esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Roller Design to Improve Web Strain Uniformity in Roll-to-Roll Process
## 2 Strain of Web in Roll-to-Roll Process
### Web Strain FEA Results: Conventional Roller  
We used the FE model to analyze the strain distribution along the position of printing line or across the width of the web under uniform tension conditions. Figure 2 shows the relationship between the position along the printing line and strain deviation in the MD with respect to the distance from the contact line of the web (width of 160 mm) and roller when a tensile force of 10 MPa is applied to the web.  
At a distance of 50 mm from the roller in the MD, the strain exhibited the most uneven distribution, while the most uniform strain was observed for a distance of 200 mm. The shorter the distance between the roller and the web in the MD, the greater the variation in strain distribution. Moreover, Figure 3 shows the deviation of the MD strain which occurs with changes in the web width for a distance 50 mm from the roller in the MD. When the film width was 160 mm, the strain deviation was minimal. The strain deviation increased as the web width increased. This strain deviation may cause a register error in the MD owing to the change in the tension in each span. In addition, when the tension is removed after the process, the elastic strain becomes zero and the positioning precision of the pattern is adversely affected. Therefore, we sought to design a roller to improve the precision of the printing pattern and to minimize the register error by compensating for the strain deviation in the MD.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Young’s Modulus** & **Mass Density** & **Poisson’s Ratio** \\ \cline{2-4}  & **E (GPa)** & **\(\rho\) (kg/m3)** & **-** \\ \hline Roll (aluminum) & 71 & 2,770 & 0.33 \\ Web (PET) & 3.5 & 1,320 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties in the finite element analysis (FEA).  
Figure 2: MD strain distribution along the printing line with respect to the change in distance between roller and line.
## 5 Performance Analysis
### Performance Analysis on Power System Monitoring Application  
The results of the existing intelligent power quality monitoring method [12], harmonic resonance mode analysis approach [13], optimal configuration method [14], multi-objective optimization approach [15], and FPM and HRMA [16] are discussed with the power system intrusion dataset collected from kaggle. The URL of the dataset is published as [70]. In total, 500 sensor nodes are deployed to conduct the simulation.  
Figure 9: Measurement analysis of energy consumption.  
Figure 11 illustrates the \(Cost\), as defined in Equation (4), for the performance evaluation of five different methods in the context of power systems. For a dataset of 700 power system data points, the \(Cost\) for these methods is recorded as 38 MB, 44 MB, 52 MB, 55 MB, and 64 MB, respectively. As the number of data points increases, the associated \(Cost\) rises correspondingly. Notably, the intelligent power quality monitoring method consistently demonstrates the lowest \(Cost\) among the evaluated methods. This advantage is attributed to its targeted monitoring capabilities, which require minimal resources while enhancing the efficiency of power quality assessments in electrical networks. Additionally, this method is faster, more economical, and easier to implement in real-time applications. Consequently, the intelligent power quality monitoring method reduces \(Cost\) by 15%, 28%, 33%, and 44% compared to the other approaches.  
Figure 11: Measurement analysis of cost.  
Figure 10: Overall performance analysis of energy consumption.  
Figure 12 presents the overall performance results regarding the _Cost_ associated with five existing methods. The analysis reveals that the _Cost_ for these methods is 35 MB, 41 MB, 38 MB, 51 MB, and 61 MB, respectively. Notably, the intelligent power quality monitoring method demonstrates the lowest _Cost_ among all methods evaluated. This indicates that it not only effectively addresses power quality issues but also does so in a more cost-efficient manner compared to its counterparts.
# Combined Channel Estimation with Interference Suppression in CPSS
## 4 Complexity Analysis and Performance Simulation
### Simulation Results  
We evaluate the performance of DFT-KNN with others by simulation comparison. The communication system parameters of simulation are shown in Table 2, and the channel parameters are shown in Table 3. To compare the performance of channel estimation objectively, the receiver is assumed to be synchronized accurately.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Modules** & **Basis of Algorithm** & **Core Operator** & **Computation** \\ \hline FFT transformation & Radix-4 & 16b \(\times\) 16b MAC & 1013.76 M \\ LS estimation & Least square & 16b \(\times\) 16b MA & 122.88 M \\ Interference detection & K-means & 16b \(\times\) 16b MAC & 122.88 M \\ Interference suppression & Linear interpolation & 16b \(\times\) 16b MA & _negligible_ \\ noise cancellation 1 & KNN & 16b \(\times\) 16b MAC & 368.64 M \\ noise cancellation 2 & Windowing & 16b \(\times\) 16b MAC & 245.76 M \\ noise cancellation 3 & Mahalanobis distance & 16b \(\times\) 16b MA & 368.64 M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Computation of algorithm modules.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Carrier frequency & 400 MHz–700 MHz \\ Modulation type & 16 QAM \({}^{a}\) \\ Transmission rate & 23.56 Mbps \\ Subcarrier number & 1024 \\ Subcarrier spacing & 15 kHz \\ Channel codes & Turbo \\ \hline \multicolumn{3}{l}{\({}^{a}\) QAM, Quadrature Amplitude Modulation.} \\ \end{tabular}
\end{table}
Table 2: Transmission parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Carrier frequency & 400 MHz–700 MHz \\ Modulation type & 16 QAM \({}^{a}\) \\ Transmission rate & 23.56 Mbps \\ Subcarrier number & 1024 \\ Subcarrier spacing & 15 kHz \\ Channel codes & Turbo \\ \hline \hline \end{tabular}
\end{table}
Table 3: Channel parameters.
# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 1 Introduction
These issues are connected with, for instance, the amount of data that is produced, data integration features, as well as a lack of experts. There have been many attempts to resolve these issues with the help of different kinds of ICTs over time, such as artificial intelligence, distributed intelligence, etc. [6; 7; 8]. Currently, with the emerging technologies such as the IoT, big data and analytics, open source solutions as well as open platforms, the entrance barrier to advanced maintenance solutions has been lowered considerably. From a developer perspective, there are a number of frameworks as well as programming languages that can provide benefits, since they were used by the community to develop both general purpose and specialized solutions to tackle many use cases that comprise machine learning and CBM. One example of these languages is Python, whose mature codebase brings new aspects into the developments of these maintenance tools, like, for instance, lower development costs. All this brings new possibilities into the domain to solve the issues with, for example, data volume, its integration, and the lack of experts in the field. Thus, such emerging ICTs as the Internet of Things (IoT) are revolutionizing several areas, including maintenance [8]. The rolling element bearing involves vital indicators to determine the machinery condition as well as its remaining lifetime in contemporary production machinery [9]. Therefore, the use of emergent technologies, such as the IoT and predictive health monitoring tools that support the rolling element bearing condition is crucial for the industry.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 7 Performance Evaluation  
In this section, we evaluate the performance of the RUL estimation obtained by the adaptive sliding window method and the Bayesian method for various experimental data. We first discuss the feature extraction and postprocessing and then evaluate the accuracy of the RUL prediction.
## 4 Experiments
### Detailed Results on Complex Scenarios  
In this section, we evaluate the extraction capability of our model in cases where there are overlapping triplets and single sentences containing multiple triplets. For comparison with previous models, we conducted experiments on two different subsets: NYT* and WebNLG*. The specific extraction results are shown in Tables 5 and 6. We can observe that our model achieved the best \(F1\) scores on 11 out of 20 subsets and the second-best scores on 3 subsets. Particularly, on the NYT* dataset with a smaller number of relations, our model achieved the best scores in all overlapping situations. On the WebNLG dataset, our model still obtained the four best results, indicating its excellent performance on datasets with a large number of relations. However, as the number of predefined relations increases, the model's ability to recognize similar semantic relationships decreases, making it more challenging to handle sentences with multiple triplets. Overall, our model is able to handle complex sentence structures well. However, as the number of relations increases,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**NYT*} & \multicolumn{3}{c}{**NYT**} & \multicolumn{3}{c}{**WebNLG*} & \multicolumn{3}{c}{**WebNLG**} \\ \cline{2-13}  & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** \\ \hline CasRel & 89.7 & 89.5 & 89.6 & - & - & - & 93.4 & 90.1 & 91.8 & - & - & - \\ TPLinker & 91.3 & 92.5 & 91.9 & 91.4 & 92.6 & 92.0 & 91.8 & 92.0 & 91.9 & 88.9 & 84.5 & 86.7 \\ PRGC & 93.3 & 91.9 & 92.6 & 93.5 & 91.9 & 92.7 & 94.0 & 92.1 & 93.0 & 89.9 & 87.2 & 88.5 \\ EmRel & 91.7 & 92.5 & 92.1 & 92.6 & 92.7 & 92.6 & 92.7 & 93.0 & 92.9 & 90.2 & 87.4 & 88.7 \\ GRTE & 92.9 & **93.1** & 93.0 & 93.4 & **93.5** & 93.4 & 93.7 & 94.2 & 93.9 & **92.3** & 87.9 & 90.0 \\ OneRel & 92.8 & 92.9 & 92.8 & 93.2 & 92.6 & 92.9 & 94.1 & **94.4** & **94.3** & 91.8 & **90.3** & **91.0** \\ Ours & **93.7** & 92.9 & **93.3** & **94.0** & 93.3 & **93.7** & **94.7** & 93.6 & 94.1 & 89.4 & 88.1 & 88.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different methods on the NYT and WebNLG datasets. The best is bold, and the next best is underlined.  
its recognition ability is also affected. Compared to other baseline models, our model can still effectively extract triplets in complex scenarios.
## 1 Introduction  
The appearance of stable structures, solitons, in laser emission is an important topic in the field of nonlinear optics. Depending on their nature, they can be classified as temporal [1] and spatial solitons [2].  
The spatial solitons are self-localized states of light capable of beating diffraction through nonlinearities; this attribute makes them useful for potential application in all optical processing and switching operations [3, 4]. Among them, cavity solitons (CS) are bistable spatially self-localized waves that exist in the transverse aperture of broad-area nonlinear optical resonators. Examples of spatial CS have been found in many different experiments, e.g., laser with a saturable absorber [5], Kerr media [6], liquid crystal light valve [7] or vertical-cavity surface-emitting laser (VCSEL) [8].  
The VCSEL is one of the most used and investigated kinds of solid state laser [9]. Since its development, it has been widely utilized in different applications [10]. In conditions of normal emission, above the threshold value for the injected current, a well characterized linear polarization is observed [11]. Depending on the state of polarization, the CS with only one component is called scalar; otherwise these structures are called vector CSs.  
In this contribution, we create the solitonic structure in a very simple system--a VCSEL with a frequency-selective feedback setup as is shown in different publications of some of the authors [12, 13]. In the aforementioned conditions, a rich collection of spatially self-localized intensity structures can be found, from the single spot CS to much more complex formations, i.e., the appearance of a ring-shaped structure, usually made by a circular intensity pattern with peaks--the vortex beam. In this particular experiment, a novel feature associated with the state of polarization of the single soliton is discovered, i.e., the existence of a hysteresis loop that connects two orthogonal orientations. Also remarkable is the discovery of a significant amount of circular light in all the cases studied, for the first time in this system, in accordance to other authors' results [14] for the VCSEL emission, making it a full Poincare beam [15].  
Our objective is to resolve the spatial distribution of the polarization in all these situations; starting from the case of homogeneous polarization found in the CS and applying also our analysis to the vortex.  
The organization of the paper is as follows. In Section 2, the experimental setup and the method are included; in Section 3, the results are presented; in Section 4, we make an interpretation of the results and the future directions of our work.
###### Abstract  
This paper presents a likelihood-based algorithm for identifying different phase shift keying (PSK) modulations, i.e., BPSK, QPSK, and 8 PSK. This algorithm selects the modulation type that maximizes a loglikelihood function that is based on the known original constellation associated with the constellation of the received signals for the candidate modulation types. However, there are two problems in non-cooperative underwater acoustic Multiple Phase Shift Keying (MPSK) modulation identification based on the likelihood method. One is the original constellation, which as prior information is unknown. The other is the underwater acoustic multipath channel makes the constellation distort seriously. In this paper, we solved these problems by combining sparse bayesian learning (SBL) with expectation maximization (EM). The specific steps are as follows. Firstly, blind channel equalization can be achieved by channel impulse response (CIR), which is estimated by sparse bayesian learning in single input multi output (SIMO) underwater acoustic channel. Subsequently, we used expectation maximization to compensate amplitude attenuation and phase offset, as the original constellation of MPSK is unknown. Finally, modulation can be successfully identified by the Quasi Hybrid Likelihood Ratio Test (QHLRT). The simulation results show that the channel estimation method based on SBL can eliminate the influence of channel effectively, and the EM algorithm can make the received constellation converge to the preset constellation in the case of unknown original transmit constellation, which effectively solves these two problems. We use the proposed SBL-EM-QHLRT method to achieve an identification rate of more than 95% in underwater acoustic multipath channels with Signal to Noise Ratio (SNR) higher than 15 dB, which provides a new idea for modulation identification of non-cooperative underwater acoustic MPSK.  
multiple phase shift keying; single input multi output; sparse bayesian learning; expectation maximization; likelihood +
Footnote †: journal: Applied Signal Processing
## 2 Materials and Methods
### Effective Wavelengths Selection  
Effective wavelengths selection played a crucial role in reducing the dimensionality of spectral data by choosing only the wavelengths with significant and important features, thereby enhancing the efficiency and accuracy of the predictive model construction [26]. By selecting wavelengths that were highly correlated with the target characteristics, irrelevant information variables were eliminated, which helped to avoid overfitting and spectral overlap. This improved the generalization capability and stability of the predictive model.  
Effective wavelength selection methods included Monte Carlo cross-validation (MC-UVE) [27], successive projections algorithm (SPA) [28], competitive adaptive reweighted sampling (CARS) [29], and random frog (RF) [30]. These methods were used to identify the wavelengths most relevant to the target characteristics from the full spectral range, thereby reducing data dimensionality and increasing model computational efficiency and predictive accuracy.
## 5 Performance Evaluation and Results Discussion
### Analysis of ME-CBCCP in Terms of Other Parameters  
If the residual energy of the nodes is considered during the execution of the routing protocols and the role of the CH is replaced by high energy nodes then connectivity of the nodes can be maintained. This can assure the transmission of the data even in the case of failure of some nodes. This aspect is required to make the routing protocol for the IoT framework more reliable, but this fact has not been taken into account in LEACH and SEP. MODLEACH, TDEEC and EESAA. ERP and genetic HCR are based on the election of the best class of CHs so they withstand well in terms of fault tolerance. This feature enhances the stability period of the protocols. ME-CBCCP also considers the residual energy of the nodes and the role of CHs with less energy is exchanged with the other (high energy) nodes in the next round. Load balancing on the nodes is achieved by nodes with the help of CHs and CCOs. CHs gathers the data of one cluster only and CCO dedicated to one cluster will always transmit the data of that particular cluster. In this way CHs and CCOs will have an approximately the same load that is the loads of one cluster only.  
From the above discussion, it is clear that ME-CBCCP successfully optimized the most stringent constraints of the WSN for an energy efficient and scalable IoT, that are time and energy. However,this novel protocol works only for large scale networks. It works well for static nodes. A static algorithm can cause problems in a dynamic environment (although failure of nodes due to energy has been considered in this protocol). This routing algorithm can suggest new directions for future work because quality of service (QoS) parameters can be enhanced further by using mobile nodes or mobile sinks to gather the data but mobility is not appropriate for IoT applications so it is a better option to optimize it in the static mode. The dumb behavior of the nodes can affect the workings of the stationary network. Nodes which can have an adverse effect on the performance of the WSN can be avoided in regular operation which can make ME-CBCCP more effective.  
From the abovementioned points and comparisons it is clear that the network lifetime of the IoT in a layered architecture (hierarchical) is longer than that of other schemes. In other schemes the relay nodes nearest to the BS can be overloaded and their energy will deplete earlier than that of other nodes. Imbalance of the load causes the nodes to deplete their energy earlier and hence network partition can occur, but with a cooperative method which reduces the communication distance, energy efficiency has been increased and it is suitable for the IoT. With the deployment of CHs, RNs and CCOs energy efficiency has been increased which would prolong the network lifetime of the IoT. Therefore, we assert that proposed hierarchical strategy is preferable for an energy efficient IoT.
# Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking
## 3 Control Design
### Fractional-Order Variable Damping Admittance Control
Although the previously described variable damping parameter strategy aids the robot in achieving compliance during the collaboration process, the controller experiences transient overshoot and has a slow tracking response. The inherent memory characteristic of fractional-order derivatives can enhance the transient response of the control system, thereby enhancing the closed-loop system's performance. Mathematically, various definitions of fractional-order derivatives exist [24]. This paper is mainly concerned with the solution of fractional-order differential equations using the Laplace transform, so the Caputo definition is used in this paper. The Caputo derivative of any order is computed using Formula (4):  
\[{}_{0}^{C}D_{t}^{p}f(t)\triangleq\frac{1}{\Gamma(n-p)}\int_{0}^{t}{(t-\tau)}^{ n-p-1}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\! The Laplace transform corresponding to the Caputo derivative of any order is represented by the following equation:  
\[ \begin{split}& L\{C_{0}^{D}I_{t}^{f}(t);s\}=L\{\{R_{0}^{L}D_{t}^{p- n}f^{(n)}(t);s\}=s^{p-n}G(s)\\ &=s^{p-n}(s^{n}Q(s)-\sum\limits_{k=0}^{n-1}s^{n-k-1}x_{e}^{(k)}( 0))\\ &=s^{p}Q(s)-\sum\limits_{k=0}^{n-1}s^{p-k-1}x_{e}^{(k)}(0),(n-1<p< n)\end{split}  \tag{5}\]  
Replacing the integer order in Equation (1) with a fractional order yields a fractional-order variable damped admittance control (FVDAC) on a single degree of freedom. \[M_{D}{}^{C}D_{t}^{\beta}(x_{e})+K_{D}(\alpha)^{C}D_{t}^{\gamma}(x_{e})=Q_{t},(1 <\beta<2,0<\gamma<1) \tag{6}\]  
The simultaneous Laplace transformation of both sides of Equation (6) yields the following:  
\[M_{D}{}^{S^{p}}X_{e}(s)+K_{D}(\alpha)s^{q}X_{e}(s)=M_{D}s^{p-1}x_{e}(0)+M_{D}s^ {p-2}\dot{x}_{e}(0)+K_{D}(\alpha)s^{q-1}x_{e}(0)+Q(s) \tag{7}\]  
The collation leads to the following:  
\[ \begin{split}& X_{e}(s)=\frac{M_{D}s^{p-1}x_{e}(0)+M_{D}s^{p-2}x_{e }(0)+K_{D}(\alpha)s^{q-1}x_{e}(0)+Q(s)}{M_{D}s^{p+}+K_{D}(\alpha)s^{q}}\\ &=\frac{M_{D}s^{p-1}x_{e}(0)}{M_{D}s^{p+}+K_{D}(\alpha)s^{q}}+ \frac{M_{D}s^{p-2}x_{e}(0)}{M_{D}s^{p+}+K_{D}(\alpha)s^{q}}+\frac{K_{D}(\alpha )s^{q-1}x_{e}(0)}{M_{D}s^{p+}+K_{D}(\alpha)s^{q}}+\frac{Q(s)}{M_{D}s^{p+}+K_{D} (\alpha)s^{q}}\end{split}  \tag{8}\]  
The Mittag-Leffler function finds extensive use in fractional-order calculus and differential equations due to its analytic nature in the complex domain, allowing for infinite series expansions that aid in solving fractional-order differential equations. The definition of the multivariate Mittag-Leffler function (in the n-dimensional case) is as follows:  
\[E_{(a_{1},\cdots,a_{n}),b}(z_{1},\cdots,z_{n})=\sum\limits_{k=0}^{\infty}\sum \limits_{l_{1}+\cdots+l_{n}=k}\frac{k!}{l_{1}!\times\cdots\times l_{n}!}\frac{ \prod\limits_{i=1}^{n}z_{i_{i}}^{l}}{\Gamma(b+\sum\limits_{i=1}^{n}a_{i}l_{i})} \tag{9}\]  
where \(b>0,a_{i}>0,|z_{i}|<\infty,l_{i}>0,i=,\cdots,n\).