esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GPP, Reco, and NEE Fluxes
The highest daily NEE rates occurred in summer months after cutting, and they were close to 11 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\) (11.92 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\) at the HWL, 11.63 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\) at the HWL, Si, 11.75 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\) at the LWL), except for the LWL\({}_{\text{Si}}\) plot, where the highest NEE fluxes reached up to 18.96 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\). What is, however, more important, is that the daily rates of NEE fluxes by most of the years were negative, which signifies the predominance of CO\({}_{2}\) assimilation processes over emissions and indicates periods when the meadow was a net sink for CO\({}_{2}\) from the atmosphere. The most negative NEE flux rates were observed in spring 2022 (Figure 3c), particularly for HWL\({}_{\text{-Si}}\), where they reached \(-\)24.28 gCO\({}_{2}\)-C\(\cdot\)m\({}^{-2}\)\(\cdot\)day\({}^{-1}\). The results of the Spearman correlation matrix carried out for the daily rates of CO\({}_{2}\) fluxes (GPP, Reco, NEE) and environmental variables (SM, TA, WTD) for all experimental plots and for both years (2021, 2022) are presented in the supplementary materials (Figures S1 and S2). Reco correlated most strongly with TA for all treatments in both years. The correlation coefficient for these regressions is between 0.89 and 0.91 for 2021 and between 0.77 and 0.84 for 2022. Correlation between Reco and WTD is negative, with correlation coefficients ranging from \(-\)0.50 to \(-\)0.59 and from \(-\)0.56 to \(-\)0.67 in 2021 and 2022, respectively. Correlations between Reco and SM were negative and rather weak, with R from \(-\)0.34 to \(-\)0.37 in 2021 and from \(-\)0.51 to \(-\)0.66 in 2022. GPP was negatively correlated with TA and positively with WTD and SM. Although correlation coefficients for GPP vs. TA regressions ranged from \(-\)0.59 to \(-\)0.63 in 2021, in 2022 they were negligible. In contrast, for GPP vs. WTD, the correlation coefficient ranged from 0.28 to 0.38 in 2021 and from 0.29 to 0.46 in 2022. For GPP vs. SM regressions, R values were at a similar rate. The differences in correlations between 2021 and 2022 are most likely due to different meteorological conditions in both years.
# Feasibility Study on Earthquake Prediction Based on Imending Geomagnetic Anomalies
## 4 Discussion
### The Reproducibility  
After the deployment of the sensor network in 2019, using the algorithm proposed in the article, the data from 2020, 2021, 2022, and 2023 all showed the same pattern and prediction results for geomagnetic anomalies.
# Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices
## 3 Methods and Results  
Many previous studies on the influencing factors of house price, neglect the analysis of the multilevel structure of house price, and most scholars use the traditional OLS or hedonic house price model. As shown in the figure above, there is a problem of spatial autocorrelation on the spatial distribution of house prices. The house prices of Ottawa hills are generally high, while those of Toledo are generally low. In addition to the ascending trace level, Lucas County has 11 school districts. The structural characteristics and accessibility of houses vary by district.  
The characteristics of the same district are somewhat similar because they will be affected by the same public policies, such as land-use zoning. Therefore, the house price data is hierarchical, the house price is nested in the city, and the effective statistical method of nested data is the multilevel modeling method (MLM). The essence of the hierarchical method is separating the variance associated with each level, and then explaining the variance accordingly. As described previously, this paper distinguishes three levels: single houses (level one) belong to census tract (level two), which are nested in school districts (level three).
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 2 The Reliability Evaluation of PR Data by Comparisons with Ground-Based Instruments  
In order to provide accurate rainfall measurements, PR needed to be evaluated or calibrated before the data can be used in further applications with confidence. A variety of studies have been carried out to address the issue through cross comparisons of PR and ground-based instruments. A fundamental difficulty with this sort of research is that all the measurement equipment including rain gauges and ground-based radars have different sampling spaces and time resolutions as well as their own systematic errors.  
In fact, before the cross comparison could be performed, it should be considered that there are objective factors affecting PR measurements. First of all, attenuation correction should be taken into account for PR since it worked at the Ku band suffering extensively from rain attenuation [18; 19]. Secondly, the non-uniform beam filling (NUBF) needed to be considered [18; 20; 21]. Additionally, non-Rayleigh scattering might frequently occur for PR because it operated at a short wavelength, inducing reflectivity differences compared with GR, particularly for rain containing large or solid drops. This difference needed to be corrected before the comparison of PR and GR [22; 23; 24]. Some of these issues had already been addressed in the products of PR data, such as corrections of attenuation and NUBF, but might be not satisfying.
## 3 Results Analysis  
### Full Life Cycle Stage Division  
This paper divides assembled M&E systems into five stages: planning and design [8], prefabrication and processing [9], installation and construction [10], operation and maintenance [6], and dismantling and reuse [11], to fine-tune the study of the impact of assembled M&E systems on future development.  
Planning and Design Stage: This stage determines the system requirements and design options. The planning and design ensure that the system can meet the desired requirements and objectives during installation and use.  
Prefabrication and machining stage: In this stage, the components and parts of the electromechanical system are prefabricated and machined according to the design plan. By advancing some of the work to the factory, quality and efficiency can be improved, and the time and labor requirements during on-site construction can be reduced.  
Installation construction stage: In this stage, prefabricated and processed components and parts are delivered to the site, assembled, and installed. By installing the prefabricated components on site, construction time and work risks can be reduced, and the quality and efficiency of the installation can be improved.  
Operation and maintenance Stage: The M&E system is formally put into operation and undergoes routine operation and maintenance. Through regular maintenance and monitoring, the system's stable operation can be ensured, and possible faults and problems can be dealt with on time.  
Figure 2: Number of papers: 1993–2024. Source: Created by authors.  
Dismantling and reuse stage: At the end of the system's life cycle, the electromechanical system needs to be dismantled and disposed of. Rational dismantling and reclamation can reduce the environmental impact, and the recovery and utilization of waste and resources can be maximized.  
The five stages help manage and control the entire lifecycle of an assembled MEP system to ensure that it operates efficiently, reliably, and in harmony with the environment. With the five research priorities identified through the stages, this paper will refine the workflow further.
###### Abstract  
Monitoring volumetric soil water content (\(\theta_{v}\)) is the key for assessing water availability and nutrient fluxes. This study evaluated the empirical accuracy of \(\theta_{v}\) measurements using standard and in situ calibrated frequency domain reflectometers (FDR) with gravimetric water content and bulk density measurements of 1512 samples gathered from 15 profiles across 5 ICP Forests level II intensive monitoring plots. The predicted \(\theta_{v}\), calibrated with standard functions, predominantly underestimated the real water content. The measurement error exceeded the threshold of 0.03 m\({}^{3}\) m\({}^{-3}\) in 93% of all soil layers. Layer specific calibration removed bias and reduced the overall prediction error with a factor up to 2.8. A simple linear regression often provided the best calibration model; temperature correction was helpful in specific cases. To adequately remove bias in our study plots, a calibration dataset of up to 24 monthly observations was required for topsoils (whereas 12 observations sufficed for subsoils). Based on estimated precision errors, 3 sensors per soil layer proved to be sufficient, while up to 16 sensors are needed to meet the required accuracy in organic topsoils. Validating FDR sensor outputs using in situ gravimetric measurements is essential for quality control and assurance of long term \(\theta_{v}\) monitoring and for improving site specific instrumentalization.  
Keywords:soil moisture; frequency domain reflectometer; forest soils; calibration; ICP Forests; soil sensors; long-term ecosystem monitoring +
Footnote †: journal: Applied Sci.
# Temperature Effect on Stability of Tunnel Face Under Unsaturated Seepage Condition
## 5 Conclusions and Discussion  
This study has developed an effective framework for integrating unsaturated seepage conditions and temperature effects to perform a stability analysis of tunnel faces. The strength's expression of unsaturated soil, when subjected to temperature fluctuations, is extended through the application of the generalized effective stress principle. The computation of internal dissipated energy and the rate of external work is anchored in the 3D logarithmic spiral failure model. The safety factor is derived by integrating the strength reduction method with a methodical iteration approach. The impact of certain parameters is discussed, with the principle outcomes summarized below.  
1. The safety factors of tunnel faces vary considerably among different soil types when subjected to the combined effects of unsaturated seepage and temperature variations, with the safety factors generally following the order silt > clay > sand; the sensitivity to temperature changes is in the order silt > clay > sand; and the sensitivity to changes in seepage rate is in the order clay > silt > sand.
2. For smaller values of \(\alpha\), \(n\), and \(k_{\rm s}\), the influence of unsaturated seepage is more pronounced. In particular, soils with a smaller \(k_{\rm s}\) value will exhibit a high sensitivity to the flow rate of seepage; meanwhile, soils with a bigger absolute value of enthalpy per unit area \(\Delta h\) are more affected by temperature. Sand has a relatively large \(\alpha\), \(n\), and \(k_{\rm s}\), and the absolute value of \(\Delta h\) is relatively small, so that is why sand is minimally affected by temperature and unsaturated seepage condition.
3. The tunnel face's safety factor is observed to decrease appreciably as the temperature increases. The maximum safety factors for both clay and silt are observed under conditions of high evaporation while sand remains virtually unchanged. In general, the impact of temperature on the stability of the tunnel face is far less than that of unsaturated seepage effects.
4. The findings suggest that considering the impacts of unsaturated soil seepage and temperature can signally enhance the tunnel face's safety factor. The approach presented in this study can be utilized as a precedent for tunnel construction and design processes.  
Methodology, Y.X.; resources, D.Z. and H.L.; date curation, Y.X.; writing--original draft, Y.X.; writing--review and editing, D.Z. and H.L.; supervision, D.Z. and H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 5: FS versus temperature for different q and temperature, with \(\rm D=10\) m, \(\rm C=20\) m: (a) \(\rm T=10\)\({}^{\circ}\)C; (b) \(\rm T=25\)\({}^{\circ}\)C; (c) \(\rm T=40\)\({}^{\circ}\)C.  
Data Availability Statement: The analysis data are available from the corresponding author on reasonable request. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 3 Spoofing Detection Method
### Threshold and Spoofing Judgment  
In the context of satellite navigation signals, binary hypothesis theory was employed to detect spoofing [29]. The null hypothesis (\(H_{0}\)) posits that there is no spoofing signal, whereas the alternative hypothesis (\(H_{1}\)) asserts that there is such a signal. A comparison was made between the height of the eye diagram \(E_{h}\) and the threshold \(\theta\) to determine whether spoofing is present. In accordance with Equation (3), the correlation result is influenced by the power of the real signal and the noise in the absence of spoofing. As previously stated, the correlation value obeys a Gaussian distribution. Under the null hypothesis \(H_{0}\), the mean value of the correlation value is \(\mu\), and the variance is \(\sigma^{2}\). Consequently, the false alarm rate \(P_{fa}\) is related to the threshold \(\theta\) as follows:  
\[\begin{split}& P_{fa}=\rho(|E_{h}|>\theta|H_{0})\\ &=\int_{\theta}^{\infty}\frac{1}{\sqrt{2\pi\delta}}e^{-\frac{(x- \mu)^{2}}{2\delta^{2}}}\,dx\end{split} \tag{15}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **ODS-P** & **ODS-R** & **ODS-F** \\ \hline Log & 99.9\% & 74.3\% & 0.852 \\ [PERSON] & 99.9\% & 67.5\% & 0.806 \\ Sobel & 99.9\% & 66.9\% & 0.801 \\ Zero Crossing & 99.9\% & 74.3\% & 0.852 \\ Canny & 99.8\% & 81.6\% & 0.898 \\ MSC-MMP & 98.7\% & 95.4\% & 0.970 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics for edge detection results.  
when \(t=\frac{\pi-\mu}{\delta}\), Equation (18) becomes  
\[\begin{split}& P_{fa}=\rho(|E_{h}|>\ \theta|H_{0})\\ &=\int_{\theta-\mu}^{\infty}\frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}} {2}}\ dt\\ &=\text{Q}\Big{(}\frac{\theta-\mu}{\delta}\Big{)}\\ &=\frac{1}{2}\text{erfc}\Big{(}\frac{\theta-\mu}{\sqrt{2\delta}} \Big{)}\end{split} \tag{16}\]  
where \(Q(x)=\int_{x}^{\infty}\frac{1}{\sqrt{2\pi}}e^{-\frac{t^{2}}{2}}\ dt\) is the right-tail function of the standard normal distribution. \(erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-t^{2}}dt\) is the complementary error function. The expression for the threshold \(\theta\) can be obtained as  
\[\theta=\sqrt{2}erfc^{-1}(2P_{fa})+\mu \tag{17}\]  
Consequently, the corresponding threshold can be calculated from the set false alarm rate. In the context of spoofing detection, the objective is to determine whether a spoofing signal is present by comparing the value measured by the metric (with the eye diagram measurement \(E_{h}\) serving as an illustrative example) with its threshold B. This can be expressed as follows:  
\[H=\left\{\begin{array}{cc}H_{1}\,&|E_{h}|>\theta\\ H_{0}\,&\text{others}\end{array}\right. \tag{18}\]  
If \(E_{h}\) exceeds \(\theta\), it is determined that there is a spoofing signal, and, otherwise, no spoofing signal is found.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 3 Related Technologies  
### Cnn  
CNN [35], a deep neural network, contains convolutional operations. Meanwhile, CNN is a representative algorithm of deep learning. It uses a convolution kernel to capture local features and has the ability of representational learning. Depending on the dimension of the input matrix, CNN can be divided into 1D-CNN and 2D-CNN, etc. Processing text usually uses 1D-CNN. The length of convolution kernel is always equal to the feature dimension of the text vector representation, whereas the width of convolution kernel determines the window size of the contextual words or characters. In practical applications, multiple convolution kernels of different widths are typically utilized in order to obtain various receptive fields and augment the feature information extracted by the model. The convolution kernels are shifted in a certain step size according to the text sequence length to obtain the feature vector representation of the text sequence. After the convolution layer, the top pooling layer or the average pooling layer is usually connected to choose features and decrease the number of parameters. For features after pooling, classification and other operations are conducted through the entire connection layer, as shown in Figure 1.  
Figure 1: Illustration of CNN (Redrawn from [35]).  
CNN has strong feature capturing capability, nevertheless, when processing text data, the convolution kernel can only pay attention to the local information in the window and lacks the learning of long-distance information, As a result, overall understanding to the text is lacking. Thus, CNN usually handles text-classification tasks rather than sequence-annotation tasks. When combined with other neural network structures, CNN can be used as a feature extractor to enhance the ability of feature representation. For example, when processing Chinese text, CNN can be used to extract stroke features or glyphs of Chinese characters.
# Mode Choice Effects on Bike Sharing Systems
## 1 Introduction  
The first bike-sharing systems (BSS) were introduced around five decades ago. Over the past two to three decades, the number of BSS has increased, and such systems are nowadays available in many cities around the globe. It is, however, surprising, that parameters for neither BSS-related mode nor route choices are currently available. This results in a lack of knowledge of behavioural patterns. Amongst other purposes, such parameters are needed for the implementation of BSS as a transport mode in transport demand models and to calculate, e.g., modal shifts.  
To estimate such parameters, a survey study was conducted in the field in Germany and collected information on mode- and route choices from around 220 participants in an existing station-based BSS. This BSS was introduced in 2012, and is located in the Rhine-Neckar area in mid-west Germany, including 20 municipalities in total; 4 of them are major cities (Mannheim, Ludwigshafen, Heidelberg, Kaiserslautern), 11 are mid-size municipalities and 5 are smaller towns. The survey study combined computer-assisted telephone interviews (CATI) for a collection of revealed preferences (RP) on the use of BSS with a follow-up paper-and-pencil survey on stated preferences (SP) for BSS users and non-users. The choice experiment considers the three transport modes: BSS, PT, and PMT.  
This effort resulted in a rich data set, which allows an analysis of behavioural patterns in terms of BSS-related mode and route choices and a quantification of the needed parameters; with that, the study closes a knowledge gap and allows the implementation of station-based BSS in local or regional transport demand models and according to simulations with these tools.  
The paper focuses on mode choices exclusively. Section 2 presents results from literature analysis and aims to identify attributes of relevance for choices pro or against BSS-use. Section 3 introduces the study area and an existing BSS, that was used to recruit survey participants and collect information on BSS use. Furthermore, the recruitment strategy, survey protocol, and experimental design for the choice experiment are presented. Next, descriptive statistics on respondents' socio-demographics and their choices are provided in Section 4 together with the model formulation. Section 5 presents results on model development and the final multinomial logit models on choices between the alternatives BSS, public transport (PT), and private motorized transport (PMT). The models are estimated for mandatory and leisure trips and model results are accompanied by interpretations. Finally, conclusions and an outlook on future research are drawn in Section 6.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 4 Analysis of Link Connectivity and Probability of Coverage
### Topological Connection Condition  
We define the probability that two SUs can topologically connect each other by \(p_{top}\), which can be expressed as  
\[p_{top}=\mathbb{P}[SNR_{dB}\geq\delta_{s}], \tag{1}\]  
where \(SNR_{dB}\) is the signal-to-noise ratio in dB between a pair of SUs, and \(\delta_{s}\) is the threshold that a receiver can successfully received information.  
The signal-to-noise ratio between a pair of SUs, denoted by \(SNR\), can be expressed as follows:  
\[SNR=\frac{P_{s}}{\int\limits_{B}A(r,f)df\int\limits_{B}N(f)df}, \tag{2}\]where \(B\) represents the bandwidth used in UCANs. Since we are concerned with the relationship between frequency \(f\) and \(SNR\), we normalize the bandwidth. Thus, Equation (2) can be simplified to  
\[SNR=\frac{P_{s}}{A(r,f)N(f)}. \tag{3}\]  
Then, \(SNR_{dB}\) can be expressed as  
\[SNR_{dB}=10\log P_{s}-10\log A(r,f)-10\log N(f). \tag{4}\]  
Combining Equation (4) and Equation (A2) (as defined in Appendix A), we can have \(p_{top}\) as follows,  
\[p_{top}=\mathbb{P}[k\cdot 10\log r+r\cdot 10\log\alpha(f)+10\log N(f)\leq 10 \log P_{s}-\delta_{s}]. \tag{5}\]  
We can see that the left-hand-side (LHS) of the inequality in Equation (5) is an increasing function of the distance \(r\). If we let LHS of the inequality be equal to RHS, we can have the _maximum communication distance_\(r_{\max}\). However, since the inequality is a transcendental function of \(r\), it is impossible to get a closed-form expression of \(r_{\max}\). Therefore, we calculate the numerical results of \(r_{\max}\). After setting \(\delta_{s}=20\) dB [29] and \(P_{p}=110\) dB, we obtain the numerical results of \(r_{\max}\) with different frequency \(f\), spreading factor \(k\), and wind speed \(w\), as shown in Figure 2. From Figure 2, we can see that \(r_{\max}\) varies with \(f\). In particular, a higher value of \(f\) leads to a lower value of \(r_{\max}\). Moreover, a higher wind speed \(w\) results in a lower \(r_{\max}\). This is because a higher wind speed brings higher noise according to the underwater acoustic channel model. Furthermore, if we compare Figure 1(a) with Figure 1(b), we can find that \(r_{\max}\) decreases with the increased spreading factor \(k\).  
According to the above analysis, we have \(p_{top}\) given as follows,  
\[p_{top}=\left\{\begin{array}{ll}1&r\leq r_{\max}\\ 0&r>r_{\max}\end{array}.\right. \tag{6}\]
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 4 Types of Persisters  
### Type I or Triggered Persisters  
Type I persisters are a subpopulation of bacterial cells that emerge during the stationary phase, typically triggered by environmental stress. These cells enter a reversible, dormant state that allows them to survive under harsh conditions, including exposure to antimicrobials [19]. The study of this type of persisters was conducted by [PERSON] et al. [15] using _E. coli_; the authors linked _E. coli_ persistence to inherent population heterogeneity. For type I persisters, the authors investigated _hipA7_ mutants and observed that, following antibiotic exposure and subsequent removal, persister cells initially in growth arrest could switch to actively dividing cells and repopulate the environment with antibiotic-sensitive microorganisms. Consequently, the authors defined type I persisters in _E. coli_ as a preexisting subpopulation of non-growing cells generated during the stationary phase. Type I persisters may also be explained through bet-hedging theory, which posits that maintaining different phenotypes within a population increases survival chances when unfavorable conditions arise [20].