esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Cyclic Mechanical Behaviors of Undisturbed Soft Clay with Different Degrees of Reconsolidation
## 2 Test Samples and Experimental Program
### Experimental Equipment  
The test instrument adopted in this study is the dynamic triaxial test system (DYNTTS, GDS, UK). Its main components include an axial loading system, cell-pressure controller, back-pressure controller, data-acquisition system, and measuring and controlling system (Figure 2). The whole test system is controlled by GDSLAB software, which allows automatic data acquisition and processing and plotting of experimental data. By changing the base and the triaxial tensile sample cap, static and dynamic tests of the samples with diameters of 38, 50, 70, and 100 mm can be conducted. In dynamic tests, not only can regular waveforms such as a sine wave, half-sine wave, and triangular wave be applied, but user-defined waveforms can be applied. The maximum allowable frequency in such experiments is 5 Hz, the maximum load capacity is 10 kN, and the maximum radial pressure is 2 MPa.  
After the post-cyclic undrained triaxial shear tests, the microscopic tests were conducted by a Quanta250 scanning electron microscope analysis system. A focused electron beam was used to scan and image the specimen surface point-by-point. The imaging signal may contain secondary, back-scattered, or absorbed electrons. The current emitted by the electron gun excites the specimen surface, producing secondary electrons. The instrument can allow microscopic characterization of the surface morphology of the specimens.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index Property** & **Mean Value** \\ \hline Natural density, \(\rho\) (g/cm\({}^{3}\)) & 1.67 \\ Coefficient of uniformity, \(C_{u}\) & 15.32 \\ Coefficient of curvature, \(C_{c}\) & 0.71 \\ Free swelling ratio, \(\delta_{\varphi}\) (\%) & 9 \\ Natural water content, \(w\) (\%) & 53.7 \\ Natural void ratio, \(e\) & 1.52 \\ Specific gravity, \(G_{s}\) & 2.71 \\ Liquid limit, \(w_{L}\) (\%) & 50.2 \\ Plasticity limit, \(w_{P}\) (\%) & 23.7 \\ Plasticity index, \(I_{P}\) (\%) & 27.2 \\ Coefficient of permeability, \(K\) (10\({}^{-7}\) cm/s) & 9.80 \\ Coefficient of compressibility, \(a_{\varphi}\) (MPa\({}^{-1}\)) & 0.82 \\ Coefficient of static lateral pressure, \(K_{0}\) & 0.44 \\ Cohesion, \(c\) (kPa) & 22.7 \\ Friction angle, \(\varphi\) (\({}^{\circ}\)) & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physico-mechanical indices of the tested soft clay.  
Figure 1: Particle size distribution of the tested soil.
## 3 Results
### Results for Refinement Factors \(r\), \(r_{mod}\), and \(r_{\text{exp}}\)  
For the ODE test problems in Section 3.1, smooth step-size sequences produced tolerance proportionality in the sequential case. The MGRIT procedure, together with (5) from the open-source XBraid library [30], was used with the Bogacki-Shampine method to take into account its stability region.  
The tolerance proportionality results can be seen in Figures 4-6. In Figures 4-6, we plot the step-size sequence for each test problem. A reasonable error tolerance proportionality is observed, as the step size on average scales in accordance with the specified error tolerance. At the finest tolerance, \(\textsc{tol}=10^{-11}\), the MGRIT-based step-size sequence is also close to that of the sequential integration for the same tolerance.  
However, in every test case, a zigzag pattern appears in the step-size sequence, which contrasts with the smooth step-size sequence expected in classical sequential implementation theory [7]. This effect arises since the refinement factor is highly sensitive to small values due to the ceiling function. Pointwise tolerance proportionality therefore suffers, and it is unlikely that the overall method is as efficient as it could be, since local errors are far from equidistributed.  
Figure 4: Step-size sequences (\(y\)-axis) for different TOL values in the sequential and MGRIT-based adaptive methods over the given time intervals (\(x\)-axis) for the linear two-compartment dilution process problem (6).
## 3 Preliminaries
### Oblivious Pseudorandom Function  
[PERSON] et al. [36] introduced the oblivious pseudorandom function (OPRF). The sender privately chooses a key k. OPRF allows the receiver to combine its own input information \(x\) with the sender's key \(k\), which is converted into the corresponding number after a series of operations, and the receiver can learn \(F_{k}(x)\). In this process, the receiver cannot know the receiver's k, and the sender does not know the final result \(F_{k}(x)\). Eachinput \(x_{i}\) can be calculated differently from the other inputs, and these numbers can then be considered as pseudorandom numbers.  
[PERSON] et al. [37] proposed an OPRF protocol based on the Diffie-Hellman assumption. It computes \(OPRF_{k}(x)=H^{\prime}(H(x)^{k})\), where H is a random prediction function in the range of values in the group \(\mathbb{Z}_{q}^{*}\). Let \(G\) be a cyclic group of order \(q\). The sender samples the key \(k\) from \(\mathbb{Z}_{q}^{*}\). Then, the receiver inputs \(x\in\left\{0,1\right\}^{*}\). The receiver first randomly chooses \(k^{\prime}\leftarrow\mathbb{Z}_{q}^{*}\), and then sends \(H(x)^{k^{\prime}}\) to the sender, who replies \(\left(H(x)^{k^{\prime}}\right)^{k}\) to the receiver. The receiver can output \(H^{\prime}\Big{(}H(x)^{k}\Big{)}\), where \(H^{\prime}\) is used to map the group elements to a sufficiently long bit string.
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 3 Materials and Methods
### Data Source and Preprocessing
#### 3.1.2 Data Preprocessing  
The data of the Chinese Meteorological Stations were obtained from the ground meteorological stations. Usually, the data of ground weather stations are considered reliable, but errors, such as missing data and some data exceeding the actual value, are inevitable. To ensure the accuracy and rationality of the experimental data, it is necessary to preprocess the site's original meteorological data.  
The preprocess starts with data cleaning, including removing meteorological factors such as evaporation that were severely missing, filling in the missing data, and calculating the averages of the 14 days for each week before and after the abnormal values to modify the values. Second, data standardization was implemented by Min-Max standardization to normalize the data and convert them into dimensionless evaluation values. Then, based on the gray correlation analysis (GRA) method, correlation attributes with a correlation degree greater than 0.88 are selected. These include average temperature, average humidity, sunshine duration, precipitation, average wind speed, maximum temperature, average pressure, and date. Finally, data reconstruction was performed using a time-sliding window. In this study, we predict the average temperature and set a time-sliding window to reconstruct the meteorological time series. For example, taking the setting value of the time sliding window as 2, the meteorological factors of days t-1 and t-2 are input into the model. Then, the outputs are the temperatures of day t. In order, the original data are slid backward by 1 and 2 cells, respectively, and the slid meteorological factors together form the new inputs. Figure 1 illustrates the workflow of data preprocessing.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 4 Conclusion  
A rectangular notched band UWB antenna with controllable bandwidth and frequency with an overall compact size of \(16\times 25\times 1.52\) mm\({}^{3}\) is presented. The antenna is fabricated on the TLY 5A (\(\varepsilon_{\text{r}}\)=2.17) substrate. The proposed antenna covers the entire UWB spectrum from 3.1 to 12.5 GHz. The UWB characteristic of the antenna is achieved by truncating the lower ends of the patch. The conventional notch is obtained by using a single EBG structure, and a rectangular notch at the WLAN band is realized by using two EBG structures with optimized parameters, which are laid on the back of the substrate and connected to the patch by shorting pins. The results showed that the notch band and frequency can be tuned by changing the EBG parameters. The measurement results of the fabricated antennas are well-matched to the simulation results.  
Investigation, K.S.S. and T.K.; Project administration, N.K.; Writing--original draft, A.A.; Writing--review & editing, N.H., M.-J.J. and J.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the ICT R&D Program of MSIT/IITP, Republic of Korea. [No. 2019-0-00102, A Study on Public Health and Safety in a Complex EMF Environment].  
The authors declare no conflict of interest.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 5 Proposed Solution
### Tools and Technologies
#### Database Management  
* **PostgreSQL** was utilized as the relational database in both models, extending conventional SQL with object-oriented capabilities and advanced functionality for data storage and manipulation. It supports complex data types including JSON, arrays, and key-value pairs, while also allowing the definition of custom types and functions [54].
* **Hibernate/JPA** is applied in the asynchronous model, providing a conventional blocking approach for data access and manipulation, object-relational mapping, and database schema generation, which allows for emphasis on business logic [55,56].
* **R2 DBC** provides streamlined connectivity and non-blocking interactions with the database, representing a specification that provides a reactive driver for PostgreSQL, integrates, and is even preferred in the development of fully reactive applications [9].
# Development of a Hybrid Electric Vehicle Simulation Tool with a Rule-Based Topology
## 1 Introduction
The bigger the battery, and consequently the electric range, the lower the impact of the ICE on the weighted CO\({}_{2}\) emission. Nevertheless, highly efficient ICEs are still important, as they guarantee high long-range efficiency when the battery is fully depleted. One issue of the current legislation regarding PHEV is that electricity use under real driving is much lower, and hence more CO\({}_{2}\) is emitted. New vehicles have to be equipped with \"on-board fuel consumption measurement\" since 2020 to determine the fuel consumption and hence the CO\({}_{2}\) emission in the field. The results might lead to adaptions to the utility factor if the difference in fuel consumption between certified value and street measurements is too large. If so, the advantage of PHEV with respect to CO\({}_{2}\) might decrease in such way that \"Not-Off-Vehicle Charging Hybrid Electric Vehicles\" (NOVC-HEVs) with smaller batteries and highly efficient ICEs become more attractive in terms of CO\({}_{2}\) reduction potential. The HEV simulation model is very important in the proper design and verification of control strategies for hybrid power plants. The vehicle simulation can be used to address the technical position of different vehicles and to estimate different scenarios of the future trend of the automotive industry. Many researchers have used different hybrid tools like GT-SUITE, AMESIM, AUTONOMIE, GREET, etc., as common vehicle simulators, and most automobile companies have their own software [5; 6] to evaluate emerging vehicle technologies and find cost-competitive solutions that minimize fuel consumption and emissions. However, most hybrid simulation tools do not share their principles and control logic algorithms in the open literature. Users have limited access in terms of modifying the control logic and hardware variation. With this motivation, many researchers have developed their own hybrid simulation models with different principles and control logic algorithms [7; 8; 9; 10; 11]. Control topologies for HEVs are algorithms that choose the power split between the ICE and electric motors (EMs) of an HEV in order to minimize the fuel consumption and emissions. There have been several proposals for the HEV control strategies in the open literature, e.g., [PERSON] et al. using heuristics [12], and dynamic programs by [PERSON] et al. [13] and [PERSON] et al. [14]. These approaches require a huge amount of time to obtain solutions and are highly complex. To avoid this significant drawback, a simple control strategy is necessary based on an optimal control theory for different hybrid architectures. In this paper, the author has developed an HEV simulation tool with such a rule-based topology. The approach described here is based on optimal control knowledge, and it can be easily applied to all types of vehicle simulation arrangements. The major advantage of this tool is enhanced flexibility to choose different control theories and energy management strategies, enabling the user to explore a wide range of hybrid topologies.
# The Emergence of Antibioticstics Resistance Genes, Bacteria, and Micropollutants in Grey Wastewater
## 4 Irrigation Spiked with EMPs
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Micropollutant Exposure** & **Methodology** & **Effect Exposure** & **Reference** \\ \hline Triclosan & Soil microcosms irrigated with synthetic GW, supplemented with 2.0 \(\upmu\)g/mL TCS & - & ARB amount increased - & [135] \\  & Biolog ECO plates were used with soil from a rice paddy. The soil was spiked with six different concentrations (0, 0.1, 1, 10, 30, 50 mg/kg), sealed inside a plastic bag, incubated at 28 \({}^{\circ}\)C in the dark, and read every 24 h over 7 days. & - & no adverse effects on the functional diversity of the soil microbial community - Shannon’s diversity index increased \\  & & - & no change in the abundance of bacterial communities - no change in the abundance of - no change in the abundance of - ammonium-oxidizing bacteria & [137] \\ A mixture of 14 pharmaceuticals experiment & Irrigation with wastewater spiked at 10 and 100 \(\upmu\)g/L in a controlled greenhouse experiment & - & no change in the abundance of ammonium-oxidizing bacteria & [138] \\  & & - & no impact on sulfamethoxazole-resistant and -degrading bacteria - limited effect on the bacterial evenness & \\ Triclosan & Soil exposed to TCS at concentrations of 0, 1, 5, 10, 50, and 100 mg/kg of soil & - & minimal effect on enzyme activity - no effect on general microbial activity & [138] \\  & Freshwater microbes exposed to TCS, alone or together with three different micropollutants & - & _Plantomyces_ and _Actinobacteria_ (more sensitive bacteria) were replaced by _Gammaproteobacteria_ and _Bacteroides_ (more tolerant) & [139] \\  & Soil irrigated long-term with wastewater compared to soil irrigation with groundwater & - & significant reduction of bacterial abundance & [130] \\ Triclosan & Soil microcosms exposed to different concentrations of TCS & - & significant reduction in soil microbial biomass & [140] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The effect of EMPs on the soil microbiome. microcosms to different concentrations of TCS changed several soil characteristics; some of them were more sensitive than others. Therefore, in order to measure soil health, more than one parameter should be measured. A significant reduction in soil microbial biomass has been detected for all levels of TCS exposure. TWW-irrigated soils have been found to have residual levels of EMPs, but currently available methodologies cannot link these EMPs to the soil microbial activity [137].
###### Abstract
[PERSON] pointed out that the destructive deformation of weak crushed perimeter rock shows rheological behavior and thought that no big deformation damage is produced in the early stage of excavation but that the crushed perimeter rock would be expanded by creep, and the tunnel would have a big bottom bulge a few years later [8]. [PERSON] et al. proposed the mechanical theory of equal-strength support for deep coal mine roadways by analyzing and summarizing the development history of deep roadway support methods domestically and abroad [9]. [PERSON] et al. used numerical simulation and theoretical analysis to derive the development law of a plastic zone of non-equal pressure in a coal roadway under broken surrounding rock, and they proposed the joint support of anchor + net + rope + spray [10]. [PERSON] et al. conducted an in-depth study on the mechanisms and conditions of rock fragmentation [11; 12; 13]. In a large cross-section roadway of broken surrounding rock, anchor rods and anchor cable support are more researched [14; 15]. Compared with anchor rods, anchor cable support, and other active support, U-shaped steel support has high tensile strength and compressive strength, a simple structure, and strong support capacity, and it is easy to install and not easy to deform; it is more widely used [16]. In engineering applications, it is used in wall-filling technology for fully closed stents, although the bearing capacity of U-shaped steel support can be greatly improved [17; 18; 19]. However, the bearing capacity of U-shaped steel support is restricted by different load conditions, support structure, and other conditions. If only relying on its passive support, the self-bearing capacity of deep complete surrounding rock is difficult to manage, and it will still cause the support to be destroyed. In summary, research on the rock characteristics of broken surrounding rock is more indepth [20; 21; 22; 23]. The deformation mechanism [24; 25], characteristics of broken surrounding rock [26; 27], and support theory for large-section broken surrounding rock roadways [28; 29] are also constantly discussed. However, for broken zones formed after years of roadway excavation, different characteristics of the stress field in a roadway's surrounding rock are presented. Therefore, with PingDingShan TianAn Coal Co., Ltd. No. 6 Mine's new digging belt inclined shaft (North Xinxin Street, Xinhua District, Pingdingshan, China) as the engineering background, and through mechanical analysis, numerical simulation experiments, on-site monitoring, and other methods to study and analyze the deformation and damage in the large cross-section of a roadway's broken peripheral rock, this paper puts forward the \"U-beam + anti-bottom arch + bottom arch interlocking beams + bottom plate anchor compensation\" joint support program, which provides a good solution for the control of broken peripheral rock. At the same time, it also provides a new support scheme for the broken surrounding rock of the roadway.
## 3 Results and Discussion
### Curing Reaction Mechanism for the Healing Agent  
In the previous section, it was revealed that when the MC120D content was 20% and the BGE content was 12.5-20%, there were two exothermic peaks at around 150 and 250 \({}^{\circ}\)C, respectively. When the BGE content was 17.5%, and the MC120D content was 10% and 20%, there were two exothermic peaks, near 150 and 250 \({}^{\circ}\)C, respectively. Instead, there was only one exothermic peak at about 150 \({}^{\circ}\)C when the content was over 30%. In order to further elucidate the mechanism of the differential thermal profile of each component, the epoxy resin cured product was analyzed. The infrared spectrum of epoxy resin E-51 displayed in Figure 6A reveals the presence of the absorption peaks at 771 cm\({}^{-1}\) for epoxy, at 1184 cm\({}^{-1}\) for C-N stretching vibration, at 1247 and 1035 cm\({}^{-1}\) for ether bond stretching vibrations, at 1361 cm\({}^{-1}\) for the bismuth symmetric vibration of bisphenol A, at 1508, 1583, 1607, and 831 cm\({}^{-1}\) for the vibrations of the benzene ring, at 2869 cm\({}^{-1}\) for the methylene stretching vibration, at 2928 cm\({}^{-1}\) for the C-H stretching vibration in CH\({}_{2}\), at 2967 cm\({}^{-1}\) for the C-H antisymmetric stretching vibration peak in CH\({}_{3}\) and at 3482 cm\({}^{-1}\) for the hydroxyl group [34]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MC120D Fraction (\%)** & \(\beta\) **(K-min\({}^{-1}\))** & \(T_{i}\) **(K)** & \(T_{P}\) **(K)** & \(T_{f}\) **(K)** & **A (I/s)** & \(k\) \\ \hline \multirow{4}{*}{10} & 5 & 351.9 & 416.9 & 446.9 & \(7.87\times 10^{5}\) & 0.18 \\  & 10 & 356.1 & 436.1 & 456.1 & \(7.35\times 10^{5}\) & 0.33 \\  & 15 & 367.9 & 442.9 & 467.9 & \(8.53\times 10^{5}\) & 0.49 \\  & 20 & 378.9 & 453.9 & 478.9 & \(7.65\times 10^{5}\) & 0.62 \\ \hline \multirow{4}{*}{20} & 5 & 345.3 & 408.5 & 448.5 & \(1.59\times 10^{5}\) & 0.17 \\  & 10 & 363.1 & 425.3 & 470.3 & \(1.70\times 10^{5}\) & 0.31 \\  & 15 & 363.1 & 438.1 & 478.1 & \(1.64\times 10^{5}\) & 0.44 \\  & 20 & 372.7 & 447.7 & 482.7 & \(1.59\times 10^{5}\) & 0.56 \\ \hline \multirow{4}{*}{30} & 5 & 346.6 & 404.2 & 439.2 & \(7.02\times 10^{6}\) & 0.21 \\  & 10 & 361.1 & 419.6 & 469.6 & \(6.90\times 10^{6}\) & 0.40 \\  & 15 & 371.1 & 426.6 & 486.6 & \(7.63\times 10^{6}\) & 0.58 \\  & 20 & 376.1 & 436.1 & 526.1 & \(6.80\times 10^{6}\) & 0.74 \\ \hline \multirow{4}{*}{40} & 5 & 344.1 & 399.1 & 434.1 & \(2.62\times 10^{6}\) & 0.20 \\  & 10 & 370.3 & 415.3 & 470.3 & \(2.56\times 10^{6}\) & 0.38 \\  & 15 & 355.0 & 425.0 & 485.0 & \(2.55\times 10^{6}\) & 0.54 \\  & 20 & 371.1 & 431.1 & 511.1 & \(2.66\times 10^{6}\) & 0.70 \\ \hline \multirow{4}{*}{50} & 5 & 350.0 & 400.1 & 430.1 & \(6.70\times 10^{8}\) & 0.27 \\  & 10 & 364.5 & 409.5 & 464.5 & \(7.77\times 10^{8}\) & 0.52 \\ \cline{1-1}  & 15 & 362.7 & 417.7 & 482.7 & \(7.41\times 10^{8}\) & 0.74 \\ \cline{1-1}  & 20 & 375.0 & 425.0 & 495.0 & \(6.69\times 10^{8}\) & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Kinetic parameters of curing reaction with different dosages of MC120D.
## 4 Discussion
The accuracy of MODIS GPP is largely determined by the accuracy of the LUE model and input parameters [48; 49]. Therefore, we suggest improving the LUE model or controlling the quality of input parameters so that they have a higher precision. In recent years, more and more researchers are exploring the wider application of SIF data of different vegetation types on the ecosystem scale [19; 50; 51]. It is a simple and effective method to use data of EC flux tower to verify the accuracy of their simulation results. However, EC flux towersare not evenly distributed on the globe, and data of most flux towers are not available to the public. Therefore, verification and use of SIF data is limited. What's more, the problem that the range of the observation of the flux tower does not match the extraction range of the SIF data seriously affected the accuracy of using SIF to estimate GPP. Ideally, we believe that the type of vegetation around the flux towers is homogeneous and single. Yet that is not the case. In general, we believe that the smaller the extraction range of SIF data around the flux tower, the higher the accuracy of the result may be. [PERSON] et al. [23] extracted 2 \(\times\) 2 km SIF data around tow flux towers and GPP data proved that OCO-2 SIF has a significant linear relationship with GPP. [PERSON] et al. [50] extracted 10 km SIF data around flux towers found that a strong correlation between GPP and SIF. [PERSON] et al. [26] and [PERSON] et al. [15] made the evaluation of OCO-2 SIF using tower GPP in corn and grassland ecosystems within a radius of 10 to 25 km. Therefore, the extraction range of SIF varies for different research purposes. However, if the extraction range of SIF data around the flux towers is too large, the more vegetation types may be included and the higher pixel heterogeneity of SIF data will be. This will eventually affect the accuracy of results. Therefore, we calculated max(LC) to ensure the accuracy of the this study. With the launch of the OCO-3 (orbiting carbon observatory 3) satellite and the TROPOMI (TROPOSpheric Monitoring Instrument) satellite with finer resolution [52]. An increasing amount of flux tower data are also public. The mismatch between extraction range of satellite's SIF data and observation range of EC flux towers may be better solved in the future.