esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Channel Reliability Modeling for 5G Communications  
### Structure of the CPDS  
The information domain of the CPDS included the protocols, software, and topology used in the information and communication equipment and communication network. The physical domain included the traditional primary equipment and new energy equipment such as photovoltaic and wind power as well as the energy storage equipment, and its structure is shown in Figure 1.  
In the figure, the information domain is divided into the backbone network and the access network. The backbone network adopted the SDH (synchronous digital hierarchy) transmission protocol to connect the power distribution master station and substations. The access network adopted the 5G wireless network based on the IEC104 protocol to connect the distribution electronic station and intelligent electronic devices. The control command of the information layer determined the operation state of the physical layer, and the operation state of the physical layer determined the data input of the information layer, and there was a close interaction between the two.  
Figure 1: Physical, converged information distribution network structure.  
The application layer realized the analysis and processing of information (decision generation) and human-computer interaction and was located in the main power distribution station (or substation). The communication layer was divided into two parts, namely, the distribution of electronic stations between the control center and the backbone communication network. It mainly adopted fiber optic SDH or a multi-service transmission platform (MSTP) ring network structure, with multiple channels and multiplexing layers. The access communication network from the substation to the distribution terminal, also known as the distribution communication network, adopted various communication methods such as Ethernet, power line carrier, wireless, etc. The interface layer included the first section of the feeder. The interface layer included the feeder first section protection device, feeder terminal unit (FTU), distribution terminal unit (DTU), inverter, and other intelligent distribution terminals.  
Due to the structure of CPDSs, wireless communication technology can serve as a supplement to wired communication, providing additional communication paths. When wired networks fail, wireless networks can serve as backups to ensure the continuous transmission of critical information, thereby improving network redundancy and reliability.
## 4 SDN-NFV Based QoS Management
### Open-Source SDN Application Development Environment  
In this study, OpenDaylight was used as the SDN controller. OpenDaylight is an open-source SDN controller maintained by the Linux Foundation. It has a microservice architecture. The Mininet [17] tool was used to create the data plane. Mininet is a network emulator. Network keys, links and hosts can be created on it. While simple network topologies can be created with a single command, customised network topologies can also be created using the Python programming language. The iPerf tool [18] was used to generate traffic. The SFlow-RT tool [19] was used to control which virtual switches on the topology the traffic flowed through.  
Figure 8 shows the SDN network topology created using virtual switches (Open vSwitch, OVS) [20] on the Mininet. The inter-switch bandwidth in the topology is 100 Mbps. Flows 1, 2 and 3, which are assumed to be initiated by the UE, represent the data flows sent to the data network through the transmission network in the 5G architecture, as shown in Figure 2. Virtual switches in the transmission network are connected to the SDN controller via the flow control interface. The traffic characteristics in the test scenario created for verification are shown in Table 5. The expected data throughput is 50 Mbps for game traffic in Flow 1, 10 Mbps for video traffic in Flow 2 and 50 Mbps for HD video traffic in Flow 3 on the test servers located on the data network side.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 6 Conclusions  
In this paper, a two-level congestion control mechanism, called 2 LCCM, is proposed for ICN. When the transmission link is under heavy congestion, the replica location of a chunk will be changed by the replica selection method to avoid the current congestion path. When the transmission link is under light congestion, the sending rate of data requests is adjusted by the receiver-driven congestion control algorithm. The replica selection method is implemented based on a node state table, and the congestion control algorithm is based on the receiver-driven BBR algorithm. This paper also provides specific definitions of heavy congestion and light congestion. When the RTT becomes abnormally long or packets are continuously lost, heavy congestion is considered to have occurred. Additionally, a detailed description of the replica switching strategy is discussed. Finally, the experimental results show that switching to another replica for successive transmission under heavy congestion can effectively shorten the transmission time. It also shows that the BDP-based BBR algorithm has better bandwidth utilization than the loss-based Reno algorithm.  
In the future, we will further investigate the replica selection method based on machine learning algorithms and the congestion control algorithm with intermediate nodes participating in congestion signal feedback. In addition, more extensive experiments will be conducted to verify the effectiveness of our proposed two-level congestion control mechanism.  
Conceptualization, Y.S., H.N. and X.Z.; methodology, Y.S., H.N. and X.Z.; software, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S., H.N. and X.Z.; supervision, X.Z.; project administration, X.Z.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Strategic Leadership Project of Chinese Academy of Sciences: SEANET Technology Standardization Research System Development (Project No. XDC02070100).  
Data Availability Statement: Data sharing not applicable.  
We would like to express our gratitude to [PERSON] and [PERSON] for their meaningful support with this work.  
The authors declare no conflict of interest.
## 2 Design and Analysis of DL-LET
### Analysis of DL-LET Joint  
An FEA model of the DL-LET joint is created to evaluate the joint behavior and verify the correctness of the theory. A 3D model is established and analyzed with the Abaqus  
Figure 3: DL-LET joint equivalent to the corresponding spring series and parallel relations.  
finite element software. NLGEOM is turned \"On\" in the step analysis to include the nonlinear effects of large displacement. In selecting two surface interaction types, the binding constraint is used to realize that the relative motion and deformation do not occur between the two surfaces. The joint is fixed at one end, and the free end is applied a bending moment. The FEA results show that the running results converge.  
Considering the feasibility of processing, the design dimensions are as follows: \(l_{\text{b1}}=25\) mm, \(w_{\text{b1}}=2\) mm, \(l_{\text{t1}}=13\) mm, \(w_{\text{t1}}=2\) mm, \(l_{\text{b2}}=50\) mm, \(w_{\text{b2}}=25\) mm, the thickness of beryllium bronze layer \(t_{\text{1}}\) is 0.5 mm, and the thickness of H18 aluminum foil \(t_{\text{2}}\) is 0.01 mm, with [PERSON]'s modulus \(E_{\text{2}}=30\) GPa, [PERSON]'s ratio \(\
u_{\text{2}}=0.32\). The equivalent stiffness of the DL-LET joint can be obtained using Equations (1)-(6):  
\[k_{\text{eq,bend}}= 118.77\text{ N}\cdot\text{mm}\cdot\text{rad}^{-1} \tag{7}\]  
The stress distribution in the joint is shown in Figure 4 when a bending moment of 50 N\(\cdot\)mm is applied. The H18 aluminum foil is so thin that the stress is negligible during deflection. The largest stress occurs in the beryllium bronze layer. At this point, the bending angle of the joint is 0.414 rad, and the maximum equivalent stress of the beryllium bronze layer is 306.0 MPa, which is smaller than the yield limit of beryllium bronze [\(s_{y}\)] = 1170 MPa. The maximum equivalent stress of H18 aluminum foil is 113.5 MPa, which is smaller than the yield limit of H18 aluminum foil, [\(s_{y}\)] = 124 MPa. Therefore, the plastic deformation will not occur and meet the design requirements.  
When the torque exerting on the joint is \(T\), we can acquire the following equations of the relationship between \(T\) and \(\theta\):  
\[T=k_{\text{eq,bend}}\theta \tag{8}\]  
where \(\theta\) is the rotation angle of the joint.  
The theoretical rotation angles of the DL-LET joint under different bending moments can be obtained by Equation (7), as plotted in Figure 5.  
Under different bending moments, the comparison between the theoretical and simulation values of the rotation angles is shown in Figure 5a, and the errors are shown in Figure 5b. It can be seen from Figure 5a that the theoretical values are consistent with the simulation values, showing an excellent linear relationship. It can be seen from Figure 5b that with the increase of bending moment, the relative error decreases slightly, and all errors are within 3%, which proves the correctness of the theoretical calculation method.  
Figure 4: Stress plot.
## 4 Results and Analysis
### Case of Corn Futures  
Although wheat, corn and soybean prices are strongly correlated [15], it is still necessary to conduct certain research on different food grain futures in order to verify the applicability and validity of the hybrid models with decomposition methods developed in our study on the aspect of prices forecasting. Thus, this subsection presents the empirical study of forecasting the corn futures prices on the basis of these hybrid models, and the empirical study of soybean futures prices is in the next subsection. Similarly, decomposition results of corn futures prices, based on the EMD, WPT, ITD and VMD techniques respectively, are shown from Figures 13-16, and the comparison between the one-day-ahead predictions and actual values are given in Figure 17, while the forecasting performance evaluation results are displayed in Table 4 and Figure 18, respectively.  
Figure 14: Decomposition results of the WPT method (corn).  
Figure 15: Decomposition results of the ITD method (corn).  
Figure 16: Decomposition results of the VMD method (corn).  
Figure 17: One-day-ahead forecasting results of different models of corn.  
Figure 18: Error graphics of different models of corn.  
Likewise, the empirical results show that the VMD-PSO-BPNN model still outperforms the EMD-PSO-BPNN model, WPT-PSO-BPNN model, ITD-PSO-BPNN model, PSO-BPNN model and ARIMA model, with respect to the forecasting performance evaluation criteria, on the basis of Table 4 and Figure 18. But the disparity of forecasting precision between the VMD-PSO-BPNN model and other models is reduced. More specifically, for the comparison among all ANN-based models, similar conclusions with the study case of wheat can be obtained; that is, in respect of the MAE, RMSE and MAPE, the VMD-PSO-BPNN model and WPT-PSO-BPNN model perform much better than the other three models, while the ITD-PSO-BPNN model and EMD-PSO-BPNN model only improve a little compared with the PSO-BPNN model. For the comparison between the ANN-based models and the ARIMA model, all hybrid models with decomposition approaches outperform the ARIMA model, while the PSO-BPNN model (1.14%) almost shares the same forecasting accuracy with the ARIMA model (1.13%) in terms of the evaluation criterion, MAPE.  
Moreover, it is worthy of note that there are three hybrid models, combined with the WPT, ITD and VMD respectively, whose forecasting accuracy is improved by one order of magnitude to 0.65%, 0.96% and 0.57%, respectively.
## 4 Machine Learning for Predicted Water Contents
### Comparison of Measured and Predicted Water Contents through Machine Learning  
The correlation between the measured and predicted water content derived through machine learning is shown in Figure 14. The R\({}^{2}\) was 0.888, which was confirmed to be very high compared to the results of simple linear regression.
## 2 Numerical Modeling
### Mathematical Model  
#### 2.2.1 Control Equations  
Continuity equation  
\[\frac{\partial\rho}{\partial t}+div(\rho\textbf{U})=0 \tag{1}\]  
Momentum equation in \(x\), \(y\) and \(z\) directions  
\[\frac{\partial}{\partial t}(\rho u)+div(\rho\textbf{U}u)=\frac{\partial}{ \partial x}\bigg{(}\mu\frac{\partial u}{\partial x}\bigg{)}+\frac{\partial}{ \partial y}\bigg{(}\mu\frac{\partial u}{\partial y}\bigg{)}+\frac{\partial}{ \partial z}\bigg{(}\mu\frac{\partial u}{\partial z}\bigg{)}-\frac{\partial p }{\partial x}+S_{u} \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho v)+div(\rho\textbf{U}v)=\frac{\partial}{ \partial x}\bigg{(}\mu\frac{\partial v}{\partial x}\bigg{)}+\frac{\partial}{ \partial y}\bigg{(}\mu\frac{\partial v}{\partial y}\bigg{)}+\frac{\partial}{ \partial z}\bigg{(}\mu\frac{\partial v}{\partial z}\bigg{)}-\frac{\partial p }{\partial x}+S_{w} \tag{3}\]  
Energy Equation  
\[\frac{\partial}{\partial t}(\rho h)+div(\rho\textbf{U}u)=div\bigg{(}\frac{k}{ c_{p}}gradh\bigg{)}+S_{h} \tag{5}\]  
\[S_{h}=\frac{\partial}{\partial t}(\rho\Delta H)+div(\rho u_{i}\Delta H) \tag{6}\]  
where \(\rho\) represents the mixing density, \(t\) represents the time, \(u\), \(v\) and \(w\) represent the components of the mixing velocity vector \(\textbf{U}\) in the \(x\), \(y\) and \(z\) directions, respectively, \(p\) represents the pressure, \(\mu\) represents the dynamic viscosity coefficient of the fluid,  
Figure 2: A schematic of calculation domain.  
and \(S_{w}\) represent the additional source terms of the momentum equation in the \(x\), \(y\), and \(z\) directions, respectively. The deposition process can be regarded as an isobaric open system process in which heat transfer results in an enthalpy change and an isobaric specific heat is used. In the energy equation, \(h\) represents the sensible enthalpy, \(\Delta H\) is the latent heat, and the latent heat is a function of temperature, which can be expressed by Equation (7) [12]:  
\[\Delta H=Lf_{l} \tag{7}\]  
\(f_{l}\) is the mass fraction of the liquid phase, and \(L\) is the value of the latent heat of solidification. This paper treats the liquid phase fraction as a linear relationship with temperature [12].  
\[f_{l}=\left\{\begin{array}{ll}0&T<T_{s}\\ \frac{T-T_{s}}{T_{l}-T_{s}}&T_{s}\leq T\leq T_{l}\\ 1&T>T_{l}\end{array}\right. \tag{8}\]  
where \(T_{s}\) represents the solidus, and \(T_{l}\) represents the liquidus.
## 3 Results  
### Chemical Measurements  
The results for the chemical measurements are shown in Table 2. Significant differences (\(p<0.05\)) were found amongst the different wine treatments for all the parameters, with the STAC and STCE treatments exhibiting the highest mean values for TDS (802.50 and 832.00 ppm, respectively) and EC (1706.80 and 1769.80 \(\upmu\)s cm\({}^{-1}\)), with the NSCE (665.70 ppm and 1415.70 \(\upmu\)s cm\({}^{-1}\)) and STCO (670.30 ppm and 1425.50 \(\upmu\)s cm\({}^{-1}\)) treatments exhibiting the lowest values. The NSCO treatment had the highest mean \({}^{\circ}\)Brix and alcohol content (6.00 Brix and 12.18%), while the STCE treatment had the lowest \({}^{\circ}\)Brix (5.09 Brix) and the STAC (9.64%) and STCE (9.41%) treatments had the lowest alcohol contents. Lastly, the NSCE treatment had the highest pH (3.87), while the STAC, STCE and STCO treatments had the lowest (3.48, 3.48 and 3.43, respectively).  
Figure 1: Two-layer feed-forward network with five hidden neurons and sigmoid function to classify wine samples according to the type of smoke taint amelioration treatment applied and smoke taint status (smoked or unsmoked). Abbreviations: w: weights; b: bias.
## 6 Real-Life Case Study
### Event Log Construction  
STEMI patients were defined by using the ICD-10 codes, resulting in 5750 patients. Patients older than 85 or younger than 40 were excluded [49]. We treated patients as traces and their medications as activities to construct the event log. After extracting the initial event log, we filtered out traces where less than one activity was presented, because they exposed less useful information regarding control-flow similarity. As a result, 2141 traces with 615 activities are presented in the event log. The event log has 1363 trace variants due to the high number of activities. The primary reason for such an enormous number of activities is the existence of redundancy in the event log. When giving prescriptions to patients, the free-text nature causes redundancy. For example, when referring to the same medicine, some doctors prefer to write the exact medication name, while others prefer the brand name (e.g., _Telnisartan_ is the medication name sold under the brand name _Micardis_). Furthermore, there are many different substitutable medications to choose from when addressing the same symptom, which is one of the other reasons for redundant activity labels in the event log. For instance, both _Candesartan_ and _Eprosartan_ can be used to treat high blood pressure. Moreover, some doctors prefer giving dose and frequency along with the medication name in one input field, such as _Aspirin one tablet per day_, which also introduces unnecessary redundancy to the event log.
## 6 Conclusions
The multi-GNSS precise clock and orbit products can be accessed at [[ftp://igs.ign.fr//pub/igs/products/mgex/](ftp://igs.ign.fr//pub/igs/products/mgex/)]([ftp://igs.ign.fr//pub/igs/products/mgex/](ftp://igs.ign.fr//pub/igs/products/mgex/)) (accessed on from 28 January 2022 to to 5 February 2022), the GPS LANV broadcast ephemeris can be achieved at [[ftp://igs.gsnswhu.cn/pub/gps/data/daily/](ftp://igs.gsnswhu.cn/pub/gps/data/daily/)]([ftp://igs.gsnswhu.cn/pub/gps/data/daily/](ftp://igs.gsnswhu.cn/pub/gps/data/daily/)) (accessed on from 28 January 2022 to to 5 February 2022), the BDS-3 CNAV1 broadcast ephemeris can be achieved at [[ftp://ftp2.csno-tarc.cn/cnav/2022/](ftp://ftp2.csno-tarc.cn/cnav/2022/)]([ftp://ftp2.csno-tarc.cn/cnav/2022/](ftp://ftp2.csno-tarc.cn/cnav/2022/)) (accessed on from 28 January 2022 to to 5 February 2022), and the PPP-B2b data were received by the K803 board from the SinoNav company (www.sinognss.com (accessed on from 28 January 2022 to to 5 February 2022)). The experimental data including the TJU-01 onboard GNSS data (accessed on from 28 January 2022 to to 5 February 2022) presented in this study are available by contacting the corresponding author with reasonable request. We are very grateful to the International GNSS Service (IGS) for providing the muti-GNSS data and products.. Thanks a lot to TianJin Yunyao Aerospace Technology Corporation for providing the TJU-01 onboard GNSS data. The authors declare no conflicts of interest.
## 3 Numerical Experiment
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Metric** & **FSS** & **FSSN** & **FSSGD** \\ \hline \multirow{3}{*}{\(f_{5}\)} & Mean & **0.67** & 0.68 & 0.68 \\  & Min & **0.64** & 0.67 & 0.66 \\  & Std. dev & 0.01 & 0.01 & 0.01 \\ \hline \multirow{3}{*}{\(f_{6}\)} & Mean & **0.63** & 0.64 & **0.63** \\  & Min & 0.61 & **0.60** & 0.61 \\  & Std. dev & 0.01 & 0.03 & 0.01 \\ \hline \multirow{3}{*}{\(f_{7}\)} & Mean & **1.01** & 1.05 & 1.03 \\  & Min & **0.99** & 1.03 & 1.01 \\  & Std. dev & 0.01 & 0.01 & 0.01 \\ \hline \multirow{3}{*}{\(f_{8}\)} & Mean & **0.67** & 0.68 & 0.68 \\  & Min & 0.65 & 0.66 & **0.63** \\ \cline{1-1}  & Std. dev & 0.01 & 0.01 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Time benchmarks of 2-dimensional test functions listed in Table 1.  
hybrid based on gradient ascent is more stable and shows more accurate results, than the original FSS algorithm; and can be used in cases, when the fitness function is differentiable in the study region.
### Role of Pre-Clinical Animal Models of Cardiotoxicity  
Animal models of cardiotoxicity represent a reasonable alternative to clinical investigations, allowing us to perform detailed and controlled studies that can address unanswered clinical questions. For example, image-based longitudinal studies in animals can reveal new biomarkers of adverse myocardial remodeling at different time points following DOX therapy, improving our mechanistic understanding regarding reversible vs. irreversible effects. Such models can be used to test more effective cardioprotective strategies prior to implementation in clinical trials. Furthermore, compared to sparse in vivo biopsy sam pling, animal models hold the great advantage of allowing us to study the entire heart after explantation for the purpose of histopathological assessment.  
With respect to this, a large body of literature has reported the development of preclinical models of chemotherapy-induced cardiotoxicity. Many chronic models using small animals (e.g., mice [12], rabbits [13], mini-pigs [14]), have clearly demonstrated that the severity of structural myocardial remodeling is dependent on the cumulative dose of the chemotherapeutic drug, leading to mechanical dysfunction with poor ejection fraction and progressive heart failure. Unfortunately, small animal models are less relevant to the clinical translation of diagnostic imaging methods. Thus, more recently, one research group developed a swine model of cardiotoxicity and used MR imaging to demonstrate that the prolongation of T2 parameter (indicating edema) is reversible in early stages post-DOX, and that the LV function declines gradually within the first few months post-DOX [15]. However, in this study, DOX was administered intracoronary through a catheter, an invasive procedure which does not mimic the chemotherapeutic plan in patients. Therefore, alternative delivery methods, such as the intravenous injection of doxorubicin (as done in patients), might be more realistic to study cardiotoxicity in large animal models.  
The specific aims of our work here are: (1) to develop a translational swine model to study DOX-induced cardiotoxicity, precisely mimicking the intravenous delivery of doxorubicin in cancer patients; and (2) to establish subtle quantitative MR imaging features and electrophysiological characteristics of DOX-induced cardiotoxic effects in the subchronic phases post-DOX (i.e., weeks and a couple of months after the DOX treatment was ceased). Specifically, in this preclinical work, we propose an MR imaging protocol that allows us to monitor, post-DOX, the temporal evolution of the left ventricular function (i.e., via Cinematic methods) and of the structural alterations (i.e., using high-resolution 3D contrast-enhanced MR to detect deposition of collagenous fibrosis), along with histological validation. In addition, our novel experimental protocol includes a complex X-ray-guided electrophysiological study to evaluate endocardial bipolar voltage maps and arrhythmia inducibility following the completion of DOX treatment.