esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Predictive Modelling of Material Removal Using ANFIS
#### 4.2.1 Membership Functions for the Input and Output Variables  
A membership function allocates grades of membership extending from numbers between zero and one to the range of the possible values of the variable. Zero membership value specifies that it is not a member of the fuzzy-set; one signifies an extensive member. A membership function such as sigmoidal membership function is created for each input variable used in belt grinding, as illustrated in Figure 11. The sigmoid function is differentiable for all values of the inputs to allow the use of powerful back-propagation learning algorithms [20]. The application of general sigmoidal membership functions to the neuro-fuzzy modelling process is a very attractive methodology to characterise nonlinear processes [21]. The advantage of sigmoidal membership functions over other membership functions is the better approximation due to tapering edges.
# Low-Code as Enabler of Digital Transformation in Manufacturing Industry
## 3 Low-Code Development Platforms
### vf-OS Platform from the Low-Code Viewpoint
For this purpose, the vf-OS platform implements a staging area to test new micro-services and means to deploy them locally. Additionally, developers can publish their newly developed assets in the curated vf-OS Marketplace, so that they can be used by other applications. All in all, it seems that low-code development platforms are the future direction for time and cost reduction in the software development context. However, and in order to give response to research question RQ2, it is worth mentioning that aspects such as integration, interoperability, communication,real-time data processing, homogenisation, ergonomics and security to mention some, should be addressed properly to facilitate adoption of the low-code paradigm. vf-OS has been tested in three different manufacturing domains: manufacturing and logistics/automation, construction-industrialisation, and manufacturing assembly and collaboration. In these domains, a total of 21 vApps (smart manufacturing applications) have been developed involving topics such as failure prevention and monitoring, collaboration analysis, quality control, product identification or document management. These vApps have been developed to meet users' needs and, as such, they have also been validated against their needs and metrics. The vf-OS platform has been applied in the European project \"Zero Defects Manufacturing Platform\" (ZDMP) for the development of apps related to products' and processes' quality to support industrial companies with the achievement of zero-defects in manufacturing. To offer an overview of the ease with which a vApp is developed, the steps for its development are described as follows: (i) developer ([PERSON]) logs in to the vf-OS Platform, where [PERSON] has access and opens the vf-Studio; (ii) [PERSON] uses the different components to create the vApps, mainly the process designer, for the skeleton or backend, and the frontend environment, for the UI. When the skeleton is being created, [PERSON] has access to the vf-Store for purchasing vf-OS assets that have some packed functionality, such as access to machine-learning or ETL routines, or access to drivers, etc. ; (iii) [PERSON] uses then the vf-Studio again to finish off the development of the vApp (e.g., bug fixing or test-run of the vApp) before publication; (iv) once the vApp has been stabilised, [PERSON] can publish the vApp in the vf-Store providing details such as the price or description; (v) industrial user, (IU) access the vf-Store and seek for a vApp that does what they need. If no vApp is found, then the IU can create a request for a new vApp (which then reaches a developer that proceeds following the steps before); (vi) IU purchases the desired vApp and, after payment, UI receives a link to download the vApp; and (vii) IU logs into their vf-OS platform and, through the system dashboard, they can install the purchased vApp. When this process is compared to the traditional one, some benefits such as rapidity, cost and complexity reduction arise.
# Quality-of-Service-Linked Privileged Content-Caching Mechanism for Named Data Networks
## 3 Literature Survey  
### NDN Traffic Equivalence Class Classification  
NDNs host regular traffic and priority traffic with diverse resource and time requirements. This statement is true for different deployments of NDNs, such as IOT, wireless sensor networks (WSNs), mobile ad hoc networks (MANETs) [31], etc., where packets originate from endpoints such as sensors, actuators, or from cloud-hosted applications through network gateways. It is necessary for NDNs to classify traffic based on equivalence classes when different traffic classes demand differential treatments in terms of resource allocation (content store memory) and forwarding preferences (PIT content name entry). Due to the distributed nature of content services, an IP-like traffic classification based on addresses and ports become unfeasible, since NDNs abandon the IP address-centric paradigm and proceeds towards a multi-source content service backed with NDN content stores [32].  
A unique characteristic of NDN content is that it is addressed through hierarchically structured and unique names for a given namespace [33]. NDN content names find a place in both interest packets and data packets. The property of all pervasive hierarchically structured NDN content names provides an opportunity to identify and classify the NDN traffic flows. Therefore, this paper [14] proposes NDN name-based flow classification using an ECNCT mechanism, as discussed in Section 2.1. The mechanism integrates with an NDN framework without any major modifications, and the strategy is computationally simple.  
This proposal [14] considers two QoS dimensions, i.e., delay and reliability. The service name dealing with delay is called \"prompt\", and the service name dealing with reliability is called \"reliable\". QoS priorities are assigned to traffic flows based on the presence of ECNCT in the content name hierarchy, corresponding to the traffic flow granularity at which QoS has to be enforced. Service classes are given priority, as shown in Figure 2. Traffic flows with both prompt and reliable service classes receive the highest priority, followed by the prompt, reliable, and regular service classes, respectively. For example, with \(Netflix/Show1-Prompt/FrameID/\#Segment\), assuming a particular content has QoS requirements, the \(Show1-Prompt\) name will indicate the equivalence class identifiedby the Flow Table installed in the NDN nodes, and all the data packets corresponding to that \(Show1-Prompt\) will receive a defined QoS treatment. Flow Tables are updated across the network on every update cycle.
## 4 Selection and Analysis of Resonant Compensation Network
### Operating Characteristics of Resonant Compensation Networks
#### 4.2.2 LCC-S Compensation  
The equivalent circuit of the LCC-S resonant compensation network is shown in Figure 12. The parameters of the transmitting side include inductor \(L_{a}\), capacitance \(C_{a}\) and \(C_{p}\), and transmitting coil \(L_{p}\). The parameters of the receiving side include capacitor \(C_{s}\), receiving coil \(L_{s}\), and load resistor \(R\). The equivalent impedance of the receiving circuit can be obtained by combining Figure 12 and Table 3 as  
\[Z_{s}=\frac{1}{j\omega_{0}C_{s}}+j\omega_{0}L_{s}+R=R \tag{25}\]  
The reflection impedance of \(Z_{s}\) reflected to the transmitting side is  
\[Z_{r}=\frac{{\omega_{0}}^{2}M^{2}}{Z_{s}}=\frac{{\omega_{0}}^{2}M^{2}}{R} \tag{26}\]  
The voltage equation of the transmitting circuit is  
\[U=j\omega L_{a}I+\frac{1}{j\omega C_{a}}(I-I_{p}) \tag{27}\]  
The expression for the input impedance of the transmitting circuit is  
\[Z_{p}=\frac{U}{I}=j\omega_{0}L_{a}+\frac{1}{j\omega_{0}C_{a}+\frac{1}{j\omega_ {0}L_{p}+1/j\omega_{0}C_{p}+Z_{r}}} \tag{28}\]  
By combining Equations (27) and (28), the current of the transmitting coil can be calculated as  
\[I_{p}=\frac{U}{j\omega_{0}L_{a}} \tag{29}\]  
According to Equation (29), \(I_{p}\) depends on \(U\) and \(L_{a}\). When these parameters are determined, \(I_{p}\) remains a constant value regardless of the load resistor, achieving a CC output in the transmitting side. Furthermore, the output voltage of the receiving circuit is  
\[U_{o}=j\omega_{0}MI_{p}=j\omega_{0}M\frac{U}{j\omega_{0}L_{a}}=\frac{UM}{L_{a}} \tag{30}\]  
Figure 11: Equivalent circuit of the S-S resonant compensation network.  
Figure 12: Equivalent circuit of the LCC-S resonant compensation network.  
According to Equation (30), \(U_{o}\) is only related to \(U\), \(M\), and \(L_{a}\), so when these parameters are determined, this topology can achieve CV output to the load. Furthermore, the output power of the LCC-S system can be calculated as  
\[P_{o}=\frac{{U_{o}}^{2}}{R}=\frac{{U^{2}}{M^{2}}}{{L_{a}}^{2}R} \tag{31}\]
## 1 Introduction  
### Problem Statement of Motivation and Methodology  
Currently, with the continuous increase in expected aesthetic requirements and the simultaneous development of manufacturing technology, there are an increasing number of parts containing free-form surfaces in daily life [1]. To meet the needs of various industries for CNC machining accuracy and efficiency, CNC machine tools have been constantly and rapidly developed in the direction of high precision and intelligence. Due to the more complex geometric structure of free-form surface parts, such parts are generally processed by CNC machine tools. There are many factors that lead to machining errors, mainly geometric, force and thermal errors [2]. Improving the machining accuracy of CNC machine tools has more important research significance, and error compensation is an effective and economic means to improve the machining accuracy of CNC machine tools [3].  
In this paper, a coarse-to-fine free-form surface machining error decomposition and compensation method was proposed which was based on the principles of decomposing the machining error from coarse to fine by variational mode decomposition (VMD)-EMD combined with the wavelet thresholding method and compensating for the decomposed systematic error. The geometric model of the free-form surface part was imported into Mastercam 2021 software to generate the corresponding NC code, which was then imported into the CNC for roughing and semifinishing. First, on-machine measurements were performed after semifinish machining was completed to obtain measurement point data on the surface of the free-form surface part, and then the machining error of each measurement point was calculated [4]. Second, the machining error was decomposed into the systematic error and random error using VMD-EMD combined with the wavelet thresholding method. Finally, the surface was reconstructed according to the decomposed systematic error to obtain the machined surface after compensating for the systematic error, generating the corresponding NC code and completing the compensated machining. This method can reduce the machining error of the free-form surface part to a greater extent and does not need to transfer or restrain the part to realize machining error compensation, which has more important research significance.
## 6 Discussion
### Study Limitations  
A limitation of our study is the reliance on manual curation to remove non-emotive expressions from the automatically expanded lexicon, which is time-consuming and reduces scalability. Additionally, in the evaluation experiment, the exact match rate decreased slightly, but the other indices were improved. Additionally, the decreased exact match rate due to single-character expressions (e.g., \"Ki\" and \"Jyou\") highlights the need to exclude emotive expressions consisting of a single character from the dictionary to further improve the system's hit probability. The study's focus on Japanese text, which requires word segmentation before processing, may limit the generalizability of our findings to languages with different linguistic structures.
## 2 Experiments
### Experimental Results
#### 2.6.1 Coplanar Crack Growth Rate  
The coplanar crack growth rates of RP, which comprises RP1, RP2, and RP3 obtained from the mixed mode I/II tests, were plotted against \(\Delta K_{\rm I}\) (Figure 7), including all \(\delta\) cases and, for comparison, the pure mode I growth data collected at \(R_{\rm I}=0\). The FCG rates for the mixed mode I/II loading were fairly faster than those for pure mode I. When the Paris-type law was applied to the crack growth rates for all the mixed mode I/II loading data, we obtained the equation  
\[\frac{da}{dN}=3.75\times 10^{-12}(\Delta K_{\rm I})^{3.39} \tag{7}\]  
where \(N\) is the number of cycles. The coefficient of determination (\(R^{2}\)) was 0.69.  
Next, the same growth rates were plotted against \(\Delta K_{\rm II}\) (Figure 8), revealing an improvement in \(R^{2}\) (0.87)  
Figure 6: Branch crack at a main crack plane and branch angle definition.  
Then, since the crack growth law can be described uniquely even under different loads and stress ratios by effective SIFs, the growth rates were plotted against \(\Delta K_{\text{Ieff}}\) (Figure 9). The crack growth rate data can be summarized by the equation  
\[\frac{da}{dN}=8.19\times 10^{-11}(\Delta K_{\text{Ieff}})^{2.59} \tag{9}\]  
Figure 8: Coplanar crack growth rates against \(\Delta K_{\text{II}}\) for RP.  
Figure 7: Coplanar crack growth rates against \(\Delta K_{\text{I}}\) comparing pure mode I data for RP.  
In this case, a further correlation improvement was obtained (\(R^{2}=0.89\)). The \(\Delta K_{\text{Ieff}}\) and \(\Delta K_{\text{Ileff}}\) values were successively used to fit the growth data.  
The crack growth rates were then plotted against the equivalent SIF range (\(\Delta K_{\text{v}}\)), as proposed by [PERSON] et al. (19) (Figure 10), which is defined as  
\[\Delta K_{\text{v}}=\frac{\Delta K_{\text{I}}}{2}+\frac{1}{2}\sqrt{\Delta K_{ \text{I}}^{2}+4(1.115\Delta K_{\text{II}})^{2}} \tag{10}\]  
The crack growth rates were expressed as  
\[\frac{da}{dN}=C(\Delta K_{\text{v}})^{m} \tag{11}\]  
In the RP, RF, and WT cases, the corresponding \(C\) and \(m\) values were \(6.08\times 10^{-11}\) and \(2.18\), \(8.70\times 10^{-11}\) and \(2.04\), and \(3.82\times 10^{-11}\) and \(2.45\), respectively. Figure 10 shows that better correlations were attained when plotting the crack growth rates against \(\Delta K_{\text{v}}\). Over all, WT exhibited the fastest crack growth rates and, among the rail steels, the rates of RP were higher than those of RF.
## 6 Conclusions  
This paper implements a routing optimization algorithm for low-orbit satellite networks based on graph neural networks. A graph neural-network-based algorithm for  
Figure 11: Average network throughput for different numbers of nodes.  
Figure 10: Average end-to-end delay of the network with different numbers of nodes.  
low-orbit satellite networks is designed to adapt to dynamic networks with frequent topological changes and to solve the network congestion problem. The GNN-DRL algorithm proposed in this paper optimizes network routing by modeling the graph data in the network with a GNN feature engineering model, generating hidden feature vector representations of its own nodes with graph neural networks and then using a fully distributed multi-intelligence DRL routing model to make routing decisions with a deep reinforcement learning algorithm. Compared with [PERSON]'s algorithm as well as the traditional DQN algorithm, the GNN-DRL algorithm proposed in this paper not only improves the overall network throughput but also reduces the average end-to-end delay. Compared with the Dijkstra and DQN algorithms, the average throughput of the GNN-DRL algorithm increases by 29.47% and 18.42%, respectively, and the average end-to-end delay is reduced by 39.76% and 15.29%, respectively. In addition, the model used in the GNN-DRL approach is able to cope with network topological changes (e.g., traffic variations, link failures and node failures) and is more suitable for real networks.  
Writing--original draft, Y.S.; Writing--review & editing, W.W., X.Z. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China (Grant Nos. 92367102, 92067101) and the Key Research and Development Plan of Jiangsu Province (Grant No. BE2021013-3).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 2 Background on Biometric Signals Processing
### Related Work  
FPGA-based works aiming at emotion identification from EEG signals are emerging in the literature. [PERSON] et al. [9] implemented a convolutional neural network (CNN) in a Virtex-7 FPGA for emotion detection from EEG signals from six channels. The classifier was integrated in a complete system containing an acquisition headset and a MATLAB program for feature extraction. Two experiments were conducted, one in real time and a second one offline using the DEAP dataset. During the real-time experiment, the system  
Figure 2: Electrode-positioning standard by International 10/20 System [2]. ’Pg’ stands for pharyngeal area, ’Fp’ for fronto polar, ’F’ for frontal, ’T’ for temporal, ’C’ for central, ’P’ for parietal, ’O’ for occipital and ’Cb’ for cerebellar.  
took 450 ms to detect an emotion, from the acquisition node. The offline processing of DEAP dataset resulted in a valence-arousal classification accuracy of 76.67%.  
The system proposed in [9] contributes with a complete execution of the classification process. However, the system is oriented to operate in a laboratory environment, rather than targeting a wearable device for daily use. This is a gap in the literature of emotion recognition, and represents an opportunity to develop a novel FPGA-based system with this scope.  
Wearable systems for biosignal acquisition and monitoring with reconfigurable technology were proposed in [10; 11; 12]. They monitor ECG, EEG, oxygen saturation and motion. However, these systems only acquire signal data to be processed by another computing system and are unable to make real-time decisions.  
In [13], the authors proposed a hardware/software system for acquisition and processing of EEG biosignals. A algorithm based on a convolutional neural network (CNN) is used to improve the classification accuracy. A hardware accelerator is proposed to speed up inference.When executed in a PYNQ-Z2 board with ZYNQ7020 FPGA, the system executes an inference in 0.22 s with 3.1 W of power. The system consumes a large percentage of on-chip memories and cannot be implemented on lower density FPGAs and only considers one type of application.  
The system proposed in this paper targets very low-density FPGAs and integrates two types of monitoring signals. The system can be straightforwardly implemented with a soft processor instead of hard processing to target non-SoC FPGAs with reduced power requirements.
# Novel Online Optimized Control for Underwater Pipe-Cleaning Robots
## 2 Catadioptric Panoramic Image-Based Vision Localization
### Feature Extraction  
The process of the method used to obtain the features of the pipe edge and the biofuling boundary in the field is shown in Figure 4. Equipped with a wide-angle lens, the camera produces images with barrel distortion, which is first corrected based on the internal and external parameters of the camera. Median filter, a nonlinear filtering method, is simultaneously applied as it can overcome the blurriness generated by the linear filtering method while maintaining and enhancing the edge details [26]. Then, two regions of interest, namely the edge of the pipe and the boundary of the biofouling, are chosen to reduce unnecessary calculation.  
With respect to Region A, threshold segmentation (TS) and an edge recognition method with canny operator were applied to extract the pipe edge [27]. Considering that the edge may not be a straight line as the pipe is not smooth, a probabilistic Hough transform (PHT) was performed to generate lines from the pipe edge, which can be formulated as follows:  
\[y=k_{i}x+b_{i}\;\;i=1,2,\ldots,n \tag{1}\]  
Thus, the feature line of the pipe edge can be computed using Equation (2).  
\[y=\frac{\sum k_{i}}{n}x+\frac{\sum b_{i}}{n}\;\;i=1,2,\ldots,n \tag{2}\]  
As for Region B, it was first overlaid by a preprocessed image to eliminate the disturbance of the robot body. Since the relative position of the robot body and the panoramic image system is constant, the preprocessed image is suitable for any situation. Second, the color-to-grey method (CtoG) and threshold segmentation (TS) were used to obtain a binary image. Third, a closed operation, combining dilation and erosion, was then applied to fill the holes that were originally occupied with the biofuling and connect with each other. Fourth, area threshold segmentation was performed to remove the small blobs that were recognized as noise. Finally, an edge recognition method with canny operator was used to extract the boundary of the area covered by biofouling.
## 2 Materials and Methods
### Environmental Availability Assays  
Using a balance, a 4 g of test sample of fresh soil was placed in a 50 mL falcon tube. Then a 40 mL volume of a 100 mmol-L\({}^{-1}\) Hydroxypropyl-\(\beta\)-cyclodextrintrin (Kleptose HP oral grade, Roquette, France) extraction solution was added to the tube and shaken horizontally for 20 h (180 rpm, 20 \(\pm\) 2 \({}^{\circ}\)C). Samples were centrifuged at (3000\(\times\)\(g\) for 15 min) to separate the solid phase from the liquid phase (clear supernatant). Supernatant corresponding to the available fraction was extracted 3 times using 20 mL of petroleum ether. The collected petroleum ether was then evaporated until dry and 150 \(\upmu\)L of nonane was added to recover analytes. Extracts were quantified as described in [11]. For each soil, 4 replicates were performed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Set**} & \multirow{2}{*}{**Sand**} & \multirow{2}{*}{**Kaolin**} & \multicolumn{2}{c}{_Sphagumm_} & \multicolumn{2}{c}{**St Cyprien**} & **Activated** \\  & & & & **Feat** & **Soil** & **Carbon** & **Biochar** \\ \hline \multirow{8}{*}{Artificial soils} & Standard Soil (SS) & 70\% & 20\% & 10\% & & & \\  & SS with oak tree & & & & & \\  & PCB 101, & 500 (BC1) & 68.6\% & 19.6\% & 9.8\% & & 2\% \\  & PCB 138, & SS with oak tree & & & & & \\  & PCB 153, & 700 (BC2) & 68.6\% & 19.6\% & 9.8\% & & 2\% \\  & PCB 180 & SS with Japanese & & & & & \\  & & knotweed (BC3) & & & & & \\  & & SS with AC1 (AC1) & 68.6\% & 19.6\% & 9.8\% & & 2\% \\  & & SS with AC2 (AC2) & 68.6\% & 19.6\% & 9.8\% & & 2\% \\  & & SS with AC3 (AC3) & 68.6\% & 19.6\% & 9.8\% & & 2\% \\  & & Control–without pollutant & & 70\% & 20\% & 10\% & & \\ \hline \multicolumn{2}{c}{Sampled soil on an} & Standard Soil (SS) & & & & 100\% & \\ \multicolumn{2}{c}{historically contaminated site} & SS with AC2 & & & 98\% & 2\% \\ \multicolumn{2}{c}{(St Cyprien, Loire, France)} & SS with AC3 & & & 98\% & 2\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the different set of soils. Each percentage represents mass proportion.