esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Inversion Experiments and Results
### Inversion of Synthetic Data
Through combining several different types of forward models, we have successfully provided the CNN inversion algorithm with a rich and diverse set of training data, enabling it to cope with a variety of complex geological situations. This training sample generation strategy not only improves the generalization ability of the algorithm but also lays a solid foundation for its application in real geological exploration. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Type** & \multicolumn{2}{c}{**Simple Model**} & \multicolumn{2}{c}{**Smooth Model**} \\ \hline
**Inversion Method** & **CNN** & **Regularization** & **CNN** & **Regularization** \\ \hline Model (a) & 0.284387 & 0.365304 & 0.087168 & 0.136331 \\ Model (b) & 0.259875 & 0.584361 & 0.086077 & 0.245371 \\ Model (c) & 0.120663 & 0.334701 & 0.105825 & 0.161201 \\ Model (d) & 0.294869 & 0.528861 & 0.118433 & 0.253228 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The RMSE (log(S/m)) of inversion results for simple models and smooth models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Model (a)**} & \multicolumn{2}{c}{**Model (b)**} & \multicolumn{2}{c}{**Model (c)**} & \multicolumn{2}{c}{**Model (d)**} \\ \cline{2-9}  & **T (m)** & \(\sigma\) **(S/m)** & **T (m)** & \(\sigma\) **(S/m)** & **T (m)** & \(\sigma\) **(S/m)** & **T (m)** & \(\sigma\) **(S/m)** \\ \hline
1 & 60 & 0.01 & 60 & 0.1 & 50 & 0.005 & 50 & 0.1 \\
2 & 70 & 0.1 & 70 & 0.002 & 50 & 0.1 & 50 & 0.005 \\
3 & \(\infty\) & 0.002 & \(\infty\) & 0.05 & 50 & 0.01 & 50 & 0.2 \\
4 & - & - & - & - & \(\infty\) & 0.1 & \(\infty\) & 0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The settings of thickness (T) and conductivity (\(\sigma\)) for each layer of four simple models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & \multicolumn{2}{c}{**Model (a)**} & \multicolumn{2}{c}{**Model (b)**} & \multicolumn{2}{c}{**Model (c)**} & \multicolumn{2}{c}{**Model (d)**} \\ \cline{2-9}  & **D (m)** & \(\sigma\) **(S/m)** & **D(m)** & \(\sigma\) **(S/m)** & **D (m)** & \(\sigma\) **(S/m)** & **D (m)** & \(\sigma\) **(S/m)** \\ \hline
1 & 30 & 0.01 & 30 & 0.1 & 25 & 0.005 & 25 & 0.1 \\
2 & 95 & 0.1 & 95 & 0.002 & 75 & 0.1 & 75 &Figure 4: The CNN inversion and regularization inversion results of four smooth models. (**a**) inversion results of model (**a**) in Table 3; (**b**) inversion results of model (**b**) in Table 3; (**c**) inversion results of model (**c**) in Table 3; (**d**) inversion results of model (**d**) in Table 3.  
Figure 3: The CNN inversion and regularization inversion results of four simple models. (**a**) inversion results of model (**a**) in Table 2; (**b**) inversion results of model (**b**) in Table 2; (**c**) inversion results of model (**c**) in Table 2; (**d**) inversion results of model (**d**) in Table 2.
## 3 Results and Discussion
### Effect of Nutrient Concentration on Pollutant Retention by Bioretention Systems  
Figure 11a,b shows the TP and NH4+-N removal efficiency rates of bioretention systems for normal and extreme events with the recurrence interval of the rainfall being 1a. In general, influent stormwater with higher nutrient concentrations would consequently result in higher concentrations in  
Figure 10: The efficiency of TP removal using bioretention systems with the Control column, DS column, and SZ column during different antecedent dry periods.  
Figure 9: The efficiency of TN removal using bioretention systems with the (**a**) Control column, (**b**) DS column, and (**c**) SZ column during different antecedent dry periods.  
the bioretention system's effluent [44]. In this study, the TP removal efficiency was found to increase in higher concentrations (extreme event) for both the Control and SZ columns. A higher phosphorus load means a larger concentration gradient, which in turn can promote the adsorption process between pollutants and media in the solution given the greater availability of chelate sites on the media.  
The extent of phosphorus removal is markedly influenced by the ratio of phosphorus in the solution (i.e., DP) and the adsorbed phosphorus [45]. Similarly, for the NO3-N removal, all columns exhibited better removal efficiency for high nutrient concentration compared with the normal concentration. This suggests that the bioretention system with a saturated area could still possess sufficient treatment capacity to handle the excessive concentration of nutrients, at least within a short period of time.  
Figure 11c,d compares the NH4+-N and TN removal efficiency rates of different bioretention systems for standard and extreme concentrations. For NH4+-N, the removal efficiency in the high nutrient concentration (extreme event) was higher than that of a normal event, especially for the column with the saturated zone (>85%). Similar observations were found for the TN removal in extreme events, where the columns with the saturated zone exhibited a higher removal efficiency of over 55%. According to the study by [PERSON] et al. [46], the removal ratio of TN in inflows with high nutrient concentrations was about 40%, which was higher compared to low nutrient inflows. Note that the improved bioretention in the SZ column (combines the saturated zone) could enable the simultaneous removal of phosphorus and nitrogen from stormwater runoff.
## 7 Conclusions  
In conclusion, we have developed an all-optical 4-bit parity generator and checker that operates at a speed of 120 Gb/s RZ data. This accomplishment was enabled by integrating a core logic module utilizing CR-SOA-based MZI configured as XOR gates. Notably, depending on the binary value of a specific bit, our circuit can serve both as a parity generator and a checker, supporting both even and odd parity configurations. In evaluating its performance, we conducted thorough simulation analyses employing numerical modeling techniques, with a focus on critical operational parameters that affect the QF metric. The proposed scheme attains impressive QF values across all functions: 32.72 for the even parity generator, 12.35 for the odd parity generator, 35.13 for the even parity checker, and 15.76 for the odd parity checker. Our investigations highlighted the significant impact of the CR-SOA's transition lifetime from CR to AR, injection current, repetition rate, and CW input power. Unlike the situation with a single XOR gate, optimizing these parameters is essential to achieving a high QF. By adhering to these technologically feasible design principles, our circuit can be fine-tuned to operate with logical accuracy and exceptional efficiency.  
Conceptualization, A.K.; Data curation, A.K.; Formal analysis, A.K.; Funding acquisition, A.K.; Investigation, A.K. and K.E.Z.; Methodology, A.K.; Project administration, A.K.; Resources, A.K.; Software, A.K.; Supervision, C.G. and W.C.; Writing--original draft, A.K.; Writing--review and editing, K.E.Z., C.G. and W.C. All authors have read and agreed to the published version of the manuscript.  
[PERSON] and [PERSON] extend their sincere gratitude to the Xi'an Jiaotong-Liverpool University Research Development Fund (RDF-23-02-001) and the Department of Education in Jiangsu Province (EFP10120240023) for their invaluable support. Data are contained within the article.  
The authors declare no conflicts of interest.
# Electrospun Porous PDLLA Fiber Membrane Coated with nHA
## 3 Results and Discussion
### Morphology of Cell Proliferation on PDLLA/nHA Composite Fibrous Membrane  
Figure 9 shows the SEM images of cells growing on non-porous PDLLA fibrous membrane (Control group) and PDLLA/nHA composite fibrous membrane (nHA particles content 10.6 wt %). Chondrocytes from rabbit could adhere to, spread and penetrate both the PDLLA and PDLLA/nHA composite fibrous scaffolds in 24 h, suggesting that the scaffolds were non-toxic. However, an undifferentiated mass formed by chondrocytes randomly dividing on the PDLLA/nHA composite fibrous scaffold was significantly larger and flatter than that on the non-porous PDLLA fiber membranes after three days. Cultured seven days later, the area of tissue pieces was further enlarged, and the surface of PDLLA/nHA composite fibrous scaffold was almost completely covered by chondrocytes. Correspondingly, morphology of the tissue block on the non-porous PDLLA fiber mats showed no remarkable change from day three to day seven. This indicates that the nHA particles  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **nHA (wt \%)** & **Young’s Modulus (MPa)** & **Tensile Strength (MPa)** \\ \hline a & 11.2 & 20.19 & 3.24 \\ b & 10.6 & 26.30 & 3.28 \\ c & 8.8 & 28.19 & 3.40 \\ d & 5.5 & 31.79 & 4.21 \\ e & 4.1 & 33.34 & 5.56 \\ Control & 0 & 84.81 & 6.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tensile strength and modulus of PDLLA/nHA composite fibrous membranes.  
Figure 8: Tensile test of PDLLA/nHA composite fibrous membranes.  
on the fibers surface of PDLLA/nHA composite scaffold could promote the adhesion and proliferation of chondrocytes from rabbits [44].  
Immunofluorescence combined with CLSM observation further showed that both PDLLA and PDLLA/nHA composite fibrous scaffolds could support chondrocytes adhesion and proliferation (Figure 10). Viability of chondrocytes cultured on two kinds of fibrous scaffolds for 24 h revealed the same cell density. After three days, chondrocytes on PDLLA/nHA composite fibrous scaffold displayed higher proliferative capability (Figure 10B-3 days), which indicated that composite scaffold could enhance proliferation of chondrocytes. On the contrary, chondrocytes proliferation on PDLLA fibrous scaffold showed a non-uniform cell distribution (Figure 10A-3 days). Cultured 7 days later, the composite fibrous scaffold was significantly improved the proliferation of the chondrocytes, which show PDLLA/nHA composite fibrous scaffold could more benefit for chondrocytes proliferation.  
Figure 10: CLSM of chondrocytes proliferation on (**A**) non-porous PDLLA fibrous membrane; (**B**) PDLLA/nHA composite fibrous membrane. Scale bars represent 100 \(\upmu\)m.  
Figure 9: SEM of chondrocyte proliferation on (**A**) non-porous PDLLA fibrous membrane; (**B**) PDLLA/nHA composite fibrous membrane. Scale bars represent 10 \(\upmu\)m.
## 3 An Adaptive Attitude Estimation Method Based on Multi-Sensor Fusion
### Attitude Estimation Based on Kalman Filter  
The accuracy of the angular attitude calculated by the accelerometer is influenced by the maneuvering acceleration in motion. In addition, the yaw angle cannot be calculated by the output data of the accelerometer. Therefore, two attitude estimation algorithms are designed based on Kalman filter.  
For the roll angle \(\gamma\) and pitch angle \(\theta\), if the STD of the acceleration output data calculated in the current period is greater than the threshold of the corresponding axis, that is the influence of maneuvering acceleration cannot be ignored and for the yaw angle \(\psi\), the angular attitude is calculated by SLAIM. If the STD of the output data of accelerometer calculated in the current period is less than the threshold of the corresponding axis, that is the influence of the maneuvering acceleration can be ignored, the angular attitude is calculated by DFBGA.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
###### Abstract  
Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions--all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998-2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.  
Keywords:gridded precipitation products; GSMaP; SWAT; CMORPH; TRMM; CPC; Mekong River Basin; water balance components +
Footnote †: journal: Remote Sensing, 2020, 12, 1858; doi:10.3390/rs12111858
## 5 Results and Discussion
### Water Surface Estimation  
Figure 11 shows the relationship between surface area (in km\({}^{2}\)) extracted from SLSTR images in the NIR band during the period from June 2016 to July 2019, and: (i) radar altimetry, which is called a \"calibration dataset\" that represents the relationship between area and level in meters via a quadratic polynomial equation with correlation coefficient equal to 0.81 and (ii) in situ water levels. Validation of the model results was made with the aid of in situ water levels in blue points which called \"validation dataset\". The accuracy of the model is evaluated using simple statistical parameters, such as the coefficient of determination R\({}^{2}\), which represents the relationship between the AHDL area estimated unsupervised method in Section 4.2 and between the altimeter level. The second parameter used normalized root mean square errors (NRMSE) to measure errors of this simple regression model, as shown in (Figure 12). The curves represent the changes of area over time, the blue curve represents surface area estimated by the model based on in situ measured water level with NRMSE equal to 0.168, the red curve represents surface area estimated by the model based on Sentinel-3 altimetry water level with NRMSE equal to 0.282. Water surface area was estimated by the unsupervised method of Sentinel-3 optical images stimulated by green curve during the period time. These values give us confidence in the extract water surface areas from this simple regression model and the optical satellite images with Sentinel-3 of 300 m resolution. [PERSON] [39] used this simple regression model to describe the relation between surface area and level for Klabsha and Allaqi Khors with a second order polynomial equation and resulting correlation coefficient from the equation equal to 0.99. [PERSON] [26] used radar altimetry technique with radiometer imagery over AHD Lake's specific area-level relationship for the periods from 1999 to 2002 and 2005 to 2009 was found with correlation coefficient R\({}^{2}\) = 0.9985.
## 3 Results and Discussion
### Simulation Results of the Calibrated Model
#### 3.2.4 Potential Energy Savings of the Different Ventilation Scenarios  
The calibrated model was used to simulate the energy demand of the surgical suite during the year 2019 under the different scenarios described in Section 2.2.3.  
Figure 16 represents the monthly heating energy demand of the surgical suite during current operation mode (COM), which corresponds to Scenario 0 in Section 2.2.3, compared to the monthly heating energy demand during off-use operation mode (OUM), which corresponds to Scenario 1 in Section 2.2.3. This demand corresponds to energy savings always above 50%. It is worth highlighting the values obtained in the winter months where these savings are above 20 MWh per month. In summer, the savings represent a higher percentage but, in comparison, thermal energy is much lower. In a complementary way, Figure 17 represents the monthly cooling energy demand of the surgical suite during current operation mode (COM) and off-use operation mode (OUM). Although cooling savings are also important, especially during the months of July and August, the thermal values are much lower due to the meteorological conditions at the hospital location. Finally, Figure 18 gathers the total monthly heating and cooling energy savings of the surgical suite, from which the importance of the decrease in thermal heating energy during the summer months can also be appreciated.  
Figure 16: Monthly heating energy demand of the surgical suite during current operation mode (COM) and off-use operation mode (OUM).  
Figure 15: Time required to reach the setpoint temperature.  
Finally, Figure 19 represents the potential energy demands and savings of the ORs and the auxiliary rooms, according to the three ventilation scenarios described in Section 2.2.3. Obviously, energy savings depend mainly on the ACH and the percentage of the outdoor fresh air intake. In any room and with any of the new scenarios, savings are kept within the range of 50% to 70%. Specifically, in ORs the savings are greater in the third scenario, exceeding 65% of savings. This is explained because it is the scenario that allows higher recirculation rates, particularly 80%, with just 20% of fresh air intake.  
Figure 17: Monthly cooling energy demand of the surgical suite during COM and OUM.  
Figure 18: Monthly heating and cooling energy savings of the surgical suite using OUM.
## 3 Materials and Methods
### Water Supply Estimation  
The SWAT model was applied to estimate water supply (water yield) between 2020 and 2029 under dry and wet year scenarios. The SWAT model is a basin-scale, continuous-time model that operates on the basis of daily data and is designed to predict the impact of management on water, sediment, and agricultural chemical yields in ungauged watersheds. The model is physically based, computationally efficient, and capable of continuous simulation over long periods. The major model components include weather, hydrology, soil temperature and properties, plant growth, nutrients, pesticides, bacteria and pathogens, and land management [32; 33; 34].
## 4 Experimental Results and Analysis
### Structured Light Experiment System  
The experiment was performed for simulation calculations in MATLAB 2018 software on a 64-bit Windows system. The experimental measurement setup includes a projector (Richon PJX2180, Coretronic, made in Jiangsu, China), a camera (Basler acA4600-7 gc, Basler, made in Germany CEFC), a computer, a displacement platform, a calibration board, and the object under test. The camera and the projector are responsible for projecting and capturing the structured light patterns. The calibration board is used for calibration [39], and the displacement platform is used to adjust the distance between the measured object and the camera and projector. Figure 11 shows the experimental environment and instruments used in the experiment.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 4 Proposed Methods  
### Work Objectives and Hypothesis  
To address the imbalance between time consumption and detection performance observed in previous work, the primary goal of this study is to develop a lightweight intrusion detection method capable of effectively detecting common injection attacks in vehicular networks. Achieving a balance between detection performance and resource efficiency is of utmost importance. In this paper, the Hamming distance method and the sliding window technique are employed to extract the temporal sequence features of CAN network data streams for detecting common and highly impactful injection attacks. This approach can be seamlessly integrated into the CAN network as an additional device or deployed directly on an existing ECU.  
Similar to previous works, we assume the attacker can already physically access the CAN bus via the OBD-II port or via Bluetooth, WiFi, etc., and the attacker has some knowledge of some CAN bus protocol contents.
## 2 Materials and Methods  
### Literature Search and Screening  
Two systematic literature searches (Figure S1, Supplementary Materials) were conducted by following the PRISMA protocol [10]. The searches were conducted without restricting publication date or type of publication, using the ISI Web of Science Core Collection, Google Scholar, China National Knowledge Infrastructure, Elsevier Scopus and Science Direct platforms, and the Sustainable Wetlands Adaptation and Mitigation Program (SWAMP) databases. The following keywords and phrases were searched:'mangrove','mangrove swamp','mangrove forest','mangrove wetland','mangal', 'primary production', forest production', 'production','respiration', 'canopy respiration', 'ecosystem respiration', 'gross primary production', 'net primary production', 'tree', 'litterfall', 'wood production','stem production', 'root production', algae', microalgae', microhydrobethon's,'macrolalgae', 'cyanobacteria', 'field-based radiation based model', 'growth curve' 'growth increment', 'aboveground', 'belowground', 'global distribution','remote sensing','modelling', 'light attenuation', 'eddy covariance', 'ECV', 'MODIS', 'NIRv', and 'gas exchange'. Pre-1980 references or data in peer-reviewed publications were also taken into consideration.  
In total, 3871 articles were identified, of which 3668 were discarded after not meeting at least one of the following criteria: (1) contains sufficient detail to judge whether methodology and level of replication were appropriate and based on published studies; (2) quantitative data were only from either empirical field or modelled studies with mangrove species clearly stated; (3) a clear description of prior land use or forest modification, if any; and (4) data of at least one environmental factor are also presented. After 101 additional articles were excluded for several reasons (e.g., inability to download or unsuccessful data acquisition from graphed data), 203 articles (see References) were finally accepted for meta-analysis.