esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamics Modeling of Vehicle/Fixed Frog Interaction  
### Vehicle Dynamics Modeling  
A dynamic model for the four-axle rail vehicle is built to more accurately simulate the dynamic characteristics of the rail vehicle passing the fixed frog area. The frog bodies of the vehicle dynamics system include 1 car body, 2 frames, and 4 wheelsets. In addition, the suspension system includes primary suspension and secondary suspension as shown in Figure 2. Assuming the vehicle runs at a uniform speed, the telescopic vibration of the wheelset, frames, and car body are negligible. Each rigid body considers 5 degrees of freedom (DOF), a total of 35 DOFs as shown in Table 1.  
Figure 1: Wheel/rail contact calculation results of key sections: (**a**) Lateral coordinates of wheel/rail contact point; (**b**) Vertical coordinates of wheel/rail contact point; (**c**) Rolling circle radius.  
Based on the matrix assembly method [21], the general representation of the dynamics equation for the railway vehicle is:  
\[\mathbf{M}\{\tilde{\mathbf{q}}\}+\mathbf{C}\{\tilde{\mathbf{q}}\}+\mathbf{K}\{ \mathbf{q}\}=\{\mathbf{F}\}\]  
where, \(\{\mathbf{q}\}\) is the DOF vector of the model;  
\(\mathbf{M}\) is the inertia matrix;  
\(\mathbf{C}\) is the damping matrix;  
\(\mathbf{K}\) is the stiffness matrix;  
\(\{\mathbf{F}\}\) is the external force matrix, ignoring the track irregularity, which is mainly composed of 3 parts:  
\[\mathbf{F}=\mathbf{F}_{uv}+\mathbf{F}_{IG}+\mathbf{F}_{c}\]  
The meaning of each force component is as follows:  
\(\mathbf{F}_{uv}\) is the force transmitted by the rail to the wheelset, including normal force and tangential force (creep force);  
\(\mathbf{F}_{IG}\) is gravity, inertia force, and Coriolis force;  
\(\mathbf{F}_{c}\) is the zero position non-equilibrium additional force due to curve line conditions.  
This paper uses MATLAB/Simulink to build the railway vehicle dynamic model for the subsequent optimization solution, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Rigid** & **Lateral** & **Vertical** & **Rolling** & **Pitch** & **Yaw** & / \\
**Body** & **Motion** & **Motion** & **Motion** & **Motion** & **Motion** & \\ \hline Car body & Yc & Zc & \(\Phi_{c}\) & \(\Theta_{c}\) & \(\Psi_{c}\) & / \\ Frames & Ybn & Zbn & \(\Phi_{bn}\) & \(\Theta_{bn}\) & \(\Psi_{bn}\) & \(n=1,2\) \\ Wheelsets & Ywi & Zwi & \(\Phi_{wi}\) & \(\Theta_{bi}\) & \(\Psi_{wi}\) & \(i=1,2,3,4\) \\ \hline \end{tabular}
\end{table}
Table 1: Model DOFs.  
Figure 2: Calculation diagram of four-axle rail vehicle.
# Cooperative Buffering Schemes for Time-Shifted Live Streaming of Distributed Appliances
## 5 Conclusions  
In this paper, we proposed a time-shifted live streaming system based on P2P technologies that are highly scalable at a low cost so that PVRs can share TV programs with each other. We also proposed two cooperative buffering schemes to provide the streaming services for time-shifted periods even when the number of PVRs playing back at the periods is insufficient. This is possible because the proposed schemes can utilize the idle resources of the PVRs playing back at the live broadcast time by working together with each other. As a result, people can watch any broadcast program at any time even on the channels that they have not watched before. To determine which chunks to be buffered, the \(D\_DP\)-based scheme prioritizes each time range depending on its degree of chunk deficiency and proximity to the live broadcast time, while the \(R\_RC\)-based scheme considers the ratio of the number of actual requests to that of chunk copies already buffered. Through extensive simulations, we showed that our proposed schemes can considerably extend the time-shifting hours compared with when PVRs buffer the broadcast programs independently of each other. The performance of the two schemes was also compared in terms of playback continuity and startup delay while varying the buffer size, playback request distribution, and the number of PVRs.  
Conceptualization, E.K.; software, Y.C.; validation, E.K. and H.S.; formal analysis, E.K. and H.S.; investigation, E.K. and H.S.; writing--original draft preparation, E.K.; writing--review and editing, Y.C. and H.S.; visualization, E.K. and Y.C.; supervision, H.S.; funding acquisition, E.K. and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C1002221 and 2019R1F1A1063698).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 16: The number of the aggregate buffermap update operations according to \(N_{p}\) with a 300 s buffer.
## 2 Research Methodology
### Performance Evaluation  
In this paper, correlation coefficient (R), explained variance score (Evar), mean absolute error (MAE) and mean squared error (MSE) were selected as evaluate indicators of predictive performance [30], which can be calculated as follows:  
\[\text{R}=\frac{\sum\limits_{i=1}^{N}{(y_{i}^{*}-\overline{y^{*}})(y_{i}- \overline{y})}}{\sqrt{\sum\limits_{i=1}^{N}{(y_{i}^{*}-\overline{y^{*}})}^{2}} \sqrt{\sum\limits_{i=1}^{N}{(y_{i}-\overline{y})}^{2}}} \tag{4}\]  
\[\text{Evar}=1-\frac{\textit{Var}\{y_{i}-y_{i}^{*}\}}{\textit{Var}\{y_{i}\}} \tag{5}\]  
\[\text{MAE}=\frac{1}{N}\sum\limits_{i=1}^{N}{|y_{i}-y_{i}^{*}|} \tag{6}\]  
\[\text{MSE}=\frac{1}{N}\sum\limits_{i=1}^{N}{(y_{i}-y_{i}^{*})}^{2} \tag{7}\]  
where \(N\) is the number of samples; \(y_{i}\) and \(y_{i}^{*}\) are the actual and estimated value of the \(i\)-th instance; \(\overline{y}\) and \(\overline{y^{*}}\) are the average values of the actual and estimated outputs; and _Var_ represents the variance.
## 2 Problem Formulation  
### Optimization Model of a Large-Scale Truss  
Based on the ground structure method, the simultaneous size and topology optimization problem for a large-scale truss is formulated in Equation (1):  
\[\left\{\begin{array}{l}Find\ \mathbf{X}=\left\{x_{1},x_{2},\cdots,x_{n}\right\}^{T} \\ \mathbf{\alpha}=\left\{\alpha_{1},\alpha_{2},\cdots,\alpha_{n}\right\}^{T}\\ Min\ W(\mathbf{X},\mathbf{\alpha})=\sum\limits_{i=1}^{n}\alpha_{i}W_{i}(x_{i})\\ S.t.\ \ g_{j}(\mathbf{X},\mathbf{\alpha})\leq 0\ j=1\cdots v\\ a_{i}x_{i}^{L}+(1-\alpha_{i})x_{i}^{b}\leq x_{i}\\ x_{i}\leq\alpha_{i}x_{i}^{ll}+(1-\alpha_{i})x_{i}^{b}\\ \alpha_{i}=0\ or\ \alpha_{i}=1\ \ i=1\cdots n\\ \end{array}\right. \tag{1}\]  
In this formulation, two kinds of variables are defined but coupled together. \(X=\left\{x_{1},x_{2},\cdots,x_{n}\right\}^{T}\) is the continuous size variable vector, where \(x_{i}\) represents the cross-sectional areas of bar members in the \(i\)-th group. \(\alpha\) is the discrete topology variable vector, where each \(\alpha_{i}\), in the \(i\)-th group, takes the value \(0/1\) to represent the absence/presence of bar members in this group. If \(\alpha_{i}=0\), members in the \(i\)-th group are removed, and the related size variables \(x_{i}\) are set to a very small value \(x_{i}^{b}\) (i.e., \(10^{-6}x_{i}^{b}\)); if \(\alpha_{i}=1\), members in the \(i\)-th group are retained, and \(x_{i}\) are optimized between the upper bound \(x_{i}^{ll}\) and the lower bound \(x_{i}^{ll}\). If some bar or node is removed, the constraint associated with it will also be removed. \(W(\mathbf{X},\mathbf{\alpha})\) means the total weight of the truss structure. \(g_{j}(\mathbf{X},\mathbf{\alpha})\) denotes the \(j\)-th normalized constraint related to structural responses, for example, nodal displacement, modal frequency, or buckling factor, and \(v\) is the total number of constraints. Additionally, two or more symmetrical bars in symmetrical response situations are usually chained to reduce the number of topology variables of the large-scale truss structure. They share the same single topology variable, while the respective size variables remain unchanged.
## Materials and Methods
### Forest Degradation Concept  
The first step in the methodological design required the identification of an operational definition of forest degradation and its component indicators. Two assumptions needed to be made to implement the trials with the methods: (a) that a comparison between an \"optimal\" and \"non-optimal\" state of the forest should be set to establish the reference state and (b) that the indicators selected reflect forest condition and help to define the reference state. Therefore, after conducting an intensive analysis of the literature, we based our definition of forest degradation mainly with reference to two approaches: the criterion/indicators [13] and the benchmark/landscape [12] methods.  
The criterion/indicators approach uses five criteria that relate to drivers of degradation, loss of ecosystem services and the need for sustainable management, unusual disturbances, protected functions, and carbon storage. Then, a subset of at least seven indicators for the five criteria should be assessed to determine forest degradation [13]. The benchmark/landscape method uses a local benchmark that represents areas of low or no degradation that have comparable biophysical characteristics for measuring forest degradation [12].  
In this approach, forest degradation is defined as the result of changes in forest structure that disrupt the capacity of a forest ecosystem to maintain carbon stocks under natural conditions. This definition is based on the use of a set of indicators, mainly derived from remote sensing data, and the establishment of a local benchmark (reference state), according to the criterion/indicators [13] and the benchmark/landscape [12] methods.  
The set of indicators of forest degradation selected for this study were Forest Canopy Cover, Above-ground Biomass and Net Primary Productivity. These indicators were deliberately selected due to the ability to estimate their spatial and temporal variability under a remote sensing approach, using the time-series image stack. These indicators emerge from the definition of forest degradation provided above.
## 5 Discussion and Conclusions  
In this paper, we propose a novel approach called Uncertainty-guided Asymmetric Consistency Domain Adaptation (UCDA) for medical image classification. The UCDA approach leverages the Uncertainty-guided Source-free Transfer Learning (USTL) and Asymmetric Consistency Learning (ACL) modules to enhance domain adaptation performance. The major issue this paper solved is to release the necessity of accessing source data in domain adaptation for medical image classification. The USTL module is introduced as an uncertainty-guided domain adaptation method, enabling effective alignment between the source and target domains. By quantifying the uncertainty of the source domain model, the UA module ensures secure knowledge transfer from the source to the target domain, preventing overconfidence in the network. Furthermore, the ACL module measures the discrepancy between the feature spaces of the source and target models. It incorporates inter-domain and intra-domain metric learning loss functions to improve the performance of the target domain model. Experimental results demonstrate the superiority of the proposed UCDA method compared to baseline methods, achieving better performance while reducing annotation workload. Across three datasets, UCDA consistently outperforms several baseline methods, revealing its potential for medical image classification tasks.  
However, it is important to note that UCDA still requires a certain amount of labeled medical images. One limitation of UCDA is that its performance might deteriorate when facing new scenarios with significant distribution differences from previous domains. Therefore, future research will focus on exploring transfer learning and unsupervised learning techniques to further enhance medical image classification performance. By addressing the limitations and continuously advancing the proposed approach, we aim to contribute to the field of medical image analysis and provide valuable insights for real-world applications.  
C.Y.: Methodology, validation, investigation, writing--original draft preparation; H.P.: Supervision, writing--review and editing, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Scientific Instruments Development Program of NSFC [61527810], and the Fundamental Research Funds for the Central Universities.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 2 Methods
### Wave Parameters Estimation  
The synthetic parameters of the waves faced by sailboats can be derived from the heave, roll and pitch motions. First, the transfer function between the boat and the sea surface movements has to be evaluated and applied to the time series estimated on the basis of GNSS data. Then, the duration of the time window to be analyzed needs to be defined. Herein, time windows of 30 min were considered. Indeed, this duration, which is typical for standard analysis of the free surface elevation signals as measured from conventional wave buoys, is considered to be long enough to properly describe, in a statistical sense, the considered sea state and short enough for considering the wave features to be stationary [22]. On the one hand, the significant wave height may be directly inferred from the heave motion by performing standard time and frequency domain analyses. On the other hand, the directional spectrum may be estimated by using the Direct Fourier Transform Method (i.e., by means of the DIWASP package, e.g., [23]) applied to the heave, pitch and roll signals without taking into account the Doppler shift effect due to the sailboat traveling. It has to be stressed that the influence of the waves upon the roll angle may be obtained by subtracting the heeling angle from the total measured roll angle, obtaining in this way a signal characterized by a mean value which is equal to zero.
# A New Linear Model for the Calculation of Routing Metrics in 802.11s Using ns-3 and RStudio
## 2 Methodology
### Networking Performance  
To model TXX network performance, researchers also used GLMs for the reasons previously explained and because it is a categorical variable. In this model, TXX is the dependent variable, METRIC is the covariable, and the remaining regressors are independent variables.  
The suggested model is:  
\[\text{glmesh2}=\text{TXX}\sim\text{METRIC}+\text{FAILAVG}+\text{MXN}+\text{M\_STEP}+\text{M\_PACKET}+\] \[\text{INTERFACES}+\text{TXDURATION} \tag{9}\]  
A BINOMIAL and LOGIT distributions worked as a link function. Then, ANCOVA analysis was conducted, consisting of three additional tests which are called statistical assumptions.  
Homogeneity of variance: The null-hypothesis shows that the variance is equal. The test LEVENE was used.  
Data independence: the null-hypothesis shows the data independence. The test ANOVA was used.  
Homogeneity of regression slopes: The null-hypothesis shows the homogeneity of the regression slopes. The test ANOVA was used.  
All the scripts and databases can be seen here [16].
## 3 Results and Discussion
### Simulation Results
(**A**) objective function \(J_{2}\); (**B**) parameter \(Y_{3}\); (**C**) parameter \(Y_{CH_{4}}\); (**D**) parameter \(\mu_{2 max}\); (**E**) parameter \(K_{S_{2}}\). outliers have been observed among parameter estimates, especially for the model parameter \(Y_{3}\). Such outliers may appear in a sample of a normally distributed population, but this kind of result would be of interest to future research. [PERSON]'s non-parametric test [61] was used to determine whether or not there was a statistically significant difference between the GA, FA, CS, and COA-derived objective function values for the 30 runs. The test is performed in Matlab R2013a using the function _friedman_. The obtained results are shown in Figure 4.  
Since the \(p\)-value is less than 0.05, the null hypothesis that the mean of the objective function is the same for all four algorithms can be rejected. This is sufficient evidence to conclude that the results obtained by GA, FA, CS, and COA are statistically significant. For practical applications, it is important to use a method that is theoretically guaranteed to converge, robust method, a method that has such a guarantee starting from any initial solution [62]. The estimates presented from 30 runs of each metaheuristic and the statistical results discussed confirm the robustness of the GA, FA, CS, and COA considered here. The observed SD, especially for FA and COA, was 0.0094 and 0.0074, respectively. It is shown that the studied algorithms, starting from 30 different initial solutions, converge to very close (statistically equal) values of the objective function. In summary, based on the results obtained and the analysis carried out, the model estimated by applying FA is assumed to be a better mathematical model of the TSAD process considered here. The obtained model parameters' estimates \(p_{1}=[\mu_{1 max}=0.017\) day\({}^{-1}\)\(K_{S_{1}}=1.1\) g/L \(Y_{1}=0.22\)\(Y_{2}=10.28\)\(Y_{H_{2}}=8.12\)] and \(p_{2}=[\mu_{2 max}=0.44\) day\({}^{-1}\)\(K_{S_{2}}=0.92\) g/L \(Y_{3}=0.1\)\(Y_{CH_{4}}=0.99\)] are physically correct and adequate based on the published mathematical models of AD processes close to the one proposed here [54; 55]. The proposed mathematical model of the TSAD process obtained by FA has satisfactory accuracy. It could be further used for process investigation as well as for process control and optimization.
## 3 Results and Discussion  
### Effect of US on MW Distribution of KGM  
Figure 1 and Table 1 show the results from the HPSEC-MALLS-Vis-RI analysis of KGM fractions before and after US treatment. The MW of KGM was significantly reduced with the increase in sonication time. Meanwhile, the polydispersity index \(M_{\mathrm{w}}/M_{\mathrm{n}}\) value of KGM decreased with increasing sonication time, indicating a narrower or more uniform MW distribution. Similarly, the MW distribution of KGM became narrower upon ultrasonic degradation [34; 35]. Usually, shear forces produced from the rapid collapse of cavitation bubbles in the liquid due to ultrasonication are mainly responsible for the disruption of the polymer aggregates and breakage of polymer chains in solution [20; 36].
# State of Health Prediction for Lithium-Ion Batteries Using Transformer-LSTM Fusion Model
## 4 Results and Discussion
### Comparative Experiments  
To verify the effectiveness of the proposed method in battery SOH prediction, we compared the performance of three models: LSTM, CNN-LSTM, and transformer-LSTM. For the experiments, we selected the battery with channel number 44 in the batch \"2017-05-12\" and the battery with channel number 48 in the batch \"2018-04-12\" for testing,  
Figure 11: (**a**) Prediction results for battery with batch number 27 on “2017-05-12”, (**b**) prediction results for battery with batch number 36 on “2017-05-12”, (**c**) prediction results for battery with batch number 11 on “2017-06-30”, (**d**) prediction results for battery with batch number 17 on “2017-06-30”, (**e**) prediction results for battery with batch number 20 on “2018-04-12”, (**f**) prediction results for battery with batch number 30 on “2018-04-12”.  
and the prediction results displayed in Figure 12. The prediction accuracy metrics of each model, including _RMSE_ and _MAE_, are documented in Table 3. The results indicate that the proposed method excels in the SOH prediction task, achieving significantly lower prediction errors compared to other models. Specifically, while the LSTM model is adept at capturing long-term dependencies, it falls short in terms of prediction accuracy. The CNN-LSTM model has an advantage in extracting local spatial features from the input data; however, the transformer-LSTM model outperforms the others in terms of prediction accuracy and generalization capability. These results clearly highlight the significant advantages of the proposed method in terms of prediction accuracy and generalization performance.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 2 JRC-CWG Model
### Receive Signal Model at the Communications User  
The signal received by the communications user is denoted as \(\mathbf{s}_{g,m}^{c}\) in Figure 4, where [42]  
\[\mathbf{s}_{g,m}^{c}=\delta_{h}\mathbf{s}_{g,m}+\mathbf{n}_{g,m}^{c} \tag{6}\]  
Figure 3: (a) Receive signal at the radar receiver. (b) Signal processing procedure.  
\(\delta_{h}\) is the communications channel coefficient [42; 43], and \(\mathbf{n}_{g,m}^{c}\) is the vector of complex Gaussian white noise at the communications receiver.  
Note that all the signals transmitted by the JRC systems, including the WTOs and the WCIs are received by the communications user. Only the WCIs need to be decoded by the user. It is assumed that the user knows the number of the WTOs \(M_{r}\), and the number of WCIs \(M_{c}\) in each JRC-CWG. In view of this, WCIs can be extracted, and the communications messages can be decoded according to the modulation scheme of the WCIs.  
The communications rate and BER are usually utilized to measure the performance of communications. The communications rate of the JRC-CWG can be represented as  
\[R_{b}=\frac{M_{c}}{M\cdot T}NB_{0}\ \text{bit/s} \tag{7}\]  
where \(M_{c}\) is the number of WCIs in each JRC-CWG; \(M\) is the number of waveforms in each JRC-CWG; \(T\) is the PRI; \(N\) is the length of each WCI; and \(B_{0}\) is the number of bits carried by each symbol of the WCI. For instance, if the BPSK waveforms are employed as the WCI, \(B_{0}=1\). If the 16-PSK waveforms are employed as the WCI, \(B_{0}=4\).  
Since the traditional unimodular communications waveforms are employed as the WCIs, the BER of the JRC-CWG is the same as that of the traditional constant modulus communications waveform under the same condition.