esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Spatial Prediction of Monthly Silitation  
The trained random forest model was further extended to perform monthly-scale spatiotemporal predictions of silitation volumes from 3 July to 31 December 2024, covering the six-month forecast period. The model produced grid-level predictions for each individual month, resulting in a continuous series of monthly siltation distribution maps across the study area (see Figure 3). As the model was trained using average daily siltation volumes, a post-processing step was applied to ensure temporal consistency. Specifically, for each target month, the model first predicted the mean daily siltation, which was then multiplied by the number of days in that month to obtain the total monthly siltation volume. This conversion ensures alignment between the training and inference phases, while producing temporally scaled outputs.  
To simulate the evolving riverbed topography over time, an iterative update mechanism was adopted. For each month, the predicted siltation volume was combined with the most recent bathymetric surface to estimate the end-of-month riverbed elevation. For example, the July prediction was applied to the bathymetric map from 3 July 2024 to derive the updated topography for 31 July. This new surface was then used as the input terrain for the August prediction, yielding a bathymetric map for 31 August, and so forth. Through this month-by-month update, a series of temporally evolving riverbed surfaces was generated, enabling dynamic visualization and analysis of sediment deposition processes throughout the forecast period. This iterative modeling strategy offers a more realistic representation of cumulative siltation effects and supports practical applications, such as navigation risk assessment, adaptive dredging planning, and channel evolution analysis, under changing hydro-environmental conditions.
# Real-Time Water Level Monitoring Based on GNSS Dual-Antenna Attitude Measurement
## 3 Field Experiment
### Experimental Equipment  
The receiver used in the experiment was jointly developed by the China Institute of Water Resources and Hydropower Research and Shanghai Sinan Satellite Navigation Technology Company Limited. The receiver supports BeiDou global signals and other mainstream global satellite navigation systems.  
Table 1 presents the parameters of the antennas used in this experiment. Figure 7a depicts the antennas used in this experiment. The RHCP antenna receives the direct satellite signal, and the LHCP antenna receives the reflected signal from the water surface. We positioned a signal clapboard at the midpoint of the connection axis between the RHCP and LHCP antennas. The signal clapboard was covered with wave-absorbing material, and its projected area on both antennas was larger than their corresponding antenna areas. As shown in Figure 7b, the clapboard reduces interference from water surface-reflected satellite signals on the RHCP antenna and from direct satellite signals on the LHCP antenna.
## 3 Results and Discussion
### Radar Sensor Operation in Different Driving Situations
#### 3.2.2 Tunnels  
It is unclear how tunnels affect the ability of the sensors to measure inter-vehicle distances. Tunnels may reduce operational complexity, as rock walls cause peripheral narrowing of roadside areas. However, walls, lighting and ventilation elements may introduce clutter. Such features are less frequent on roads in natural terrain.  
All _Tumel_ driving occurred in truck order 1-2-3. Figure 10 shows excerpts from six representative _Tumel_ traversals (green shading). Driving periods outside tunnels have white backdrops. All excerpts were coded as _LWRR_, except for the period between the two tunnels in the top excerpt, when data were lacking for truck 2. This period was coded as _LBRR_, as truck 1 was located far away. Comparing Figures 8 and 10, it seems as if filter 4 (target selection) chooses erroneous data points at comparable frequencies both inside and outside tunnels. Thus, tunnel driving does not appear to degrade radar operating conditions. In tunnels, maximum distance values for trucks 2 and 3 were reduced by 8-9%, and the distance standard deviation dropped by 18% and 15%, respectively. Thus, inter-vehicle distances were moderated by _Tunnels_, causing closer, more uniform driving at lower speeds. This made preceding trucks occupy a larger part of radar the field  
Figure 9: Inter-vehicle distances measured from three separate driving excerpts, _LWRR_, during which radar range was exceeded. Horizontal lines show farthest measurements.  
of-view. However, measurements collected in tunnels were noisier than those collected during _Driving_, indicated by lower SNR values (weaker signal). For both followers, _Ttunnel_ filtering reduced mean SNR by 3%, maximum values by 6-8% and standard deviations by 7-11%. This did not affect the curated radar data when plotted: In Figure 9, erroneously selected targets in tunnels appear to have distance values at similar deviations to the trend, compared to erroneously selected targets outside tunnels. Thus, it appears as if inter-vehicle distance measurements between platooning trucks are not adversely affected by tunnels.
###### Abstract  
Model predictive control, without strict constraints on the control model, effectively overcomes problems, such as poor system dynamic quality caused by time delay, can improve control accuracy to a certain extent, and can directly process input and output constraints of the system online. It is applied in greenhouse system control. The appropriate objective optimization function and its corresponding constraint conditions have a direct impact on the solution of the optimal control rate of the model predictive control. In response to this issue, this paper proposes a simple and fast optimal predictive tracking control method. Based on the current prediction model of the greenhouse system, which reflects the dynamic relationship between various control equipment actions and greenhouse environmental factors, a multi degree of freedom discrete time state space model with tracking errors is established. Based on this model, in establishing the corresponding objective optimization function, the gradient descent theory and the two-norm definition are applied, and combined with actual constraints, iterative constraint conditions for real-time error tracking updates are established. Compared with traditional constraint ranges, a constraint function with real-time update characteristics is formed, achieving more accurate constraint conditions. By using rolling optimization and iterative methods, the optimal control rate corresponding to the minimum value of the objective optimization function within a finite time is solved. Through simulation examples, it is demonstrated that the model predictive control with optimization constraints can achieve a more accurate prediction and tracking control of indoor environmental parameters. This method has the advantages of simple control, energy-saving optimization, stable control, and accurate tracking, providing a reference for online real-time prediction and tracking control of future greenhouse environmental parameters.  
model predictive control; rolling optimization method; gradient descent theory; tracking predictive control +
Footnote †: journal: Applied sciences
## 3 Results
### Microstructure
#### 3.1.2 Laser-Hardened Quench-Tempered Gray Cast Iron (LHQTGI)  
Figure 5 shows the microstructure of LHQTGI specimens with various tempering temperatures. Three different regions were found beneath the laser-hardened surface in the matrix of LHQTGI. Zone 3 was the substrate containing the tempered martensite, which is a typical phase component for QTGI. The tempered martensite became coarse when increasing tempering temperature from 316 \({}^{\circ}\)C to 482 \({}^{\circ}\)C. Similar to the LHAGI specimens, the laser-hardened region (Zone 1) was also full of ledeburitic structures without the presence of graphite flakes, which indicates that Zone 1 experienced a solid-liquid-solid phase transformation during the laser-hardening process. Zone 2 was a heat-affected zone, which contained martensite due to the effects of self-quenching. The graphite flakes remained with no changes in morphology or size. The martensite around Zone 1 was coarser than that around Zone 3 due to the different temperature distributions. Table 5 displays the dimensions of the laser-hardened areas and heat-affected regions in the matrix of LHQTGI specimens with various tempering temperatures. The width and depth of Zone 1 and Zone 2 in LHQTGI specimens were similar to those in LHAGI specimens due to the same parameters being used in the laser-hardening treatment. The lower heat-affected zone was not produced in the matrix of LHQTGI specimens due to the higher thermal stability of tempered martensite as compared with acicular ferrite and high carbon content austenite.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Austempering** & **W\({}_{1}\)** & **D\({}_{1}\)** & **W\({}_{2}\)** & **D\({}_{2}\)** & **W\({}_{3}\)** & **D\({}_{3}\)** & \\
**Temperature** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
232 \({}^{\circ}\)C & 2.171 & 0.755 & 2.823 & 1.085 & 3.555 & 1.529 & \\
288 \({}^{\circ}\)C & 2.168 & 0.716 & 2.726 & 1.071 & 3.445 & 1.613 & \\
343 \({}^{\circ}\)C & 2.187 & 0.745 & 2.851 & 1.058 & 3.483 & 1.575 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Width and Depth of the Laser-Hardened, Upper Heat-Affected, and Lower Heat-Affected Zones for LHAGI Specimens with Various Austempering Temperatures.  
Figure 5: Microstructure of Difference Zones in the Matrix of Laser-Hardened Quench-Tempered Gray Cast Iron (LHQTGI) Specimens with Various Tempering Temperatures **(a)** 316 \({}^{\circ}\)C; **(b)** 399 \({}^{\circ}\)C; **(c)** 482 \({}^{\circ}\)C.
## 3 Results and Discussion
### Effectiveness Analysis
#### 3.2.1 Completion Time for Each Question  
The test that was developed to assess IVEE performance comprised 26 questions. Sixteen out of the 26 questions asked the participants to generate a visualization, and the participant created each type of representation four times. Three out of the 26 questions asked the participants to subset the data, two questions asked the participants to merge visualizations, and five were knowledge questions that asked the participants to visually identify certain values.  
The time it took the participants to complete each question on the test, as well as the entire test, using both experimental conditions--R and VR--was recorded. Since this study follows a within-subject experimental design, where participants undergo both treatment conditions, a total of 52 variables (26 \(\times\) 2) were collected from each participant.  
Shapiro-Wilk and Kolmogorov-Smirnov tests were conducted for all 52 variables [31; 32]. The visual inspection of their histograms, normal Q-Q plots, and boxplots showed that percent difference values were not normally distributed. Hence, a non-parametric test was administered to determine whether a statistical significance exists in the time it took the participants to complete each question in both R and VR environments.  
A Wilcoxon signed-rank test belongs to non-parametric tests and is an alternative non-parametric statistical hypothesis test to a dependent \(t\)-test. Since this test does not require that data points fall on a normal curve, it can be performed when parametric assumptions are not met and when parametric statistical methods are not suitable [33]. The Wilcoxon signed-rank test is employed to decide whether two treatments, given to the same group of participants (in a within-subject study), are statistically different in relation to a variable of interest. The latter must be continuous, and the sample must be randomly selected. As time is a continuous variable and the study sample was randomly selected, this test is appropriate for our analysis. It also tests the hypothesis that the medians from the two samples are the same. In this work, this test looks to find whether the use of the IVEE engine had enhanced participant performance in comparison to the use of R software. The null and alternative hypotheses for each tutorial question are as follows:  
* H0: Participants took the same amount of time to complete question \(i\) using \(R\) and VR environments; \(i=1,2,\ldots,26\).
* H1: Participants took less time to complete question \(i\) using the VR environment; \(i=1,\) 2,..., 26.  
To avoid confusion, \(R\) data-related questions are labeled \(R_{Qi}\), where \(i=1,\) 2,..., 26, and \(VR\) data-related questions are labeled \(VR_{Qi}\), where \(i=1,\) 2,..., 26. Table 1 shows the summary of hypothesis testing.  
The summary of our hypothesis testing shows that for 21 out of 26 questions, the significance value (  
criteria of 0.5, indicating a significant effect size. This means that the VR environment can reduce the time it takes users to perform data analysis for most of the questions.
## 3 Results
### Construction and Detection of _satP_ Strains  
The knockout of _satP_ in _PmS_, _PmR_ and _PmHR_ was conducted with the suicide vector pRE112. To determine whether _APmS_, _APmR_, _APmHR_ and _CPmS_ were successfully constructed, hereditary stability, PCR detection and qRT-PCR were performed. PCR showed that the _satP_ deletion strains were 700 bp less than that of the wild-type strain and _CPmS_. Real-time qRT-PCR showed that no signal of the _satP_ gene was detected from the deletion strains. Genetic stability showed that _APmS_, _APmR_, _APmHR_ and _CPmS_ were successfully constructed. The deletion of _satP_ did not affect the MIC and growth of the strains (Figure 2A-D).  
Figure 1: Characterization and verification of differentially expressed genes. (**A**) Differentially expressed gene analysis of resistance groups (_PmS_ vs. _PmR_). (**B**) Differentially expressed gene analysis of resistance groups (_PmS_ vs. _PmHR_). (**C**) _satP_, (**D**) _focA_, (**E**) _napF_, (**F**) _glgP_ and (**G**) _napD_ gene expression level verification using qRT-PCR. (All data are expressed as the mean \(\pm\) SD. \(n\) = 3. \(**\)\(p\) < 0.01 vs., \(*\)\(p\) < 0.05 vs. control group).  
We found that when the _satP_ gene was deleted, \(\Delta PmS\), \(\Delta PmR\) and \(\Delta PmHR\) had no QDRR mutation compared with those without deletion (Table 2). The MIC of ENR was determined by the agar dilution method in the presence and absence of CCCP (4 g/mL, MIC/2) and verapamil (8 g/mL, MIC/2). The results showed that the function of the efflux pump had no effect on the phenotype of \(Pm\) to ENR.
## 3 Materials and Methods
### Characterization of Nanoparticles  
XRD (PANalytical Empyrean, Almelo, The Netherlands) was employed for the characterization of the produced materials. The scan angle ranged from 5 to 80\({}^{\circ}\) with a scan step of 0.05\({}^{\circ}\), and a 30 mA current was applied with an accelerating voltage of 40 KV. The vibration of chemical bonds was analyzed using a Bruker apparatus (Vertex 70 FTIR-FT, Karlsruhe, Germany). Surface morphology of the produced materials was examined using a scanning electron microscope (SEM). Additionally, an automatic surface analyzer (TriStar II 3020, Micromertics, Norcross, GA, USA) was utilized for determining the BET-specific surface area, pore volume, and pore size distribution of the produced materials through the N2 adsorption-desorption method. Particle sizes and zeta potentials were measured using a Malvern instrument (Malvern Instruments Ltd., Worcestershire, UK). Samples were prepared in accordance with standard protocols [84]. Thermogravimetric-differential thermal studies (TGA-DT) were conducted by TA Instruments (SDT 2960, New Castle, DE, USA). The behaviors of the produced samples were assessed in an air environment ranging from 25 \({}^{\circ}\)C to 1400 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C min\({}^{-1}\).  
The prepared samples intended for DLS analysis should be dispersed in a liquid phase. Deionized or Milli-Q water was used as the solvent in this case. Filtration may also be necessary for samples obtained in suspension to remove undesirable species with large molecular masses. It is important to ensure the suspension is homogeneous and the samples are evenly distributed. This can be achieved by gentle pipetting or, for delicate samples, by bath sonication at maximum power for up to 15 min [85].
# A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
###### Abstract  
Mobile Cyber-Physical Systems (MCPS) have extended the application domains by exploiting the advantages of Cyber-Physical Systems (CPS) through the mobile devices. The cooperation of various mobile equipment and workers based on the MCPS further improved efficiency and productivity in the industry. To support this cooperation of groups of workers (hereafter referred to as the Mobile Sink Groups), data should be delivered to appropriate groups of workers in a timely manner. Traditionally, the data dissemination for MSG relies on flooding-based geocasting into the movable area of the group due to frequent movements of each group member. However, the flooding-based data dissemination could not be directly applied to real-time data delivery that demands the required time deadline and the end-to-end delivery distance, because the flooding could not define the end-to-end distance and progress to each member in a group. This paper proposes a real-time data delivery mechanism for supporting MSG in time-critical applications. In our mechanism, a ring-based modeling and data transfer scheme on a virtual grid in the ring for group mobility provides the end-to-end distance and the progress to forward real-time data to each member. Simulation results show our mechanism is superior to the existing ones in terms of real-time communication for MSG.  
Mobile Cyber-Physical Systems (MCPS); industry; Mobile Sink Groups (MSG); group mobility; real-time data delivery +
Footnote †: journal: Applied Physics Letters  
_Article_  
A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
## 4 Results and Discussion  
The Algorithm 1 speed-up and performance is shown in Figure 9. Looking at the graph, we can notice that the speed-up of the algorithm is achieved and the processing time improves with the increase in the number of threads.  
In this sub-subsection we introduce parallel Algorithm 2, which is based on different approach of fitting track trajectory [15].  
```
0: All Tracks Table with \(x\), \(y\), \(z\), \(N\), \(s\), \(d\), and \(T\)
1:for each track in All Tracks Table do
2: Compute initial parametrization \(u\) using centripetal method
3: Define polynomials \(f_{x}(u)\), \(f_{y}(u)\), \(f_{z}(u)\) of degree \(k\)
4:for each parameter \(u_{i}\) in \(u\)do
5: Compute \(n\) using Equation (12)
6: Initialize search interval \([a,b]\) around \(u_{i}\)
7:repeat
8: Apply Golden Ratio search on interval \([a,b]\)
9: Update \(a\) and \(b\) based on Golden Ratio search
10: Compute residuals and update \(f_{x}(u)\), \(f_{y}(u)\), \(f_{z}(u)\)
11:until convergence criterion is met or maximum iterations are reached
12:endfor
13: Compute final polynomials \(f_{x}(u)\), \(f_{y}(u)\), \(f_{z}(u)\)
14:endfor
15: Fitted polynomial parameters for each track
```  
**Algorithm 2** Polynomial Fit Method  
Figure 9: Execution time of parallel Algorithm 1 as a function of thread count.
## 2 Data and Methodology
### COSMIC Mission  
COSMIC is a constellation of Low Earth Orbit satellites that probe the Earth's ionosphere and atmosphere using the mechanism of trans\(-\)ionospheric radio wave propagation. The constellation consists of six satellites circled in six circular orbits inclined at \(\sim\)72\({}^{\circ}\) and about 800 km altitude. The satellites provide different kinds of measurements, inclusive GPS-based Radio Occultation (RO) measurements for probing the Earth's ionosphere and atmosphere. In our study, we investigated the storm-induced variations in the profile shape and the altitude distribution of ionospheric irregularities at different levels of magnetic activities. For this purpose, we used the main COSMIC\(-\)RO ionospheric product \"ionPrf\" ionospheric electron density profiles. We selected IBI events where COSMIC satellites are within \(\pm\)30\({}^{\circ}\) longitudes of the same event observed by the Swarm satellites. The reason for that is to investigate the altitude of the F2 layer peak height (hmF2) and the degree of depletion using COSMIC data for the same IBI event confirmed by the Swarm satellite.
### Satellite and Climate Data  
At present, Landsat mission satellites offer useful spatial resolution and historical temporal coverage for cropland applications. Several software applications readily support GPP calculations from Landsat images. We used the Python script PySEBAL because it produces a biomass estimate based on GPP from the CASA equation, and also gives outputs required to calculate stress scalars, such as transpiration and evaporation [12, 23].  
Figure 1: Flowchart of methodology showing data sources (circles), data items (trapezoids), algorithms or estimations (rectangles), and accuracy assessments of GPP estimations (rounded rectangles).  
Both Landsat-7 and Landsat-8 imagery were used to cover the epoch from 2001 to 2018 (Table 1). Collection 1 Level-1 images with less than 20% cloud cover were downloaded from the USGS Earth Explorer website. Landsat band digital numbers were converted to top of atmosphere reflectance values using the scaling coefficients in metadata files. Landsat-7 imagery must be spatially gapfilled to correct the scan line corrector (SLC) failure. [PERSON] et al. [38] reported more consistent outputs from PySEBAL when Landsat-7 bands were gapfilled using the 'focal' function from the R package 'raster' [39]. Therefore, a 3 \(\times\) 3 moving window was applied to Landsat-7 bands using the 'focal' function to fill pixels identified as having null values in R version 4.1.1 [40].  
PySEBAL requires 24 average and instantaneous shortwave radiation, wind speed, air temperature, air pressure and relative humidity as inputs. These were derived from the National Aeronautics and Space Administration's Global Land data assimilation system (GLDAS) [41]. Climate observations closest in time to the Landsat overpass time were used as the instantaneous values while means were calculated across the eight observations for each day to form 24 h average values [11; 12].  
To complete the required inputs for PySEBAL, a digital elevation model (DEM) was acquired from the Shuttle Radar Topography Mission (SRTM) [42] and soil physical properties were derived from the HiHydroSoil dataset [43].
## 3 Attacks in Blockchain
### Classification of Blockchain Attacks
#### 3.1.2 Smart Contract Vulnerabilities  
Smart contracts, which are self-executing programs running on blockchain networks, play a crucial role in automating and enforcing agreements without intermediaries. Despite their advantages, smart contracts are vulnerable to coding errors and design flaws that can result in unauthorized access or loss of funds [32; 33; 34]. These vulnerabilities are particularly critical due to the immutability of blockchain, which makes it challenging to correct errors once deployed [35]. Notable cases, such as the Poly Network hack in 2021, illustrate how attackers exploit smart contract vulnerabilities to siphon funds, demonstrating the need for rigorous security audits and robust coding practices.