esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 6 Conclusions  
The current study represents the problem of prolonging the network lifetime in a rechargeable wireless sensor network scenario, where a wireless charging vehicle travels through the network and recharge the battery of randomly deployed sensor nodes. It has been attempted to minimize the energy consumption and maximize the vacation time of wireless charging vehicle considering the overall network lifetime. The FWA-ATF algorithm is proposed to evaluate the selection of the cluster head based on the sensor node position and remaining energy. Numerical results verify our design, which shows that our methodology outperforms in extension of network lifetime compared to the baseline method. Moreover, we have validated our approach efficiently by employing a three-layered neural network. Five algorithms are used in the artificial neural network training to evaluate the performance of the proposed method. The simulation results corroborate the superiority of the resilient backpropagation over the other algorithms applied in this study.  
The authors would like to express their deepest gratitude to the Hebei University of Technology for extending all the essential materials required for the successful completion of this research paper. My special thanks are due to China Scholarship Council (CSC) for providing timely funds towards my Doctoral study. All the authors have contributed equally to this research work. The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hidden Layer Algorithm** & \begin{tabular}{c} **Output Layer** \\ **Function** \\ \end{tabular} & \begin{tabular}{c} **Transfer** \\ **Function** \\ \end{tabular} & \
\begin{tabular}{c} **Transfer** \\ **Function** \\ \end{tabular} &
\begin{tabular}{c} **Correlation** \\ **Coefficient (R)** \\ \end{tabular} \\ \hline Resilient backpropagation & [PERSON] & Poslin & Tansig & 0.992 \\ \hline Conjugate gradient backpropagation with & Traincp & Poslin & Tansig & 0.756 \\ Polak–[PERSON] updates & [PERSON] & Poslin & Tansig & 0.731 \\ \hline Gradient descent with momentum and & & & & \\ adaptive learning rate backpropagation & & & & \\ \hline Levenberg–Marquardt backpropagation & [PERSON] & Poslin & Tansig & 0.674 \\ \hline Scaled conjugate gradient backpropagation & [PERSON] & Poslin & Tansig & 0.668 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of algorithms employed in this present study.  
Figure 6: Regression plot (experimental vs. predicted) employing 3 factors, 10 neurons present in the hidden layer, and 1 output factor applying the ANN model.
# Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs
## 4 Experiments and Results
### Baselines  
* **Character Matching**: Character matching method counts the number of characters that are the same in the question and the answer.
* **Word Matching**: Similar to the character matching method, the word matching method counts the number of words that are the same. However, this method requires word segmentation; therefore, we use two word segmentation toolkits and present a discussion of the impact of different toolkits.
* **BM25**: BM25 (Best Matching) is a ranking function in Information Retrieval (IR); it has also been used in question answer matching [8; 16]. The definition of BM25 is given as follows: \[\text{BM25}(q_{i},a_{i})=\sum_{w_{j}\in q_{i}}\text{IDF}(w_{j})\cdot\frac{f(w_ {j},a_{i})\cdot(k+1)}{f(w_{j},a_{i})+k\cdot(1-b+b\cdot\frac{|a_{i}|}{|a_{\text {leg}}})},\] (11) where \(\text{IDF}(w_{j})\) is the inverse document frequency (IDF) value of word (character) \(w_{j}\) in question, \(|a_{i}|\) is the length of the answer \(a_{i}\), \(|a|_{avg}\) is the average length of answers, and \(f(w_{j},a_{i})\) is the frequency of \(w_{j}\) in \(a_{i}\). \(k\) and \(b\) are free parameters. In our experiment, according to [PERSON] et al. [39], we set \(k=2.0\) and \(b=0.75\).
* **Average of Embeddings**: Character (Word) embeddings contain semantic and syntactic information, and an average of these can be used to directly represent features. Thus, we compute the average of each question and answer embeddings, and calculate the cosine similarity between the two average embeddings.
* **Embedding Matching**: Inspired by [PERSON] et al. [14], we compute the similarity of two sentences by calculating the similarity of words (characters). We modified the algorithm by computing the similarity of every two embeddings, and find the best matching score of each word (character) of the question. The answer with the best matching score will be chosen.
* **biLSTM**: [PERSON] et al. [2] used bi-directional LSTM networks to extract the semantic representations of questions and answers. LSTM networks are a variant of recurrent neural networks (RNN), which are capable of capturing sequence information [2; 40; 41].
# Development of a Dual-Modality Gamma-ray/Fast Neutron Imaging System for Air Cargo Inspection
## 2 Materials and Methods
### Gamma-ray Generation and Detection  
The gamma-ray beam is produced using an electron linear accelerator system (LINAC) to output gamma-rays. The LINAC is composed of an electron gun, radio frequency (RF) generator, RF cavity, vacuum system, cooling system, high-voltage power system, tungsten target, and collimator, as shown Figure 2. The generated electrons were accelerated in a gamma-ray generator system with a size of 965 \(\times\) 1515 \(\times\) 1115 mm\({}^{3}\) (width \(\times\) length \(\times\) height) and a repetition rate of 200 Hz for a tungsten target with a thickness of 2 mm. The gamma-rays produced from the tungsten target by the bremsstrahlung process have an energy of 6 MeV and a dose rate of 1.25 Gy/min. The gamma-ray beam passes through the slit collimator in front of the tungsten target and radiates towards the gamma-ray detectors.  
Figure 1: Air cargo inspection system: (**a**) image of the system, (**b**) diagram of the gamma-ray imaging system, and (**c**) diagram of the neutron imaging system.  
The gamma-ray radiation that passed through the air cargo container was detected using a gamma-ray detection tower. The structure of the gamma-ray detection tower is built from a steel frame, as shown in Figure 1b, to vertically arrange the 30 gamma-ray detection modules. They are connected to the gamma-ray control units and transmit the gamma-ray image pixel values to a user-controlled computer. One gamma-ray detection module combines CdWO4 scintillators (PSs, EPIC Crystal Co., 30,000 photons/MeV in scintillation efficiency), a Si-PIN photodiode (Si-PIN), and a control board. The PSs with a size of 4.6 \(\times\) 7 \(\times\) 30 mm\({}^{3}\) (width \(\times\) length \(\times\) height) convert gamma-rays to visible light with a wavelength of 480 nm, and the Si-PINs detect the visible light and generate electrical signals. The control board in the gamma-ray detection module counts the number of electrical signals and restructures the pixel values of the images. The number of vertically arranged image pixel values in one gamma-ray detection module was 32. The image  
Figure 2: Gamma-ray generator. The gamma-ray beam is produced from the linear accelerator (LINAC) in the tungsten target by the bremsstrahlung interaction. In front of the LINAC, the reference detector is installed.  
frame was composed of the arrangement of the image pixel values transmitted from the gamma-ray detection modules. The other gamma-ray detection module was installed in front of the gamma-ray generator to detect a reference signal with pure gamma-ray beam information with no air cargo signals. The reference signal was useful for gamma-ray image correction.
## 1 Introduction
[6] addressed the allocation problem using simulated annealing (SA), albeit limited to improving the retrieval phase. In this paper, the authors expand on the aforementioned work by enhancing both the retrieval and storage operations. Here, the problem is approached using a simulation optimization methodology that combines discrete event simulation (DES) principles with a biased-randomized (BR) algorithm [7]. While the DES component handles intricate time dependencies among different events, the BR component facilitates intelligent decision making. The resulting BR-DES is then integrated into a multi-start framework, enabling the generation of multiple high-quality solutions within short computing times. Figure 1: A picture of a shuttle-lift-crane AS/RS. The remainder of this paper is organized as follows: Section 2 provides an overview of related work. Section 3 offers a detailed description of the AS/RS under analysis. Section 4 introduces the optimization problem to be solved. Section 5 details the deterministic heuristic used by the authors to evaluate solutions. The proposed biased-randomized algorithm, along with its integration into a multi-start framework, is described in Section 6. The obtained solutions for different instances are compared with those generated by previous approaches in Section 7. Finally, conclusions and future research directions are presented in Section 8.
# PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything
## 3 Proposed Approach--PINE Protocol
### Assumptions  
* All the nodes in the network are aware of each other's location through Global Navigation Satellite Systems (_GNSS_), that is, a node maintains a neighbour table containing the Media Access Control (MAC) address, the Internet Protocol (IP) address, and the location of the neighbours; it transmits this to the neighbour nodes from time to time, promoting location exchange.
* In the network, non-cooperative nodes \(NC_{j}\) are only authorized to alter their residual energy, as the current energy level is in the direct control of \(NC_{j}\), whereas other attributes (except for EnergyStatus, i.e., PacketForwardCount, PacketDropCount, Timestamp and Incentive) are not in the control of such nodes, as such information is monitored and stored by peer nodes.
* Nodes in the network behave cooperatively at the beginning of the communication sessions, i.e., until network stabilization (refer to Section 3.3.1 and Figure 3), and the network nodes may tend to misbehave after a certain period of time, especially in the operation phase (refer to Section 3.3.2).
* The incentive procedure performed by node \(S\) may happen after certain periodic intervals, though not after every round-trip session cycle in order to reduce the network overhead and save the computing resources. Furthermore, the incentive for the cooperative or non-cooperative relays is communicated through the trusted nodes.
## 4 OpenFlow Flow Table Memory Management
### Machine Learning Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Related Work**} & **Controller** & \multirow{2}{*}{**Focus**} & **Technique** & **Use Case** \\ \cline{3-4}  & & & Flow monitoring to reduce congestion and & Experimental & \\  & & packet loss in SDN & validation & Routing rules \\ \hline [PERSON] et al. [82] & \multirow{2}{*}{Proactive} & \multirow{2}{*}{Traffic classification} & Supervised & \\  & & & learning & \\ \hline \multirow{3}{*}{[PERSON] et al. [89]} & \multirow{3}{*}{UDP flow classification} & Support vector & \multirow{3}{*}{Routing rules} \\  & & & machine & \\  & & supervised & & \\  & & learning method & & \\ \hline [PERSON] et al. [92] & \multirow{3}{*}{Proactive} & Elephant flow-aware traffic classification at & Machine learning & Traffic flow routing \\  & & the edge of the network & technique & rules in DC. \\ \hline [PERSON] et al. [93] & \multirow{2}{*}{Proactive} & Learning method to detect elephant & \multirow{2}{*}{Decision tree} & Routing rules \\  & & flows in SDN & & \\ \hline [PERSON] et al. [94] & \multirow{2}{*}{Proactive} & Predict the duration of the flow entry & Machine learning & Routing and distributed \\  & & & technique & ACL rules \\ \hline FlowMaster [95] & \multirow{2}{*}{Proactive} & Predict when flow entry becomes stale & Probability & Routing rules \\ \hline [PERSON] et al. [83] & \multirow{2}{*}{Proactive} & Machine learning techniques to decide the & \multirow{2}{*}{Deep learning neural network} & \multirow{2}{*}{Routing rules} \\  & & preserved flow between long-lived & & neural network \\  & & (elephant) and short-lived (mice) & & \\ \hline [PERSON] et al. [88] & \multirow{2}{*}{Proactive} & Classify flows into active and inactive to & \multirow{2}{*}{Machine learning} & \multirow{2}{*}{Routing rules} \\  & & decide the right flow to remove intelligently & & techniques \\ \hline [PERSON] al. [96] & \multirow{2}{*}{Proactive} & Q, learning approach to an efficiently select & \multirow{2}{*}{Machine learning} & \multirow{2}{*}{Distributed ACL rules} \\  & & flow timeout value & & techniques \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of machine learning related work to improve flowtable storage. built-in features in the OpenFlow network and the global networking knowledge of the controller. It is important to leverage the features and achieve better routing optimization, QoS provision, traffic flow classification, and network resource management. Therefore, more research work is required with features selections to predict flows and decide on the right flow to preserve in the switch flowtable in case of a large-scale network.
## 3 Results and Discussion
### CFD Simulation for Validation of Analytical Expressions
As shown in Figure 2B(b), the ND between the analytical formula and CFD simulation was less than 6%. Two Equations (1) and (2) were then used to consistently estimate the pressure of each stream in the coflowing channel. Next, it was necessary to validate the shear stress relationship of each stream in the coflowing channel (i.e., Equations (3) and (4)). Figure 2D showed the variations in \(\tau_{r}\) and \(\tau_{t}\) with respect to \(\beta\). The ND between \(\tau_{r}\) and \(\tau_{t}\) was obtained with respect to \(\beta\). As the ND was less than 1% when \(\beta\) was relocated from 0.1 to 0.9, the results indicated that both streams had the same shear stress (\(\tau_{r}=\tau_{t}\)). Both shear stresses increased at higher interface values. Furthermore, the shear stress of each stream exhibited remarkably similar variations with respect to \(\beta\) when compared with the pressure of each stream. Based on the pressure correction factor obtained from the CFD simulation results, it was validated that both streams in the coflowing channel had the same pressure and shear stress. The  
Figure 2: Quantitative validation of pressure and shear stress. (**A**) Distribution of pressure and interface obtained with CFD simulation results with respect to flow rate of test fluid. The flow rate of the reference fluid was fixed at \(Q_{r}=1\) mL/h. Both fluids had the same viscosity (i.e., \(\mu_{r}=\mu_{t}=1\) cP). The corresponding interface for each flow rate of test fluid was obtained at (a) \(\beta=0.095\) for \(Q_{t}=0.1\) mL/h, (b) \(\beta=0.5\) for \(Q_{t}=1\) mL/h, and (c) \(\beta=0.897\) for \(Q_{t}=10\) mL/h. The right-side panel showed distributions of pressure along the coflowing channel with respect to \(Q_{t}\). (**B**) Quantitative comparison of pressure between CFD simulation and analytical formula. (a) Variations of pressure obtained with CFD simulation with respect to \(\beta=0.095\), 0.5, 0.743, 0.847, and 0.897. (b) Variations of pressure obtained with both methods and ND (normalized difference) between two pressures with respect to interface (\(\beta\)). (**C**) Correction factor (\(C_{P}\)) for correcting the same pressure of both streams in the coflowing channel. (a) Variations of the pressure of \(P_{r}\) and \(P_{t}\) with respect to \(\beta\). (b) Variations of a correction factor of pressure (\(C_{P}\)) with respect to \(\beta\). Based on \(C_{P}=(Q_{t}/Q_{r})\times(1-\beta)/\beta\), the \(C_{P}\) was obtained as \(C_{P}=2.7378\)\(\beta^{4}-4.3067\)\(\beta^{3}+2.1783\)\(\beta^{2}-0.2644\)\(\beta+0.9592\) (\(R^{2}=0.9918\)). (**D**) Variations of shear stress of \(\tau_{r}\) and \(\tau_{t}\) and ND between two shear stresses with respect to \(\beta\). The right-side panel showed shear stress expression for each stream. analytical expression of the shear stress and pressure could be used to monitor the test fluid (i.e., pure liquid or blood) flowing in the microchannel.
## 3 Analysis of Engineering Example
### Database of Training Samples
Taking into account the computational efficiency, the lateral offset value and the jacking difference are changed at intervals of 5 mm in [0,50], and the refined model is used to calculate the local maximum stress of the beam corresponding to the random combination of the two parameters. There are 121 sets of parameter combinations, and the results are shown in Figure 11. From the figure, it can be seen that the magnitude of stress increase in each major stress member under the influence of a single parameter is smaller than the magnitude of stress increase under the combined influence of two parameters. Therefore, when the jacking height difference and lateral offset exist at the same time, the stress change in each component will be aggravated. If the control threshold is only set from the perspective of a single parameter influence, it is easy to ignore some unsafe states, which is not enough to ensure the safety of the structure during the jacking process. It is necessary to integrate the influence of the two to set the control threshold. On the basis of the previous calculation, if we want to set a control threshold with higher precision (such as 1 mm) from the perspective of the beam force, it is necessary to calculate the local maximum stress of the beam when the two parameters change at an interval of 1 mm, as shown in [1,50]. At this time, there are 2500 parameter combinations, and the calculation efficiency is low when using the finite element model. Therefore, the machine-learning model can be trained through the existing 121 sets of data to achieve the purpose of predicting the local stress of the beam according to the difference between the jacking height and the lateral offset value, thereby reducing the calculation time.
## 3 Analysis of the Fault State and Establishment of the Fault Data Sample
### Analysis of Fault Signal
#### 3.1.3 Leaks of the Exhaust Pipe  
In this paper, the simulation of the leaks of the exhaust pipe is realized by loosening the connecting bolts between the exhaust pipe of the number two cylinder and the cylinder block. Compared with the normal state, when leaks of the exhaust pipe occur, it will cause a large amount of oxygen to enter the exhaust pipe, which will directly affect the value of the oxygen sensor, thereby increasing the air-fuel ratio. However, leaks of the exhaust pipe have less effect on the engine block vibration, torque, and other signals.  
Figure 5: Vibration signal waveform under low oil pressure and normal conditions. (**a**) Vibration waveform in the normal state, (**b**) Vibration waveform when fuel pressure is low.  
Figure 6: Temperature change chart of two cylinders under normal condition and low fuel pressure condition. (**a**) Change chart of cylinder temperature in normal condition, (**b**) Change chart of cylinder temperature when fuel pressure is low.  
Figure 7 is a comparison diagram of the air-fuel ratio of the two cylinders under normal conditions and when the number two-cylinder exhaust pipe leaks. Figure 7a represents the relationship between the air-fuel ratio and the rotational speed, and Figure 7b shows the change of the air-fuel ratio within 0.1 s in the engine at 3000 r/min, where the black line represents the air-fuel ratio of the number two cylinder in the normal state, and the red line represents the air-fuel ratio when the exhaust pipe leaks. It can be seen from the figure that whether in the time domain or in the frequency domain, the leaks of the exhaust pipe will cause the air-fuel ratio to increase compared with the normal state.
## 7 Rationale for ka-band selection
### In terms of radiometer instrument  
As far as the radiometer is concerned, the aim is to perform the measurements necessary to get the wet troposphere correction with a sufficient accuracy. Moreover, the instrument has to be compact in order to be possibly embarked on a micro-satellite. This led to the selection of a dual frequency radiometer of the total power type and based on the direct detection principle. The 23.8 and 36.8 GHz frequencies allow sharing of the antenna with the altimeter.
## 5 Experimental Results
### Comparative Analysis
#### 5.2.6 Reliability before Coverage Hole Repair  
In Figure 10, the number of transmitted packets for the proposed MiA-CODER mechanism before coverage hole repair is compared with other existing approaches with respect to the simulation time. The proposed MiA-CODER mechanism has a high number of successful transmitted packets even before coverage hole repair due to the adoption of coverage hole prevention. This reduces the number of coverage holes formation in the network. In particular, the proposed mechanism is found to be reliable during the 150 th second of simulation time with a maximum number of transmitted packets compared to other approaches. The existing approaches have reduced the number of successful transmitted packets due to the lack of coverage prevention techniques.  
Figure 9: Number of sensor nodes vs. throughput.  
Figure 11 presents the comparison of the proposed MiA-CODER mechanism before coverage hole repair with several existing works in terms of the number of successfully received packets with respect to the simulation time. The proposed mechanism is found to have the maximum number of successfully received packets due to the implementation of the unequal cluster-tree topology and effective selection of cluster head using the MoBWo algorithm. The number of packets received successfully is low for the existing approaches because they provide solutions only for coverage holes detection and repair but not for the prevention of coverage holes in the network.  
The comparison of the proposed MiA-CODER mechanism with other existing works in terms of the number of control packets before coverage hole repair with respect to simulation time is presented in Figure 12. The proposed mechanism has a high number of control packets due to the formation of unequal Sierpinski cluster-tree topology (USCT) and the implementation of dynamic sleep scheduling in the network. This reduces the load in the network, thereby facilitating the effective transmission of data. The existing approaches did not focus on proper management of the load in the network which affects the reliability of those approaches.  
Figure 11: Number of received packets (before coverage hole repair) vs. simulation time.  
Figure 10: Number of transmitted packets (before coverage hole repair) vs. simulation time.
## 1 Introduction
### Organization of the Paper  
The rest of the chapter is organized as follows. Section 2 presents the Materials and Methods. In-depth analysis of the statistical data fusion approach in chain and tree routing structures as well as bidirectional graph networks are discussed. In Section 3, both analytical and simulation results are presented. The data accuracy, computational complexity and energy consumption are evaluated. Finally, we conclude this paper in Section 4.
## 5 Performance Testing of Hydraulic Support FBG Inclinometers
### Analysis of Test Results
When the FBG inclinometer of the hydraulic support is tilted to the left (\(-15^{\circ}\)\(\sim\)0\({}^{\circ}\)), the right grating (\(\Delta\lambda_{2}\)) will increase linearly due to stretching, and the linear equation is y = \(-66.5\)x, while the left grating (\(\Delta\lambda_{1}\)) will change non-linearly due to compression. And the sensitivities of FBG1 and FBG2 are 66.4 pm/1\({}^{\circ}\) and 66.5 pm/1\({}^{\circ}\), respectively.