esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a new atmospheric-correcting approach over bright and stable land surface, such as desert and the Gobi, for most of the MHSR imagery is developed. The major contributions of the new method include five aspects: (i) it takes consideration of the BRDF effect, so it can be applied to various MHSR remotely sensed imageries, especially for the Chinese MHSR imageries with very wide swath; (ii) it is specifically developed for bright and stable land surfaces; (iii) it is completely automatic; (iv) the deep blue band is not required, which is more applicable for most of the MHSR data; and (v) due to the relatively even distribution of the aerosol in the arid areas, the reduced spatial resolution of the retrieved AOD is accurate enough to capture the actual AOD distribution and greatly increases the efficiency. For the spatial expansion method [15], the initial AOD retrieved from the images in arid areas is rarely successful, so the spatial expansion procedure may fail or needs to be done iteratively, which is very time consuming; thus, the proposed algorithm will be an effective complement for the spatial expansion method in this situation. The derived AOD at four different locations around the world is validated with AERONET measurements at four sites (Fowlers_Gap, Mezaira, Tamanrasset_INM, and Dalanzadgad), which indicates that AOD estimated using the proposed method agrees very well with the AOD from the AERONET ground measurements. The AOD retrieved from Landsat/OLI is the best among the three different sensors validated, which probably benefits from its nadir viewing design.  
[PERSON] developed the algorithm and wrote the paper. [PERSON] wrote the program and produced the AOD from imagery. [PERSON] collected and processed the remote-sensing data. [PERSON] compared the BRDF models. [PERSON] made the thematic maps of atmospheric correction. [PERSON] validated the results. [PERSON] and [PERSON] provided the Chinese Satellite data and did some preprocessing. [PERSON] reviewed the manuscript and provided valuable suggestions. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the GF6 Project under Grant 30-Y20A02-9003-17/18 and the National Key R&D Program of China under Grant under Grant 2018 YFA0605500 and 2017 YFA0603000.  
The GF1/WFV imagery used in this research is supported by the China Centre for Resources Satellite Data and Application ([[http://www.cresda.com](http://www.cresda.com)]([http://www.cresda.com](http://www.cresda.com))). The Landsat@OLI data were downloaded from the USGS website ([[http://landsat.usgs.gov](http://landsat.usgs.gov)]([http://landsat.usgs.gov](http://landsat.usgs.gov))). The Sentinel2 MSI data were downloaded from the ESA website ([[https://earth.esa.int](https://earth.esa.int)]([https://earth.esa.int](https://earth.esa.int))). The AERONET data were downloaded from GSFC/NASA. The authors sincerely thank the reviewers who provided helpful comments to the manuscript.  
The authors declare no conflict of interest.
## 3 Method
### Neighborhood Microimage Selection Under Multiple Constraints  
Conducting neighborhood microimage selection under multiple constraints is a preliminary step in light field depth estimation algorithms. This process involves screening multiple microlens images to determine a collection of microimages with various degrees of blur to use as neighbors for the subsequent depth estimation process. This step provides high-quality data for depth estimation purposes, which is crucial for achieving enhanced accuracy and reducing errors.  
The quality of the neighborhood selection process directly affects the subsequent disparity calculation and depth estimation tasks [28; 29]. By employing constraints such as photometric consistency, the degree of overlap, and blur, unsuitable neighborhood images are eliminated, and the degree of blur exhibited by the microimages is defined, ensuring that the participating microimages are highly reliable and accurate. The selection of reliable neighborhood images can improve the precision and robustness of the final depth map.
## 3 Results
### Carbon Flux Predicted by the RS-CFLUX Model  
Figures 6 and 7 show the comparison between the carbon flux predicted by the RS-CFLUX model and the carbon flux estimated from EC at Daman superstation and Zhangye wetland station. The model predictions and observations were consistent. The RS-CFLUX model can correctly simulate the seasonal dynamics and quantities of GPP and NEP at the two stations. R\({}^{2}\), RMSE and NSE of the GPP between the RS-CFLUX model and EC were, respectively 0.92, 1.96 g C/m\({}^{2}\)/d and 0.87 at Daman superstation, and 0.94, 1.00 g C/m\({}^{2}\)/day and 0.85 at Zhangye wetland station. R\({}^{2}\), RMSE and NSE of the NEP between the RS-CFLUX model and EC were 0.88, 1.31 g C/m\({}^{2}\)/d and 0.87 at the Daman superstation, and 0.78, 1.02 g C/m\({}^{2}\)/day and 0.61 at the Zhangye wetland station (see Table 3). The sum of the EC-estimated GPP was 1442.04 g C/m\({}^{2}\)/year at the Daman superstation and 928.89 g C/m\({}^{2}\)/year at the Zhangye wetland station (the GPP missed for 68 days in 2013 and was not filled for the Zhangye wetland station); The sum of predicted GPP was 1196.93 g C/m\({}^{2}\)/year at the Daman superstation and 775.24 g C/m\({}^{2}\)/year at the Zhangye wetland station at the corresponding time. At the peak-growing season, GPP was obviously underestimated at Daman superstation; this is mainly caused by model parameter \(\rm{LUE_{max}}\). \(\rm{LUE_{max}}\) is smaller when both _in-situ_ GPP and NEE were simultaneously used to calibrate CFLUX model than when only _in-situ_ GPP was used. The sum of the EC-estimated NEP was 409.38 g C/m\({}^{2}\)/year at the Daman superstation and 422.60 g C/m\({}^{2}\)/year at the Zhangye wetland station (the GPP missed for 68 days in 2013 were not filled at Zhangye wetland station); The sum of the predicted NEP was 409.46 g C/m\({}^{2}\)/year at the Daman superstation and 433.12 g C/m\({}^{2}\)/year at the Zhangye wetland station at the corresponding time. The linear regression slope between the model and observations showed GPP was only slightly underestimated at the Daman superstation.  
Figure 6: Validation of the GPP estimated by the RS-CFLUX model at the Daman superstation (Daman) and Zhangye wetland station (Wetland). The slope and residual of the linear fit are shown in Table 3.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 4 Analysis of Passenger Flow Characteristics and Influencing Factors of URT
### Analysis of Factors Affecting Passenger Flow
The passenger flow at a certain moment is related to both the past historical passenger flow and the future passenger flow. Figure 10 is the hourly average inbound/outbound passenger flow statistics for all stations. It can be seen that there are two passenger flow peaks at 8:00 and 18:00, and the passenger flow before and after the peak is affected. Taking the time granularity of 1 h as an example, the impact of historical passenger flow on the current passenger flow in the short-term passenger flow prediction of URT was analyzed. The calculated Pearson correlation coefficient is shown in Table 2, and it can be seen that the correlation is significant (within the 99% confidence interval). The short-term prediction of URT passenger flow in units of days also needs to consider the influence of the natural environment and weather. The weather has a great impact on residents' travel, especially those residents who go out shopping and playing, who flexibly choose travel methods and times according to weather conditions. This paper mainly analyzes the influence of weather conditions such as temperature, weather conditions, wind power, and air quality index on passenger flow. The calculated Pearson correlation coefficients are shown in Table 2. According to Table 2, except for the wind factor, the other influencing factors are all within the 99% confidence interval. According to the above analysis, it can be found that there are several factors affecting the URT passenger flow. In this paper, six factors except the wind factor are used as the  
Figure 10: Hourly average inbound/outbound passenger flow statistics. Figure 9: Influence of date attribute on URT passenger flow. (**a**) Daily inbound/outbound passenger flow statistics; (**b**) Statistics of daily average inbound/outbound passenger flow within a week. parameter indicators to analyze and predict the short-term passenger flow. Details are shown in Table 3.
## 4 Mathematical Analysis of Handover Delay and Packet Loss of CoMP  
In this section, we mathematically analyze both handover delay and packet loss for mobility management using CoMP. In particular, we compare the performance of the proposed CoMP with that of the IETF MIPv6 and IETF HMIPv6 mobility management protocols.
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 3 Experimental Data and Analysis Indicators
### Observational Data from GNSS Systems  
To verify the degree of improvement of the positioning precision of this method on the GNSS system, the GPS satellite ephemeris data provided by the IGS station on 8 September 2021 at the Fangshan in Beijing, China for 24 h with a sampling interval of 30 s was selected as the satellite constellation of the simulation experiment. The visible number of satellites during the observation time is shown in Figure 6.  
At present, the GNSS system can correct the ephemeris error and clock error by using the precise ephemeris and precise clock error products provided by IGS, and use the dual-frequency ionospheric elimination combined model to weaken the ionospheric error; use the Saastamonien model to correct the tropospheric delay. In the simulation experiment of GNSS, on the premise that the correlation models are used to weaken the correlation system error, a random error with a variance of 1 and a mean of 0 that satisfies the Gaussian distribution is added to the geometric distance from the position coordinates of the satellite to the position coordinates of the Fangshan station to simulate the measurement pseudo-ranges of multiple frequency points. The position coordinates of the observation station are the coordinates of the IGS annual solution.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Data Management
#### 4.1.1 Data Management and Storage
Although blockchain technology has found widespread use in the financial sector, supply chains for agriculture, and food traceability, its adoption for managing breeding data remains notably less frequent. Confronted with the escalating volume of breeding data and the increased demand for concurrent database access, traditional data storage approaches are facing significant performance hurdles. Blockchain provides a decentralized alternative to these conventional methods. Addressing these challenges, article [87] introduces an effective and secure framework for storing crop breeding data at high throughput utilizing enhanced blockchain technology. Distinct from conventional blockchain structures, this framework organizes breeding information by geographical location and breeding stages, allocating it across various databases while encapsulating the core data's summary within a streamlined blockchain. This framework not only facilitates swift and secure data storage and retrieval but also seamlessly scales alongside growing datasets, thereby elevating the system's operational efficiency. Furthermore, the framework's robust security and high efficiency eliminate the need for high-caliber computing resources in both its database and blockchain layers, substantially lowering the financial burden associated with system hardware.
###### Abstract  
GPU-based deep neural networks (DNNs) are powerful for electrocardiogram (ECG) processing and rhythm classification. Although questions often arise about their practical application in embedded systems with low computational resources, few studies have investigated the associated challenges. This study aims to show a useful workflow for deploying a pre-trained DNN model from a GPU-based development platform to two popular ARM-based microcontrollers: Raspberry Pi 4 and ARM Cortex-M7. Specifically, a five-layer convolutional neural network pre-trained in TensorFlow (TF) for the detection of ventricular fibrillation is converted to Lite Runtime (LiteRT) format and subjected to post-training quantization to reduce model size and computational complexity. Using a test dataset of 7482 10 s cardiac arrest ECGs, the inference of LiteRT DNN in Raspberry Pi 4 takes about 1 ms with a sensitivity of 98.6% and specificity of 99.5%, reproducing the TF DNN performance. An optimization study with 1300 representative datasets (RDSs), including 10 to 4000 calibration ECG signals selected by random, rhythm, or amplitude-based criteria, showed that choosing a random RDS with a relatively small size of 80 resulted in a quantized integer LiteRT DNN with minimal quantization error. The inference of both non-quantized and quantized LiteRT DNNs on a low-resource ARM Cortex-M7 microcontroller (STM32F7) shows rhythm accuracy deviation of \(<\)0.4%. Quantization reduces internal computation latency from 4.8 s to 0.6 s, flash memory usage from 40 kB to 20 kB, and energy consumption by 7.85 times. This study ensures that DNN models retain their functionality while being optimized for real-time execution on resource-constrained hardware, demonstrating application in automated external defibrillators.  
Footnote †: journal: Applied sciences  
_Article_
## 2 Method and Data
### Main Data for GLOPEM-CEVSA Model Input and Validations
#### 2.2.2.2 Remotely Sensed Data  
(1) GIMMS FPAR  
FPAR was linearly related with NDVI or the NDVI-derived Simple Ratio (SR, \(SR=(1+NDVI)/(1-NDVI)\)). Previous study has shown that the NDVI-based FPAR model (\(FPAR_{NDVI}\)) and SR-based FPAR model (\(FPAR_{SR}\)) can overestimate or underestimate FPAR respectively, and an integrated model by combining \(FPAR_{NDVI}\) and \(FPAR_{SR}\) would provide an improved estimate [55]. We used this integrated strategy to calculate the FPAR,  
\[FPAR=\alpha FPAR_{NDVI}+(1-\alpha)FPAR_{SR} \tag{9}\]  
where \(\alpha\) was arbitrarily set to 0.5. We calculated the FPAR\({}_{SR}\) by using this empirical algorithm [35] that is for a horizontally homogeneous, closed canopy composed almost exclusively of green material,  
\[FPAR_{SR}=\frac{\big{(}SR-SR_{i,\min}\big{)}\big{(}FPAR_{\max}-FPAR_{\min}\big{)} }{\big{(}SR_{i,\max}-SR_{i,\min}\big{)}}+FPAR_{\min} \tag{10}\]  
where \(SR_{\max}\) and \(SR_{\min}\) are the simple ratios corresponding to the 98% and 2% of the NDVI frequency distributions of the \(i\) vegetation type. The 98% NDVI is assumed to represent vegetation with the maximum FPAR value, \(FPAR_{\max}\), here assumed to be 0.95; the 2% value is assumed to represent no vegetation cover and is assumed to correspond to a FPAR value close to 0, \(FPAR_{\min}\), here was assumed to be 0.001 [35]. NDVI-based FPAR was calculated by the following empirical equation,  
\[FPAR_{NDVI}=\frac{\big{(}NDVI-NDVI_{i,\min}\big{)}\big{(}FPAR_{\max}-FPAR_{\min }\big{)}}{\big{(}NDVI_{i,\max}-NDVI_{i,\min}\big{)}}+FPAR_{\min} \tag{11}\]  
where the \(NDVI_{\max}\) and \(NDVI_{\min}\) were estimated as 0.96 and 0.03 corresponding to the 98% and 2% thresholds of the NDVI frequency distributions of all the vegetated area. In order to match with MODIS data, the GIMMS FPAR was interpolated from the half month time step to the eight-day step by the spline algorithms in Matlab [56].  
(2) MODIS FPAR  
The MOD15A2 FPAR is retrieved from the reflectance of two bands (648 and 858 nm) and its algorithm is based on three-dimensional radiative transfer theory and a back-up method based on relations between the NDVI and LAI/FPAR [57, 58, 59, 60, 61]. The FPAR data were downloaded from the Land Processes Distributed Active Archive Center (LP DAAC) [62], then filtered to remove the noise in FPAR time series by using the adaptive Savitzky-Golay (SG) method in the TIMESAT software [62, 63]. The results had a 1-km spatial resolution and were resampled to 8-km. We will refer to this processed FPAR dataset as FPAR\({}_{\text{MOD15}}\) from here on.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
###### Abstract  
To clarify the copyrights of 3D models of oblique photography (3 DMOP) and guarantee their security, a novel security protection scheme of 3 DMOP was proposed in this study by synergistically applying digital watermarking and data encryption. In the proposed scheme, point clouds were clustered first, and then the centroid and feature points of each cluster were calculated and extracted, respectively. Afterward, the watermarks were embedded into the point clouds cluster-by-cluster, taking distances between feature points and centroids as the embedding positions. In addition, the watermarks were also embedded using texture coordinates of 3 DMOP to further enhance the robustness of the watermarking algorithm. Furthermore, Arnold transformation was performed on texture images of 3 DMOP for security protection of classified or sensitive information. Experimental results have verified the strong imperceptibility and robustness of the proposed watermarking algorithm, as well as the high security of the designed data encryption algorithm. The outcomes of this work can refine the current security protection methods of 3 DMOP and thus further expand their application scope.  
3D models of oblique photography; digital watermarking; data encryption; security protection
# A Heterogeneous Battlefield Situation Information Sharing Method Based on Content
## Sharing Framework of Heterogeneous Battlefield Situation Information
### Content Consistency Analysis of Heterogeneous Battlefield Situation Information
No matter which system the heterogeneous information comes from, it essentially describes the information of categories, attributes, states, and inter-relationships owned by combat units. Thus, we take combat units as the core description objects to model the battlefield situation, which possesses the features of its nature, easy understanding, and close to reality.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Model Assumptions
#### 3.1.1 System Assumptions  
* The total number of slots available for downlink data transmission is constant, denoted as \(N_{S}\).
* All connection requests are accepted, with no blocking or resource denial.
* Each mobile station has unlimited transmission capacity, meaning that if only one user is active, they can use all available slots.
# Sonoporation, a Novel Frontier for Cancer Treatment: A Review of the Literature
## 3 Results
### Analysis of Results by Body District
#### 3.2.5 Prostate  
In 2007, [PERSON] et al. demonstrated the effectiveness of sonoporation, in non-viral gene delivery to prostate tumors. Their study successfully transfected prostate cancer cells and endothelial cells in vitro, showcasing significant inhibition of EC migration and proliferation. In vivo, the research led to substantial tumor growth inhibition. It is necessary to specify that, in this case, genes are discussed as molecules that penetrate the membrane. This is why they have been included in the review, even though it does not specifically address bubbles encapsulating drugs or drugs themselves. In [PERSON] et al. [23] (2012), the combination therapy involving low-frequency and low-intensity ultrasound along with microbubbles (UM) was explored for the targeted delivery of mitoxantrone HCl (MIT) to DU145 cells, which are known for their drug-resistant nature. The results demonstrated that this approach significantly improved drug uptake and therapeutic efficacy without impacting cell viability. Furthermore, the treatment exhibited notable effects on the migration capability of DU145 cells. Overall, the ultrasonic parameters (frequency, 21 kHz; power density, 2 min at a duty cycle of 70%) used in this research hold promise for enhancing chemotherapeutic outcomes while mitigating MIT's adverse side effects. It is important to note that ultrasound-mediated microbubble therapy has broader applications in cancer treatment and is particularly relevant in addressing the challenges posed by prostate cancer.  
Figure 7: In (**A**) the two probes for stimulation and diagnostics are shown. The mouse is to be placed under the water tank that allows coupling with the transducer. (**B**) An illustrative reproduction of the instrumentation shown in section A is presented with the mouse under the water tank on the Vevo imaging tank. Images from [21].
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
### Flight Safety Vulnerabilities  
ADS-B consists of several other systems to collect the data for the ADS-B Out messages. It includes the GPS to determine the aircraft's coordinates, a barometric altimeter for the aircraft's altitude, and a transponder to send the messages. Each of these components has additional vulnerabilities to cyberattacks. However, this paper focused on the ADS-B system and the GPS being resilient on radio wave communication, while being vulnerable to cyberattacks such as jamming and spoofing (Figure 2), on a commercial aircraft.  
Figure 1: Aircraft use the GPS to determine their location and velocity and then transmit an ADS-B message containing these data using the ADS-B Out subsystem. ATCs use the ADS-B In subsystem to collect ADS-B messages from all aircraft in the area and then display a visual map containing these aircraft. There are aircraft equipped with the ADS-B In subsystem in addition to the ADS-B Out subsystem.  
Figure 2: Jamming and spoofing cyberattacks on flight safety.
# Modeling of Geophysical Flows through GPUFOW
## 3 Sensitivity Analysis
### Impact of the Thermal Model  
We performed a sensitivity analysis of the variable emissivity and the windchill included in the thermal model of GPUFLOW through different synthetic simulations. To avoid the influence due to the real rough topography, GPUFLOW simulations were run on planes with different inclinations (from \(5^{\circ}\) to \(30^{\circ}\) with \(5^{\circ}\) steps) and different constant effusion rates (10, 20, and 30 m\({}^{3}\)/s for one day), while keeping the same single vent located on the top of the plane. For the rheological properties, we used the typical parameters retrieved from literature for a'a lavas: 1360 K and 1143 K as extrusion and solidification temperatures, respectively, 2600 kg\(\cdot\)m\({}^{-3}\) as density, and 0.05 wt% as the water content.  
The influence of constant and variable emissivity on the maximum length reached by the simulated lava flows is shown in Figure 1. In particular, we plotted the length ratios between the simulations run with a constant emissivity of 0.6 and 0.9, with respect to the simulations run with a temperature-dependent emissivity (Equation (3)), taken as reference.  
Figure 2 shows the maximum lengths reached by lava flows simulated on a \(20^{\circ}\) inclined plane using constant and variable emissivity and a windchill coefficient for the cooling from 0 (heat loss due to air convection is negligible compared to radiation) to 25.  
Figure 1: Length ratio of the lava flows simulated with a constant emissivity of 0.6 and 0.9 _vs._ variable emissivity, by varying the effusion rate (**a**) and the inclination of the plane used as DSM (**b**).