esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 New Adaptable Strategy for RT and NRT troposphere Monitoring
### Retrieving Slant Tropospheric Delays from Both RT and NRT Processing
There are negligible differences in terms of variants using the solution (F vs. S) or input products (PP vs. RT). Standard deviations show however different behavior for all three variants of slant delays. As expected, the model SDs are in the best agreement, but it should be stressed that they do not contain full information about the atmosphere, but only that approximated by the ZTD and horizontal gradients. The clean SD performs much better compared to the raw SDs; both contain additional spatio-temporal information about the water vapor content in the troposphere [47]. The most interesting is the impact of the backward smoothing which shows the largest improvement for the model SD compared to the clean SDs and the raw SDs. A similar impact is visible for the solutions using precise and real-time products. The backward smoothing has a dominating effect on improving the estimated parameters, which has a positive impact on slant delays too. Finally, Figure 11 shows similar statistics for the GFZ post-processing solution (GFZ) and our solution (GOP_S), both previously evaluated in [47]. The new post-processing variants from the above figure are also included for the comparison purposes. Interestingly, we have significantly improved the original GOP_S solution mainly by resolving specific problems related to the backward smoothing combined with the SD retrievals from the post-fit carrier-phase residuals. Thanks to the troposphere stochastic modeling, the new solution now shows the best performance, even when compared to the deterministic model and the LSQ with a lower-parameter sampling rate.
## 5 Experiment and Case Study
### Human Evaluation for Quality Assessment of Generated Summaries  
In order to verify the quality of the text summary results, we conducted a survey on the example resulting summaries (in Figures 7 and A4-A11) with human judges for quality assessment of generated summaries. In this work, we invited 39 participants from University graduate students, including 19 males and 20 females. Their age ranges from 20 to 30 years old. Participants were asked to rate the summaries on a 1-5 scale, with higher being better. The assessed summaries were selected using random sampling. Thus, we randomly selected 10 sample summaries from our summarization system for evaluation. In this work, the summaries are evaluated from three aspects. The first aspect is to assess the consistency of the topic between the tweets and the summary. The second aspect is to evaluate the closeness to the \"facts\" of the summary. The third aspect is to assess the degree of grammatical correctness of the summary. The summaries are reviewed on a scale of 1 to 5 points, and the evaluation results are shown in Table 7. Options for grading summaries are as follows: 1: Bad 2: Below Average 3: Average 4: Good 5: Excellent. In Table 7, we find that the mean values (i.e., 3.92, 3.89, 3.92, respectively) are close to 4 points, which show that the experimental result is acceptable and very close to the performance of human. In addition, we used [PERSON]'s alpha test to check whether the multi-question Likert scale survey is reliable and acquires results ([PERSON]'s alpha coefficients are 0.725, 0.812, 0.846). It shows that [PERSON]'s alpha coefficient is greater than 0.7, and the resulting coefficient indicates that the results are reliable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Consistency of the** & **Closeness to the** & **Grammatical** \\  & **Topic** & **Facts** & **Correctness** \\ \hline No. of Participant & 39 & 39 & 39 \\ Minimum & 2 & 2 & 1 \\ Maximum & 5 & 5 & 5 \\ Mean & 3.92 & 3.89 & 3.92 \\ Median & 4 & 4 & 4 \\ Std. Deviation & 0.824 & 0.819 & 0.928 \\ Std. Error & 0.132 & 0.131 & 0.148 \\ [PERSON]’s alpha & 0.725 & 0.812 & 0.846 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quality assessment of generated summaries.  
Figure 7: A sample resulting summary of formulated event topics (9 January 2020).
## 4 Experimental Results and Performance Evaluations
### Power Conversion Efficiencies  
The power conversion efficiencies and input/output for the soft-switching high-frequency inverter are given in Figure 9. Changing the PDM duty ratio \(D_{\text{PDM}}\) causes linear regulation of the output. The PDM operates at \(D_{\text{PDM}}\) = 1.0 in the printing mode and at \(D_{\text{PDM}}\) = 0.05 in the stand-by mode. More than 94% power conversion efficiency \(\eta\) can be achieved with a \(D_{\text{PDM}}\) = 0.05 to 1.0. Thus, the proposed IH fixed roller application in printing and copy machines can achieve high efficiency using the proposed high-frequency inverter system.  
Figure 8: Current and voltage waveforms for switches \(S_{1}\) and \(S_{2}\). (**a**) Steady-state condition for switch \(S_{1}\). (**b**) Steady-state condition for switch \(S_{2}\). (**c**) Beginning of power injection for switch \(S_{1}\). (**d**) Beginning of power injection for switch \(S_{2}\).  
Figure 9: Power conversion efficiency and input/output characteristics.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 3 Materials and Methods
### Our Approach
#### 3.3.4 Architecture of Our Travel Time Prediction Model
```
Input:input_ttr //a vector representing the input of previous travel times (24-dim) \(\text{W}_{\text{Q}}\), \(\text{W}_{\text{K}}\), \(\text{W}_{\text{V}}\) //3 vectors for self-attention (24-dim) \(\text{C}_{0}\)//a cell state input vector (256-dim) \(\text{H}_{0}\)//a hidden state input vector (256-dim) \(\text{W}_{\text{LSTM}}\)//a weight tensor of LSTM for 24 units \(\text{W}_{\text{FC}}\)//a weight matrix for the fully connected layer (256-dim) Output:pred_tt //the prediction of a following travel time (scalar)
```
1://Butterworth Low-Pass Filter
2:input_ttr \(\leftarrow\) BF(input_ttr)//24-dim vector
3://Self-Attention Layer
4:Q \(\leftarrow\) W\({}_{\text{Q}}\)\(\times\) input_ttr //24-dim vector
5:K \(\leftarrow\) W\({}_{\text{K}}\)\(\times\) input_ttr //24-dim vector
6:V \(\leftarrow\) W\({}_{\text{V}}\)\(\times\) input_ttr //24-dim vector
7:s_map \(\leftarrow\) SoftMax(Q\(\cdot\)K\({}^{\text{T}}\div\sqrt{24}\))\(\cdot\text{V}\)//24-dim vector
8:input_features \(\leftarrow\) input_ttr + sa_map //24-dim vector
9://LSTM Layer
10:lstm_model \(\leftarrow\) LSTM(C\({}_{0}\), H\({}_{0}\), \(\text{W}_{\text{LSTM}}\))//24 LSTM units
11:output_features \(\leftarrow\)lstm_model.predict(input_features)//256-dim vector
12://Fully Connected Layer ```  
**Algorithm 1**Model: Travel Time Prediction
## 4 Conclusions  
The excavation process of the foundation pit supported by large-diameter recyclable steel-pipe piles was analyzed by a field test and the finite element simulation method, and the mechanical performance of the pile was acquired. The following conclusions were drawn from this study:  
1. Upon completing the foundation pit excavation, front- and rear-side strains of the pile body were approximately symmetrical. The value of the pile-body strain was 0 near the undersurface of the foundation pit. The pile body also experienced negative bending at the position above the undersurface of the foundation pit. The maximum pile-body strain occurred at 0.62 \(H\) below the ground surface. In addition, the pile body went through positive bending at the position below the undersurface of the foundation pit. The maximum pile-body strain occurred at 0.23 \(H\).
2. During the foundation pit excavation process, the pile body experienced displacement towards the pit. Pile-top displacement gradually increased with the excavation depth. After the excavation, the pile-top displacement reached the maximum value of 5.63 mm.
3. The ground surface settlement curve caused by the foundation pit excavation presented a \"Spoon\" shape. In the direction away from the foundation pit, ground surface settlement initially increased. Then, the tendency decreased with the increase in distance. The maximum ground surface settlement occurred at 0.23 \(H\) behind the supporting structure. The main influence area of the ground surface settlement caused by foundation pit excavation was 0.92 \(H\).
4. The calculation result, obtained through the finite element analysis, showed that the pile-body deformation laws were consistent with the measured ones. The maximum normal stress was 56.9 MPa, which was smaller than the design value of steel flexural strength. Therefore, the pile-body structure was safe.
5. A suitable mathematical expression of ground surface settlement curve shape was proposed. Through a comparison, the proposed formula can express the shape of the settlement curve well.
6. Given the condition restriction in this test, the obtained ground surface settlement monitoring data were limited. Therefore, in future work, it is necessary to analyze the parameters of mathematical expressions in the case of more abundant monitoring data.  
Conceptualization, J.L. and Y.L.; methodology, J.L.; software, J.L.; validation, J.L. and Y.L.; formal analysis, J.L.; investigation, J.L., Y.L. and A.Y.; resources, Y.L.; data curation, J.L. and A.Y.; writing--original draft preparation, J.L.; writing--review and editing, Y.L. and A.Y.; visualization, J.L.; supervision, A.Y.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Youth Technology Top Talent Project of RIGH and China Scholarship Council (CSC).  
Not applicable.  
Data Availability Statement: Data will be made available on reasonable request.  
Thanks for the support from [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.
# Extending Battery Lifetime by Avoiding High SOC
## 2 Methods  
### Battery Cell Test Set-Up  
A 26 Ah commercial pouch battery cell is used as reference object. The SOC window is defined, based on the recommendations from the cell manufacturer, by a lower and upper voltage limit of 2.8 and 4.15 V.  
The cells are put in a test set-up as presented in Figure 1. The set-up comprises of the test cell, current connections, voltage and temperature sensors, a bakelite bottom and two aluminum plates to represent the cell placed in a battery pack. The equipments used for the lifetime testing were a MACCOR Series 4000, a PEC SBT0550, and a Digatron MCT 100-05-08 ME.  
Apart from registering the voltage and current, the temperature of the cell was also monitored. The ambient temperature was controlled by climate chambers Votsch VT3 7034, VT3 4060, VC 4033, and Climate Temperature System (CTS) T-40/350.  
The cells were tested using symmetric synthetic constant current (CC) cycles in different SOC levels, C-rates, and temperatures. The charge and discharge currents were always kept the same. A test cycle with a 1C charge and 1C discharge is here represented by the shorter notation 1C. In Table 1, the full test matrix is provided.
# Can Bioelectrical Impedance Analysis (BIA) Be Used to Predict Pig's Meat Quality In Vivo?
## 4 Conclusions  
The results showed a significant Pearson correlation between the bioelectrical impedance parameter R\({}_{\text{z}}\) and pH1 (r = 0.48, \(p<0.05\)). A significant Spearman correlation was shown between color b* value and the Rz/Xc/HCW ratio (r = \(-\)0.62, \(p<0.05\)) and Xc (r = \(-\)0.51, \(p<0.05\)), as well as between the Rz/Xc ratio with pH1 (r = 0.48, \(p<0.05\)).  
PCA and cluster analysis showed that bioimpedance measurements combined with the following meat quality traits make it possible to distinguish three groups with different BIA and quality parameters: resistance (R\({}_{\text{z}}\)), ultimate pH (pH2\({}_{\text{4}}\)), glycolytic potential (GP), and color b* value. However, the relationships between them are complex and still require analysis.  
The conducted research indicates potential possibilities to use bioelectrical impedance measurements for the empirical estimation of particular features of pork meat quality. However, the enhancement of a number of the methodological aspects seems to be necessary.  
Conceptualization, W.P. and D.J.; formal analysis, W.P. and D.J.; investigation, D.J., W.P., S.N. and M.S.; data curation, W.P., S.N., L.S., M.S. and D.J.; writing--original draft preparation, W.P., M.S. and D.J.; writing--review and editing, W.P., K.D. and I.W.-K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the statutory funds from Polish Ministry of Science and Higher Education within funds of Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS), for scientific research.  
Not applicable.  
Figure 2: The effects of cluster analysis using the K-means method.
## 2 Heat-to-Power Applications
### Supercritical CO\({}_{2}\) Brayton Cycles
#### Experimental Results
Tests were carried out on their EPS100 installation, which integrates printed circuit heat exchangers (PCHE) as recuperator and cooler, heater of fine tube design, a turbopump that consists of a hermetically sealed unit, and a power turbine coupled to a four-pole synchronous generator [72]. Initial testing took place without the power turbine, and off-design performance of components were measured and data recorded in comparison with model predictions showed in good agreement. At the KAERI, a simple Brayton was tested [79; 80]. The test rig comprises an oil loop, a double low-pressure motor-compressor unit, a power turbine (200 kW power output) and a pre-cooler. The turbine was designed to stand a pressure up to 12.5 MPa, a temperature around 435 \({}^{\circ}\)C, and has a shaft speed of 80 kRPM. Compressor performance maps were drawn, transient analysis and cycle operation performed. After recording cycle parameters, they were able to draw the thermodynamic cycle in a TS diagram and succeeded in producing 1.2 kW output power.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 4 Field-Test Results and Discussion
### Position Domain Analysis  
Carrier phase based positioning was performed using RTKLib open source software. The position errors using PM and MPDR are shown in Figure 14a and the east-north position errors scatter plot using MPDR is shown in Figure 14b. The error statistics are shown in Table 9. MPDR is able to provide fixed solutions. However, PM was not able to provide fixed solution and errors up to 1 m were observed.  
Figure 14: **(a)** Carrier phase based position errors with actual data with chirp interference using PM and MPDR; **(b)** East-north position errors scatter plot with MPDR.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Position Errors**} & \multicolumn{2}{c}{**PM**} & \multicolumn{2}{c}{**MPDR**} \\ \cline{2-5}  & **Mean** & **Standard Deviation** & **Mean** & **Standard Deviation** \\ \hline East error (cm) & \(-63.4\) & \(36.2\) & \(-0.04\) & \(0.08\) \\ North error (cm) & \(104.5\) & \(65.0\) & \(-0.25\) & \(0.06\) \\ Height error (cm) & \(-5.09\) & \(78.6\) & \(-0.06\) & \(0.16\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Carrier phase based position errors with actual data.
## 1 Introduction  
The application of thermal energy storage (TES) is a key technology for power plants by offering improved operating flexibility for conventional power plants and extended operating time for power plants driven by renewable energy sources [1; 2]. Other power generating applications for TES are adiabatic compressed air energy storage systems [3; 4]. Further TES concepts exist in industrial heat management, e.g., in the steel industries (in the form of \"Cowper\" storage), in glass producing industries and air purification systems [5; 6]. Here, the aim is a decrease in operational costs and an increase in system efficiency and flexibility.  
When air is used as a heat transfer medium, regenerator-type heat storage with a packed bed inventory is a straightforward design option, offering cost-effective solutions and flexibility in terms of arrangement and material choices for the storage medium. Possible choices for packed bed materials are ceramic pebbles or simply natural stone [7]. One of the major challenges for such a packed bed TES system is its vulnerability to mechanical failures caused by the punctiform contacts between particles and the encircling container wall [8]. During the thermal charging and discharging processes of the heat storage system, the pebbles or particles of the packed bed start to expand and shrink, respectively. As a consequence, thermo-mechanical loads are induced which lead to high technical risks for the storage inventory itself and the encircling containment insulation of the heat storage system. Due to the operating mode consisting of repeated cyclic thermal loading and unloading, the induced thermo-mechanical loads potentially lower the life expectancy of the storage system. Therefore, knowledge of the occurring stresses and loads are required in order to adequately adjust the packed bed installation and container design if necessary. Hence, appropriate numerical models and simulation tools for calculating these loads are needed. Conventional approaches for mechanical packed bed modeling are based on either particle discrete models [9; 10] or continuum models [11]. As discussed in [12], both of these approaches offer advantages and disadvantages: While the particle discrete modelsare able to model packed beds with high accuracy at the expense of high computing times, continuum models generally offer faster simulation results, but are in need of complex model parametrizations.  
The work at hand aims to provide a methodology for a simplified approach to approximate the thermo-mechanical stresses of a large-scale regenerator type TES with a packed bed as the storage medium. The present work is a continuation of a previously published paper [12] and builds upon its findings. Therefore, it is recommended to first study the findings in [12] in order to obtain additional background information and deeper insight of the discussed subjects in the present work. Subsequently, the results of an exemplarily large-scale regenerator type TES, as described in [4], is used as a basis for a validation of the developed simulation approach.
# Coupled Elastic-Plastic Damage Modeling of Rock Based on Irreversible Thermodynamics
## 5 Model Parameter Sensitivity Analysis  
In the constitutive model, the plastic hardening rate \(b\), the damage evolution rate \(B_{\omega}\), the weighting coefficient \(r\), and the damage softening \(a\) are four important coupled parameters requiring further parameter sensitivity analysis.  
From Figure 12, during the pre-peak stage, \(b\) controls the rate of plastic hardening. A larger \(b\) value makes plastic hardening more pronounced and increases the peak strength. A smaller \(b\) enhances the nonlinear deformation. However, \(b\) has a minor impact on residual strength and damage evolution.  
From Figure 13, it is evident that the damage evolution rate \(B_{\omega}\) plays a role in elastoplastic and damage behavior. The value of \(B_{\omega}\) has a minor impact on pre-peak deformation but a significant impact on post-peak deformation. The smaller \(B_{\omega}\) is, the faster the  
Figure 11: Stress–strain curves of theoretical and experimental for Eastern Yunnan–Western Chongqing shale: (**a**) Comparison when the bedding angle \(\theta\) is \(0^{\circ}\); (**b**) Comparison when the bedding angle \(\theta\) is \(22.5^{\circ}\); (**c**) Comparison when the bedding angle \(\theta\) is \(45^{\circ}\); (**d**) Comparison when the bedding angle \(\theta\) is \(90^{\circ}\)[110].  
Figure 12: Sensitivity analysis of \(b\): (**a**) The relationship between axial strain and deviatoric stress; (**b**) The relationship between axial strain and damage variable \(D\).  
post-peak stress decreases, the deformation under the same stress condition significantly increases, the post-peak residual stress notably reduces, and the rate of damage increase accelerates. A larger \(B_{\omega}\) value implies a rapid development of the damage variable, enhancing the damage softening effect, accelerating the post-peak stress decrease, and leading to a reduction in peak strength.  
From Figure 14, it is known that in the post-peak phase, the weighting coefficient \(r\) has a significant impact on plastic behavior and damage behavior. The larger \(r\) is, the slower the damage grows, and the smaller the stress peak and residual strength are. Where \(r=0\) indicates that only \(Y^{P}\) drives damage development, with no damage occurring during the elastic deformation phase, i.e., \(\omega=0\). \(r=0.5\) means that \(Y^{e}\) and \(Y^{P}\) contribute equally to damage development.  
Figure 14: Sensitivity analysis of \(r\): (**a**) The relationship between axial strain and deviatoric stress; (**b**) The relationship between axial strain and damage variable \(D\).  
Figure 13: Sensitivity analysis of \(B_{\omega}\): (**a**) The relationship between axial strain and deviatoric stress; (**b**) The relationship between axial strain and damage variable \(D\).  
From Figure 15, \(a\) reflects the degree of post-peak stress decrease. The larger \(a\) is, the faster the damage grows, the quicker the stress decreases, and the lower the peak and residual strengths are.