esa-sceva/satcom-chunk-collection huggingface dataset:
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Simulation Models
#### 3.7.3 Device's Lifetime Model  
The battery level and energy consumption of devices depend on the device lifetime model, while longer device vitality leads to further network life. This is due to the fact that the network is connected for a specific period of time unless the battery of the paired device has been exhausted. The drain rate is dependent upon the device load, and in cases where the initial energy level of all devices is even, the first device to completely exhaust its battery will have the maximum drain rate. The lifetime of devices is calculated by the ratio of residual battery, while the drain rate of device \(j\) is described as follows:  
\[LT_{j}(t)=\frac{RB_{j}(t)}{DR_{j}(t)} \tag{15}\]  
where \(LT_{j}(t)\) denotes the lifetime of device \(j\), \(RB_{j}(t)\) stands for the device \(j\) residual battery and \(DR_{j}(t)\) is the drain rate of device \(j\) at a time instant \(t\) which is calculated in Equation (5). If the value of the drain rate is zero, then the lifetime of the device is considered to be a maximum value. Extending the individual device lifetime will ensure that the whole network lifetime is prolonged. In addition, the lifetime of the device depends upon the drain rate and battery level; therefore, the proposed scheme considers the device's lifetime to measure the battery level and drain rate. Therefore, the device will have the longest lifetime with the highest battery energy level and lowest drain rate.
## 1 Introduction  
Worldwide, acute gastroenteritis (AGE) is one of the leading causes of morbidity and mortality of children younger than 5 years [1, 2]. AGE cases are predominantly caused by viruses, most commonly norovirus, rotavirus, and enteric adenovirus [3]. Despite the fact that the majority of virus-related AGE infections are self-limiting, they still significantly contribute to the burden of disease in many countries [1].  
Human norovirus is the leading etiology of non-bacterial AGE among individuals of all ages globally [1; 2; 3], and is responsible for 60% of AGE cases in the United States, accounting for 400,000 emergency department (ED) visits and 71,000 hospitalizations each year [4]. Norovirus is highly contagious due to its low infective dose, various transmission routes, and persistence in the environment. Noroviruses were recently grouped into ten genogroups (GI-GX), with GII being the most predominant [5]. Infected individuals can shed over \(10^{9}\) copies of viral particles in one gram of feces [6]. In immunocompetent individuals, norovirus infection is usually self-limiting, with clinical symptoms lasting for 2-3 days [6; 7].  
Rotavirus was the most common cause of AGE in infants and young children worldwide before the introduction of the rotavirus vaccine. According to the Global Burden of Disease report, rotavirus infection caused 128,500 deaths and more than 250 million episodes of diarrhea among children younger than 5 years in 2016 [8]. Although the rotavirus vaccine has substantially reduced its incidence in countries with high vaccine coverage [9; 10], rotavirus remains a major cause of AGE in children. Although most children with rotavirus gastroenteritis have high amounts of virus in their feces, shedding usually ceases within 10 days of symptom onset [10].  
Adenovirus consists of non-enveloped, double-stranded DNA viruses, and belongs to the family of _Adenoviridae_. In humans, there are 88 serotypes in seven species (species A-G) [11]. Enteric adenovirus, which classically refers to serotypes 40 and 41 of specie F, was estimated to be responsible for over 52,000 deaths globally among children younger than 5 years in 2016, behind only rotavirus and Shigella [1]. The incubation period for adenovirus, typically 8-10 days, is longer than that of norovirus and rotavirus, and the illness can last as long as two weeks [12; 13].  
Although it is known that viruses can be shed for an extended period after the resolution of acute symptoms, the duration of virus shedding in stool varied among different studies [14; 15]. Few studies have reported the kinetics and duration of viral shedding in stool samples of children with AGE infected with norovirus, rotavirus, and adenovirus [16]. In this prospective cohort study, we evaluated the viral load and virus shedding in stool samples collected from children with AGE at an index ED visit and during follow-up 5 and 28 days after the visit using real-time reverse transcription quantitative PCR (RT-qPCR).
## Appendix A  
The computation of the state matrices \(A\), \(B\), \(C\) is elaborated upon in this appendix. Drawing upon the notations presented in Figure 11, the state vector \(\mathbf{X}\), the output vector \(\mathbf{Y}\), and the AC sources vector \(\mathbf{U}\), as delineated by expression (6), encompass the following variables:  
* \(\mathbf{X}\): _Vdc, i15, i16, i18, i19, i111, i12, i115, i116, i117_;
* \(\mathbf{Y}\): _i1R1, iR2, iR3, iR4, iR5, iR6, iR7, iR7, iR9, iR10, iR11, iR12, iD1, iD2, iD3, iD4, iD5, iD6, iD7, iD8, iD9, iD10, iR0, iR5, iR6, iR11, iR12_;
* \(\mathbf{U}\): _Va, Vb, Vc_. The expression (6) can be rewritten with block matrices, as in Equation (A1). \[\left\{\begin{array}{cc}\frac{\mathrm{d}X}{\mathrm{d}t}=\begin{pmatrix} \mathrm{A11}&\mathrm{A12}\\ \mathrm{A21}&\mathrm{A22}\end{pmatrix}*\mathrm{X}+\begin{pmatrix}\mathrm{B11} \\ \mathrm{B12}\end{pmatrix}*\mathrm{U}(t)\\ \mathrm{Y}=\begin{pmatrix}\mathrm{C11}&\mathrm{C12}\\ \mathrm{C21}&\mathrm{C22}\end{pmatrix}*\mathrm{X}\end{array}\right.\] (A1) The block matrices are given by the following equations [49; 50]. \[\mathrm{A11}=-\mathrm{T1}^{-1}*\mathrm{s6^{T}*H1^{-1}*s6}\] (A2) \[\mathrm{A12}=\mathrm{T1}^{-1}*\left(\mathrm{s10^{T}-86^{T}*H1^{-1}*s7*Rb1*s11^ {T}}\right)\] (A3) \[\mathrm{A21}=\mathrm{T2}^{-1}*\left(\mathrm{s11*H2^{-1}*Rb1*s7^{T}*Rm2^{-1}*s6 -s10}\right)\] (A4) \[\mathrm{A22}=-\mathrm{T2}^{-1}*s11*H2^{-1}*Rb1*s11^{T}\] (A5) \[\mathrm{B11}=-\mathrm{T1}^{-1}*\mathrm{s6^{T}*H1^{-1}*s5}\] (A6) \[\mathrm{B21}=\mathrm{T2}^{-1}*\left(\mathrm{s11*H2^{-1}*Rb1*s7^{T}*Rm2^{-1}*s 5-s9}\right)\] (A7) \[\mathrm{C11}=-\mathrm{s7}^{T}*H1^{-1}*s7\] (A8) \[\mathrm{C12}=\mathrm{s11^{T}-s7^{T}*H1^{-1}*s7*Rb1*s11^{T}}\] (A9) \[\mathrm{C21}=-\mathrm{H1}^{-1}*s6\] (A10) \[\mathrm{C22}=-\mathrm{H1}^{-1}*\mathrm{s7*Rb1*s11^{T}}\] (A11) with: \[\mathrm{H1}=\mathrm{Rm2}+\mathrm{s7*Rb1^{-1}*s7^{T}}\] (A12) \[\mathrm{H2}=\mathrm{Id}+\mathrm{Rb1*s7^{T}*Rm2^{-1}*s7}\] (A13) \[\mathrm{T1}=\mathrm{MC1}\] (A14) \[\mathrm{T2}=\mathrm{MLm2}+\mathrm{MM}^{T}*\mathrm{s12^{T}}+\mathrm{s12*MM}+ \mathrm{s12*MLb1*s12^{T}}\] (A15) where:
* The _si_ matrices are the sub-matrices of the incidence matrix of the aircraft power channel;
* \(Rb1\) is the diagonal matrix of branch resistances;
* \(Rm2\) is the diagonal matrix of mesh resistances;
* \(MLb1\) is the diagonal matrix of branch inductances;
* \(MLm2\) is the diagonal matrix of mesh inductances;
* \(MC1\) is the diagonal matrix of branch capacitances;
* \(Id\) is the identity matrix of the same dimension of Rb1.
## 2 Correlation Method
### Vision Transformer  
ViT is mainly composed of an embedding layer, an encoder (composed of \(L\) Encoder Blocks), and a classifier, and its structure is shown in Figure 1.  
The role of the embedding layer is to process the input images into the tokens required by the Transformer model. For the input image \(x\in R^{h\times w\times c}\), where \(h\), \(w\), and \(c\) are the height, width, and number of channels of the image, respectively, the image is first divided into \(N\) patches of \(p\times p\), and then each patch is linearly mapped to a one-dimensional vector, resulting in a token of length \(p\times p\times c\). In this paper, this part was implemented by a convolutional layer, which directly applied a convolutional kernel of size \(p\times p\), stride \(p\), and number of kernels \(p^{2}\times c\) to the input image. The output of the convolutional layer is \(x_{p}\in R^{N\times(p^{2}\times c)}\).  
Then, a special category label \(x_{class}\in R^{1\times(p^{2}\times c)}\) was inserted into \(x_{p}\), and the shape of the concatenated token was \([N+1,p^{2}\times c]\). In order to make full use of the location information, the ViT model introduced a learnable position encoding matrix \(E_{pos}\). The sum of position coding information \(E_{pos}\) and feature vector space was used as input \(z_{0}\) of the Transformer Encoder:  
\[z_{0}=\left[x_{class};\ x_{p}^{1}E;\ x_{p}^{2}e;\ldots;\ x_{p}^{N}E\right]+E_{ pos}. \tag{8}\]  
The Transformer encoder consists of \(L\) identical stacked modules, and its internal structure is shown in Figure 2a. It mainly consists of two sub-layers, namely multi-head self-attention layer (MSA) and multilayer perception (MLP). Before data enters each sublayer, it was normalized using a normalization layer, and then fused by adding the output of each sublayer to the input of that sublayer. The encoder was set to \(L\) layers, and the computation process of the \(l\)-th layer is as follows:  
\[z_{l}^{\prime}=MSA(LN(z_{l-1}))+z_{l-1},\ l=1,\ldots,L, \tag{9}\]  
\[z_{l}=MLP(LN(z_{l}^{\prime}))+z_{l}^{\prime},\ l=1,\ldots,L, \tag{10}\]  
The encoding result after \(L\) layers of encoder is \(z_{L}=[z_{L}^{0},\ z_{L}^{1},\ldots,\ z_{L}^{N}]\).  
ViT model adopts a multi-head self-attention mechanism, the multi-head self-attention layer uses multiple self-attention heads for parallel calculation, and finally splices the output of all self-attention heads together to get the result. The multilayer perception (MLP) is mainly composed of the fully connected layer, the GELU activation function, and the dropout layer, and its structure is shown in Figure 2b.  
The classifier applies a nonlinear transformation to the encoder's output and produces the final result of fault diagnosis.  
Figure 1: The structure of Vision Transformer.
## 4 Numerical Results
### Results of Parametric Analysis
#### 4.3.2 Influence of Internal Pressure  
The relationship between the CLS and internal pressure for YPT and RHT pipes is illustrated by the plots of CLS vs. \(f_{p}\) in Figures 10 and 11, respectively. The plots in Figure 10 are obtained for YPT pipes, with YPL = 1.25%, while the plots in Figure 11 are obtained for RHT pipes, with PLUS ratio = 0.691. Similar to the plots in Figures 8 and 9, the plots for combined axial compression + bending in Figures 10c and 11c are both obtained for pipes subjected to compression factor, \(f_{c}\) = 0.6. The CLS trends are presented for all four \(D/t\) ratios considered.  
Unlike the relationship between the CLS and the \(D/t\) ratio, the correlation between the CLS and internal pressure is characteristically positive and is more evident in YPT pipes. In conformance with the deductions from the CLS vs. \(D/t\) ratio plots in Figure 9, variation of the internal pressure is observed to have a negligible influence on the CLS of RHT pipes for all \(D/t\) ratios. On the other hand, the influence of internal pressure on the CLS of YPT pipes is only minimal at intermediate \(D/t\) ratios (DT2 and DT3) and high \(D/t\) ratios (DT4) for internal pressures \(\leq\) 60% YS. The most significant influence of internal pressure is observed in YPT pipes of low \(D/t\) ratios (DT1).  
Figure 8: Plots of CLS vs. \(D/t\) ratio of YPT pipes with YPL = 1.50% for (**a**) uniform axial compression, (**b**) uniform bending, and (**c**) combined loading (\(f_{c}\) = 0.6).  
Figure 9: Plots of CLS vs. \(D/t\) ratio of RHT pipes with PL/US = 0.634 for (**a**) uniform axial compression, (**b**) uniform bending, and (**c**) combined loading (\(f_{c}\) = 0.6).
## 2 Problem Formulation
### Optimal Economic Environmental Dispatch of Power System  
#### 2.1.1 Objectives  
(1) Minimum Fuel Cost of the Power System  
\[\text{Min}\quad F_{p}=\sum\limits_{i=1}^{N_{C}}\sum\limits_{t=1}^{T}a_{i}P_{Gi}(t )^{2}+b_{i}P_{Gi}(t)+c_{i} \tag{1}\]  
(2) Minimum \(SO_{x}\) Pollultant Emissions of the Power System  
\[\text{Min}\quad E_{SO_{x}}=\sum\limits_{i=1}^{N_{C}}\sum\limits_{t=1}^{T} \left(\alpha_{i}+\beta_{i}P_{Gi}(t)+\gamma_{i}P_{Gi}(t)^{2}+\delta_{i}e^{ \lambda_{i}P_{Gi}(t)}\right) \tag{2}\]  
(3) Minimum Load Loss Rate of the Power System  
\[\text{Min}\quad L_{p}=\frac{\sum\limits_{t=1}^{T}\left[P_{L}(t)+\sum\limits_{ k=1}^{N_{C}}P_{P2 Ck}(t)-\sum\limits_{i=1}^{N_{C}}P_{Gi}(t)\right]}{\sum\limits_{t=1}^{T}P_{ L}(t)} \tag{3}\]  
where \(F_{p}\) is the fuel cost of power system; \(N_{C}\) is the number of power generations; \(T\) is the number of time periods; \(P_{Gi}\) (\(t\)) is the power generation output at time \(t\); \(a_{i}\), \(b_{i}\), \(c_{i}\) are coefficient of the fuel cost;  
Figure 2: Flow chart.  
\(E_{SOx}\) is the pollutant emission of \(SO_{x}\); \(\alpha_{i}\), \(\beta_{i}\), \(\gamma_{i}\), \(\delta_{i}\), \(\lambda_{i}\) are coefficient of the pollutant emission; \(L_{p}\) is the load loss rate presenting the reliability of power supply; \(N_{P2G}\) is the number of P2G; \(P_{L}(t)\) is the power load at time \(t\); \(P_{P2G}\) (\(t\)) is the power supplied to the P2G facilities at time \(t\).  
The power output of gas-fired units is calculated by the product of the gas flow injected to the gas-fired units \(Q_{GT}(t)\), higher heating value of natural gas \(HHV_{g}\) and the energy conversion efficiency \(\eta_{GT}(t)\). In this paper, the last objective is converted into a constraint by being less than a given value \(\varepsilon\).
## 2 Virtual Surgical Planning Protocol Between Surgeons and Bioengineers  
### Generation of 3D Models  
Accurate segmentation of the mandible and lesions from cone-beam computed tomography (CBCT) data to create stereolithography (STL) files is critical for both precise surgical guide fabrication and accurate surgical planning. To take full advantage of virtual surgical planning, which provides a three-dimensional understanding of anatomical structures and lesions, it is necessary to segment not only the external shape of the mandible and lesions, but also anatomical structures such as tooth roots and inferior alveolar nerve bundles. However, these processes are time-consuming.  
Hounsfield unit slices are used to segment the mandible and surrounding anatomical structures for virtual surgical planning. The resulting 3D mandibular model, while too large for computer-aided design due to the inclusion of internal bone structures, is advantageous for virtual surgical planning because it allows simultaneous visualization of anatomical structures, such as tooth roots and the inferior alveolar nerve bundle. For computer-aided planning, a 3D model of the mandible focused on the cortical bone area without inappropriate holes is preferred, and this task is best performed by a bioengineer.  
Lesion segmentation is the most critical aspect of virtual surgical planning materials. Accurate lesion identification and appropriate safety margin setting are essential to reduce the risk of recurrence and minimize deviations from the plan during actual surgery. Therefore, lesion segmentation should be performed by the surgeon. This allows identification of ambiguous areas in the lesion and can be supplemented with other medical imaging data, such as magnetic resonance imaging (MRI) or positron emission tomography-computed tomography (PET-CT). It also assists in setting resection margins during virtual surgery planning.  
Surgeon's role: Provide high-quality CT scans, identify critical anatomical structures, and segment lesions.  
Bioengineer's role: Segment CT data to create accurate 3D models of the mandible.
## 5 An Innovative Robustness and Reliability Analysis
MCRAT is identified as the method with the highest sensitivity, while FUCA exhibits the lowest sensitivity and, consequently, the highest correlation with the constant factor. Similarly, the SAW method is categorized among the low-sensitivity methods, whereas the TOPSIS method, like MCRAT, is determined to be highly sensitive.
## 5 Simulation Results and Performance Analyses
### Performance Evaluation of \(CLCQ\)
Such a design can optimize data transmission in different network environments, significantly improving communication reliability and packet delivery rates.
## 4 Test Results and Discussion  
### Evolution of the Damage  
The tested columns, as shown in Figure 2, have no steel stirrups in the body of the connection of the column with the beam. Therefore, the damage is expected to be located in the body of the connection. Figure 6 presents the evolution of the developing cracking system of the tested specimens. In particular, cracking systems of both specimens are presented at the third loading step in Figure 6a, at the fifth loading step in Figure 6b, and at the seventh loading step in Figure 6c. In Figure 6a, it is observed that in the first loading steps cracks appeared only in specimen JC0V, whereas the column of specimen JC0V2X2c along with the connection part remained intact. Further, in the subsequent loading steps, many new cracks appeared in the column and the joint body of the specimen JC0V, as shown in Figure 6b.  
On the other hand, in the column and the joint body of the strengthened specimen JC0V2X2c, only cracks with a crack width equal to 0.1-0.4 mm appeared. In this specimen, flexural cracks with larger width appeared only in the beam. Finally, in the seventh loading step, damage characterized by concrete disgoranization and final failure of concrete was  
Figure 5: Testing setup. (a) Specimen JC0V2X2c and instrumentation; (b) layout of the test setup; (c) loading protocol comprises 7 loading steps and each step includes 3 full cycles.  
observed in the joint body and in parts of the column of the unstrengthened specimen; further, as it can be obtained from the strain gauge measurements (see next Section 5), flexural steel bars of the column developed high tensile strains. In the strengthened specimen, severe cracks appeared in the beam and particularly at the end of the beam near the column where eventually a plastic hinge was formed.
## 5 Conclusions  
This paper reports a novel study that focuses on the classification and sizing of pinhole corrosions on pipelines using deep learning algorithms. We propose a CNN classification model to classify six different types of corrosions (i.e., \(\mathrm{G_{in}}\), \(\mathrm{G_{ex}}\), \(\mathrm{P_{in}}\), \(\mathrm{P_{ex}}\), \(\mathrm{PIC_{in}}\), and \(\mathrm{PIC_{ex}}\)) on pipelines and a CNN regression model to estimate the sizes and location of the pinhole defects based on MFL signals generated using magneto-static FEA. Extensive 3D parametric FEA cases involving box-shaped general corrosions and cylinder-shaped pinholes are used to simulate the 3D MFL signals by varying the defect depth, length, width, and longitudinal and circumferential locations. The CNN classification and regression models are then trained and validated using the simulated MFL signals.  
The proposed classification model is shown to have excellent accuracy in classifying six types of corrosion defects with a misprediction rate of 0.34% (33/9600 = 0.34%). The  
Figure 13: Comparison of \(R^{2}\) between noise-free and SNR = 20 scenarios for different corrosion types.  
proposed regression model is highly accurate in predicting the location of the pinhole and achieves good accuracy in predicting the depth and diameter of the pinhole. Through a comparative analysis of the CNN regression model with noise-free and noisy signals (SNR = 20) as input, it is observed that the noise impact on the predictive accuracy of the regression model is moderate. This study demonstrates the application of deep learning algorithms to facilitate the integrity management of pipelines containing complex-shaped corrosion defects.  
Y.S.: Investigation, Visualization, Formal analysis, Methodology, Writing--original draft. W.Z.: Funding acquisition, Supervision, Conceptualization, Methodology, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support received from the Natural Sciences and Engineering Research Council of Canada (NSERC) (Grant No. RGPIN-2019-05160) and the Faculty of Engineering of the University of Western Ontario.  
Data availability Statement: Data are available on request from the authors.  
The authors declare no conflicts of interest.
# Dispatching Strategy for Low-Carbon Flexible Operation of Park-Level Integrated Energy System
## 5 Case Study
### Flexibility Analysis  
PFI is the combined result of the adjustability of multi-equipment participation. It can be seen in Figure 10 that PFI is zero in the period of 11:00-18:00 when the electricity price is off-peak and the MT utilization is high. The presence of load-side demand response capability partially contributes to the flexibility margin of the system and enhances the flexibility of the system operation. This analysis shows that multiple resources have complementary characteristics in terms of system operation flexibility indexes and the participation of multiple resources gives rise to improved system operation flexibility compared to the conventional practice of only considering controllable units.  
As a load-side dispatchable resource, the load demand response characteristic effectively improves PFI. Meanwhile, it impacts the system economics. To further evaluate the impact of the degree of the integrated demand response on PIES operation, the test shown in Figure 11 was conducted.  
Figure 11: Impact of the comprehensive demand response ratio on PIES.  
Figure 10: PIES flexibility index of each equipment.  
As illustrated in Figure 11, as the integrated demand share increases, the PIES system operating cost first gradually decreases, and then slowly increases when the share exceeds 25%. In contrast, the flexibility of the demand response operation consistently increases. As far as the operating cost is concerned, the integrated demand response changes the power consumption plan of the original load, which makes the load distribution more consistent with the power supply characteristics while lowering the PIES operating cost. Nevertheless, as the demand response grows further, the load that the system is free to allocate increases. The final power consumption plan is no longer changed, and the operating cost does not decrease. In addition, the increase in the demand response share further enhances the flexibility of the system, and the cost incurred from simultaneously maintaining system flexibility and performing system optimization increases the total operating cost.
###### Abstract  
In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm's QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme.  
QoE fairness; video quality; federated learning; deep reinforcement learning; congestion control Article