esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Validation of Heterotrophic Medium Optimization  
Hereupon, _C. amblystomatis_ cultures grown with the company's heterotrophic basal medium were compared to those grown with the optimized heterotrophic medium (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Degree of Freedom** & **Mean Square** & **Coefficient Estimate** & **F-Value** & _p-Value_ \\ \hline Model & 0.0005 & 6 & 0.0001 & & 65.25 & \textless{}0.0001 \\ \hline _A_-(NH\({}_{2}\))\({}_{2}\)CO & 0.0001 & 1 & 0.0001 & 0.0034 & 76.66 & \textless{}0.0001 \\ \hline _B_-NaH\({}_{2}\)PO\({}_{4}\)\(\cdot\)H\({}_{2}\)O + K\({}_{2}\)HPO\({}_{4}\) & 0.0003 & 1 & 0.0003 & \(-\)0.0064 & 186.76 & \textless{}0.0001 \\ \hline C-CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O & 0.0001 & 1 & 0.0001 & \(-\)0.0035 & 67.35 & \textless{}0.0001 \\ \hline _AB_ & 0.0000 & 1 & 0.0000 & \(-\)0.0010 & 8.40 & 0.0200 \\ \hline _AC_ & 0.0001 & 1 & 0.0001 & \(-\)0.0042 & 47.02 & 0.0001 \\ \hline _BC_ & 0.0000 & 1 & 0.0000 & 0.0013 & 5.33 & 0.0498 \\ \hline Residual & 0.0000 & 8 & 0.0000 & & & \\ \hline Lack of Fit & 0.0000 & 6 & 0.0000 & & 1.24 & 0.5107 \\ \hline Pure Error & 0.0000 & 2 & 0.0000 & & & \\ \hline Corr. Total & 0.0006 & 14 & & & \\ \hline \hline \end{tabular}  
* Model: \(R^{2}\) = 0.980; \(R_{\text{adj}}\) = 0.965; \(R_{\text{pred}}\) = 0.937.  
\end{table}
Table 3: Statistical analysis of Box–Behnken experiment for specific growth rates of heterotrophic cultures.  
Cultures grown with the optimized medium registered an improvement of 44.9% in specific growth rate, from 0.034 to 0.050 h\({}^{-1}\) (\(p<0.05\)). Consequently, improvements of 67.4% were registered for global productivity, increasing from 0.12 to 0.19 g\(\cdot\)L\({}^{-1}\).h\({}^{-1}\) (\(p<0.05\)) and over 109.7% for maximum productivity, from 0.29 to 0.60 g\(\cdot\)L\({}^{-1}\).h\({}^{-1}\) (\(p<0.05\)). Furthermore, the highest biomass concentration of 10.53 g\(\cdot\)L\({}^{-1}\), in comparison to 7.66 g\(\cdot\)L\({}^{-1}\), was obtained from cultures grown with the optimized medium (\(p<0.05\)). This appropriate culture medium recipe brought improvements in C. _ambly stomatis_ growth rates and productivity and resulted in higher cell concentrations, which is key to nutrient cost reduction and the operational costs of production in an industrial setting [18].
## 3 Data
### Capacitance Stations  
In February 2014, eleven stations equipped with an EM50 datalogger were installed over the Segezia site, with an average spacing of approximately 500 m (Figure 2). They record volumetric soil moisture values every 15 min using two 5 TM and two 10 HS probes, which are installed horizontally at 0.025 m, 0.10 m, 0.20 m and 0.40 m depths. Furthermore, the 5 TM device also measures soil temperature. The capacitance sensors were calibrated by thermogravimetric method collecting soil moisture samples throughout a year, and it is periodically controlled. Moreover, intensive campaigns collecting vegetation and soil parameters in coincidence with radar and optical acquisitions (e.g., PRISMA, COSMO-SkyMed, RADARSAT-2, Sentinel-1/2, 9 LSO-2, SAOCOM) are frequently carried out. In this study, the capacitance stations at points 2, 3, 5, 7, 9, and 10 are used, having the most consistent and continuous data for the years 2018 to 2020. For validation, soil moisture values from 0.025 m and 0.10 m are used, matching with each orbit overpass time.
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 7 Validation and Discussion
### Results and Discussion
#### 7.5.3 Noise Sensitivity Assessment  
Noise during the actual measurement process is unavoidable and has always been a major challenge for this measurement technology. Noise sensitivity limits the widespread application and further development of the technology. To address this challenge, we specifically introduce corresponding countermeasures in the design of the reconstruction model. In this section, we perform a comprehensive assessment of the robustness of the MOOR algorithm by analyzing capacitance data collected under varying noise conditions (5%, 10%, and 15%). The evaluation results are presented both qualitatively and quantitatively, with Figure 6 illustrating the visual reconstruction results and Tables 7 and 8 providing statistical metrics, such as image error and correlation coefficients  
The precise reconstruction of images and the performance of algorithms in complex noise environments have always been central issues of concern in academia and engineering applications. Achieving high-precision and stable reconstruction in complex noise scenarios is not only directly related to the practicality and reliability of this technology but also holds significant implications for technological developments in related fields. As shown in Figure 6 and Tables 7 and 8, the MOROR algorithm demonstrates outstanding performance advantages under different noise conditions, consistently maintaining image errors at a relatively low level. Specifically, the maximum image error of the MOROR algorithm is only 1.21%, while the minimum correlation coefficient is 0.9996. This fully proves the robustness and superior performance of the algorithm in noisy environments.  
The exceptional robustness of the MOROR algorithm is attributed to several key features and innovative designs. Firstly, the MOROR algorithm introduces a multi-objective robust imaging model that reduces sensitivity to noise during the image reconstruction process. This model considers the balance between imaging accuracy and algorithm robustness, allowing the algorithm to maintain stable performance in various noise scenarios. Secondly, the MOROR algorithm incorporates regularization techniques and integrates multiple types of prior information. The enhancement in the diversity and complementarity of prior information helps improve imaging quality and the robustness of the algorithm. Additionally, the use of regularization techniques effectively mitigates the interference of measurement noise. Thirdly, the MOROR algorithm has the capability to automatically calculate model parameters. Compared to traditional algorithms that rely on manual adjustments, this adaptive optimization strategy significantly improves the model's adaptability and reconstruction quality in complex and high-noise scenarios.
# An Efficient Optimization of the Monte Carlo Tree Search Algorithm for Amazons
## 5 Experiment and Results
### Quantitative and Qualitative Experiments  
#### 5.2.1 Winning Percentage Statistics and Analysis  
In order to objectively and accurately assess the effectiveness of the MG-PEO algorithm optimization strategy, we selected the MCTS algorithm without Move-Groups optimization and without Parallel Evaluation optimization for comparison, and we played a total of 100 matches under the same conditions, with each match divided into 3 games each (first hand and second hand). Specifically, we chose 33 players who participated in a computer gaming competition and 17 players who open-sourced their rankings (ladder list) on the Botzone platform ([[https://www.botzone.org.cn/](https://www.botzone.org.cn/)]([https://www.botzone.org.cn/](https://www.botzone.org.cn/)), accessed on 2 March 2024), totaling 50 opponents. Due to the requirement for third-party libraries, we locally exported the exe programs for all players and then conducted the games against each other on the SAU platform ([[http://computergames.caai.cn/platform/SAU_Game_Platform_2.1.0_r3.rar](http://computergames.caai.cn/platform/SAU_Game_Platform_2.1.0_r3.rar)]([http://computergames.caai.cn/platform/SAU_Game_Platform_2.1.0_r3.rar](http://computergames.caai.cn/platform/SAU_Game_Platform_2.1.0_r3.rar)), accessed on 20 March 2024), and the results of the games are shown in Table 5. Detailed information on all the games can be found in Appendix C.  
In Table 5, the MCTS algorithm has a winning rate of 74% in Amazons Duel on the SAU matchmaking platform. In the same test environment, the MCTS algorithm with the MG-PEO strategy shows higher stability, and its winning rate in the Amazons Match on the SAU Matchmaking Platform reaches 97%. In summary, the MCTS algorithm with the MG-PEO strategy has more comprehensive and in-depth optimization when considering and utilizing strategies than the MCTS algorithm and, thus, shows a higher winning rate in this test scenario.  
It is worth noting that--by observing the matches of the two algorithms--we found that the optimized MCTS algorithm often makes more accurate decisions than the unoptimized MCTS algorithm in some critical rounds. These key rounds often involve crucial positions for \"encircle territory\" or \"block the opponent\" strategies. Additionally, during the matches, we noticed that the optimized MCTS algorithm has a clear advantage over some traditional algorithms (such as Minimax and traditional MCTS). However, its performance is slightly lacking when competing against neural network programs trained with a large amount of game data. Nevertheless, this provides a direction for further improving the algorithm's performance in the next step.
## Abbreviations  
Acronym Full Form  
TWSA Terrestrial Water Storage Anomalies  
FPI Flood Potential Index  
GRACE Gravity Recovery and Climate Experiment  
GRACE-FO Gravity Recovery and Climate Experiment Follow-On  
GAIN Generative Adversarial Imputation Networks  
LSTM Long Short-Term Memory  
KNN k-Nearest Neighbors  
GPM Global Precipitation Measurement  
IMERG Integrated Multi-satellite Retrievals for GPM  
ROC Receiver Operating Characteristic  
AUC Area Under Curve  
RMSE Root Mean Square Error  
MAE Mean Absolute Error  
CSR Center for Space Research  
JPL Jet Propulsion Laboratory  
GSFC Goddard Space Flight Center  
SSA Singular Spectrum Analysis  
MSSA Multichannel Singular Spectrum Analysis  
ARMA Autoregressive Moving Average  
EM-DAT Emergency Events Database  
TP True Positive  
TN True Negative  
FP False Positive  
FN False Negative
## 3 Results and Discussion
### Spme  
The extraction time study for the SPME fiber, using C4 samples as the primary explosive material, was conducted across various substrates, including cotton, wood, metal, glass, and cardboard. Figure 4 highlights the extraction results for cotton, with data for other substrates available in the Supplementary Materials. A 20 min extraction time was found to be optimal across all substrates. The SPME fiber exhibited logarithmic trendlines for the extraction of DMNB, 2E1H, and cyclohexanone, which are the expected volatiles of C4. The logarithmic trendline for DMNB suggests a rapid initial uptake by the SPME fiber, slowing as it approaches saturation, a behavior consistent with SPME fibers. Although 2E1H and cyclohexanone also followed logarithmic trendlines, their concentrations were much lower due to their lower volatility compared to DMNB. These differences underscore the importance of optimizing extraction times to ensure reliable results, leading to the adoption of a 20 min extraction time for all subsequent SPME testing to ensure consistent analyte recovery across different sample matrices.  
The extraction efficiency of volatiles from C4 was evaluated across substrates--cardboard, wood, cotton, glass, and metal--using 20 min SPME extraction times. Figure 5 compares peak areas of 1-BO with the C4 volatiles DMNB, cyclohexanone, and 2E1H. Cardboard showed the highest retention and release of volatiles, followed by wood, with cotton retaining significant DMNB and 2E1H but less cyclohexanone. The metal had large peak areas for 1-BO and DMNB but showed no 2E1H or cyclohexanone, while glass had the  
Figure 3: Comparison of 1-bromoetane peak areas obtained for wood, cardboard, sheet metal, and glass substrates.  
lowest retention overall. 1-BO consistently produced large peak areas, making it a reliable standard. DMNB was detected across all substrates, confirming its role as a key C4 volatile, whereas 2E1H and cyclohexanone were less consistent, particularly on non-porous metal and glass. The findings suggest that porous substrates like cardboard, wood, and cotton are more effective in retaining and releasing volatile compounds compared to non-porous substrates like glass and metal.
# Methods and Means of Eddy Current Testing of Soldered Lap Joints of Electrical Machines
## 2 Materials and Methods
### Description of Problems in Eddy Current Testing of Soldered Lap Joints  
In the process of testing the TO using eddy current methods, a range of various stray parameters had a significant influence on the results of the probe measurements.  
1. The mutual arrangement and relative offset of the ECP and TO: the dimensions of the ECP and TO are comparable, so the displacement of the ECP relative to the TO in any of the X, Y, and Z axes noticeably impacts the ECP signal.
2. The geometric parameters of the TO: The technological tolerance for the thickness of the tested copper busbars is 1 mm, respectively, and the tolerance for the thickness of the soldered joint is \(\pm\)2 mm. The measurement results of such joints are affected not only by the thickness of the conductive material in the TO but also by the gap between the TO and the installation plane of the ECP, exponentially reducing the  
Figure 2: Vector field of propagation of eddy currents in two-element tangential ECP in **(a)** TO without defects and **(b)** TO with defects: 1—TO; 2—defect location plane; 3—tangentially placed ECP coils; 4—current direction in ECP coils; 5—eddy current propagation lines in TO.  
penetration depth of eddy currents. Therefore, the existing geometrical tolerances of the TO impose significant limitations on the probe design [33].
3. Adjacent solder joints: In the frontal part of the stator winding of electrical machines, the spacing of the tested joints is dense. When one joint is tested, adjacent copper busbars affect the excitation electromagnetic field, resulting in distorted ECP readings [34; 35].
4. The electromagnetic parameters of the TO material: ECP signals are sensitive to variations in the electrical conductivity of the TO material. Therefore, a difference in the values of electrical conductivity for the upper and lower copper plates of the TO distorts ECP measurements. The variation in electrical conductivity in the considered application can be caused mainly for two reasons: a technological variation in the electrical conductivity of the upper and lower plates of the TO and temperature changes affecting the electrical conductivity value of the TO metal [36; 37; 38].
## 3 Methodology Description
### Numerical Approach
#### 3.2.3 Sloshing Force and Fluid Structure Interaction  
In order to control the tank's vertical motion, a second-order equation is derived from the application of [PERSON]'s second law to the solid mass \(m_{s}\). It is assumed that the forces that are applied to the tank come from three different components, in the same fashion as it has been proposed by [8]: the spring forces \(-Ky\), the vertical component of the sloshing force coming from the internal fluid action \(F_{slosh}\) and the vertical component of the damping force \(F_{D}=B_{0d}\cdot sign(dy/dt)-B_{1d}\cdot dy/dt\). The last component is assumed to include all sources of energy dissipation in the system that are not due to fluid slosh. In the present work, these are a Coulomb friction and a viscous friction term, respectively. The resulting equation reads:  
\[F_{slosh}-B_{0d}\cdot sign\left(\frac{dy}{dt}\right)-B_{1d}\cdot\frac{dy}{dt} -K\cdot y=m_{s}\frac{d^{2}y}{dt^{2}}\,, \tag{18}\]  
where \(B_{0d}=0.38\) N represents the Coulomb damping coefficient and \(B_{1d}=1.73\) kg/s is the viscous damping coefficient. Finally, the vertical sloshing force \(F_{slosh}\) is given, for a Newtonian fluid, by  
\[F_{slosh}=-\int_{S_{tank}}p\,\mathbf{n}\,dS+2\,\mu\int_{S_{tank}}\mathbb{D}\,\mathbf{n }\,dS \tag{19}\]  
The first term in Equation (19) represents the contribution of the pressure forces, and the second term the one from the viscous forces. \(S_{tank}\) corresponds to the tank's internal walls, and the normal \(\mathbf{n}\) is defined such that it is pointing outwards. These forces can be computed in SPH, according to the boundary integrals approach, on the boundary elements. The resulting expressions are:  
\[\begin{array}{rcl}F_{slosh}^{p}&=-&\sum_{i}\sum_{j\in BE}\left(p_{j}+p_{i} \right)\mathbf{n}_{j}\,W_{ij}\,V_{i}\,S_{j}\,,\\ F_{slosh}^{V}&=\mu&\sum_{i}\sum_{j\in BE}\left[\pi_{ij}\,\mathbf{n}_{j}+\frac{ \left(\mathbf{I}-\mathbf{n}_{j}\otimes\mathbf{n}_{j}\right)\,\mathbf{u}_{ji}}{\left|\mathbf{r}_{ ij}\cdot\mathbf{n}_{j}\right|}\right]W_{ij}\,V_{i}\,S_{j}\,.\end{array} \tag{20}\]  
The second term of the viscous force \(F_{slosh}^{V}\) is taken into account if no slip boundary conditions are enforced. A similar procedure to compute the forces can be found in [44].  
Then, forces obtained according to expressions in Equation (20) can be applied to Equation (18) to obtain the vertical motion of the tank.  
A fourth-order Runge-Kutta integrator (RK4) is used to obtain accelerations, velocities and positions at each time step. The resulting fields are then applied to the boundary elements in which the tank walls are discretized. This exchange of information is done at every time step. SPH integration algorithm implies much smaller time steps than the ones needed by the RK4 algorithm used for tank motion evolution, and this way it receives updated information every loop, assuring the stability of the coupled system.
# A Text-Generated Method to Joint Extraction of Entities and Relations
## 7 Conclusions  
In this paper, we propose to completely transform the entity-relation extraction task into the text generation task to solve the entity overlap problem in relation extraction. We use a pointer-based sequence-to-sequence framework to enable the model to copy words from the source text or to select words from the predefined vocabulary. We further analyze the extraction ability of our model on different degrees of entity overlap, and classify the sentences according to the different number of relations between two entities, and test the extraction effects of our model on these subclasses. We conduct experiments on the public datasets NYT10 and NYT11. The experimental results show that our method outperforms the baselines.  
Conceptualization, H.E. and S.X.; Data curation, H.E.; Formal analysis, H.E.; Funding acquisition, H.E.; Investigation, S.X.; Methodology, S.X.; Project administration, H.E.; Resources, H.E.; Software, H.E.; Supervision, H.E.; Validation, H.E., S.X. and M.S.; Visualization, H.E.; Writing--original draft, S.X.; Writing--review & editing, H.E., S.X. and M.S.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **1** & **2** & **3** & **>=4** & **Percentage** \\ \hline sentences containing **2** relation triples & 514 & 943 & - & - & 0.647 \\ sentences containing **3** relation triples & 210 & 72 & 182 & - & 0.547 \\ sentences containing **4** relation triples & 70 & 71 & 12 & 194 & **0.800** \\ \hline \hline \end{tabular} Bold numbers represent the largest proportion of entity pair overlap in the three cases.  
\end{table}
Table 4: Statistics Results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Input** & **Output of Our Model** \\ \hline [PERSON] is helping complete arrangements for & & \\ a family trip to **Houston**, **America**. & contains, America, Houston. \\ \hline You can take the train from many cities in **Italy to Lecce**, & & contains, Italy, Lecce. \\ which is about 45 min from **Otranto** by car. & & contains, Italy, Otranto. \\ \hline The real power at **Microsoft** resides with its longtime & & work\_in, [PERSON], Microsoft. \\ leaders—**[PERSON]**, the co-founder and chairman. & & founder, Microsoft, [PERSON]. \\ \hline
**Somerset County** has experienced disaster, with the crash & & contains, County, Quecreek \\ of flight and nine coal miners trapped at **Quecreek**. & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Extraction examples of our models. The first column in the table is the input of the model, and the second column is the corresponding sentences of the output of the model. As described in Section 3.1 above, multiple relational triples in the model output are separated by ’\(\cdot\)’ and ’\(\cdot\)’ separates relational words and two entities within each triple.  
**Funding:** This work was supported in part by the National Key R&D Program of China under Grant SQ2018 YFB140079.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
High temperatures and external force can easily lead to a decline in the bond strength of reinforced concrete components. Microbial-induced calcium carbonate precipitation (MICP) technology has considerable potential for repairing concrete. Given this, this study utilized MICP technology to improve the bond strength of heat- and pull-damaged lightweight aggregate concrete (LWAC). The specimens of a control group (Group A) and two experimental groups (Group B and Group C) were prepared. The experimental group was prepared using lightweight aggregates (LWAs) that had been immersed in a nutrient solution and a bacterial solution. The control group was prepared using LWAs that were not immersed in a nutrient solution or bacterial solution. These specimens healed themselves in different ways after exposure to high temperatures (300 \({}^{\circ}\)C and 500 \({}^{\circ}\)C) and pull-out damage. Groups A and B adopted the same self-healing method; that is, their specimens were placed in a computer-controlled incubator at 40 \({}^{\circ}\)C. Group C used different self-healing methods. The specimens in this group were soaked in a mixed solution of urea and calcium acetate at 40 \({}^{\circ}\)C for two days and then taken out and placed in an incubator at 40 \({}^{\circ}\)C for two days. A cycle took four days until the expected self-healing age was reached. After being exposed to 300 \({}^{\circ}\)C and self-healinged for 90 days, the residual bond strengths of the secondary pull-out tests in Groups A, B, and C were 20.63, 22.13, and 25.69 MPa, respectively. Moreover, compared with Group A, the relative bond strength ratios of the secondary pull-out tests in Groups B and C increased by 5.8% and 20.3%, respectively. This demonstrates that MICP technology could effectively improve the bond strength of LWAC after high-temperature and pull-out damage.  
microbial-induced calcium carbonate precipitation; heat damage; self-healing; pull-out test; bond-slip relationship +
Footnote †: journal: Applied Sciences