esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Accuracy Assessment of GAMIT/GLOBK Adjustment Results
The dataset consists of approximately 5500 control stations, observed intermittently every 2 to 3 years. While most stations remained stable and continued to function as part of Korea's long-term geodetic framework, a few were inevitably reinstalled at nearby locations, leading to significant outliers in both horizontal and vertical components. To minimize the influence of these outliers and to more precisely evaluate the internal consistency of the adjustment, a 3-sigma outlier filtering was applied. The residual distributions in the east, north, and up directions are illustrated in Figure 11.  
Table 8 presents the coordinate residual statistics after applying a 3-sigma outlier filtering process. The RMSE and standard deviation values for all three components--east, north, and up--are substantially reduced compared to the unfiltered results shown in Table 7. This improvement demonstrates that a small number of extreme residuals had a significant impact on the original statistics. After filtering, the RMSE values were reduced to 0.79 cm (east), 0.50 cm (north), and 1.93 cm (up), with corresponding standard deviations of 0.5 cm, 0.48 cm, and 1.93 cm, respectively. The residual ranges are now tightly constrained, with maximum values not exceeding 2.48 cm (east), 2.35 cm (north), and 9.46 cm (up). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & **RMSE (cm)** & **Coordinate Difference (cm)** & **Standard** \\  & & **Min/Max/Mean** & **Deviation (cm)** \\ \hline East (E) & 1.4 & 0.00/85.69/0.63 & 1.25 \\ North (N) & 1.33 & 0.00/81.09/0.60 & 1.18 \\ Vertical (U) & 64.8 & 0/4826.36/3.49 & 64.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary statistics of coordinate residuals from the GAMIT/GLOBK adjustment, including RMSE, coordinate differences, and standard deviation (E, N, and U directions). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Direction** & **RMSE (cm)** & **Coordinate Difference (cm)** & **Standard** \\  & & **Min/Max/Mean** & **Deviation (cm)** \\ \hline East (E) & 0.79 & 0.00/2.48/0.61 & 0.5 \\ North (N) & 50.0 & 0.00/2.35/0.58 & 0.48 \\ Vertical (U) & 64.8 & 0.00/9.46/2.35 & 1.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary statistics of coordinate residuals from the GAMIT/GLOBK adjustment after applying 3-sigma outlier filtering, including RMSE, coordinate differences, and standard deviation for the east, north, and up directions. These results emphasize the necessity of establishing a consistent nationwide geodetic framework to eliminate discrepancies and reduce regional deviations that may arise from localized network adjustments. By excluding the influence of extreme outliers, the refined residual statistics demonstrate that a unified adjustment approach can minimize positional inconsistencies across regions and ensure uniform accuracy and internal coherence throughout the national control network. This underscores the importance of adopting a nationwide geodetic adjustment strategy to support high-precision positioning and sustainable geospatial infrastructure management.
## 2 Grounding Grid Boundary Voltage Calculation
The simulation example is shown in Figure 2. The conductivity of the normal uncorroded flat steel material is \(1\times 10^{7}\) S/m, and the conductivity of the soil part is \(5\times 10^{-3}\) S/m. This paper adopts a 16-electrode adjacent excitation model. The electrodes surround the measured area, inject the current in turn and measure the voltage value of the adjacent electrodes, and the injection current density is set to 1 A/m. The principle of adjacent excitation cycle measurement is to select 16 accessible nodes around the measured grounding grid, inject the current from adjacent nodes, \(P_{1}-P_{2}\), \(P_{2}-P_{3}\), \(\ldots\ldots\), \(P_{15}-P_{16}\), \(P_{16}-P_{1}\), in turn, and cyclically measure the adjacent potentials of the remaining 13 nodes except the measured node, that is, 208 boundary voltage values are obtained, and then the voltage values are reconstructed through the improved CGAN algorithm. The conductivity ratio of grounding grid materials under normal conditions and under corrosion conditions is generally within 10 times, so this paper mainly diagnoses the corrosion of grounding grids with conductivity of \(1\times 10^{6}\) S/m to \(1\times 10^{7}\) S/m. The dataset includes 4500 sets of boundary voltage values obtained by simulation and the corresponding real conductivity distribution images, of which 80% are randomly selected as training sets, 10% as validation sets and 10% as test sets. Each pair consists of a set of boundary voltage measurements and the corresponding true conductivity map. The boundary voltage values are input into the generator to generate a conductivity distribution map, and the corresponding true conductivity distribution map is input into the discriminator to determine whether the generated image is correct or not. Since the traditional EIT imaging method struggles to effectively reconstruct the regular structure and corrosion conditions of each branch of the grounding grid, this paper uses the color coding method to diagnose the corrosion conditions. The defined real corrosion diagnosis image is shown in Figure 3, which is used to guide the supervised learning of the CGAN to generate the grounding grid electrical impedance imaging map. Taking the \(3\times 3\) structure as an example, the conductivity value and the color value are linearly mapped, and the conductivity imaging map is smoothly transitioned from blue to yellow. In the figure, the blue area indicates lower conductivity, and the yellow area indicates higher conductivity, which is conducive to image analysis after imaging the CGAN grounding grid in order to solve its corrosion location and corrosion degree.
## 2 Materials and Methods  
### Substrate and Inoculum  
The ESBP samples came from a high-size sugar beet factory (AB SugarTM) located in Jerez de la Frontera (Cadiz, Spain). The ESBP samples have a very high solid content (TS range 80%-90%) that facilitated their conservation. The samples of PM were collected in a local farm placed in San Jose del Valle (Cadiz, Spain). The farm did not have any device for the separation of urine and feces. The samples were frozen at \(-20\)\({}^{\circ}\)C, unfrozen, and stored at 4 \({}^{\circ}\)C, when they were close to being used.  
The mixtures of ESBP and PM were prepared, considering the proportions of each waste, on a wet weight basis, and a total solid content of 8% was maintained. ESBP samples were previously rehydrated for 24 h, in deionized water [11]. ESBP is highly hygroscopic and, hence, the total solids percentage must be limited to 8%, to avoid rheological problems into the reactors. Indeed, previous studies determined that the increase in solids content impeded an adequate mixing in the reactor, due to the high water holding capacity of ESBP [26; 27; 28; 29; 30]. The following mixtures of ESBP:PM (weight basis) were studied: 0:100, 10:90, 25:75, 50:50, and 100:0.  
The inoculum used in the experiments came from a lab-scale semi-continuous reactor for the AcoD of ESBP and PM. The reactor has been working in the thermophilic temperature range (55 \({}^{\circ}\)C)and was fed with an ESBP:PM mixture of (10:90), also with a total solids content of 8%. When the inoculum samples were taken, the reactor was operating in stable conditions with a hydraulic retention time of 30 days, a methane yield around 105-110 mL CH\({}_{4}\)/gVS\({}_{\text{added}}\), and a productivity of 0.22-0.24 LCH\({}_{4}\)/L\({}_{\text{reactor}}\)-day. The use of an adapted inoculum to carry out the AD assays guaranteed an efficient start-up of the process [30]. The inoculum to substrate ratio was at 1:1 (volume basis).  
The physicochemical characteristics of the substrates and the inoculum are shown in Table 1.
## 3 Proposed Method
### Proposed Patient Confidential Data Hiding Technique  
Since the energy and quality of a signal are concentrated in the low frequency, the high frequency is susceptible to noise interference and can be easily removed or filtered without affecting the quality of the signal. Therefore, the binary bits are usually embedded in low frequencies to prevent them from being removed, filtered out, or interfered with by noise. In order to define a better threshold when embedding and extracting the binary bits in low frequencies, original audio signal of length \(L\) is firstly segmented by \(I\) frames, and then k-level DFT is realized on each frame. Therefore, the total number of lowest-frequency coefficients in each frame is \(n=L/(I\cdot 2^{k})\) and the mean of these coefficients is  
\[m=\frac{1}{n}\underset{i=0}{\overset{n}{\sum}}|c_{i}| \tag{6}\]  
Then, two thresholds are defined by  
\[h_{1}=m+\varepsilon \tag{7}\]  
and  
\[h_{0}=m-\varepsilon \tag{8}\]  
where \(\varepsilon>0\) is the embedding strength. Finally, the binary bit of value \(0\) or \(1\) is embedded into low frequencies by the following rules.  
\[\underset{i=0}{\overset{n}{\sum}}|\varepsilon_{i}|\geq h_{1},\text{ if }b_{i}=1 \tag{9}\]  
\[\underset{i=0}{\overset{n}{\sum}}|\varepsilon_{i}|<h_{0},\text{ if }b_{i}=0 \tag{10}\]  
where \(\varepsilon_{i}\) is the embedded DWT low-frequency coefficient corresponding to original DWT coefficient \(c_{i}\).  
optimization model. The clear process of SA in solving our proposed optimization model will be listed in the following steps:  
Step 1. Given the initial value of parameters, including initial solution \(C_{0}\), initial temperature \(T_{0}\), final temperature \(T_{f}\), cooling rate \(r\), and iteration number \(D\) for each temperature, \(C^{\prime\prime}=\{|c^{\prime\prime}1|,|c^{\prime\prime}2|,\cdots,|c^{\prime \prime}n|\}\), where \(C^{\prime\prime}=\{|c^{\prime\prime}1|,|c^{\prime\prime}2|,\cdots,|c^{\prime \prime}n|\}\).  
Step 2. For each \(d\)=1..., \(D\) at a temperature \(T\), repeat the following:  
(1) Randomly generate a new solution \(C^{\prime\prime}=\{|c^{\prime\prime}1|,|c^{\prime\prime}2|,\cdots,|c^{\prime \prime}n|\}\) and evaluate the difference  
\[\Delta E=E(C^{\prime})-E(C^{\prime})=\left\{-10\log\left[\sum\limits_{i=1}^{n} \left(|c^{\prime\prime}
## 2 Related Work
A sequential antenna switching IA algorithm was proposed in [27], where the QoS is improved by selecting proper antenna configurations. Another challenge that IA confronts is the acquisition of channel state information (CSI), and a large number of works [28, 29, 30, 31, 32, 33] have focused on this problem. [PERSON] _et al._ proposed feedback topologies in [29], which can reduce the signaling overhead significantly. An analogy CSI feedback strategy was proposed in [30], which can achieve lower feedback burden while maintaining the full multiplexing gain in the case of comparable forward and backward SNR levels. [PERSON] _et al._ designed a CSI training and feedback scheme to maximize the effective sum rate of the IA networks in [32]. [PERSON] _et al._ have concentrated on the CSI feedback latency in [33] and proposed a new IA scheme by taking advantage of the channel prediction.
# Performance Evaluation of China's First Ocean Dynamic Environment Satellite Constellation
## 3 Results
### Sea-Level Performances
#### 3.2.2 Cross-Calibration with Multiple Satellite Systems  
The Jason-3 satellite is the successor mission to the OSTM/Jason-2 satellite and is mainly used to study ocean circulations and sea surface height variability with high accuracy calibrations. The data from the Jason-3 satellite have been used as references to validate other altimeter data. This analysis of the GDR data of the Jason-3 satellite were used to verify the quality of the HY-2B SLA data. The HY-2B data period was utilized as a reference, and the cross-calibration of the HY-2B and Jason-3 data was analyzed. As detailed in Figure 4, the mean value of the standard deviation of the SLA differences at the dual-crossovers points of the HY-2B and Jason-3 satellites was 3.40 cm, with the maximum standard deviation of 3.61 cm for the data of Cycle 75. The data of the remainder of the  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Min** & **Max** \\ \hline pole\_tide & \(-\)0.15 m & 0.15 m \\ swh\_ku & 0 m & 11 m \\ sig0\_ku & 7 db & 30 db \\ altimeter wind speed & 0 m/s & 30 m/s \\ off\_nadir\_angle\_ku\_wvf & \(-\)0.2 deg\({}^{2}\) & 0.64 deg\({}^{2}\) \\ sig0\_rms\_ku & - & 1 db \\ sig0\_numval\_ku & 10 & - \\ alt\_range\_ku & \(-\)130 m & 100 m \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._cycles were stable (between 3.30 cm and 3.50 cm). The standard deviation of the SLA at the dual-crossovers points of HY-2B and Jason-3 was found to be consistent with the SLA from the self-crossing points recorded by the HY-2B satellite altimeter. These findings reconfirm that the observations from the radar altimeter of the HY-2B satellite are stable and that the accuracy of this product is consistent with that of the Jason-3.  
Figure 4: Cycle-by-cycle distributions of the sea level anomaly (SLA) differences at the crossover points of HY-2B and Jason-3 and HY-2B and HY-2C/D satellites (black line: HY-2B; red line: HY-2C; blue line: HY-2D).  
Figure 3: Cycle-by-cycle distributions of the sea level anomaly (SLA) differences at the mono-mission crossovers of the HY-2B/C/D satellite.  
The HY-2C, HY-2D, and Jason-3 have inclined orbits, with orbital inclination of 66\({}^{\circ}\). The orbits of the three satellites are almost parallel, with a small number of crossover points in the low-latitude regions. Jason-3 cannot be used to evaluate HY-2C/D. The HY-2B altimetry data were determined to be of higher quality. Therefore, the HY-2B data can be used to verify the quality of HY-2C and HY-2D altimetry data.  
As detailed in Figure 4, the HY-2B and HY-2C/D track crossover points of the SLA differences' standard deviation had an average value of 3.48 cm and 4.25 cm. Except for one cycle with large discrepancy, the data quality of HY-2C is high and the data are stable. The SLA measured by HY-2D showed some deviation from HY-2B data, possibly due to differences in the orbit determination methods used for the two satellites.
# A Blockchain Solution for Remote Sensing Data Management Model
## 3 The Architecture of the System
### Block Data Structure Serving Large-Scale Remote Sensing Applications
At the same time, because it is a public chain, the data are all public, which is convenient for transactions between various alliance chains through the public chain, which is obtained via the synchronization of the alliance chains; the other type is the class B block, which is obtained by the general public after they have submitted open data. The data storage structure of this blockchain system is based on the design idea of Ethernet. The remote sensing information blocks form a chain structure in a linear way, consisting of a block header and a block body. The data block of the public chain is referred to as the main chain block, and the main chain block body includes the ledger transaction data and the IPFS hash value of the telemetry metadata. The public searchable data recorded by the transaction data with storage incidentally store information, such as the serial number of the coalition chain where the corresponding telemetry data are located, the block height, and the block hash value of the block where the data are located in the coalition chain. The federation chain maintains the actual transactions in each chain, and the data block is called the slave chain block. The IPFS hash value in the block body contains the hash value of the remote sensing data block, and the hash value of the remote sensing metadata.
## 3 Results and Discussion  
### Effects of Biochar and Humic Acid on Heavy Metal Content in Crops and Soil  
#### 3.1.1 Heavy Metal Content in Crops  
In the analysis of HM contents in the crops, it was found that different treatments significantly altered the distribution pattern of HM within the crops (Figure 1). Notably, the Mn content in the aboveground parts of maize under the YB3, YB6, YB3H, and YB6H treatments was significantly lower than that in the Y treatment, with reductions of 10.81%, 11.05%, 26.03%, and 47.02%, respectively (\(p\) < 0.05). Furthermore, in the YB3H and YB6H treatments, the Mn content in the underground parts of maize decreased significantly by 7.07% and 11.55%, respectively, to the Y treatment (\(p\) < 0.05). Conversely, the Mn content in the underground parts of maize under YB3 and YB6 treatments did not differ significantly from that in the Y treatment (\(p<0.05\)). This suggests that biochar alone was effective in reducing Mn uptake in the aboveground maize, and this deterrent effect was further enhanced when biochar was used in conjunction with HA ([PERSON] et al., 2018). On the contrary, Cu content in aboveground parts of maize under YB3, YB6, YB3H, and YB6H treatments was significantly higher than that under Y treatment, and it increased synchronously with the increase of biochar and HA application amount (YB3-YB6-YB3H-YB6H). The underground Cu content in YB6, YB3H, and YB6H treatments was significantly lower than in Y treatment by 23.85%, 23.83%, and 40.40% (\(p<0.05\)), respectively. Overall, all of the high remediation material treatments (YB6, YB3H, and YB6H) increased aboveground Cu content but significantly decreased underground Cu content.
# Invasive Prenatal Diagnostics: A Cornerstone of Perinatal Management
## 3 Technological Advances in Prenatal Diagnostics  
In recent years, we have observed the rapid development of artificial intelligence (AI). Numerous self-learning algorithms have already been implemented in various aspects of our lives, including medicine. Initially, machine learning (ML) and deep learning (DL) models were found to be extremely useful in the field of radiologic image analysis [185; 186; 187; 188]. The overall performance of ML in analysing MRI images was approximately 90% and was significantly higher than that of clinicians. Those findings captured the attention of obstetricians, raising questions regarding the utility of AI in prenatal diagnostics. As a response, in 2022, [PERSON] et al. [187] introduced an ML model created by Trisomy21 Net to assess the risk of trisomy 21 in first-trimester screening. In their study, they obtained 3303 images of a standard midsagittal presentation used to assess nuchal translucency with FTS standards. Scans were shown to a team of experienced, certified sonographers and simultaneously analysed by AI. Surprisingly, the model had better detective performance for foetuses with trisomy 21. This finding suggests that implementing AI into the prenatal screening algorithm would reduce the risk of bias by examinations' experience and suboptimal visualisation. A year later, [PERSON] et al. [188] developed an AI model called Pgds-ResNet, a fully automated prenatal screening algorithm for various genetic diseases. This tool achieved sensitivity and specificity in detecting trisomy 21, 18, and 13 comparable to those of experienced sonographers. Analysing the heatmap of the AI framework showed that the most informative features of the foetal face in the context of abnormalities were the nose, jaw, and forehead. This contrasts the region of interest of clinicians, who mostly analyse nuchal translucency. On this basis, [PERSON] et al. [189] introduced a model targeted to analyse the features of foetal faces in the context of various abnormalities. Their study once again confirmed the high ability to detect trisomy 21 and 18 by AI. Moreover, they established a high diagnostic value for certain facial markers, such as the inferior facial angle (IFA), maxilla-nasion-mandible (MNM), facial-maximally angle (FMA), and frontal space distance (FS). Such discoveries only tap into a fraction of the capabilities of artificial intelligence, thus fuelling imagination for the future. The practice of prenatal genetic testing is continually changing, and old methods are constantly being substituted by novel ones.
## 4 Layered Models
### Application Layer  
The application layer, here referred to as the fourth layer, is tasked with the storage, analysis, and processing of data collected through network operations [62]. To fulfill this function, it incorporates high-capacity storage and processing infrastructure, as well as tools for the analysis of data sourced from the network and its components.  
The fundamental objective of this layer is to equip nodes with the capability to process extensive data from diverse origins, thereby enabling the evaluation of various risks and circumstances arising in traffic scenarios [58]. Such scenarios may encompass hazardous vehicle movement conditions, traffic congestion, or emergency events.  
The implementation described in [44] incorporates a pair of functionalities within this layer; they are specifically denoted as the \"see-through\" and \"collision warning\" services. The operation of these services relies on cloud computing technologies featuring adaptable resource scheduling.  
In the work presented by [45], services designed for the coordination and collaboration of multiple vehicles and autonomous driving are instantiated. The model they employed facilitates the deployment of additional services, thereby categorizing them into two distinct groups: customized application services targeted at mitigating safety risks in traffic and intelligent transportation applications encompassing intelligent driving and transportation management.  
The model outlined in [47] employs protocols like HTTP REST, advanced message queuing protocol, and extensible messaging. It elucidates the protocol employed within the Internet of Things (IoT) network, thereby illustrating its applicability in the context of the Internet of Vehicles (IoV).
## 3 Results and Discussion
### Volatile Compounds  
Forty volatile constituents were identified as shown in Table 5. The typical analytical ion current (AIC) chromatogram is shown in Figure S2. The chemical composition of volatiles was found in agreement with previous reports [43]. The main volatile compounds identified were nonanal (18.829%, **13**), \(\beta\)-pinene (12.179%, **14**), 1-octen-3-ol (12.129%, **15**), nervl alcohol (10.135%, **16**), \(\alpha\)-pinene (8.046%, **17**), 6-methyl-3,5-heptadiene-2-one (6.803%, **18**), \(p\)-cymen-9-ol (3.108%, **19**), and sylvestrene (3.022 %, **20**) (Figure S3). It is important to note that the content of the compounds differed from a previous study carried out on the volatile compounds in propolisis samples from Yucatan (Mexico). In that sample, hexadecanoic acid (10.9%) and trans-verbenol (7.0%) were the predominant compounds [44].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{4}{c}{**MIC b (ug/mL)**} \\ \cline{2-5}  & _S. mutans_ & _S. oralis_ & _S. sanguinis_ & _P. gingivalis_ \\ \hline EEP & 250 & 125 & 125 & 500 \\ EOP & 500 & 500 & 500 & 1000 \\ pinooembrin (**1**) & 256 & 128 & 128 & 512 \\ chrysin (**2**) & 512 & 512 & 512 & 1024 \\ galangin (**3**) & 256 & 256 & 256 & 1024 \\ alpinetin (**4**) & 128 & 256 & 128 & 516 \\ ferulic acid (**10**) & 500 & 250 & 250 & 500 \\ syringic acid (**11**) & 250 & 250 & 250 & 500 \\ caffeic acid (**12**) & 128 & 128 & 128 & 256 \\ nonanal (**13**) & 256 & 256 & 128 & 512 \\ nervl alcohol (**15**) & 1024 & 512 & 512 & 1024 \\ \(\alpha\)-pinene (**16**) & 250 & 250 & 250 & 500 \\ \(\alpha\)-pinene (**17**) & 500 & 500 & 500 & 500 \\ \({}^{\text{a}}\) CHX & 0.02 & 0.02 & 0.02 & 0.12 \\ \hline \hline \end{tabular}  
* positive control; b minimum inhibitory concentration.  
\end{table}
Table 4: Antimicrobial activity of ethanolic extract and compounds **1**–**4**, **10**–**13**, **15**–**17**, and chlorhexidine digluconate (CHX) a.