esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 4 Experimental Verifications
### Outcome of Experiment  
The established ResNet method was adopted in fault diagnosis based on vibration using a dataset collected from bearings. In the training of the improved ResNet model, the Adam optimization algorithm was also adopted to improve the overfitting problem. The Adam (Adaptive Moment Estimation) algorithm is an algorithm that combines Momentum algorithm and RMSProp algorithm. Also, data augmentation and Dropout technology were used to improve the overfitting problem. The mini-batch was 16, the ReLU activation function was used, and the number of cycle iterations was 100 rounds. The final improved ResNet has the highest accuracy of 99.83% on the test set, and its diagnostic results are shown in Table 3.  
By comparing with Table 3, it can be seen that the performance of the improved RESNET algorithm is significantly improved compared with the traditional fully connected RESNET algorithm. In terms of time, the number of model parameters is greatly reduced and the training time is significantly reduced in the improved RESNET algorithm because the full connection layer is removed, which is of great significance for the model to be applied to the online rapid diagnosis and monitoring of faults. In terms of accuracy, the accuracy of the improved RESNET algorithm has reached 99.83%, while the accuracy of the traditional RESNET algorithm is 98.48%; Figure 8 shows the training and testing result of one trial. Figure 8 shows the relationship between the epoch and the accuracy of the model. Figure 8 shows the relationship between the epoch and the cross entropy.  
Figure 8: Experimental result of bearing dataset. (**a**) Accuracy curve. (**b**) Loss curve.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Testing Accuracy** & **Training Time per Model** & **Testing Time per Observation** \\ \hline ResNet + GAP & 99.83\% & 228.53 s & 0.193 s \\ ResNet + FC & 98.48\% & 245.96 s & 0.270 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of fault diagnosis results.
###### Abstract  
Maritime route planning under minimal-risk conditions plays an important part in the development and utilization of marine resources. High-resolution weather forecasting data places higher demands on the algorithms' ability to optimize and compute, and existing algorithms are significantly deficient in these aspects. Therefore, we propose a parallel computing-based planning method, segment parallel A* (SPA*), which splits the path into small segments and runs A* separately on CPU cores through a control algorithm. In segment planning, we propose an adaptive heuristic function on A*. It automatically balances the order of magnitude difference between the risk assessment value and the estimated distance, thus significantly reducing the A* expansion useless grid to improve the performance and running speed of the algorithm. Finally, the complete route is obtained by splicing the above segments. In the static planning experiments, the time of SPA* is reduced by about 5-12,425 times compared with 6 traditional and swarm intelligence-based algorithms, i.e., Dijkstra, A*, bidirectional A* (BA*), ant colony optimization (ACO), Harris hawts optimization (HHO), and sparrow search algorithm (SSA). And the abilities to control the risk caused by wind and waves and the comprehensive risk are improved by 7.68%\(\sim\)25.14% and 8.44%-14.38%, respectively; in the dynamic planning experiments, the above results are 4.8-1262.9 times, 3.87%\(\sim\)9.47% and 7.21%\(\sim\)10.36%, respectively. By setting the recommended range of the number of segments for each case, SPA* shows stable performance in terms of the calculation and risk control. SPA* demonstrates a unique structure for using parallel computing in route planning, which is representative and general in both reducing time and improving efficiency.  
parallel computing; A-star algorithm; weather ship routing; minimum risk route (MRR)
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 1 Introduction  
### Background  
Since its inception in the mid-20 th century, satellite communication has emerged as a vital component in the global communication system [1]. Its wide coverage, high reliability, and adaptability to various geographical environments have made it indispensable in various fields, including military operations, meteorology, navigation, broadcasting, and the Internet [2]. With the relentless progression of science and technology, modern satellite communication systems have witnessed remarkable enhancements in data transmission rates, spectrum utilization efficiency, and anti-interference capabilities. In recent years, the deployment and utilization of low Earth orbit (LEO) satellites have facilitated faster and lower-latency communication services worldwide, further accelerating the development and application of satellite communication technology. These technologies will play a pivotal role in the development of 6G and other networks that integrate new functions such as sensing and computing, allow the use of new services, and leverage improved environmental information for machine learning (ML) and artificial intelligence (AI) [3, 4, 5].  
However, satellite-ground communication currently faces numerous challenges in the process of image transmission. These challenges primarily involve environmental interference, spectrum resources, and energy management [6]. Firstly, as the signal traverses the atmosphere, it is subjected to atmospheric attenuation and meteorological conditions, resulting in path loss and signal degradation. Concurrently, multipath effects cause signal interference, further diminishing transmission quality, increasing bit error rates, and reducing the accuracy and integrity of the transmission [7]. Additionally, limited spectrum resources constrain the transmission bandwidth and rate of image data. Furthermore, the finite energy resources of satellites necessitate a careful balance between transmission power and energy consumption to ensure the normal operation and longevity of the satellite [8].
# An Environmental Perception Framework for Robotic Fish Formation Based on Machine Learning Methods
## 3 The Velocity Prediction of Single Robotic Fish
### The Construction and Validation of Predictive Models  
We used polynomial regression to predict the inlet flow velocity. The construction phase of predictive models and their validation were done by a powerful library called _scikit-learn_, which is a Python module that integrates many machine learning algorithms [43]. Our predictive models are presented as a polynomial in the following form:  
\[U=C_{0}+C_{1}\times p_{i}+C_{2}\times p_{i}^{2}+C_{3}\times p_{i}^{3}+\dots+C_{n }\times p_{i}^{n}, \tag{6}\]  
where \(C_{j}(0\leq j\leq n)\) is the coefficient of the polynomial, \(n\) is the order of the polynomial, and \(p_{i}(0\leq i\leq 7)\) has limited scope to ensure the validity of polynomial.  
We used the corresponding relations of \(p_{i}(0\leq i\leq 7)\) and \(U\) to train predictive models, respectively, and set a series of \(U\) to validate predictive models. The velocity settings for validating predictive models are shown in Table 2. While building the predictive model, we used different orders and calculated the training error. Then, we used the test data to validate the predictive model and calculate the predictive error. Errors are the mean square deviation of standard and calculated values. The training and predictive error of these models are shown in Table 3, respectively.  
Figure 4: The data monitored by symmetrical sensors in a cycle of fish swimming.  
Figure 5: The change of \(p_{i}\) with the inlet flow velocity \(U\).  
Figure 6: The fitting image of the predictive model trained by \(p_{0}\)–\(p_{7}\), respectively.  
Through Figures 6 and 7 and Table 3, we can get the best predictive model with the minimum predictive error in all polynomials, that is, the polynomial predicted by \(p_{7}\) (the kinematic pressure of sensor \(S_{7}\) at \(x=0.35\ L\)) is as follows:  
\[U=0.0006+(-5.9127)\times p_{7}+(-6.3910)\times p_{7}^{2}+(-3.3478)\times p_{7}^{3 }\qquad-1\leq p_{7}\leq 0, \tag{7}\]  
Equation (7) is applicable to the velocity prediction of single robotic fish. In the case of two or more fishes, the fish is affected by the swing of other adjacent fish, the kinematic pressure of which is different from that of single robotic fish.
# Analytical Study of a Circular Thin Plate Contacting with an Elastic Sphere
## 2 Formulation and Governing Equations
### Contact Deformation of the Circular Thin Plate
Furthermore, considering the case of \(b<r\leq a\), the expression of \(\psi(y)\) is written as  
\[\psi(y)=\frac{3a^{2}\big{(}1-\mu^{2}\big{)}\sqrt{3(1-\mu^{2})}}{4\pi Et^{4}y} \int_{0}^{r}q(r)2\pi r\mathrm{d}r= \tag{33}\]  
Under this condition, the exact solution to Equation (28) can be expressed as follows [24; 25; 26]  
\[\left\{ \begin{array}{c}\varphi(y)=\sum\limits_{i=1}^{\infty}\sum\limits_{j=0 }^{i}A_{ij}y^{i}\ln^{j}y\\ S(y)=\sum\limits_{i=1}^{\infty}\sum\limits_{j=0}^{i}B_{ij}y^{i}\ln^{j}y\end{array}  \right., \tag{34}\]  
where the constant coefficients \(A_{\bar{\eta}}\) and \(B_{\bar{\eta}}\) can be determined by the boundary condition of \(y=1\). The deflection of the circular thin plate on the external contact area can then be obtained by combining Equations (27) and (34)  
\[w_{12}(r)=\frac{h}{\sqrt{3(1-\mu^{2})}}\left\{\sum\limits_{i=1}^{\infty}\sum \limits_{j=0}^{i}\sum\limits_{t=j}^{i}\frac{(-1)^{t-j}t!}{j!^{t-j+1}}A_{it} \left(\frac{r}{a}\right)^{2i}\ln^{j}\left(\frac{r}{a}\right)^{2}+\sum\limits_{ i=1}^{\infty}\sum\limits_{j=0}^{i}\frac{(-1)^{j}!}{j!^{t}}A_{ij}\right\}. \tag{35}\]  
Substituting Equations (24) and (32) into Equation (8) yields  
\[\frac{h}{\sqrt{3(1-\mu^{2})}}\sum\limits_{i=1}^{\infty}\frac{a_{i}}{i}\left( \frac{r}{a}\right)^{2i}+C_{1}+\frac{3P\big{(}1-\mu_{b}^{2}\big{)}\big{(}2b^{2 }-r^{2}\big{)}}{8E_{b}b^{3}}=\delta-\frac{r^{2}}{2r_{b}}. \tag{36}\]  
Because this relation must be satisfied for all values of \(r\), equating the like powers of \(r^{i}\) gives the results  
\[\left\{ \begin{array}{c}\frac{3P\big{(}1-\mu_{b}^{2}\big{)}}{4E_{b}b}+C_{1} =\delta\\ \frac{3P\big{(}1-\mu_{b}^{2}\big{)}}{8E_{b}b^{3}}-\frac{h}{\sqrt{3(1-\mu^{2}) }}\frac{a_{1}}{a^{2}}=\frac{1}{2r_{b}}\\ a_{i}=0\qquad\quad i\geq 2\end{array} \right. \tag{37}\]  
Assuming that \(w(r)\) and \(w^{\prime}(r)\) are both continuous across the interface \(r=b\), we have  
\[\frac{h}{\sqrt{3(1-\mu^{2})}}\frac{a_{1}b^{2}}{a^{2}}+C_{1}=\frac{h}{\sqrt{3( 1-\mu^{2})}}\left\{\sum\limits_{t=1}^{\infty}\sum\limits_{j=0}^{i}\sum\limits _{t=j}^{i}\frac{(-1)^{t-j+1}t!}{i^{t-j+1}j!}A_{it}\left(\frac{b}{a}\right)^{2i }\ln^{j}\left(\frac{b}{a}\right)^{2}+\sum\limits_{i=1}^{\infty}\sum\limits_{j= 0}^{i}\frac{(-1)^{j}!}{j!^{t+1}}A_{ij}\right\} \tag{38}\]  
and  
\[a_{1}=\sum\limits_{i=1}^{\infty}\sum\limits_{j=0}^{i}A_{ij}y^{i-1}\ln^{j}y \left|{}_{y=\frac{\mu^{2}}{a^{2}}}=\sum\limits_{i=1}^{\infty}\sum\limits_{j= 0}^{i}A_{ij}\left(\frac{b}{a}\right)^{2i-2}\ln^{j}\left(\frac{b}{a}\right)^{2}. \tag{39}\]  
Accordingly, the relationship between the contact force \(F\) and the relative penetration \(\delta\) can be obtained from Equations (37)-(39).
###### Abstract  
As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area.  
class purity; training samples; patch-based classification; convolutional neural network +
Footnote †: journal: Accepted: 27 May 2020; Published: 29 May 2020
## 3 Fuzzy Logic Q-Learning Routing Scheme with Asymmetric Link Aware
### Neighbors Evaluation Criteria
#### 3.3.1 Link Quality Estimation Using ETX  
Link instability has a significant impact on network connectivity, routing performance, packet delivery ratio, network throughput, and end-to-end delay. Wireless links are directly affected by signal attenuation and fading. Thus, to elevate forwarding reliability and minimize packet re-transmissions and delay, we consider the link quality between the current forwarding node and the potential next-hop candidates. To estimate link quality, we utilize the well-known Expected Transmission Count (ETX) module [25; 33] to select the most reliable wireless links. Mainly ETX measurement depends on dedicated probe packets. Instead, we consider Hello packets, which is a fundamental factor of an opportunistic cooperative awareness scheme, and it is also used by our routing protocol to discover direct neighbors.  
We define the probability of successfully receiving data packet as \(d_{packet}\) and \(d_{ACK}\) is the probability of receiving an ACK packet. ETX can be defined as:  
\[ETX(V_{n},C_{i})=\frac{1}{d_{packet}\cdot d_{ACK}} \tag{1}\]  
Figure 2: FQ-AGO system architecture.  
Every node estimates link quality for its direct neighbors by calculating \(d_{packet}\) and \(d_{ACK}\) using the ratios of Hello packets that have been successfully received (HRR). The forwarder maintains a counter of each neighbor to calculate the number of received Hello messages within a sliding window size of w seconds. Therefore, the ratios of the successfully received Hello packets (HRR) is calculated at any given time instance (t) by using the following equation:  
\[HRR_{hello}(t)=\frac{counter(t-w,t)}{w/\tau} \tag{2}\]  
where \(count(t-w,t)\) is the number of Hello packets that have been delivered at time t during the sliding window w, and \(w/\tau\) indicate to the total number of Hello packets that should have been received during time t.  
For the calculation of the HRR(t), ETX model relys on the sequence number that exists in every received Hello packet, which is used to determine the number of received and lost Hello packets. The packet is either received or lost in the current window size, w, based on a Hello packet sequence number. This determination happens through a comparison process between the sequence number of the freshly received packet and the sequence number of the last packet sequence number recorded in the neighbors' table (Figure 2). Accordingly, a node can detect any lost Hello packet, and the HRR value is calculated by updating the numerator and denominator in Equation (2).
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 4 Results and Discussion
### Comparison with Previous Research  
Table 11 compares the accuracy of the proposed models and other approaches developed by researchers. As can be seen, our GRU and LSTM models achieve higher accuracy with fewer features than all methods using deep learning. Furthermore, our LSTM model achieves higher accuracy than LSTM models [18; 25], which use features selected automatically by the algorithm. However, it is essential to note that the GRU model achieves superior accuracy to LSTM. This accuracy highlights the efficiency and performance of our model compared to other existing approaches, outperforming previously developed LSTM models. Thus, the implementation of the GRU architecture can result in further improvements in SDN traffic classification accuracy.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Ref.** & **Algorithm** & **Accuracy** & **Number of Features** \\ \hline \multirow{4}{*}{Proposed} & GRU & 99.65\% & \multirow{4}{*}{5} \\  & BiGRU & 98.60\% & \\  & LSTM & 99.63\% & \\  & BiLSTM & 99.32\% & \\  & ML-LSTM & 97.14\% & \\
[18] & SAE & 99.14\% & Automatic by algorithm \\  & CNN & 99.30\% & \\
[19] & SAE & \textgreater{}90.00\% & Automatic by algorithm \\
[20] & ML-LSTM & 97.14\% & Automatic by algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 11: Accuracy comparison with previous research on traffic classification.  
Figure 11: Accuracy and loss comparison of the algorithms.
# The Extraction of Maximal-Sum Principal Submatrix and Its Applications
## 2 Related Work
### Color Combination Selection  
There are many applications related to the \(k\)-order maximal-sum principal submatrix (MSPS). One of them is color combination selection. Constructing an optimal color combination is a critical factor for data visualization [13; 14], and a lot of literature paid their attention to it [15; 16; 17; 18; 19]. A method to improve the readability of the data after clustering and visualizing is to consider the degree of color difference, which can be measured by the distance of two colors [20].  
Our MSPS algorithm can be applied to automatically construct the optimal color combination to improve the readability of visualized data. The optimal color combination here is defined by the largest color difference among all selected colors. The issue that extracts \(k\) colors from \(n\) different colors to form a color combination with maximum color difference, corresponds to the problem that extracts the \(k\)-order maximal-sum principal submatrix from an \(n\)-order real symmetric matrix.
## 3 Materials and Methods
### Data Transmission
#### 3.5.2 Network Functionality Virtualization (NFV)  
NFV is the total virtualization of multiple network functions, such as routers or firewalls. Networking hardware companies such as Cisco [31], Juniper [32], Huawei [33], or FortiNet [34] are providing kernel-based virtual machines (KVMs), which are digital versions of their equipment. For the implementation of user equipment (guest users), companies such as Canonical [35] also provide a collection of KVMs. KVM solutions provide similar functions to their physical counterparts, a feature highly valuable for equipment testing, which helps reduce the error rate during deployment. In addition, KVM enables the connection between the simulated environment and the natural world (two-way connection), providing a hybrid combination of hardware and software components.
# Improvement of Porous GaN-Based UV Photodetector with Graphene Cladding
## 5 Conclusions  
Graphene has been synthesized using two methods, namely CVD and EC. The graphene materials were cladded onto the surface of porous GaN (Pr-GaN), where the overall product would be a UV-photodetector, and subsequently, their performances were evaluated. Between the two types of graphene claddings, there were some notable differences. Morphologically, the CVD method could be used to grow a continuous 2D graphene sheet, while micron-sized graphene flakes were obtained using the EC method. Despite both types of graphene having almost similar crystallite size L\({}_{\text{a}}\) and comparable average distance between defects L\({}_{\text{D}}\). Although Pr-GaN had a higher exposed surface area for UV sensing, its performance was significantly improved with the integration of graphene, i.e., graphene/Pr-GaN heterojunction. Through such integration, the L\({}_{\text{D}}\) was slightly reduced. However, in terms of performance, CG-GaN showed the highest response, followed by EC-GaN, with sensitivities of 4.6 and 4.2, respectively. It should be noted that there were lesser differences between the aforementioned in this manner. On the responsivity and EQE, it was found that the as-received GaN possessed values of 0.066 A/W and 23%, respectively, while CG-GaN showed 1.23 A/W and 430%. The presence of the potential barrier was deduced to be present for Pr-GaN in the form of surface passivation, hence resulting in rectifying behaviours in the J-V profile. The inclusion of graphene claddings induced a built-in electric field at the heterojunction that further suppressed reverse leakage current, while serving as a current collector to further enhance the sensitivity of the photodetector.  
Conceptualization, K.P.B., A.R., N.M.A. and Z.H.; methodology, K.P.B., N.M.A., A.R. and F.K.Y.; software, F.K.M. and K.P.B.; validation, K.P.B., A.R. and N.M.A.; formal analysis, F.K.M. and K.P.B.; investigation, F.K.M. and K.P.B.; resources, K.P.B., A.R. and N.M.A.; data curation, F.K.M. and K.P.B.; writing--original draft preparation, F.K.M. and K.P.B.; writing--review and editing, K.P.B., A.R., N.M.A. and F.K.Y.; visualization, F.K.M. and K.P.B.; supervision, K.P.B., A.R. and N.M.A.; project administration, K.P.B., F.K.Y. and Z.H.; funding acquisition, K.P.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the short-term grant of Universiti Sains Malaysia (USM), grant number 304/PFIZIK/6315307. The work is financially supported by the short-term grant of Universiti Sains Malaysia (USM), titled \"Template-Assisted Growth of Group III-Nitrides Nanomaterials and Their Integration in Sensing Applications\", with grant no.: 304/PFIZIK/6315307. The first author would like to express her gratitude and thankfulness for IPS in sponsoring her work through the graduate research assistance (GRA) scheme. In addition, all supports from USM, IPS, and INOR are gratefully acknowledged.  
The authors declare no conflict of interest.