esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Latent Thermal Storage in a Compact Heat Exchanger Using Mini-Channels
## 2 Materials and Methods
### Physical Model  
Simulations of melting and solidification processes of RT22 as PCM were performed with two types of mini-channels. The air-PCM heat exchanger, as shown in Figure 1, is a cuboid of 0.15 \(\times\) 0.15 \(\times\) 0.1 m\({}^{3}\) comprised of a duct shell and the mini-channels. The PCM is located in the shell, and air, as the HTF, flows through mini-channels. To reduce the computational effort of modeling the phase change process, a symmetric domain of one channel was simulated.  
Figure 1: The physical model and the simulation domain.  
Two types of channels, with specified dimensions shown in Figure 2, were considered in the study. The mini-channel type one (channel-1) is an existing product of Hydro Aluminum, manufactured through an extrusion and studied for the first time in an LTES component. Channel-1 has hydraulic diameters of 1.63 mm and 1.59 mm for the middle and the side passages, respectively. The mini-channel type two (channel-2) is a conceptual design with the purpose of improving the thermal performance of channel-1. This channel could be potentially manufactured via metal 3D printing as a fast and quick production method for innovative prototypes, using AlSi\({}_{10}\)Mg alloy as the material, with and without external fins. Channel-2 has a slightly higher area-to-volume ratio (\(\frac{\text{A}_{\text{s}}}{\text{Vol}}\)) than channel-2, providing a higher heat transfer surface given a similar PCM packing factor. Channel-2 was proposed with a hydraulic diameter and aspect ratio higher than Channel 1 to achieve a potentially higher heat transfer coefficient of air and a lower pressure drop. It has a hydraulic diameter of 2.27 mm and an aspect ratio of 17.5. The geometrical characteristics of the channels with their material properties are summarized in Table 1.  
Figure 2: Dimensions of the simulated configurations: (**a**) channel-1, (**b**) channel-2 and (**c**) channel-2 filmed.  
In the cuboid, the mini-channels with varying quantities are oriented vertically. In the case of channel-1, due to limitations in the production procedures of the cuboid, including the restrictions associated with brazing and welding processes, the channels need to maintain a minimum distance of 5 mm. In the case of channel-2, due to the inherited characteristics of the 3D-printing method, this distance could be lowered down to 1-2 mm. Thus, the number of channels per the height of the cuboid was set to five and six for channel-1 and channel-2, respectively. In the case of externally finned channel-2, four fins with a length of 3 mm and thickness of 0.2 mm on each side of the channel-2 were considered, almost doubling the external surface area. The number of channels in width of the cuboid is varied from 12 to 20 with packing factor (PF), defined as a ratio of the PCM volume to the total volume of the cuboid, range of 77.4-86.8%. The details of simulated configurations and the corresponding PFs are summarized in Table 2.
## 2 Method
### Optimization Framework  
The \(U^{j}\) estimated in the above ptychographic imaging correlography manner can only provide the Fourier magnitude of the \(j\)-th subobject. To obtain the image of the entire object, a reconstruction algorithm is required to retrieve the phase information associated with \(U^{j}\). Here, the Wiener filter is introduced before invoking the phase retrieval algorithms, which produces the minimum mean-square estimate of Fourier magnitude from the recorded speckle patterns [2; 7]. This can greatly improve the visual quality of the recovered image. The filtered Fourier modulus is described by  
\[\phi^{j}(u,v)=W(u,v)U^{j}(u,v) \tag{8}\]  
where \(W(u,v)\) is the Wiener filter operator and is given as  
\[W(u,v)=\frac{OTF(u,v)E_{s}(u,v)}{\left|OTF(u,v)\right|^{2}}E_{s}(u,v)+E_{n} \tag{9}\]  
where \(OTF(u,v)\) is the aperture transfer function, \(E_{s}(u,v)\) is an estimate of the object's power spectrum, and \(E_{n}\) is an estimate of the noise-energy spectrum. For this work, the parameters of the Wiener filter are estimated from the data recorded at the central position. The \(OTF\) is taken to be the autocorrelation of the subaperture. The squared Fourier modulus \([U(u,v)]^{2}\) is used in place of \(E_{s}(u,v)\). \(E_{n}\) is assumed to be a constant, whose value is obtained by averaging the squared Fourier modulus estimate over those high spatial frequencies. According to the actual situation, one can control the suppression of the high-frequency noise by adjusting the value of \(E_{n}\).
## 3 Results and Discussion
### Effect of FMFRF on Blowoff Transient  
Instantaneous images of OH*-CL with a time interval of 2 ms from an individual blowoff event of an unforced flame are visualized and shown in Figure 10, corresponding to a blowoff air velocity of 16.5 m/s. As described in Ref. [56], the flame may survive for a few seconds before the global extinction, characterized by a diminished-size flame and a sudden drop in OH*-CL. It can be seen that the flame becomes highly fragmented when approaching blowoff, and its shape changes significantly, both spatially and temporally. The size of the heat release zone gradually decreases and shrinks towards the fuel nozzle. The blowoff transients of the unforced and forced cases are very similar, which is related to the fact that only the central axis region was directly affected by FMFRF.  
Every time-resolved OH*-CL image was area-integrated, and the global OH*-CL signal intensity was calculated for all test conditions with varying FMFRF amplitude, as shown in Figure 11. The average number of precursor events that occurred per second in every condition is also shown in Figure 11. Specifically, a precursor before global blowoff is defined as long as the OH*-CL signal drops below a certain threshold, which was set as 50% of the averaged OH*-CL signal of the steady case in the current study. The statistical results of the global OH*-CL signal in Figure 11 are consistent with the observation in Figure 5. The frequency of the precursor occurrence dramatically decreases when the external acoustic forcing is applied to the fuel. The flame is more stable under a higher FMFRF applied on the fuel jet, and the corresponding blowoff limit is extended. When the FMFRF is beyond 100%, the frequency of the precursor events is approximately the same for different conditions, which has already been demonstrated in Figure 9.  
Figure 10: Time sequence of instantaneous OH*-CL images during an individual blowoff event at a mean fuel mass flow rate of 0.142 g/s with no acoustic forcing corresponding to a blowoff velocity of 16.5 m/s.
## 2 Materials and Methods
### Mapping Structures Vulnerability  
Once the seismic hazard due to ground shaking and local site effects are adequately characterized, the next step is the damage estimation to structural facilities. To evaluate the seismic vulnerability of structures, a detailed buildings inventory of the interest regional area is needed and well-defined relationships between earthquake motion (including local site effects) and both structural and non-structural damage have to be identified. Vulnerability estimation allows measuring the structures and infrastructures susceptibility to be damaged by a seismic event. For the study area, buildings inventory has been derived [22] by institutional ISTAT Census dataset (Italian National Institute of Statistics. [[http://www.istat.it/en/](http://www.istat.it/en/)]([http://www.istat.it/en/](http://www.istat.it/en/))). Aggregated data related to buildings such as built-up density, structural typology (2 classes: Masonry or Reinforced Concrete), age of construction (7 classes), number of storeys (4 classes), that can be derived by buildings datasets by using geo-processing and spatial analysis tools, can be attributed to census sections as spatial unit. Thus, the vulnerability index (\(I_{\text{V}}\)) for each census section can be calculated by using the Lagomarsino and Giovinazzi method [31].  
According to the proposed approach, buildings can basically be classified (Table 1) in Masonry (M) or Reinforced Concrete (RC). Buildings attributes, generally available in a common buildings inventory, such as the number of floors and period of construction, can be used to correct the vulnerability index for each category and considered as behavior modifiers (Table 2) [32].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Construction age** & \multicolumn{2}{c}{\(\boldsymbol{I_{V}}\)} \\ \cline{3-4}  & & **Masonry** & **RC** \\ \hline
**1** & Before 1919 & 50 & - \\
**2** & 1919–1945 & 40 & - \\
**3** & 1946–1961 & 30 & 20 \\
**4** & 1962–1971 & 30 & 20 \\
**5** & 1972–1981 & 20 & 20 \\
**6** & 1982–1991 & 20 & 0 \\
**7** & After 1991 & 20 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vulnerability indices for building typologies and construction age for the study area (Campania, Southern Italy).  
The listed modifiers can be used to increase or decrease the \(I_{\mathrm{V}}\) index, depending on the characteristics of the buildings within the area considered. However, vulnerability index is a building intrinsic value. Census sections might therefore contain buildings with different vulnerability indices. In this case, \(I_{\mathrm{V}}\) can be calculated for each census section as weighted average of the different building indices. The resulting map is depicted in Figure 2.
## 3 Results and Discussion
### Comparison of Leaf Segmentation Performance
#### 3.2.1 Comparison of Camera Types and Segmentation Algorithms
These results are affected by the condition at night, where due to the low power of infrared LEDs, the leaf objects partially appeared in the images, as discussed previously. \begin{table}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{2}{c}{**Isodata**} & \multicolumn{2}{c}{**Li**} & \multicolumn{2}{c}{**Local**} & \multicolumn{2}{c}{**Mean**} \\ \hline TP & FN & TP & FN & TP & FN & TP & FN & FN \\ \hline
22.54\% & 5.68\% & 20.28\% & 7.93\% & 15.43\% & 12.79\% & 21.34\% & 6.88\% \\ \hline FP & TN & FP & TN & FP & TN & FP & TN \\
41.08\% & 30.70\% & 35.62\% & 36.16\% & 39.49\% & 32.29\% & 36.88\% & 34.90\% \\ \hline \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Otsu**} & \multicolumn{2}{c}{**Multi-Otsu**} & \multicolumn{2}{c}{**Triangle**} \\ \hline TP & FN & TP & FN & TP & FN & TP & FN \\
13.02\% & 15.20\% & 22.59\% & 5.63\% & 19.10\% & 9.12\% & 0.43\% & 27.79\% \\ \hline FP & TN & FP & TN & FP & TN & FP & TN \\
25.07\% & 46.72\% & 41.20\% & 30.59\% & 30.72\% & 41.06\% & 4.90\% & 66.88\% \\ \hline \multicolumn{2}{c}{**Yen**} & \multicolumn{2}{c}{**K-Means (K = 2)**} & \multicolumn{2}{c}{K-Means (K = 3)} & \multicolumn{2}{c}{} \\ \hline TP & FN & TP & FN & TP & FN & FN & & & \\
23.97\% & 4.25\% & 21.41\% & 7.19\% & 16.68\% & 11.92\% & & & & \\ \hline FP & TN & FP & TN & FP & TN & FP & TN & & \\
45.54\% & 26.24\% & 37.10\% & 34.30\% & 24.62\% & 46.78\% & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: Normalized confusion tables of segmentation results using thermal camera (daytime). Figure 19: Segmentation performance of thermal camera (daytime and nighttime). Figure 18: Segmentation performance of thermal camera (nighttime).
# Surgical Phase Recognition: From Public Datasets to Real-World Data
## 2 Methods
### Evaluation Methodology  
We report the frame-wise performance and compare the metrics on the test split of our dataset. Accuracy, precision, and recall have been used previously for phase recognition [8]. Precision, recall, and f1-score are first computed per phase for each video and then averaged across videos for each phase. Finally, phase-wise scores are averaged to obtain a single score. For these metrics, the reported mean \(\pm\) std correspond to variations over phases. On the contrary, we first compute the accuracy per video and then report its average across videos. For accuracy, the reported mean \(\pm\) std correspond to variations over videos.  
These frame-wise metrics fail to evaluate the phase segmentation performance of the model. Therefore, we additionally report segment-based metrics, e.g., metrics computed over segments of predictions and ground truths. Specifically, we incorporate the use of segmental f1-score [21] and segmental edit score [23], which have been used to assess video action detection and segmentation algorithms [21], and lately considered for surgical phase recognition [24].  
The segmental edit score penalizes out-of-order predictions while still allowing minor temporal shifts between the prediction and ground truth. For each frame-wise ground-truth sequence \(G\), the segment-wise labels \(G_{s}\) are defined such that if \(G=\{[AAA],[BB],[CCC]\}\) then \(G_{s}=\{ABC\}\), where \(A,B,C\) are different labels. The predicted frame-wise (\(P\)) and segment-wise (\(P_{s}\)) sequences are defined analogously. The edit score is defined using an edit distance [25], \(s_{e}(G_{s},P_{s})\), with insertions, deletions, and replacements. Then, the score is normalized by taking the maximum between the length of \(G_{s}\), \(L_{G}\), and of \(P_{s}\), \(L_{P}\). It can be formally defined as:  
\[S_{edit}=(1-\frac{s_{e}(G_{s},P_{s})}{max(L_{G},L_{P})})\times 100 \tag{1}\]  
with 100 being the best and 0 being the worst score.  
The segmental f1-score measures the overlap between ground truth and predicted segments, thereby penalizing over-segmentation errors. True and false positives are computed over each predicted segment, by comparing its intersection over union (IoU) with respect to the corresponding ground truth using a fixed threshold, \(k\). Precision and recall are computed for each class and then summed over classes. The segmental f1-score can be formally defined as follows,  
\[F1@k=2\times(\frac{precision\times recall}{precision+recall})\times 100, \tag{2}\]  
where \(k\in\{25,50,75\}\) is the IoU threshold.
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 3 Results
### Data Validation
#### 3.2.1 Personnel Validation  
After the training program, it is crucial to validate the performance of the inspectors to ensure the repeatability and accuracy of the measurements. The following standards were established for this validation:  
1. **Repeatability:** Inspectors performed four measurements on the validation road unit. One extreme outlier from the four sets of data was excluded, and the Coefficient of Variation (CV) was calculated for the remaining three sets. The CV must be less than or equal to 0.1 to ensure consistency in the measurement results.
2. **Accuracy:** Inspectors performed four measurements on the validation road unit. One extreme outlier from the four sets of data was excluded, and the remaining three sets were recorded. The average of 12 measurements collected by all inspectors was taken as the reference value. Each inspector's measurements were then compared against the reference value to calculate the relative error, and the Mean Relative Error (MRE) was determined. The MRE must not exceed 10% to ensure the reliability of measurement results across the inspection team.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Inspector**} & \multicolumn{4}{c}{**Coefficient of Variation (\%)**} & \multicolumn{4}{c}{**Mean Relative Error (\%)**} \\ \cline{2-7}  & **Before** & **After** & **Percentage** & **Before** & **After** & **Training** & **Percentage** \\  & **Training** & **Training** & **Percentage** & **Training** & **Training** & **Percentage** \\ \hline Inspector A & 19.9\% & 7.7\% & 61.3\% & & & \\ Inspector B & 33.4\% & 5.6\% & 83.2\% & & & \\ Inspector C & 8.8\% & 4.5\% & 48.9\% & 45.28\% & 5.88\% & 87.0\% \\ Inspector D & 56.1\% & 8.8\% & 84.3\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of precision and accuracy before and after training.
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
## 7 Capacity Analysis
Specially, if \(m=\Omega(n)\), most of packets can be delivered to the destination nodes with the help of backbone network. It implies that if the number of helping nodes is large enough, network capacity can stay constant order, and the delay can be limited, too. Similarly, if \(m=o(n)\), most of the packets will be transmitted by _ad hoc_ transmission mode. It means that if the number of helping nodes is small, though the capacity is also constant order, the delay can not be ensured. If the delay is limited (\(D=\Omega(n)\)), the network capacity is \(\Theta\left(\frac{\sqrt{m}W_{2}}{n}\right)\). In this condition, most of the packets will be transmitted by helping transmission mode. The number of helping nodes is the determining factor of the throughput capacity. 2. In three-dimensional mobile networks: \[\lambda\left(n,m\right)=\left\{ \begin{array}{cc}\Theta\left(1\right)+\Theta \left(\frac{\sqrt[3]{m^{2}}W_{2}}{n}\right),&\text{if }D=\Omega\left(n^{2}\right)\\ \Theta\left(\frac{\sqrt[3]{m^{2}}W_{2}}{n}\right),&\text{if }D=O\left(n^{2} \right)\end{array} \right.\]  
We can reach similar conclusions to two-dimensional mobile networks. According the analysis above, we can conclude that the number of helping nodes in the network is a key factor which affects the throughput capacity. If the number of helping nodes is large enough, the capacity can stay constant with the help of the backbone network. In this case, the time delay can be ensured. If the number of helping node is small, most of the packets have to be delivered in multi-hop fashion. In this case, though the throughput capacity can achieve constant order too, the delay will go to infinity. The delay  
Figure 12: The relationship of between capacity and the number of helping nodes. constraint \(D\) is also an important factor on the network capacity. If \(D\) is not limited, the capacity contributed by _ad hoc_ transmission mode can achieve \(\Theta(1)\), which means that the network capacity of the whole network can achieve a constant order. If \(D\) is constrained, the capacity contributed by _ad hoc_ mode is almost zero and most of packets have to be delivered by helping transmission mode.
# An Object Detection and Localization Method Based on Improved YOLOv5 for the Teleoperated Robot
###### Abstract  
In the traditional teleoperation system, the operator locates the object using the real-time scene information sent back from the robot terminal; however, the localization accuracy is poor and the execution efficiency is low. To address the issues, we propose an object detection and localization method for the teleoperated robot. First, we improved the classic YOLOv5 network model to produce superior object detection performance and named the improved model YOLOv5_Tel. On the basis of the classic YOLOv5 network model, the feature pyramid network was changed to a bidirectional feature pyramid network (BiFPN) network module to achieve the weighted feature fusion mechanism. The coordinate attention (CA) module was added to make the model pay more attention to the features of interest. Furthermore, we pruned the model from the depth and width to make it more lightweight and changed the bounding box regression loss function GIOU to SIOU to speed up model convergence. Then, the YOLOv5_Tel model and ZED2 depth camera were used to achieve object localization based on the binocular stereo vision ranging principle. Finally, we established an object detection platform for the teleoperated robot and created a small dataset to validate the proposed method. The experiment shows that compared with the classic YOLOv5 series network model, the YOLOv5_Tel is higher in accuracy, lighter in weight, and faster in detection speed. The mean average precision (mAP) value of the YOLOv5_Tel increased by 0.8%, 0.9%, and 1.0%, respectively. The model size decreased by 11.1%, 70.0%, and 86.4%, respectively. The inference time decreased by 9.1%, 42.9%, and 58.3%, respectively. The proposed object localization method has a high localization accuracy with an average relative error of only 1.12%.  
Required: 26 September 2022  
Accepted: 2 November 2022  
Published: 11 November 2022  
Published: **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Network Architecture and Hierarchical Model for Leak Detection and Localization  
### Architecture of Pipeline Monitoring Sensor Networks  
Natural gas pipeline monitoring sensor networks are made up of a great number of sensor nodes, the sinks and a control and management center. The whole networks adopt a sort of clustering structure, which is described as Figure 1. The sensor nodes, installed along the pipeline, are in charge of signal collecting and data preprocessing. Then the processing results are sent to the sink via a multi-hop route. The monitoring networks take an in-tube communication mode for message exchange between sensor nodes. The sink, as the cluster leader, is responsible for managing sensor nodes in its cluster,combining preprocessing results for final decision and localizing leak position if a leak has occurred. Finally, the diagnosis result is sent to the control and management center, which judges whether a leak warning alarm should be announced.
## 3 Materials and Methods
### Proposed Models
#### 3.1.2 Proposed GRU-CNN Model  
The GRU is simpler than LSTM, has the ability to train sequential patterns, and takes less time to train the model with improved network performance. To utilize both GRU and one-dimensional CNN, we propose a stacked architecture where a GRU and a one-dimensional CNN are combined, namely the GRU-CNN model. The parameters used for the GRU-CNN model were similar to those of the CNN-LSTM model, as described in Section 3.1.1. The difference between the CNN-LSTM and GRU-CNN models is in the order of stacking the RNN and CNN layers.  
The GRU-CNN model consists of a GRU layer with 128 units and the tanh activation function; a one-dimensional convolutional layer with 32 filters of size 3 with a stride of 1,  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Model** & **Description** \\ \hline CNN-LSTM & One-dimensional convolutional layer with 32 filters of size 3 with a stride of 1 \\  & LSTM layer with 128 units and tanh activation function \\  & Batch-normalization layer \\  & Dropout layer with a rate of 0.2 \\  & Dense layer with a prediction window size of units \\ \hline GRU-CNN & GRU layer with 128 units and the tanh activation \\  & One-dimensional convolutional layer with 32 filters of size 3 with a stride of 1 \\  & One-dimensional global max-pooling layer \\  & Batch-normalization layer \\  & Dense layer with 10 units and the ReLU activation \\  & Dropout layer with a rate of 0.2 \\  & Dense layer with a prediction window size of units \\ \hline Ensemble & RNN layer with 128 units and the tanh activation function \\  & LSTM layer with 128 units and the tanh activation function \\  & GRU layer with 128 units and the tanh activation function \\  & Average of all the hidden states from RNN, LSTM, and GRU \\  & Dropout layer with a rate of 0.2 \\  & Dense layer with 32 units and the ReLU activation function \\  & Dense layer with a prediction window size of units \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model configuration of the proposed models.  
Figure 1: (**a**) Architecture of the CNN-LSTM model. (**b**) Architecture of the GRU-CNN model. (**c**) Architecture of the ensemble model.  
causal padding, and the ReLU activation function; a one-dimensional global max-pooling layer; a batch-normalization layer; a dense layer with 10 units and the ReLU activation function; a dropout layer with a rate of 0.2; and a dense layer with a prediction window size of units and the ReLU activation function. In the GRU-CNN model, the GRU layer returns a sequence, and the one-dimensional global max-pooling layer takes only important features and reduces the dimension of the feature map. The architecture of the proposed GRU-CNN model is illustrated in Figure 1, while the configuration is listed in Table 1.