esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of Scale Effects in the Validation of GOCI-Derived SSC
### Proposed Method for Scale Effect Evaluation
Table 6 details the R\({}^{2}\) and MRE values for the regression relationships corresponding to each CV interval, and Figure 12 illustrates the trend of R\({}^{2}\) and MRE as the CV increases. Notably, when CV > 0.10, the correlation between the central pixel SSC of the HJ CCD and the GOCI retrieval drops markedly, and the MRE increases significantly; in contrast, the regression using the mean SSC shows a smaller deterioration in terms of both correlation and error. Therefore, in this study, for the HJ CCD SSC and GOCI SSC data, when the CV of SSC within the HJ CCD 17 \(\times\) 17 window corresponding to a GOCI pixel exceeds 0.1, scale effects become significant. In such regions, directly validating GOCI SSC retrievals with in situ measurements may introduce considerable uncertainty into the evaluation results. Figure 11: SSC sample pixels and regression relationships between different scales of HJ CCD SSC and GOCI SSC for eight variation intervals **(a–h)**. **(a)** SSC sample pixel and regression relationship when CV is 0–0.07. **(b)** SSC sample pixel and regression relationship when CV is 0.07–0.08. **(c)** SSC sample pixel and regression relationship when CV is 0.08–0.10. **(d)** SSC sample pixel and regression relationship when CV is 0.10–0.15. **(e)** SSC sample pixel and regression relationship when CV is 0.15–0.20. **(f)** SSC sample pixel and regression relationship when CV is 0.20–0.25. **(g)** SSC sample pixel and regression relationship when CV is 0.25–0.30. **(h)** SSC sample pixel and regression relationship when CV is 0.30–0.74. This method, through the construction of a CV-based stratification framework, systematically elucidates the scale effect in validating SSC retrievals from remote sensing data in highly dynamic water bodies. The experimental results demonstrate that the validation method utilizing the HJ CCD window mean enhances the accuracy of correlating GOCI-derived suspended sediment concentration (SSC) products in Hangzhou Bay, particularly under specific heterogeneity conditions. When the coefficient of variation (CV) ranges from 0.10 to 0.15, this method yields a marked improvement in correlation with GOCI products, achieving an R\({}^{2}\) value of approximately 0.83 compared to approximately 0.71 for traditional single-point validation. Additionally, the mean relative error (MRE) decreases to approximately 11.63% from approximately 14.89% in the same CV range. However, in higher CV intervals (CV > 0.15), although the window-averaged validation continues to outperform single-point validation, both the correlation (R\({}^{2}\)) and error (MRE) metrics exhibit a gradual decline, with R\({}^{2}\) dropping below 0.83 and MRE exceeding 11.63%. This approach quantitatively defines the critical threshold for scale effects in highly heterogeneous waters. By integrating multi-scale validation concepts within a CV-based zoning framework, this method offers a solution that is both theoretically robust and practically viable for validating remote sensing products in high-dynamic water environments.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Model** & **R.Train** & **R.Test** & **MSE.Train** & **MSE.Test** & **MAE.Train** & **MAE.Test** & **NSE** & **RMSE** \\ \hline
0 min. & 0.9237 & 0.8525 & 14.2629 & 29.9912 & 1.4419 & 2.5535 & 0.8529 & 3.7766 \\
30 min. & 0.9512 & 0.9355 & 9.5954 & 11.4159 & 0.6157 & 0.7815 & 0.9043 & 3.0976 \\
1 h & 0.8932 & 0.8473 & 19.5178 & 28.9255 & 0.9876 & 1.3884 & 0.7976 & 4.4179 \\ \(\delta Q\) & 0.9757 & 0.9046 & 4.7187 & 19.8966 & 0.4672 & 0.8565 & 0.9514 & 2.1723 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance of LSTM with bat algorithm models for streamflow. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Model** & **R.Train** & **R.Test** & **MSE.Train** & **MSE.Test** & **MAE.Train** & **MAE.Test** & **NSE** & **RMSE** \\ \hline
0 min. & 0.9420 & 0.7773 & 10.9191 & 46.4891 & 1.4316 & 2.9865 & 0.8873 & 3.3044 \\ \hline
30 min. & 0.9741 & 0.9102 & 5.1395 & 15.7782 & 0.5536 & 0.8486 & 0.9487 & **2.2670** \\ \(1\) h & 0.9401 & 0.7928 & 11.2678 & 38.2200 & 0.8736 & 1.5270 & 0.8836 & 3.3568 \\ \(\delta Q\) & 0.9738 & 0.9010 & 5.1252 & 20.5746 & 0.4766 & 0.8563 & 0.9475 & 2.2639 \\ \hline \end{tabular}
\end{table} 
Table 5: Performance of LSTM with Java algorithm models for streamflow simulation. A further check is performed on the hybrid optimization models to determine the MAPE, MAE and maximum error values for the flows equal to or greater than 150 m\({}^{3}\)/s. This ensures the accuracy of forecasting high flow values, which is important in a flood mitigation operation.
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 4 Discussion  
The aim of this study is to fully leverage the information from high-resolution remote sensing images. By processing and analyzing the multi-spectral and multi-band multi-label remote sensing image data, a wealth of geographic information can be obtained, which has extensive applications in various fields. For example, [PERSON] et al. [34] extracted information from mountainous road networks, which is of great significance for road planning, traffic management, and environmental protection in mountainous areas. [PERSON] et al. [35] conducted research on identifying agricultural land parcels, providing accurate land use information for agricultural management and decision-making. [PERSON] et al. [36] developed a method for water extraction, which is crucial for water resource management, environmental monitoring, and disaster response. [PERSON] et al. [37] investigated land cover change, providing important references for urban planning and land management. [PERSON] et al. [38] studied land use classification, contributing to land management and environmental monitoring in the region. These studies demonstrate the potential of our research in obtaining surface information and assisting scientific research and decision-making processes.  
The research findings demonstrate that the MMDL-Net model outperforms the ResNet model in handling categories with complex landform features, such as pastures and agroforestry mixed areas. The MMDL-Net model performs exceptionally well in classifying marine waters, achieving an accuracy of over 95%. Although the accuracy of the MMDL-Net model is slightly lower in categories like beaches, sand dunes, and sandy land, it still shows improvement compared to the ResNet model.  
This study involves the identification and classification of various land features, such as forests, farmland, urban areas, water bodies, and natural vegetation. It provides valuable information for decision-making processes related to sustainable development, land management, and conservation efforts. It plays a crucial role in managing land resources, urban planning, environmental monitoring, and natural resource management.
###### Abstract  
Satellite altimetry provides exceptional means for absolute and undisputable monitoring of changes in sea level and inland waters (rivers and lakes), over regional to global scales, with accuracy and with respect to the center of mass of the Earth. Altimetry system's responses have to be continuously monitored for their quality, biases, errors, drifts, etc. with calibration. Absolute calibration of altimeters is achieved by external and independent to satellite facilities on the ground. This is the mainstay for a continuous, homogenous, and reliable monitoring of the earth and its oceans. This paper describes the development of the Permanent Facility for Altimetry Calibration in Gavdos/Crete, Greece, as of 2001 along with its infrastructure and instrumentation. Calibration results are presented for the reference missions of Jason-1, Jason-2, and Jason-3. Then, this work continues with the determination of relative calibrations with respect to reference missions for Sentinel-3A, HY-2A, and SARAL/AltiKa. Calibration results are also given for Jason-2 and Jason-3 altimeters using the transponder at the CDN1 Cal/Val site on the mountains of Crete, with simultaneous comparisons against sea-surface calibration and during their tandem mission. Finally, the paper presents procedures for estimating uncertainties for altimeter calibration to meet the Fiducial Reference Measurement standards.  
altimetry; calibration; Jason; transponder; sea surface; fiducial reference measurements 10 1557; doi:10.3390/rs10101557
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 3 Results
### Experimental Formation of Interferograms
The results confirm that complex interference fields were obtained with the amplitude-phase modulation technique [50].
## 4 Discussion
### Volatile Organic Compounds  
It was worth studying the effect of a kiwifruit extract on the volatile organic compounds (VOCs) of cheese, since these substances determine the taste and flavour of cheese, and thus influence the consumer's choice. The key odorants of _Actinidia delicosa_ are (E)-2-hexenal, hexanal, ethyl butyrate and 1-penten-3-one, which give a herbal, sweet, marzipan odour [31]; in addition, ethyl butyrate, ethyl caproate, 2-hexen 1-ol and 1-hexanol and 1-penten-3 are contained in non-negligible quantities in kiwifruit (Table 4). They are fat soluble substances and thus we expected them to have been transferred to the cheese during cheesemaking, negatively affecting its organoleptic properties. However, we found that only (E)-2-hexenal (which represents about 80% of VOCs in kiwifruit extract), ethyl caproate and 2-hexen-1-ol, as well as two others volatile substances, 3-methyl eicosane and dibutyl formaldehyde, with a lower content in the kiwi extract, were transferred from kiwifruit into the buffalo and sheep cheese. These substances were found in the \"kiwi cheeses\" in quantities never higher than 2 \(\mu\)g/kg of cheese and in total accounted for less than 3.4 \(\mu\)g/kg, making up less than 2.5% of the total VOCs of both cheeses.  
On the other hand, 2-butanone 2,3-butenedione made up over 30% of the total VOCs in all the cheeses and thus were not impacted by the cheesemaking. Given that the substances that most affected the aroma of the cheeses were the same, irrespectively of the cheesemaking procedure, and that the typical odorants of the kiwi aroma were transferred to the cheese to a very low extent, this would seem to indicate that cheesemaking with the kiwi extract led to a transfer of some volatile substances into the cheese. Further and specific organoleptic tests to assess whether this affects the aroma and taste of cheese should to be done. Finally, VOCs can be effective in tracing of the cheesemaking process.
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 2 Motivation and Background
### Background on 5G AKA and Cryptographic Primitives
#### 2.2.1 5G-AKA and SUCI
The key difference between Profile A and Profile B arises in the elliptic curve domain parameters, elliptic curve Diffie-Hellman (ECDH) primitive, and point compression. However, other elements remain consistent across both profiles. Profile A uses Curve25519 as its domain parameter and X25519 as an ECDH primitive while Profile B uses secp256r1 as its domain parameter and elliptic curve cofactor Diffie-Hellman as an ECDH primitive. Additionally, Profile B implements point compression to reduce overhead, while Profile A does not apply this compression technique. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Scheme ID** & **Algorithms** & **Output Scheme** \\ \hline Null scheme & 0 & N/A & Size of MSIN (if using IMSI) \\ \hline Profile A & 1 & ECIES (X25519) & \
\begin{tabular}{c} 256 bits public keys + 64 bits \\ MAC + cipher-text value \\ \end{tabular} \\ \hline Profile B & 2 & ECIES (secp256r1) &
\begin{tabular}{c} 256 bits public keys + 64 bits \\ MAC + cipher-text value \\ \end{tabular} \\ \hline Reserved for future & 3–11 & N/A & N/A \\ \hline Reserved for proprietary & 12–15 & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Protection schemes standardized by 3 GPP [6]. Figure 2: Subscriber Concealed Identifier (SUCI) data fields.
## 4 Discussion
### Treatment Effect on LUE\({}_{\text{P}}\), LUE\({}_{\text{f}}\), Fesc\({}_{\text{fw}}\)  
The relative stability among treatments of LUE\({}_{\text{P}}\), which is significantly different for the N treatment only in Campaign 6 and shows an increase of NP in Campaign 5 in 2015, suggests that our Mediterranean grasslands is quite constrained in its photosynthetic efficiency, and that any nutrient induced changes in GPP (Figure 2) are mostly modulated by changes in structural parameters such as fAPAR.  
The increase in LUE\({}_{\text{f}}\) in the NP treatment compared to N alone suggests a co-limitation of nitrogen and phosphorus on fluorescence efficiency. The role of P on the functional modulation of fluorescence efficiency at canopy scale has not yet been shown in the literature. However, a series of studies at leaf level showed a positive relationship between photochemical quenching and P in leaves as well as an effect of P on active fluorescence measurements [57]; these support the differences in LUE\({}_{\text{f}}\) observed in our study. Our study suggests that P, and in particular the co-limitation N and P, might have an important role on determining F\({}_{760}\) but is not conclusive on the mechanism, and more research is needed to understand the mechanism and also to support the current efforts to include P in terrestrial biosphere and photosynthesis models [27]. The fact that the magnitude of increase of Fesc\({}_{\text{fw}}\) is very similar in N and NP treatments support the idea that N addition is the main factor regulating canopy structure (Fesc\({}_{\text{fw}}\) and APAR). Other works show that N addition strongly impacts canopy structural parameters such as LAI and plant height in a short-grass prairie [58], although there are no studies focused on the effect of N and NP on Fesc.  
Overall, the ecosystem responded in the first year to the fertilization, mainly in a functional way (higher LUE\({}_{\text{f}}\)), whereas, in the second year of fertilization, we observed structurally mediated increase in GPP and F\({}_{760}\) (through higher APAR and Fesc\({}_{\text{fw}}\)) (Figures 21 and 3d). The structurally mediated changes in 2015, driven by a decrease in abundance of erectophiles plants as the graminoids in the N containing treatments, caused a change in slope in the GPP-F\({}_{760}\) relationship for the N and NP treatment (Figure 4c), which is almost significantly different from the C for F\({}_{760\text{leaf,fw}}\), but significantly different from the C for F\({}_{760\text{leaf,fw}}\) in the NP treatment (Figure 4d).  
The N treatment has proven to affect plant functioning and canopy structure (APAR and Fesc\({}_{\text{fw}}\)), while P has only a marginal role on the LUE\({}_{\text{f}}\). For this reason, in the next paragraphs, more attention is paid to the role of N%, together with meteorology and canopy structure, as driver of in \(\mathrm{LUE_{p}}\), \(\mathrm{LUE_{f}}\) and \(\mathrm{Fesc_{fw}}\), as well as GPP and \(\mathrm{F_{760}}\).
## 2 Materials and Methods
### Base Classifiers
#### 2.2.4 Logistic Regression  
Logistic regression (LR) [26] assumes that the data obey the Bernoulli distribution and performs classification using the method of maximizing the likelihood function to solve the parameters via gradient descent. The advantage is that the classification possibility is directly modeled, avoiding the problems caused by inaccurate distribution assumptions. Itis easy to use, with strong interpretability. However, LR is prone to underfitting and has relatively poor classification accuracy. When the feature space is large, the performance is relatively poor. The logistic regression model can be expressed as:  
\[\begin{split} P(y=1|x)&=\frac{exp(w\cdot x+b)}{1+exp(w \cdot x)+b}\\ P(y=0|x)&=\frac{1}{1+exp(w\cdot x)+b}\end{split} \tag{6}\]  
where \(x\) represents the input features, \(y\) is the output class, \(w\) is the weight vector, \(b\) is the bias term, and \(w\cdot x\) denotes the dot product of \(w\) and \(x\).
## 3 Outage Rate Analysis
### Optimal \(\Delta_{t}^{*}\) with Varying Path-Loss Exponent \(n\)  
In Figures 7 and 8, we observe the impact of the path-loss exponent \(n\). Since path attenuation increases, as \(n\) increases, the performance degradation by increasing \(\Delta_{t}^{*}\) becomes more significant. As derived in analysis in Section 3, the outage rates increase, as \(n\) increases due to the increased path attenuation for the same distance, as indicated in Figure 7. As in the previous simulation results, the outage probability with MRC is always smaller compared that with SC. Furthermore, the shape of the surfaces tell us that there exists an optimal \(\Delta_{t}\) for a fixed \(n\). Figure 8 illustrates how the optimal inter-separation \(\Delta_{t}^{*}\) varies under the change in \(n\). As before, the curves with 'x'- and 'o'-markers indicate \(\Delta_{t}^{*}\) with MRC and SC, respectively. In general, for both MRC and SC, \(\Delta_{t}^{*}\) decreases as \(n\) increases, because the path attenuation becomes more important factor with higher \(n\), while the impact of correlated shadowing remains the same. For the reason, when \(n\) is large, a method to take advantage of macro-diversity by incrementing \(\Delta_{t}\) is not as efficient as in the channel environments with small \(n\).
# A Dynamic Service Placement Based on Deep Reinforcement Learning in Mobile Edge Computing
## 4 Dynamic Service Placement Framework Based on Deep Reinforcement Learning
### Migration Conflicting Resolution Mechanism
#### 4.2.2 Migration Conflict Resolution Mechanism
```
0: The action \(a_{t}\) at time slot \(t\);
0: The updated action \(a_{t}\) of service placement decisions under the migration conflict edge servers;
1:for each service \(v_{h}\in\mathbf{V}\)do
2: Pre-migration according to \(a_{t}\);
3:endfor
4:for each edge server \(m_{j}\in\mathbf{M}\)do
5: Calculate the total number of services \(N_{m_{j}}\) pre-migrated to \(m_{j}\);
6:if\(N_{m_{j}}>R(m_{j})\)then
7: Construct conflict set \(\mathbb{C}_{m_{j}}=\{v_{h}^{m_{j}}\}\);
8: Choose service \(v_{h}\) in set \(\mathbb{C}_{m_{j}}\) with maximum \(\zeta(v_{h})\);
9: Update set \(\mathbb{C}_{m_{j}}=\mathbb{C}_{m_{j}}/v_{h}\);
10:for each service \(v_{h^{\prime}}\) in set \(\mathbb{C}_{m_{j}}\)do
11: Migrate \(v_{h^{\prime}}\) to the nearest edge server that meets the storage resource;
12:endfor
13: Record current state of services placement in \(a_{t}^{\prime}\) and update \(a_{t}=a_{t}^{\prime}\);
14:else
15: Keep the original service placement decisions of action \(a_{t}\);
16:endif
17:endfor
```  
**Algorithm 1** Migration conflict resolution method
###### Abstract  
This study presents the results of applying the methods of synthesizing mesoporous carbon and mesoporous polymer materials with an extended porous mesostructure as adsorbents for cationic dye molecules. Both types of adsorbents are synthetic materials. The aim of the presented research was the preparation, characterisation, and utilisation of obtained mesoporous adsorbents. The physicochemical properties, morphology, and porous structure characteristics of the obtained materials were determined using low-temperature nitrogen sorption isotherms, X-ray diffraction (XRD), small angle X-ray scattering (SAXS), and potentiometric titration measurements. The morphology and microstructure were imaged using scanning electron microscopy (SEM). The chemical characterisation of the surface chemistry of the adsorbents, which provides information about the surface-active groups, the elemental composition, and the electronic state of the elements, was carried out using X-ray photoelectron spectroscopy (XPS). The adsorption properties of the mesoporous materials were determined using equilibrium and kinetic adsorption experiments for three selected cationic dyes (derivatives of thiazol (methylene blue) and triarylmethane (malachite green and crystal violet)). The adsorption capacity was analysed to the nanostructural and surface properties of used materials. The Generalized Langmuir equation was applied for the analysis of adsorption isotherm data. The adsorption study showed that the carbon materials have a higher sorption capacity for both methylene blue and crystal violet, e.g., 0.88-1.0 mmol/g and 0.33-0.44 mmol/g, respectively, compared to the polymer materials (e.g., 0.038-0.04 mmol/g and 0.038-0.050 mmol/g, respectively). The kinetics of dyes adsorption was closely correlated with the structural properties of the adsorbents. The kinetic data were analysed using various equations: first-order (FOE), second-order (SOE), mixed 1,2-order (MOE), multi-exponential (m-exp), and fractal-like MOE (f-MOE).  
mesoporous carbon; mesoporous polymer; hierarchical porosity; dye adsorption; methylene blue; crystal violet; malachite green; nanostructure; environment protection
## 3 Results and Discussion
The longitudinal relaxivities of our Gd-CNPs obtained by both methods are higher than those values for the commercially available Gd-based molecular CAs [33; 34] and theranostic Gd-based NPs [35; 36]. The high values of both longitudinal and transverse relaxivities of the prepared Gd-CNPs are probably determined by the employed synthesis and purification procedures, which ensure the desired surface arrangement and molecular coordination of Gd\({}^{3+}\) in CNPs.