline{-}\)**13.86** & \(\underline{-}\)**5924.12** & NS & NS & \(-\)0.0019 & \(-\)3.0757 & NS \\ (Vs/Vs)2 & NS & NS & \(-\)978.97 & NS & \(-\)0.0014 & \(-\)0.0012 & \(-\)0.1183 & \(-\)0.0013 \\ \hline \multicolumn{8}{c}{Error term (\(\epsilon\))} \\ R2 & 0.9858 & 0.9830 & 0.9954 & 0.9885 & 0.9922 & 0.9944 & 0.9947 & 0.9975 \\ Adj-R2 & 0.9802 & 0.9793 & 0.9910 & 0.9808 & 0.9897 & 0.9916 & 0.9912 & 0.9942 \\ Predicted R2 & 0.9683 & 0.9720 & 0.9753 & 0.9506 & 0.9827 & 0.9847 & 0.9709 & 0.9794 \\ \hline \multicolumn{8}{c}{NS: Non-significant term. The values in bold are the three most significant parameters and the underlined ones are the most significant terms.} \\ \end{tabular}
\end{table} 
Table 12: Fitted models for mechanical properties F,24h and Rc,24h for Full, Fractioned and Small Central Composite designs (design variables in coded values).
## 2 Background
### Bandit Theory  
The classic _Multi-Armed Bandit_ problem can be formulated as follows. The agent repeatedly chooses from a set of actions (arms) with rewards that are initially unknown and observes the associated reward. In the next iterations, the agent uses the knowledge it has gained during the previous iterations. The goal of the agents is to minimize a regret function, which is mathematically formulated as the difference between actual accumulated rewards (with unknown statistics) and the optimal reward [14]. In parallel with optimizing the accumulated reward, the agent aims to learn about the environment. The optimal policy consists of always choosing the action with the maximum expectation of reward. Intuitively, the faster an agent identifies the best action, the less regret it is likely to have.  
Bounding the regret can be realized by balancing the tradeoff between exploration and exploitation. Exploration can be defined by examining arms that have not been used before. If the agents overspend their resources by excessively exploring actions, they lose long-term rewards and increase their regret. [PERSON] et al. [15] proved a lower bound for the number of times that each arm needs to be pulled to learn about the environment. This translates into an instance-independent lower bound of the regret of the agents. This lower bound is usually used as a measure to assess the robustness of an exploration policy.  
Unlike the classic \(\varepsilon\)-greedy, modern exploration policies such as the _Upper Confidence Bound_ (UCB) bring about fast convergence and optimal results. The intuition behind the UCB algorithm is based on a principle called optimism in the face of uncertainty. Agents employing this algorithm always take the action that is as large as plausibly possible.  
[PERSON] et al. [16] proved that UCB achieves sublinear regret in time, outperforming the linear regret associated with \(\varepsilon\)-greedy and matching the lower bound achieved by [PERSON] [15].  
Depending on how the problem is formulated, bandit theory proposes different solutions and methods, including non-stationary bandits, adversarial bandits, and contextual bandits. Non-stationary bandits can be applied when the distribution governing rewards is dynamic. Secondly, for the adversarial version, the distributions not only are non-stationary, but also, an adversary is trying to work against the agent by reducing the rewards. The algorithms that are designed for these classes of problems are Discounted UCB and EXP3, respectively. For the contextual bandits, the agent associates one model to each context, and when it detects the context, it uses its associated model to choose the action.
###### Abstract
As a result, unlike remote control systems in which the sensors present information in a form that is as operator-friendly as possible, in a UAV, a system platform that performs the autonomous mission usually possesses an onboard navigation system instead of sensors of various physical nature. So, the measurement results should be converted into input signals of the control system, which requires other approaches. At the same time, in an autonomous flight, the observing system should be able to search for characteristic objects in the observed landscape and give the control system their coordinates and estimate the distances between them [7]. So, it is very important to study how to produce a decision/control that can be executed directly by the onboard control system. As far as a path planning application is concerned, not only should the direct impact of various constraints on the UAV movement be considered, but also the real-time control instructions should be calculated according to feedback information that might have an effect on the current motion state of the UAV. Furthermore, the planning system sometimes could not sense the current exact state completely because of the threats and the unknown environment as well as the error and imperfection owing to the precision of the sensors and the current position, velocity, acceleration, and poses of the UAV to be estimated according to the action strategy it adopted. As a result, not only the uncertainty of the actions but also the uncertainty of the states should be considered in the actual path planning for UAV; this happens to accord with the partially observable problem, and belongs to the category of partially observable [PERSON] decision process theoretical research. Consequently, a dynamic path planning solution framework for UAV constructed based on Q-learning theory is suitable, and in this solution framework, we consider the effects of the uncertainty environment along with the optimal control strategy and the dynamics of the UAV together during the path planning. This is in line with the application of UAV path planning, and further improves the navigation accuracy. The rest of this paper is organized as follows. First, some related work is introduced in Section 2. Then, Q-learning theory and a path planning model based on Q-learning strategy are described in Section 3; meanwhile, a path planning solving framework for UAV based on Q-learning strategy is constructed. In this framework, the uncertainty owning to the environment is considered as well as the effect of the movement changing after the UAV adopted the planned control strategies. Following that, the state transition of the path planning for UAV is described, the cost function is defined, and strategy creation for UAV path planning based on Q-learning theory is elaborated on in Section 4. The findings of the method proposed in this paper are illustrated with simulations and analysis in Section 5. Finally, Section 6 concludes this paper.
## 2 Literature Review
### Unified Theory of Acceptance and Use of Technology (UTAUT)  
According to [PERSON] [14], UTAUT is one of the most intensive and well-developed models used for testing the acceptance of technology adoption. [PERSON] further notes that this model is robust and trusted for use with different types of technology. Furthermore, the model is not overly complicated, as it includes a limited number of constructs and moderating variables, thereby facilitating an examination of the acceptance of technology integration.  
Based on the above description, this study applies UTAUT [15] to create a model built on four variables, which reflect the behavioural intention to use technology [16]. Each variable measures different perspectives of the acceptance of technology use. [PERSON] et al. [17] define these variables as follows: performance expectancy (PE) is \"the degree to which an individual believes that using the system will help him or her achieve gains in job performance\"; effort expectancy (EE) is \"the degree of ease associated with the use of a system\"; social influence (SI) is \"the degree to which an individual perceives that important people believe they should use the new system\"; and facilitating conditions (FC) are \"the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of a system\". Use of this theoretical framework is common among studies that investigate participants' perceptions of using different kinds of technology for teaching and learning. In a systematic review investigating studies on the impact of technology use around the world from the perspective of UTAUT, the resulting data effectively predicted technology adoption in higher education settings [18].  
Nevertheless, UTAUT is a model that has not been tested extensively on EFL courses, although one recent study in Saudi Arabia examined the continuity of English teachers' use of online education in elementary schools in the Kingdom [19]. Through a multiple regression analysis of the four factors mentioned above, the results revealed that performance expectancy, social influence, and facilitating conditions can predict teachers' use of online learning.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 2 Watermarking Algorithm in Hybrid Domain
### The Design of the Adaptive Embedding Depth
Five groups of audio segments are obtained according to the value of \(M_{l}\) from large to small, and each group contains \(M/5\) audio segments which are named from Group 5 to Group 1, respectively. Finally, \(M/5\) bit binary watermarks are embedded into each group. The experimental results about SNR and BER (under the Gaussian noise with 20 dB) are shown in Figure 2.  
As shown in Figure 2, under the same embedding depth, the SNR of Group 5 with the larger amplitude is lower than the other groups, but the BER is superior to the other groups, which indicates that imperceptibility is not good but the accuracy of the watermark extraction is very high when the watermark is embedded in this audio segment with larger average amplitude. Therefore, a smaller embedding depth can be selected in Group 5 to enhance the imperceptibility of the algorithm. The SNR of the Group 1 with the smaller amplitude is higher than the other groups, but the BER is inferior to the other groups, which indicates that when the watermark is embedded in this audio segment with smaller average amplitude, the imperceptibility is good but the accuracy of the watermark extraction is not high. Therefore, a larger embedding depth can be selected in Group 1 to improve the robustness. Those experimental results in Figure 2 are consistent with the above theoretical analysis results. The embedding depth can be adjusted according to the overall average amplitude of each audio segment so as to balance among imperceptibility and robustness. The embedded depth \(\lambda_{l}\) can be set up by the following formula:  
\[\lambda_{l}=a+(b-a)\frac{Max-M_{l}}{Max-Min} \tag{31}\]  
where \(a,b\in(0,1)\), \(a<b\), \(1\leq l\leq M\), \(Max\) and \(Min\) are the maximum and minimum of \(M_{l}\).
# Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management
## 3 Method
### Multi-Modal Spatio-Temporal Knowledge Graph Storage
#### 3.3.1 Time Expression Model Based on Neo4j Graph Database  
Time expressions in spatio-temporal data models can be categorized into three types: interval-based methods, point-based methods, and time-based methods. Interval-based methods partition time into discrete intervals, which are defined by their relationships, such as 'before' or 'after'. Point-based methods represent time as specific moments when entity objects exist or events occur. In this study, a time-based method that integrates both point-based and interval-based approaches is employed. The interval-based method is expressed using a link table in the graph database, while the point-based method is represented using a timeline tree. By combining these two techniques, a comprehensive time-based method is achieved, enabling the direct inclusion of time as an attribute of node entities based on the domain model.
## 2 Optimization of the Positioning Method of the Nano-Scale Double-Helix Point Spread Function
### Algorithm Simulation and Optimization Results
Through the iterative optimization process, the main lobes of the double spot become more rounded, the connection between the spots becomes weaker, the stray light range of the side lobes decreases, and the energy intensity is lowered. The efficiency of the main lobe of the spot increased from 35.07 to 53.26%. The energy dispersion induced by the side lobe is significantly reduced. After the optimization process, the main lobes become clearer.
###### Abstract  
In this study, a multi-input, multi-output-based artificial neural network (ANN) was constructed by classifying output parameters into different groups, considering the physical meanings and characteristics of product quality factors in the injection molding process. Injection molding experiments were conducted for bowl products, and a dataset was established. Based on this dataset, an ANN model was developed to predict the quality of molded products. The input parameters included melt temperature, mold temperature, packing pressure, packing time, and cooling time. The output parameters included mass, diameter, and height of the molded product. The output parameters were divided into two cases. In one case, diameter, and height, representing length, were grouped together, while mass was organized into a separate group. In the other case, mass, diameter and height were separated individually and applied to the ANN. A multi-task learning method was used to group the output parameters. The performance of the two constructed multi-task learning-based ANNs was compared with that of the conventional ANN where the output parameters were not separated and applied to a single layer. The comparative results showed that the multi-task learning architecture, which grouped the output parameters considering the physical meaning and characteristics of the quality of molded products, exhibited an improved prediction performance of about 32.8% based on the RMSE values.  
injection molding; artificial neural network (ANN); Multi-input multi-output (MIMO); multi-task learning; quality prediction +
Footnote †: journal: _Applied_  
_aciences_
## 3 Results and Discussion
### Seasonal Variations in Biophysical Factors and CH\({}_{4}\) Fluxes
The CH\({}_{4}\) fluxes decreased gradually in the reproductive stage and remained low thereafter. The average CH\({}_{4}\) fluxes during the vegetative stage, reproductive stage and ripening stage were 0.498, 0.058 and 0.006 umol m\({}^{-2}\) s\({}^{-1}\), respectively. Figure 4: Seasonal variations in (**a**) net solar radiation (R\({}_{\rm a}\)); (**b**) T\({}_{\rm a}\) and soil temperature at 5 cm depth (T\({}_{\rm s}\)5) and (**c**) soil water content at 5 cm depth (SWC). Figure 5: Seasonal variations of (**a**) CH\({}_{4}\) flux, (**b**) ecosystem respiration (R\({}_{\rm eco}\)), (**c**) GPP, and (**d**) net ecosystem exchange (NEE) during flooded (gray shaded area) and non-flooded (non-shaded area) periods. Black rectangles indicate periods of mid-season drainage in each season. [PERSON] et al. [49], [PERSON] et al. [50] and [PERSON] et al. [34] observed that the maximum daily CH\({}_{4}\) flux was 0.355 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) (from a single-season paddy field in Yancheng, China), 1.15 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) (from a rice-wheat rotation agroecosystem in Nanjing, China) and 0.69 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) (from a double-cropping paddy field in Yiyang, China) by EC. [PERSON] et al. [66] observed that the maximum daily CH\({}_{4}\) flux from a double-cropping paddy field in the Philippines during the growing period from 2013 to 2014 was 0.03 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\), 0.27 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) and 0.46 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). The maximum daily CH\({}_{4}\) flux observed by [PERSON] et al. [1] in Korea was 0.27 \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\). The maximum daily CH\({}_{4}\) flux observed from late rice in Nanchang is higher than that of many places, while the maximum daily CH\({}_{4}\) flux of early rice is comparable to that of other places. The reason is that high temperatures and sufficient water during the growing season of late rice were favourable for the production of CH\({}_{4}\).
## 4 Conclusions  
The proposed new model takes into account the time-varying helix angle, the transverse pressure angle, the gear pair center distance, and the contact ratio. The model consists of finite element shafts with a helical gear pair and thrust bearing. The Lagrange method was used to derive the equations of the motion of the system, which were solved by the numerical integration method. Compared with the results of other researchers, the time-varying verifications of the spur gear pair, the natural frequencies, and corresponding mode shapes of the geared rotor-bearing system obtained by the present approach are very consistent. The time-varying helical-geared rotor-bearing system dynamic characteristics with 3-D motion due to rotating shaft deformation were examined. Some advantages of the proposed model are summarized as follows:  
1. This research proposed a new nonlinear dynamic model that has a time-varying helix angle, transverse pressure angle, gear pair center distance, and contact ratio. These time-varying variables provide more realistic numerical simulation values than previous models in which these aspects are considered as constant.
2. The time-varying variables for the higher initial helix angle are not negligible.
3. The time-varying variables have more influence on soft or thinner rotating shaft material and less influence on hard or thicker rotating shaft material. Similarly, the time-varying variables have a stronger influence on the common bearing stiffness and a weaker effect on the large bearing stiffness. Thus, it can be concluded that, in the case of a sufficiently soft rotating shaft or bearing, the numerical simulation results will be very different from the actual results when the time-varying effect is not considered.
4. The time-varying effect has a significant influence on the gear vibration analysis of the helical-geared rotor-bearing system and cannot be ignored. The results of this research present some useful information for gear vibration analysis.  
Figure 9: Time-varying and constant (**a**) helix angle, (**b**) transverse pressure angle, (**c**) gear pair center distance, and (**d**) total contact ratio under different degrees of the bearing stiffness.  
**Author Contributions:** This research has only one author. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Ministry of Science and Technology of Taiwan (Grant No. 1072218E013001 MY2).  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Routing
### Routing Tree Construction  
In CENet, beacons are divided into three categories: request, reply and pull. All of these beacons are transmitted by broadcasting. A request beacon is to advertise that the sender doesn't have a proper parent and it is searching for a parent. A reply beacon is to indicate that the sender has a route to BS and can be a candidate parent. Finally, a pull beacon indicates that the sender has better or updated route information and notifies its neighbors to maintain their routes.  
When a node does not have a parent or its parent turns to fail, it starts searching for a parent by periodically broadcasting request beacons to its neighbors. A neighbor node with a determined parent broadcasts a reply beacon when it receives the request. The request and reply beacons need to exchange many times to accurately estimate the link qualities. We use expected transmission (ETX [10]) as a cost metric, which reflects the quality of the link to one neighbor. The more reliable the link quality is, the smaller the ETX. The route cost is the sum of link ETX values along the entire route. A node selects a parent with the least route cost. Once a node has found its parent, it stops sending requests.  
Figure 4: (a) A routing topology. (b) Focus on the nodes in the circle of (a). For node C, only the bidirectional link between C and A effects the reliability of packet transmission.  
The frequency of sending request beacons is determined by user requirements. For cabinet monitoring application, if a user wants to construct a routing tree quickly after sensor deployment, they can set the interval to several hundreds of milliseconds.  
After sensor deployment, only BS has a parent which is an upper computer connected over UART. Other nodes do not have parents and broadcast requests periodically. When BS receives the requests from the nodes closer to it, it sends reply beacons. After enough exchanges of the beacons, the sensor nodes at the 1 st level in the routing tree choose BS as parent and stop sending requests. Similarly the nodes at the 2 nd level exchange request and reply beacons with the nodes at the 1 st level to determine their parent. The whole routing tree can be constructed after several rounds. When the routing tree is constructed, none of the nodes in the network sends request beacons. If the communication links in the network remain good, there will be no beacon exchange at all and the link quality is estimated by transmitted data.