esa-sceva/satcom-chunk-collection huggingface dataset:
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
###### Abstract  
In recent years information and communication technologies (ICT) have played a significant role in all aspects of modern society and have impacted socioeconomic development in sectors such as education, administration, business, medical care and agriculture. The benefits of such technologies in agriculture can be appreciated only if farmers use them. In order to predict and evaluate the adoption of these new technological tools, the technology acceptance model (TAM) can be a valid aid. This paper identifies the most commonly used external variables in e-learning, agriculture and virtual reality applications for further validation in an e-learning tool designed for EU farmers and agricultural entrepreneurs. Starting from a literature review of the technology acceptance model, the analysis based on Quality Function Deployment (QFD) shows that computer self-efficacy, individual innovativeness, computer anxiety, perceived enjoyment, social norm, content and system quality, experience and facilitating conditions are the most common determinants addressing technology acceptance. Furthermore, findings evidenced that the external variables have a different impact on the two main beliefs of the TAM Model, Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). This study is expected to bring theoretical support for academics when determining the variables to be included in TAM extensions.  
T 2020 The contribution of tools such as FARMER 4.0, should not be overlooked as it helps to the strengthening of two fundamental sectors for sustainable development, namely e-learning and agriculture. As pointed out by [PERSON] et al. [2], the e-learning sector and sustainability are closely related. Online education, aiming to improve the quality of learning programs, promote the acquisition of sustainability-related values and skills, and increase awareness about sustainable development initiatives, can positively affect local and global development.
# ISAR Image Quality Assessment Based on Visual Attention Model
## 5 Conclusions  
In this paper, a new backbone model, called Gram-T, is used for the quality assessment of ISAR images. The Gramin Transformer computes the Gramin matrix of features to reinforce the score token through the attention layer in order to strengthen the correlation between features and the image quality score in the process of feature extraction rather than the final fully connected layer. Furthermore, this paper applies the attention mechanism in both the channel and spatial dimensions. The spatial attention module captures long semantic association information across locations, while the channel attention module captures long semantic association information across channels. Experimental results show that our method achieves the best results in SRCC and PLCC metrics, respectively, compared to state-of-the-art networks. Ablation studies demonstrate the effectiveness of the network's architecture and the spatial and channel attention mechanisms.  
Conceptualization, J.Z.; methodology, J.Z.; software, J.Z.; validation, J.Z.; formal analysis, J.Z.; investigation, J.Z.; resources, J.Z.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, Z.Z. and X.T.; visualization, [PERSON]; supervision, Z.Z. and X.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Random Vibration Fatigue Life Analysis of Airborne Electrical Control Box
## 6 Random Vibration Test
### Test Results  
Based on the GJB150.16A-2009 standard, the electrical control box experienced random vibration under given load spectrum excitation for 1.5 h (0.5 h per direction), and its dimensions, weight, and internal components were not abnormal. The electrical control box passed the durability test, indicating the correctness of the random vibration fatigue life analysis results and proving that the anti-fatigue performance of the electrical control box structure meets the design requirements.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 5 LoRaCog Protocol
### LoRaCog End-Device  
On the ED side, LoRaCog imposes two modifications: the first one concerns the join/activation request, and the second is a new arrangement for the default receive window.  
* Activation/Join request: During the activation, each ED should share the transmission time with the NS. The NS will use this information to send available spectrum holes to ED prior to its transmission time. The ED will be addressed uniquely through GW with the best RSSI.  
Figure 5: LoRaCog Logarithm.  
* Receive window: Unlike LoRaWAN, the receive window RX2 will be shifted to before the uplink with no delay in between. This window will be used to receive the MAC commands from NS which could be the channel's parameter configuration based on the sensing identified by the GW. The motivation behind this shifting is related to the relevance of the sensing decision. That means the available channels diagnosed by the GW and selected by the NS to use are not guaranteed to be free for a long time, since PU may become active at any time. Thus, the EDs should be informed of the channel to use just before transmitting. As shown in Figure 6, the uplink for ED will start right after the RX2. The RX1 will remain at a scheduled time after each transmission.  
The purpose for choosing RX2 and not RX1 is that in LoRaWAN, RX2 uses a predefined channel and a fixed data rate known to all EDs while RX1 uses the channel of the last Uplink [42]. The default parameters in EU are 869.525 MHz/DR0 (SF12, 125 kHz) [47]. Hence, the gateway can manage the EDs using such controlled channels for configurations. In this case, the channel decision, such as the SF, the channel frequency and the data rate will be communicated to the ED just before the uplink transmission.  
Then, the ED wakes up, receives the frame and decides to transmit directly or configure before transmitting. It is important to mention that the ED, in case of no instructions from NS, will switch back to default channels and not use the ones used in the previous uplink.
## 2 Overview of the Power Distribution Control  
### Hybrid Energy Storage System  
Figure 2 shows a circuit diagram of the hybrid energy storage system (HESS) and its connection to a 1500 V dc bus. The HESS is composed of a battery pack and a supercapacitor module as energy storage devices (ESDs). Each ESD has its own bidirectional switching converter with the output port connected in parallel with the other converter. The current demanded by the charging points is supplied by the addition of the SST current and HESS current.  
Figure 1: Electrical architecture of the ultrafast charging station studied in [14].  
A half-bridge converter is used to implement the power bidirectional converter for both ESDs. It operates as a voltage step-up converter when the power is transferred from the ESDs to the main bus and as a voltage step-down converter when the power flows in the opposite way. Thus, the direction of the currents \(i_{s}\) and \(i_{b}\) in Figure 2 defines the operation of the converter.  
The current-voltage characteristics of the ESDs have been modeled using simple equivalent-circuit models. The supercapacitor is represented by a capacitance \(C_{sc}\), accounting for the total capacitance, and the series resistance \(R_{s}\) represents the equivalent series resistance, which models the losses between the electrodes and the electrolyte. The battery pack model considers a dependent voltage source \(v_{OCV}\), which is a function of the state of charge (SOC) of the pack, and the internal resistance \(R_{b}\).  
In our system, each ESD and its corresponding converter constitute an integrated module, namely, a battery energy storage system (BESS) and supercapacitor energy storage system (SESS). Thus, the total current of the HESS \(i_{HESS}\) results from the contributions of both modules.  
\[i_{HESS}=i_{SESS}+i_{BESS} \tag{1}\]  
As depicted in Figure 2, the current demanded by the charging points (ChPs) \(i_{EV}\) is supplied by the \(i_{HESS}\) and the SST current \(i_{SST}\).  
\[i_{HESS}+i_{SST}=i_{EV} \tag{2}\]
## 5 Multi-Drone Guidance Law
### Prediction of the Target's Position after One Sample-Index
Utilizing Equations (7) and (10), the covariance matrix is predicted as  
\[\mathbf{P}_{k+1|k}=\mathbf{F}*\mathbf{P}_{k|k}*\mathbf{F}^{T}+\mathbf{Q}. \tag{15}\]  
The measurement update step is  
\[\hat{\mathbf{X}}_{k+1|k+1}=\hat{\mathbf{X}}_{k+1|k}+\mathbf{W}_{k}*(\mathbf{m }_{k}-\mathbf{H}*\hat{\mathbf{X}}_{k+1|k}), \tag{16}\]  
where  
\[\mathbf{W}_{k}=\mathbf{P}_{k+1|k}*\mathbf{H}^{T}*\mathbf{S}^{-1}. \tag{17}\]  
Here, we use  
\[\mathbf{S}=\mathbf{H}\mathbf{P}_{k+1|k}\mathbf{H}^{T}+\mathbf{R}_{k}. \tag{18}\]  
In addition, the covariance matrix is updated using  
\[\mathbf{P}_{k+1|k+1}=\mathbf{P}_{k+1|k}-\mathbf{W}_{k}*\mathbf{S}*\mathbf{W}_{ k}^{T}. \tag{19}\]  
The ship at sample-index \(k\) predicts the target state after one sample-index forward in time using Equation (14). Let \(\hat{\mathbf{r}}_{k+1}^{t}\) denote the target position at sample-index \(k+1\), which is predicted using all measurements up to sample-index \(k\). Using Equation (14), \(\hat{\mathbf{r}}_{k+1}^{t}\) is predicted as  
\[\hat{\mathbf{r}}_{k+1}^{t}=\mathbf{H}*\hat{\mathbf{X}}_{k+1|k}. \tag{20}\]  
Here, recall that \(\mathbf{H}\) was defined in Equation (13). We acknowledge that the prediction of the target's position may not be accurate due to the target's elusive maneuvers or measurement noise. This is the motivation for using a formation of drones instead of a single drone. In our paper, a formation of drones is used instead of a single drone in order to increase the capture rate. Since we use a drone formation, we can increase the capture rate even when the target prediction is erroneous. Through MATLAB simulations, the effectiveness of our formation-based guidance law is verified by comparing it with other state-of-the-art guidance controls.
## 1 Introduction
So, the GAs are quite useful in optimizing the design of PAs, which is a nonlinear problem of multiple objectives, in which conventional deterministic methods are vulnerable to a local optimum [13, 16, 20, 21, 22, 23]. Thus, the GA can calculate the optimal values of excitation signals of the array elements, which will be used to control the radiation pattern in several applications, such as directing the main beam, determining the width and the number of beams, increasing gain and scanning speed, reducing sidelobes, rejecting interfering signals or improving the coverage area of the radiated signal. In this context, the Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC) optimization method was developed to control the radiation pattern of PAs. The GA-MMC uses a reconfigurable algorithm with multiple objectives, and has two special features. The first is the real coding with a differentiated technique, inspired by the physical structure of the antenna array itself, in order to simplify the algorithm and reduce the processing time. Another remarkable feature of GA is the new crossover genetic operator, called Maximum-Minimum Crossover (MMC), which combines the most and least fit individuals to increase the genetic diversity of the population and, consequently, the global search, improving the final fitness of the solution set and reducing the processing time. To test the GA-MMC validity, a linear antenna array was adopted, because it serves as the basis for composing more complex arrays and has proven practical utility (as in radars with rotating base or cell phone towers). It also represents a more challenging task for the optimization method, since it has less freedom to format the radiation pattern compared to a planar array. This requires a more precise control of the elements excitation signals, in order to achieve extreme operating conditions, such as main beam near endfire direction. Moreover, as the GA-MMC is generic and allows adaptation to any type of element, isotropic radiators were chosen, which can be easily extended to other types of elements, such as dipoles or microstrip antennas [7]. With that said, this paper is organized as follows: in Section 2, PARs are described, considering RTS operation and the theory of antenna arrays. In Section 3, GA-MMC is presented, using GA concepts in optimization problems in controlling the radiation pattern of PAs. In Section 4, the simulation results are shown and discussed. Finally, the paper is concluded in Section 5.
# Timing Performance Testing and Regularity Analysis of eLoran System
## 2 eLoran Timing Error
### Authorization Performance Assessment Metrics
#### 2.2.4 Timing Stability  
The timing stability is quantified by the standard deviation of the monitoring value of the timing deviation during the valid data period, which characterises the degree of dispersion of the national standard time broadcast by the eLoran timing system [26]:  
\[\sigma=\pm\sqrt{\frac{\sum\limits_{i=1}^{n}\left(\tau_{i}-\overline{\tau} \right)^{2}}{n-1}}. \tag{2}\]  
where \(n\) indicates the number of data points in the valid data period, \(\tau_{i}\) indicates the monitoring value of the timing deviation, and \(\overline{\tau}\) indicates the average value of the timing deviation in the valid data period.  
Currently, China's eLoran system requires timing accuracy in the order of microseconds (millionths of a second). According to the timing characteristics of the eLoran system and the above analysis, we have stipulated how to use receivers to receive eLoran timing signals; monitor the deviation of the time broadcasted by the eLoran timing system from the national standard time; and analyse indices such as the maximum timing deviation, the minimum timing deviation, timing accuracy, and timing stability, based on the value of the monitoring of deviation, in order to carry out a comprehensive assessment of the performance of the eLoran system's timing.
## 3 Results and Discussion
### Feature Importance
\begin{table}
\begin{tabular}{c c} \hline \hline
**Figure 2** & **Description** \\ \hline avg\_dev GSvolume\_pa & \begin{tabular}{c} Average deviation in the Goldschmidt volume per atom from \\ the dataset mean provides a measure of the variation in \\ atomic volume. \\ \end{tabular} \\ \hline avg\_dev SpaceGroupNumber & \begin{tabular}{c} Average deviation of the space group numbers represents \\ the variances in symmetry and periodicity between \\ crystal structures. \\ \end{tabular} \\ \hline avg\_dev Electronegativity & \begin{tabular}{c} Average variation in electronegativity of the elements present, \\ giving insight into the distribution of electrons. \\ \end{tabular} \\ \hline mode CovalentRadius & \begin{tabular}{c} Most frequently occurring covalent radius among the \\ elements in the material, which impacts bonding and \\ electronic structure. \\ \end{tabular} \\ \hline range MendeleevNumber & \begin{tabular}{c} Difference between the highest and lowest Mendeleev \\ numbers in the compound. \\ \end{tabular} \\ \hline mean NUnfilled & \begin{tabular}{c} Average number of unfilled electron states. \\ \end{tabular} \\ \hline mode MeltingT & Most common melting point among the constituent elements \\ \hline mode Electronegativity & \begin{tabular}{c} Most common value of electronegativity found among \\ the elements. \\ \end{tabular} \\ \hline minimum CovalentRadius & \begin{tabular}{c} Smallest covalent radius of the elements present. \\ \end{tabular} \\ \hline mean Electronegativity & \begin{tabular}{c} Average electronegativity of the elements. \\ \end{tabular} \\ \hline avg\_dev NdValence & \begin{tabular}{c} Average deviation in the valence electron counts for the \\ f-block elements (Nd). \\ \end{tabular} \\ \hline avg\_dev NpValence & \
\begin{tabular}{c} Similar to NdValence, but for the p-block elements. \\ \end{tabular} \\ \hline minimum Electronegativity &
\begin{tabular}{c} The lowest value of electronegativity among the elements in \\ the material. \\ \end{tabular} \\ \hline
2-norm & \
\begin{tabular}{c} Mathematical norm that is used here as a measure of the \\ magnitude of vectors. \\ \end{tabular} \\ \hline minimum NValence &
\begin{tabular}{c} The smallest valence electron counts of the elements present. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Significant features for predicting superconductor Tc using the Random Forest model. Each panel on the figure compares the actual Tc of the materials with the Tc predicted by the model, with the identity line representing perfect prediction. The purpose of these plots is twofold. Firstly, they validate the model's ability to generalize across a range of material classes, ensuring that the patterns learned are not specific to a single type of composition. Secondly, the plot corresponding to nitrides (panel d) is of particular interest as it provides assurance that the model can reliably predict the behavior of the superconductors within the composition type central to our focus. With confidence in the model's generalization capability, we apply it to a virtual search space within the nitride group.
## 4 Discussion
### Recommendations for Environmental Noise Management  
The predicted environmental noise has a particular reference for noise monitoring. The environmental noise standards for Chinese cities are divided into daytime (06:00-22:00) and nighttime (22:00-6:00). According to the Chinese environmental quality standard for noise [61], the study area belongs to residential, cultural, and educational institutions. The ambient noise limit is 55 dB(A) during the daytime and 45 dB during the nighttime. As shown in Table 4, during the two monitoring periods, except for monitoring point 02, the noise levels of monitoring points 01, 03, and 04 did not meet the standards. The prediction of noise in Figure 7 also reflects that the noise level of the monitoring point exceeds the standard. From this point of view, the prediction of environmental noise is conducive to a more comprehensive grasp of the noise level at the monitoring point, to promptly remind relevant departments to take practical actions.  
There are many complaints about environmental noise in the city, if the predicted environmental noise can be regarded as a reference state to compare the actual observed noise level, it can facilitate the determination of a noise pollution incident that may be harmful to the health near the monitoring point, and further attract the attention of the environmental management department. On the other hand, the LSTM prediction model proposed in this study can predict the environmental noise at different time intervals, and can adapt to refined and diverse environmental management needs.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 5 Scalability and Discussion
### Network Performance under Larger Network Scales
Different with target data line access, the end-to-end latency for data page access increases with larger MCAR regardless of network scales. Scaling the amount of decomposed hardware from 64 cubes to 4096 cubes, the intra-rack end-to-end access latency increases by 11.1% under an MCAR of 0.9 (671.8 ns for 64-cube network and 755.8 ns for 4096-cube network). As for inter-rack interconnection, the end-to-end latency for data page access performs only a small increase of 7.4% under large network sizes (more than 256 cubes). It can be referred that the optical computing architecture keeps similar performance when increasing the port count of NOS and interconnecting more decomposed hardware cubes. Figure 8: (**a**) End-to-end access latency of fetching (**a**) target data line and (**b**) data page with 40 Gb/s TRX bandwidth.
## 3 Model Assumptions and Notations  
The droplet model served as a working hypothesis to explore what would be the consequences of assuming a non-singular electron model. For this reason, drastic simplifying assumptions were introduced in order to facilitate an easy analysis with the limited aim to just get a first indication of the consequences. The model analysed is shown in Figure 2. The droplet is assumed to move along the Z-axis inside a one-dimensional potential well, one half of which is schematically indicated by the barrier at a distance \(a/2\) from the well centre at \(z=0\). What is sketched suggests a potential box, but the barrier may be soft as well, such as in a parabolic well. The motion of the droplet, as well as the motion of all its elements, is likewise assumed to be one-dimensional. Consequently, the charge elements have a velocity in the \(Z\)-direction only. The charge distribution is assumed to have rotational symmetry around the Z-axis, and fore-aft symmetry w.r.t. an \"equator plane\". We can thus define a midpoint which is the centre of mass as well as the centre of charge.  
The model has two degrees of freedom: The variable position of the midpoint \(z_{m}(t)\), and the elongation \(s(t)\). A quasi-static deformation mode is assumed, where stretching (i.e., an increase of the total length \(s\)) takes place as in a spring without dynamic effects. For each charge element the relative position w.r.t. the midpoint, expressed as a fraction of the total length, is therefore constant:  
\[\frac{z_{elem}(t)-z_{m}(t)}{s(t)/2}=constant \tag{1}\]  
The constant in this expression and the constant values \(x_{elem}\) and \(y_{elem}\) together form a set of \"labels\" which identify a particular element of the total charge, no matter the changes in time of \(z_{m}(t)\) and \(s(t)\). The velocity, acceleration and jerk of any element can, by Equation (1), be determined in terms of \(\ddot{z}_{m}\), \(\ddot{z}_{m}\),... and \(\dot{s}\), \(\ddot{s}\),... by differentiation.  
A final assumption is that the droplet has an elongated shape in the Z-direction (i.e., varying from a Zeppelin-shape to a needle or even a long wire or string), so that the analysis of the electromagnetic field and the self-forces can make use of \"slender-body\" approximations.  
Figure 2: Configuration and notations.
###### Abstract  
In order to realize effective attitude-predictive control during large-diameter shield tunneling, this study established an intelligent framework for attitude prediction. Specifically, a principal component analysis-support vector regression (PCA-SVR) hybrid model was constructed, based on principal component analysis. The principal component analysis method was used to mine the relevant input parameters and reduce the accompanying data noise. SVR used statistical learning and structural risk minimization to overcome data overfitting. Taking a large-diameter shield tunnel, the Zhuhai Xingye Express Tunnel, as an example, the proposed PCA-SVR model was validated by considering tunnel excavation parameters, geometric parameters, and geological parameters. At the same time, the correlation coefficient was used to analyze the relationship between input parameters and attitude parameters. The results show that the propulsion cylinder pressure is an important factor affecting the trajectory of attitude motion. The geometrical and geological parameters of the shield have a strong correlation with the attitude parameters. The attitude parameters predicted by the model are within the range of the corresponding monitoring data. The high prediction accuracy verifies that the proposed PCA-SVR hybrid model can accurately predict the attitude parameters during shield tunneling. The prediction framework can be used for reference for the attitude-prediction control of shields in similar projects.  
superc-large-diameter shield tunnel; support vector regression; moving trajectory; attitude prediction; sensitivity analysis +
Footnote †: journal: Applied sciences  
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