esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We have investigated the statistical indoor EZ that can be efficiently applied to an NPP by investigating electromagnetic fields inside the actual NPP. To obtain the statistical indoor EZ, the indoor environment of the KINS simulator room was modeled. In this simulation model, both the transmitting and receiving antenna used a measured radiation pattern of the dipole antenna to make the simulation results more consistent with the actual values. The indoor space around the transmitting antenna was classified as multiple observation regions, and the EZ boundaries of each region were independently defined within each separate observation region. The EZ boundaries were obtained using a margined regression model that included 95% of the data that were defined based on the general regression model, which can determine a reasonable boundary of the statistical indoor EZ. The EZ boundary in all regions was observed from the minimum of 2.8 m to the maximum of 7.4 m when the transmitting antenna was placed at _Tx_. 1 (near the center of the room), and the height of the receiving antenna was 1.4 m. On the other hand, when the transmitting antenna was located in _Tx_. 2 (the corner of the room), the distance between the transmitting antenna and the EZ boundary ranged from a minimum of 1.3 m to a maximum of 11.7 m. Additionally, to understand the EZ in a 3D environment, the EZ was extended into a 3D volume space by investigating at various heights. To validate the statistical indoor EZ, we measured the received power inside the KINS simulator room when the transmitting antenna was placed at _Tx_. 2. The measured received power according to the distance between the transmitting and receiving antenna was observed to have a distribution range from \(-\)23.5 dBm to \(-\)7 dBm in _Region 3_ and from \(-\)31.5 dBm to \(-\)8.5 dBm in _Region 7_. The measured received power agreed well with the simulated result. The results demonstrated that the proposed statistical indoor EZ can be properly obtained not only from the simulation data but also from the measurement data, and, therefore, the I&C equipment can be easily placed in the safe zone without complication.  
Conceptualization, D.J., S.Y., [PERSON]-Y.L. and H.C.; formal analysis, D.J. and S.Y.; funding acquisition, H.C.; investigation, D.J. and S.Y.; methodology, D.J. and S.Y.; project administration, H.C.; software, D.J.; supervision, H.C.; validation, D.J., S.Y., [PERSON]-Y.L. and H.C.; visualization, D.J.; writing--original draft, D.J. and S.Y.; writing--review and editing, D.J. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2017R1A5A1015596) and the NRF grant funded by the Korean government (No.2015R1A6A1A03031833; NRF-2017R1D1A1B04031890).  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Quantitative Evaluation Using SNR  
SNR is a critical metric in infrared thermography, commonly used to assess the performance of image processing algorithms for defect detection in NDT [48; 49; 50]. It quantitatively compares the intensity of a signal originating from a defect region with the level of background noise, thereby serving as an indicator of image quality and defect visibility [49].  
The most widely used formulation for SNR in thermographic applications is defined as follows [48; 49]:  
\[\text{SNR (dB)}=20\log_{10}\left(\frac{|\mu_{\text{defect}}-\mu_{\text{ background}}|}{\sigma_{\text{background}}}\right) \tag{3}\]  
where \(\mu_{\text{defect}}\) is the mean intensity value within the defect region, \(\mu_{\text{background}}\) is the mean intensity in a nearby sound area, and \(\sigma_{\text{background}}\) is the standard deviation of the background noise [48; 49].  
In the study by [PERSON] et al. [48], the SNR was used to quantitatively compare several thermographic image processing techniques. These included differential absolute contrast (DAC), pulsed phase thermography (PPT), thermographic signal reconstruction (TSR), principal component thermography (PCT), as well as statistical approaches such as skewness and kurtosis. The authors demonstrated that SNR is a useful metric for evaluating the effectiveness of each method in enhancing defect visibility. By calculating the difference in average intensity between a defect region and a nearby background region and normalising it by the background noise level, SNR provides an objective measure of image contrast and quality.  
[PERSON] et al. [49] demonstrated the application of this SNR metric in lock-in thermography, comparing discrete Fourier transform (DFT) and harmonic approximation (HA) methods for phase image generation. Their results highlighted the importance of sufficient SNR (e.g., \(>\)0 dB) to ensure reliable automatic defect detection.  
In this study, SNR was employed to quantitatively evaluate the effectiveness of PCA and RPCA in enhancing defect visibility in the upper CFRP specimens.
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 5 Results  
The performance of dynamical optimization inverse kinematics solver is compared to instantaneous optimization implementations. The evaluation is done in terms of computational load and accuracy on a 2.3 GHz Intel Core i7 processor with 16 GB of RAM. The accuracy is measured with two metrics, one for the orientation targets and one for the velocity targets. The _mean normalized trace error_ (\(MNTE\)) is a dimensionless metric measuring the overall accuracy of the orientation targets:  
\[MNTE=\frac{1}{n_{o}}\sum_{j=1}^{n_{o}}\frac{\text{tr}(I_{3\times 3}-h_{\mathcal{ O}_{j}}^{\sigma}(q)^{\mathcal{T}\,\mathcal{I}}R_{\mathcal{O}_{j}})}{2}, \tag{15}\]  
where the \(\frac{1}{2}\) factor normalize the value of the trace between 0 and 1. For the angular velocities, the overall error is evaluated as _root mean squared error_ (\(RMSE\)):  
\[RMSE=\sqrt{\frac{1}{n_{o}}\sum_{j=1}^{n_{o}}\frac{\left\|\mathcal{I}\omega_{ \mathcal{O}_{j}}-\mathcal{I}\hat{\omega}_{\mathcal{O}_{j}}(q,\
u)\right\|_{2} ^{2}}{3}}, \tag{16}\]  
where \(\hat{\omega}_{\mathcal{O}_{j}}(q,\
u)\) is the estimated frame velocity given the configuration \((q,\
u)\). The computational load is evaluated as the time for computing the state \((q,\
u)\). The statistics have been collected discarding the transient of 2 second from the initial time \(t_{0}\).  
Figure 5: Real-time retargeting of the human motion to iCub humanoid robot configuration.
## 1 Introduction
However, the one-dimensional CNN still suffers from the shortcomings of a long training time and slow convergence in fault diagnosis, leading to low efficiency in fault identification. A new framework that integrates grey wolf optimized variational modal decomposition (IGVMD) and a parametric rectified linear unit (PReLU) activation layer in a 1 DCNN model is proposed in this paper for bearing fault diagnosis to overcome the above problems. Firstly, the grey wolf algorithm is improved based on three adjustment strategies, and then the improved GWO method is used to optimize the parameters of VMD (IGVMD). The objective of the IGVMD is to extract features of fault signals from a noisy background. The fault signal is decomposed by the IGVMD algorithm to obtain \(K\) modal components, and the valid modal components are obtained by correlation coefficient threshold screening. Then, a PReLU-based 1 DCNN is introduced to promote the efficiency and accuracy of model training. IGVMD is used to preprocess the fault signals to effectively extract the fault features, and then the extracted fault features are used as the input of 1 DCNN to train the network for accurate classification of the fault signals. The main contributions of this paper are summarized as follows:  
1. An improved grey wolf optimization algorithm is designed to improve the global search performance of the GWO by introducing three improved strategies. We propose a hybrid diagnosis framework for rolling bearings with integrated signal denoising and dynamic training. The improved grey wolf algorithm adaptive optimization variational modal method is used for effectively extracting weak fault signals under a noise background. 2. A one-dimensional convolutional neural network is designed by introducing grid search and proposing a PReLU-1 DCNN with a dynamically trained activation layer and adaptive learning rate. This can achieve high accuracy and a fast convergence rate by solving the problems of mean shift and negative interval death of neurons. The arrangement of this paper is as follows. Section 2 briefly describes the relevant theories of the grey wolf algorithm, variational modal decomposition, and one-dimensional convolutional neural networks. Section 3 introduces the fault diagnosis framework based on IGVMD-1 DCNN. In Section 4, the experimental dataset collected in the ElectricalEngineering Laboratory of Case Western Reserve University (CWRU) and the gearbox at Southeast University is used for experiments to assess the effectiveness of the proposed method, and the results are analyzed and discussed in detail. Finally, our conclusions and prospects are shown in Section 5.
## 4 Experimental Results
### HI Results  
Since the normal stage images are used to train the deep learning model, the \(HI\) is solely constructed for the failure stage. The trained deep learning model was used to construct on-line \(HI\) by applying the \(MD\) formula to measure the distance of the values of the bottleneck nodes between the normal stage and failure stage. The constructed \(HIs\) for the four IMS bearings are shown in Figure 10a-d. Intuitively, it can be observed that \(HIs\) evolved gradually at the beginning and dramatically at the end; they revealed the true degradation in bearings. Although the initial \(HI\) curves represented global monotonicity, there still were severe local spurious fluctuations that may have been the result of highly inaccurate and unreliable data. Therefore, the smoothness and monotonicity of the constructed \(HIs\) were improved by using the exponential function defined in Equation (12); the modified \(HIs\) are shown in Figure 10e-h. The exponential function is an increasing function that uses the mean from the starting time to the current time; thus, it can effectively eliminate oscillation and enhance monotonicity. It can be seen by the naked eye that the modified \(HIs\) were smoother, and gradually increasing, while the degradation trends were effectively captured as well. The results indicate that the proposed method has a good performance in detecting the early bearing defects and abnormal bearing health conditions.  
Figure 9: Comparison of the original and reconstructed images of the wavelet power spectrum during the run-to-failure experiment of the subset 2 bearing 1.  
Moreover, this method provides an \(HI\) that is well correlated with progressively increasing bearing degradations, and it can lead to better RUL prediction.  
Figure 10: \(HI\) results for the four IMS bearings: (**a–d**) raw \(HI\), (**e–h**) modified \(HI\).
## 4 Results
To evaluate the segmentation performance, we computed the Jaccard similarity between the synapse segmentations and the manually-labeled ground truth of synapses, as shown in \[Recall=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}. \tag{13}\]2 Table 5 shows the statistics of different types of MSBs: approximately 94.31% axon-two postsynaptic sites (dendrite spines or shafts), 4.99% axon-three postsynaptic sites (dendrite spines or shafts), and 0.70% axon-four postsynaptic sites (dendrite spines or shafts). The morphological changes in MSBs can induce pattern and functional strength alterations in neural connections, such as synchronous activity, learning, and memory [12; 38]. Apart from the percentage ratio of different MSBs' profiles, the area of the synapse, especially the presynaptic bouton and PSD (postsynaptic density) along the portion of the plasma membrane, are a characteristic feature of the synapses. The area of the presynaptic axon boutons and PSD are also shown in \[Recall=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}. \tag{13}\]3 The average area of the presynaptic axon boutons and PSD was 0.5731 \(\upmu\)m\({}^{2}\) and 0.0121 \(\upmu\)m\({}^{2}\), respectively. A previous study indicated that structural alterations of the presynaptic boutons and the PSD led to sustained changes in neurotransmitter release, which played an important role in synapse-specific forms of plasticity, as well as neuropathy [39]. Deep learning method application in MSBs' detection and recognition will be a useful tool in neuroscience study. \(\mu\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**J(Synapses, Ground truth)**} & \multirow{2}{*}{**Area(Neuron) (\(\upmu\)m\({}^{2}\))**} & \multirow{2}{*}{**Area(Synapse) (\(\upmu\)m\({}^{2}\))**} & \multirow{2}{*}{**Area(Neuron)/ Area(Synapses)**} \\  & & & & \\ \hline A & 62.54\% & 0.2050 & 0.0266 & 12.98\% \\ B & 57.48\% & 0.3634 & 0.0334 & 9.20\% \\ C & 48.40\% & 0.6497 & 0.0048 & 7.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of MSBs in Figure 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Manual**} & \multicolumn{3}{c}{**Our Method**} \\ \cline{3-5}  & & **Total** & **False Positive** & **False Negative** \\ \hline Layer 1 & 8 & 10 & 2 & 0 \\ \hline Layer 21 & 7 & 10 & 3 & 0 \\ \hline Layer 41 & 6 & 7 & 1 & 0 \\ \hline Layer 61 & 3 & 5 & 2 & 0 \\ \hline Layer 81 & 3 & 4 & 2 & 1 \\ \hline Layer 101 & 4 & 7 & 3 & 0 \\ \hline Layer 121 & 5 & 6 & 2 & 1 \\ \hline Layer 141 & 4 & 7 & 4 & 1 \\ \hline Layer 16
## 4 Results and Discussion
### Comparison of Natural Gas and Hydrogen  
#### 4.2.1 Comparison of the Thermodynamic Properties of Gasses in Cavity  
Under the same injection and withdrawal mass rate, the variation in cavity pressure over a three-year injection-withdrawal cycle is illustrated in Figure 4. The pressure fluctuation patterns of the two gasses within a single injection-withdrawal cycle are strikingly similar, yet the pressure differences are substantial. Under identical injection and withdrawal parameters, the cavity pressure of hydrogen can reach up to 17.9 MPa, while the maximum pressure of natural gas is approximately 15 MPa. The reason for the aforementioned results lies in the fact that a fixed mass injection rate condition is set in the simulation. Based on the ideal gas law, under fixed volume and equal mass injection rates, the pressure of a gas with a lower molecular weight is greater.  
The temperature variations in hydrogen during a three-year injection-withdrawal cycle are illustrated in Figure 5. The temperature fluctuation of hydrogen within a single injection-withdrawal cycle bears striking resemblance to that of natural gas. However, due to differences in thermodynamic parameters such as specific heat capacity and thermal conductivity between hydrogen and natural gas, their temperature discrepancies are substantial. At identical injection-withdrawal pressures, the temperature of hydrogen can  
Figure 4: Variation in cavity pressure over a three-year injection–withdrawal cycle.  
Figure 3: Variation in natural gas temperature within the salt cavern storage.  
reach up to 76 \({}^{\circ}\)C, while the maximum temperature of natural gas is approximately 60 \({}^{\circ}\)C, a difference of around 16 \({}^{\circ}\)C. During the withdrawal process, the temperature drop of hydrogen is generally more pronounced than that of natural gas, amounting to approximately 5 \({}^{\circ}\)C. The reason for the aforementioned results lies in the fact that the compression process is a transformation of mechanical energy into internal energy. Hydrogen is more challenging to compress than natural gas. The hydrogen molecule is diminutive in size and readily experiences intermolecular forces from surrounding hydrogen molecules when compressed. In contrast, methane possesses a tetrahedral spatial structure, resulting in slightly stronger intermolecular interactions; however, it benefits from spatial advantages. Therefore, greater work must be performed during the injection process, resulting in a more significant increase in the temperature of hydrogen.
## 5 Discussion of Main Findings and Implications  
The results presented above (Section 4) provide relevant proof of the good performance of PCM-based mortars to limit overheating situations and reduce the energy consumption of air-conditioning equipment in lightweight constructions. This has been proven at a real-scale relevant environment, thus stepping over previous studies which strictly relied on lab-scale characterizations or numerical simulations.  
The main findings derived from this work have revealed peak temperature reductions by 1-2 \({}^{\circ}\)C and air-conditioning energy savings by 5-10% as a result of the application of two specific PCM-containing mortars as indoor coating solutions at a real-scale lightweight demonstrator. These represent the first promising results that spearhead a step forward from laboratory to field research on this topic and promote advances in the technology readiness of PCM-based solutions integrated into sustainable built environments.  
At the same time, these promising findings pave the way for further studies, which are required to extend the conclusions to other materials and constructive assemblies, address cost-benefit aspects, and consolidate the above-mentioned positive impacts under different building contexts and climate conditions.  
From a long-term perspective, PCM-containing mortars are expected to enable a relevant impact on lightweight prefabricated constructions, which will become increasingly frequent in the construction sector over the coming years. Indeed, according to recent market studies [44, 45], the prefabricated building market in Europe is expected to sustainably grow at a Compound Annual Growth Rate (CAGR) of approximately 4-5.5% up to 2026.  
This will bring important challenges to ensure high-quality indoor comfort and reduced energy consumption since the low thermal inertia of lightweight buildings may lead to higher temperature fluctuations and thermal demands. Overheating is a major risk in Southern Europe, while increased heating and cooling energy use would also affect colder climates where the prefabricated housing market is particularly rising [46].  
However, based on the findings presented in this work, it has been demonstrated that PCM-containing mortars can successfully be applied to this kind of constructions with clear benefits to face the above-mentioned challenges in the next future.
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 4 Experiment
### Analysis of Experimental Results
#### 4.4.4 Meter Automatic Reading Evaluation Index  
In this experiment, the absolute error, relative error, and accuracy are used as the measurement indicators of the meter reading, and their calculation formulas are in Formulas (17)-(19). Among them, the manual reading is performed by 20 workers, and their average value is taken as the manual reading. Results are rounded to two decimal places. The comparison between automatic identification and manual identification of pointer meters is shown in Table 5. The interpretation of each abbreviation is shown in Table 6.  
\[\text{Absolute Error}=\mid\text{Automatic Reading}-\text{Manual Reading}\mid, \tag{17}\]  
\[\text{Relative Error}=\frac{\text{Absolute Error}}{\text{Maximum Range}}, \tag{18}\]  
\[\text{Accuracy}=1-\text{Relative Error}, \tag{19}\]  
In this data table, we can see that there are certain errors between the automatic readings and the manual readings, but in most cases the relative errors are small, and the accuracy is high. The accuracy of the readings obtained by this scheme is not less than 95%. At the same time, we can find that the automatic identification meter type is consistent with the manual identification meter type, and the maximum range of automatic identification is basically the same as that of manual identification, which shows that the automatic identification algorithm of the meter proposed in this experiment is relatively reliable. Therefore, this solution can be used as an effective application of intelligent measurement technology in the industrial field, thereby improving the accuracy and efficiency of measurement and reducing the cost of manual operation.  
Figure 10: The situation where the pointer covers the scale value.
# State-of-the-Art Internet of Things in Protected Agriculture
## 3 The Crucial Technologies of IoT in Protected Agriculture
### Wsn  
The WSN consists of a lot of sensor nodes that are usually powered by battery and it is a multi-hop self-organizing network system formed by wireless communication to collaboratively sense, collect and process various information of the perceived object in the network coverage area [47; 48]. It can be divided into terrestrial WSN and wireless underground sensor networks (WUSN). The agricultural sensors are usually planted into soil and lower frequency wireless technologies are more selected due to low attenuation in WUSN. Besides, antenna size and energy consumption in WUSN is higher than terrestrial WSN [49]. With development of LPWAN, IoT may not need a mesh-style WSN with power-based routing, where one node forwards packets of other nodes.
## 5 Conclusions  
In this paper, the DOFS was applied inside an operating power transformer and verified by the corresponding tests. Then, the transformer with a completely internal temperature sensing capability was successfully developed and qualified for actual application through the ex-factory type tests. The internal temperature was obtained in a distributed manner and it can serve as an important reference for both the field operation staff and the relevant engineers.  
From this research, it was proven that the distributed optical fiber sensors can be applied into the power transformers for a spatiotemporally continuous temperature monitoring with a space resolution of 80 cm and temperature accuracy less than 0.5 \({}^{\circ}\)C. For the large transformers, the spatial positioning accuracy is enough to locate the exact overheated winding turn. For the small transformers, especially the ones with layered winding structure, the proposed densely winded optical fiber laying scheme is possibly a practical way to obtain the temperature where located under the spatial resolution.  
Meanwhile, the real time internal temperature revelation shows that the hotspot real location is more likely at 90% of the winding height rather than the conventional cognition which asserts the hottest region is always at the winding top. The followed numerical simulation may partially account for this 10% deviation from the fluid field and thermal flux perspective.  
In conclusion, the distributed sensing technology displays a promising future in the power transformer online temperature monitoring and may play an increasingly important role in the future electrical apparatus field.  
[PERSON] and [PERSON] proposed the idea and designed the experiment; [PERSON] and [PERSON] performed the calculation; [PERSON] conducted the experiment; [PERSON] and [PERSON] prepared the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of State Grid Corporation of China (SGCC) (No. SGHEDK00 DYJS1900063).  
The authors declare no conflict of interest.  
Figure 15: Oil stream vector diagram from the (**a**) front view and (**b**) side view.