esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Life-Cycle Cost Analysis of Small Particle-Size Asphalt Pavement  
In this section, life-cycle cost analysis (LCCA) was used to assess the economic effectiveness of small particle-size thin overlay on an SMA asphalt pavement and a typical Hot Mixed Asphalt (HMA) \begin{table}
\begin{tabular}{c c} \hline
**Mixture** & **Bending Strain Energy Density/(kJ/m3)** \\ \hline SMA-I & 14.75 \\ SMA-II-1 & 17.72 \\ SMA-II-3 & 20.38 \\ SMA-II-2 & 24.28 \\ \hline \end{tabular}
\end{table} 
Table 20: Bending strain energy density of asphalt mixtures. Figure 13: Relationship between load and flexural strain of low temperature bend test at \(-10\)\({}^{\circ}\)C, at loading rate of 50 mm/min. pavement. LCCA is a method based on the principles of economic analysis, which has been widely applied in pavement design and management [43]. Among the economic indices of the economic evaluation of projects, the internal rate of return (IRR), equivalent uniform annual cost (EUAC), benefit/cost ratio (B/C) and Net Present Value (_NPV_) are the most commonly used. In this case study, Net Present Value (_NPV_) was selected as the economic indicator. Equation (6) can be applied for an asphalt pavement case [43]. \[ \begin{array}{c}\mathit{NPV}=\mathit{Initial}\quad\mathit{Cons.Cost}+\sum_{ K=1}^{N}\mathit{Future}\;\;\mathit{Cost}_{K}\bigg{[}\frac{1}{(1+i)^{n_{k}}} \bigg{]}-\mathit{Salvage}\;\;Value\bigg{[}\frac{1}{(1+i)^{n_{e}}}\bigg{]}\end{array}  \tag{6}\]  
where:  
\(N\) = number of future costs incurred over the analysis period,  
\(i\) = discount rate in percent,  
\(n_{K}\) = number of years from the initial construction to the Kth expenditure,  
\(n_{e}\) = analysis period in years. In this case study, it is assumed that the AC-13 thin overlay was applied to the ordinary HMA pavement every five years. The SPS thin overlay was applied to an SMA pavement every seven years because of the good crack resistance, there will therefore be one less thin overlay treatment than an ordinary HMA pavement in a 20-year service life. For initial construction cost, according to average market price, the price of ordinary HMA was set to $70/ton, the price of small particle-size asphalt mixture was set to $100/ton. The analysis results can be seen in Table 21. The design life was 20 years, the discount rate in percent (\(i\)) was 3%, the annual maintenance cost of a new HMA pavement was 11% of the initial construction cost and the annual maintenance cost of a new SMA pavement was 9% of the initial construction cost. The total cost was calculated based on one 3.5 m-wide lane/km. Table 21 shows that, compared with a traditional HMA pavement, the initial cost of an SMA asphalt pavement with an SPS asphalt mixture top layer increases from $107,939/km to $109,652/km. But the maintenance cost decreases by 20.5% (from $228,965 to $181,952) because of the reduction of maintenance times. Across the whole service life, the total NPV decreases by 13.4% (from $336,904 to $291,604). Considering the increase of user delay costs during the maintenance period and the high
## 2 Experimental Setup and Methods
### Experimental Equipment and Testing System
The camera is arranged from the tip to the root of the wing to avoid the stabilizer shielding the spatial flow field on the rudder during the flow field measurement. The Dantec Company developed the PIV system used in the experiment, mainly composed of a workstation, controller, particle generator, high-resolution CCD camera, and synchronizer. The double-pulse laser model is Beamtech Vliet-500. The maximum output power is 500 mJ/pulse, the pulse laser wavelength is 532 nm, and the pulse time is 15 ns. The beam waist thickness of the adjustable focal length sheet light source is 1 mm, and the focal length is adjustable within the range of 0.2 m\(\sim\)2 m. The CCD camera is Flowsense EO 4M with a pixel resolution of 2048 \(\times\) 2048. The sampling rate was set at 10 Hz, and 100 snapshots were averaged to obtain a steady-state spatial flow field. The raw material of the tracer particle is DEHS (diethyl hexyl sebacate), and the particle diameter is less than 5 \(\upmu\)m. It has good properties in the flow field, low toxicity, and noncorrosive and stable chemical properties.
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 2 Related Research
In [20], [PERSON] selected nine parameters and used the method of system dynamic cluster analysis to divide the stuck pipe accidents into three categories: mechanical stuck pipe, differential pressure stuck pipe and circulation stuck pipe. In [21], [PERSON] have obtained the downhole engineering parameters according to the downhole measuring tools and established an intelligent model using stochastic forest algorithm. In [22], [PERSON] have developed three indicators based on mudlog data, which aim to detect three different physical phenomena associated with the insurgence of stuck pipe. A statistical model that relates these features to documented stuck-pipe events was then developed using advanced machine learning tools. Through the analysis of the above related research, it is found that the stuck pipe analysis method based on knowledge-driven model is mainly used for monitoring and qualitative analysis of stuck pipe, but it is difficult to achieve prediction and quantitative analysis. Meanwhile, the accuracy and reliability of data-driven stuck pipe analysis method need to be further improved due to the number and quality of stuck pipe samples. Therefore, on the basis of previous studies, we put forward a comprehensive evaluation method which combines the knowledge-driven model with the data-driven model through fuzzy mathematics theory.
## 5 Numerical Results and Performance Analysis
### Numerical Results
#### 5.2.4 Serviceability  
The average serviceability of a network indicates the ratio of NSIs that substrate networks can serve simultaneously for a given set of concurrent slice requests. In Figure 10, we analyze the relationship between the latency threshold of eRTC slice and the serviceability of three models under the same simulation scenario. As expected, our model has the highest growth trend and can reach more acceptance with extreme service requirements, for example, approximately 85% acceptance when the latency threshold of eRTC slice is set to the common value, 80 ms. It reveals that our model can simultaneously serve a larger scale of concurrent NS requests or support lower latency. When relaxing the latency threshold of the eRTC slice to 110 ms, the serviceabilities of our model and NSDP scheme reach 100% acceptance. However, the QoS of eRTC will decline with poor transport network latency, as its latency threshold is set to 110 ms, which is much higher than the extreme latency requirements. Therefore, the advantages of our model are no longer obvious after increasing the certain latency threshold. The reason is that our model pays more attention to reduce the transport network latency, rather than attaining the optimal use of network resources.  
Figure 9: Average substrate link load in three-layer networks comparing among proposed model, NSDP and RFPA.
###### Abstract  
With the continuous development of science and technology, laser ranging technology will become more efficient, convenient, and widespread, and it has been widely used in the fields of medicine, engineering, video games, and three-dimensional imaging. A time-of-flight (ToF) camera is a three-dimensional stereo imaging device with the advantages of small size, small measurement error, and strong anti-interference ability. However, compared to traditional sensors, ToF cameras typically exhibit lower resolution and signal-to-noise ratio due to inevitable noise from multipath interference and mixed pixels during usage. Additionally, in environments with scattering media, the information about objects gets scattered multiple times, making it challenging for ToF cameras to obtain effective object information. To address these issues, we propose a solution that combines ToF cameras with single-pixel imaging theory. Leveraging intensity information acquired by ToF cameras, we apply various reconstruction algorithms to reconstruct the object's image. Under undersampling conditions, our reconstruction approach yields higher peak signal-to-noise ratio compared to the raw camera image, significantly improving the quality of the target object's image. Furthermore, when ToF cameras fail in environments with scattering media, our proposed approach successfully reconstructs the object's image when the camera is imaging through the scattering medium. This experimental demonstration effectively reduces the noise and direct ambient light generated by the ToF camera itself, while opening up the potential application of ToF cameras in challenging environments, such as scattering media or underwater.  
time-of-flight; computational correlation imaging; scattering media; compressed sensing; untrained neural network +
Footnote †: journal: _Photonics_
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 7 Conclusions  
This paper describes the effective implementation of 5G NR multiband OFH utilising Convolutional Neural Networks to reduce RoF nonlinearities. To begin, a novel CNN approach was developed that not only has less complexity than conventional machine learning methods but also performs better with a 75% decrease in complexity overheads and multiplications. The theoretical foundations and pieces needed to design a Neural Network are also explained in the article. To a distance of 10 km, 5G New Radio multiband transmissions at 3 GHz and 10 GHz are used. The proposed CNN-DPD approach reduces EVM by 8% to 2.1% bringing the performance within 3 GPP limits.
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
###### Abstract  
Considering the unknown compound interference in manufacturing systems, the finite-time tracking and synchronization performance of the multi-motor system significantly affects the production safety, reliability, and quality, which can be considered a multi-agent system with unmeasured speed and uncertainty. In recent years, the synchronous control schemes of the multi-motor system have grown to maturity, but the research on the speed sensorless finite-time consensus tracking control remains to be extended. This paper proposes an observer-based leader-follower consensus tracking control for the synchronous coordination control of the multi-motor system. The speed and position of all motors can be tracked by consensus in a finite time when only some motors realize partial interaction. First, a finite-time observer is designed to estimate the unknown composite disturbance and unmeasurable speed variable of each motor. Second, the distributed finite-time consensus tracking control protocol is designed using the observed value and local information interaction. The stability of the overall closed-loop system is theoretically analyzed based on Lyapunov theory and graph theory, which shows that the consensus tracking error converges to an arbitrary small neighborhood of zero, and all signals are globally bounded in finite time. Finally, simulation results are provided to illustrate the effectiveness of the proposed control method.  
leader-follower; multi-motor system; finite-time observer; finite-time consensus; multi-agent system +
Footnote †: journal: Applied
## 5 Experiment and Evaluation
### Benchmark Evaluations
#### 5.2.1 Cross-Subject Evaluation  
We split the 10 subjects into training and testing groups. The training group consisted of seven randomly selected subjects; the remaining subjects were reserved for testing. For this evaluation, the training and testing sets had 1585 and 584 samples, respectively. The IDs of training subjects in this evaluation were 2, 4, 6, 10, 13, 16, and 18. The remaining subjects were reserved for testing.
## 7 Conclusions  
Hepatocellular carcinoma (HCC) and liver abscess are among the most dangerous liver diseases worldwide. Due to the need to diagnose liver disease based on ultrasound images, developing a computer-aided diagnosis (CAD) system will greatly assist inexperienced physicians in their decision making. Therefore, this research proposes a system to reduce the erroneous classification of HCC and liver abscess. First, 96 textural features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) were extracted from the regions of interest (ROIs), which were verified by radiologists and recognized by biopsy. To obtain the important features, we applied the following three feature selection schemes: (i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score. Then we determined the most discriminative feature set. Finally, the support vector machine (SVM) classifier was trained from the features of the training set and tested by 10-fold cross-validation to obtain a reliable result. The final results show that the proposed methods for a CAD system can provide diagnostic help while distinguishing HCC from liver abscess with high accuracy (up to 89.25%) of identification.  
Conceptualization, S.S.-D.X., C.-C.C. and C.-T.S.; Formal analysis, S.S.-D.X., C.-C.C., C.-T.S. and P.Q.P.; Funding acquisition, S.S.-D.X., C.-C.C. and C.-T.S.; Investigation, S.S.-D.X., C.-C.C., C.-T.S. and P.Q.P.; Methodology, S.S.-D.X., C.-C.C., C.-T.S. and P.Q.P.; Project administration, S.S.-D.X. and C.-T.S.; Resources, C.-C.C. and C.-T.S.; Software, P.Q.P.; Supervision, S.S.-D.X.; Validation, S.S.-D.X. and P.Q.P.; Visualization, C.-C.C. and C.-T.S.; Writing--original draft, S.S.-D.X., C.-C.C., C.-T.S. and P.Q.P.; Writing--review & editing, S.S.-D.X., C.-C.C., C.-T.S. and P.Q.P.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No Feature** & \multirow{2}{*}{**SFS**} & \multirow{2}{*}{**SBS**} & **F-Score** & **F-Score** \\  & **Selection** & & & **(Threshold)** & **(Search All)** \\ \hline GLCM + SVM & 75.75\% & 78\% & 75.5\% & 76.25\% & 77.25\% \\ GLRLM + SVM & 54.53\% & 88.13\% & 88.25\% & 87.125\% & 87.125\% \\ All features + SVM & 61\% & 89.25\% & 88.87\% & 87.375\% & 88.875\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overall results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Threshold** & **Search All** \\ \hline GLCM & 76.25\% & 77.25\% \\ GLRLM & 87.125\% & 87.125\% \\ GLCM + GLRLM & 87.375\% & 88.875\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison between searching all and using a threshold.  
**Acknowledgments:** This research was supported in part by Taipei Medical University (TMU), Taiwan, and National Taiwan University of Science and Technology (NTUST), Taiwan, under the Grants TMU-NTUST-100-11, TMU-NTUST-101-10, and TMU-NTUST-103-12, and by the Ministry of Science and Technology (MOST), Taiwan, under the Grant MOST 107-2221-E-011-145.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Hardware Acceleration of Satellite Remote Sensing Image Object Detection Based on Channel Pruning
## 2 Related Work  
### Lightweight Network  
Using a lightweight network as the backbone feature extraction network can better deploy CNN models to embedded devices. At present, the mainstream lightweight networks mainly include the ShuffleNet series [20], NasNet [21], GhostNet [19], SqueezeNet, WeightNet, MicroNet, the EfficientNet series, the MobileNet series, etc. SqueezeNet reduces the number of input channels for pooling and images in the network model by employing \(1\times 1\) convolution and packet convolution [22]; The SqueezeNext network structure borrows from the residual structure and uses the separation convolution reduction operation [23]. Figure 1 shows the network architecture diagram of ShuffleNet, MobileNetv1, MobileNetv2, and WeightNet. In the ShuffleNet series of networks, the ShuffleNetv1 network proposesa channel shuffle operation, which can not only reduce the amount of network computation, but also increase the convolution dimension; The ShuffleNetv2 network proposes a channel split operation, which divides the input features into two parts, which can achieve the effect of feature reuse while achieving the effect of accelerating the network [24]; The WeightNet network integrates CondConv and SENet on the weight space, and directly generates convolution kernel weights by adding a packet fully connected layer after the activation vector, thereby improving the computational efficiency [25]; MobileNetv1 is a lightweight network developed by Google for the deployment of neural networks on the mobile terminal, using depthwise convolution (DWC) and pointwise convolution (PWC) to build deep separable convolution greatly improves the running speed, but also bringing a large amount of information loss of images [26]; MobileNetv2 network is a lightweight convolutional neural network launched by Google in 2018, which has two innovations compared to the MobileNetv1 network: inverted residuals (inverted residual structure) and linear bottlenecks. The inverted residual structure is first on the input feature matrix, through \(1\times 1\) convolution to upgrade the dimension and increase the channel size. Then, the DW convolution kernel of \(3\times 3\) is used for convolution processing, and finally the dimensionality reduction is carried out by the convolution kernel of \(1\times 1\)[27].  
Figure 1: Comparison of convolutional blocks with different network structures.
# [PERSON]: An HPC Job Scheduler Using Deep Reinforcement Learning and Remaining Time Prediction
## 4 Experimental Results
### Convergence Behavior  
The policy function of RLSchert is learned by the policy gradient (PG) algorithm, which is randomly initialized. Specifically, we used the state-of-the-art method PPO. The convergence curves of RLSchert are shown in Figure 6(a)-c. It can be seen from the policy convergence curve that the PPO initially maintains a similar quality to the random policy and rapidly improves the policy quality. After training to 100 steps, the PPO algorithm surpasses the best static scheduling policy, the SAF, and converges to the optimal policy after 250 steps. From Figure 6(a)-c, we notice that RLSchert performs better than DeepRM, which only considers the runtime prediction before the job runs. In addition, RLSchert is better than RLSchert without the kill policy, which shows that the kill policy according to the rules is reasonable. Since the heuristic policy does not consider the future available resources in the cluster, it has no effect on them based on the time predictions before and during the job runs, which is shown as a straight line in the figure.  
In addition, we verified the policy convergence behavior of each scheduler on the completion time metric, as shown in Figure 8. The experimental results are similar to the convergence curve of the slowdown. The policy training was completed at about 200 steps, and RLSchert was also significantly better than the heuristic static policies. The SAF scheduler can also be surpassed at about 100 steps by RLSchert.  
The kill policy used in the previous experiment is a rule-based policy. If the policy network can learn when and which job needs to be killed, the effect may be better than the fixed policy. Therefore, we added the kill policy as an additional action to the policy network and trained it together with selecting the job. The addition of the kill policy significantly increases the action dimension, and the state transition function of the simulator is more complicated. In order to train the kill policy effectively, we used the \"rule-based kill\" and \"RLSchert without kill\" as the \"teacher\" policies, let the agent pretrain the RLSchert policy through imitation learning, and then performed PPO reinforcement learning on the pretrained model. Imitation learning reduces the random exploration in the simulator and ensures that the agent starts with a better policy to improve. After completing the  
Figure 8: The convergence behavior of each scheduler on the completion time metric.  
Figure 7: The convergence behavior of each scheduler on the average slowdown.  
two stages of imitation learning and reinforcement learning, the PPO-based dynamic kill policy finally surpasses the rule-based kill policy. The kill policy training curve of imitation learning and reinforcement learning is shown in Figure 9.
## 6 Conclusions  
We have presented the development and several applications of a flexible multilayer Si-SiN CMOS-compatible photonics platform. Regarding datacom applications, we have shown how SiN allows the realization of robust and quasi-temperature insensitive (de-)multiplexers for CWDM optical transceivers, as well as for broadband fiber grating couplers. Another promising application of SiN in telecom and datacom is for frequency comb generation, which we have demonstrated in a non-annealed Si\({}_{3}\)N\({}_{4}\) micro-resonator. Finally, by exploiting the wide transparency range of SiN, we presented a functioning integrated OPA for 2D beam steering, operating in the near infrared, at 905 nm. In the context of silicon photonics platform maturation, these results show the versatility, as well as the relative simplicity, of SiN guiding layer integration, thus opening the way to a wide range of large-scale and low-cost applications.  
Conceptualization, C.S., D.F., S.M. and S.O.; Investigation, Q.W., H.E.D., N.T., C.S., D.F., S.G., M.C., S.K., K.H. and A.K.; Project administration, X.L., L.K.O., W.R., B.S. and S.O.; Supervision, C.S., D.F., S.M., C.M., X.L., M.P., K.Y., L.K.O., W.R. and S.O.; Writing--original draft, Q.W., C.S. and D.F.; Writing--review & editing, Q.W., D.F. and S.O.  
This work has received funding from the European Union Horizon H2020 Program (H2020-ICT27-2015) under grant agreement COSMICC n\({}^{\circ}\)688516, the DOPT 2020 internal funding program (CEA-LETI), the Danish Research Council SPOC (DNRF-123) center of excellence silicon photonics for communications (SPOC) (DTU), the ERC H2020 GRAPHICS (648546), the Institut Universitaire de France (INL), and the French Direction Generale des Entreprises (DGE) via the DEMO3S project.  
Figure 7: (**a**) Cross-sections in \(\phi\) and \(\theta\) of the main lobe to determine the FWHM. (**b–d**) Examples of output beam intensity profiles showing beam steering in two dimensions.  
**Acknowledgments:** The authors acknowledge [PERSON], [PERSON] and [PERSON] for valuable help in the measurement setup mounting, and [PERSON], [PERSON] and [PERSON] for their help in the fabrication process.  
**Conflicts of Interest:** The authors declare no conflict of interest.