esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Content of Monitoring of Large-Segment Hoisting in the Large Cross-Sea Bridge
### The Main Work of Construction Monitoring  
Based on different construction states, construction monitoring can be divided into the preparation stage, prefabrication stage, installation stage, post-installation, and bridge operation and maintenance stage, as shown in Figure 2. On this basis, a perfect monitoring system has been established, combined with digital technology.  
Based on the structural configuration and construction technology characteristics of Xiang'an Bridge, the following four aspects of work will be carried out with the aim of construction control: (1) analysis of structure state during construction: determine the ideal construction parameters by comparing the structure state during construction with design calculations; (2) monitoring the key parameters in the structure during construction: monitor the deformations and stresses of the steel box girders; (3) analysis and adjustment of construction errors: analyze the linear errors during the installation of the steel box girders and adjust errors which exceed the control values; (4) prediction of subsequent construction state: predict the subsequent states of the bridges based on the current state and ensure the realization of the designed bridge state.  
\begin{table}
\begin{tabular}{c c c} \hline
**Bridge** & **Span Composition** & **Notes** \\ \hline Huandao East Road Interchange Main Line Bridge & 4 \(\times\) 90 m & Steel box girder with variable width \\ West Non-Navigable Channel Bridge 1 & 5 \(\times\) 90 m & Steel box girder with constant section \\ West Navigation Channel Bridge & 4 \(\times\) 90 m & Steel box girder with constant section \\ West Non-Navigable Channel Bridge 2 & 4 \(\times\) 90 m & Steel box girder with constant section \\ Middle Navigation Channel Bridge & 2 \(\times\) 90 + 2 \(\times\) 150 + 2 \(\times\) 90 m & Steel box girder with variable height \\ East Non-Navigable Channel Bridge 1 & 4 \(\times\) 90 m & Steel box girder with constant section \\ East Non-Navigable Channel Bridge 2 & 4 \(\times\) 90 m & Steel box girder with constant section \\ East Navigation Channel Bridge & 4 \(\times\) 90 m & Steel box girder with constant section \\ \hline \end{tabular}
\end{table}
Table 1: Overview of steel box girders in the sea of the large cross-sea bridge.  
Figure 2: Main work and digital monitoring system of the lifting construction monitoring of the large-segment steel box girder of the large cross-sea bridge.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis
### Effects of Hyperparameters of the Hybrid Loss Function  
The hyperparameters \(\lambda_{c}\) and \(\lambda_{r}\) in the hybrid loss function directly affect the performance of HCPL. We carry out OSR experiments on the Jamming-7 dataset using HCPL-CNN. When \(\lambda_{r}=0\), i.e., without using the prototype radius loss, the curves of the classification accuracy and AUROC with JNR under specific \(\lambda_{c}\) are shown as Figure 7a,b, respectively. When \(\lambda_{c}=0.3\), the curves of the classification accuracy and AUROC with JNR under specific \(\lambda_{r}\) are shown as Figure 7c,d, respectively.  
Figure 6: Two feature extractors that are used in HCPL: (**a**) FRNet and (**b**) CNN.  
It can be seen from Figure 7a,b that the rejection performance of the unknown classes is improved greatly as \(\lambda_{c}\) increases. However, when \(\lambda_{c}\) is too large (\(\lambda_{c}=1\)), the classification performance of the known classes at a low JNR, and the rejection performance of the unknown classes under various JNR decreases; this is because, when \(\lambda_{c}\) becomes larger, the model increasingly focuses on the intraclass compactness of the features and ignores the interclass separability of the features. Therefore, \(\lambda_{c}=0.3\) is chosen below.  
As shown in Figure 7c,d, the prototype radius loss does not affect the classification performance of the known classes, while the rejection performance of the unknown classes increases first and then decreases with the increase of \(\lambda_{r}\). Therefore, the prototype radius loss can improve the rejection performance of the unknown classes in HCPL, but when \(\lambda_{r}\) is too large, the prototype radius loss cannot converge on the optimal value, resulting in a decrease of the rejection performance of the unknown classes. Therefore, \(\lambda_{r}=0.1\) is chosen below.
# Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids
## 1 Introduction
In this study, a cooperative combination of the fractional order and SMC scheme is developed and implemented for the grid frequency control in a hybrid MG. The simulation study is accomplished on a complex MG, including various RESs, to indicate the significance of the suggested model-based FOSMC control theory. The classic methodologies, as mentioned earlier, may not be applicable in such circumstances to guarantee the stability of the hybrid power plant. The controller parameters are adjusted automatically by using an effective combination of the Sine Cosine algorithm and harmony search (SCA-HS). Designing the suggested control strategy is based on the model specifications of the components configured in the controlled MG, and thus the time domain design of such a scheme is very valuable. The proposed FOSMC scheme offers superior frequency regulation of MGs, which are composed of numerous DGs and RESs, in comparison with the MPC and conventional SMC approaches. Moreover, the proposed scheme has more robustness, to tackle more uncertainties than the above-mentioned approaches, making it more suitable for practical applications. For investigating the performance and robustness of the suggested model-based FOSMC technique, experimental validation using hardware-in-the-loop (HIL) simulations are also given in this paper.
## 2 Data Quality  
The observation data of 10 stations (CEDU, DARW, JFNG, MIZU, NNOR, PIMO, SIN1, USUD, YARR) from the MGEX network located in the Asia-Pacific region from day of year (DOY) 283 to 289 in 2021 are applied. The distribution of these stations is shown in Figure 1, and the latitude, longitude, receiver type, and antenna type of each station are listed in Table 2. These 10 stations are evenly distributed in different latitudes and longitudes around the Asia-Pacific region, and all frequencies of GPS, BDS-2, BDS-3, GLONASS, Galileo, and QZSS satellites can be received by these stations, which can better reflect the observation data quality and positioning performance of multi-GNSS in the Asia-Pacific region. The observation data quality was studied in the Asia-Pacific region in terms of two indicators: C/N0 and pseudorange multipath, in which the C/N0 can reflect the ability of the signal strength from satellite to receiver, whereas the impact of the satellite signal due to ground interference on the receiver can be reflected in pseudorange multipath. The C/N0 and pseudorange multipath are important indicators in the observation data quality assessment, and their performance directly affects the performance of PNT services.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Longitude** & **Latitude** & **Receiver Type** & **Antenna Type** \\ \hline CEDU & 133.81 & \(-\)30.13 & SEPT POLARX5 & AOAD/M\_T \\ DARW & 131.13 & \(-\)11.16 & SEPT POLARX5 & JAVRINGANT\_DM \\ JFNG & 114.49 & 30.52 & TRIMBLE ALLOY & TRM59800.00 \\ LAUT & 177.45 & \(-\)16.39 & SEPT POLARX5 & JAVRINGANT\_DM \\ MIZU & 141.13 & 39.14 & SEPT POLARX5 & SEPCHOKE\_B3E6 \\ NNOR & 116.19 & \(-\)30.95 & SEPT POLARXSTR & SEPCHOKE\_B3E6 \\ \hline \end{tabular}
\end{table}
Table 2: Receiver information.
## 1 Introduction
Although previous studies explored how drought affected the WUE at the global and point scales, the responses of regional WUE across different biomes and eco-climatic zones to drought were still not well understood. [PERSON] (1973) proposed the concept of system resilience to disturbances [27], which has been widely applied to terrestrial ecosystems in the past few decades [19; 28]. The terrestrial ecosystem resilience represents the ability of an ecosystem to absorb disturbances from hydro-climate (e.g., drought) and to sustain or recover its structure and functions with ever-changing environments [28]. A resilient ecosystem could sustain or increase WUE to e nsure its productivity under water shortage conditions due to drought [29]. [PERSON] et al. (2013) explored the relationship between WUE and drought during the drought in the early 21 st century and found that Australian and North American ecosystems had strong biome-scale resilience according to increased WUE in the dry year [19]. [PERSON] et al. (2017) examined ecosystem resilience at the river basin scale in India during 2000-2014 based on the MODIS (Moderate Resolution Imaging Spectroradiometer) dataset and indicated that most basins in India were not resilient to disturbances [29]. Recently, previous studies have explored the spatiotemporal changes in WUE and the resilience in China [30; 31], but how drought affected the WUE and the ecosystem resilience to drought in different biomes and nine sub-regions over China are not understood. In this study, the objectives are to (1) explore the spatiotemporal responses of WUE to drought over China during the period from 1982 to 2015 based on model tree ensemble-based evapotranspiration (MTE ET) and satellite-retrieved Global Land Surface Satellite (GLASS) Gross Primary Productivity (GPP) product; (2) assess the ecosystem resilience to drought in China at different scales (e.g., vegetation type and eco-climate regions). It will provide critical advice to the government for making reasonable ecosystem and water resources management strategies under natural and anthropogenic disturbances (e.g., drought).
## 4 Experimental Results
### Training Size  
Collecting enough data for creating location statistical fingerprints is the key to achieving good performance. As pointed out by [PERSON] [63], for a grid localisation system, 5-6 APs deployed strategically within the test area would be ideal and each location should have enough calibration samples (e.g., 200-300 samples). To evaluate the performance of the proposed system with smaller training samples, we chose different training sizes ranging from 10% to 100% of the collected training data.  
The plot in Figure 12 shows that, with 25% of measurements, the method can achieve the best performance with 98% accuracy in over 50% of the trials. Generally, all sample sizes are enough to train the MVGMM and can get over 97% matching accuracy for half of the trials. However, we also noticed in Figure 12 that the proposed algorithm is insensitive to the size of the training samples, even presenting more robust localisation accuracy to lower sample sizes. This result is similar to the analysis in the work of [PERSON] [63] who found that, given denser calibration samples for the area may introduce more noise to distinguish from other areas. [PERSON] et al. [64] also pointed out that, given larger training samples, it is unlikely that additional sampling will increase accuracy. The possible reason might be that larger training data contain more time-varying features and signal interference from the environment. We observe that approximately 15%-25% of training data per cell is sufficient to attain comparable level of accuracy.  
Figure 11: Kinematic Trajectory 2 repeated by Device 4 and 9.  
Figure 12: Matching accuracy in dependence of training size.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 4 Data Analysis Result
### Adjusted COST231  
Since the measurement was conducted in an outdoor-indoor environment, a considerable part of the propagation was outdoors. Therefore, a similar path loss exponent (\(n_{A231}\)) was added to the COST231 indoor model to account for the outdoor propagation losses. For the adjusted model, the constant loss was found as \(L_{c}=4.1\) dB, and path loss exponent \(n_{A231}=2.4\). Attenuation of the walls for both the COST231 and the adjusted model are depicted and compared in Figure 4. For the adjusted model, these attenuations are range from 1 dB to 8 dB.
# Effects of Epigallocatechin-3-O-Gallate on Bone Health
## 3 Biochemical Effects of EGCG
### Increasing Expression of the OSL
In _Xenopus_ embryos, the binding of BMP to the BMP receptor complex triggers three sequential phosphorylations in Smad1 that control the timing of the BMP signal [97]. BMP activity has also been shown to initiate transcription factor Sox9 expression as cell condensation takes place during embryonic skeletons [93,94,98]. What is more, BMP-2 induces the synthesis of OPG in human osteoblasts [99]. [PERSON] et al. studied whether the inhibition of angiogenesis enhances chondrogenesis and inhibits osteogenesis using EGCG [49]. Bone morphogenetic protein-induced osteo- and chondrogenesis was chosen as the experimental system to elucidate the biochemical mechanism of bone formation. In the study, a fibrous glass membrane (FGM) was used as a BMP carrier, mixed with 1.2 ug of rhBMP-2 and 1 or 10 ug of EGCG, and subcutaneously implanted in rats. The results showed that its supplementation increases cartilage formation and reduces bone formation. The FGM, coupled with BMP and implanted subcutaneously in rats, produced only cartilage in the membrane after 1 and 2 weeks. Increasing the dose of EGCG resulted in a decrease in the activity of alkaline phosphatase and calcium content, while it increased the content of type II collagen [49]. In numerous studies, the effect of EGCG on the expression of bone morphogenetic protein 2 has been investigated, consistently demonstrating its role in promoting osteogenesis (Table 4). In an in vivo model using ovariectomy-induced osteogenic rats, EGCG mitigated bone loss by improving bone microarchitecture parameters, including BMD and trabecular bone structure, while also upregulating BMP-2 synthesis in both the proximal tibia and lumbar spine [35]. In vitro, EGCG was shown to enhance BMP-2 expression in hBM-MSCs, particularly when combined with osteoinductive agents, thereby promoting osteogenic differentiation [31]. Similarly, in hMSCs, EGCG increased BMP-2 expression alongside RUNX2 and VEGF, contributing to accelerated osteogenic commitment [32]. Additional studies confirmed that EGCG upregulated BMP-2 mRNA levels in hBM-MSCs, along with markers such as RUNX2 and ALP, leading to enhanced mineralisation and osteogenic differentiation [41]. Furthermore, in a rat tibial fracture model, EGCG treatment increased BMP-2 expression and promoted callus formation, supporting its role in fracture healing [21].
## 3 Experimental Assessment of Mixed Metal Oxide Systems for TCES  
None of the test has led to a melting of any redox pairs in the single-metal oxide systems, and complete reduction was generally achieved. However, the re-oxidation rates were low for all compounds except cobalt oxide [7]. Since Co\({}_{3}\)O\({}_{4}\)/CoO and Mn\({}_{2}\)O\({}_{3}\)/Mn\({}_{3}\)O\({}_{4}\) are the two most promising materials for TCES, the potential of Co and Mn-based mixed oxides was investigated through the addition of a secondary metal, for example with copper [36; 41], iron [27; 41; 44; 35], or with one another [36; 45]. A summary of the experimental data obtained in the present work is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**TCES System**} & \multicolumn{2}{c}{**Onset Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Temperature**} & \multicolumn{2}{c}{**Conversion**} & \multirow{2}{*}{**Exp. Reaction**} & \multirow{2}{*}{**Phase Transition \&**} \\ \cline{2-2} \cline{5-6}  & **Reduction** & & & & & **Co(\({}^{\circ}\)C)** & & & **Enthalpy (k/kg)** & **Redev Couple** \\ \hline \(Co_{3}O_{4}\)/\(CoO\) & 909 & 843 & 66 & 95.6 & 597 & \(\mathbf{S}\) & \(\mathbf{M}\) \\  & 0.1 & 867 & 845 & 22 & 97.4 & 570 & \\  & 0.2 & 864 & 824 & 40 & 97.3 & 520 & \\ \(Co\)-Cu-O x(Cu) & 0.25 & 863 & 820 & 43 & 99.7 & 503 & \(\mathbf{S}\) & \(\mathbf{M}\) \\  & 0.3 & 864 & 813 & 51 & 97.5 & 436 & \(Co^{III}\) & \(\mathbf{\rightarrow}\) & \(Co^{II}\) \\  & 0.4 & 864 & 816 & 48 & 93 & 327 & \\  & 0.6 & 861 & 818 & 43 & 98.8 & 351 & \\  & 0.8 & 867 & 813 & 54 & 93.4 & 212 & \\ \hline \multirow{2}{*}{\(Co\)-Fe-O x(Fe)} & 0.05 & 921 & 848 & 73 & 84.4 & 454 & \\  & 0.1 & 931 & 896 & 35 & 83.9 & 365 & \(\mathbf{S}\) & \(\mathbf{M}\) \\  & 0.25 & 933 & 914 & 19 & 82.4 & 224 & \(Co^{III}\) & \(Co^{II}\) \\  & 0.4 & 941 & 945 & 4 & 82.5 & 51 & \\ \hline \multirow{2}{*}{\(Mn\)-Fe-O x(Co)} & 0.5 & 989 (Ar) & - & - & 64.2 & 133 & \\  & 0.6 & 942 (Ar) & - & - & 84.7 & 200 & \\ \multirow{2}{*}{\(Mn\)-Fe-O x(Co)} & 0.7 & 930 & 877 & 53 & 96.7 & 306 & \(\mathbf{S}\) & \(\mathbf{M}\) \\  & 0.8 & 933 & 873 & 60 & 96.6 & 296 & \(Co^{III}\) & \(Co^{II}\) \\  & 0.9 & 920 & 871 & 49 & 97.9 & 427 & \\  & 0.95 & 937 & 991 & 54 & 94.9 & 319 & \\ \hline \end{tabular}
\end{table}
Table 1: Experimental data obtained for the investigated oxide systems (with 20% O\({}_{2}\) in Ar), comprising onset temperatures, chemical conversions and reaction enthalpy (B = Bixbyite, C = Corundum, S = Spinel, H = Hausmannite, M = Monoxide, D = Delafossite).
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 4 Single Locale Experiments
### Method Comparison on Simulation Data  
We have made use of the simulation model of the sister project EpiPolicy [3]. The purpose of EpiPolicy is to help policymakers combat epidemics by allowing the policy makers to simulate different parameter settings in an SEIRS model and see the impact on death rates and other health indicators. In our use of EpiPolicy, we have separated the populations for each locale into three groups: children, adults, and seniors.  
In the SEIRS model, there are four main parameters: \(\beta\), \(\sigma\), \(\gamma\), and \(\eta\). They represent the rate of transitioning from susceptible to exposed (\(\beta\)), the rate of transitioning from exposed to infected (\(\sigma\)), the recovery rate (\(\gamma\)), and the rate of transitioning from the recovered state back to susceptible (\(\eta\)).  
In all of the EpiPolicy experiments, the values of \(\sigma\), \(\gamma\), and \(\eta\) are fixed at 0.1428, 0.1, and 0.1, respectively. The value of \(\sigma\) was chosen to be the reciprocal of 7, because, as noted above, seven days is a commonly seen duration of the incubation period in pandemics like COVID-19 [26].  
What varies in these models, based on population type, is \(\beta\). Because the number of daily contacts directly affects the value of the parameter \(\beta\) in the SEIRS model, the effect of different numbers of daily contacts was achieved by setting a different value of \(\beta\) for each age group. For this experiment, the value of \(\beta\) for children is 0.65. For adults, it is 0.75. For seniors, it is 0.5.  
Table 1 shows the Root Mean Squared Error of the results for forecasting values from 1 to 20.  
Figure 2 shows the predictions from the two best methods ARIMA and EpiInfer.  
Figure 3, by contrast, shows the relative Root Mean Squared Error, RMSE, which is the square root of the squared relative errors ( \(\left(\frac{\hat{q}-\underline{y}}{\hat{y}}\right)^{2}\), where \(\hat{y}\) is the prediction and \(y\) is the actual value) of the top two methods (EpiInfer and ARIMA) for days running between 11 and 70 days into the simulated pandemic. Predictions for each such day \(d\) are 1, 5, 10, and 20 days in the future (i.e., \(d+1\), \(d+5\), \(d+10\), \(d+20\)).  
Both ARIMA and EpiInfer have lower Root Mean Squared Errors (RMSE) than LSTM or differential equation models regardless of how far in the future to forecast. Table 1 shows that ARIMA is better for forecasts up to 7 days in the future after which EpiInfer is better.  
In Appendix A, we include comparisons with a graph neural network and a transformer model. Those methods perform similarly to LSTM and thus are inferior to both EpiInfer and ARIMA.
## 2 Data and Methods  
### Data  
The national land use datasets were sourced from the Chinese Academy of Sciences [47]. This institution produced high-quality land use datasets from 1980 to 2020, with a resolution of 1 km. Six land use classes have been identified from the datasets, including cropland, forests, grassland, water, construction land, and unused land. The remaining datasets required for the land use projection are the spatial driving factors, including climate and socioeconomic data from the Chinese Academy of Sciences, the elevation dataset from the Geographic and Spatial Data Cloud, and transportation and water system data from OpenStreetMap (Table 1). Furthermore, the technical framework of our research comprises two aspects: habitat quality assessment and land use projection (Figure 1).  
OpenStreetMap was selected due to three main advantages over China's official transportation data: First, its crowdsourced nature ensures a higher frequency of transportation information updates. Second, it offers comprehensive coverage of geographical features with detailed transportation networks. Third, its open-access nature allows unrestricted use of the data for research purposes.
###### Abstract  
In recent years, a growing interest has been shown in the use of microalgae due to their interesting nutritional and bioactive profiles. Green innovative processing technologies such as ultrasound-assisted extraction (UAE) avoid the use of toxic solvents and high temperatures, being a sustainable alternative in comparison with traditional extraction methods. The present study aims to evaluate the recovery of high added-value compounds from _Phaeodactylum tricornutum_ assisted by ultrasound. To optimize the UAE of proteins, carbohydrates, pigments and antioxidant compounds, a response surface methodology was used. Carbohydrate extraction was positively affected by the temperature. However, for the extraction of carotenoids, the most influential factor was the extraction time. The total polyphenols were only significantly affected by the extraction time. Finally, the antioxidant capacity, measured by 2,2\({}^{\prime}\)-azino-bis-3-ethylbenzothazoline-6-sulfonic acid (ABTS), was strongly modulated by the extraction time, while for the oxygen radical antioxidant capacity (ORAC) assay, the most important parameter was the temperature, followed by the extraction time. The optimal conditions for the maximum extraction of nutrients, bioactive compounds and antioxidant capacity were 30 min, 50 \({}^{\circ}\)C and a pH of 8.5. Finally, it has been seen that with these conditions, the extraction of fucoxanthin is allowed, although no differences were found between an ultrasound-assisted extraction and a shaking extraction (control).  
microlalgae; ultrasounds; bioactive compounds; nutrients; extraction; optimization; _Phaeodactylum tricornutum_