esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Ecosystem Services across the Land-Sea Interface in Baltic Case Studies
## 3 Results
### Ecosystem Services Flow
Differences between land and sea can only be assessed based on the ES 'natural heritage' (C3). For the German CSS, the proportion of protected areas, and thus the ES flow value, is higher for marine areas (Schlei: 0.07 terrestrial vs. 0.74 marine; GWB: 0.14 terrestrial vs. 0.95 marine). In contrast, the ES flow is higher for marine areas for the CSS Curonian Lagoon (0.77 terrestrial vs. 0.56 marine). All CSS cover coastal Lagoons, which are included in the list of specific habitats that are of European interest in accordance with the HD. As such, they targeted for conservation and included in the Natura 2000 network. The marine area of the sea-dominated CSS Curonian Lagoon has a large share of open coastal waters, which are often not designated as protected areas. Hence, the flow is lower in comparison to the land-dominated CSS Schlei and the balanced CSS Greifswald Bay. To generate ES flow maps, the relative flow values for the selected ES were multiplied with the corresponding ES potentials across all habitats. Resulting maps for ES flows, based on the CSS-specific flow matrices, are shown in Figure 4.  
A generally increasing trend from lower provisioning ES to higher cultural ES is also shown for ES flows, with the exception of CSS Schlei. Here, the habitat-weighted flow values for the overall area and the terrestrial area are higher for regulating ES than for cultural ES. Differences in aggregated ES flows across land and sea are shown between the CSS. On an aggregated level, low ES flows are shown in all CSS. In contrast to the other CSS, there is no difference between the habitat-weighted flow values for terrestrial and marine areas. The habitat-weighted flow of regulating and maintenance ES is very similar for the German CSS (RES-S: O:25, T:23, M:30 vs. RES-GWB: O:26, T:21, M:16). In contrast, the overall habitat-weighted flow is lower in the Lithuanian CSS and higher for terrestrial the terrestrial area compared to the marine area (RES-CL: O:17, T: 27, M:16). The habitat-weighted ES flow is highest for Greifswald Bay (CES-GWB: O:39, T:22, M:47) and Curonian Lagoon (CES-CL: O:30, T:46, M:29). Additionally, in this case, the higher  
Figure 4: Ecosystem services (ES) flows shown on an aggregated (averaged) level for provisioning, regulating, and maintenance, and cultural ES. Lowest ES flows are shown in red (<10) and highest ES flows are shown in dark green (>90). Habitat-weighted ES flows are indicated for each site for the overall areas (O), and for terrestrial (T) and marine (M) areas separately. contribution of terrestrial areas in the CSS Curonian Lagoon in comparison to Greifswald Bay is directly visible in the spatial maps. The maps presented in Figure 4 allow a general comparison of ES flow across land and sea and among CSS. However, for a better interpretation a comparison of ES potentials and flows is needed, as low ES flows indicated in the maps can both be a result of low case study flow values (cf. Table 2) or a result of low ES potentials values in the expert-based Baltic ESP Matrix.
# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 2 Preparatory Knowledge
### Probabilistic Public-Key Cryptosystems  
Probabilistic public-key encryption is a non-deterministic cryptography that generates randomly varying ciphertexts for the same plaintext. Under the computational security assumption, it is impossible to acquire any reliable information about the plaintext within polynomial time by using ciphertext correlation attacks. [PERSON] and [PERSON] designed a probabilistic public-key scheme [16] (referred to as the GM probabilistic public-key encryption algorithm) using the quadratic residual theorem. However, the GM scheme has a high ciphertext expansion rate, leading to low transmission efficiency. [PERSON] and [PERSON] proposed a more efficient probabilistic encryption scheme [17] (referred to as the BG cryptosystem), significantly reducing the expansion of ciphertext data. Hence, we primarily employ the BG scheme for encrypting user identity information. The BG probabilistic encryption mainly utilizes the BBS [18] generator to enhance ciphertext randomness. The detailed algorithm is as follows:  
Parameter Setting: Let \(n=p\cdot q\), where \(p\) and \(q\) are large primes, and \(p\equiv q\equiv 3 mod4\). Here, \(n\) is the public key, while \(p\) and \(q\) serve as the private keys. Define the plaintext space as \(P=\left(Z^{2}\right)^{m}\), the ciphertext space as \(C=\left(Z^{2}\right)^{m}\times Z_{n}^{*}\), and the key space as \(K=\left\{(n,p,q)\right\}\).  
Encryption: For plaintext message \(x\in\left(Z^{2}\right)^{m}\) to be encrypted, the process is as follows:  
1. Randomly select a seed \(\mathrm{s_{o}}\) and use the BBS generator to produce \(m\) random bits \(z_{1}\cdots z_{m}\) as the keystream;
2. Calculate \(s_{m+1}=s_{o}^{2m+1}modn\);
3. Calculate \(y_{i}=(x_{i}+z_{i})mod2\), \(1\leq i\leq m\);
4. The ciphertext is \(\mathrm{c}=\mathrm{E_{K}(x,r)}=(y_{i^{\prime}}\cdots,y_{i^{\prime}}s_{m+1})\).  
Decryption: The process of decrypting the ciphertext \(c=(y_{i},\cdots,y_{i^{\prime}}s_{m+1})\) is as follows:  
Figure 1: Example of a Bitcoin UTXO trading model.  
1. Calculate \(a_{1}=\left(\frac{p+1}{4}\right)^{m+1}mod(p-1)\) ;
2. Calculate \(a_{2}=\left(\frac{q+1}{4}\right)^{m+1}mod(q-1)\) ;
3. Calculate \(b_{1}=s_{m+1}^{a_{1}}modp\);
4. Calculate \(b_{2}=s_{m+1}^{a_{2}}modq\);
5. Utilize the Chinese remainder theorem to calculate \(r\), satisfying \(r\equiv b_{1}modp\) and \(r\equiv b_{2}modq\);
6. Using the BBS generator, derive \(z_{1}\cdots z_{m}\) from the seed \(s_{o}=r\);
7. For each bit \(1\leq i\leq m\), compute \(x_{i}=(y_{i}+z_{i})mod2\);
8. The decrypted plaintext is \(x=x_{1}\cdots x_{m}\).
# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
## 2 Previous Research
### Gradient Boosting, LightGBM, CatBoost  
[PERSON] et al. [23] used the gradient boosting decision tree model with the gradient-based one-side sampling and exclusive feature bundling to further enhance the prediction capability of GBM. Approximation ratios for the developed LightGBM were compared against the XgBoost model, and results showed fast prediction of data among the same base line. Furthermore, LightGBM leads to 20 times increase in the training speed of the gradient boosting decision tree. [PERSON] et al. [24] predicted the skid resistance using the LightGBM, Xgboost and SVR algorithms, and 3D point cloud data for the asphalt surface were used to train the algorithms based on the surface texture of asphalt pavements. The performance of each algorithm was evaluated based on the dataset and training. Results concluded with LightGBM exhibiting the highest accuracy with R\({}^{2}\) of 92.83%.  
[PERSON] et al. [25] used the ensemble learning model developed with LightGBM for roughness and rut depth predictions. Results were compared with that of ANN and showed good capability of the developed LightGBM to predict rut depth and IRI. [PERSON] et al. [26] used the LightGBM for prediction of pavement condition index of airport pavements by comparing it with non-linear regression models, random forest models and artificial neural networks. Results showed good capability of the LightGBM to detect cracks using the imagery and outperforming the other models' capability to predict with accuracy to real site conditions. [PERSON] et al. [27] used various versions of gradient boosting techniques including Adaptive Boosting (AdaBoost), LightGBM, gradient boosting decision tree (GBDT) and categorial boost (CatBoost) for faulting predictions in rigid pavements. The models were combined with the tree-structured Parzen simulator (TPE). Data were obtained from a long-term pavement performance database with over 160 observations. Results showed good capability of models to predict the faulting, with CatBoost being the most accurate with an R\({}^{2}\) value of 0.906. Historical data consisting of 17 variables with 160 instances of observations from a long-term pavement performance program (LTPP) were utilized. Results concluded with the CatBoost algorithm showing the highest accuracy with R\({}^{2}\) of 0.906. Furthermore, CatBoost was able to identify the primary affecting factors in terms of faulting.
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 5 Conclusions  
This study proposes a simple but effective and portable approach for the monitoring of river hydromorphology through remote sensing. A case study focusing on the Po River is presented, providing evidence of the impact of the 2022 drought on river width. The use of PWC time series calculated from Sentinel-2 NDWI, together with the derived indicators (i.e., MWF, MWFA, DPM, and OWF), contributed to a more comprehensive understanding of the river dynamics and spatio-temporal variations compared to an assessment based only on the available hydrological dataset. The findings demonstrate that (i) during June and July 2022, due to a reduction in precipitation and an increase in temperature and evapotranspiration, the Po River experienced a significant shrinkage; (ii) the annual and interannual satellite-based analysis conducted from 2016 to 2023 highlighted higher sensitivity areas; and (iii) variation in river width was evident as a result of both climate change and river hydromorphological dynamics.  
The potential for generalizing the methodology through the proposed approach, in terms of applicability to other geographic sites and the possible use of various optical multi-spectral sensors, allows detailed assessments of surface water dynamics across entire river basins, enhancing monitoring capabilities and supporting the effective management of water resources. Application of the procedure to imagery acquired by a virtual constellation of high- and very-high-resolution satellite sensors can provide a more detailed understanding of the phenomenon, stimulating the development of operational services like hydromorphological assessment. While the results confirm the reliability of the proposed approach, future improvements could further refine the methodology, with the aim of advancing sustainable strategies in response to climate change and anthropogenic pressures.  
Conceptualization, F.F. and G.F.; methodology, F.F., G.C. and G.F.; software, F.F.; formal analysis, F.F., G.C., E.V. and G.F.; investigation, F.F., G.C., E.V. and G.F.; data curation, F.F.; writing--original draft preparation, F.F. and G.C.; writing--review and editing, F.F., G.C., E.V., C.C. and G.F.; supervision, C.C. All authors have read and agreed to the published version of the manuscript.  
This manuscript is not funded by a specific project grant.  
Dataset available upon request from the authors.  
G.C. acknowledges the support of project PNRR-NGEU, which has received funding from the MUR-DM 118/2023. This work contains modified Copernicus Sentinel data (2025). Authors are grateful to the many individuals working on the development of free and open-source software for supporting the sharing of knowledge. The authors thank the anonymous reviewers for their careful reading of our manuscript and their insightful comments and suggestions.  
The authors declare no conflicts of interest.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 4 Trajectory Feature Extraction and Restoration Model Construction
### Construction of Seq2 Seq-Based Restoration Model
#### 4.3.2 Basic Neural Network Models  
In this paper, RNN, gated recurrent unit (GRU), LSTM, BILSTM, and BIGRU models are used as the underlying network architectures for comparison experiments. Its internal structures are shown in Figure 7.  
The mechanism of operation of the RNN model is that the output of each step acts directly on itself at the next time step. The inputs from the previous moment are processed and extracted for information and then passed directly into the network along with the inputs from the next moment. This mechanism enables the hidden state to be passed backward sequentially as the sequence advances, but at the same time, it may also lead to the problem of gradient vanishing or gradient explosion, and the RNN structure is shown in Figure 7a.  
Figure 6: Seq2 Seq model structure.  
Compared to RNN, the LSTM model introduces a gating mechanism, and this improvement allows the model to better control the long-term information transfer and capture the long-distance dependence, thus improving the problem of gradient vanishing or gradient explosion. The structure of LSTM is mainly composed of four parts, namely the input gate, the forgetting gate, the output gate, and the memory unit, and the structure is shown in Figure 7b.  
The GRU model is further simplified in terms of network structure, containing only update gates and reset gates, which do not degrade the model performance but realize the functions of \"forgetting\" and \"selecting memory\" by resetting the gating unit at the same time. The GRU model not only reduces the time required for model training but also helps to alleviate the problems of gradient vanishing and explosion. The GRU model not only effectively reduces the time needed for model training but also helps to alleviate the problems of gradient vanishing and explosion, as shown in Figure 7c.  
The BILSTM and BIGRU models, on the other hand, are bi-directionally trained to ensure that the trajectory data are fully utilized. This class of model is a bi-directional architecture composed of two independent LSTM/GRU networks, which are computed in forward and direction and merged to produce the final result, with the structure shown in Figure 7d.
# Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
## 4 Results and Discussion
### The Northeast Area of Dianchi Lake  
The surface subsidence in the northeast of Dianchi Lake has been serious for a long time. As can be seen from the monitoring results, the surface subsidence in this region is still significant, with the maximum deformation rate exceeding 37 mm/year, as shown in Figure 7a. The area with obvious subsidence characteristics is divided into three areas, namely P1, P2 and P3. The P1 region is the area of Fuhai-Hewei village- Yuhu village, which shows a zonal distribution, and the average annual subsidence rate is about \(-20.51\) mm/year-- \(2.64\) mm/year. Area P2 is the convention and exhibition center with a zonal distribution, and the average annual subsidence rate is about \(-33.71\) mm/year-- \(-9.94\) mm/year. Area P3 is the area of Xiaobanqiao-Guangwei village. It is a traditional settlement area with an oval shape, and the average annual subsidence rate is \(-24.66\) mm/year-- \(6.51\) mm/year.  
In order to analyze the deformation law of each subsidence area in this region over time, time series cumulative subsidence maps of P1, P2 and P3 are extracted, as shown in Figure 7b. The cumulative deformation of P1, P2 and P3 in this period reached 30 mm, 70 mm and 50 mm respectively, showing a continuous downward trend. In the area of the convention and exhibition center, the cumulative subsidence accelerated from February 2019-March 2019 and September 2019-October 2019 but slowed down from November 2019-December 2020.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Num**} & \multicolumn{2}{c}{**T0 (Pre Treatment)**} & \multicolumn{2}{c}{**T1 (Post Treatment)**} & \multicolumn{2}{c}{**\(\Delta\) Mean**} & \multirow{2}{*}{**\(p\) Value**} \\  & & & **T0 (Pre Treatment)** & & **T1 (Post Treatment)** & & \\ \hline SNA & 45 & 79.50 \(\pm\) 3.376 & 79.84 \(\pm\) 3.483 & \(-\)0.34 & 0.1 \\ SNB & 45 & 74.17 \(\pm\) 2.913 & 75.71 \(\pm\) 2.871 & +1.538 & 0.00002 * \\ ANB & 45 & 5.349 \(\pm\) 1.221 & 4.124 \(\pm\) 1.299 & +1.224 & 0.00002 * \\ Wits & 45 & 3.231 \(\pm\) 2.068 & 0.442 \(\pm\) 1.540 & \(-\)2.789 & 0.00003 * \\ FMA & 45 & 22.7 \(\pm\) 5.104 & 23.8 \(\pm\) 3.841 & +1.102 & 0.2 \\ MP\_SN & 45 & 32.63 \(\pm\) 4.327 & 33.49 \(\pm\) 4.303 & +0.8622 & 0.04 \\ UI\_OCel plane & 45 & 58.55 \(\pm\) 8.459 & 59.62 \(\pm\) 6.344 & +1.076 & 0.5 \\ U1\_Palatal Plane & 45 & 111.8 \(\pm\) 9.411 & 111.0 \(\pm\) 15.216 & \(-\)0.7267 & 0.7 \\ L1\_Ocl Plane & 45 & 67.86 \(\pm\) 4.251 & 66.96 \(\pm\) 4.375 & \(-\)0.9089 & 0.4 \\ IMPA & 45 & 97.7 \(\pm\) 5.493 & 99.0 \(\pm\) 4.545 & +1.3 & 0.00003 * \\ SN\_PP & 45 & 8.198 \(\pm\) 2.954 & 8.540 \(\pm\) 2.727 & +0.3422 & 0.5 \\ PP\_GoGn & 45 & 20.75 \(\pm\) 5.162 & 21.77 \(\pm\) 4.523 & +1.018 & 0.1 \\ Go\_Pg & 45 & 59.01 \(\pm\) 3.752 & 62.44 \(\pm\) 4.343 & +3.422 & 0.00003 * \\ Co\_Gn & 45 & 94.80 \(\pm\) 5.586 & 97.82 \(\pm\) 5.811 & +3.016 & 0.00001 * \\ Overjet (OJ) & 45 & 5.409 \(\pm\) 1.94 & 3.089 \(\pm\) 0.922 & \(-\)2.32 & 0.0001 * \\ Overbite (OB) & 45 & 3.600 \(\pm\) 1.480 & 2.282 \(\pm\) 1.131 & \(-\)1.318 & 0.0004 * \\ Right molar class & 45 & 4.005 \(\pm\) 1.303 & 1.460 \(\pm\) 1.350 & \(-\)2.545 & 0.0000008 * \\ Left molar class & 45 & 3.871 \(\pm\) 1.356 & 1.201 \(\pm\) 1.357 & \(-\)2.67 & 0.0000001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptive information (mean/standard deviation for each value) at T0 and T1, and relative statistical significance (\(p<0.05\) *). Figure 8: Trend in Wits appraisal over time from T0 to T1. Figure 6: Trend in SNB over time from T0 to T1. Figure 7: Trend in ANB over time from T0 to T1. Figure 11: Trend in Go-Pg over time from T0 to T1. Figure 10: Trend in IMPA over time from T0 to T1. Figure 9: Trend in MP-SN over time from T0 to T1.
## 4 The Proposed Model
### Prediction
#### Scoring Function  
The predicted preference score of user \(u\) for item \(i\) is computed via a dot product scoring function. By integrating the global semantic abstraction of VRKG and the local dynamic adaptation of GAT, GLARA generates more accurate and contextually relevant predictions. While the objective function combines the recommendation loss and regularization terms, the regularization term \(L_{\text{cluster}}\) is not explicitly included in the final loss function. Instead, it plays an important role during the virtual relation clustering process, ensuring that the clustering is semantically consistent. The main focus of the loss function is to optimize the recommendation accuracy, while \(L_{\text{cluster}}\) ensures the quality of the virtual relation embeddings by regulating the clustering step. This design allows the model to concentrate on improving the recommendation task while maintaining the integrity of the semantic relationships learned during clustering. The synergy between these components ensures that both long-tail relations and recent user preferences contribute to the final prediction, providing a more personalized and adaptive recommendation:  
\[\hat{y}_{ui}=f(u,i)=\left(\mathbf{e}_{u}^{\text{GAT}}\right)^{\top}\mathbf{e }_{i}^{\text{GAT}}. \tag{26}\]  
This formulation assumes that user preferences are proportional to the similarity between latent representations in the shared embedding space.  
Alternatively, a bilinear scoring function can be adopted for higher expressiveness:  
\[\hat{y}_{ui}=\left(\mathbf{e}_{u}^{\text{GAT}}\right)^{\top}\mathbf{W}_{s} \,\mathbf{e}_{i}^{\text{GAT}},\quad\mathbf{W}_{s}\in\mathbb{R}^{d\times d}, \tag{27}\]  
where \(\mathbf{W}_{s}\) is a learnable interaction weight matrix.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 5 Performance Evaluation
### Packet Delivery Ratio  
It can be seen in Figure 8 that as the node number increases, PDR begins to increase in all of the schemes. This is because the PNN per node increases as the node number increases. Hence, the void hole probability reduces due to the availability of qualified nodes in the ATD. PDR increases till a certain threshold; after that, collision at the receiver results in reduction of PDR. Below 300 nodes, 2 hop-AHH-VBF and QF-2 hop-AHH-VBF perform better than AHH-VBF due to the optimal forwarder selection in the ATD. This selection ensures successful packet transmission even in sparse regions  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Nodes & 100–500 Random deployment \\ Sinks & 1, on the surface \\ Network area & 3D region of 10 km \(\times\) 10 km \(\times\) 10 km \\ Max power for transmission & 90 dB re \(\mu\) Pa \\ Max power for reception & 10 dB re \(\mu\) Pa \\ Max Transmission range & 2000 m \\ Initial energy of each node & 100 Joule \\ Node mobility & 1–3 m/s \\ Data rate & 16 kbps \\ Data packet size & 111 bytes \\ Neighbor request packet size & 66 bytes \\ Acknowledgment packet size & 114 bytes \\ Center frequency & 12 kHz \\ Bandwidth & 4 kHz \\ Mobility model & Random walk mobility mode \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter setting.  
in the network. As the node number increases beyond 300 nodes, the difference of PDR of all of the schemes reduces due to the fact that the increase in the node number itself reduces the void hole probability. However, QF-2 hop-AHH-VBF outperforms AHH-VBF because of an optimal forwarder selection criterion in terms of node position and residual energy. Hence, QF-2 hop-AHH-VBF performs better in sparse and dense environments. In dense network regions, duplication of packets is handled with the constrained pipeline radius as set by the AHH-VBF scheme. QF-2 hop-AHH-VBF performs 5.6% better than AHH-VBF, while 2 hop-AHHH-VBF and WDFAD-DBR perform slightly better than AHH-VBF in sparse regions. In dense regions, duplication of packets is avoided by opting for the mechanism to restrict the forwarding area in all four schemes. Hence, the performance pattern of all of the schemes is similar for a high number of nodes.
# CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA
## 2 Related Works
Table 1 compares previous studies on crosswalk pedestrian situation recognition with our study on five items and shows actual detected sample images: accuracy of detecting a pedestrian and a crosswalk, use of deep learning, detect crosswalk, the sight of the car driver, method of detecting crosswalk. Our system showed the highest accuracy and satisfied all other items, showing superior results to the systems presented in previous research. When we tried to detect the crosswalk with YOLO, we did not make an exact crosswalk shape but only a simple box shape. Additionally, with Mask R-CNN, we cannot satisfy detecting crosswalks when crosslines are invisible according to the road condition. So, we developed our CDA, which can draw the crosswalk shape even if some parts of zebra cross lines are erased. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Systems**} & **Accuracy of Detecting a Pedestrian \& Deep** & **Detect** & **The Sight of the Car** & **Method of Detecting** & **Samples of Detecting Crosswalk** \\  & **a Crosswalk** & **Learning** & **Crosswalk** & **Driver** & **Crosswalk** \\ \hline \multirow{2}{*}{Ours} & \multirow{2}{*}{96.7–98.7} & \multirow{2}{*}{O} & \multirow{2}{*}{crosswalk shape} & \multirow{2}{*}{O} & \multirow{2}{*}{CDA} \\  & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [23]} & \multirow{2}{*}{82–89} & \multirow{2}{*}{X} & \multirow{2}{*}{box shape} & \multirow{2}{*}{X} & \multirow{2}{*}{optical sensors} \\  & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al. [30]} & Detect only a pedestrian & \multirow{2}{*}{O} & \multirow{2}{*}{box shape} & \multirow{2}{*}{X} & \multirow{2}{*}{YOLO} \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of our system to others; Accuracy and CDA.