esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Cross-Attention Fusion Network Based on Image Super-Resolution
## 4 Results
### Ablation Study
#### 4.4.2 Study of Multi-Scale Trans-Module Synthesis  
In this part of this study, we analyze the influence of MTM and the number of PMMs in MTM on the network performance and conduct a series of ablation experiments. As  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Scale**} & \multicolumn{2}{c}{**Set5**} & \multicolumn{2}{c}{**Set14**} & \multicolumn{2}{c}{**B100**} \\ \cline{3-8}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline PMMs & & 31.797 & 0.889 & 28.362 & 0.776 & 27.424 & 0.731 \\ MTMs\_PMMs + IAEM & & 31.828 & 0.889 & 28.413 & 0.776 & 27.444 & 0.731 \\ MTMs\_PAM + IAEM & & 31.861 & 0.890 & 28.391 & 0.776 & 27.459 & 0.732 \\ MTMs\_CAM + IAEM & \(\times 4\) & 31.943 & 0.891 & 28.447 & 0.777 & 27.487 & 0.732 \\ MTMs (PMMs + CFM) & & 31.995 & 0.891 & 28.517 & 0.780 & 27.529 & 0.734 \\ MTMs + CAM & & 31.997 & 0.890 & 28.372 & 0.776 & 27.430 & 0.731 \\ MCFN (MTMs + IAEM) & & 32.047 & 0.893 & 28.520 & 0.780 & 27.543 & 0.735 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of MCFN properties.  
Figure 9: Line plots of the training process: (**a**) plot comparing the results of the fusion module network and (**b**) plot comparing the results of integrating the augmentation module and CAM.  
shown in Table 3, we set the number of MTMS M to 4, 5, and 6 and evaluate its impact on the number of parameters and network performance in the test set Set 5. The results show that with the increase in M, the PSNR value of the network improves, and the network performance improves, but the growth rate becomes gradually smaller. In addition, we analyze the number N of PMMs, setting them to 6, 7, and 8, respectively, and record the comparative experimental results, as shown in Table 4. The experimental results show that when N increases from 6 to 7, the PSNR value increases by 0.034. However, when N grows to 8, the increase in PSNR value is only 0.01. Therefore, to effectively balance the reconstruction quality and the number of parameters, we set the number of MTM and PMM to 5 and 7.
# End-to-End Object Detection with Enhanced Positive Sample Filter
## 4 Experiments
### Comparisons with State-of-the-Arts
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Methods** & **Epochs** & **mMR\(\downarrow\)** & **AP\({}_{\text{50}}\)** & **Recall** \\ \hline \multicolumn{4}{l}{_NMS-based Detectors:_} \\ Faster RCNN [1] & - & 50.4 & 85.0 & 90.2 \\ RetinaNet [2] & 32 & 57.6 & 81.7 & 88.6 \\ FCOS [3] & 32 & 54.9 & 86.1 & 94.2 \\ ATSS [41] & 32 & 49.7 & 87.2 & 94.0 \\ AdaptiveNMS [9] & - & 49.7 & 84.7 & 91.3 \\ \hline \multicolumn{4}{l}{_NMS-free Detectors:_} \\ DETR [13] & 300 & 80.1 & 72.8 & 82.7 \\ Deformable DETR [30] & 32 & 54.0 & 86.7 & 92.5 \\ OneNet [11] & 50 & 48.2 & 90.7 & 97.6 \\ DeFCN [12] & 32 & 48.9 & 89.1 & 96.5 \\ Ours & 32 & 46.0 & 90.8 & 97.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparisons with the state-of-the-art object detectors on CrowdHuman val set. Note that the lower mMR is better. Their union set is denoted as R + HO. As shown in Table 4, our detector achieves the best performance on all subsets, which validates its great robustness in terms of handling samples in different situations. **Advantages** In summary, the proposed detector enjoys the following advantages:  
* The proposed detector shows superiority in performance against other NMS-free detectors and many mainstream NMS-based detectors. Thus, we think it is likely to serve as a strong alternative to current mainstream detectors;
* The proposed detector shows a consistent excellent performance on these mainstream detection datasets including with different scenes, which demonstrates its robustness in terms of handling various situations;
* The proposed end-to-end detector was designed in a one-stage anchor-free manner without involving the heuristic attention mechanism and NMS post-processing step. The entire detection pipeline is simple and efficient, and can be easily deployed in real-world industrial applications.
## 2 Literature Review
### Smart Agriculture for Supporting Community-Based Tourism to Sustainability  
Smart agriculture orchards and smart farming are the applications of computer technology; such as, sensors and artificial intelligence (AI) and communication systems like the Internet to be used to aid farming and to ensure accurate management, save labor, and reduce costs in various fields. This could also create a point of interest for the community in terms of creating new learning attractions.  
According to the 'Agriculture 2050' program, the Food and Agriculture Organization of the United Nations (FAO)has projected that the global population would reach approximately 10 billion by the end of 2050 [24]. Therefore, food production should increase approximately by at least 70% to support this population growth [25]. As such, there is an urgent need to improve crop yields, so precision farming and smart farming are very important in today's world. In this regard, technologies related to smart agriculture that include robotics, AI, information and communication technology (ICT), unmanned aerial vehicles (UAV), deep learning (DL), IoT, and big data analytics could effectively address the challenges involved. This would have a positive effect in terms of reducing food scarcity and resource loss [26].
# Assessment of Smart Mechatronics Applications in Agriculture: A Review
## 2 Literature Review
However, these failures or malfunctions occur periodically, and third-party service providers may provide remote troubleshooting, maintenance, and repair. The farmer may also need to keep a large inventory of machine spare parts to minimize machine downtime and ensure that work continues even after a breakdown. The implementation of agricultural technical systems increases farm yields and thus productivity [79; 80]. * Training requirements It is possible that farmers may find it difficult to adapt to digital farming technology and interpret computerized results and may also experience operational difficulties due to various integrated technical systems. This may require the farmer to invest in practical training and introduction to the use of agricultural machinery, and even learn the basic concepts of calculation to effectively operate, implement, and operate agricultural systems. Sometimes, these steps can be time consuming, difficult, stressful, or even inadequate [81; 82]. * Employment opportunities  
A downside of the introduction of new farming techniques is that it will make agricultural workers unemployed. The fact that these farming systems are almost completely autonomous means that less human labor is required. Therefore, a tradeoff arises between the level of implementation of mechanized agricultural systems and the loss of livelihoods [83]. * Land use  
The use of highly mechanized, faster, and large-scale farm automation technology and machinery can result in more land being used for useful and productive agriculture and reduce the need for human labor. This means that farm yields increase, which in turn ensures better returns for farmers and food sustainability for the economy [84]. * Mobile applications  
With the latest smartphone technology, farmers can now more easily and conveniently integrate farm automation technologies with remote monitoring from their smartphones and tablets [85]. * Blockchain technology  
This allows for accurate tracking along the supply chain of all products within the food system. With block chain technology, food contamination can be traced back to the exact source [86]. * Mini chromosome technology  
This new technology retains the plant's original chromosomes, making it a more socially acceptable means of crop enhancement than other methods of genetic modification [87]. * Mechatronics system applications in different countries  
Most developed countries have spent much time and effort developing smart mechatronics systems. These include: Clearpath Robotics in Canada, Earth Dynamics, Modular Robotics, and Soreria Mechatronics in the USA, Reshape Biotech in Denmark, VERHAERT in Belgium, Stanley Robotics and ESTEC in France, Cambridge Mechatronics in the UK, Advantest in Japan, Sartorius in Germany, TREVENTUS Mechatronics in Austria, and BFG Group in Russia [88].
## 3 Proposed Approach
### Temporal Convolutional Network for IFGD Process Identification
The overall structure of the TCN model used in our IFGD modeling problem is presented in Figure 4.  
It should be highlighted in Figure 4 that a unidimensional kernel instead of a two-dimensional one is used to convolve the multivariate input signal, which is marked in a yellow shaded area in the input tensor. The commonly used two-dimensional kernel acts on two-dimensional samples, say images, and the kernel moves along two directions (i.e., the height and width dimension). As for unidimensional kernel used here, it moves only along the time dimension such that information in time sequences can be extracted. In addition, only the input tensor for Residue Block 1 has two channels, while the input signal for other blocks is unichannel. In this case, the linear projection is a convolution kernel of size 2 \(\times\) 1, while for other residue blocks can either be fixed to an identity matrix or just removed. In what follows, the feasibility of using TCN for process modeling is illustrated from the system theory perspective. We shall use notations introduced in (2) and attempt to establish a link between the real process and TCN model. Irrespective of the residue architecture, the TCN model presented above can be mathematically expressed as follows. \[\hat{y}(k+1)=\varphi^{(N)}(O^{N-1}(k)) \tag{8}\]  
\[o^{(n)}(k)=\varphi^{(n)}(O^{n-1}(k)),n=1,2,\cdots,N-1 \tag{9}\]  
\[o^{(0)}(k)=x(k)=[u(k),y(k)] \tag{10}\]  
Figure 4: The overall architecture of TCN-based identification model for IFGD process. where, \(x(k)\) denotes input vector for the model, \(k\in\mathbb{Z}^{+}\) is the time instant, and \(u(k)\), \(y(k)\in\Re\) denotes the system input and output, respectively. \(\varphi(\cdot)\) is the sigmoidal activation function and \(O^{n-1}(k)\) is defined as,  
\[O^{n-1}(k)=[o^{n-1}(k),o^{n-1}(k-d),\cdots,o^{n-1}(k-(i-1)d)] \tag{11}\]  
where \(n\) is the number of layers and \(N\) is a positive constant. For mathematical convenience, both biases and kernel weights in (8) and (9) are omitted. \(i,d\in\mathbb{Z}^{+}\) denotes the kernel size and dilation factor, respectively. In comparison with the real process in form of (3), it is found that a complete TCN model can be regarded as the cascade connections of a finite number of models that in a simplified form of (3). In this sense, given a complicated dynamic process with long-term dependencies, from a theoretical point of view it can be well-approximated by increasing the depth or receptive field of a TCN model. In the following section, the performance of proposed BEMD-TCN model will be evaluated in an IFGD context.
## 5 Experiments and Performance Evaluation
### Effect of Communication Range  
The network reliability and cost is not only related to the number of nodes deployed at each tower but is also related to communication range of nodes. To comprehensively illustrate the tradeoff between cost and reliability in extreme weather, we consider an 80-tower network consisting of five practical types of nodes that has different RF modules and thus different prices (Table 3). Figure 7 shows the effect of the communication range with respect to network reliability per unit cost. In the case of fixed node cost, the total cost grows with an increasing number of nodes deployed at each tower. In the case of adaptive node cost depending upon communication range, total costs increase rapidly if using better nodes. Thus, it can be profitable to deploy nodes according to the network reliability per unit cost. According to results in Table 3, deploying two five-tower coverage nodes at each tower is suggested.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Types 1** & **DTK DRF1601** & **ATZGB-780F1** & **Xbee-ZB SMT** & **Xbee-PRO** & **DTK DRF1605H** \\ \hline RF Range & \textless{}400 m & \textless{}750 m & \textless{}1200 m & \textless{}1500 m & \textgreater{}1600 m \\ \hline Coverage of nodes 2 & 1 & 2 & 3 & 4 & 5 \\ \hline Price 3 & \textless{}25 & \textless{}39 & \textless{}550 & \textless{}65 & \textless{}84 \\ \hline \multirow{2}{*}{Producer} & DTK Electronics & Atmel (San & Digi (Minnetonka, & Digi (Minnetonka, & DTK Electronics \\  & (Shenzhen, China) & Jose, CA, USA) & MN, USA) & MN, USA) & (Shenzhen, China) \\ \hline \hline \end{tabular} Notes: 1 The types of sensor node is based on some Zigbee node makers and suppliers. It indicates available devices or systems on chip; 2 The coverage of nodes means the number of tower in communication range; 3 The price of sensor node devices depends mainly on the microcontroller Units (MCUs), radio frequency (RF) modules and Printed Circutloud Board (PCB) manufacture. In this case, we only study the difference of RF modules assuming other factors are the same.  
\end{table}
Table 3: Communication Range@ 2.4 GHz and prices of sensor node devices.  
Figure 6: Effect of the number of nodes deployed at each tower.  
Figure 7: Effect of communication range.
# Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion
## 5 Results
### Weight Analysis of Target Function  
Another analysis on the influence of the target function weight \(\lambda\) is conducted, in which we fix the TCN model parameters to 256 channels, and change the \(\lambda\) from 0 to 1 with a stride of 0.1. 11 sets of contrast experiments are recorded. The Table 2 shows the results in the contrast experiments.  
The line graph results in Figure 6 demonstrate that the combination of the two-part measurement is effective in promoting the model performance in the two concerning aspects. And the model achieves the best result reported in Section 5.1 with 1.18 mm RMSE and 0.845 PCC when \(\lambda\) is 0.5, which means an even optimization of RMSE and PCC is valid for improving the AAI system. We specially note that when \(\lambda\) approaches 1, which means the RMSE is of predominant in the error back-propagation, the performance is analogous to that achieved with \(\lambda\) approaches 0.1. Nevertheless, it can not gain any promotion from pure PCC target function as the \(\lambda\) is 0, which achieves an RMSE of 15.72 mm with a 0.82 PCC. The RMSE is too large to be shown in the line graph Figure 6. This is due to the PCC only concerned with the overall trend, rather than focus on absolute error. The model will give a line with similar trajectory to the predicted target, but the absolute value, which indicates the articulators' position, achieves a rather large difference, showing that the model did not learn the mapping from acoustic features to the articulatory features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Weight \(\lambda\)** & **RMSE** & **PCC** & **Weight \(\lambda\)** & **RMSE** & **PCC** \\ \hline
0.1 & 1.276 & 0.812 & 0.6 & 1.200 & 0.836 \\
0.2 & 1.238 & 0.829 & 0.7 & 1.220 & 0.820 \\
0.3 & 1.241 & 0.825 & 0.8 & 1.285 & 0.812 \\
0.4 & 1.219 & 0.838 & 0.9 & 1.275 & 0.812 \\
0.5 & 1.180 & 0.845 & 1 & 1.280 & 0.811 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Part of the results of the contrast experiments. An evenly trained model with \(\lambda=0.5\) achieves the best performance.  
Figure 6: A line graph for analyze the impact of \(\lambda\) on the two aspects. the red line with diamond illustrates RMSE trend, and the blue line with circle represents PCC trend. \(\lambda=0.5\) the model acquires the best performance in the two measurements’ aspects. The results when \(\lambda\) approaches 0.1 or 0.9 is nearly the same. But using pure PCC target achieves a fairly large RMSE which cannot be illustrated in the line graph.
# Temporal Variations Dataset for Indoor Environmental Parameters in Northern Saudi Arabia
## 5 Proposed Applications  
Recognizing the significance of the produced dataset in different fields, the following are some of its applications:  
Figure 6: Kernel Density Estimation.  
* Smart Homes: The datasets may be used in developing and enhancing smart home applications, especially in locations with comparable environmental features to Saudi Arabia. Controlling the home environment for comfort, energy efficiency, or special health requirements falls under this category.
* Remote Patient Monitoring Systems: This dataset may be used to enhance these systems, which are used to monitor patients at home. For example, healthcare practitioners might utilize this data to evaluate how changes in a patient's home environment may affect their health.
* Aged Care Technologies: This dataset may help technologies that let the elderly live independently at home. Sensors that monitor environmental conditions might give useful information about possible hazards or health problems.
* Environmental Health Research: Researchers may use this data to investigate the influence of various environmental factors on health outcomes. This might result in creating treatments or recommendations for protecting people's health in certain surroundings.
* Models for Disease Prediction: These models might utilize environmental data and personal health data to predict the probability of specific diseases or health problems.
* Personalized Health Recommendations: This data may be used by mobile apps that deliver individualized health recommendations to account for the environmental context. For example, depending on the current situation at hand, an app may recommend certain activities or precautions.
* Climate Control Systems: Maintaining a certain range of environmental conditions in hospitals or healthcare facilities may be critical for patient comfort and health. This dataset may be used to train machine learning models that improve the performance of these systems.
* Emergency Alert Systems: These systems may advise users or healthcare practitioners to take appropriate action in the event of severe environmental conditions (e.g., high temperature).
* Indoor Farming and Agriculture: Indoor farming and agriculture may also benefit from these statistics since they can give insights into improving plant growth conditions. Although this application does not directly relate to healthcare, it is nonetheless essential due to the need for environmental monitoring.
* Air Quality Monitoring Systems: Although the dataset does not directly assess air quality, it may be used with other data sources to construct or enhance such systems. Machine learning algorithms, for example, might estimate air quality based on temperature, humidity, and pressure data.
# Automation of Experimental Modal Analysis Using Bayesian Optimization
## 3 Application
### Synthetic Test Data

Table 2: Value range and initial values of the hyperparameters used in the optimization stage of the modal parameter optimization for both use cases; a description of the parameters can be found in Table 1 and Section 2.1. The regularization parameter \(r\) and the minimum modal damping \(k_{Dmin}\) were set constant. Figure 5: Comparison of the receptance amplitude and the phase between the input (solid) and the fitted FRF (AutoEMA: dashed, MATLAB®: dash-dotted) from the modal model from a force \(f_{1}\) to the displacement \(x_{1}\) of the first mass; normal (Gaussian) noise was added to the (synthetic) displacement data to simulate noisy real-world measurements. shapes. It can be seen that both approaches estimated the reference mode shapes equally well with very high concordance. To assess the accuracy of eigenfrequencies \(f_{i}^{e}\), the natural frequency difference (NFD) criterion can be used [21]:  
\[\mathrm{NFD}(f_{1}^{e},f_{2}^{e})=\frac{|f_{1}^{e}-f_{2}^{e}|}{\min\bigl{(}f_{1 }^{e},f_{2}^{e}\bigr{)}} \tag{6}\]  
By slightly adapting Equation (6), a similar comparison can be made regarding the modal damping ratios \(\xi_{i}\) using the natural damping difference (NDD):  
\[\mathrm{NDD}(\xi_{1},\xi_{2})=\frac{|\xi_{1}-\xi_{2}|}{\min(\xi_{1},\xi_{2})} \tag{7}\]  
Both criteria indicate perfect coincidence with a value of 0% and increase with a rising difference between the damping ratios and eigenfrequencies to be compared, respectively. Tables 3 and 4 show these criteria along with the absolute values for the synthetic data use case. Again, it can be seen that both approaches estimated the reference modal damping ratios and eigenfrequencies well with maximum deviations of 0.3% and 17.1% of the NFD and NDD, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Mode** & \(\mathbf{\omega_{ref}}\) & \(\mathbf{\omega_{ML}}\) & \(\mathbf{\omega_{AE}}\) & \(\mathrm{NFD_{ref-ML}}\) & \(\mathrm{NFD_{ref-AE}}\) \\ \hline \(1\) & \(33.8\,\mathrm{Hz}\) & \(33.8\,\mathrm{Hz}\) & \(33.7\,\mathrm{Hz}\) & \(0.0\%\) & \(0.2\%\) \\ \(2\) & \(86.6\,\mathrm{Hz}\) & \(86.6\,\mathrm{Hz}\) & \(86.6\,\mathrm{Hz}\) & \(0.0\%\) & \(0.0\%\) \\ \(3\) & \(186.7\,\mathrm{Hz}\) & \(186.6\,\mathrm{Hz}\) & \(186.6\,\mathrm{Hz}\) & \(0.0\%\) & \(0.0\%\) \\ \(4\) & \(237.3\,\mathrm{Hz}\) & \(236.6\,\mathrm{Hz}\) & \(257.3\,\mathrm{Hz}\) & \(0.3\%\) & \(0.0\%\) \\ \(5\) & \(250.5\,\mathrm{Hz}\) & \(250.3\,\mathrm{Hz}\) & \(250.5\,\mathrm{Hz}\) & \(0.1\%\) & \(0.0\%\) \\ \(6\) & \(318.9\,\mathrm{Hz}\) & \(318.4\,\mathrm{Hz}\) & \(319.5\,\mathrm{Hz}\) & \(0.1\%\) & \(0.2\%\) \\ \hline \end{tabular}
\end{table} 
Table 3: Overview of the eigenfrequency results for the synthetic data use case (see Section 3.1) showing the absolute values and the NFD for both the MATLAB® (ML) and AutoEMA (AE) approach. Figure 6: Comparison of the found mode shapes using the MAC; here, the mode shapes from solving the damped eigenvalue problem resulting from the MDOF oscillator in Section 3.1 were used as ground truth, (a) MATLAB® modalfit, (b) AutoEMA.
## 2 Background
#### Target  
A target is a physical entity or an object located in an exploration space. It is considered static if it does not present any form of mobility in its environment or dynamic in the case of spatio-temporal changes.
### Main Contribution  
Although quite a few survey papers have talked about the overview of the TI, they are not mainly focussed on the design architecture, applications, and current issues. Thus, they do not provide illustrative diagrams for the reader to comprehend the gist of the research altogether. Here, we briefly highlight the main contributions of this survey as follows.  
* We emphasise the TI design aspects with proposed application-centric design architecture, where the main sections of the architecture, i.e., master, network, and slave sections, with their functions, are explored.
* We identify the key application areas and provide discussions with proposed illustrative diagrams of use cases. Moreover, the multiple application scenarios with required the TI competencies and their performance metrics are tabulated.
* We provide an in-depth discussion of the identified current issues and challenges with potential enablers of the TI. In addition, some possible future research directions are provided to give an insight to researchers.
* We present a comprehensive review focussing on the related articles on enabling technologies such as 5G, SDN, NFV, Cloud/Edge/Fog Computing, Multiple Access, and Network Coding to realise the TI. Here, the contribution of the related articles is summarised according to enabling technologies.