esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of Cyclic Plasticity and Creep Ratchet Behavior of PTFE
## 3 Results and Discussion
### Static Crep Tests and Determination of Crep Equations
#### 3.3.2 Determination of the Creep Equation  
Considering only the steady state creep stage, the Arrhenius equation [36; 37] describes the creep rate in Equation (3).  
\[\dot{\varepsilon}=A\sigma^{n}\exp(-\frac{Q_{a}}{RT}), \tag{3}\]  
where \(\dot{\varepsilon}\) is the steady state creep rate, \(A\) is a material constant, \(\sigma\) is the creep stress, \(Q_{\rm a}\) is the nominal creep activation energy, \(n\) is the stress index [38; 39], \(R\) is the gas constant, and \(T\) is the absolute temperature. As shown in Figure 8a, the steady creep rate is derived from the stable stage of the creep curve at various temperatures and stress levels.  
The stress index \(n\) and the creep activation energy \(Q_{\rm a}\) of the creep steady state phase are determined according to Equations (4) and (5), respectively.  
\[n=\frac{\partial\ln\dot{\varepsilon}}{\partial\ln\sigma}. \tag{4}\]  
\[Q_{a}=-R(\frac{\partial\ln\dot{\varepsilon}}{\partial T^{-1}}). \tag{5}\]  
Figure 8: Creep parameters at various temperatures: (**a**) steady state creep rate; (**b**) stress index; (**c**) creep stress threshold value; (**d**) creep activation energy.  
Due to the material's inherent resistance to deformation, it is believed that the propelling force for creep deformation is not the applied stress, but rather the effective stress \(S\)- \(S_{\text{th}}\), where \(S_{\text{th}}\) is the creep stress threshold. In this paper, the trial algorithm is used to solve the stress index and creep threshold. Firstly, \(S_{\text{th}}\) is assumed to be equal to 0. Then the \(ln\dot{\varepsilon}\sim ln\sigma\) curve is plotted based on the experimental data, and the slope of the fitted curve in the figure is the initial \(n\) value. After plotting the \(\dot{\varepsilon}^{\frac{1}{n}}\sim\sigma\) curve and extrapolating the fitted curve to \(\dot{\varepsilon}^{\frac{1}{n}}=0\), the creep threshold stress value \(S_{\text{th}}\) can be obtained. According to this method, repeated trial calculations can finally obtain the stress index and creep stress threshold at different temperatures. The creep parameters at each temperature are shown in Table 3.  
From the experimental data, it can be seen that the creep stress index \(n\) (Figure 8b) and creep stress threshold value \(Q_{\text{a}}\) (Figure 8c) decrease with the increase in temperature, which can be divided into two stages of change. At 23 to 50 \({}^{\circ}\)C, \(n\) and \(Q_{\text{a}}\) decrease rapidly, while at 50 to 200 \({}^{\circ}\)C, \(n\) and \(Q_{\text{a}}\) decrease at a relatively low rate.
###### Abstract  
Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G x E x M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUE\({}_{\text{Canopy}}\)). The simulations are used to both explore the sensitivity of the satellite-estimated genotype x management (G x M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUE\({}_{\text{Canopy}}\) are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G x M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUE\({}_{\text{Canopy}}\). The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.  
LAI; GPP; MODIS; LANDSAT; G x E x M; crop growth models; CO\({}_{2}\) flux towers; Ameriflux; GHG-Europe 22 July 2019; Accepted: 15 August 2019; Published: 17 August 2019
## 3 Results and Discussion
### SCCO\({}_{2}\) Regeneration
#### 3.2.1 Effect of Temperature
Figure 5: N\({}_{2}\) adsorption-desorption isotherms for the original ACF and the ACFs regenerated at 200 \({}^{\circ}\)C and 400 \({}^{\circ}\)C.  
It must be noted that, in all of these previous works, the influence of the extraction pathway was outstanding regarding the influence of the thermal desorption pathway because temperatures did not exceed 100 \({}^{\circ}\)C. In the current research the influence of the thermal desorption pathway is evidently more noticeable since temperatures above 200 \({}^{\circ}\)C are used. Thermal desorption is the main pathway when the exhausted adsorbents are regenerated with hot inert gases at temperatures lower than 300 \({}^{\circ}\)C [3]. Regeneration with hot inert gases is an easier procedure than SCCO\({}_{2}\) regeneration. Consequently, it is important to study the relative influence of each pathway in SCCO\({}_{2}\) regeneration at high temperatures to assess if the use of the supercritical fluid is necessary, or the adsorbents would be efficiently regenerated with a simple flow of a hot inert gas. This point was studied by regenerating an exhausted ACF with a N\({}_{2}\) flow of 300 cm\({}^{3}\cdot\)min\({}^{-1}\) during 15 min at 250 \({}^{\circ}\)C. The RE after two adsorption-regeneration cycles with the hot inert gas was 79.2%, in comparison to 93.5% with SCCO\({}_{2}\) regeneration with the same conditions of temperature and time in both samples. The RE achieved by thermal desorption with hot inert gases was not low, but the concomitant extraction pathway related to the supercritical fluid provided a meaningful improvement that turned SCCO\({}_{2}\) regeneration into a highly efficient regeneration method.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction
### Novelty and Contribution  
Our work presents a new hybrid method for LS-based channel estimation in LIS systems. It overcomes key limitations of previous studies and introduces practical improvements. We compare different channel estimation techniques and thoroughly evaluate their accuracy, complexity, and energy efficiency. Below, we clearly explain how our innovations go beyond past research.
# Energy-Based Economic Sustainability Protocols
## 5 Results
### The Role of a Payment Tracking System in the Depletion of the Commons  
Let us consider two companies that own land located above a natural water spring (having neither a well nor smart metering hardware), and let us suppose that the local legislation allows these companies to be entitled to exert full control over the raw materials located on their property. Now let us suppose both companies decide to bottle natural water, collecting it from two natural springs, and both are harvesting it from the same groundwater reservoir. Satellite imagery may provide an effective tool for assessing water withdrawals for better planning of water reservoirs [43]. However, discriminating stakeholders' individual consumption for nearby withdrawals from the same reservoir may be challenging. Therefore, tracing the amount of water harvested from the environment by focusing on monitoring the event of water depletion while maintaining spatio-temporal resolution is not as effective as focusing on anonymous stakeholders' behaviour in known locations would be.
## 4 Definitions and Basic Properties
Through this mapping, we can precisely determine how many vertices the algorithm selects relative to the optimal solution, which is essential for proving the approximation ratio. The function \(\alpha\) accounts for both direct vertex selections and cases where the algorithm performs optimization by replacing one vertex with another (cases where vertex \(\gamma(v)\) is added to set \(D_{2}\) instead of \(\beta(v)\)). Definition 10: For each \(m\in M\) we also define counting functions:  
\[c_{IN}(m) =|\{v\in\Gamma_{1}^{IN}:\alpha(v)=m\}|\] \[+|\{v\in\Gamma_{2}^{IN}\setminus\Psi_{2}^{\prime}:\alpha(v)=m\}|\] \[+|\{v\in\Psi_{2}:\alpha(v)=m\wedge\exists_{u}:\beta_{2}(u)\in \Gamma_{2}^{IN}\wedge\gamma_{2}(u)=v\}|\] \[+ \begin{cases}1&\text{if }m\in D_{1}\cup\,D_{2}\\ 0&\text{otherwise}\end{cases} \] \[c_{OUT}(m) =|\{v\in V\setminus M:\alpha(v)=m,\text{ such that }v\text{ was not counted in }c_{IN}(m)\}|\] \[c(m) =c_{IN}(m)+c_{OUT}(m)\]  
The function \(c_{IN}(m)\) counts some internal vertices in bunches that are added to \(D\). More precisely, \(c_{IN}(m)\) includes the number of vertices added to \(D_{1}\) lying inside a bunch and for which \(m\) is responsible. Additionally, it counts the vertices added to \(D_{2}\) in phase 2 of the algorithm, for which vertex \(m\) is also responsible. The function also accounts for the vertices added in phase 2 as vertices \(\gamma\) instead of vertices \(\beta\) (where vertex \(\gamma\) was selected by a vertex \(v\), for which \(m(v)=m\)). Furthermore, we also count vertex \(m\) itself if it belongs to the set \(D_{1}\cup D_{2}\). Definition 11: We divide the set \(M\) into two disjoint subsets:  
* \(M_{HIGH}=\{m\in M:\deg(m)\geq 4\}\)--vertices with a high number of neighbors;
* \(M_{\text{LOW}}=\{m\in M:\deg(m)\leq 3\}\)--vertices with a low number of neighbors.
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
###### Abstract  
Nowadays, with the increasingly prominent contradiction between environment and development, renewable energy technology has received extensive attention. However, due to natural fluctuation, there are many challenges in safety and stationarity in an electric power system with a large-scale portion of renewable energy. Meanwhile, the conventional electric power system in which the power supply follows the load change paradigm is gradually broken. Thanks to advanced information and communication technology, energy storage technology, and automatic control technology, the power supply, grid, load, and energy storage interactive electric power system regulation paradigm came into being. A virtual power plant (VPP) is a concrete realization of this regulation paradigm. In this paper, based on the characteristics of VPP service requirements, we propose a data-driven method to estimate and predict the network latency. First, we investigate and summarize the characteristics of a VPP participating in various auxiliary services and their demand for communication networks. Second, the practical implementation of a VPP network architecture, which combines the local network and the backhaul network, is presented based on a secure access gateway and VPP monitoring and scheduling platform for flexible resource aggregation and regulation. Then, based on a delay probe, a network latency acquisition method is proposed for a public backhaul network. Finally, a data-driven network latency processing method is proposed to support the VPP's participation in different communication requirements of auxiliary services.  
virtual power plant; digital power; network architecture; time series analysis Article
## 4 Recurrence-Analysis-Based Detector
### Analytical Expression of the Probability of False Alarm
#### 4.2.2 The PDF of \(\overline{\mathbf{u}}_{d}\) under Hypothesis \(\mathcal{H}_{0}\)  
Under \(\mathcal{H}_{0}\), the average value \(\overline{\mathbf{u}}_{d}\) of \(\mathbf{u}_{d}\) is defined as follows:  
\[\overline{\mathbf{u}}_{d/\mathcal{H}_{0}}=\frac{1}{K_{d}}\sum_{k=1}^{K_{d}}u_{ d_{k}/\mathcal{H}_{0}}^{(k)} \tag{32}\]  
where \(u_{d/\mathcal{H}_{0}}^{(k)}\in\{d_{i;i+1}\};1\leq i\leq K_{d}=K-1\).  
Because of the structure of the upper triangular distance matrix, the coefficients of the first top diagonal \(u_{d/\mathcal{H}_{0}}^{(k)}\) are strongly decorrelated. In addition, \(K_{d}\) is a large number. Thus, we can approximate \(\overline{\mathbf{u}}_{d}\) by a Gaussian variable by using the central limit theorem:  
\[\overline{\mathbf{u}}_{d/\mathcal{H}_{0}}\mapsto\mathcal{N}\left(\mu_{d/ \mathcal{H}_{0}},\sigma^{2}_{d/\mathcal{H}_{0}}\right) \tag{33}\]  
Using a similar approach to the calculation of the expectation value \(\mu_{\Delta/\mathcal{H}_{0}}\) and variance \(\sigma^{2}_{\Delta/\mathcal{H}_{0}}\) of \(\overline{\mathbf{u}}_{\Delta/\mathcal{H}_{0}}\), we obtain  
\[\mu_{d/\mathcal{H}_{0}}=2\sigma_{b}\frac{\Gamma\left(\frac{m+1}{2}\right)}{ \Gamma\left(\frac{m}{2}\right)} \tag{34}\]  
\[\sigma^{2}_{d/\mathcal{H}_{0}}=\frac{2\sigma^{2}_{b}}{K_{d}}\left[m-2\left( \frac{\Gamma\left(\frac{m+1}{2}\right)}{\Gamma\left(\frac{m}{2}\right)}\right) ^{2}\right] \tag{35}\]
# CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
## 2 Background and Related Work  
### Defect Detection for the Injection Molding Process  
Various studies have been implemented to understand shrinkage and to control the dimensions of injection molding. [PERSON] et al. [12; 13] applied an experimental design to conduct quantitative studies of the shrinkage and warping of fine-porosity and existing injection molds. [PERSON] et al. [14] studied anisotropic contraction in injection molding of amorphous polymers considering the pressure-volume-temperature equation of state, molecular orientation, and elastic recovery. [PERSON] et al. [15] investigated the effect of packing pressure, melting temperature, and cooling time on shrinkage of injection molds. [PERSON] et al. [16] explored the effects of suppression, time, and geometric constraints on the contraction of semi-crystalline polymers with strain gauges. [PERSON] et al. [17; 18] applied gas backpressure to reduce the shrinkage of parts during injection molding. [PERSON] et al. [19] found that mixing of polypropylene copolymers can effectively reduce the molding shrinkage of isletic polypropylene. [PERSON] et al. [20] identified the transition temperature used in injection molding simulations (i.e., moldflow) to calculate contractions. [PERSON] et al. [21] used artificial neural network (ANN) simulations to evaluate the effectiveness of molding parameters on molding shrinkage. [PERSON] et al. [22] developed a shrinkage prediction of injection molded dense polyethylene parts using the Taguchi approach and ANN. [PERSON] et al. [23] and [PERSON] et al. [24] studied the tensile strength and shrinkage of thermoplastic complexes in injection molding. [PERSON] et al. [25] applied the Taguchi approach to reduce shrinkage of injection-molded hybrid biocomposites. [PERSON] et al. [26] conducted a comprehensive review of the effects of molding parameters on the strength, shrinkage, and bending of plastic parts. All of these studies are very useful for improving the under standing of molding shrinkage and optimizing machine parameter settings. Furthermore, [PERSON] et al. [27; 28; 29; 30] investigated the vibration and thermal behavior of functional class materials considering vibration and material distribution in the study.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
## 3 Results and Discussion
### Result Analysis  
#### 3.1.1 Mean Drop Rate  
The lowest mean drop rate was observed for LIA as the MPTCP CC algorithm combined with BBR at the path level for both \(d_{1}\) and \(d_{2}\). Thus, this combination is highly efficient in terms of losses and uninterrupted transmission. In turn, wVegas with Reno and BALIA with Cubic at the path level resulted in the highest drop rate, indicating a loss of reliability and proclivity to cause stream pauses or even breaks, e.g., when keyframes are missing.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline  & & \multicolumn{4}{c}{**LIA**} & \multicolumn{4}{c}{**OLIA**} & \multicolumn{4}{c}{**BALIA**} & \multicolumn{4}{c}{**wVegas**} \\  & & **Reno** & **Cubic** & **BBR** & **Reno** & **Cubic** & **BBR** & **Reno** & **Cubic** & **BBR** & **Reno** & **Cubic** & **BBR** \\ \hline Protocol delay & \(v_{\text{inv}}\) & 96 & 97 & 231 & 98 & 96 & 95 & 95 & 99 & 95 & 271 & 99 & 99 \\ (ms) & \(v_{\text{max}}\) & 225 & 224 & 569 & 256 & 235 & 238 & 231 & 295 & 243 & 816 & 259 & 254 \\ \hline HoL Degree & \(\zeta_{\text{inv}}\) & 17 & 17 & 151 & 19 & 16 & 16 & 15 & 18 & 15 & 191 & 19 & 19 \\ (ms) & \(\zeta_{\text{max}}\) & 143 & 143 & 488 & 175 & 150 & 155 & 148 & 211 & 157 & 733 & 178 & 174 \\ \hline SRTT (ms) & \(\tau_{1,\text{inv}}\) & 79 & 79 & 79 & 78 & 78 & 77 & 79 & 80 & 79 & 77 & 79 & 78 \\ path 1 & \(\tau_{1,\text{max}}\) & 94 &Figure 3: Transmission characteristics of LIA, OLIA, BALIA, and mVegas at the MPTCP level, juxtaposed with Reno (top row: (**a,b**)), Cubic (middle row: (**c,d**)), and BBR (bottom row: (**e,f**)) at the SPTCP level. The left column (**a,c,e**) showcases Path Delay, and the right column (**b,d,f**) shows protocol delay.  
Figure 4: Transmission characteristics of LIA, OLIA, BALIA, and mVegas at the MPTCP level, juxtaposed with Reno (top row: (**a**,**b**)), Cubic (middle row: (**c**,**d**)), and BBR (bottom row: (**e**,**f**)) at the SPTCP level. The left column (**a**,**c**,**e**) showcases HoL Blocking Degree, and the right column (**b**,**d**,**f**) shows throughput.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 2 Preliminaries
### Computing Time-Dependent Shortest Paths
#### 2.1.3 The CFLAT Oracle  
The time-dependent oracle CFLAT [21] precomputes approximations \(\overline{D}[\ell,v]\) (called _travel-time summaries_) of minimum-travel-time functions \(D[\ell,v]\) from each element \(\ell\) of a small set of carefully selected nodes (called _landmarks_), towards each reachable destination \(v\). These travel-time summaries are succinctly represented as a collection of time-stamped minimum-travel-time trees. The appropriate construction of these summaries ensures both \((1+\epsilon)\) approximation guarantees (for any given \(\epsilon>0\)) for the landmark-to-destination travel-time functions \(\overline{D}\) and efficient (subquadratic) space requirements.
## 2 Materials and Methods
### Classifier Evaluation  
The classifier was first evaluated with a single-blind test containing 17 patients. The diagnosis results were unknown to the classifier trainers. Their malignancy predictions were compared with clinical diagnosis, and the concordance was calculated to evaluate the generalization ability of the trained classifier.  
Then, the classifier was used to predict the malignancy of patients in undetermined group (42 patients). Patient follow-up was performed for these patients. A final clinical diagnosis was made according to the follow-up, cytopathology, BRAF gene, and ultrasound features. In detail, Benign: (1) the clinical treatment was ultrasound recheck after 3-6 months; (2) the ultrasound recheck indicates low risk; and (3) reported benign after surgery. Malignancy: reported malignancy after surgery. Suspicious for malignancy: (1) BRAF (+), no recheck or surgery; (2) the ultrasound recheck is high-risk; (3) the cytopathologic recheck is suspicious for malignancy or malignancy; (4) a reported borderline tumor after surgery. The concordance of classifier prediction with the final diagnosis was calculated. Because the classifier could only provide a benign or malignant mark, when calculating the concordance of prediction with a final diagnosis, suspicious for malignancy was treated as malignant.
## 4 Experimental and Results Analysis
### Experimental Results and Analysis
#### 4.3.3 Experiment 3  
After conducting an HPO with Optuna, we found that the batch size of 16, model depth of 256, hidden layer depth of 1 layer, and patch size and stride of 300 resulted in the best performance for the UniTS-SinSpec model. The final results of Experiment 3 are shown in Table 8.  
The results demonstrate that the UniTS-SinSpec model, optimized through HPO, achieves an average accuracy rate of 97.47% in classifying ENF data, significantly surpassing both the baseline model and other enhanced models. Notably, the optimized UniTS-SinSpec model exhibits a nearly 6 percentage point improvement in accuracy compared to the baseline UniTS model.  
It is noteworthy that the optimal patch length and stride step of 300, corresponding to inputting ENF data sequentially at 5 min intervals, yields the model's highest classification performance and an average reduced running time of approximately 9.0597 s. We posit that, due to its one-dimensional nature, ENF data exhibit lower complexity; thus, a reduction in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Number of** & **Sequence** & **UniTS** & **THF\_TSC** & **LSTM\_TSC** \\
**Number** & **Valid Samples** & **Length** & & & \\ \hline A1 & 1,575,357 & 60 & 88.95\% & 57.86\% & 26.6\% \\ A2 & 188,928 & 3600 & 91.49\% & 58.29\% & 32.54\% \\ A3 & 1046 & 86,400 & none & none & 6.25\% \\ B1 & 188,921 & 60 & 77.03\% & 48.93\% & 24.02\% \\ B2 & 22,656 & 3600 & 79.61\% & 50.21\% & 29.37\% \\ B3 & 124 & 86,400 & none & none & 5.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classification accuracy comparison of models based on the sequence length and effective sample size (Experiment 1).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Index** & **UniTS** & **UniTS\_SAF** & **UniTS\_SAM** & **UniTS-SinSpec** \\ \hline Time-consuming & 33.28 s & 44.01 s & 45.674 s & 46.549 s \\ Average verification & 91.49\% & 94.43\% & 94.11\% & 96.24\% \\ Accuracy & \multicolumn{1}{c}{} & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of ablation models (Experiment 2).  
model complexity and an appropriate patch length and stride step can effectively enhance both the model's performance and efficiency.