esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 6 Conclusions  
In this article, we presented and implemented a clustering algorithm for performing inferential network monitoring based on multicast end-to-end measurements. Specifically, we addressed the problems of inferring the routing tree topology (both logical and physical) and estimating two link performance parameters (i.e., loss rate and jitter). The experimental results regarding the accuracy and the execution time of the proposed algorithm, as well as the presented motivating application leveraging change point analysis, demonstrated its promising potential as a close to real-time monitoring tool and an enabler of performance anomaly detection.  
Regarding future work, we plan to explore and experimentally validate more applications of the proposed NT monitoring approach. One such example is the development of a utility-based framework enabling advanced decision-making. The envisaged utility function can incorporate network-centric QoS parameters provided and updated by the clustering algorithm to promote informed decisions based on network utility maximization.  
All authors contributed to the conceptualization of the paper. [PERSON] and [PERSON] outlined the structure of the article. [PERSON] was responsible for software development, performance evaluation, and preparation of the original draft. [PERSON] and [PERSON] performed the critical review and overall consistency check. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Commission in the framework of the H2020-ICT-19-2019 project 5G-HEART (Grant Agreement No. 857034).  
Figure 6: Loss rate estimates and detected change points.
# Key Performance Indicators for Food Supply Chain: A Bibliometric and Systematic Literature Review
## 3 Results
### Outcomes of the Content Analysis
As shown in this figure, the combination of all three types of KPIs (Economic, Environmental, and Social) is the most studied in the literature (56 out of 92 studies), reflecting the Triple Bottom Line approach, which corresponds to the most complete level of analysis of an activity or of a process. In line with the fact that quite often the Social aspect is neglected both in the literature and in real cases, the combination of Economic and Environmental KPIs follows, with 22 papers. Conversely, Social and Environmental is the least studied combination, with one contribution only; also, this result was expected if noting again that the production stage is the most widely investigated.
# The Montreal Experience: A Retrospective Study Part I--Basic Principles and Treatment Algorithm
## 1 Introduction  
It is known that myopia is becoming epidemic. High myopia is associated with a higher risk of developing significant ocular pathologies, which may lead to visual impairment at an older age. This is why the World Health Organization has identified myopia andhigh myopia as a public health issue [1] and has challenged governments, public agencies, eyecare professionals, and other stakeholders to develop strategies to reduce the burden of this avoidable blindness.  
Moreover, the World Council of Optometry has adopted a resolution advising myopia management, mitigation and measurement to be incorporated as a standard of care for optometrists [2]. More specifically, mitigation implies that eye care practitioners (ECP) must educate and counsel parents and children, during early and regular eye exams, on lifestyle, dietary, and other factors to prevent or delay the onset of myopia. Measurement means that ECP must evaluate the status of a patient during regular comprehensive vision and eye health exams, such as measuring refractive error and axial length whenever possible. Finally, management asks ECP to address patients' needs of today by correcting myopia, while also providing evidence-based interventions (e.g., contact lenses, spectacles, pharmaceuticals) that slow the progression of myopia, for improved quality of life and better eye health today and into the future.  
If this position seems innovative, it is not new for experts at the Clinique Universitaire de la Vision, Universite de Montreal (CUV). Indeed, for more than 15 years, CUV professionals have been practicing myopia control techniques which have been proven. With time and experience, they have refined their methods. Their clinical approach is now known as the Montreal Experience (ME), based on three pillars [3]. This approach differs from others because it is highly customized to the patient, takes into account binocular vision status, and relies on the pupil area and the myopic defocus generated to establish a dose response strategy.
# Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form
## 3 Grey Decision-Making for Product Form Driven by Fuzzy Relationship between Kansei Images
### Construction of the Grey Decision-Making Model for Product Form  
#### 3.2.1 Identification of the Fuzzy Relationship and Angle between Kansei Images  
In product development, designers cater to the diverse emotional needs of users with concrete product forms, which makes a product a collection of multiple Kansei images [25]. All Kansei images can be divided into main and secondary Kansei images according to their strengths in product form; that is, the strongest subjective feeling generated by users is the main Kansei image, and other images are secondary Kansei images [26]. The main and secondary Kansei images are not independent of each other, but form a user's overall cognition of a product through coupling. Therefore, when constructing the grey decision-making model for product form, it is necessary to explore the fuzzy relationship and correlation degree between Kansei images to improve the accuracy of the grey decision-making model.  
The Pearson correlation coefficient is a method of measuring the linear relationship between two variables, and its value range is \([-1,1]\). In this range, the greater the absolute value of the correlation coefficient is, the stronger the correlation between the two variables. In this study, Pearson correlation coefficient analysis is used to explore the degree of correlation between Kansei images. The expression [27] is as follows:  
\[B=\frac{\sum\limits_{j=1}^{n}\big{(}V_{j}-\overline{V}\big{)}\big{(}U_{j}- \overline{U}\big{)}}{\sqrt{\sum\limits_{j=1}^{n}\big{(}V_{j}-\overline{V} \big{)}^{2}\sum\limits_{j=1}^{n}\big{(}U_{j}-\overline{U}\big{)}^{2}}} \tag{9}\]  
where \(V\) and \(U\) are columns of Kansei image evaluation data in a standardised matrix \(X\), respectively.  
Based on the Kansei image weights \(W_{j}\), combined with Equation(10) for calculating the angles of Kansei images proposed in reference [26], the angle \(\alpha_{j}\) of each Kansei image is obtained, which are then used to determine the range of each Kansei image area. In this study, a single Kansei image is used as the main Kansei image, and other Kansei images are ranked from left to right based on the Kansei image correlation, as shown in Figure 3.  
\[\alpha_{j}=\frac{360^{\circ}\cdot w_{j}}{\sum\limits_{j=1}^{n}w_{j}} \tag{10}\]To facilitate the representation of the fuzzy relationship between Kansei images, a Kansei image is represented by the angular bisector of the Kansei image area [28], as shown in Figure 4.
# Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
## 3 Simulation Results and Discussion
### Comparison of SGD and MBGD Algorithm for DRSMO  
We have previously used the SGD algorithm to solve multi-objective SMO [25; 26] and it converged well with fast speed. However, due to the wider sampling range of defocusing in the DRSMO framework, it was hard for the SGD algorithm to search for the global optimal direction if each iteration was only driven by one sample gradient in the training set. To compare the SGD and MBGD optimization performance for the DRSMO in terms of speed and accuracy, we generated the same training set with 900 sample points for both MBGD and SGD optimization processes (for the MBGD algorithm, there were a total of three samples per iteration and 300 iterations. For the SGD algorithm, there was a total of one sample per iteration and 900 iterations). Figure 8 illustrates that the defocus-PAE curves for target 1 optimization in the case of the same weight factor, \(\omega=0.1\), was proceeded by the SGD and MBGD algorithms, respectively. It was demonstrated that the slope of the DRSMO was proceeded by the MBGD (red curve), which was lower than that of the SGD (jasper curve). It indicates the better optimization performance for the MBGD in the same training set. Similarly, Figure 9 shows that the PWs with the same weight factor were proceeded by the SGD and MBGD algorithms, respectively. It was the MBGD algorithm that provided a wider PW due to the lower defocus sensitivity.  
Table 4 summarizes the comparison of the optimization performances for target 1 proceeded by the MBGD and SGD, respectively. It clearly shows that the lower \(S_{\beta}\) and larger DOF were improved by MBGD optimization. Meanwhile, Table 4 shows the comparison of run times, although SGD had a relatively weak optimization performance but was faster in regard to convergence rates. In addition, all computations were carried out on a server with an Intel core i5 8400 CPU, 2.8 GHz, 16.0 GB of RAM.  
Figure 8: The defocus–PAE curves of target 1 for the DRSMO with the same weight factor \(\omega=0.1\) which was proceeded by the SGD algorithm (jasper curve) and MBGD algorithms (red curve), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(S_{\beta}\) & **DOF (EL = 5\%)** & **DOF (EL = 8\%)** \\ \hline
**Initial SMO** & 54.2 & 77 & 0 \\
**DRSMO (\(\omega\) = 0)** & 40.3 & 82 & 0 \\
**DRSMO (\(\omega\) = 0.2)** & 32.6 & 119 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The target 2 optimized values of \(S_{\beta}\), DOF (nm) corresponding to ELs equal to 5% and 8% for the conventional SMO, and DRSMO with \(\omega=0\), \(\omega=0.1\), \(\omega=0.2\), and \(\omega=0.3\), respectively.  
In conclusion, both the MBGD and SGD were beneficial for improving DOF and PW. However, the massive samples taken from the defocusing disturbances made it hard for the SGD to converge to a global search direction. Therefore, the MBGD algorithm was applied to the DRSMO multi-objective optimization problem most effectively.
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 3 Results
### Results of the Confusion Matrix  
Four different BPNNs were trained and tested with the data from each one of the four loads at each speed. The overall results are shown in Figure 5. Each data point in the three matrixes represents an accuracy of gait recognition across all subjects for the designated training and testing loads. In the matrixes, the vertical axis represents the training loads and the horizontal axis represents the test loads. The data shown in the main diagonal comes from the cases where the data that was used for training and testing was obtained from the same load, i.e., intra-load. The other data that was not in the main diagonal came from the cases where the data was used for training and testing as obtained from the different loads, i.e., inter-loads. The \(t\)-test was employed to demonstrate that there was a significant difference (\(p<0.05\)) between the accuracy of the gait recognition from the main diagonal and the gait recognition off the diagonal. This implies the load variation has a great impact on the accuracy of gait recognition. For instance, the results (83.61, 97.56, 65.93, and 67%, respectively) from training with L20 and testing with L0, L20, L30, and L40 at V5, were considerably different. Similar results were found at V3 and V7 under other loads. The poorest results appeared when training with the data from L20, and testing with data from L0, L30 (L20-L0: 52.78% and L20-L30: 53.62%, respectively) at V3. The poorest results at V5 came from L20-L30, L0-L30 (65.93% and 66.23%, respectively). As for V7, the poorest results came from L20-L30, L20-L40 (62.75% and 66.48%, respectively).  
Figure 4: Comparison of three classification algorithms at four different loads under three different speeds.  
The statistical results of the three recognition accuracy matrices, including recognition accuracy of intra-load, inter-loads, and overall, are shown in Table 2, and expressed as mean \(\pm\) sd. The total mean intra-load recognition accuracy was 91.81%, but the total mean overall recognition accuracy was 75.02%, which was far away from the intra-load recognition accuracy. The lowest mean recognition accuracy was the inter-load accuracy, which was only 69.42%. Different speeds were found to have a great impact on the accuracy of gait recognition in Table 2. For instance, the mean overall recognition accuracy at V3 (70.98%) was substantially different from those at V5 (76.96%) and V7 (77.11%).
## 3 Methodology
### Data Processing and Analysis
#### 3.2.2 Travel Feature Extraction  
According to the concept of outliers in pattern recognition theory, anomalous behaviors can be regarded as an outlier behavior that deviates from the travel behavior of most passengers [2]. In order to accurately identify normal and abnormal passengers, multiple travel feature indicators are extracted from the dimensions of time, space, and attributes for identification. Finally, seven key features are extracted to construct a feature sequence for passenger travel to quantify passenger travel patterns. These features are used as input data for cluster analysis [1].  
Referring to the work of [PERSON]. [PERSON] et al. [5], considering the diversity of passenger travel in time, space and travel categories, we extract seven travel key features?abStas (number of visits to the station), staTmEn (temporal entropy of passengers at station level), staZnEn (spatial entropy of passengers at station level), freTraPct (proportion of high-frequency travel during weekdays), peakTmPtct (proportion of travel during rush hours), maxODptct (proportion of most frequent travel), shortTraPct (proportion of short travel) and the characteristic attribute lof (anomaly extent of a passenger). In the visual analytics module, we use these features to visually explore passenger travel. Due to the different calculation methods of each feature, the values of each feature not only have large differences, but also have different dimensions. In order to use these features more conveniently, we have normalize these features. The calculation formula of the normalization process is shown in Formula (1), where \(v_{i}\) represents the value of feature A, B represents the maximum value allowed for each feature in the visual analytics. In our method, the value of B is 10.  
\[v^{\prime}_{i}=\frac{v_{i}-\min(A)}{\max(A)-\min(A)}B \tag{1}\]
###### Abstract  
This paper presents the quantitative measurement through an experimental test of 640 Gbps 16-QAM coherent-optical orthogonal frequency-division multiplexing (CO-OFDM) over 800 km optical fiber with mid-link optical phase conjugation (OPC) using highly nonlinear fiber (HNLF). The first focus is the OPC parameter optimization, including the optimization of HNLF length and signal/pump power that inputs into OPC. Four different HNLFs, as the illustrative examples, are investigated. The second focus is to investigate the effects of fiber dispersion, nonlinearity, and amplified spontaneous emission (ASE) noise on the long-haul transmission of 16-QAM CO-OFDM signal, and the OPC compensation efficiency. The performance evaluation focuses on the conversion efficiency (CE), received signal constellation, Q-factor improvement, and bit error rate (BER) at the receiver end. Such end-to-end performance evaluation is important because the 16-QAM CO-OFDM signal status is heterogeneous and the mitigation of transmission impairments to the signal is still unclear. The OPC parametric optimization is achieved experimentally using commercially available HNLFs with different scenarios and the numerical results are interpreted in conjunction with simulations.  
16-QAM CO-OFDM; optical phase conjugation (OPC); four-wave mixing (FWM); high nonlinear fiber (HNLF); OPC parameter
# An Extended Car-Following Model Considering Generalized Preceding Vehicles in V2X Environment
## 1 Introduction
However, urban roads are not all one-lane roads, and traffic flow on each lane of multi-lane roads is not independent of each other. Common driving experience also suggests that drivers will pay attention to motion state of vehicles ahead in the current and adjacent lanes at the same time. In recent years, a large number of research upon car-following behavior for different aims, such as traffic flow prediction [20], feedback control [21] or the safety analysis [22; 23], and based on various idea, such as considering driver' memory effect [24; 25; 26] or driver's visual characteristics [27], communication delay [28], reaction delay [29], have been worked out. Among them, several efforts upon vehicle platoon control in V2X environment [30; 31] or the influence of V2X technology on driving behavior [32; 33] have been conducted. However, understanding about the impact exerted by motion state information, which is available for the drivers using V2X technology in ITS, of preceding vehicles including ones in the adjacent lanes on car-following behavior and traffic flow is still limited. Motivated by the above contents, a concept called GPV is proposed to stand for the vehicles group consisted of the front vehicle, non-neighboring front vehicle, and neighboring front vehicles in the adjacent lanes (also known as left/right front vehicle), and average velocity is employed to represent motion state of GPV. Based on these, an extended car-following model is established in Section 2 and then calibrated with the NGSIM data set using the genetic algorithm in Section 3. The stability condition of the model is derived through linear stability analysis in Section 4, and the performance of our model is studied utilizing numerical simulation in Section 5. Based on these efforts, the impact of GPV motion state information on car-following behavior and traffic flow in the V2X environment is explored. Research results are discussed in Section 6, and the conclusion is given in Section 7.
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 3 Methods  
### RBF Neural Network Fundamentals  
The RBF (radical basis function) neural network is an efficient feed-forward network that has the best approximation performance and global optimum properties that other forward networks do not have, and has a simple structure and fast training speed. Its network topology is shown in Figure 2, which contains an input layer, a radial basis function hidden layer, and an output layer. The main advantage is that it can approximate and predict any continuous nonlinear function within arbitrary accuracy and has global approximation capability, which fundamentally solves the local optimum problem of the BP neural network, the topology is compact, and the structural parameters can be separated for learning [31]. In addition, the generalization ability of the RBF network is also better than the BP network in several aspects. In the RBF neural network, the input data enter the input layer without any transformation. When the data enter the hidden layer, the hidden layer neural nodes perform a kind of spatial nonlinear mapping transformation on the data through the basis function. The output layer uses a linear optimization strategy, so the learning speed can be faster. Finally, the mapped values are output by a linearly weighted combination of the output layer neural nodes.  
Figure 1: Twenty-five typhoon paths affecting Ningbo area during 2014–2019.  
The input layer of RBF neural network realizes the spatial nonlinear mapping transformation \(x_{p}\to R(x_{p}-c_{i})\) by a radial basis function, where the commonly used basis functions are Gaussian functions with the following activation functions:  
\[R(x_{p}-c_{i})=\exp(-\frac{1}{2\sigma^{2}}\|x_{p}-c_{i}\|^{2}) \tag{1}\]  
where \(\|x_{p}-c_{i}\|\) is the Euclidean norm; \(x_{p}=\left(x_{1}^{p},x_{2}^{p},\ldots,x_{m}^{p}\right)^{\mathsf{T}}\) representing the \(p\)th input data; \(c_{i}\) is the implicit layer node center; and \(\sigma\) is the variance of the basis function.  
The output layer of the RBF neural network achieves a linear mapping \(R(x_{p}-c_{i})\to y_{j}\) by weighted combinations, denoted as:  
\[y_{j}=\sum_{i=1}^{h}\omega_{ij}R(x_{p}-c_{i})j=1,2,\ldots,n \tag{2}\]  
where \(\omega_{ij}\) is the connection weight from the implicit layer to the output layer; \(i=1,2,3,\ldots,h\), \(h\) is the number of nodes in the hidden layer; and \(y_{j}\) is the \(j\)th actual output value corresponding to the data.
# Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications
## 2 MIMO System Design with CoI Cancellation
### Computational Complexity Analysis  
In this subsection, we simply analyze the computational complexity of the proposed methods, including the channel estimation method and channel equalization method.  
Let us first consider the channel estimation model. When canceling the interference from \(k\)-th transmitter, the computational complexity of (10) is \(\mathcal{O}(kS^{3})+\mathcal{O}(S^{2}L)=\mathcal{O}(S^{3})\). The computational complexity of the term \(\mathbf{B}_{k}^{H}\mathbf{B}_{k}\) is \(\mathcal{O}(L^{2}S)\); the complexity of the term \((\mathbf{B}_{k}^{H}\mathbf{B}_{k})^{-1}\) is \(\mathcal{O}(L^{3})\); the complexity of the term \(\mathbf{B}_{k}(\mathbf{B}_{k}^{H}\mathbf{B}_{k})^{-1}\) is \(\mathcal{O}(SL^{2}+S^{2}L)=\mathcal{O}(S^{2}L)\). Therefore, the total computational complexity of (9) is \(\mathcal{O}(S^{3})+\mathcal{O}(S^{2}L)+\mathcal{O}(SL)=\mathcal{O}(S^{3})\). The computational complexity of the term \((\prod_{i=1}^{M}\mathbf{P}_{i})\mathbf{y}_{n}\) in (12) is \(\mathcal{O}(S^{2}L)\). When solving the (12) using least square, the computational complexity is also \(\mathcal{O}(S^{3})\). Since the number of transmitters for underwater acoustic MIMO communication is very limited, the computational complexity of the proposed channel estimation is still \(\mathcal{O}(S^{3})\).  
The main complexity for the channel equalizer is the matrix inverse process. The computational complexity of the term \(\mathbf{g}_{k}\mathbf{g}_{k}^{H}\) is \(\mathcal{O}(L_{ff}^{2})\); the complexity complexity matrix inverse in (21a) is \(\mathcal{O}(L_{ff}^{3})\); the computational complexity of (21b) and (21c) is \(\mathcal{O}(L_{fb}L_{ff})\).  
It can be concluded that the computational complexity of the proposed receiver is determined by the training length \(S\) and the length of the feedforward filter \(L_{ff}\).