esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
###### Abstract  
Visual tracking is one of the key research fields in computer vision. Based on the combination of correlation filter tracking (CFT) model and deep convolutional neural networks (DCNNs), deep correlation filter tracking (DCFT) has recently become a critical issue in visual tracking because of CFT's rapidity and DCNN's better feature representation. However, DCNNs are often complex in structure, which most possibly results in the conflict between the rapidity and accuracy of DCFT. To reduce such conflict, this paper proposes a model mainly including: (1) Based on the pre-pruning network obtained by feature channel importance, an optimal global tracking pruning rate (_GTPR_) is determined in terms of the contribution of filter channels to tracking response. (2) Based on (_GTPR_), an alternative convolutional kernel is defined to replace non-important channel kernels, which leads to the further pruning of the feature network. (3) An online updating pruned feature network with a structural similarity index is employed to adapt the model to tracking scene changes. (4) The proposed model was performed on OTB2013; experimental results demonstrate the model can effectively enhance speed with a 45% increment while guaranteeing tracking accuracy, and improve tracking accuracy with a 4% increment when tracking scene changes take place.  
object tracking; correlation filter; deep convolutional neural network; network pruning; network online updating +
Footnote †: journal: Medical Image Processing  
1
## 3 Results
### Thermoluminescence (TL) and PersL  
We measured the TL and PersL of sample A10. The results are shown in Figure 10a,b. The excitation in all cases is performed using 254 nm emitting UV light for 5 min. The choice of 254 nm light was based on the PL excitation spectrum. The TL results obtained immediately (after stopping excitation) show an initial signal at room temperature followed by a maximum at 175 \({}^{\circ}\)C, cf. Figure 10a. The initial signal at room temperature is a clear indication of PersL in sample A10. The reason for PersL is the charge trapping and detrapping at room temperature, suggesting that room temperature provides sufficient energy for charges to become detrapped. These charges have an equal probability of becoming detrapped (and retrapped) at room temperature, producing PersL. We measured PersL at room temperature, and the results are shown in Figure 10b. The log-log plot shows that the signal decays almost linearly and reaches the background signal after 15 min of measurement time. The background signal is also shown for a comparison of the two signals. In the third measurement, we recorded TL after recording PersL (Figure 10a [green line]). In this case, the TL maximum is obtained at 225 \({}^{\circ}\)C. By assuming first-order kinetics and using the equation 25\(kT_{m}\)[23], the activation energy (\(E_{ac}\)) of the charge storing  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Sample** & **Emission** & **t1 (ns)** & **t2 (ns)** \\ \hline A10 & 325 & 3.25 \(\pm\) 0.04 & 11.49 \(\pm\) 0.28 \\ A10 & 346 & 3.35 \(\pm\) 0.11 & 11.61 \(\pm\) 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The variation of decay constants obtained after fitting luminescence decay curves in Figure 9.  
Figure 9: A Semi-log plot of decay curves measured at prominent emissions at wavelengths 325 and 346 nm is shown. The fitting of the decay curves is depicted using gray and yellow lines.  
defects is determined to be 1.07 eV. Considering an electron trap model, we infer that these defects are approximately 1.07 eV below the conduction band.
# Synthesis and Properties of Octet NiCr Alloy Lattices Obtained by the Pack Cementation Process
## 3 Results and Discussion
### Energy Absorption  
The energy absorption capacity wi an important technical property of porous materials. The energy absorption capacity of a hollow lattice material can be expressed by the energy absorbed per unit volume, the energy absorbed per unit of mass (i.e., specific energy absorption) and the energy absorption efficiency [34; 35; 36; 37; 38]. In general, the energy absorbed per unit volume W and unit of mass is calculated as:  
\[W=\int_{0}^{\epsilon_{\rm d}}\sigma{\rm d}\varepsilon \tag{1}\]  
\[{\rm SAE}=\frac{\int_{0}^{\epsilon_{\rm d}}\sigma{\rm d}\varepsilon}{\rho} \tag{2}\]  
To quantify the relationship between the energy absorption of different materials and the first peak stress, the energy absorption efficiency I is defined as:  
\[I=\frac{\int_{0}^{\epsilon_{\rm d}}\sigma{\rm d}\varepsilon}{\sigma_{\rm m}*10 0\%} \tag{3}\]  
where \(\epsilon_{\rm d}\) is the densification strain (the corresponding strain when the stress value reaches the peak stress value for the second time), and \(\sigma_{\rm m}\) generally refers to the first peak stress. However, there were no obvious stress peaks in the Ni and NiCr lattice alloy materials.  
Figure 9: Energy absorption capacity of different NiCr alloys.  
Therefore, it was specified that \(\varepsilon_{\text{d}}=\varepsilon_{\text{a}}=0.6\), and \(\sigma_{\text{m}}\) was the stress corresponding to the compression curve when the strain was 0.6.  
The long stress plateau zone made the octet lattice have a large energy absorption capacity. The external impact energy can be dissipated by the plastic work and friction between components during macroscopic deformation [39]. Figure 9 shows the energy absorption, specific energy absorption and energy absorption efficiency of Ni-based lattices with different components after homogenization. The change in energy absorption of the NiCr alloy lattice was similar to the change in compressive strength. The energy absorbed per unit volume by the pure nickel lattice was 0.8 g/cm\({}^{3}\), and the specific energy absorption was 2.1 J/g. The energy absorbed per unit volume by Ni-16 Cr, Ni-23 Cr, Ni-34 Cr and Ni-45 Cr lattice was 2.3 J/cm\({}^{3}\), 4.1 J/cm\({}^{3}\), 5.2 J/cm\({}^{3}\) and 6.6 J/cm\({}^{3}\), respectively, and the respective energy absorption values were 5.1 J/g, 7.7 J/g, 9.1 J/g and 10 J/g. The energy absorbed per unit volume and unit mass by the Ni-45 Cr alloy lattice were eight times and five times higher than those of the pure nickel lattice, because the lattices showed relatively high strength after homogenization [10; 21; 36; 38].  
Octet lattice materials had high energy absorption efficiency, and the energy absorption efficiency of the octet lattice of the Ni and NiCr alloys was mainly concentrated between 30% and 40%.
## 2 Materials and Methods
### Complex Terrain Recognition
#### 2.3.2 Image Infilling for Mixed Terrain
Their results indicate that the simple linear iterative cluster (SLIC) super-pixel segmentation algorithm has good performance in terms of the controllability of pixel numbers and the close degree of controllability. Aiming at segmentation, the SLIC algorithm is used for mixed terrain regions. The most super-pixels are selected as the target area in a multi-super-pixel area and the boundary pixels of the pixel coordinates of curve fitting are extracted as the terrain boundary segmentation of a complex terrain image. The procedure and results are shown in Figure 9.  
Figure 8: Distribution of terrain types: (**a**) single terrain; (**b**) mixed terrain. Figure 9: Segmentation result of mixed terrain images: (**a**) simple linear iterative cluster (SLIC) algorithm; (**b**) maximum super-pixel extraction; (**c**) filtering out smaller areas; (**d**) finding the boundary and fitting the line; (**e**) results. Classification results after image segmentation are shown in Table 3. The output labels do not match actual terrain types. For this mismatch, the number of points of interest extracted from segmented images is shown in Figure 10. Compared with the original terrain image in Figure 4, the number of feature points of segmented images is still related to terrain type but much smaller. Obviously, it is impossible to realize an accurate prediction using the segmentation image, because the feature points are inadequate. Thus, the segmented images are spliced together to enhance the terrain features. Segmented color images would have only some of the pixels of the original color image collected by the Kinect camera and the blank pixels would be infilled by duplication of the segmented image. In the test, the rotation-inversion operation is used for image infilling. The results are shown in Figure 11.  
terrain type increased after image infilling. On the contrary, confidence scores of wrong terrain types decreased. That means the proposed method can effectively magnify image features for the classifier.
## 3 Results and Discussion
### Validation of the HPLC Analytical Method Performance
#### 3.2.1 Sensitivity and Linearity of the Test Method
The established standard calibration curve correlating the HNS concentration with peak area is shown in Figure 6a. The equation for the curve is provided below:  
\[\text{y}=2.4665\text{x}+0.0567 \tag{1}\]  
where x represents the peak area of HNS measured during detection, y denotes the corresponding HNS concentration in mg/L, and the linear fit achieved an R\({}^{2}\) value of 0.9999. The calibration curve for DMF was determined using methods similar to those aforementioned. With all other conditions held constant, the detection wavelength was set at 226 nm. A standard calibration curve was established from seven data points, correlating the concentration of DMF with the corresponding peak areas, as shown in Figure 6b. The equation for the curve is provided below:  
\[\text{y}=0.0783\text{x}+2.7751 \tag{2}\]  
where x represents the peak area of DMF measured during detection, y denotes the corresponding DMF concentration in mL/L, and the linear fit achieved an R\({}^{2}\) value of 0.9999. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample Preparation** & \textbackslash{1} & \textbackslash{5} & \textbackslash{6} & \textbackslash{7} & \textbackslash{8} & \textbackslash{9} \\ \hline Sampling \textbackslash{1} (mL) & 0.000 & 0.000 & 0.000 & 0.000 & 0.600 & 1.500 \\ Sampling \textbackslash{2} (mL) & 0.125 & 0.250 & 5.000 & 7.500 & 0.000 & 0.000 \\ Sampling \textbackslash{3} (mL) & 7.375 & 7.250 & 2.500 & 0.000 & 0.000 & 0.000 \\ Corresponding HNS concentration (mg/L) & 0.50 & 1.00 & 20.00 & 30.00 & 60.00 & 150.00 \\ \hline \end{tabular}
\end{table} 
Table 4: Detailed preparation methods for standard solutions. In the selected concentration range, the mass concentrations of all target analytes exhibited a good linear relationship with the peak areas. The linear fit R\({}^{2}\) values for both HNS and DMF reached 0.9999, indicating that the method provides a robust linear response when HNS concentrations range from 0.50 to 150.00 mg/L. The detection and quantification limits, at 0.10 mg/L and 0.40 mg/L, respectively, were relatively low, demonstrating the method's high sensitivity and its compliance with analytical testing requirements.
## 4 Experimental Evaluation of the GAGAN Model  
The practical experiments within the GAGAN framework were designed to evaluate the effectiveness of integrating GAs with GANs, specifically to improve the discriminator's performance. The primary aim of this work is to address common challenges in GAN training, such as instability and mode collapse, by using GAs to optimize the discriminator's weights. This approach involves treating the discriminator's weights as individuals within a genetic population, allowing selection, crossover, and mutation to iteratively fine-tune the discriminator. By enhancing the discriminator's ability to distinguish between real and generated images, the GAGAN framework provides more accurate feedback to the generator, facilitating the production of higher-quality synthetic data.
# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Lung Segmentation Result
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **IOU(\(\uparrow\))** & **DSC(\(\uparrow\))** & **ACD(\(\downarrow\))** & **ASD(\(\downarrow\))** \\ \hline \multirow{4}{*}{JSRT} & U-Net w/o aug & 0.950 \(\pm\) 0.003 & 0.974 \(\pm\) 0.002 & 1.376 \(\pm\) 0.120 & 0.810 \(\pm\) 0.023 \\  & U-Net & 0.955 \(\pm\) 0.002 & 0.977 \(\pm\) 0.001 & 1.116 \(\pm\) 0.083 & 0.745 \(\pm\) 0.017 \\  & ACNN & 0.955 \(\pm\) 0.001 & 0.977 \(\pm\) 0.000 & 1.036 \(\pm\) 0.025 & 0.745 \(\pm\) 0.007 \\  & SRM & 0.956 \(\pm\) 0.001 & 0.977 \(\pm\) 0.001 & 1.074 \(\pm\) 0.023 & 0.732 \(\pm\) 0.010 \\  & Proposed & **0.959 \(\pm\) 0.002** & **0.979 \(\pm\) 0.001** & **0.936 \(\pm\) 0.052** & **0.691 \(\pm\) 0.013** \\ \hline \multirow{4}{*}{MC} & U-Net w/o aug & 0.940 \(\pm\) 0.006 & 0.968 \(\pm\) 0.004 & 1.547 \(\pm\) 0.156 & 0.848 \(\pm\) 0.039 \\  & U-Net & 0.950 \(\pm\) 0.005 & 0.974 \(\pm\) 0.003 & 1.212 \(\pm\) 0.148 & 0.749 \(\pm\) 0.032 \\ \cline{1-1}  & ACNN & 0.953 \(\pm\) 0.005 & 0.976 \(\pm\) 0.003 & 1.069 \(\pm\) 0.154 & 0.727 \(\pm\) 0.044 \\ \cline{1-1}  & SRM & 0.952 \(\pm\) 0.003 & 0.975 \(\pm\) 0.002 & 1.159 \(\pm\) 0.139 & 0.726 \(\pm\) 0.031 \\ \cline{1-1}  & Proposed & **0.956 \(\pm\) 0.003** & **0.978 \(\pm\) 0.002** & **1.032 \(\pm\) 0.144** & **0.687 \(\pm\) 0.026** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of lung segmentation test performance. The means and standard deviations over five runs are reported. For each dataset, the best result is shown in boldface. Figure 4: Visualization of the lung segmentation results on test samples from JSRT (**top**) and MC (**bottom**). The left part shows the results of each method, and the right part presents the results from \(f_{DAE}\to g_{DAE}\) and \(f_{SEG}\to g_{DAE}\) of the proposed method. The green color represents the segmentation results of each method, and the ground-truth is in red. As described in Section 3.2, the proposed method is designed to control the gradient flows through the embedding loss function \(L_{E}\) in Equation (3). The gradients from \(L_{E}\) do not contribute to the DAE encoder, and thus, the DAE encoder is not affected when learning to build the anatomical structure features. Table 4 illustrates the effect of the proposed strategy on the JSRT dataset. Proposed-BI represents a method in which the gradient update from \(L_{E}\) occurs in the segmentation and DAE encoder simultaneously, and Proposed-UN indicates the proposed strategy that only updates the segmentation encoder. From this experiment, we observed that Proposed-BI outperforms the baseline U-Net, which shows that constraining the feature space of the segmentation network by the autoencoder is effective to improve the segmentation performance. Moreover, the results of Proposed-UN demonstrate that preventing the gradient from \(L_{E}\) from being propagated to the DAE further enhances the segmentation performance by encouraging the DAE to effectively learn the structural information.
# User QoS-Based Optimized Handover Algorithm for Wireless Networks
## 5 Conclusions  
This paper proposes an optimized handover algorithm and an optimized handover algorithm with QoS requirements to improve the handover performance. The experimental results show that, in Scenario 1, the OHA algorithm and the OHAQR algorithm outperformed the Mininet-WiFi algorithm by 23.53% and 11.76%, respectively, in the number of handovers. The OHAQR algorithm had excellent performance and outperformed the Mininet-WiFi algorithm by 11.76%, 51.85%, 6.3%, and 37.5% in the number of handovers, Act. TP, Avg. TP, and PLR, respectively. Similarly, in Scenarios 2 and 3, the OHA algorithm and OHAQR algorithm were better than the Mininet-WiFi algorithm in terms of Act. TP, Avg. TP, and PLR. The DoTHa algorithm only had better Avg. TP and PLR in Scenario 3, but as there were more than 200 handovers in Scenarios 1 and 2, the ping-pong effect was serious. Since the OHAQR has to meet the requirements of QoS, the number of handovers was not the least, but it did have excellent performance in Act. TP, Avg. TP, and PLR.  
In future work, we will continue to explore not only improving the system performance of the handover algorithm with QoS requirements, but also continue to explore the applicability of other network service quality indicators and handover algorithms. We expect to design a general handover algorithm suitable for various network service usage scenarios to provide an efficient and stable network usage environment.  
Conceptualization, H-C.C., C-E.W., W-M.C., and H-C.L.; methodology, H-C.C., C-E.W., and W-M.C.; software, C-E.W.; validation, H-C.C., C-E.W., and W-M.C.; formal analysis, H-C.C. and C-E.W.; investigation, H-C.C., C-E.W., W-M.C., and H-C.L.; resources, H-C.C. and C-E.W.; data curation, H-C.C., C-E.W., W-M.C., and H-C.L.; writing--original draft preparation, H-C.C., C-E.W., and W-M.C.; writing--review and editing, H-C.C., C-E.W., W-M.C., and H-C.L.; visualization, C-E.W. and H-C.L.; supervision, H-C.C. and W-M.C.; project administration, H-C.C. and W-M.C.; funding acquisition, H-C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology, Taiwan, under grants MOST 110-2221-E-324-001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used in the current study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
###### Abstract  
A common concern in smart cities is the focus on sensing procedures to provide city-wide information to city managers and citizens. To meet the growing demands of smart cities, the network must provide the ability to handle a large number of mobile sensors/devices, with high heterogeneity and unpredictable mobility, by collecting and delivering the sensed information for future treatment. This work proposes a multi-wireless technology communication platform for opportunistic data gathering and data exchange with respect to smart cities. Through the implementation of a proprietary long-range (LoRa) network and an urban sensor network, our platform addresses the heterogeneity of Internet of Things (IoT) devices while conferring communications in an opportunistic manner, increasing the interoperability of our platform. It implements and evaluates a medium access communication (MAC) protocol for LoRa networks with multiple gateways. It also implements mobile Opportunistic VEhicular (mOVE), a delay-tolerant network (DTN)-based architecture to address the mobility dimension. The platform provides vehicle-to-everything (V2X) communication with support for highly reliable and actionable information flows. Moreover, taking into account the high mobility pattern that a smart city scenario presents, we propose and evaluate two forwarding strategies for the opportunistic sensor network.  
s 20181184; 10.3390/s18041184
## 1 Introduction
### Paper Contributions and Structure  
There are several unique contributions that this paper makes when compared to other works available in the literature that has been studied by the authors of this proposal:  
1. A protocol that has been specifically defined for underwater environments is put forward. This has been done so by collecting information among the consortium members of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) research project [20], which have provided their feedback regarding the kind of information that must be transferred among the different components of a CPS with autonomous maritime vehicles.
2. After the design was completed and deemed satisfactory, an implementation of that protocol was carried out. This was done so as a way to test the reliability of the Protocol Data Units (PDUs) that were previously designed, as well as the possibility to develop an actual piece of work based on a regular programming language. Java has been used for such a purpose, although it was not the only software resource that has been utilized.
3. The underlying implementation works that were performed made use of Data Distribution Service (DDS) as a way to provide data interoperability among the different devices that can be integrated by the same protocol. While DDS facilities imply several software developments that have been already tested in constrained environments (such as a Data-Centric Publish/Subscribe--referred to as DCPS--or a Real-Time Publish Subscribe--RTPS--wire protocol used for interoperability among DDS solutions), the protocol that has been designed and implemented is completely new and, to the best of the authors' knowledge, there is not a development like this in any other proposal with this kind of characteristics.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 8 Land-Based Systems Implementation--Other Projects
### Technical Contributions Provided by the Xi'an University of Technology  
A research group working at the Xi'an University of Technology has provided relevant contributions to the development of the electronics employed to drive the piezoelectric transducers that excite ultrasonic guided waves in rails. Their specific contributions include:  
* The design of a high-voltage pulser achieving an improved transmission efficiency [63].
* The development of a tracking method able to estimate the optimal excitation frequency, i.e., the one maximizing the received signal power [64].
* The use of Baker coded UGW signals for enhancing the SNR of the received signal and the development of an adaptive peak detection algorithm [78].  
As far as the last point is concerned, it is worth mentioning that Baker coding, albeit simple, is characterized by a autocorrelation with lower sidelobes than those observed in the autocorrelation of binary codes having the same length; moreover, its simplicity enables minimizing hardware complexity at the transmit side. It is also worth mentioning that the proposed peak detection algorithm offers the advantage of a low energy consumption and achieves better performance than other more sophisticated techniques, like those based on the discrete wavelet transform [79].