esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Thermoelectric Conversion and Conjugate Heat Transfer for PCBA by Finite Element Analysis
## 1 Introduction  
High thermal resistance for printed circuit board assembly (PCBAs) will decrease the heat transfer efficiency, and the performance reduction is inevitable for the power chips on PCBAs, due to the over-heat problem. Conventionally, filling the gap between the chips and the board with thermal grease or thermal Interface materials (TIMs), it is expected that the capability of thermal conductivity will be increased to overcome the overheating problem for the power chips on the PCBA. Taking light-emitting diode (LED) lights as an example, LED lights have excellent luminous efficacy and energy-saving features, as compared to those of the traditional bulbs. In industry, high brightness and illuminance is achieved by multiple LED arrays. In the case of single LED light chip, the input voltage will be raised to qualify the requirements for brightness and illuminance. No matter what the solution is, using LED chip arrays or raising the input voltage, a non-uniform and over-heating temperature distribution is often observed for the PCBAs inside the LED lamps. The corresponding result is the performance and working life reductions for LED lights. Another similar case is the electronic virtual reality devices (VR). Due to the huge and fast operations for image processing, VR devices have several high-power chips which are considered as heat sources on the PCBA. As thepower input is high and the volume of the VR device is small, cooling the power chips will be very difficult. Obviously, the traditional TIMs or grease is not enough for the cooling requirements for the up-to-date electronic devices. There should be new heat sinkers or dissipators which are able to solve the over-heat problems for the powerful and compact electronic devices.  
In this study, the conjugate heat transfer model provided by the commercial finite volume method (FVM) software, FLUENT, will be used for the analysis about the temperature distribution for the electronic devices with high-power chips and compact volumes, such as LEDs and VRs. The values of power for the dominant chips on the PCBA will be derived experimentally by the optical sphere and the power analyzer. In the FEA model, the power chips will be simulated as heat sources. Heat by conduction will pass through the printed circuit board from the power chips. On the boundary between the PCBA and air, heat transfer by convection will happen. In addition, when the power chips are applied with TIMs of graphene, the heat transfer by radiation will be introduced in the analytic model. Finite space will be constructed to simulate the cases for LED lights. The results of simulation will be verified by those data obtained by the experiments, which confirms the validity of the proposed FEA model.
# Aircraft Wake Recognition Based on Improved ParNet Convolutional Neural Network
## 6 Conclusions  
This paper proposes a new method of aircraft wake recognition based on the improved ParNet. In the improved ParNet, DSC is introduced to significantly reduce the model's parameters. Concurrently, to improve the model's capability to extract spatial features, the CBAM replaces the parallel attention module in the original model. Considering the wind field data of Hong Kong International Airport obtained by lidar as the dataset, this paper discusses the recognition effects of the ParNet and its different improved versions on wake.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Value (\%)** & **MFLOPs** \\ \hline SVM & 93.59 & 94.85 & 92.19 & 93.50 & — \\ KNN & 92.65 & 94.17 & 90.94 & 92.53 & — \\ CNN & 94.84 & 96.14 & 93.44 & 94.77 & 0.12 \\ VGG16 & 98.44 & 98.74 & 98.13 & 98.43 & 15.00 \\ Improved ParNet & 98.91 & 99.06 & 98.90 & 98.90 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition effect of different recognition models.  
The DSC and the CBAM are experimentally shown to be able to considerably improve the recognition performance of the model. Further experimental results show that the performance of the proposed aircraft wake recognition model is optimal compared with that of the SVM, KNN, traditional CNN, and VGG16 in terms of recognition effect and required software and hardware costs. The method based on the improved ParNet proposed herein can realise the accurate identification of aircraft wakes in complex environments.  
Conceptualization, Y.M.; methodology, Y.M. and J.Z.; software, J.Z. and H.H.; supervision, P.-w.C. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number U1833111.  
Not applicable.  
The data used to support the findings of the study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet
### Summary  
The above internetworking strategy of marine Internet tries to exploit any communication resources available in oceans as much as possible without relying on any particular one. This makes marine Internet very robust with strong viability. Among them, the ICN plays an important role to provide cost-effective marine Internet services in water areas near coastlines, and various types of WANETs can intensify, extend such service and provide opportunistic networking. The satellite networks are used as reliable backups.
# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 2 Infrared and Visible Image Fusion Methods
### Methods Based on Deep Learning  
Deep learning is used to perform feature extraction, data fusion, and image reconstruction in order to get around the constraints of conventional techniques. The deep-learning-based image fusion technique can utilize the network's capacity for learning and adaptively train and update the model parameters to create an end-to-end input-output mode. Deep learning significantly lessens the impact of human factors on the results of fusion by avoiding activity-level assessment and fusion rule design in comparison to conventional methods. Furthermore, the deep learning technique fully utilizes the capacity of network feature extraction and fully stores the supplementary data of the original image in the fused image, enhancing the fused image's quality. A range of deep-learning-based fusion algorithms for combining infrared and visible images have evolved in recent yearsas a result of the growth of deep learning. Fusion methods based on deep learning can be classified into the following groups: convolutional neural network-based methods, generative adversarial network-based methods, and automated encoder-based methods, depending on the various traits and guiding principles of the algorithms. Table 2 displays the benefits and drawbacks of each deep-learning-based technique.
## 3 Materials and Methods
### Dataset  
The dataset used in this study was obtained from the delivery operations belonging to the Chiang Rai post office in the Chiang Rai province of Thailand. The operations mainly involved postal delivery service to residential and commercial locations. The Chiang Rai post office holds delivery duties for customers in the area of the Viang and Rob-Viang districts, which are clustered into six delivery zones. Detailed data of the delivery operation were collected for 50 days, tracking two delivery vehicles equipped with Global Positioning System (GPS)-enabled android devices with location-tracking applications installed. The average number of customer locations to be delivered to per day by both vehicles was 97.22. Those two vehicles were motorcycles, each with large parcel pouches strapped on both sides, and both had duties to deliver parcels to customers in specific areas under the Chiang Rai post office's responsibility.  
The GPS devices recorded routing data, including all customer locations and where the vehicles visited, in geographic coordinate form. Then, for each day of delivery operations, the total travel distance of both vehicles was extracted. Moreover, the stored latitude and longitude coordinates, which corresponded to the post office and customer locations, were used to construct a distance matrix of \(n\times n\) dimensions, where \(n\) was the number of all location coordinates. Each cell of the distance matrix represented the smallest distance between a pair of location coordinates, and this distance value was determined by a python script using the googlemaps module from the Google Maps Application Programming Interface (API) [31]. The obtained matrix might not have been the symmetric type, since the shortest route from one coordinate to another may not have been the same the other way around, taking into account that one-way traffic could be enforced in some routes. In total, 50 matrices were constructed, each one representing one day of delivery operations. These matrices were essential inputs to the optimization algorithms, acting as a lookup distance table.
# Analysis and Design of Single-Phase Unidirectional Transducers with High Directivity
## 4 Experimental Results and Discussion  
The other COM parameters can also be obtained by the FEM method; then, the P-matrix for each EWC-SPUDT cell, which is a very effective tool for analyzing and designing SAW devices, can be constructed [41; 42; 43]. The overall response of the whole device can be determined by the cascading of the P-matrixes of each individual part of the device. In this way, we can easily calculate the transmission scattering parameters (\(S_{21}\)) of a SAW delay line and further calculate its insertion loss (by the equation \(IL=-20\log_{10}(S_{21})\)) through combining the input IDTs and the output IDTs with electrical source or load. Therefore, the theoretical value of electrode insertion loss can be calculated by COM theory, while the experimental value can be obtained by network analyzer (Agilent E5061B).  
Figure 9 shows the calculated and experimental results of transducers with the optimized EWC-SPUDT cells. These results include the calculated and experimental responses with the transducers face-to-face, and the experimental response of the transducers back-to-back as a contrast. The results indicate that the simulation results coincide with the experimental results expected at the side-lobes, that is to say, the experiment confirms that our theoretical analysis process is reasonable. It is easy to see that the response of the transducers based on two structures is indeed different near the center frequency, and that the transducers face-to-face have better performance than the other one.  
Figure 8: Image of the fabricated test device with transducers face-to-face.  
Figure 7: Fabrication procedure for a test device.  
In addition, as a contrast, two SAW delay lines with traditional EWC-SPUDT cells were designed, fabricated, and measured. The insertion loss of the transducers face-to-face (\(IL=-20\log_{10}(S_{21})\)) and back-to-back (\(IL=-20\log_{10}(S_{21}^{\prime})\)) are shown in Figure 10. The results show that the response near the center frequency with transducers face-to-face is better than with the transducers back-to-back, but the difference is not obvious.  
The elements of P-matrix are related to the scattering coefficient, therefore, the directivity \(D\) in logarithmic form can also be approximately expressed as \(-20\log_{10}(S_{21}/S_{21}^{\prime})\). The frequency dependence of \(D\) for an optimized EWC-SPUDT compared with that for a traditional EWC-SPUDT is shown in Figure 11. The maximum directivity of transducer based on the optimized EWC-SPUDT cells reaches 30.1 dB, which is much larger than that based on the traditional EWC-SPUDT cell. It suggests that the optimized EWC-SPUDT cell can be used to achieve miniaturization and higher performance in SAW device design.  
Figure 10: Frequency responses of the test devices with traditional EWC-SPUDT cells.  
Figure 9: Frequency responses of the test devices with optimized EWC-SPUDT cells.
# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 5 Conclusions  
As discussed throughout the paper, LMD is critical in supply chains, and E-commerce logistics providers must stay up-to-date with industry standards. Logistics for LMD are complex, inefficient, and costly, and in response to customer demand, many businesses are racing to provide faster, more efficient, and accurate delivery. To meet customer needs and gain a competitive advantage, new technologies are introduced and implemented. According to the references in this paper, AI has an impact on LMD's challenges and opportunities. Hence, this literature review aimed to frame and maximize the best technologies in LMD.  
As a result, it has been demonstrated that AI-powered technologies increased efficiency, decreased costs, and opened up new avenues for improving logistics and businesses. Companies that incorporate new technologies into their business development strategies perform better than those that do not. Powerful AI can aid in route optimization, but it is frequently rigid and does not adapt to a changing environment, rendering it obsolete. LMD can use AI to detect patterns in historical data, build algorithms based on real-world data, and optimize delivery routing. The advantages of AI in optimization extends beyond routing. Autonomous cars, drones, and robots, for example, use AI to minimize labor costs while enhancing productivity. While a fully AI-based system for last-mile logistics is still in its early stages, with parcel-pooling in warehouses for the most efficient delivery route and being delivered by autonomous vehicles and robots, industry leaders, such as Amazon and DHL, are embracing existing AI-driven technologies (e.g., autonomous vehicles, robots, route-planning systems) and driving the industry's development.  
Moreover, it has been identified that big data science and ML are data-driven decision-making technologies in the AI category and can help businesses rationalize decision-making and, with further development, assist them with realistic forecasts and so on. New markets are being created by AI's new technologies and many LMDs have jumped on the innovation bandwagon and begun their journey toward a smarter future. LMD's AI-powered autonomous vehicles, drones, and robots improve productivity and cost optimization. Additionally, AI-powered routing optimization assists businesses in lowering emissions and meeting sustainability targets.  
However, modern LMDs, focused on AI-powered technologies, are feasible and beneficial but existing challenging and limitations are impeding their development. To conclude, AI implementation provides numerous opportunities for modern LMD, but it can not be fully realized by AI's potential in LMD or logistics until the FFF challenging categories are resolved and industry standards are established. This indicates these should be suggested by research and precisely directed by industry in order to benefit from the gains of modern LMD as much as possible.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 2 Methodology
### Date Detector  
There are commonly two object detection methods in the current deep learning field: the anchor-based object detection model and anchor-free object detection [14]. FCOS (fully convolutional one-stage object detection) [12] is adopted in this work. FCOS is an anchor-free object detection model designed to simplify the detection process while maintaining high accuracy. The basic structure of the model is shown in Figure 2.  
In this study, the backbone network is tasked with deriving features from the input image, utilizing the Repghost module [15] as the foundation. Compared to the Ghost module [16], this choice lowers computational demands on hardware while delivering enhanced accuracy with fewer parameters and similar latency, which is advantageous for mobile devices. Unlike the Ghost module, the RepGhost module eliminates the use of the inefficient concatenation operator, which considerably reduces inference time. Additionally, the process of information fusion is implicitly handled by the add operator rather than delegating it to other convolutional layers. Ghost-PAN [17] substitutes the traditional Feature Pyramid Networks (FPNs) due to the substantial computational load of typical FPNs. The network head produces three outputs: quality focal loss (QFL), distribution focal loss (DFL), and GIoU loss. QFL and DFL are based on generalized focal loss [18]. QFL is designed to refine both the classification and localization aspects of object detection, enhancing its effectiveness in dense object detection tasks. QFL, in particular, targets improving classification by emphasizing hard-to-classify samples. The QFL function is defined as (1)  
\[QFL(\sigma)=-|y-\sigma|^{\beta}[(1-y)log(1-\sigma)+ylog\sigma] \tag{1}\]  
where \(\sigma\) is the predicted classification probability, \(y\) is the annotated label, and \(\beta\) is the weight.  
GIoU ranges from \(-1\) to \(1\), where \(1\) means perfect overlap, \(0\) means no overlap, and negative values indicate that the predicted box is outside the ground truth box. GIoU function is shown in (2)  
\[GIoU=\frac{|A\cap B|}{|A\cup B|}-\frac{|A\cup B|-A\cap B}{|A\cup B|} \tag{2}\]  
where \(A\) and \(B\) are areas of the predicted and ground truth bounding boxes, respectively. \(|A\cup B|\) stands for the area of the union of the predicted and ground truth bounding boxes, while \(|A\cap B|\) is the area of the intersection of the predicted bounding box \(A\) and the ground truth bounding box \(B\).  
DFL enhances the bounding box regression by encouraging the model to predict more distributed and qualified bounding boxes,  
\[DFL(s_{i},s_{i+1})=-[(y_{i+1}-y)log(s_{i})+(y-y_{i})log(s_{i+1})] \tag{3}\]  
where \(y_{i}\) and \(y_{i+1}\) are the two closest intervals of the true label \(y\), and \(s_{i}\) and \(s_{i+1}\) are the corresponding model outputs.  
Figure 2: The architecture of the detection model.
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 3 Small-Strain Tests for Unsaturated Sand  
Small-strain shear modulus tests on unsaturated sands with different net stresses were conducted on silty-fine sand from Hangzhou Bay under the dehumidification stress path based on the improved BE-RC test device.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods
### Semantic Segmentation-Based Tea Classification Models
#### 3.3.2 SE-UNet  
The squeeze-and-excitation (SE) attention module (Figure 9) is a type of channel attention module commonly applied in visual models. Through it, channel-wise feature enhancement can be performed on input feature maps without altering their size [37]. Specifically, it first passes the input through a standard convolution operation to obtain feature maps. The formula is defined in Equation (7).  
\[U_{C}=V_{C}\times X=\sum_{s=1}^{c_{2}}V_{c_{2}}^{s}\times X^{s} \tag{7}\]  
where \(X\in R^{(h\times w\times c_{1})}\) represents the input, \(U\in R^{(h\times w\times c_{2})}\) represents the output feature map, \(V_{C}\) represents a set of convolutional kernels corresponding to channel \(C\), and this set of kernels is trainable. Next, to address channel dependency issues, global spatial information is compressed into a \(1\times 1\times c_{2}\) feature vector \(z\) through global average pooling, which defined in Equation (8).  
\[Z_{c}=F_{sq}(u_{c})=\frac{1}{h\times w}\sum_{i=1}^{h}\sum_{j=1}^{w}u_{c}(i,j) \tag{8}\]  
Figure 8: Diagram of the UNet model structure used in this study.  
Then, a gate mechanism consisting of two fully connected layers is employed. The channel is first compressed to \(c_{2}/r\), then activated through a ReLU function, and finally restored to \(c_{2}\) in the second fully connected layer. A sigmoid activation is applied to obtain the weight vector \(s\in 1\times 1\times c_{2}\), which characterizes the channel attention weights. Here, \(r\) represents the compression ratio.  
\[s=F_{ex}(z,W)=\sigma(g(z,W))=\sigma(W_{2}\delta(W_{1}z)) \tag{9}\]  
Finally, through reweighting, adaptive allocation of feature weights for each channel is achieved to implement the channel attention mechanism.  
\[\widetilde{x}_{c_{2}}=F_{scale}(u_{c_{2}},s_{c_{2}})=s_{c_{2}}u_{c_{2}} \tag{10}\]  
The SE module achieves channel weight allocation adaptively through the network without excessive manual intervention and maintains a consistent mechanism with unchanged input feature dimensions, contributing to its effective performance enhancement in various remote sensing recognition tasks. Therefore, considering the powerful feature extraction and deep-to-shallow feature fusion capabilities of the SE module combined with UNet, it can more effectively extract tea plantation features. We incorporate the SE module after every two convolution and activation function operations in the downsampling and upsampling processes of UNet to adaptively calculate channel attention. Since the SE module does not change the input feature size, its feature dimensions and spatial size changes align with UNet in the overall network (Figure 10). During the specific training process, we computed the binary cross-entropy loss function as the error to update parameters through backpropagation.  
Figure 10: Diagram of the SE-UNet model structure.  
Figure 9: Diagram of the SE module structure.