esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 NFV and SFC Frameworks Taxonomy
### Performance Tuning Frameworks
#### 4.2.8 Opennetvm Framework  
The OpenNetVM framework was developed by [PERSON] et al. [13] as a framework that is ideal for high-performance vNF deployments using the Intel Dataplane Development Kit (DPDK) [[https://www.dpdk.org/](https://www.dpdk.org/)]([https://www.dpdk.org/](https://www.dpdk.org/)) (accessed on 8 December 2021) and Docker Containers [[https://www.docker.com/](https://www.docker.com/)]([https://www.docker.com/](https://www.docker.com/)) (accessed on 17 December 2021). To achieve high-speed packet I/O transfer, OpenNetVM implements zero-copy to reduce the I/O overhead associated with copying packets from the NIC for processing in the user space. Packets are DMA'd directly from the NIC to a shared memory space, which is accessible to DPDK-based network functions supported by the framework.  
As depicted in Figure 11, a shared memory space is created by the manager, which stores the metadata information, list of service chains, and flow tables. Dependencies are encapsulated in Docker containers that host the network functions; thus, packet transfer between NFs is handled by the TX and RX threads that carry useful descriptors. High-speed packet processing is also made possible by utilising the DPDK poll-mode driver rather than interrupts.  
In terms of NF-to-NF communication, OpenNetVM uses a centralised logical controller, which communicates using the OpenFlow protocol to orchestrate NF activities. The network function manager is responsible for managing memory and the NF life-cycle by handling inter-NF communication and sending keepalive messages. The transfer of packets between the NICs and NFs is also handled by the manager using the TX and RX threads. Network functions are either implemented using DPDK or as a user-space container process, which are tied to specific CPU cores in both scenarios. The framework was evaluated using metrics such as the scalability of multiple ports, overflow of the flow director, performance of service chains, and the flexibility of the framework can steer packets, which yields a much better performance when compared with ClickOS, especially in terms of throughput with variable chain length.
###### Abstract  
The capacity of passive optical networks (PONs) is continuously increasing, and it has been standardized up to 50 Gbit/s. The two main standardization organizations, IEEE and ITU-T, are actively working on the next-generation PON, which appears to be a 100G-PON still based on intensity modulation. Even though direct detection would be preferred for its cost and simplicity, the choice of coherent detection seems inevitable when the bit rate reaches 200-400 Gbit/s, specifically to guarantee the optical power budget requirement of an access network. The introduction of coherent systems in the PON scenario, allowing high-power-budget performances, should encouragetelecom operators to merge the metro and access networks into a single domain. This paper analyzes the mentioned metro + PON convergence scenario with experimental results focusing on a 400 Gbit/s fully coherent transmission (50 GBaud PM-16 QAM). We characterize three different transceivers, two of which are pre-commercials. We perform experimental demonstrations, with real urban fiber and laboratory set ups, of the metro-access convergence network in terms of the minimum OSNR value of the metro path, producing an acceptable optical power budget within the access network. Our work demonstrates feasibility of merging the metro-access network by using currently coherent optical transceivers for PON applications.  
accs network; high-capacity passive optical network; metro-access convergence; coherent transmission +
Footnote †: journal: _Electronics_  
0000-0001-6699-2276-2276][PERSON]. [PERSON]  
1 Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, 10129 Torino, Italy 1
## 7 Research Gaps and Future Opportunities
### Protection against Computational Attacks
#### 7.2.3 Lack of Support for Business Evolution  
Typical BC-SCM solutions implement various agreements among involved parties/stakeholders in smart contracts, which hard codes agreements as rules. In practice, businesses keep evolving due to several reasons (e.g., changing business policies) and also their underlying processes and agreements also evolve. On the other hand, due to recent governance developments, various involved stakeholders, products, producers, and consumers also need to comply with various general directives. These directives can be GDPR [94] or domain specific directives, such as unfair trading in the agriculture and food supply chain [95; 96]. Hard coded nature of such process agreements make these solutions incapable of integrating evolved business processes and changing business requirements as well as their compliance against emerging directives. Therefore, such solutions become infeasible on one hand and introduce inconsistencies between actual business process agreements and their corresponding implementation in smart contract on the other hand.
# Performance Analysis of IoT-Based Health and Environment WSN Deployment
## 3 Health and Environment Sensor Network Deployment  
Based on what can be inferred from the previous research in several applications of health and environment (Section 2), the sensor network deployment is a multi-objective optimization problem to maximize coverage and lifetime with considering the connectivity constraint. To solve this multi-objective optimization problem, GIS based optimization algorithms are applied in this research. The methodology of applying and evaluating GIS based optimization algorithms to optimize coverage and lifetime in health and environmental applications are shown in Figure 1. In the first step, GIS based optimization is modeled for the WSN deployment. In this regard, coverage and lifetime functions are presented to calculate the total coverage and lifetime of the network. The MST routing protocol is applied to generate a unit distance graph of the sensors to optimize the topology and then minimize the energy consumption. Second, the BA optimization algorithm is designed to step by step deploy health and environment sensors. BA is one of the artificial algorithms that has been most extensively used in many real-world applications [66] and it is an efficient algorithm that needs fewer control parameters and easy to implement [67; 68]. Third, the PSO algorithm procedure is designed to solve GIS based multi-objective WSN deployment. PSO is one of the swarm intelligence algorithms, which simulates the behavior of bird flocks and fish schools [69; 70]. Fourth, the BA and PSO algorithms are implemented for a simulated region. Before testing the algorithms, the algorithm parameters are set using the parameter tuning method. Finally, two BA and PSO algorithms are compared with three criteria.
# Use of Granulated Rubber Tyre Waste as Lightweight Backfill Material for Retaining Walls
## 4 Results and Discussion
### Mechanical Properties
#### 4.1.2 Unit Weight Test  
In order to determine the optimum moisture content of the mix and to prepare the samples for unconfined compression and direct shear tests, Standard Proctor tests are performed in accordance with Spanish code UNE-EN 13286-2 [53]. With the results obtained, shown in Table 4, compaction curves are plotted in order to find the maximum dry unit weight and the optimum water content. Nine 5% increments of water content are studied, which are introduced with between 10% and 50% weight of the rubber granulates. The compaction energy (Ee) is difficult to determine due to the high deformability of the rubber granulates.  
It can be observed in Figure 3 that increases in water content do not increase the dry unit weight. In none of the six rubber granulate mixtures is a clear trend line observed in the moisture curve, nor can one be found in the drying curve. The different percentages of particles that have been verified and compared reveal similar values, thus establishing a range of values from 5.39 kN/m3 as minimum to 6.03 kN/m3 as maximum. In verifying the retaining wall, 6.03 kN/m3 has been considered.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline Samples & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** \\ \hline Particle size & & & & & & 90\% 2.0–7.0 + & 50\% 2.0–7.0 + \\ (mm) & 0.0–0.8 & 0.6–2.0 & 2.0–4.0 & 2.0–7.0 & 10\% 0.6–2.0 & 50\% 0.6–2.0 \\ D10 & - & 1.15 & 1.20 & 2.35 & 2.35 & 2.05 \\ D60 & - & 1.50 & 3.00 & 5.20 & 5.20 & 3.00 \\ Cu & - & 1.30 & 2.50 & 2.21 & 2.21 & 1.46 \\ \(\rho_{\text{La}}\) (kN/m3) & - & - & - & 10.05 & 10.02 & - \\ \(\rho_{\text{s}}\) (kN/m3) & - & 9.79 & 10.20 & 10.30 & 10.80 & 10.32 \\ \hline \end{tabular}
\end{table}
Table 3: D10, D60, Coefficient of uniformity (Cu); apparent density (\(\rho_{\text{La}}\)); solid particle density (\(\rho_{\text{s}}\)) of the rubber granulates.  
Figure 2: **Left-hand side**. Distribution of the samples. S1: 0.0–0.8 mm/S2: 0.6–2.0 mm/S3: 2.0–4.0 mm/S4: 2.0–7.0 mm/S5: 90% 2.0–7.0 mm + 10% 0.6–2.0 mm/S6: 50% 2.0–7.0 mm + 50% 0.6–2.0 mm. **Right-hand side**. The particle-density analysis of the rubber granulates.  
It is observed that up to a 20% water content helps workability; however, for higher values, the water drains out of the test mould.  
The unit weight of soils ranges from 17.55 to 26.77 kN/m\({}^{3}\)[16; 28; 43; 54]. Therefore, the unit weight of rubber granulates is less than one third of the unit weight of soils, and hence rubber granulates are considered as a lightweight backfill material in retaining walls.
## 4 Governing Differential Equations and Boundary Conditions
Subsequently, the system's total potential energy \(\Pi\) is expressed as follows:  
\[\Pi=U+V=\int_{0}^{L}\left[Mw^{\prime\prime}+\frac{E}{2}N_{1}\left(w^{\prime \prime}\right)^{2}+EN_{2}w^{\prime\prime}\varphi^{\prime}+\frac{E}{2}N_{3} \left(\varphi^{\prime}\right)^{2}+\frac{9G}{10}N_{4}\varphi^{2}\right]dz \tag{33}\]  
where \(N_{i}\) is the parameter related to the cross-sectional properties. For single-cell box girders, the parameters \(N_{i}\) can be expressed as follows:  
\[\left\{ \begin{array}{l}N_{1}=\sum\limits_{i=1}^{3}I_{1i}+I_{1w2}\qquad\qquad N _{2}=\sum\limits_{i=1}^{3}I_{1i}\left(\frac{\eta_{i}}{4}+d_{i}\right)-\frac{3h _{1}(h_{2}-h_{1})(\eta_{1}+d_{1})}{2\left(h_{2}^{2}-h_{1}h_{2}+h_{1}^{2}\right) }I_{1w2}\\ N_{3}=\sum\limits_{i=1}^{3}I_{1i}\left(\frac{\eta_{i}^{2}}{7}+\frac{\eta_{i}d _{i}}{2}+d_{i}^{2}\right)+\frac{3h_{1}^{2}(\eta_{1}+d_{1})^{2}}{h_{2}^{2}-h_{1 }h_{2}+h_{1}^{2}}I_{1w2}\qquad\qquad N_{4}=\sum\limits_{i=1}^{3}I_{1i}\frac{ \eta_{i}^{2}}{b_{k}^{2}}\end{array} \right. \tag{34}\]  
For double-cell box girders, the parameters \(N_{i}\) can be expressed as follows:  
\[\left\{ \begin{array}{l}N_{1}=\sum\limits_{i=1}^{5}I_{2i}+\sum\limits_{i=1}^{ 2}I_{2 wi}\qquad\qquad N_{2}=\sum\limits_{i=1}^{5}I_{2i}\left(\frac{\eta_{i}}{4} +d_{i}\right)-\sum\limits_{i=1}^{2}\frac{3h_{1}(h_{2}-h_{1})}{2\left(h_{2}^{2} -h_{1}h_{2}+h_{1}^{2}\right)}(\eta_{i}+d_{i})I_{2 wi}\\ N_{3}=\sum\limits_{i=1}^{5}I_{2i}\left(\frac{\eta_{i}^{2}}{7}+\frac{\eta_{i}d _{i}}{2}+d_{i}^{2}\right)+\frac{3h_{1}^{2}(\eta_{1}+d_{1})^{2}}{\left(h_{2}^{2} -h_{1}h_{2}+h_{1}^{2}\right)}I_{2w1}+\frac{3h_{1}^{2}(\eta_{2}+d_{2})^{2}}{ \left(h_{2}^{2}-h_{1}h_{2}+h_{1}^{2}\right)}I_{2w2}\\ N_{4}=\sum\limits_{i=1}^{5}I_{2i}\frac{\eta_{i}^{2}}{b_{k}^{2}}\end{array} \right. \tag{35}\]  
Substituting Equation (33) into Equation (24), and then applying the partial integration method, governing differential equations and boundary conditions are deduced as follows:  
\[\left[EN_{1}w^{\prime\prime}+EN_{2}\varphi^{\prime}+M\right]\delta w^{\prime \prime}=0 \tag{36}\]  
\[\left[EN_{2}w^{\prime\prime\prime}+EN_{3}\varphi^{\prime\prime}-\frac{9G}{5}N_{4 }\varphi\right]\delta\varphi=0 \tag{37}\]  
\[\left[EN_{2}w^{\prime\prime}+EN_{3}\varphi^{\prime}\right]\delta\varphi\big{|}_{ 0}^{L}=0 \tag{38}\]According to Equations (36) and (37), the differential equation of the maximum difference in the shear angle \(\varphi\) can be expressed as follows:  
\[\varphi^{\prime\prime}-\alpha^{2}\varphi=\beta Q(z) \tag{39}\]  
In which,  
\[\alpha=\sqrt{\frac{9 GN_{1}N_{4}}{5E\big{(}N_{1}N_{3}-N_{2}^{2}\big{)}}}\qquad \beta=\frac{N_{2}}{E\big{(}N_{1}N_{3}-N_{2}^{2}\big{)}}\qquad Q(z)=M^{\prime}(z) \tag{40}\]
# Modeling the Trip Distributions of Tourists Based on Trip Chain and Entropy-Maximizing Theory
## 4 The Trip Distribution Forecasting Model Based on Trip Chains
### Trip Distribution Forecasting Model
#### 4.2.4 Model Inputs, Outputs, and Application  
Application of the Entropy-Maximizing -based trip chain distribution model requires several types of input data, including the tourist survey data, traffic network, and traffic zones of the tourism city, as well as the number of produced and attracted tourists of each traffic zone obtained through the questionnaire survey of tourists. The prior probability of each trip chain is needed too. Then, we can get the actual probability of each trip chain and the trip distributions of traffic zones.  
1. Model inputs.  
The input of the model includes the number of produced and attracted tourists of each traffic zone, the prior probability of trip chains, and the total trip distance between each traffic zone.  
The numbers of tourists in each traffic zone are a precondition of the model. Thus, the number of origin and destination zones should be determined before preparing model input data. The origin zone is the area primarily influenced by transportation hubs, and the destination zone is the area dominated by a scenic spot. Tourist productions or attractions of these traffic zones are important input data of the model.  
The prior probability of the initial travel path can be obtained from the survey data. Trip distance is not only a model constraint but also an important input of the model. It can be divided into different classes according to the types of tourists' trips by means of aggregation. Then, the trip distance of each trip chain can be calculated separately, and the trip type of each trip distance class is taken as the input parameter of the model.  
2. Model outputs.  
The output of the model is the trip probability of each trip chain. Then, we can perform a statistic analysis on the number of trips and verify whether the classification of the number of trips is consistent with the actual trips of tourists. Furthermore, trip distances can also be obtained from the model, and the distribution of the trip distances of tourists analyzed. Finally, the spatial distribution of tourists' trips can be obtained from the model as well.  
3. Model application.  
The proposed model can be used to predict the number of tourists in traffic zones in the targeted year based on the current status of the traffic zones in recent years. Specifically, according to the annual growth of tourists, the gray prediction theory can be used to predict the number of tourists in the traffic zone. Then, taking the number of tourists in the traffic zone as the input value, the model can be used to predict the trip distribution.
# Fraud Detection Using Neural Networks: A Case Study of Income Tax
## 3 Methodology
### Structure of Artificial Neural Networks  
The smallest element of any ANN is a neuron, also known as a node. See Figure 1, a neuron structure and sample ANN Multi-layer perceptron structure. These are connected by synapses, which also define how strong the connection is using the weight. The synapse is mathematically represented as a weight vector, and this weight decides how the preceding layer's output is routed via the activation function for each node. Each neuron or perceptron is characterized by its input, an activation function, and the output provided [19].  
A neuron's inputs \(x_{0},x_{1},\dots,x_{n}\) with their corresponding weights \(w_{0},w_{1},\dots,w_{n}\) are processed by an activation function, and the result is the neuron's output, which corresponds to:  
\[Output=f(w_{1}x_{1}+w_{2}x_{2}+\dots+w_{n}x_{n}+bias)=f\left(\sum_{i=1}^{n}x_{ i}w_{i}+bias\right), \tag{1}\]  
where \(x_{i}\) and \(w_{i}\) are the input vector and weight vector, respectively, \(f\) is the activation function applied on the sum of products of each input and its associated weight and the bias.  
An activation function of a neuron in an ANN defines the neuron's output given a set of inputs. They convert input signals into output, which is then fed as input to the next layer in a network. This function is designed to introduce non-linearity in a network by either activating or deactivating a perceptron [23]. In its optimization process, ANN employs optimizers such as gradient descent to minimize prediction errors; the prediction error is computed using a loss function, which quantifies how good or bad the model performance is. Optimizers modify a perceptron's weight and bias, as well as the network's learning rate, to reduce the loss function as much as possible, thereby improving the model's performance [24].
## 5 Capacity Evaluation Analysis
### Model Validation  
According to the model fitting in Section 5, FD and FLR are related to capacity. This paper used the traffic data around the four bus stops (as mentioned in Section 3) around 7:30-8:30 in 17 November 2021 to calculate the traffic capacity through fitting model, and compare it with actual capacity value, therefore, further verifying the effectiveness of the fitting model. Figure 13 reflects the relationship between measured values and observed values of the four groups of data. Table 13 shows the R\({}^{2}\) of the fitting model of each group of data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function Model** & **Function Expression** & **R\({}^{2}\)** \\ \hline Linear function & \(y=-28,739.9x+1798.4\) & 0.91744 \\ Polynomial function & \(y=-228,679.2x^{2}-22,452.1x+1758.6\) & 0.91789 \\ Logarithmic function & \(y=-374.3 ln(x)-216.44\) & 0.90707 \\ Exponential function & \(y=1855.4e^{-205}\)x & 0.91569 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The fitting function used and the R\({}^{2}\) of FLR/Q-Capacity (Ra = 0%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function Model** & **Function Expression** & **R\({}^{2}\)** \\ \hline Linear function & \(y=-46,171.4x+2518.1\) & 0.96177 \\ Polynomial function & \(y=-929,568.1x^{2}-26,888.4x+2425.8\) & 0.96427 \\ Logarithmic function & \(y=-446.6 ln(x)-18.459\) & 0.96115 \\ Exponential function & \(y=2576.5e^{-22.77x}\) & 0.9581 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The fitting function used and the R\({}^{2}\) of FLR/Q-Capacity (Ra = 50%).  
Figure 13: The measured values and observed values of traffic capacity around four bus stops: **(a)** no. 1 bus stop; **(b)** no. 2 bus stop; **(c)** no. 3 bus stop; **(d)** no. 4 bus stop.  
It can be seen from the figure that the fitting accuracy of the above FD and FLR fitting models for the traffic capacity of the four stations is between 0.85 and 0.95, and the fitting effects are good, so the fitting models can be considered effective. The results reveal a significant second linear fitting relationship between both failure duration and capacity and forced lane-changing and capacity, which further verifies that this index can better reflect the impact on the capacity of the road near the stop.  
In general, after the above research, an effective relationship model can be established between FD index, the FLR index and capacity, which reflects that FD and FLR can effectively evaluate the capacity of sections within bus stops under a CAVs environment. The study provides the theoretical basis for urban public transport planning and management.
# AutoElbow: An Automatic Elbow Detection Method for Estimating the Number of Clusters in a Dataset
## 4 Results and Discussion  
We considered four notable case studies to evaluate the performance of the proposed method: the iris (real) dataset (with three clusters), a seven-cluster [20], a forty-cluster synthetic dataset [21], and the Cleveland heart disease dataset [22]. The iris dataset with three clusters was considered suitable for assessing the different approaches for a small cluster number, whereas the seven- and forty-cluster datasets were considered for analyzing datasets with both medium and high cluster numbers, while the Cleveland dataset sufficed for a high dimensional and unbalanced dataset.  
The idea of experimenting with different \(K\) values to generate the elbow graph was motivated by the fact that \(K\) typically determines the shape of the elbow graph and also because the actual number of clusters in the dataset is not always known in advance. Consequently, small \(K\) values typically generate graphs with short tails and may not correspond to the actual number of clusters in datasets, particularly datasets with many clusters. Large \(K\) values, on the other hand, may account for datasets with many actual cluster numbers but will generate long-tailed graphs, which will ultimately shift the potential location of the elbow \(k^{*}\).  
Hence, it is essential to evaluate the performance of the various methods for varying \(K\) values. As a result, we evaluated the different approaches with \(K=10\) and \(K=40\) for the various datasets, and the results are discussed in the following subsections. In addition, because the K-means algorithm is based on statistics, 1000 independent Monte Carlo simulations were conducted for each dataset. The average results for each method are displayed in the graphs below. We should also mention that, because the essence of each method is to determine \(k^{*}\) along the \(x\)-axis of the graph, each method, thus, produces different magnitudes along the \(y\)-axis based on their specific measure of interest. To ensure consistency of interpretation, we normalized these measures between 0 and 1 as can be seen in the different performance graphs.
## 4 Experimental Verification  
The experimental platform is setup as depicted in Figure 5. It comprises GNSS signal simulator, host computer control software, test receiver, and connection feeder. GNSS signal simulator is used as an authentic signal and spoofing signal generating device. The host computer control software controls the code offset (unit: m), carrier phase offset (unit: m), and code rate (unit: m/s) of each spoofing signal relative to the authentic signal and carrier phase rate (unit: m/s), and relative power gain (unit: dB), power increase/decrease rate (unit: dB/s) by writing instructions. In addition, the Space Vehicle Identification (SVID), number of satellites, and signal power value of authentic signal and spoofing signal can be selected initially. The experiment uses the PolaRx5 receiver of Septentrio as the target receiver, which has advanced interference monitoring and anti-interference ability.  
The following two groups of experiments are designed to compare the spoofing effect of the newly designed GNSS spoofing signal power control algorithm and spoofing signal high power control algorithm on a test receiver. In experiment 1, the high-power control algorithm was performed for the spoofing signal, and in experiment 2, the new power control algorithm was performed for the spoofing signal. In the two groups of experiments, except the GNSS spoofing signal power control algorithm, the other experimental conditions were the same.
# A Novel Approach to Enhance the Energy Efficiency of a NOMA Network
## 5 The Sub-Optimal Solution  
In this section, we propose and examine how to maximize the minimum individual EE under transmit power and QoS constraints to ensure fairness among users. As a result of the fractional structure of the EE expression and the binary variable in the channel allocation indicator, the optimization problem at hand is a non-convex problem that is difficult to solve directly. Next, we will introduce the problem optimization solutions; first USFMA will be discussed, then power allocation by DC programming will be detailed.